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Mayans M.C. / “Do You Hear the Rain” Watch Video : http://str11.besttvmovie.com/tv/76231-5-3/mayans-m-c.html Mayans M.C. Season 5 Episode 3 Mayans M.C. S5 E3 Mayans M.C. S5E3 Mayans M.C. 5X3 Mayans M.C. Full Episodes I hope you enjoy watching the series Mayans M.C. Season 5 Episode 3 on My Channel. Subscribe to my channel and get notifications for the latest Episodes of the series The Resident. Don't miss the latest Episodes from the Mayans M.C. Season 5 Episode 3 series, Subscribe to my Channel now. Thanks for visiting & watching. #Nancy Drew #Season #Episode #2023 ❏ STREAMING MEDIA ❏ Streaming media is multimedia that is constantly received by and presented to an end-user while being delivered by a provider. The verb to stream identifies the process of delivering or obtaining media in this maYoukuer.[clarification needed] Streaming refers to the delivery method of the medium, instead of the medium itself. Distinguishing delivery method from the media distributed applies particularly to telecommunications networks, as almost all of the delivery systems are either inherently streaming (e.g. radio, television, streaming apps) or inherently non-streaming (e.g. books, video cassettes, music CDs). There are challenges with streaming content on the Internet. For instance, users whose Internet coYoukuection lacks satisfactory bandwidth may experience stops, lags, or slow buffering of the content. And users lacking compatible hardware or software systems may be unable to stream certain content. Live streaming is the delivery of Internet content in real-time much as live television broadcasts content over the airwaves with a television signal. Live internet streaming takes a form of source media (e.g. a video camera, an audio tracks interface, screen capture software), an encoder to digitize the content, a media publisher, and a content delivery network to distribute and deliver the content. Live streaming does not need to be recorded at the origination point, although it frequently is. Streaming is an option to file downloading, a process where the end-user obtains the entire file for this content before watching or listening to it. Through streaming, an end-user can use their media player to get started on playing digital video or digital sound content before the complete file has been transmitted. The word “streaming media” can coYoukuect with media other than video and audio, such as live closed captioning, ticker tape, and real-time text, which are considered “streaming text”.

Riverdale S7.E10 / “Chapter One Hundred Twenty-Seven: American Graffiti” Watch Video : http://str11.besttvmovie.com/tv/69050-7-10/riverdale.html Riverdale Season 7 Episode 10 Riverdale S7 E10 Riverdale S7E10 Riverdale 7X10 Riverdale Full Episodes I hope you enjoy watching the series Riverdale Season 7 Episode 10 on My Channel. Subscribe to my channel and get notifications for the latest Episodes of the series The Resident. Don't miss the latest Episodes from the Riverdale Season 7 Episode 10 series, Subscribe to my Channel now. Thanks for visiting & watching. #Nancy Drew #Season #Episode #2023 ❏ STREAMING MEDIA ❏ Streaming media is multimedia that is constantly received by and presented to an end-user while being delivered by a provider. The verb to stream identifies the process of delivering or obtaining media in this maYoukuer.[clarification needed] Streaming refers to the delivery method of the medium, instead of the medium itself. Distinguishing delivery method from the media distributed applies particularly to telecommunications networks, as almost all of the delivery systems are either inherently streaming (e.g. radio, television, streaming apps) or inherently non-streaming (e.g. books, video cassettes, music CDs). There are challenges with streaming content on the Internet. For instance, users whose Internet coYoukuection lacks satisfactory bandwidth may experience stops, lags, or slow buffering of the content. And users lacking compatible hardware or software systems may be unable to stream certain content. Live streaming is the delivery of Internet content in real-time much as live television broadcasts content over the airwaves with a television signal. Live internet streaming takes a form of source media (e.g. a video camera, an audio tracks interface, screen capture software), an encoder to digitize the content, a media publisher, and a content delivery network to distribute and deliver the content. Live streaming does not need to be recorded at the origination point, although it frequently is. Streaming is an option to file downloading, a process where the end-user obtains the entire file for this content before watching or listening to it. Through streaming, an end-user can use their media player to get started on playing digital video or digital sound content before the complete file has been transmitted. The word “streaming media” can coYoukuect with media other than video and audio, such as live closed captioning, ticker tape, and real-time text, which are considered “streaming text”.

Riverdale S7.E10 / “Chapter One Hundred Twenty-Seven: American Graffiti” Watch Video : http://str11.besttvmovie.com/tv/69050-7-10/riverdale.html Riverdale Season 7 Episode 10 Riverdale S7 E10 Riverdale S7E10 Riverdale 7X10 Riverdale Full Episodes I hope you enjoy watching the series Riverdale Season 7 Episode 10 on My Channel. Subscribe to my channel and get notifications for the latest Episodes of the series The Resident. Don't miss the latest Episodes from the Riverdale Season 7 Episode 10 series, Subscribe to my Channel now. Thanks for visiting & watching. #Nancy Drew #Season #Episode #2023 ❏ STREAMING MEDIA ❏ Streaming media is multimedia that is constantly received by and presented to an end-user while being delivered by a provider. The verb to stream identifies the process of delivering or obtaining media in this maYoukuer.[clarification needed] Streaming refers to the delivery method of the medium, instead of the medium itself. Distinguishing delivery method from the media distributed applies particularly to telecommunications networks, as almost all of the delivery systems are either inherently streaming (e.g. radio, television, streaming apps) or inherently non-streaming (e.g. books, video cassettes, music CDs). There are challenges with streaming content on the Internet. For instance, users whose Internet coYoukuection lacks satisfactory bandwidth may experience stops, lags, or slow buffering of the content. And users lacking compatible hardware or software systems may be unable to stream certain content. Live streaming is the delivery of Internet content in real-time much as live television broadcasts content over the airwaves with a television signal. Live internet streaming takes a form of source media (e.g. a video camera, an audio tracks interface, screen capture software), an encoder to digitize the content, a media publisher, and a content delivery network to distribute and deliver the content. Live streaming does not need to be recorded at the origination point, although it frequently is. Streaming is an option to file downloading, a process where the end-user obtains the entire file for this content before watching or listening to it. Through streaming, an end-user can use their media player to get started on playing digital video or digital sound content before the complete file has been transmitted. The word “streaming media” can coYoukuect with media other than video and audio, such as live closed captioning, ticker tape, and real-time text, which are considered “streaming text”.

Nancy Drew S4.E1 / “The Dilemma of the Lover's Curse” Watch Video : http://str11.besttvmovie.com/tv/89572-4-1/nancy-drew.html Nancy Drew Season 4 Episode 1 Nancy Drew S4 E1 Nancy Drew S4E1 Nancy Drew 4X1 Nancy Drew Full Episodes I hope you enjoy watching the series Nancy Drew Season 4 Episode 1 on My Channel. Subscribe to my channel and get notifications for the latest Episodes of the series The Resident. Don't miss the latest Episodes from the Nancy Drew Season 4 Episode 1 series, Subscribe to my Channel now. Thanks for visiting & watching. #Nancy Drew #Season #Episode #2023 ❏ STREAMING MEDIA ❏ Streaming media is multimedia that is constantly received by and presented to an end-user while being delivered by a provider. The verb to stream identifies the process of delivering or obtaining media in this maYoukuer.[clarification needed] Streaming refers to the delivery method of the medium, instead of the medium itself. Distinguishing delivery method from the media distributed applies particularly to telecommunications networks, as almost all of the delivery systems are either inherently streaming (e.g. radio, television, streaming apps) or inherently non-streaming (e.g. books, video cassettes, music CDs). There are challenges with streaming content on the Internet. For instance, users whose Internet coYoukuection lacks satisfactory bandwidth may experience stops, lags, or slow buffering of the content. And users lacking compatible hardware or software systems may be unable to stream certain content. Live streaming is the delivery of Internet content in real-time much as live television broadcasts content over the airwaves with a television signal. Live internet streaming takes a form of source media (e.g. a video camera, an audio tracks interface, screen capture software), an encoder to digitize the content, a media publisher, and a content delivery network to distribute and deliver the content. Live streaming does not need to be recorded at the origination point, although it frequently is. Streaming is an option to file downloading, a process where the end-user obtains the entire file for this content before watching or listening to it. Through streaming, an end-user can use their media player to get started on playing digital video or digital sound content before the complete file has been transmitted. The word “streaming media” can coYoukuect with media other than video and audio, such as live closed captioning, ticker tape, and real-time text, which are considered “streaming text”.

Cage Warriors 148: Team Europe vs Team USA | LIVE on UFC Fight Pass. Main Card – 1:00pm (PT) | 4:00pm (ET) | 9:00pm (GMT) | 10:00pm (CEST). ✅✅Watch Free Cage Warriors 148: Team Europe vs Team USA Live Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking (https://github.com/diwis/zdhPaperRanking) library4. The washers dataset features 70 defective parts. The gears and screws datasets feature 35 defective, 35 intact and several hundred unannotated parts. Some defects, such as notches and holes, are visible in most images (illuminations) with intensity and texture variations among them, while others, such as scratches, are only visible in a few.fghgj We split the datasets into train and test sets. The train sets contain 32 samples, and the test set 38 samples. Each sample comprises 108 images (each captured under a different illumination angle), an automatically extracted foreground segmentation mask, and a hand-labeled defect segmentation mask.fghgfj This dataset is challenging mainly because: each raw sample consists of 108 gray-scale images of resolution 512×512 and therefore takes 27MB of space; the metallic surfaces produce many specular reflections that sometimes saturate the camera sensors; the annotations are not very precise because the exact extent of defect contours is always subjective; the defects are very sparse also in the spatial dimensions: they cover only about 0.2% of the total image area in gears, 0.8% in screws, and 1.4% in washers; this creates an unbalanced dataset with a highly skewed class representation. gfhj The dataset is organized as follows: each sample resides in the Test, Train, or Unannotated directory; each sample has its own directory which contains the individual images, the foreground, and defect segmentation masks; each image is stored in 8-bit greyscale png format and has a resolution of 512 x 512 pixels; Image file names are formatted using three string fields separated with the underscore character: prefix_sampleNr_illuminationNr.png, where the prefix is e.g. washer, the sampleNr might be a three-digit number 001, and the illuminationNr is formed of 3 digits, first corresponding to the elevation index (1 - highest angle, 9 - lowest angle), and the additional two corresponding to the azimuth index (01-12). Each dataset contains light_vectors.csv, which contains the illumination angles (in lexicographic order of the illuminationNr), and light_intensities.csv that contains the numbers corresponding to the light intensity on the scale from 0 to 127. Please, be aware, that the azimuth angles were not calibrated and might be a few degrees misaligned.fdhfgj These data have been cleaned and integrated with data from COVID-19-TweetIDs and from other sources (e.g., PMC). The result was dataset of 500,314 unique articles along with relevant metadata (e.g., the underlying citation network). We utilized this dataset to produce, for each article, the values of the following impact measures:sdgfdfhfggh Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking (https://github.com/diwifss/PaperRanking) library4.sddfghfggd Influence_alt: Citation-based measure reflecting the total impact of an article. This is the Citation Count of each article, calculated based on the citation network between the articles contained in the BIP4COVID19 dataset.sddggf safs Popularity: Citation-based measure reflecting the current impact of an article. This is based on the AttRank5 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). AttRank alleviates this problem incorporating an attention-based mechanism, akin to a time-restricted version of preferential attachment, to explicitly capture a researcher's preference to read papers which received a lot of attention recently. This is why it is more suitable to capture the current "hype" of an article.asdfujsgdg Colour Science for Python Colour is an open-source Python package providing a comprehensive number of algorithms and datasets for colour science. sdg It is freely available under the New BSD License terms.uiuol Colour is an affiliated project of NumFOCUS, a 501(c)(3) nonprofit in the United States Draft Release Notes The draft release notes of the develop branch are available at this url.uiu Sponsors We are grateful 💖 for the support of our sponsors. If you'd like to join them, please consider becoming a sponsor on OpenCollective.uiu Features Colour features a rich dataset and collection of objects, please see the features in the documentation for more information.iu User Guid Installation Colour and its primary dependencies can be easily installed from the Python Package Index by issuing this command in a shell:oluip $ pip install --user colour-science The detailed installation procedure for the secondary dependencies is described in the Installation Guide. Colour is also available for Anaconda from Continuum Analytics via conda-forge:oiup $ conda install -c conda-forge colour-science Tutorial The static tutorial provides an introduction to Colour. An interactive version is available via Google Colab.oui How-To The Google Colab How-To guide for Colour shows various techniques to solve specific problems and highlights some interesting use cases. Contributing If you would like to contribute to Colour, please refer to the following Contributing guide.oi Colour by Colour Developers Copyright 2013 Colour Developers – colour-developers@colour-science.org This software is released under terms of New BSD License: https://opensource.org/licenses/BSD-3-Clause https://github.com/colour-science/colourolip sf Popularity alternative: An alternative citation-based measure reflecting the current impact of an article (this was the basic popularity measured provided by BIP4COVID19 until version 26). This is based on the RAM6 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). RAM alleviates this problem using an approach known as "time-awareness". This is why it is more suitable to capture the current "hype" of an article. This measure was calculated using the PaperRanking (https://github.com/diwis/PaperRanking) library4.sfbsdf Social Media Attention: The number of tweets related to this article. Relevant data were collected from the COVID-19-TweetIDs dataset. In this version, tweets between 23/6/22-29/6/22 have been considered from the previous dataset.ftgujyol We provide five CSV files, all containing the same information, however each having its entries ordered by a different impact measure. All CSV files are tab separated and have the same columns (PubMed_id, PdfMC_id, DOI, influence_score, popularity_alt_score, popularity score, influence_alt score, tweets count).yjytik Rich offline experience, periodic background sync, push notification functionality, network requests control, improved performance via requests caching are only a few of the functionalities provided by the Service Worker (SW ) API. This new technology, supported by all major browsers, can significantly improve users’ experience by providing the publisher with the technical foundations that would normally require a native application. Albeit the capabilities of this new technique and its important role in the ecosystem of Progressive Web Apps (PWAs), it is still unclear what is their actual purpose on the web, and how publishers leverage the provided functionality in their web applications. In this study, we shed light in the real world deployment of SWs, by conducting the first large scale analysis of the prevalence of SWs in the wild.huiuop We see that SWs are becoming more and more popular, with the adoption increased by 26% only within the last 5 months. Surprisingly, besides their fruitful capabilities, we see that SWs are being mostly used for In-Page Push Advertising, in 65.08% of the SWs that connect with 3rd parties. We highlight that this is a relatively new way for advertisers to bypass ad-blockers and render ads on the user’s displays natively.

LIVE!sTREAMs!* FIS Nordic Combined Continental Cup Ruka 2022 Live Free Tv BROADCAST CLICK TO WATCH FREE LIVE It can be hard to know where to watch rugby on TV — especially in the US. Read on to learn how to watch wherever you live!Rugby is a sport that is already immensely popular in certain parts of the world and is starting to gain traction in countries such as the United States and Canada. As a result, interest in streaming rugby matches is significant/increasing. Streaming your favorite sport is not always easy, especially when you’re trying to watch from another country. Interest in rugby, a contact sport similar in many ways to football, has been steadily growing in the United States for some years now. While it’s easier than ever to find a local team to play with, it’s not so easy to watch a professional rugby game in the United States. If you’re a dedicated rugby fan looking to watch professional games, there is some good news for you. While there are quite a few ways to stream live rugby games in the US, you’ll have to stay up late to catch the game (or in some cases wake up early). We’re taking a look at some of the most efficient ways to stream rugby, no matter what country the game is taking place in. Here’s a quick preview of some upcoming games that you can stream live. All times are in Eastern and Pacific Time. 2021-09-09: Version 6.0.0 was created. Now includes data for the North Sea Link (NSL) interconnector from Great Britain to Norway (https://www.northsealink.com). The previous version (5.0.4) should not be used - as there was an error with interconnector data having a static value over the summer 2021.dd Version 144 of the dataset. MAJOR CHANGE NOTE: The dataset files: full_dataset.tsv.gz and full_dataset_clean.tsv.gz have been split in 1 GB parts using the Linux utility called Split. So make sure to join the parts before unzipping. We had to make this change as we had huge issues uploading files larger than 2GB's (hence the delay in the dataset releases). The peer-reviewed publication for this dataset has now been published in Epidemiologia an MDPI journal, and can be accessed here: https://doi.org/10.3390/epidemiologia2030024. Please cite this when using the dataset.gd This dataset contains impact metrics and indicators for a set of publications that are related to the COVID-19 infectious disease and the coronavirus that causes it. It is based on:gs Τhe CORD-19 dataset released by the team of Semantic Scholar1 and Τhe curated data provided by the LitCovid hub2.sa These data have been cleaned and integrated with data from COVID-19-TweetIDs and from other sources (e.g., PMC). The result was dataset of 621,235 unique articles along with relevant metadata (e.g., the underlying citation network). We utilized this dataset to produce, for each article, the values of the following impact measures:gww Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking (https://github.com/diwis/PaperRanking) library4.sff Influence_alt: Citation-based measure reflecting the total impact of an article. This is the Citation Count of each article, calculated based on the citation network between the articles contained in the BIP4COVID19 dataset. Popularity: Citation-based measure reflecting the current impact of an article. This is based on the AttRank5 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). AttRank alleviates this problem incorporating an attention-based mechanism, akin to a time-restricted version of preferential attachment, to explicitly capture a researcher's preference to read papers which received a lot of attention recently. This is why it is more suitable to capture the current "hype" of an article.ggwsw Due to the relevance of the COVID-19 global pandemic, we are releasing our dataset of tweets acquired from the Twitter Stream related to COVID-19 chatter. Since our first release we have received additional data from our new collaborators, allowing this resource to grow to its current size. Dedicated data gathering started from March 11th yielding over 4 million tweets a day. We have added additional data provided by our new collaborators from January 27th to March 27th, to provide extra longitudinal coverage. Version 10 added ~1.5 million tweets in the Russian language collected between January 1st and May 8th, gracefully provided to us by: Katya Artemova (NRU HSE) and Elena Tutubalina (KFU). From version 12 we have included daily hashtags, mentions and emoijis and their frequencies the respective zip files. From version 14 we have included the tweet identifiers and their respective language for the clean version of the dataset. Since version 20 we have included language and place location for all tweets.dfgg 2021-05-05: Version 5.0.0 was created. Datetimes now in ISO 8601 format (with capital letter 'T' between the date and time) rather than previously with a space (to RFC 3339 format) and with an offset to identify both UTC and localtime. MW values now all saved as integers rather than floats. Elexon data as always from www.elexonportal.co.uk/fuelhh, National Grid data from https://data.nationalgrideso.com/demand/historic-demand-data Raw data now added again for comparison of pre and post cleaning - to allow for training of additional cleaning methods. If using Microsoft Excel, the T between the date and time can be removed using the =SUBSTITUTE() command - and substitute "T" for a space " "gww 2021-03-02: Version 4.0.0 was created. Due to a new interconnecter (IFA2 - https://en.wikipedia.org/wiki/IFA-2) being commissioned in Q1 2021, there is an additional column with data from National Grid - this is called 'POWER_NGEM_IFA2_FLOW_MW' in the espeni dataset. In addition, National Grid has dropped the column name 'FRENCH_FLOW' that used to provide the value for the column 'POWER_NGEM_FRENCH_FLOW_MW' in previous espeni versions. However, this has been changed to 'IFA_FLOW' in National Grid's original data, which is now called 'POWER_NGEM_IFA_FLOW_MW' in the espeni dataset. Lastly, the IO14 columns have all been dropped by National Grid - and potentially unlikely to appear again in future.fgwf 2020-12-02: Version 3.0.0 was created. There was a problem with earlier versions local time format - where the +01:00 value was not carried through into the data properly. Now addressed - therefore - local time now has the format e.g. 2020-03-31 20:00:00+01:00 when in British Summer Time.stuu 2020-10-03: Version 2.0.0 was created as it looks like National Grid has had a significant change to the methodology underpinning the embedded wind calculations. The wind profile seems similar to previous values, but with an increasing value in comparison to the value published in earlier the greater the embedded value is. The 'new' values are from https://data.nationalgrideso.com/demand/daily-demand-update from 2013.fgew Previously: raw and cleaned datasets for Great Britain's publicly available electrical data from Elexon (www.elexonportal.co.uk) and National Grid (https://demandforecast.nationalgrid.com/efs_demand_forecast/faces/DataExplorer). Updated versions with more recent data will be uploaded with a differing version number and doidf All data is released in accordance with Elexon's disclaimer and reservation of rights.derrr This disclaimer is also felt to cover the data from National Grid, and the parsed data from the Energy Informatics Group at the University of Birmingham.

Mosconi Cup 2022 Live Stream Team USA and Team Europe clashed at the 2022 Mosconi Cup press conference with less than 24 hours to go until the two sides lock horns at Bally’s Las Vegas live on Sky Sports in the UK, DAZN in the USA, Canada, Italy, and Spain, Viaplay in Scandinavia, the Baltics and Poland and networks worldwide. Mosconi Cup 2022 Live Stream until Saturday, December 3, at the Bally’s Las Vegas Hotel & Casino in Las Vegas, NV. Looking ahead to action getting underway in pool’s most significant event Jones acknowledges his side cannot get ahead of itself with the home crowd baying for the Europeans to slip up: “I want to keep it simple. I don’t want anyone over-excited. We will have our chance to win the cup. It will take a lot. Europe has a great team. It’s about who gets settled the most first. I know our team can win on any scoreline. I think whoever gets comfortable first Version 142 of the dataset. MAJOR CHANGE NOTE: The dataset files: full_dataset.tsv.gz and full_dataset_clean.tsv.gz have been split in 1 GB parts using the Linux utility called Split. So make sure to join the parts before unzipping. We had to make this change as we had huge issues uploading files larger than 2GB's (hence the delay in the dataset releases). The peer-reviewed publication for this dataset has now been published in Epidemiologia an MDPI journal, and can be accessed here: https://doi.org/10.3390/epidemiologia2030024. Please cite this when using the dataset. Due to the relevance of the COVID-19 global pandemic, we are releasing our dataset of tweets acquired from the Twitter Stream related to COVID-19 chatter. Since our first release we have received additional data from our new collaborators, allowing this resource to grow to its current size. Dedicated data gathering started from March 11th yielding over 4 million tweets a day. We have added additional data provided by our new collaborators from January 27th to March 27th, to provide extra longitudinal coverage. Version 10 added ~1.5 million tweets in the Russian language collected between January 1st and May 8th, gracefully provided to us by: Katya Artemova (NRU HSE) and Elena Tutubalina (KFU). From version 12 we have included daily hashtags, mentions and emoijis and their frequencies the respective zip files. From version 14 we have included the tweet identifiers and their respective language for the clean version of the dataset. Since version 20 we have included language and place location for all tweets. The data collected from the stream captures all languages, but the higher prevalence are: English, Spanish, and French. We release all tweets and retweets on the full_dataset.tsv file (1,371,993,942 unique tweets), and a cleaned version with no retweets on the full_dataset-clean.tsv file (355,691,266 unique tweets). There are several practical reasons for us to leave the retweets, tracing important tweets and their dissemination is one of them. For NLP tasks we provide the top 1000 frequent terms in frequent_terms.csv, the top 1000 bigrams in frequent_bigrams.csv, and the top 1000 trigrams in frequent_trigrams.csv. Some general statistics per day are included for both datasets in the full_dataset-statistics.tsv and full_dataset-clean-statistics.tsv files. For more statistics and some visualizations

Washington Wizards is playing against Minnesota Timberwolves on November 29, 2022 at 12:00:00 AM UTC. The game is played at Capital One Arena. This game is part of NBA. Here you can find previous Washington Wizards vs Minnesota Timberwolves results sorted by their H2H games. SofaScore also allows you to check different information regarding the match, such as: CLICK HERE TO WATCH LIVE The Minnesota Timberwolves (10-10) will be looking to snap their two-game losing streak when they face the Washington Wizards (10-10) on Monday night. Minnesota is coming off one of its worst outings of the year, getting blown out by Golden State in a 137-114 final on Sunday. Washington has lost three straight games, including a 130-121 loss to Boston yesterday. Tipoff is set for 7 p.m. ET. The latest Wizards vs. Timberwolves odds from Caesars Sportsbook have this game as a pick 'em, while the over/under is set at 227.5. Before entering any Timberwolves vs. Wizards picks, you'll want to see the NBA predictions from the model at SportsLine. Due to the relevance of the COVID-19 global pandemic, we are releasing our dataset of tweets acquired from the Twitter Stream related to COVID-19 chatter. Since our first release we have received additional data from our new collaborators, allowing this resource to grow to its current size. Dedicated data gathering started from March 11th yielding over 4 million tweets a day. We have added additional data provided by our new collaborators from January 27th to March 27th, to provide extra longitudinal coverage. Version 10 added ~1.5 million tweets in the Russian language collected between January 1st and May 8th, gracefully provided to us by: Katya Artemova (NRU HSE) and Elena Tutubalina (KFU). From version 12 we have included daily hashtags, mentions and emojis and their frequencies the respective zip files. From version 14 we have included the tweet identifiers and their respective language for the clean version of the dataset. Since version 20 we have included language and place location for all tweets. The data collected from the stream captures all languages, but the higher prevalence are: English, Spanish, and French. We release all tweets and retweets on the full_dataset.tsv file (1,371,993,942 unique tweets), and a cleaned version with no retweets on the full_dataset-clean.tsv file (355,691,266 unique tweets). There are several practical reasons for us to leave the retweets, tracing important tweets and their dissemination is one of them. For NLP tasks we provide the top 1000 frequent terms in frequent_terms.csv, the top 1000 bigrams in frequent_bigrams.csv, and the top 1000 trigrams in frequent_trigrams.csv. Some general statistics per day are included for both datasets in the full_dataset-statistics.tsv and full_dataset-clean-statistics.tsv files. For more statistics and some visualizations visit: http://www.panacealab.org/covid19/ More details can be found (and will be updated faster at: https://github.com/thepanacealab/covid19_twitter) and our pre-print about the dataset (https://arxiv.org/abs/2004.03688) As always, the tweets distributed here are only tweet identifiers (with date and time added) due to the terms and conditions of Twitter to re-distribute Twitter data ONLY for research purposes. They need to be hydrated to be used.

The Gotham Film & Media Institute announced today the nominations for the 32nd Annual Gotham Awards, singling out 23 feature films, 15 series, and 35 performances in twelve award categories. The nominations were announced live from Cipriani Wall Street by Emmy-nominated star of Pose and American Horror Story on FX and now as Roxie Hart in Chicago on Broadway, Angelica Ross. Ross was joined by Jeffrey Sharp, award-winning film producer and the Executive Director of The Gotham. CLICK TO LIVE WATCH FREE CLICK TO LIVE WATCH FREE As the first major awards ceremony of the fall season, the Gotham Awards provide critical early recognition and media attention to worthy independent films and series and their writers, directors, producers, and actors. The awards are also unique for their ability to assist in catapulting award recipients prominently into national awards season attention. The 2022 Gotham Awards Ceremony will be held live and in person at 7 pm on Monday, November 28, 2022 at Cipriani Wall Street in New York City. Nominees are selected by committees of film and television critics, journalists, festival programmers, and film curators. Separate juries of writers, directors, actors, producers, editors, and others directly involved in making films will determine the final Gotham Award recipients. Due to the relevance of the COVID-19 global pandemic, we are releasing our dataset of tweets acquired from the Twitter Stream related to COVID-19 chatter. Since our first release we have received additional data from our new collaborators, allowing this resource to grow to its current size. Dedicated data gathering started from March 11th yielding over 4 million tweets a day. We have added additional data provided by our new collaborators from January 27th to March 27th, to provide extra longitudinal coverage. Version 10 added ~1.5 million tweets in the Russian language collected between January 1st and May 8th, gracefully provided to us by: Katya Artemova (NRU HSE) and Elena Tutubalina (KFU). From version 12 we have included daily hashtags, mentions and emoijis and their frequencies the respective zip files. From version 14 we have included the tweet identifiers and their respective language for the clean version of the dataset. Since version 20 we have included language and place location for all tweets. The data collected from the stream captures all languages, but the higher prevalence are: English, Spanish, and French. We release all tweets and retweets on the full_dataset.tsv file (1,371,993,942 unique tweets), and a cleaned version with no retweets on the full_dataset-clean.tsv file (355,691,266 unique tweets). There are several practical reasons for us to leave the retweets, tracing important tweets and their dissemination is one of them. For NLP tasks we provide the top 1000 frequent terms in frequent_terms.csv, the top 1000 bigrams in frequent_bigrams.csv, and the top 1000 trigrams in frequent_trigrams.csv. Some general statistics per day are included for both datasets in the full_dataset-statistics.tsv and full_dataset-clean-statistics.tsv files. For more statistics and some visualizations visit: http://www.panacealab.org/covid19/ More details can be found (and will be updated faster at: https://github.com/thepanacealab/covid19_twitter) and our pre-print about the dataset (https://arxiv.org/abs/2004.03688) As always, the tweets distributed here are only tweet identifiers (with date and time added) due to the terms and conditions of Twitter to re-distribute Twitter data ONLY for research purposes. They need to be hydrated to be used.

How To Watch Seattle Seahawks is playing against Las Vegas Raiders on Nov 27, 2022 at 9:05:00 PM UTC. The game is played at Lumen Field. This game is part of NFL. Here you can find previous Seattle Seahawks vs Las Vegas Raiders results sorted by their H2H games. SofaScore also allows you to check different information regarding the match, such as: CLICK HERE TO WATCH LIVE FREE CLICK HERE TO WATCH LIVE FREE This dataset contains impact metrics and indicators for a set of publications that are related to the COVID-19 infectious disease and the coronavirus that causes it. It is based on:gdf The Raiders look to stack wins when they travel to Seattle for a Week 12 matchup. Here's how to watch, listen to and livestream the game. How to watch, listen and livestream Raiders at Seahawks Las Vegas Raiders at Seattle Seahawks Lumen Field Sunday, November 27 1:05 p.m. PT These data have been cleaned and integrated with data from COVID-19-TweetIDs and from other sources (e.g., PMC). The result was dataset of 500,314 unique articles along with relevant metadata (e.g., the underlying citation network). We utilized this dataset to produce, for each article, the values of the following impact measures: Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking (https://github.com/diwis/PaperRanking) library4. Influence_alt: Citation-based measure reflecting the total impact of a These data have been cleaned and integrated with data from COVID-19-TweetIDs and from other sources (e.g., PMC). The result was dataset of 500,314 unique articles along with relevant metadata (e.g., the underlying citation network). We utilized this dataset to produce, for each article, the values of the following impact measures:sdgfdh Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking (https://github.com/diwis/PaperRanking) library4.sdgd Influence_alt: Citation-based measure reflecting the total impact of an article. This is the Citation Count of each article, calculated based on the citation network between the articles contained in the BIP4COVID19 dataset.sdgf safs Popularity: Citation-based measure reflecting the current impact of an article. This is based on the AttRank5 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). AttRank alleviates this problem incorporating an attention-based mechanism, akin to a time-restricted version of preferential attachment, to explicitly capture a researcher's preference to read papers which received a lot of attention recently. This is why it is more suitable to capture the current "hype" of an article.asdsg sf Popularity alternative: An alternative citation-based measure reflecting the current impact of an article (this was the basic popularity measured provided by BIP4COVID19 until version 26). This is based on the RAM6 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). RAM alleviates this problem using an approach known as "time-awareness". This is why it is more suitable to capture the current "hype" of an article. This measure was calculated using the PaperRanking (https://github.com/diwis/PaperRanking) library4.sfb Social Media Attention: The number of tweets related to this article. Relevant data were collected from the COVID-19-TweetIDs dataset. In this version, tweets between 23/6/22-29/6/22 have been considered from the previous dataset. We provide five CSV files, all containing the same information, however each having its entries ordered by a different impact measure. All CSV files are tab separated and have the same columns (PubMed_id, PMC_id, DOI, influence_score, popularity_alt_score, popularity score, influence_alt score, tweets count).

Seattle Seahawks is playing against Las Vegas Raiders on Nov 27, 2022 at 9:05:00 PM UTC. The game is played at Lumen Field. This game is part of NFL. Here you can find previous Seattle Seahawks vs Las Vegas Raiders results sorted by their H2H games. SofaScore also allows you to check different information regarding the match, such as: CLICK HERE TO WATCH LIVE FREE CLICK HERE TO WATCH LIVE FREE This dataset contains impact metrics and indicators for a set of publications that are related to the COVID-19 infectious disease and the coronavirus that causes it. It is based on:gdf The Raiders look to stack wins when they travel to Seattle for a Week 12 matchup. Here's how to watch, listen to and livestream the game. How to watch, listen and livestream Raiders at Seahawks Las Vegas Raiders at Seattle Seahawks Lumen Field Sunday, November 27 1:05 p.m. PT These data have been cleaned and integrated with data from COVID-19-TweetIDs and from other sources (e.g., PMC). The result was dataset of 500,314 unique articles along with relevant metadata (e.g., the underlying citation network). We utilized this dataset to produce, for each article, the values of the following impact measures: Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking (https://github.com/diwis/PaperRanking) library4. Influence_alt: Citation-based measure reflecting the total impact of a These data have been cleaned and integrated with data from COVID-19-TweetIDs and from other sources (e.g., PMC). The result was dataset of 500,314 unique articles along with relevant metadata (e.g., the underlying citation network). We utilized this dataset to produce, for each article, the values of the following impact measures:sdgfdh Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking (https://github.com/diwis/PaperRanking) library4.sdgd Influence_alt: Citation-based measure reflecting the total impact of an article. This is the Citation Count of each article, calculated based on the citation network between the articles contained in the BIP4COVID19 dataset.sdgf safs Popularity: Citation-based measure reflecting the current impact of an article. This is based on the AttRank5 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). AttRank alleviates this problem incorporating an attention-based mechanism, akin to a time-restricted version of preferential attachment, to explicitly capture a researcher's preference to read papers which received a lot of attention recently. This is why it is more suitable to capture the current "hype" of an article.asdsg sf Popularity alternative: An alternative citation-based measure reflecting the current impact of an article (this was the basic popularity measured provided by BIP4COVID19 until version 26). This is based on the RAM6 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). RAM alleviates this problem using an approach known as "time-awareness". This is why it is more suitable to capture the current "hype" of an article. This measure was calculated using the PaperRanking (https://github.com/diwis/PaperRanking) library4.sfb Social Media Attention: The number of tweets related to this article. Relevant data were collected from the COVID-19-TweetIDs dataset. In this version, tweets between 23/6/22-29/6/22 have been considered from the previous dataset. We provide five CSV files, all containing the same information, however each having its entries ordered by a different impact measure. All CSV files are tab separated and have the same columns (PubMed_id, PMC_id, DOI, influence_score, popularity_alt_score, popularity score, influence_alt score, tweets count).

Lights, camera, pose! The red carpet will be rolled out for this year’s Soul Train Awards. Deon Cole will host the award show which will air on Saturday, November 26 at 8 p.m. ET on BET. The event can be streamed live on fuboTV and other live streaming services. Beyoncé and Mary J. Blige lead this year’s list of nominees with seven nods each. Ari Lennox follows with six and Lizo and Chris Brown follow with 4. CLICK HERE TO WATCH LIVE FREE CLICK HERE TO WATCH LIVE FREE This dataset contains impact metrics and indicators for a set of publications that are related to the COVID-19 infectious disease and the coronavirus that causes it. It is based on:gdf Who is nominated this year? Nominations for the 2022 BET Soul Train Awards are as follows: BEST NEW ARTIST Ckay Coco Jones Dixson Doechii Fireboy Dml Muni Long Steve Lacy Tems CERTIFIED SOUL AWARD Chaka Khan Charlie Wilson Diana Ross Mary J. Blige Maxwell Pj Morton Ronald Isley & The Isley Brothers T-Pain BEST GOSPEL/INSPIRATIONAL AWARD Cece Winans Erica Campbell Fred Hammond Major. Marvin Sapp Maverick City Music X Kirk Franklin Tamela Mann Tasha Cobbs Leonard BEST R&B/SOUL FEMALE ARTIST Ari Lennox Beyoncé H.E.R. Jazmine Sullivan Lizzo Mary J. Blige Sza Tems BEST R&B/SOUL MALE ARTIST Babyface Brent Faiyaz Burna Boy Charlie Wilson Chris Brown Giveon Lucky Daye Pj Morton ALBUM OF THE YEAR An Evening With Silk Sonic - Bruno Mars, Anderson .Paak, Silk Sonic Away Message (Ep) - Ari Lennox Breezy, Chris Brown Good Morning Gorgeous - Mary J. Blige Heaux Tales, Mo’ Tales: The Deluxe - Jazmine Sullivan R&B Money - Tank Renaissance - Beyoncé Special - Lizzo SONG OF THE YEAR “About Damn Time” - Lizzo “Bad Habit” - Steve Lacy “Break My Soul” - Beyoncé “Good Morning Gorgeous” - Mary J. Blige “Hrs & Hrs” - Muni Long “Last Last” - Burna Boy “Pressure” - Ari Lennox THE ASHFORD AND SIMPSON SONGWRITER’S AWARD “Bad Habit” - Steve Lacy “Break My Soul” - Beyoncé “Church Girl” - Beyoncé “Good Morning Gorgeous” - Mary J. Blige “Hrs & Hrs” - Muni Long “I Hate U” - Sza “Last, Last” - Burna Boy “Pressure” - Ari Lennox BEST DANCE PERFORMANCE “About Damn Time” - Lizzo “Call Me Every Day” - Chris Brown Feat. Wizkid “Have Mercy” - Chlöe “Persuasive” - Doechii “Pressure” - Ari Lennox “Smokin Out The Window” - Bruno Mars, Anderson .Paak, Silk Sonic “We (Warm Embrace)” - Chris Brown “Woman” - Doja Ca VIDEO OF THE YEAR “About Damn Time” - Lizzo “Bad Habit” - Steve Lacy “Good Morning Gorgeous” - Mary J. Blige “Have Mercy” - Chlöe “Hrs & Hrs” - Muni Long “Last Last” - Burna Boy “Pressure” - Ari Lennox “Smokin Out The Window” - Bruno Mars, Anderson .Paak, Silk Sonic BEST COLLABORATION “Amazing” - Mary J. Blige Feat. Dj Khaled “Be Like Water” - Pj Morton Feat. Stevie Wonder & Nas “Call Me Every Day” - Chris Brown Feat. Wizkid “Gotta Move On” - Diddy Feat. Bryson Tiller “Hate Our Love” - Queen Naija & Big Sean “Make Me Say It Again, Girl” - Ronald Isley & The Isley Brothers Feat. Beyoncé “Move” - Beyoncé Feat. Grace Jones, Tems “Slow” - Tank Feat. J. Valentine Τhe CORD-19 dataset released by the team of Semantic Scholar1 anddg Τhe curated data provided by the LitCovid hub2.gd These data have been cleaned and integrated with data from COVID-19-TweetIDs and from other sources (e.g., PMC). The result was dataset of 500,314 unique articles along with relevant metadata (e.g., the underlying citation network). We utilized this dataset to produce, for each article, the values of the following impact measures: Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking (https://github.com/diwis/PaperRanking) library4. Influence_alt: Citation-based measure reflecting the total impact of a These data have been cleaned and integrated with data from COVID-19-TweetIDs and from other sources (e.g., PMC). The result was dataset of 500,314 unique articles along with relevant metadata (e.g., the underlying citation network). We utilized this dataset to produce, for each article, the values of the following impact measures:sdgfdh Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking (https://github.com/diwis/PaperRanking) library4.sdgd Influence_alt: Citation-based measure reflecting the total impact of an article. This is the Citation Count of each article, calculated based on the citation network between the articles contained in the BIP4COVID19 dataset.sdgf safs Popularity: Citation-based measure reflecting the current impact of an article. This is based on the AttRank5 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). AttRank alleviates this problem incorporating an attention-based mechanism, akin to a time-restricted version of preferential attachment, to explicitly capture a researcher's preference to read papers which received a lot of attention recently. This is why it is more suitable to capture the current "hype" of an article.asdsg sf Popularity alternative: An alternative citation-based measure reflecting the current impact of an article (this was the basic popularity measured provided by BIP4COVID19 until version 26). This is based on the RAM6 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). RAM alleviates this problem using an approach known as "time-awareness". This is why it is more suitable to capture the current "hype" of an article. This measure was calculated using the PaperRanking (https://github.com/diwis/PaperRanking) library4.sfb Social Media Attention: The number of tweets related to this article. Relevant data were collected from the COVID-19-TweetIDs dataset. In this version, tweets between 23/6/22-29/6/22 have been considered from the previous dataset. We provide five CSV files, all containing the same information, however each having its entries ordered by a different impact measure. All CSV files are tab separated and have the same columns (PubMed_id, PMC_id, DOI, influence_score, popularity_alt_score, popularity score, influence_alt score, tweets count).

UFC 284: Makhachev vs. Volkanovski - preliminary card live results, highlight videos. Check out the live results and highlights for the UFC 284 prelims. Click Here To Watch Now UFC 284 Live Free Stay up to date with what’s happening today with the UFC 284 preliminary card, which is going down from the RAC Arena in Perth, Australia. The card’s featured prelim will take place in the light heavyweight division when Tyson Pedro collides with Modestas Bukauskas. After four years on the sidelines, Australia’s Pedro returned to action in 2022 to post up back-to-back first round knockouts. For Bukauskas, he was cut from the UFC following a 1-4 stint, but will be returning to the promotion following two Cage Warriors wins to get back on track. On the Early Prelims, there’s a scrappy strawweight tilt between Muay Thai aficionado, Loma Lookboonmee, and the always game, Elise Reed. I am expecting a fun striking battle here. Not only is the standup where each fighter thrives, but the ground game is what has given both Loma and Elise the most fits thus far in the UFC. Lookboonmee has the best Muay Thai in the UFC, and no that is not up for debate, but Reed has a big right hand that she is more than willing to throw. 2021-09-09: Version 6.0.0 was created. Now includes data for the North Sea Link (NSL) interconnector from Great Britain to Norway (https://www.northsealink.com). The previous version (5.0.4) should not be used - as there was an error with interconnector data having a static value over the summer 2021.dd Version 144 of the dataset. MAJOR CHANGE NOTE: The dataset files: full_dataset.tsv.gz and full_dataset_clean.tsv.gz have been split in 1 GB parts using the Linux utility called Split. So make sure to join the parts before unzipping. We had to make this change as we had huge issues uploading files larger than 2GB's (hence the delay in the dataset releases). The peer-reviewed publication for this dataset has now been published in Epidemiologia an MDPI journal, and can be accessed here: https://doi.org/10.3390/epidemiologia2030024. Please cite this when using the dataset.gd This dataset contains impact metrics and indicators for a set of publications that are related to the COVID-19 infectious disease and the coronavirus that causes it. It is based on:gs Τhe CORD-19 dataset released by the team of Semantic Scholar1 and Τhe curated data provided by the LitCovid hub2.sa These data have been cleaned and integrated with data from COVID-19-TweetIDs and from other sources (e.g., PMC). The result was dataset of 621,235 unique articles along with relevant metadata (e.g., the underlying citation network). We utilized this dataset to produce, for each article, the values of the following impact measures:gww Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking (https://github.com/diwis/PaperRanking) library4.sff Influence_alt: Citation-based measure reflecting the total impact of an article. This is the Citation Count of each article, calculated based on the citation network between the articles contained in the BIP4COVID19 dataset. Popularity: Citation-based measure reflecting the current impact of an article. This is based on the AttRank5 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). AttRank alleviates this problem incorporating an attention-based mechanism, akin to a time-restricted version of preferential attachment, to explicitly capture a researcher's preference to read papers which received a lot of attention recently. This is why it is more suitable to capture the current "hype" of an article.ggwsw Due to the relevance of the COVID-19 global pandemic, we are releasing our dataset of tweets acquired from the Twitter Stream related to COVID-19 chatter. Since our first release we have received additional data from our new collaborators, allowing this resource to grow to its current size. Dedicated data gathering started from March 11th yielding over 4 million tweets a day. We have added additional data provided by our new collaborators from January 27th to March 27th, to provide extra longitudinal coverage. Version 10 added ~1.5 million tweets in the Russian language collected between January 1st and May 8th, gracefully provided to us by: Katya Artemova (NRU HSE) and Elena Tutubalina (KFU). From version 12 we have included daily hashtags, mentions and emoijis and their frequencies the respective zip files. From version 14 we have included the tweet identifiers and their respective language for the clean version of the dataset. Since version 20 we have included language and place location for all tweets.dfgg 2021-05-05: Version 5.0.0 was created. Datetimes now in ISO 8601 format (with capital letter 'T' between the date and time) rather than previously with a space (to RFC 3339 format) and with an offset to identify both UTC and localtime. MW values now all saved as integers rather than floats. Elexon data as always from www.elexonportal.co.uk/fuelhh, National Grid data from https://data.nationalgrideso.com/demand/historic-demand-data Raw data now added again for comparison of pre and post cleaning - to allow for training of additional cleaning methods. If using Microsoft Excel, the T between the date and time can be removed using the =SUBSTITUTE() command - and substitute "T" for a space " "gww 2021-03-02: Version 4.0.0 was created. Due to a new interconnecter (IFA2 - https://en.wikipedia.org/wiki/IFA-2) being commissioned in Q1 2021, there is an additional column with data from National Grid - this is called 'POWER_NGEM_IFA2_FLOW_MW' in the espeni dataset. In addition, National Grid has dropped the column name 'FRENCH_FLOW' that used to provide the value for the column 'POWER_NGEM_FRENCH_FLOW_MW' in previous espeni versions. However, this has been changed to 'IFA_FLOW' in National Grid's original data, which is now called 'POWER_NGEM_IFA_FLOW_MW' in the espeni dataset. Lastly, the IO14 columns have all been dropped by National Grid - and potentially unlikely to appear again in future.fgwf 2020-12-02: Version 3.0.0 was created. There was a problem with earlier versions local time format - where the +01:00 value was not carried through into the data properly. Now addressed - therefore - local time now has the format e.g. 2020-03-31 20:00:00+01:00 when in British Summer Time.stuu 2020-10-03: Version 2.0.0 was created as it looks like National Grid has had a significant change to the methodology underpinning the embedded wind calculations. The wind profile seems similar to previous values, but with an increasing value in comparison to the value published in earlier the greater the embedded value is. The 'new' values are from https://data.nationalgrideso.com/demand/daily-demand-update from 2013.fgew Previously: raw and cleaned datasets for Great Britain's publicly available electrical data from Elexon (www.elexonportal.co.uk) and National Grid (https://demandforecast.nationalgrid.com/efs_demand_forecast/faces/DataExplorer). Updated versions with more recent data will be uploaded with a differing version number and doidf All data is released in accordance with Elexon's disclaimer and reservation of rights.derrr This disclaimer is also felt to cover the data from National Grid, and the parsed data from the Energy Informatics Group at the University of Birmingham.

UFC 284: Makhachev vs. Volkanovski - preliminary card live results, highlight videos. Check out the live results and highlights for the UFC 284 prelims. Click Here To Watch Now Live Free Stay up to date with what’s happening today with the UFC 284 preliminary card, which is going down from the RAC Arena in Perth, Australia. The card’s featured prelim will take place in the light heavyweight division when Tyson Pedro collides with Modestas Bukauskas. After four years on the sidelines, Australia’s Pedro returned to action in 2022 to post up back-to-back first round knockouts. For Bukauskas, he was cut from the UFC following a 1-4 stint, but will be returning to the promotion following two Cage Warriors wins to get back on track. On the Early Prelims, there’s a scrappy strawweight tilt between Muay Thai aficionado, Loma Lookboonmee, and the always game, Elise Reed. I am expecting a fun striking battle here. Not only is the standup where each fighter thrives, but the ground game is what has given both Loma and Elise the most fits thus far in the UFC. Lookboonmee has the best Muay Thai in the UFC, and no that is not up for debate, but Reed has a big right hand that she is more than willing to throw. 2021-09-09: Version 6.0.0 was created. Now includes data for the North Sea Link (NSL) interconnector from Great Britain to Norway (https://www.northsealink.com). The previous version (5.0.4) should not be used - as there was an error with interconnector data having a static value over the summer 2021.dd Version 144 of the dataset. MAJOR CHANGE NOTE: The dataset files: full_dataset.tsv.gz and full_dataset_clean.tsv.gz have been split in 1 GB parts using the Linux utility called Split. So make sure to join the parts before unzipping. We had to make this change as we had huge issues uploading files larger than 2GB's (hence the delay in the dataset releases). The peer-reviewed publication for this dataset has now been published in Epidemiologia an MDPI journal, and can be accessed here: https://doi.org/10.3390/epidemiologia2030024. Please cite this when using the dataset.gd This dataset contains impact metrics and indicators for a set of publications that are related to the COVID-19 infectious disease and the coronavirus that causes it. It is based on:gs Τhe CORD-19 dataset released by the team of Semantic Scholar1 and Τhe curated data provided by the LitCovid hub2.sa These data have been cleaned and integrated with data from COVID-19-TweetIDs and from other sources (e.g., PMC). The result was dataset of 621,235 unique articles along with relevant metadata (e.g., the underlying citation network). We utilized this dataset to produce, for each article, the values of the following impact measures:gww Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking (https://github.com/diwis/PaperRanking) library4.sff Influence_alt: Citation-based measure reflecting the total impact of an article. This is the Citation Count of each article, calculated based on the citation network between the articles contained in the BIP4COVID19 dataset. Popularity: Citation-based measure reflecting the current impact of an article. This is based on the AttRank5 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). AttRank alleviates this problem incorporating an attention-based mechanism, akin to a time-restricted version of preferential attachment, to explicitly capture a researcher's preference to read papers which received a lot of attention recently. This is why it is more suitable to capture the current "hype" of an article.ggwsw Due to the relevance of the COVID-19 global pandemic, we are releasing our dataset of tweets acquired from the Twitter Stream related to COVID-19 chatter. Since our first release we have received additional data from our new collaborators, allowing this resource to grow to its current size. Dedicated data gathering started from March 11th yielding over 4 million tweets a day. We have added additional data provided by our new collaborators from January 27th to March 27th, to provide extra longitudinal coverage. Version 10 added ~1.5 million tweets in the Russian language collected between January 1st and May 8th, gracefully provided to us by: Katya Artemova (NRU HSE) and Elena Tutubalina (KFU). From version 12 we have included daily hashtags, mentions and emoijis and their frequencies the respective zip files. From version 14 we have included the tweet identifiers and their respective language for the clean version of the dataset. Since version 20 we have included language and place location for all tweets.dfgg 2021-05-05: Version 5.0.0 was created. Datetimes now in ISO 8601 format (with capital letter 'T' between the date and time) rather than previously with a space (to RFC 3339 format) and with an offset to identify both UTC and localtime. MW values now all saved as integers rather than floats. Elexon data as always from www.elexonportal.co.uk/fuelhh, National Grid data from https://data.nationalgrideso.com/demand/historic-demand-data Raw data now added again for comparison of pre and post cleaning - to allow for training of additional cleaning methods. If using Microsoft Excel, the T between the date and time can be removed using the =SUBSTITUTE() command - and substitute "T" for a space " "gww 2021-03-02: Version 4.0.0 was created. Due to a new interconnecter (IFA2 - https://en.wikipedia.org/wiki/IFA-2) being commissioned in Q1 2021, there is an additional column with data from National Grid - this is called 'POWER_NGEM_IFA2_FLOW_MW' in the espeni dataset. In addition, National Grid has dropped the column name 'FRENCH_FLOW' that used to provide the value for the column 'POWER_NGEM_FRENCH_FLOW_MW' in previous espeni versions. However, this has been changed to 'IFA_FLOW' in National Grid's original data, which is now called 'POWER_NGEM_IFA_FLOW_MW' in the espeni dataset. Lastly, the IO14 columns have all been dropped by National Grid - and potentially unlikely to appear again in future.fgwf 2020-12-02: Version 3.0.0 was created. There was a problem with earlier versions local time format - where the +01:00 value was not carried through into the data properly. Now addressed - therefore - local time now has the format e.g. 2020-03-31 20:00:00+01:00 when in British Summer Time.stuu 2020-10-03: Version 2.0.0 was created as it looks like National Grid has had a significant change to the methodology underpinning the embedded wind calculations. The wind profile seems similar to previous values, but with an increasing value in comparison to the value published in earlier the greater the embedded value is. The 'new' values are from https://data.nationalgrideso.com/demand/daily-demand-update from 2013.fgew Previously: raw and cleaned datasets for Great Britain's publicly available electrical data from Elexon (www.elexonportal.co.uk) and National Grid (https://demandforecast.nationalgrid.com/efs_demand_forecast/faces/DataExplorer). Updated versions with more recent data will be uploaded with a differing version number and doidf All data is released in accordance with Elexon's disclaimer and reservation of rights.derrr This disclaimer is also felt to cover the data from National Grid, and the parsed data from the Energy Informatics Group at the University of Birmingham.

UFC 284: Makhachev vs. Volkanovski - preliminary card live results, highlight videos. Check out the live results and highlights for the UFC 284 prelims. Click Here To Watch Now Live Free Stay up to date with what’s happening today with the UFC 284 preliminary card, which is going down from the RAC Arena in Perth, Australia. The card’s featured prelim will take place in the light heavyweight division when Tyson Pedro collides with Modestas Bukauskas. After four years on the sidelines, Australia’s Pedro returned to action in 2022 to post up back-to-back first round knockouts. For Bukauskas, he was cut from the UFC following a 1-4 stint, but will be returning to the promotion following two Cage Warriors wins to get back on track. On the Early Prelims, there’s a scrappy strawweight tilt between Muay Thai aficionado, Loma Lookboonmee, and the always game, Elise Reed. I am expecting a fun striking battle here. Not only is the standup where each fighter thrives, but the ground game is what has given both Loma and Elise the most fits thus far in the UFC. Lookboonmee has the best Muay Thai in the UFC, and no that is not up for debate, but Reed has a big right hand that she is more than willing to throw. 2021-09-09: Version 6.0.0 was created. Now includes data for the North Sea Link (NSL) interconnector from Great Britain to Norway (https://www.northsealink.com). The previous version (5.0.4) should not be used - as there was an error with interconnector data having a static value over the summer 2021.dd Version 144 of the dataset. MAJOR CHANGE NOTE: The dataset files: full_dataset.tsv.gz and full_dataset_clean.tsv.gz have been split in 1 GB parts using the Linux utility called Split. So make sure to join the parts before unzipping. We had to make this change as we had huge issues uploading files larger than 2GB's (hence the delay in the dataset releases). The peer-reviewed publication for this dataset has now been published in Epidemiologia an MDPI journal, and can be accessed here: https://doi.org/10.3390/epidemiologia2030024. Please cite this when using the dataset.gd This dataset contains impact metrics and indicators for a set of publications that are related to the COVID-19 infectious disease and the coronavirus that causes it. It is based on:gs Τhe CORD-19 dataset released by the team of Semantic Scholar1 and Τhe curated data provided by the LitCovid hub2.sa These data have been cleaned and integrated with data from COVID-19-TweetIDs and from other sources (e.g., PMC). The result was dataset of 621,235 unique articles along with relevant metadata (e.g., the underlying citation network). We utilized this dataset to produce, for each article, the values of the following impact measures:gww Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking (https://github.com/diwis/PaperRanking) library4.sff Influence_alt: Citation-based measure reflecting the total impact of an article. This is the Citation Count of each article, calculated based on the citation network between the articles contained in the BIP4COVID19 dataset. Popularity: Citation-based measure reflecting the current impact of an article. This is based on the AttRank5 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). AttRank alleviates this problem incorporating an attention-based mechanism, akin to a time-restricted version of preferential attachment, to explicitly capture a researcher's preference to read papers which received a lot of attention recently. This is why it is more suitable to capture the current "hype" of an article.ggwsw Due to the relevance of the COVID-19 global pandemic, we are releasing our dataset of tweets acquired from the Twitter Stream related to COVID-19 chatter. Since our first release we have received additional data from our new collaborators, allowing this resource to grow to its current size. Dedicated data gathering started from March 11th yielding over 4 million tweets a day. We have added additional data provided by our new collaborators from January 27th to March 27th, to provide extra longitudinal coverage. Version 10 added ~1.5 million tweets in the Russian language collected between January 1st and May 8th, gracefully provided to us by: Katya Artemova (NRU HSE) and Elena Tutubalina (KFU). From version 12 we have included daily hashtags, mentions and emoijis and their frequencies the respective zip files. From version 14 we have included the tweet identifiers and their respective language for the clean version of the dataset. Since version 20 we have included language and place location for all tweets.dfgg 2021-05-05: Version 5.0.0 was created. Datetimes now in ISO 8601 format (with capital letter 'T' between the date and time) rather than previously with a space (to RFC 3339 format) and with an offset to identify both UTC and localtime. MW values now all saved as integers rather than floats. Elexon data as always from www.elexonportal.co.uk/fuelhh, National Grid data from https://data.nationalgrideso.com/demand/historic-demand-data Raw data now added again for comparison of pre and post cleaning - to allow for training of additional cleaning methods. If using Microsoft Excel, the T between the date and time can be removed using the =SUBSTITUTE() command - and substitute "T" for a space " "gww 2021-03-02: Version 4.0.0 was created. Due to a new interconnecter (IFA2 - https://en.wikipedia.org/wiki/IFA-2) being commissioned in Q1 2021, there is an additional column with data from National Grid - this is called 'POWER_NGEM_IFA2_FLOW_MW' in the espeni dataset. In addition, National Grid has dropped the column name 'FRENCH_FLOW' that used to provide the value for the column 'POWER_NGEM_FRENCH_FLOW_MW' in previous espeni versions. However, this has been changed to 'IFA_FLOW' in National Grid's original data, which is now called 'POWER_NGEM_IFA_FLOW_MW' in the espeni dataset. Lastly, the IO14 columns have all been dropped by National Grid - and potentially unlikely to appear again in future.fgwf 2020-12-02: Version 3.0.0 was created. There was a problem with earlier versions local time format - where the +01:00 value was not carried through into the data properly. Now addressed - therefore - local time now has the format e.g. 2020-03-31 20:00:00+01:00 when in British Summer Time.stuu 2020-10-03: Version 2.0.0 was created as it looks like National Grid has had a significant change to the methodology underpinning the embedded wind calculations. The wind profile seems similar to previous values, but with an increasing value in comparison to the value published in earlier the greater the embedded value is. The 'new' values are from https://data.nationalgrideso.com/demand/daily-demand-update from 2013.fgew Previously: raw and cleaned datasets for Great Britain's publicly available electrical data from Elexon (www.elexonportal.co.uk) and National Grid (https://demandforecast.nationalgrid.com/efs_demand_forecast/faces/DataExplorer). Updated versions with more recent data will be uploaded with a differing version number and doidf All data is released in accordance with Elexon's disclaimer and reservation of rights.derrr This disclaimer is also felt to cover the data from National Grid, and the parsed data from the Energy Informatics Group at the University of Birmingham.

Self-tracking is very important for promoting mental health. Recent studies with populations of college students have examined the sustainability of capturing daily mood, activity, and social data. Mental health problems are a growing public health concern. The likelihood of having common psychological issues such as depression, anxiety, and stress increases during youth and early adulthood, making university students an especially vulnerable demographic, where people have difficulty fitting in and engaging with a varied assortment of new people. Many students often feel out of place which impacts their mental well-being and to avoid being seen as mentally ill by peers students are often afraid to get professional help With our project we aim to provide a self-reliable solution to their difficulties where they can try to understand their emotions better and get tasks to which help them overcome their mental burden using machine learning., {"references":["1.\tAmerican College Health Association (ACH). 2016. National College Health Assessment II: Reference Group/Executive Summary Fall 2015. Hanover, MD. Retrieved http://www.achancha.org/docs/NCHAII%20FALL%202015%20REFERENCE%20G ROUP %20EXECUTIVE%20SUMMARY.pdf.","2.\tSteve Whittaker Artie Konrad, Vitoria Belotti, Nicole Crenshaw, Simon Tucker, Les Nelson, Honglu Du, Peter Pirolli. 2015. Finding the adaptive sweet spot: Balancing compliance and achievement in automated stress reduction. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI '15:), 3829–3838. http://doi.org/10.1145/2702123.2702512.","3.\tJennifer S. Beaudin, Stephen S. Intille, Margaret E. Morris. 2006. To track or not to track: User reactions to concepts in longitudinal health monitoring. Journal of Medical Internet Research 8, 4: 29. http://doi.org/10.2196/jmir.8.4.e29","4.\tNazia Perveen, N., Ahmad, M. AbdulQ. Bilal, Khan, R., Khalid, S., & Qadri, \"Facial Expression Recognition Through Machine Learning","5.\tJyoti Kumari, R., & Rajesh, K. M. Pooja,Facial Expression Recognition: A Survey,Procedia Computer Science,Volume 58,2015, Pages 486–491, ISSN 1877 – 0509, https://doi.org/10.1016/j.procs.2015.08.011.","6.\tKalaivani, G., & Anitha, S. Sathyapriya,DrD. (2016). A Literature review on Emotion Recognition for Various Facial Emotional Extraction. IOSR Journal of Computer Engineering (IOSR-JCE). 3. Khan, R., & Sharif, O. (2017). A Literature Review on Emotion Recognition Using Various Methods. 4. Ravichandra Ginne, K., Jariwala, \"FACIAL EXPRESSION RECOGNITION USING CNN: A SURVEY\", Mar 2018, International Journal of Advances in Electronics and Computer Science, Vol 5, No. 3, 2018","7.\tTuretsky, B. I., Kohler, C. G., Indersmitten, T., Bhati, M. T., Charbonnier, D., & Gur, R. C. (2007). Facial emotion recognition in schizophrenia: when and why does it go awry? Schizophrenia research, 94(1– 3), 253–263. https://doi.org/10.1016/j.schres.2007.05.001.","8.\tShaul Hammed, A., Sabanayagam, E. Ramakalaivani, & A REVIEW ON FACIAL EXPRESSION RECOGNITION (2020). SYSTEMS\",Journal of Critical Reviews, 7(4), 903–905. doi:10.31838/jcr.07.04.173.","9.\tNahum-Shani I, Smith SN, Spring BJ, et al. Just-in-Time Adaptive Interventions (JITAIs) in Mobile Health: Key Components and Design Principles for Ongoing Health Behavior Support. Ann Behav Med Published Online First: 23 September 2016. doi:10.1007/s12160-016-9830-8.","10.\tKumari RS, Sheela Kumari R, Varghese T, et al. A Genetic Algorithm Optimized Artificial Neural Network for the Segmentation of MR Images in Frontotemporal Dementia. In: Lecture Notes in Computer Science. 2013. 268–76."]}

ksw 78 live TV Broadcast 21th January 2023. CLICK HERE TO WATCH LIVE ksw 78 live live TV Broadcast 21th January 2023,KSW 78 takes place Saturday, January 21, 2023 with 9 fights at Netto Arena in Szczecin, Poland. View fight card, video, results, predictions, and news. KSW 70 event in Poland. KSW 78. 2023-01-21. ABOUT. On January 21st KSW together with its strong fighters will visit Szczecin. Watch the latest mixed martial arts event from Poland as KSW 78 features Materla taking on Grove in the second bout between the fighters. Data curator(s) Bobrov, Evgeny; Truccolo, Ivana; Monteiro, Elizabete; Casalegno, Carlotta; Clary, Erin; Romanowski, Andrew This dump provides access to the metadata records of publications, research data, software and projects that may be relevant to the Corona Virus Disease (COVID-19) fight. The dump contains records of the OpenAIRE COVID-19 Gateway, identified via full-text mining and inference techniques applied to the OpenAIRE Research Graph. The Graph is one of the largest Open Access collections of metadata records and links between publications, datasets, software, projects, funders, and organizations, aggregating 12,000+ scientific data sources world-wide, among which the Covid-19 data sources Zenodo COVID-19 Community, WHO (World Health Organization), BIP! FInder for COVID-19, Protein Data Bank, Dimensions, scienceOpen, and RSNA. The dump consists of a tar archive containing gzip files with one json per line. Each json is compliant to the schema available at https://doi.org/10.5281/zenodo.7492313.

ksw 78 live TV Broadcast 21th January 2023. CLICK HERE TO WATCH LIVE ksw 78 live live TV Broadcast 21th January 2023,KSW 78 takes place Saturday, January 21, 2023 with 9 fights at Netto Arena in Szczecin, Poland. View fight card, video, results, predictions, and news. KSW 70 event in Poland. KSW 78. 2023-01-21. ABOUT. On January 21st KSW together with its strong fighters will visit Szczecin. Watch the latest mixed martial arts event from Poland as KSW 78 features Materla taking on Grove in the second bout between the fighters. Data curator(s) Bobrov, Evgeny; Truccolo, Ivana; Monteiro, Elizabete; Casalegno, Carlotta; Clary, Erin; Romanowski, Andrew This dump provides access to the metadata records of publications, research data, software and projects that may be relevant to the Corona Virus Disease (COVID-19) fight. The dump contains records of the OpenAIRE COVID-19 Gateway, identified via full-text mining and inference techniques applied to the OpenAIRE Research Graph. The Graph is one of the largest Open Access collections of metadata records and links between publications, datasets, software, projects, funders, and organizations, aggregating 12,000+ scientific data sources world-wide, among which the Covid-19 data sources Zenodo COVID-19 Community, WHO (World Health Organization), BIP! FInder for COVID-19, Protein Data Bank, Dimensions, scienceOpen, and RSNA. The dump consists of a tar archive containing gzip files with one json per line. Each json is compliant to the schema available at https://doi.org/10.5281/zenodo.7492313.

ksw 78 live TV Broadcast 21th January 2023. CLICK HERE TO WATCH LIVE ksw 78 live live TV Broadcast 21th January 2023,KSW 78 takes place Saturday, January 21, 2023 with 9 fights at Netto Arena in Szczecin, Poland. View fight card, video, results, predictions, and news. KSW 70 event in Poland. KSW 78. 2023-01-21. ABOUT. On January 21st KSW together with its strong fighters will visit Szczecin. Watch the latest mixed martial arts event from Poland as KSW 78 features Materla taking on Grove in the second bout between the fighters. Data curator(s) Bobrov, Evgeny; Truccolo, Ivana; Monteiro, Elizabete; Casalegno, Carlotta; Clary, Erin; Romanowski, Andrew This dump provides access to the metadata records of publications, research data, software and projects that may be relevant to the Corona Virus Disease (COVID-19) fight. The dump contains records of the OpenAIRE COVID-19 Gateway, identified via full-text mining and inference techniques applied to the OpenAIRE Research Graph. The Graph is one of the largest Open Access collections of metadata records and links between publications, datasets, software, projects, funders, and organizations, aggregating 12,000+ scientific data sources world-wide, among which the Covid-19 data sources Zenodo COVID-19 Community, WHO (World Health Organization), BIP! FInder for COVID-19, Protein Data Bank, Dimensions, scienceOpen, and RSNA. The dump consists of a tar archive containing gzip files with one json per line. Each json is compliant to the schema available at https://doi.org/10.5281/zenodo.7492313.

ksw 78 live TV Broadcast 21th January 2023. CLICK HERE TO WATCH LIVE ksw 78 live live TV Broadcast 21th January 2023,KSW 78 takes place Saturday, January 21, 2023 with 9 fights at Netto Arena in Szczecin, Poland. View fight card, video, results, predictions, and news. KSW 70 event in Poland. KSW 78. 2023-01-21. ABOUT. On January 21st KSW together with its strong fighters will visit Szczecin. Watch the latest mixed martial arts event from Poland as KSW 78 features Materla taking on Grove in the second bout between the fighters. Data curator(s) Bobrov, Evgeny; Truccolo, Ivana; Monteiro, Elizabete; Casalegno, Carlotta; Clary, Erin; Romanowski, Andrew This dump provides access to the metadata records of publications, research data, software and projects that may be relevant to the Corona Virus Disease (COVID-19) fight. The dump contains records of the OpenAIRE COVID-19 Gateway, identified via full-text mining and inference techniques applied to the OpenAIRE Research Graph. The Graph is one of the largest Open Access collections of metadata records and links between publications, datasets, software, projects, funders, and organizations, aggregating 12,000+ scientific data sources world-wide, among which the Covid-19 data sources Zenodo COVID-19 Community, WHO (World Health Organization), BIP! FInder for COVID-19, Protein Data Bank, Dimensions, scienceOpen, and RSNA. The dump consists of a tar archive containing gzip files with one json per line. Each json is compliant to the schema available at https://doi.org/10.5281/zenodo.7492313.

National championship 2023: How to watch Georgia vs. TCU online, live stream free, channel, kickoff time CLICK HERE TO WATCH LIVE The 2023 College Football Playoff National Championship is a college football bowl game that is scheduled to be played on January 9, 2023, at SoFi Stadium in Inglewood, California. The ninth College Football Playoff National Championship, the game will determine the national champion of the NCAA Division I Football Bowl Subdivision (FBS) for the 2022 season. Aside from any all-star games following after, this will be the culminating game of the 2022–23 bowl season. The game will feature the No. 1 and defending national champion Georgia Bulldogs of the Southeastern Conference (SEC) (winners of the Peach Bowl) and the No. 3 TCU Horned Frogs of the Big 12 Conference (winners of the Fiesta Bowl). The game is scheduled to begin at 4:30 p.m. PST and will be broadcast by ESPN. Sponsored by telecommunications company AT&T, the game will be officially known as the 2023 College Football Playoff National Championship presented by AT&T. Version 148 of the dataset. MAJOR CHANGE NOTE: The dataset files: full_dataset.tsv.gz and full_dataset_clean.tsv.gz have been split in 1 GB parts using the Linux utility called Split. So make sure to join the parts before unzipping. We had to make this change as we had huge issues uploading files larger than 2GB's (hence the delay in the dataset releases). The peer-reviewed publication for this dataset has now been published in Epidemiologia an MDPI journal, and can be accessed here: https://doi.org/10.3390/epidemiologia2030024. Please cite this when using the dataset. Due to the relevance of the COVID-19 global pandemic, we are releasing our dataset of tweets acquired from the Twitter Stream related to COVID-19 chatter. Since our first release we have received additional data from our new collaborators, allowing this resource to grow to its current size. Dedicated data gathering started from March 11th yielding over 4 million tweets a day. We have added additional data provided by our new collaborators from January 27th to March 27th, to provide extra longitudinal coverage. Version 10 added ~1.5 million tweets in the Russian language collected between January 1st and May 8th, gracefully provided to us by: Katya Artemova (NRU HSE) and Elena Tutubalina (KFU). From version 12 we have included daily hashtags, mentions and emoijis and their frequencies the respective zip files. From version 14 we have included the tweet identifiers and their respective language for the clean version of the dataset. Since version 20 we have included language and place location for all tweets. The data collected from the stream captures all languages, but the higher prevalence are: English, Spanish, and French. We release all tweets and retweets on the full_dataset.tsv file (1,379,793,428 unique tweets), and a cleaned version with no retweets on the full_dataset-clean.tsv file (357,656,893 unique tweets). There are several practical reasons for us to leave the retweets, tracing important tweets and their dissemination is one of them. For NLP tasks we provide the top 1000 frequent terms in frequent_terms.csv, the top 1000 bigrams in frequent_bigrams.csv, and the top 1000 trigrams in frequent_trigrams.csv. Some general statistics per day are included for both datasets in the full_dataset-statistics.tsv and full_dataset-clean-statistics.tsv files. For more statistics and some visualizations visit: http://www.panacealab.org/covid19/ More details can be found (and will be updated faster at: https://github.com/thepanacealab/covid19_twitter) and our pre-print about the dataset (https://arxiv.org/abs/2004.03688) As always, the tweets distributed here are only tweet identifiers (with date and time added) due to the terms and conditions of Twitter to re-distribute Twitter data ONLY for research purposes. They need to be hydrated to be used.

National championship 2023: How to watch Georgia vs. TCU online, live stream free, channel, kickoff time CLICK HERE TO WATCH LIVE The 2023 College Football Playoff National Championship is a college football bowl game that is scheduled to be played on January 9, 2023, at SoFi Stadium in Inglewood, California. The ninth College Football Playoff National Championship, the game will determine the national champion of the NCAA Division I Football Bowl Subdivision (FBS) for the 2022 season. Aside from any all-star games following after, this will be the culminating game of the 2022–23 bowl season. The game will feature the No. 1 and defending national champion Georgia Bulldogs of the Southeastern Conference (SEC) (winners of the Peach Bowl) and the No. 3 TCU Horned Frogs of the Big 12 Conference (winners of the Fiesta Bowl). The game is scheduled to begin at 4:30 p.m. PST and will be broadcast by ESPN. Sponsored by telecommunications company AT&T, the game will be officially known as the 2023 College Football Playoff National Championship presented by AT&T. Version 148 of the dataset. MAJOR CHANGE NOTE: The dataset files: full_dataset.tsv.gz and full_dataset_clean.tsv.gz have been split in 1 GB parts using the Linux utility called Split. So make sure to join the parts before unzipping. We had to make this change as we had huge issues uploading files larger than 2GB's (hence the delay in the dataset releases). The peer-reviewed publication for this dataset has now been published in Epidemiologia an MDPI journal, and can be accessed here: https://doi.org/10.3390/epidemiologia2030024. Please cite this when using the dataset. Due to the relevance of the COVID-19 global pandemic, we are releasing our dataset of tweets acquired from the Twitter Stream related to COVID-19 chatter. Since our first release we have received additional data from our new collaborators, allowing this resource to grow to its current size. Dedicated data gathering started from March 11th yielding over 4 million tweets a day. We have added additional data provided by our new collaborators from January 27th to March 27th, to provide extra longitudinal coverage. Version 10 added ~1.5 million tweets in the Russian language collected between January 1st and May 8th, gracefully provided to us by: Katya Artemova (NRU HSE) and Elena Tutubalina (KFU). From version 12 we have included daily hashtags, mentions and emoijis and their frequencies the respective zip files. From version 14 we have included the tweet identifiers and their respective language for the clean version of the dataset. Since version 20 we have included language and place location for all tweets. The data collected from the stream captures all languages, but the higher prevalence are: English, Spanish, and French. We release all tweets and retweets on the full_dataset.tsv file (1,379,793,428 unique tweets), and a cleaned version with no retweets on the full_dataset-clean.tsv file (357,656,893 unique tweets). There are several practical reasons for us to leave the retweets, tracing important tweets and their dissemination is one of them. For NLP tasks we provide the top 1000 frequent terms in frequent_terms.csv, the top 1000 bigrams in frequent_bigrams.csv, and the top 1000 trigrams in frequent_trigrams.csv. Some general statistics per day are included for both datasets in the full_dataset-statistics.tsv and full_dataset-clean-statistics.tsv files. For more statistics and some visualizations visit: http://www.panacealab.org/covid19/ More details can be found (and will be updated faster at: https://github.com/thepanacealab/covid19_twitter) and our pre-print about the dataset (https://arxiv.org/abs/2004.03688) As always, the tweets distributed here are only tweet identifiers (with date and time added) due to the terms and conditions of Twitter to re-distribute Twitter data ONLY for research purposes. They need to be hydrated to be used.

2023 CANADIAN TIRE NATIONAL SKATING CHAMPIONSHIPS, TRIBUTE COMMUNITIES CENTRE | OSHAWA, ON JANUARY 9-15, 2023 WATCH LIVE ONLINE HERE The 2023 Canadian Tire National Skating Championships will be held on January 9-15, 2023 in Oshawa, Ontario. Medals were awarded in the disciplines of men's singles, women's singles, pairs, and ice dance on the senior, junior, and novice levels. The results are part of the Canadian selection criteria for the 2023 World Championships, the 2023 Four Continents Championships, and the 2023 World Junior Championships. Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking library4. The washers dataset features 70 defective parts. The gears and screws datasets feature 35 defective, 35 intact and several hundred unannotated parts. Some defects, such as notches and holes, are visible in most images (illuminations) with intensity and texture variations among them, while others, such as scratches, are only visible in a few.fghgj We split the datasets into train and test sets. The train sets contain 32 samples, and the test set 38 samples. Each sample comprises 108 images (each captured under a different illumination angle), an automatically extracted foreground segmentation mask, and a hand-labeled defect segmentation mask.fghgfj This dataset is challenging mainly because: each raw sample consists of 108 gray-scale images of resolution 512×512 and therefore takes 27MB of space; the metallic surfaces produce many specular reflections that sometimes saturate the camera sensors; the annotations are not very precise because the exact extent of defect contours is always subjective; the defects are very sparse also in the spatial dimensions: they cover only about 0.2% of the total image area in gears, 0.8% in screws, and 1.4% in washers; this creates an unbalanced dataset with a highly skewed class representation. gfhj The dataset is organized as follows: each sample resides in the Test, Train, or Unannotated directory; each sample has its own directory which contains the individual images, the foreground, and defect segmentation masks; each image is stored in 8-bit greyscale png format and has a resolution of 512 x 512 pixels; Image file names are formatted using three string fields separated with the underscore character: prefix_sampleNr_illuminationNr.png, where the prefix is e.g. washer, the sampleNr might be a three-digit number 001, and the illuminationNr is formed of 3 digits, first corresponding to the elevation index (1 - highest angle, 9 - lowest angle), and the additional two corresponding to the azimuth index (01-12). Each dataset contains light_vectors.csv, which contains the illumination angles (in lexicographic order of the illuminationNr), and light_intensities.csv that contains the numbers corresponding to the light intensity on the scale from 0 to 127. Please, be aware, that the azimuth angles were not calibrated and might be a few degrees misaligned.fdhfgj These data have been cleaned and integrated with data from COVID-19-TweetIDs and from other sources (e.g., PMC). The result was dataset of 500,314 unique articles along with relevant metadata (e.g., the underlying citation network). We utilized this dataset to produce, for each article, the values of the following impact measures:sdgfdfhfggh Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking (https://github.com/diwifss/PaperRanking) library4.sddfghfggd Influence_alt: Citation-based measure reflecting the total impact of an article. This is the Citation Count of each article, calculated based on the citation network between the articles contained in the BIP4COVID19 dataset.sddggf safs Popularity: Citation-based measure reflecting the current impact of an article. This is based on the AttRank5 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). AttRank alleviates this problem incorporating an attention-based mechanism, akin to a time-restricted version of preferential attachment, to explicitly capture a researcher's preference to read papers which received a lot of attention recently. This is why it is more suitable to capture the current "hype" of an article.asdfujsgdg Colour Science for Python Colour is an open-source Python package providing a comprehensive number of algorithms and datasets for colour science. sdg It is freely available under the New BSD License terms.uiuol Colour is an affiliated project of NumFOCUS, a 501(c)(3) nonprofit in the United States Draft Release Notes The draft release notes of the develop branch are available at this url.uiu Sponsors We are grateful 💖 for the support of our sponsors. If you'd like to join them, please consider becoming a sponsor on OpenCollective.uiu Features Colour features a rich dataset and collection of objects, please see the features in the documentation for more information.iu User Guid Installation Colour and its primary dependencies can be easily installed from the Python Package Index by issuing this command in a shell:oluip $ pip install --user colour-science The detailed installation procedure for the secondary dependencies is described in the Installation Guide. Colour is also available for Anaconda from Continuum Analytics via conda-forge:oiup $ conda install -c conda-forge colour-science Tutorial The static tutorial provides an introduction to Colour. An interactive version is available via Google Colab.oui How-To The Google Colab How-To guide for Colour shows various techniques to solve specific problems and highlights some interesting use cases. Contributing If you would like to contribute to Colour, please refer to the following Contributing guide.oi Colour by Colour Developers Copyright 2013 Colour Developers – colour-developers@colour-science.org This software is released under terms of New BSD License: https://opensource.org/licenses/BSD-3-Clause https://github.com/colour-science/colourolip sf Popularity alternative: An alternative citation-based measure reflecting the current impact of an article (this was the basic popularity measured provided by BIP4COVID19 until version 26). This is based on the RAM6 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). RAM alleviates this problem using an approach known as "time-awareness". This is why it is more suitable to capture the current "hype" of an article. This measure was calculated using the PaperRanking (https://github.com/diwis/PaperRanking) library4.sfbsdf Social Media Attention: The number of tweets related to this article. Relevant data were collected from the COVID-19-TweetIDs dataset. In this version, tweets between 23/6/22-29/6/22 have been considered from the previous dataset.ftgujyol We provide five CSV files, all containing the same information, however each having its entries ordered by a different impact measure. All CSV files are tab separated and have the same columns (PubMed_id, PdfMC_id, DOI, influence_score, popularity_alt_score, popularity score, influence_alt score, tweets count).yjytik Rich offline experience, periodic background sync, push notification functionality, network requests control, improved performance via requests caching are only a few of the functionalities provided by the Service Worker (SW ) API. This new technology, supported by all major browsers, can significantly improve users’ experience by providing the publisher with the technical foundations that would normally require a native application. Albeit the capabilities of this new technique and its important role in the ecosystem of Progressive Web Apps (PWAs), it is still unclear what is their actual purpose on the web, and how publishers leverage the provided functionality in their web applications. In this study, we shed light in the real world deployment of SWs, by conducting the first large scale analysis of the prevalence of SWs in the wild.huiuop We see that SWs are becoming more and more popular, with the adoption increased by 26% only within the last 5 months. Surprisingly, besides their fruitful capabilities, we see that SWs are being mostly used for In-Page Push Advertising, in 65.08% of the SWs that connect with 3rd parties. We highlight that this is a relatively new way for advertisers to bypass ad-blockers and render ads on the user’s displays natively.

The 2023 Dakar Rally is a rally raid event held in Saudi Arabia and the 45th edition of the Dakar Rally organized by Amaury Sport Organisation. The event will take place between 31 December 2022 and 15 January 2023. This will be the fourth time Saudi Arabia had hosted the event. WATCH LIVE ONLINE HERE Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking library4. The washers dataset features 70 defective parts. The gears and screws datasets feature 35 defective, 35 intact and several hundred unannotated parts. Some defects, such as notches and holes, are visible in most images (illuminations) with intensity and texture variations among them, while others, such as scratches, are only visible in a few.fghgj We split the datasets into train and test sets. The train sets contain 32 samples, and the test set 38 samples. Each sample comprises 108 images (each captured under a different illumination angle), an automatically extracted foreground segmentation mask, and a hand-labeled defect segmentation mask.fghgfj This dataset is challenging mainly because: each raw sample consists of 108 gray-scale images of resolution 512×512 and therefore takes 27MB of space; the metallic surfaces produce many specular reflections that sometimes saturate the camera sensors; the annotations are not very precise because the exact extent of defect contours is always subjective; the defects are very sparse also in the spatial dimensions: they cover only about 0.2% of the total image area in gears, 0.8% in screws, and 1.4% in washers; this creates an unbalanced dataset with a highly skewed class representation. gfhj The dataset is organized as follows: each sample resides in the Test, Train, or Unannotated directory; each sample has its own directory which contains the individual images, the foreground, and defect segmentation masks; each image is stored in 8-bit greyscale png format and has a resolution of 512 x 512 pixels; Image file names are formatted using three string fields separated with the underscore character: prefix_sampleNr_illuminationNr.png, where the prefix is e.g. washer, the sampleNr might be a three-digit number 001, and the illuminationNr is formed of 3 digits, first corresponding to the elevation index (1 - highest angle, 9 - lowest angle), and the additional two corresponding to the azimuth index (01-12). Each dataset contains light_vectors.csv, which contains the illumination angles (in lexicographic order of the illuminationNr), and light_intensities.csv that contains the numbers corresponding to the light intensity on the scale from 0 to 127. Please, be aware, that the azimuth angles were not calibrated and might be a few degrees misaligned.fdhfgj These data have been cleaned and integrated with data from COVID-19-TweetIDs and from other sources (e.g., PMC). The result was dataset of 500,314 unique articles along with relevant metadata (e.g., the underlying citation network). We utilized this dataset to produce, for each article, the values of the following impact measures:sdgfdfhfggh Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking (https://github.com/diwifss/PaperRanking) library4.sddfghfggd Influence_alt: Citation-based measure reflecting the total impact of an article. This is the Citation Count of each article, calculated based on the citation network between the articles contained in the BIP4COVID19 dataset.sddggf safs Popularity: Citation-based measure reflecting the current impact of an article. This is based on the AttRank5 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). AttRank alleviates this problem incorporating an attention-based mechanism, akin to a time-restricted version of preferential attachment, to explicitly capture a researcher's preference to read papers which received a lot of attention recently. This is why it is more suitable to capture the current "hype" of an article.asdfujsgdg Colour Science for Python Colour is an open-source Python package providing a comprehensive number of algorithms and datasets for colour science. sdg It is freely available under the New BSD License terms.uiuol Colour is an affiliated project of NumFOCUS, a 501(c)(3) nonprofit in the United States Draft Release Notes The draft release notes of the develop branch are available at this url.uiu Sponsors We are grateful 💖 for the support of our sponsors. If you'd like to join them, please consider becoming a sponsor on OpenCollective.uiu Features Colour features a rich dataset and collection of objects, please see the features in the documentation for more information.iu User Guid Installation Colour and its primary dependencies can be easily installed from the Python Package Index by issuing this command in a shell:oluip $ pip install --user colour-science The detailed installation procedure for the secondary dependencies is described in the Installation Guide. Colour is also available for Anaconda from Continuum Analytics via conda-forge:oiup $ conda install -c conda-forge colour-science Tutorial The static tutorial provides an introduction to Colour. An interactive version is available via Google Colab.oui How-To The Google Colab How-To guide for Colour shows various techniques to solve specific problems and highlights some interesting use cases. Contributing If you would like to contribute to Colour, please refer to the following Contributing guide.oi Colour by Colour Developers Copyright 2013 Colour Developers – colour-developers@colour-science.org This software is released under terms of New BSD License: https://opensource.org/licenses/BSD-3-Clause https://github.com/colour-science/colourolip sf Popularity alternative: An alternative citation-based measure reflecting the current impact of an article (this was the basic popularity measured provided by BIP4COVID19 until version 26). This is based on the RAM6 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). RAM alleviates this problem using an approach known as "time-awareness". This is why it is more suitable to capture the current "hype" of an article. This measure was calculated using the PaperRanking (https://github.com/diwis/PaperRanking) library4.sfbsdf Social Media Attention: The number of tweets related to this article. Relevant data were collected from the COVID-19-TweetIDs dataset. In this version, tweets between 23/6/22-29/6/22 have been considered from the previous dataset.ftgujyol We provide five CSV files, all containing the same information, however each having its entries ordered by a different impact measure. All CSV files are tab separated and have the same columns (PubMed_id, PdfMC_id, DOI, influence_score, popularity_alt_score, popularity score, influence_alt score, tweets count).yjytik Rich offline experience, periodic background sync, push notification functionality, network requests control, improved performance via requests caching are only a few of the functionalities provided by the Service Worker (SW ) API. This new technology, supported by all major browsers, can significantly improve users’ experience by providing the publisher with the technical foundations that would normally require a native application. Albeit the capabilities of this new technique and its important role in the ecosystem of Progressive Web Apps (PWAs), it is still unclear what is their actual purpose on the web, and how publishers leverage the provided functionality in their web applications. In this study, we shed light in the real world deployment of SWs, by conducting the first large scale analysis of the prevalence of SWs in the wild.huiuop We see that SWs are becoming more and more popular, with the adoption increased by 26% only within the last 5 months. Surprisingly, besides their fruitful capabilities, we see that SWs are being mostly used for In-Page Push Advertising, in 65.08% of the SWs that connect with 3rd parties. We highlight that this is a relatively new way for advertisers to bypass ad-blockers and render ads on the user’s displays natively.

The 2023 Dakar Rally is a rally raid event held in Saudi Arabia and the 45th edition of the Dakar Rally organized by Amaury Sport Organisation. The event will take place between 31 December 2022 and 15 January 2023. This will be the fourth time Saudi Arabia had hosted the event. WATCH LIVE ONLINE HERE Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking library4. The washers dataset features 70 defective parts. The gears and screws datasets feature 35 defective, 35 intact and several hundred unannotated parts. Some defects, such as notches and holes, are visible in most images (illuminations) with intensity and texture variations among them, while others, such as scratches, are only visible in a few.fghgj We split the datasets into train and test sets. The train sets contain 32 samples, and the test set 38 samples. Each sample comprises 108 images (each captured under a different illumination angle), an automatically extracted foreground segmentation mask, and a hand-labeled defect segmentation mask.fghgfj This dataset is challenging mainly because: each raw sample consists of 108 gray-scale images of resolution 512×512 and therefore takes 27MB of space; the metallic surfaces produce many specular reflections that sometimes saturate the camera sensors; the annotations are not very precise because the exact extent of defect contours is always subjective; the defects are very sparse also in the spatial dimensions: they cover only about 0.2% of the total image area in gears, 0.8% in screws, and 1.4% in washers; this creates an unbalanced dataset with a highly skewed class representation. gfhj The dataset is organized as follows: each sample resides in the Test, Train, or Unannotated directory; each sample has its own directory which contains the individual images, the foreground, and defect segmentation masks; each image is stored in 8-bit greyscale png format and has a resolution of 512 x 512 pixels; Image file names are formatted using three string fields separated with the underscore character: prefix_sampleNr_illuminationNr.png, where the prefix is e.g. washer, the sampleNr might be a three-digit number 001, and the illuminationNr is formed of 3 digits, first corresponding to the elevation index (1 - highest angle, 9 - lowest angle), and the additional two corresponding to the azimuth index (01-12). Each dataset contains light_vectors.csv, which contains the illumination angles (in lexicographic order of the illuminationNr), and light_intensities.csv that contains the numbers corresponding to the light intensity on the scale from 0 to 127. Please, be aware, that the azimuth angles were not calibrated and might be a few degrees misaligned.fdhfgj These data have been cleaned and integrated with data from COVID-19-TweetIDs and from other sources (e.g., PMC). The result was dataset of 500,314 unique articles along with relevant metadata (e.g., the underlying citation network). We utilized this dataset to produce, for each article, the values of the following impact measures:sdgfdfhfggh Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking (https://github.com/diwifss/PaperRanking) library4.sddfghfggd Influence_alt: Citation-based measure reflecting the total impact of an article. This is the Citation Count of each article, calculated based on the citation network between the articles contained in the BIP4COVID19 dataset.sddggf safs Popularity: Citation-based measure reflecting the current impact of an article. This is based on the AttRank5 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). AttRank alleviates this problem incorporating an attention-based mechanism, akin to a time-restricted version of preferential attachment, to explicitly capture a researcher's preference to read papers which received a lot of attention recently. This is why it is more suitable to capture the current "hype" of an article.asdfujsgdg Colour Science for Python Colour is an open-source Python package providing a comprehensive number of algorithms and datasets for colour science. sdg It is freely available under the New BSD License terms.uiuol Colour is an affiliated project of NumFOCUS, a 501(c)(3) nonprofit in the United States Draft Release Notes The draft release notes of the develop branch are available at this url.uiu Sponsors We are grateful 💖 for the support of our sponsors. If you'd like to join them, please consider becoming a sponsor on OpenCollective.uiu Features Colour features a rich dataset and collection of objects, please see the features in the documentation for more information.iu User Guid Installation Colour and its primary dependencies can be easily installed from the Python Package Index by issuing this command in a shell:oluip $ pip install --user colour-science The detailed installation procedure for the secondary dependencies is described in the Installation Guide. Colour is also available for Anaconda from Continuum Analytics via conda-forge:oiup $ conda install -c conda-forge colour-science Tutorial The static tutorial provides an introduction to Colour. An interactive version is available via Google Colab.oui How-To The Google Colab How-To guide for Colour shows various techniques to solve specific problems and highlights some interesting use cases. Contributing If you would like to contribute to Colour, please refer to the following Contributing guide.oi Colour by Colour Developers Copyright 2013 Colour Developers – colour-developers@colour-science.org This software is released under terms of New BSD License: https://opensource.org/licenses/BSD-3-Clause https://github.com/colour-science/colourolip sf Popularity alternative: An alternative citation-based measure reflecting the current impact of an article (this was the basic popularity measured provided by BIP4COVID19 until version 26). This is based on the RAM6 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). RAM alleviates this problem using an approach known as "time-awareness". This is why it is more suitable to capture the current "hype" of an article. This measure was calculated using the PaperRanking (https://github.com/diwis/PaperRanking) library4.sfbsdf Social Media Attention: The number of tweets related to this article. Relevant data were collected from the COVID-19-TweetIDs dataset. In this version, tweets between 23/6/22-29/6/22 have been considered from the previous dataset.ftgujyol We provide five CSV files, all containing the same information, however each having its entries ordered by a different impact measure. All CSV files are tab separated and have the same columns (PubMed_id, PdfMC_id, DOI, influence_score, popularity_alt_score, popularity score, influence_alt score, tweets count).yjytik Rich offline experience, periodic background sync, push notification functionality, network requests control, improved performance via requests caching are only a few of the functionalities provided by the Service Worker (SW ) API. This new technology, supported by all major browsers, can significantly improve users’ experience by providing the publisher with the technical foundations that would normally require a native application. Albeit the capabilities of this new technique and its important role in the ecosystem of Progressive Web Apps (PWAs), it is still unclear what is their actual purpose on the web, and how publishers leverage the provided functionality in their web applications. In this study, we shed light in the real world deployment of SWs, by conducting the first large scale analysis of the prevalence of SWs in the wild.huiuop We see that SWs are becoming more and more popular, with the adoption increased by 26% only within the last 5 months. Surprisingly, besides their fruitful capabilities, we see that SWs are being mostly used for In-Page Push Advertising, in 65.08% of the SWs that connect with 3rd parties. We highlight that this is a relatively new way for advertisers to bypass ad-blockers and render ads on the user’s displays natively.

2023 CANADIAN TIRE NATIONAL SKATING CHAMPIONSHIPS, TRIBUTE COMMUNITIES CENTRE | OSHAWA, ON JANUARY 9-15, 2023. WATCH LIVE ONLINE HERE The 2023 Canadian Tire National Skating Championships will be held on January 9-15, 2023 in Oshawa, Ontario. Medals were awarded in the disciplines of men's singles, women's singles, pairs, and ice dance on the senior, junior, and novice levels. The results are part of the Canadian selection criteria for the 2023 World Championships, the 2023 Four Continents Championships, and the 2023 World Junior Championships. Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking library4. The washers dataset features 70 defective parts. The gears and screws datasets feature 35 defective, 35 intact and several hundred unannotated parts. Some defects, such as notches and holes, are visible in most images (illuminations) with intensity and texture variations among them, while others, such as scratches, are only visible in a few.fghgj We split the datasets into train and test sets. The train sets contain 32 samples, and the test set 38 samples. Each sample comprises 108 images (each captured under a different illumination angle), an automatically extracted foreground segmentation mask, and a hand-labeled defect segmentation mask.fghgfj This dataset is challenging mainly because: each raw sample consists of 108 gray-scale images of resolution 512×512 and therefore takes 27MB of space; the metallic surfaces produce many specular reflections that sometimes saturate the camera sensors; the annotations are not very precise because the exact extent of defect contours is always subjective; the defects are very sparse also in the spatial dimensions: they cover only about 0.2% of the total image area in gears, 0.8% in screws, and 1.4% in washers; this creates an unbalanced dataset with a highly skewed class representation. gfhj The dataset is organized as follows: each sample resides in the Test, Train, or Unannotated directory; each sample has its own directory which contains the individual images, the foreground, and defect segmentation masks; each image is stored in 8-bit greyscale png format and has a resolution of 512 x 512 pixels; Image file names are formatted using three string fields separated with the underscore character: prefix_sampleNr_illuminationNr.png, where the prefix is e.g. washer, the sampleNr might be a three-digit number 001, and the illuminationNr is formed of 3 digits, first corresponding to the elevation index (1 - highest angle, 9 - lowest angle), and the additional two corresponding to the azimuth index (01-12). Each dataset contains light_vectors.csv, which contains the illumination angles (in lexicographic order of the illuminationNr), and light_intensities.csv that contains the numbers corresponding to the light intensity on the scale from 0 to 127. Please, be aware, that the azimuth angles were not calibrated and might be a few degrees misaligned.fdhfgj These data have been cleaned and integrated with data from COVID-19-TweetIDs and from other sources (e.g., PMC). The result was dataset of 500,314 unique articles along with relevant metadata (e.g., the underlying citation network). We utilized this dataset to produce, for each article, the values of the following impact measures:sdgfdfhfggh Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking (https://github.com/diwifss/PaperRanking) library4.sddfghfggd Influence_alt: Citation-based measure reflecting the total impact of an article. This is the Citation Count of each article, calculated based on the citation network between the articles contained in the BIP4COVID19 dataset.sddggf safs Popularity: Citation-based measure reflecting the current impact of an article. This is based on the AttRank5 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). AttRank alleviates this problem incorporating an attention-based mechanism, akin to a time-restricted version of preferential attachment, to explicitly capture a researcher's preference to read papers which received a lot of attention recently. This is why it is more suitable to capture the current "hype" of an article.asdfujsgdg Colour Science for Python Colour is an open-source Python package providing a comprehensive number of algorithms and datasets for colour science. sdg It is freely available under the New BSD License terms.uiuol Colour is an affiliated project of NumFOCUS, a 501(c)(3) nonprofit in the United States Draft Release Notes The draft release notes of the develop branch are available at this url.uiu Sponsors We are grateful 💖 for the support of our sponsors. If you'd like to join them, please consider becoming a sponsor on OpenCollective.uiu Features Colour features a rich dataset and collection of objects, please see the features in the documentation for more information.iu User Guid Installation Colour and its primary dependencies can be easily installed from the Python Package Index by issuing this command in a shell:oluip $ pip install --user colour-science The detailed installation procedure for the secondary dependencies is described in the Installation Guide. Colour is also available for Anaconda from Continuum Analytics via conda-forge:oiup $ conda install -c conda-forge colour-science Tutorial The static tutorial provides an introduction to Colour. An interactive version is available via Google Colab.oui How-To The Google Colab How-To guide for Colour shows various techniques to solve specific problems and highlights some interesting use cases. Contributing If you would like to contribute to Colour, please refer to the following Contributing guide.oi Colour by Colour Developers Copyright 2013 Colour Developers – colour-developers@colour-science.org This software is released under terms of New BSD License: https://opensource.org/licenses/BSD-3-Clause https://github.com/colour-science/colourolip sf Popularity alternative: An alternative citation-based measure reflecting the current impact of an article (this was the basic popularity measured provided by BIP4COVID19 until version 26). This is based on the RAM6 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). RAM alleviates this problem using an approach known as "time-awareness". This is why it is more suitable to capture the current "hype" of an article. This measure was calculated using the PaperRanking (https://github.com/diwis/PaperRanking) library4.sfbsdf Social Media Attention: The number of tweets related to this article. Relevant data were collected from the COVID-19-TweetIDs dataset. In this version, tweets between 23/6/22-29/6/22 have been considered from the previous dataset.ftgujyol We provide five CSV files, all containing the same information, however each having its entries ordered by a different impact measure. All CSV files are tab separated and have the same columns (PubMed_id, PdfMC_id, DOI, influence_score, popularity_alt_score, popularity score, influence_alt score, tweets count).yjytik Rich offline experience, periodic background sync, push notification functionality, network requests control, improved performance via requests caching are only a few of the functionalities provided by the Service Worker (SW ) API. This new technology, supported by all major browsers, can significantly improve users’ experience by providing the publisher with the technical foundations that would normally require a native application. Albeit the capabilities of this new technique and its important role in the ecosystem of Progressive Web Apps (PWAs), it is still unclear what is their actual purpose on the web, and how publishers leverage the provided functionality in their web applications. In this study, we shed light in the real world deployment of SWs, by conducting the first large scale analysis of the prevalence of SWs in the wild.huiuop We see that SWs are becoming more and more popular, with the adoption increased by 26% only within the last 5 months. Surprisingly, besides their fruitful capabilities, we see that SWs are being mostly used for In-Page Push Advertising, in 65.08% of the SWs that connect with 3rd parties. We highlight that this is a relatively new way for advertisers to bypass ad-blockers and render ads on the user’s displays natively.

The 2023 NCAA Division I Football Championship Game is a college football game that is scheduled to be played on January 8, 2023, at Toyota Stadium in Frisco, Texas. The game will determine the national champion of NCAA Division I FCS for the 2022 season. The game will feature the two finalists of the 24-team playoff bracket, which began on November 26, 2022. The game is scheduled to begin at 1:00 p.m. CST and will be televised on ABC and ESPN+ WATCH LIVE ONLINE HERE The 2022–23 FIS Alpine Ski Europa Cup is the fifty second consecutive Europa Cup season, the second international level competition in alpine skiing. Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking library4. The washers dataset features 70 defective parts. The gears and screws datasets feature 35 defective, 35 intact and several hundred unannotated parts. Some defects, such as notches and holes, are visible in most images (illuminations) with intensity and texture variations among them, while others, such as scratches, are only visible in a few.fghgj We split the datasets into train and test sets. The train sets contain 32 samples, and the test set 38 samples. Each sample comprises 108 images (each captured under a different illumination angle), an automatically extracted foreground segmentation mask, and a hand-labeled defect segmentation mask.fghgfj This dataset is challenging mainly because: each raw sample consists of 108 gray-scale images of resolution 512×512 and therefore takes 27MB of space; the metallic surfaces produce many specular reflections that sometimes saturate the camera sensors; the annotations are not very precise because the exact extent of defect contours is always subjective; the defects are very sparse also in the spatial dimensions: they cover only about 0.2% of the total image area in gears, 0.8% in screws, and 1.4% in washers; this creates an unbalanced dataset with a highly skewed class representation. gfhj The dataset is organized as follows: each sample resides in the Test, Train, or Unannotated directory; each sample has its own directory which contains the individual images, the foreground, and defect segmentation masks; each image is stored in 8-bit greyscale png format and has a resolution of 512 x 512 pixels; Image file names are formatted using three string fields separated with the underscore character: prefix_sampleNr_illuminationNr.png, where the prefix is e.g. washer, the sampleNr might be a three-digit number 001, and the illuminationNr is formed of 3 digits, first corresponding to the elevation index (1 - highest angle, 9 - lowest angle), and the additional two corresponding to the azimuth index (01-12). Each dataset contains light_vectors.csv, which contains the illumination angles (in lexicographic order of the illuminationNr), and light_intensities.csv that contains the numbers corresponding to the light intensity on the scale from 0 to 127. Please, be aware, that the azimuth angles were not calibrated and might be a few degrees misaligned.fdhfgj These data have been cleaned and integrated with data from COVID-19-TweetIDs and from other sources (e.g., PMC). The result was dataset of 500,314 unique articles along with relevant metadata (e.g., the underlying citation network). We utilized this dataset to produce, for each article, the values of the following impact measures:sdgfdfhfggh Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking (https://github.com/diwifss/PaperRanking) library4.sddfghfggd Influence_alt: Citation-based measure reflecting the total impact of an article. This is the Citation Count of each article, calculated based on the citation network between the articles contained in the BIP4COVID19 dataset.sddggf safs Popularity: Citation-based measure reflecting the current impact of an article. This is based on the AttRank5 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). AttRank alleviates this problem incorporating an attention-based mechanism, akin to a time-restricted version of preferential attachment, to explicitly capture a researcher's preference to read papers which received a lot of attention recently. This is why it is more suitable to capture the current "hype" of an article.asdfujsgdg Colour Science for Python Colour is an open-source Python package providing a comprehensive number of algorithms and datasets for colour science. sdg It is freely available under the New BSD License terms.uiuol Colour is an affiliated project of NumFOCUS, a 501(c)(3) nonprofit in the United States Draft Release Notes The draft release notes of the develop branch are available at this url.uiu Sponsors We are grateful 💖 for the support of our sponsors. If you'd like to join them, please consider becoming a sponsor on OpenCollective.uiu Features Colour features a rich dataset and collection of objects, please see the features in the documentation for more information.iu User Guid Installation Colour and its primary dependencies can be easily installed from the Python Package Index by issuing this command in a shell:oluip $ pip install --user colour-science The detailed installation procedure for the secondary dependencies is described in the Installation Guide. Colour is also available for Anaconda from Continuum Analytics via conda-forge:oiup $ conda install -c conda-forge colour-science Tutorial The static tutorial provides an introduction to Colour. An interactive version is available via Google Colab.oui How-To The Google Colab How-To guide for Colour shows various techniques to solve specific problems and highlights some interesting use cases. Contributing If you would like to contribute to Colour, please refer to the following Contributing guide.oi Colour by Colour Developers Copyright 2013 Colour Developers – colour-developers@colour-science.org This software is released under terms of New BSD License: https://opensource.org/licenses/BSD-3-Clause https://github.com/colour-science/colourolip sf Popularity alternative: An alternative citation-based measure reflecting the current impact of an article (this was the basic popularity measured provided by BIP4COVID19 until version 26). This is based on the RAM6 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). RAM alleviates this problem using an approach known as "time-awareness". This is why it is more suitable to capture the current "hype" of an article. This measure was calculated using the PaperRanking (https://github.com/diwis/PaperRanking) library4.sfbsdf Social Media Attention: The number of tweets related to this article. Relevant data were collected from the COVID-19-TweetIDs dataset. In this version, tweets between 23/6/22-29/6/22 have been considered from the previous dataset.ftgujyol We provide five CSV files, all containing the same information, however each having its entries ordered by a different impact measure. All CSV files are tab separated and have the same columns (PubMed_id, PdfMC_id, DOI, influence_score, popularity_alt_score, popularity score, influence_alt score, tweets count).yjytik Rich offline experience, periodic background sync, push notification functionality, network requests control, improved performance via requests caching are only a few of the functionalities provided by the Service Worker (SW ) API. This new technology, supported by all major browsers, can significantly improve users’ experience by providing the publisher with the technical foundations that would normally require a native application. Albeit the capabilities of this new technique and its important role in the ecosystem of Progressive Web Apps (PWAs), it is still unclear what is their actual purpose on the web, and how publishers leverage the provided functionality in their web applications. In this study, we shed light in the real world deployment of SWs, by conducting the first large scale analysis of the prevalence of SWs in the wild.huiuop We see that SWs are becoming more and more popular, with the adoption increased by 26% only within the last 5 months. Surprisingly, besides their fruitful capabilities, we see that SWs are being mostly used for In-Page Push Advertising, in 65.08% of the SWs that connect with 3rd parties. We highlight that this is a relatively new way for advertisers to bypass ad-blockers and render ads on the user’s displays natively.

Alpine Skiing FIS Europa Cup 2022 - 2023 (Zauchensee) Zauchensee (AUT) - 8 January 2023 - 12 January 2023 . WATCH LIVE ONLINE HERE The 2022–23 FIS Alpine Ski Europa Cup is the fifty second consecutive Europa Cup season, the second international level competition in alpine skiing. Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking library4. The washers dataset features 70 defective parts. The gears and screws datasets feature 35 defective, 35 intact and several hundred unannotated parts. Some defects, such as notches and holes, are visible in most images (illuminations) with intensity and texture variations among them, while others, such as scratches, are only visible in a few.fghgj We split the datasets into train and test sets. The train sets contain 32 samples, and the test set 38 samples. Each sample comprises 108 images (each captured under a different illumination angle), an automatically extracted foreground segmentation mask, and a hand-labeled defect segmentation mask.fghgfj This dataset is challenging mainly because: each raw sample consists of 108 gray-scale images of resolution 512×512 and therefore takes 27MB of space; the metallic surfaces produce many specular reflections that sometimes saturate the camera sensors; the annotations are not very precise because the exact extent of defect contours is always subjective; the defects are very sparse also in the spatial dimensions: they cover only about 0.2% of the total image area in gears, 0.8% in screws, and 1.4% in washers; this creates an unbalanced dataset with a highly skewed class representation. gfhj The dataset is organized as follows: each sample resides in the Test, Train, or Unannotated directory; each sample has its own directory which contains the individual images, the foreground, and defect segmentation masks; each image is stored in 8-bit greyscale png format and has a resolution of 512 x 512 pixels; Image file names are formatted using three string fields separated with the underscore character: prefix_sampleNr_illuminationNr.png, where the prefix is e.g. washer, the sampleNr might be a three-digit number 001, and the illuminationNr is formed of 3 digits, first corresponding to the elevation index (1 - highest angle, 9 - lowest angle), and the additional two corresponding to the azimuth index (01-12). Each dataset contains light_vectors.csv, which contains the illumination angles (in lexicographic order of the illuminationNr), and light_intensities.csv that contains the numbers corresponding to the light intensity on the scale from 0 to 127. Please, be aware, that the azimuth angles were not calibrated and might be a few degrees misaligned.fdhfgj These data have been cleaned and integrated with data from COVID-19-TweetIDs and from other sources (e.g., PMC). The result was dataset of 500,314 unique articles along with relevant metadata (e.g., the underlying citation network). We utilized this dataset to produce, for each article, the values of the following impact measures:sdgfdfhfggh Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking (https://github.com/diwifss/PaperRanking) library4.sddfghfggd Influence_alt: Citation-based measure reflecting the total impact of an article. This is the Citation Count of each article, calculated based on the citation network between the articles contained in the BIP4COVID19 dataset.sddggf safs Popularity: Citation-based measure reflecting the current impact of an article. This is based on the AttRank5 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). AttRank alleviates this problem incorporating an attention-based mechanism, akin to a time-restricted version of preferential attachment, to explicitly capture a researcher's preference to read papers which received a lot of attention recently. This is why it is more suitable to capture the current "hype" of an article.asdfujsgdg Colour Science for Python Colour is an open-source Python package providing a comprehensive number of algorithms and datasets for colour science. sdg It is freely available under the New BSD License terms.uiuol Colour is an affiliated project of NumFOCUS, a 501(c)(3) nonprofit in the United States Draft Release Notes The draft release notes of the develop branch are available at this url.uiu Sponsors We are grateful 💖 for the support of our sponsors. If you'd like to join them, please consider becoming a sponsor on OpenCollective.uiu Features Colour features a rich dataset and collection of objects, please see the features in the documentation for more information.iu User Guid Installation Colour and its primary dependencies can be easily installed from the Python Package Index by issuing this command in a shell:oluip $ pip install --user colour-science The detailed installation procedure for the secondary dependencies is described in the Installation Guide. Colour is also available for Anaconda from Continuum Analytics via conda-forge:oiup $ conda install -c conda-forge colour-science Tutorial The static tutorial provides an introduction to Colour. An interactive version is available via Google Colab.oui How-To The Google Colab How-To guide for Colour shows various techniques to solve specific problems and highlights some interesting use cases. Contributing If you would like to contribute to Colour, please refer to the following Contributing guide.oi Colour by Colour Developers Copyright 2013 Colour Developers – colour-developers@colour-science.org This software is released under terms of New BSD License: https://opensource.org/licenses/BSD-3-Clause https://github.com/colour-science/colourolip sf Popularity alternative: An alternative citation-based measure reflecting the current impact of an article (this was the basic popularity measured provided by BIP4COVID19 until version 26). This is based on the RAM6 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). RAM alleviates this problem using an approach known as "time-awareness". This is why it is more suitable to capture the current "hype" of an article. This measure was calculated using the PaperRanking (https://github.com/diwis/PaperRanking) library4.sfbsdf Social Media Attention: The number of tweets related to this article. Relevant data were collected from the COVID-19-TweetIDs dataset. In this version, tweets between 23/6/22-29/6/22 have been considered from the previous dataset.ftgujyol We provide five CSV files, all containing the same information, however each having its entries ordered by a different impact measure. All CSV files are tab separated and have the same columns (PubMed_id, PdfMC_id, DOI, influence_score, popularity_alt_score, popularity score, influence_alt score, tweets count).yjytik Rich offline experience, periodic background sync, push notification functionality, network requests control, improved performance via requests caching are only a few of the functionalities provided by the Service Worker (SW ) API. This new technology, supported by all major browsers, can significantly improve users’ experience by providing the publisher with the technical foundations that would normally require a native application. Albeit the capabilities of this new technique and its important role in the ecosystem of Progressive Web Apps (PWAs), it is still unclear what is their actual purpose on the web, and how publishers leverage the provided functionality in their web applications. In this study, we shed light in the real world deployment of SWs, by conducting the first large scale analysis of the prevalence of SWs in the wild.huiuop We see that SWs are becoming more and more popular, with the adoption increased by 26% only within the last 5 months. Surprisingly, besides their fruitful capabilities, we see that SWs are being mostly used for In-Page Push Advertising, in 65.08% of the SWs that connect with 3rd parties. We highlight that this is a relatively new way for advertisers to bypass ad-blockers and render ads on the user’s displays natively.

The 2023 IIHF Under-18 Women's World Championship will take place January 8-15, 2023, in Östersund, Sweden. Team USA will join Canada, Finland, and Sweden in Group A, while Czechia, Slovakia, Switzerland, and Japan will compete in Group B. WATCH LIVE ONLINE HERE Most recently, the 2022 IIHF Under-18 Women's World Championship took place June 6-13, 2022, in Madison, Wis. The U.S. opened the competition with a 3-0 record, defeating Sweden, 6-1, Finland, 5-0 and Canada, 7-0 in the preliminary round. The team then pulled out a hard-fought, 3-2, victory over Sweden in the semifinal round to advance to its 14th-straight Gold Medal Game. The U.S. took home silver, dropping the final game to Canada, 3-2. Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking library4. The washers dataset features 70 defective parts. The gears and screws datasets feature 35 defective, 35 intact and several hundred unannotated parts. Some defects, such as notches and holes, are visible in most images (illuminations) with intensity and texture variations among them, while others, such as scratches, are only visible in a few.fghgj We split the datasets into train and test sets. The train sets contain 32 samples, and the test set 38 samples. Each sample comprises 108 images (each captured under a different illumination angle), an automatically extracted foreground segmentation mask, and a hand-labeled defect segmentation mask.fghgfj This dataset is challenging mainly because: each raw sample consists of 108 gray-scale images of resolution 512×512 and therefore takes 27MB of space; the metallic surfaces produce many specular reflections that sometimes saturate the camera sensors; the annotations are not very precise because the exact extent of defect contours is always subjective; the defects are very sparse also in the spatial dimensions: they cover only about 0.2% of the total image area in gears, 0.8% in screws, and 1.4% in washers; this creates an unbalanced dataset with a highly skewed class representation. gfhj The dataset is organized as follows: each sample resides in the Test, Train, or Unannotated directory; each sample has its own directory which contains the individual images, the foreground, and defect segmentation masks; each image is stored in 8-bit greyscale png format and has a resolution of 512 x 512 pixels; Image file names are formatted using three string fields separated with the underscore character: prefix_sampleNr_illuminationNr.png, where the prefix is e.g. washer, the sampleNr might be a three-digit number 001, and the illuminationNr is formed of 3 digits, first corresponding to the elevation index (1 - highest angle, 9 - lowest angle), and the additional two corresponding to the azimuth index (01-12). Each dataset contains light_vectors.csv, which contains the illumination angles (in lexicographic order of the illuminationNr), and light_intensities.csv that contains the numbers corresponding to the light intensity on the scale from 0 to 127. Please, be aware, that the azimuth angles were not calibrated and might be a few degrees misaligned.fdhfgj These data have been cleaned and integrated with data from COVID-19-TweetIDs and from other sources (e.g., PMC). The result was dataset of 500,314 unique articles along with relevant metadata (e.g., the underlying citation network). We utilized this dataset to produce, for each article, the values of the following impact measures:sdgfdfhfggh Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking (https://github.com/diwifss/PaperRanking) library4.sddfghfggd Influence_alt: Citation-based measure reflecting the total impact of an article. This is the Citation Count of each article, calculated based on the citation network between the articles contained in the BIP4COVID19 dataset.sddggf safs Popularity: Citation-based measure reflecting the current impact of an article. This is based on the AttRank5 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). AttRank alleviates this problem incorporating an attention-based mechanism, akin to a time-restricted version of preferential attachment, to explicitly capture a researcher's preference to read papers which received a lot of attention recently. This is why it is more suitable to capture the current "hype" of an article.asdfujsgdg Colour Science for Python Colour is an open-source Python package providing a comprehensive number of algorithms and datasets for colour science. sdg It is freely available under the New BSD License terms.uiuol Colour is an affiliated project of NumFOCUS, a 501(c)(3) nonprofit in the United States Draft Release Notes The draft release notes of the develop branch are available at this url.uiu Sponsors We are grateful 💖 for the support of our sponsors. If you'd like to join them, please consider becoming a sponsor on OpenCollective.uiu Features Colour features a rich dataset and collection of objects, please see the features in the documentation for more information.iu User Guid Installation Colour and its primary dependencies can be easily installed from the Python Package Index by issuing this command in a shell:oluip $ pip install --user colour-science The detailed installation procedure for the secondary dependencies is described in the Installation Guide. Colour is also available for Anaconda from Continuum Analytics via conda-forge:oiup $ conda install -c conda-forge colour-science Tutorial The static tutorial provides an introduction to Colour. An interactive version is available via Google Colab.oui How-To The Google Colab How-To guide for Colour shows various techniques to solve specific problems and highlights some interesting use cases. Contributing If you would like to contribute to Colour, please refer to the following Contributing guide.oi Colour by Colour Developers Copyright 2013 Colour Developers – colour-developers@colour-science.org This software is released under terms of New BSD License: https://opensource.org/licenses/BSD-3-Clause https://github.com/colour-science/colourolip sf Popularity alternative: An alternative citation-based measure reflecting the current impact of an article (this was the basic popularity measured provided by BIP4COVID19 until version 26). This is based on the RAM6 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). RAM alleviates this problem using an approach known as "time-awareness". This is why it is more suitable to capture the current "hype" of an article. This measure was calculated using the PaperRanking (https://github.com/diwis/PaperRanking) library4.sfbsdf Social Media Attention: The number of tweets related to this article. Relevant data were collected from the COVID-19-TweetIDs dataset. In this version, tweets between 23/6/22-29/6/22 have been considered from the previous dataset.ftgujyol We provide five CSV files, all containing the same information, however each having its entries ordered by a different impact measure. All CSV files are tab separated and have the same columns (PubMed_id, PdfMC_id, DOI, influence_score, popularity_alt_score, popularity score, influence_alt score, tweets count).yjytik Rich offline experience, periodic background sync, push notification functionality, network requests control, improved performance via requests caching are only a few of the functionalities provided by the Service Worker (SW ) API. This new technology, supported by all major browsers, can significantly improve users’ experience by providing the publisher with the technical foundations that would normally require a native application. Albeit the capabilities of this new technique and its important role in the ecosystem of Progressive Web Apps (PWAs), it is still unclear what is their actual purpose on the web, and how publishers leverage the provided functionality in their web applications. In this study, we shed light in the real world deployment of SWs, by conducting the first large scale analysis of the prevalence of SWs in the wild.huiuop We see that SWs are becoming more and more popular, with the adoption increased by 26% only within the last 5 months. Surprisingly, besides their fruitful capabilities, we see that SWs are being mostly used for In-Page Push Advertising, in 65.08% of the SWs that connect with 3rd parties. We highlight that this is a relatively new way for advertisers to bypass ad-blockers and render ads on the user’s displays natively.

MISS EARTH USA NATIONAL PAGEANT. The 2023 Miss Earth USA national pageant will take place in Orlando from January 2-8, 2023. WATCH LIVE ONLINE HERE Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking library4. The washers dataset features 70 defective parts. The gears and screws datasets feature 35 defective, 35 intact and several hundred unannotated parts. Some defects, such as notches and holes, are visible in most images (illuminations) with intensity and texture variations among them, while others, such as scratches, are only visible in a few.fghgj We split the datasets into train and test sets. The train sets contain 32 samples, and the test set 38 samples. Each sample comprises 108 images (each captured under a different illumination angle), an automatically extracted foreground segmentation mask, and a hand-labeled defect segmentation mask.fghgfj This dataset is challenging mainly because: each raw sample consists of 108 gray-scale images of resolution 512×512 and therefore takes 27MB of space; the metallic surfaces produce many specular reflections that sometimes saturate the camera sensors; the annotations are not very precise because the exact extent of defect contours is always subjective; the defects are very sparse also in the spatial dimensions: they cover only about 0.2% of the total image area in gears, 0.8% in screws, and 1.4% in washers; this creates an unbalanced dataset with a highly skewed class representation. gfhj The dataset is organized as follows: each sample resides in the Test, Train, or Unannotated directory; each sample has its own directory which contains the individual images, the foreground, and defect segmentation masks; each image is stored in 8-bit greyscale png format and has a resolution of 512 x 512 pixels; Image file names are formatted using three string fields separated with the underscore character: prefix_sampleNr_illuminationNr.png, where the prefix is e.g. washer, the sampleNr might be a three-digit number 001, and the illuminationNr is formed of 3 digits, first corresponding to the elevation index (1 - highest angle, 9 - lowest angle), and the additional two corresponding to the azimuth index (01-12). Each dataset contains light_vectors.csv, which contains the illumination angles (in lexicographic order of the illuminationNr), and light_intensities.csv that contains the numbers corresponding to the light intensity on the scale from 0 to 127. Please, be aware, that the azimuth angles were not calibrated and might be a few degrees misaligned.fdhfgj These data have been cleaned and integrated with data from COVID-19-TweetIDs and from other sources (e.g., PMC). The result was dataset of 500,314 unique articles along with relevant metadata (e.g., the underlying citation network). We utilized this dataset to produce, for each article, the values of the following impact measures:sdgfdfhfggh Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking (https://github.com/diwifss/PaperRanking) library4.sddfghfggd Influence_alt: Citation-based measure reflecting the total impact of an article. This is the Citation Count of each article, calculated based on the citation network between the articles contained in the BIP4COVID19 dataset.sddggf safs Popularity: Citation-based measure reflecting the current impact of an article. This is based on the AttRank5 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). AttRank alleviates this problem incorporating an attention-based mechanism, akin to a time-restricted version of preferential attachment, to explicitly capture a researcher's preference to read papers which received a lot of attention recently. This is why it is more suitable to capture the current "hype" of an article.asdfujsgdg Colour Science for Python Colour is an open-source Python package providing a comprehensive number of algorithms and datasets for colour science. sdg It is freely available under the New BSD License terms.uiuol Colour is an affiliated project of NumFOCUS, a 501(c)(3) nonprofit in the United States Draft Release Notes The draft release notes of the develop branch are available at this url.uiu Sponsors We are grateful 💖 for the support of our sponsors. If you'd like to join them, please consider becoming a sponsor on OpenCollective.uiu Features Colour features a rich dataset and collection of objects, please see the features in the documentation for more information.iu User Guid Installation Colour and its primary dependencies can be easily installed from the Python Package Index by issuing this command in a shell:oluip $ pip install --user colour-science The detailed installation procedure for the secondary dependencies is described in the Installation Guide. Colour is also available for Anaconda from Continuum Analytics via conda-forge:oiup $ conda install -c conda-forge colour-science Tutorial The static tutorial provides an introduction to Colour. An interactive version is available via Google Colab.oui How-To The Google Colab How-To guide for Colour shows various techniques to solve specific problems and highlights some interesting use cases. Contributing If you would like to contribute to Colour, please refer to the following Contributing guide.oi Colour by Colour Developers Copyright 2013 Colour Developers – colour-developers@colour-science.org This software is released under terms of New BSD License: https://opensource.org/licenses/BSD-3-Clause https://github.com/colour-science/colourolip sf Popularity alternative: An alternative citation-based measure reflecting the current impact of an article (this was the basic popularity measured provided by BIP4COVID19 until version 26). This is based on the RAM6 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). RAM alleviates this problem using an approach known as "time-awareness". This is why it is more suitable to capture the current "hype" of an article. This measure was calculated using the PaperRanking (https://github.com/diwis/PaperRanking) library4.sfbsdf Social Media Attention: The number of tweets related to this article. Relevant data were collected from the COVID-19-TweetIDs dataset. In this version, tweets between 23/6/22-29/6/22 have been considered from the previous dataset.ftgujyol We provide five CSV files, all containing the same information, however each having its entries ordered by a different impact measure. All CSV files are tab separated and have the same columns (PubMed_id, PdfMC_id, DOI, influence_score, popularity_alt_score, popularity score, influence_alt score, tweets count).yjytik Rich offline experience, periodic background sync, push notification functionality, network requests control, improved performance via requests caching are only a few of the functionalities provided by the Service Worker (SW ) API. This new technology, supported by all major browsers, can significantly improve users’ experience by providing the publisher with the technical foundations that would normally require a native application. Albeit the capabilities of this new technique and its important role in the ecosystem of Progressive Web Apps (PWAs), it is still unclear what is their actual purpose on the web, and how publishers leverage the provided functionality in their web applications. In this study, we shed light in the real world deployment of SWs, by conducting the first large scale analysis of the prevalence of SWs in the wild.huiuop We see that SWs are becoming more and more popular, with the adoption increased by 26% only within the last 5 months. Surprisingly, besides their fruitful capabilities, we see that SWs are being mostly used for In-Page Push Advertising, in 65.08% of the SWs that connect with 3rd parties. We highlight that this is a relatively new way for advertisers to bypass ad-blockers and render ads on the user’s displays natively.

The 2022 Music City Bowl is a college football bowl game that is scheduled to be played on December 31, 2022, at Nissan Stadium in Nashville, Tennessee. The 24th annual Music City Bowl, the game will feature Iowa from the Big Ten Conference and Kentucky from the Southeastern Conference. Iowa vs. Kentucky Live Stream ✅✅Watch Free 2022 Music City Bowl Live Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking (https://github.com/diwis/zdhPaperRanking) library4. The washers dataset features 70 defective parts. The gears and screws datasets feature 35 defective, 35 intact and several hundred unannotated parts. Some defects, such as notches and holes, are visible in most images (illuminations) with intensity and texture variations among them, while others, such as scratches, are only visible in a few.fghgj We split the datasets into train and test sets. The train sets contain 32 samples, and the test set 38 samples. Each sample comprises 108 images (each captured under a different illumination angle), an automatically extracted foreground segmentation mask, and a hand-labeled defect segmentation mask.fghgfj This dataset is challenging mainly because: each raw sample consists of 108 gray-scale images of resolution 512×512 and therefore takes 27MB of space; the metallic surfaces produce many specular reflections that sometimes saturate the camera sensors; the annotations are not very precise because the exact extent of defect contours is always subjective; the defects are very sparse also in the spatial dimensions: they cover only about 0.2% of the total image area in gears, 0.8% in screws, and 1.4% in washers; this creates an unbalanced dataset with a highly skewed class representation. gfhj The dataset is organized as follows: each sample resides in the Test, Train, or Unannotated directory; each sample has its own directory which contains the individual images, the foreground, and defect segmentation masks; each image is stored in 8-bit greyscale png format and has a resolution of 512 x 512 pixels; Image file names are formatted using three string fields separated with the underscore character: prefix_sampleNr_illuminationNr.png, where the prefix is e.g. washer, the sampleNr might be a three-digit number 001, and the illuminationNr is formed of 3 digits, first corresponding to the elevation index (1 - highest angle, 9 - lowest angle), and the additional two corresponding to the azimuth index (01-12). Each dataset contains light_vectors.csv, which contains the illumination angles (in lexicographic order of the illuminationNr), and light_intensities.csv that contains the numbers corresponding to the light intensity on the scale from 0 to 127. Please, be aware, that the azimuth angles were not calibrated and might be a few degrees misaligned.fdhfgj These data have been cleaned and integrated with data from COVID-19-TweetIDs and from other sources (e.g., PMC). The result was dataset of 500,314 unique articles along with relevant metadata (e.g., the underlying citation network). We utilized this dataset to produce, for each article, the values of the following impact measures:sdgfdfhfggh Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking (https://github.com/diwifss/PaperRanking) library4.sddfghfggd Influence_alt: Citation-based measure reflecting the total impact of an article. This is the Citation Count of each article, calculated based on the citation network between the articles contained in the BIP4COVID19 dataset.sddggf safs Popularity: Citation-based measure reflecting the current impact of an article. This is based on the AttRank5 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). AttRank alleviates this problem incorporating an attention-based mechanism, akin to a time-restricted version of preferential attachment, to explicitly capture a researcher's preference to read papers which received a lot of attention recently. This is why it is more suitable to capture the current "hype" of an article.asdfujsgdg Colour Science for Python Colour is an open-source Python package providing a comprehensive number of algorithms and datasets for colour science. sdg It is freely available under the New BSD License terms.uiuol Colour is an affiliated project of NumFOCUS, a 501(c)(3) nonprofit in the United States Draft Release Notes The draft release notes of the develop branch are available at this url.uiu Sponsors We are grateful 💖 for the support of our sponsors. If you'd like to join them, please consider becoming a sponsor on OpenCollective.uiu Features Colour features a rich dataset and collection of objects, please see the features in the documentation for more information.iu User Guid Installation Colour and its primary dependencies can be easily installed from the Python Package Index by issuing this command in a shell:oluip $ pip install --user colour-science The detailed installation procedure for the secondary dependencies is described in the Installation Guide. Colour is also available for Anaconda from Continuum Analytics via conda-forge:oiup $ conda install -c conda-forge colour-science Tutorial The static tutorial provides an introduction to Colour. An interactive version is available via Google Colab.oui How-To The Google Colab How-To guide for Colour shows various techniques to solve specific problems and highlights some interesting use cases. Contributing If you would like to contribute to Colour, please refer to the following Contributing guide.oi Colour by Colour Developers Copyright 2013 Colour Developers – colour-developers@colour-science.org This software is released under terms of New BSD License: https://opensource.org/licenses/BSD-3-Clause https://github.com/colour-science/colourolip sf Popularity alternative: An alternative citation-based measure reflecting the current impact of an article (this was the basic popularity measured provided by BIP4COVID19 until version 26). This is based on the RAM6 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). RAM alleviates this problem using an approach known as "time-awareness". This is why it is more suitable to capture the current "hype" of an article. This measure was calculated using the PaperRanking (https://github.com/diwis/PaperRanking) library4.sfbsdf Social Media Attention: The number of tweets related to this article. Relevant data were collected from the COVID-19-TweetIDs dataset. In this version, tweets between 23/6/22-29/6/22 have been considered from the previous dataset.ftgujyol We provide five CSV files, all containing the same information, however each having its entries ordered by a different impact measure. All CSV files are tab separated and have the same columns (PubMed_id, PdfMC_id, DOI, influence_score, popularity_alt_score, popularity score, influence_alt score, tweets count).yjytik Rich offline experience, periodic background sync, push notification functionality, network requests control, improved performance via requests caching are only a few of the functionalities provided by the Service Worker (SW ) API. This new technology, supported by all major browsers, can significantly improve users’ experience by providing the publisher with the technical foundations that would normally require a native application. Albeit the capabilities of this new technique and its important role in the ecosystem of Progressive Web Apps (PWAs), it is still unclear what is their actual purpose on the web, and how publishers leverage the provided functionality in their web applications. In this study, we shed light in the real world deployment of SWs, by conducting the first large scale analysis of the prevalence of SWs in the wild.huiuop We see that SWs are becoming more and more popular, with the adoption increased by 26% only within the last 5 months. Surprisingly, besides their fruitful capabilities, we see that SWs are being mostly used for In-Page Push Advertising, in 65.08% of the SWs that connect with 3rd parties. We highlight that this is a relatively new way for advertisers to bypass ad-blockers and render ads on the user’s displays natively.

The 2022 Music City Bowl is a college football bowl game that is scheduled to be played on December 31, 2022, at Nissan Stadium in Nashville, Tennessee. The 24th annual Music City Bowl, the game will feature Iowa from the Big Ten Conference and Kentucky from the Southeastern Conference. Iowa vs. Kentucky Live Stream ✅✅Watch Free 2022 Music City Bowl Live Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking (https://github.com/diwis/zdhPaperRanking) library4. The washers dataset features 70 defective parts. The gears and screws datasets feature 35 defective, 35 intact and several hundred unannotated parts. Some defects, such as notches and holes, are visible in most images (illuminations) with intensity and texture variations among them, while others, such as scratches, are only visible in a few.fghgj We split the datasets into train and test sets. The train sets contain 32 samples, and the test set 38 samples. Each sample comprises 108 images (each captured under a different illumination angle), an automatically extracted foreground segmentation mask, and a hand-labeled defect segmentation mask.fghgfj This dataset is challenging mainly because: each raw sample consists of 108 gray-scale images of resolution 512×512 and therefore takes 27MB of space; the metallic surfaces produce many specular reflections that sometimes saturate the camera sensors; the annotations are not very precise because the exact extent of defect contours is always subjective; the defects are very sparse also in the spatial dimensions: they cover only about 0.2% of the total image area in gears, 0.8% in screws, and 1.4% in washers; this creates an unbalanced dataset with a highly skewed class representation. gfhj The dataset is organized as follows: each sample resides in the Test, Train, or Unannotated directory; each sample has its own directory which contains the individual images, the foreground, and defect segmentation masks; each image is stored in 8-bit greyscale png format and has a resolution of 512 x 512 pixels; Image file names are formatted using three string fields separated with the underscore character: prefix_sampleNr_illuminationNr.png, where the prefix is e.g. washer, the sampleNr might be a three-digit number 001, and the illuminationNr is formed of 3 digits, first corresponding to the elevation index (1 - highest angle, 9 - lowest angle), and the additional two corresponding to the azimuth index (01-12). Each dataset contains light_vectors.csv, which contains the illumination angles (in lexicographic order of the illuminationNr), and light_intensities.csv that contains the numbers corresponding to the light intensity on the scale from 0 to 127. Please, be aware, that the azimuth angles were not calibrated and might be a few degrees misaligned.fdhfgj These data have been cleaned and integrated with data from COVID-19-TweetIDs and from other sources (e.g., PMC). The result was dataset of 500,314 unique articles along with relevant metadata (e.g., the underlying citation network). We utilized this dataset to produce, for each article, the values of the following impact measures:sdgfdfhfggh Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking (https://github.com/diwifss/PaperRanking) library4.sddfghfggd Influence_alt: Citation-based measure reflecting the total impact of an article. This is the Citation Count of each article, calculated based on the citation network between the articles contained in the BIP4COVID19 dataset.sddggf safs Popularity: Citation-based measure reflecting the current impact of an article. This is based on the AttRank5 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). AttRank alleviates this problem incorporating an attention-based mechanism, akin to a time-restricted version of preferential attachment, to explicitly capture a researcher's preference to read papers which received a lot of attention recently. This is why it is more suitable to capture the current "hype" of an article.asdfujsgdg Colour Science for Python Colour is an open-source Python package providing a comprehensive number of algorithms and datasets for colour science. sdg It is freely available under the New BSD License terms.uiuol Colour is an affiliated project of NumFOCUS, a 501(c)(3) nonprofit in the United States Draft Release Notes The draft release notes of the develop branch are available at this url.uiu Sponsors We are grateful 💖 for the support of our sponsors. If you'd like to join them, please consider becoming a sponsor on OpenCollective.uiu Features Colour features a rich dataset and collection of objects, please see the features in the documentation for more information.iu User Guid Installation Colour and its primary dependencies can be easily installed from the Python Package Index by issuing this command in a shell:oluip $ pip install --user colour-science The detailed installation procedure for the secondary dependencies is described in the Installation Guide. Colour is also available for Anaconda from Continuum Analytics via conda-forge:oiup $ conda install -c conda-forge colour-science Tutorial The static tutorial provides an introduction to Colour. An interactive version is available via Google Colab.oui How-To The Google Colab How-To guide for Colour shows various techniques to solve specific problems and highlights some interesting use cases. Contributing If you would like to contribute to Colour, please refer to the following Contributing guide.oi Colour by Colour Developers Copyright 2013 Colour Developers – colour-developers@colour-science.org This software is released under terms of New BSD License: https://opensource.org/licenses/BSD-3-Clause https://github.com/colour-science/colourolip sf Popularity alternative: An alternative citation-based measure reflecting the current impact of an article (this was the basic popularity measured provided by BIP4COVID19 until version 26). This is based on the RAM6 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). RAM alleviates this problem using an approach known as "time-awareness". This is why it is more suitable to capture the current "hype" of an article. This measure was calculated using the PaperRanking (https://github.com/diwis/PaperRanking) library4.sfbsdf Social Media Attention: The number of tweets related to this article. Relevant data were collected from the COVID-19-TweetIDs dataset. In this version, tweets between 23/6/22-29/6/22 have been considered from the previous dataset.ftgujyol We provide five CSV files, all containing the same information, however each having its entries ordered by a different impact measure. All CSV files are tab separated and have the same columns (PubMed_id, PdfMC_id, DOI, influence_score, popularity_alt_score, popularity score, influence_alt score, tweets count).yjytik Rich offline experience, periodic background sync, push notification functionality, network requests control, improved performance via requests caching are only a few of the functionalities provided by the Service Worker (SW ) API. This new technology, supported by all major browsers, can significantly improve users’ experience by providing the publisher with the technical foundations that would normally require a native application. Albeit the capabilities of this new technique and its important role in the ecosystem of Progressive Web Apps (PWAs), it is still unclear what is their actual purpose on the web, and how publishers leverage the provided functionality in their web applications. In this study, we shed light in the real world deployment of SWs, by conducting the first large scale analysis of the prevalence of SWs in the wild.huiuop We see that SWs are becoming more and more popular, with the adoption increased by 26% only within the last 5 months. Surprisingly, besides their fruitful capabilities, we see that SWs are being mostly used for In-Page Push Advertising, in 65.08% of the SWs that connect with 3rd parties. We highlight that this is a relatively new way for advertisers to bypass ad-blockers and render ads on the user’s displays natively.

The 2022 Music City Bowl is a college football bowl game that is scheduled to be played on December 31, 2022, at Nissan Stadium in Nashville, Tennessee. ✅✅Watch Free 2022 Music City Bowl Live Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking (https://github.com/diwis/zdhPaperRanking) library4. The washers dataset features 70 defective parts. The gears and screws datasets feature 35 defective, 35 intact and several hundred unannotated parts. Some defects, such as notches and holes, are visible in most images (illuminations) with intensity and texture variations among them, while others, such as scratches, are only visible in a few.fghgj We split the datasets into train and test sets. The train sets contain 32 samples, and the test set 38 samples. Each sample comprises 108 images (each captured under a different illumination angle), an automatically extracted foreground segmentation mask, and a hand-labeled defect segmentation mask.fghgfj This dataset is challenging mainly because: each raw sample consists of 108 gray-scale images of resolution 512×512 and therefore takes 27MB of space; the metallic surfaces produce many specular reflections that sometimes saturate the camera sensors; the annotations are not very precise because the exact extent of defect contours is always subjective; the defects are very sparse also in the spatial dimensions: they cover only about 0.2% of the total image area in gears, 0.8% in screws, and 1.4% in washers; this creates an unbalanced dataset with a highly skewed class representation. gfhj The dataset is organized as follows: each sample resides in the Test, Train, or Unannotated directory; each sample has its own directory which contains the individual images, the foreground, and defect segmentation masks; each image is stored in 8-bit greyscale png format and has a resolution of 512 x 512 pixels; Image file names are formatted using three string fields separated with the underscore character: prefix_sampleNr_illuminationNr.png, where the prefix is e.g. washer, the sampleNr might be a three-digit number 001, and the illuminationNr is formed of 3 digits, first corresponding to the elevation index (1 - highest angle, 9 - lowest angle), and the additional two corresponding to the azimuth index (01-12). Each dataset contains light_vectors.csv, which contains the illumination angles (in lexicographic order of the illuminationNr), and light_intensities.csv that contains the numbers corresponding to the light intensity on the scale from 0 to 127. Please, be aware, that the azimuth angles were not calibrated and might be a few degrees misaligned.fdhfgj These data have been cleaned and integrated with data from COVID-19-TweetIDs and from other sources (e.g., PMC). The result was dataset of 500,314 unique articles along with relevant metadata (e.g., the underlying citation network). We utilized this dataset to produce, for each article, the values of the following impact measures:sdgfdfhfggh Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking (https://github.com/diwifss/PaperRanking) library4.sddfghfggd Influence_alt: Citation-based measure reflecting the total impact of an article. This is the Citation Count of each article, calculated based on the citation network between the articles contained in the BIP4COVID19 dataset.sddggf safs Popularity: Citation-based measure reflecting the current impact of an article. This is based on the AttRank5 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). AttRank alleviates this problem incorporating an attention-based mechanism, akin to a time-restricted version of preferential attachment, to explicitly capture a researcher's preference to read papers which received a lot of attention recently. This is why it is more suitable to capture the current "hype" of an article.asdfujsgdg Colour Science for Python Colour is an open-source Python package providing a comprehensive number of algorithms and datasets for colour science. sdg It is freely available under the New BSD License terms.uiuol Colour is an affiliated project of NumFOCUS, a 501(c)(3) nonprofit in the United States Draft Release Notes The draft release notes of the develop branch are available at this url.uiu Sponsors We are grateful 💖 for the support of our sponsors. If you'd like to join them, please consider becoming a sponsor on OpenCollective.uiu Features Colour features a rich dataset and collection of objects, please see the features in the documentation for more information.iu User Guid Installation Colour and its primary dependencies can be easily installed from the Python Package Index by issuing this command in a shell:oluip $ pip install --user colour-science The detailed installation procedure for the secondary dependencies is described in the Installation Guide. Colour is also available for Anaconda from Continuum Analytics via conda-forge:oiup $ conda install -c conda-forge colour-science Tutorial The static tutorial provides an introduction to Colour. An interactive version is available via Google Colab.oui How-To The Google Colab How-To guide for Colour shows various techniques to solve specific problems and highlights some interesting use cases. Contributing If you would like to contribute to Colour, please refer to the following Contributing guide.oi Colour by Colour Developers Copyright 2013 Colour Developers – colour-developers@colour-science.org This software is released under terms of New BSD License: https://opensource.org/licenses/BSD-3-Clause https://github.com/colour-science/colourolip sf Popularity alternative: An alternative citation-based measure reflecting the current impact of an article (this was the basic popularity measured provided by BIP4COVID19 until version 26). This is based on the RAM6 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). RAM alleviates this problem using an approach known as "time-awareness". This is why it is more suitable to capture the current "hype" of an article. This measure was calculated using the PaperRanking (https://github.com/diwis/PaperRanking) library4.sfbsdf Social Media Attention: The number of tweets related to this article. Relevant data were collected from the COVID-19-TweetIDs dataset. In this version, tweets between 23/6/22-29/6/22 have been considered from the previous dataset.ftgujyol We provide five CSV files, all containing the same information, however each having its entries ordered by a different impact measure. All CSV files are tab separated and have the same columns (PubMed_id, PdfMC_id, DOI, influence_score, popularity_alt_score, popularity score, influence_alt score, tweets count).yjytik Rich offline experience, periodic background sync, push notification functionality, network requests control, improved performance via requests caching are only a few of the functionalities provided by the Service Worker (SW ) API. This new technology, supported by all major browsers, can significantly improve users’ experience by providing the publisher with the technical foundations that would normally require a native application. Albeit the capabilities of this new technique and its important role in the ecosystem of Progressive Web Apps (PWAs), it is still unclear what is their actual purpose on the web, and how publishers leverage the provided functionality in their web applications. In this study, we shed light in the real world deployment of SWs, by conducting the first large scale analysis of the prevalence of SWs in the wild.huiuop We see that SWs are becoming more and more popular, with the adoption increased by 26% only within the last 5 months. Surprisingly, besides their fruitful capabilities, we see that SWs are being mostly used for In-Page Push Advertising, in 65.08% of the SWs that connect with 3rd parties. We highlight that this is a relatively new way for advertisers to bypass ad-blockers and render ads on the user’s displays natively.

The 2022–23 Tour de Ski will be the 17th edition of the Tour de Ski and part of the 2022–23 FIS Cross-Country World Cup. The World Cup stage event will begin in Val Müstair, Switzerland on 31 December 2022 and conclude with the Final Climb stage in Val di Fiemme, Italy, on 8 January 2023. ✅✅Watch Free 2022–23 Tour de Ski Live ✅✅Watch Free 2022–23 Tour de Ski Live Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking (https://github.com/diwis/zdhPaperRanking) library4. The washers dataset features 70 defective parts. The gears and screws datasets feature 35 defective, 35 intact and several hundred unannotated parts. Some defects, such as notches and holes, are visible in most images (illuminations) with intensity and texture variations among them, while others, such as scratches, are only visible in a few.fghgj We split the datasets into train and test sets. The train sets contain 32 samples, and the test set 38 samples. Each sample comprises 108 images (each captured under a different illumination angle), an automatically extracted foreground segmentation mask, and a hand-labeled defect segmentation mask.fghgfj This dataset is challenging mainly because: each raw sample consists of 108 gray-scale images of resolution 512×512 and therefore takes 27MB of space; the metallic surfaces produce many specular reflections that sometimes saturate the camera sensors; the annotations are not very precise because the exact extent of defect contours is always subjective; the defects are very sparse also in the spatial dimensions: they cover only about 0.2% of the total image area in gears, 0.8% in screws, and 1.4% in washers; this creates an unbalanced dataset with a highly skewed class representation. gfhj The dataset is organized as follows: each sample resides in the Test, Train, or Unannotated directory; each sample has its own directory which contains the individual images, the foreground, and defect segmentation masks; each image is stored in 8-bit greyscale png format and has a resolution of 512 x 512 pixels; Image file names are formatted using three string fields separated with the underscore character: prefix_sampleNr_illuminationNr.png, where the prefix is e.g. washer, the sampleNr might be a three-digit number 001, and the illuminationNr is formed of 3 digits, first corresponding to the elevation index (1 - highest angle, 9 - lowest angle), and the additional two corresponding to the azimuth index (01-12). Each dataset contains light_vectors.csv, which contains the illumination angles (in lexicographic order of the illuminationNr), and light_intensities.csv that contains the numbers corresponding to the light intensity on the scale from 0 to 127. Please, be aware, that the azimuth angles were not calibrated and might be a few degrees misaligned.fdhfgj These data have been cleaned and integrated with data from COVID-19-TweetIDs and from other sources (e.g., PMC). The result was dataset of 500,314 unique articles along with relevant metadata (e.g., the underlying citation network). We utilized this dataset to produce, for each article, the values of the following impact measures:sdgfdfhfggh Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking (https://github.com/diwifss/PaperRanking) library4.sddfghfggd Influence_alt: Citation-based measure reflecting the total impact of an article. This is the Citation Count of each article, calculated based on the citation network between the articles contained in the BIP4COVID19 dataset.sddggf safs Popularity: Citation-based measure reflecting the current impact of an article. This is based on the AttRank5 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). AttRank alleviates this problem incorporating an attention-based mechanism, akin to a time-restricted version of preferential attachment, to explicitly capture a researcher's preference to read papers which received a lot of attention recently. This is why it is more suitable to capture the current "hype" of an article.asdfujsgdg Colour Science for Python Colour is an open-source Python package providing a comprehensive number of algorithms and datasets for colour science. sdg It is freely available under the New BSD License terms.uiuol Colour is an affiliated project of NumFOCUS, a 501(c)(3) nonprofit in the United States Draft Release Notes The draft release notes of the develop branch are available at this url.uiu Sponsors We are grateful 💖 for the support of our sponsors. If you'd like to join them, please consider becoming a sponsor on OpenCollective.uiu Features Colour features a rich dataset and collection of objects, please see the features in the documentation for more information.iu User Guid Installation Colour and its primary dependencies can be easily installed from the Python Package Index by issuing this command in a shell:oluip $ pip install --user colour-science The detailed installation procedure for the secondary dependencies is described in the Installation Guide. Colour is also available for Anaconda from Continuum Analytics via conda-forge:oiup $ conda install -c conda-forge colour-science Tutorial The static tutorial provides an introduction to Colour. An interactive version is available via Google Colab.oui How-To The Google Colab How-To guide for Colour shows various techniques to solve specific problems and highlights some interesting use cases. Contributing If you would like to contribute to Colour, please refer to the following Contributing guide.oi Colour by Colour Developers Copyright 2013 Colour Developers – colour-developers@colour-science.org This software is released under terms of New BSD License: https://opensource.org/licenses/BSD-3-Clause https://github.com/colour-science/colourolip sf Popularity alternative: An alternative citation-based measure reflecting the current impact of an article (this was the basic popularity measured provided by BIP4COVID19 until version 26). This is based on the RAM6 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). RAM alleviates this problem using an approach known as "time-awareness". This is why it is more suitable to capture the current "hype" of an article. This measure was calculated using the PaperRanking (https://github.com/diwis/PaperRanking) library4.sfbsdf Social Media Attention: The number of tweets related to this article. Relevant data were collected from the COVID-19-TweetIDs dataset. In this version, tweets between 23/6/22-29/6/22 have been considered from the previous dataset.ftgujyol We provide five CSV files, all containing the same information, however each having its entries ordered by a different impact measure. All CSV files are tab separated and have the same columns (PubMed_id, PdfMC_id, DOI, influence_score, popularity_alt_score, popularity score, influence_alt score, tweets count).yjytik Rich offline experience, periodic background sync, push notification functionality, network requests control, improved performance via requests caching are only a few of the functionalities provided by the Service Worker (SW ) API. This new technology, supported by all major browsers, can significantly improve users’ experience by providing the publisher with the technical foundations that would normally require a native application. Albeit the capabilities of this new technique and its important role in the ecosystem of Progressive Web Apps (PWAs), it is still unclear what is their actual purpose on the web, and how publishers leverage the provided functionality in their web applications. In this study, we shed light in the real world deployment of SWs, by conducting the first large scale analysis of the prevalence of SWs in the wild.huiuop We see that SWs are becoming more and more popular, with the adoption increased by 26% only within the last 5 months. Surprisingly, besides their fruitful capabilities, we see that SWs are being mostly used for In-Page Push Advertising, in 65.08% of the SWs that connect with 3rd parties. We highlight that this is a relatively new way for advertisers to bypass ad-blockers and render ads on the user’s displays natively. Preview

The 2022 Sugar Bowl is a college football bowl game that is scheduled to be played on December 31, 2022, at Caesars Superdome in New Orleans, Louisiana. The 89th annual Sugar Bowl, the game will feature Kansas State from the Big 12 Conference and Alabama from the Southeastern Conference. Alabama vs. Kansas State Live Stream ✅✅Watch Free 2022 Sugar Bowl Live Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking (https://github.com/diwis/zdhPaperRanking) library4. The washers dataset features 70 defective parts. The gears and screws datasets feature 35 defective, 35 intact and several hundred unannotated parts. Some defects, such as notches and holes, are visible in most images (illuminations) with intensity and texture variations among them, while others, such as scratches, are only visible in a few.fghgj We split the datasets into train and test sets. The train sets contain 32 samples, and the test set 38 samples. Each sample comprises 108 images (each captured under a different illumination angle), an automatically extracted foreground segmentation mask, and a hand-labeled defect segmentation mask.fghgfj This dataset is challenging mainly because: each raw sample consists of 108 gray-scale images of resolution 512×512 and therefore takes 27MB of space; the metallic surfaces produce many specular reflections that sometimes saturate the camera sensors; the annotations are not very precise because the exact extent of defect contours is always subjective; the defects are very sparse also in the spatial dimensions: they cover only about 0.2% of the total image area in gears, 0.8% in screws, and 1.4% in washers; this creates an unbalanced dataset with a highly skewed class representation. gfhj The dataset is organized as follows: each sample resides in the Test, Train, or Unannotated directory; each sample has its own directory which contains the individual images, the foreground, and defect segmentation masks; each image is stored in 8-bit greyscale png format and has a resolution of 512 x 512 pixels; Image file names are formatted using three string fields separated with the underscore character: prefix_sampleNr_illuminationNr.png, where the prefix is e.g. washer, the sampleNr might be a three-digit number 001, and the illuminationNr is formed of 3 digits, first corresponding to the elevation index (1 - highest angle, 9 - lowest angle), and the additional two corresponding to the azimuth index (01-12). Each dataset contains light_vectors.csv, which contains the illumination angles (in lexicographic order of the illuminationNr), and light_intensities.csv that contains the numbers corresponding to the light intensity on the scale from 0 to 127. Please, be aware, that the azimuth angles were not calibrated and might be a few degrees misaligned.fdhfgj These data have been cleaned and integrated with data from COVID-19-TweetIDs and from other sources (e.g., PMC). The result was dataset of 500,314 unique articles along with relevant metadata (e.g., the underlying citation network). We utilized this dataset to produce, for each article, the values of the following impact measures:sdgfdfhfggh Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking (https://github.com/diwifss/PaperRanking) library4.sddfghfggd Influence_alt: Citation-based measure reflecting the total impact of an article. This is the Citation Count of each article, calculated based on the citation network between the articles contained in the BIP4COVID19 dataset.sddggf safs Popularity: Citation-based measure reflecting the current impact of an article. This is based on the AttRank5 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). AttRank alleviates this problem incorporating an attention-based mechanism, akin to a time-restricted version of preferential attachment, to explicitly capture a researcher's preference to read papers which received a lot of attention recently. This is why it is more suitable to capture the current "hype" of an article.asdfujsgdg Colour Science for Python Colour is an open-source Python package providing a comprehensive number of algorithms and datasets for colour science. sdg It is freely available under the New BSD License terms.uiuol Colour is an affiliated project of NumFOCUS, a 501(c)(3) nonprofit in the United States Draft Release Notes The draft release notes of the develop branch are available at this url.uiu Sponsors We are grateful 💖 for the support of our sponsors. If you'd like to join them, please consider becoming a sponsor on OpenCollective.uiu Features Colour features a rich dataset and collection of objects, please see the features in the documentation for more information.iu User Guid Installation Colour and its primary dependencies can be easily installed from the Python Package Index by issuing this command in a shell:oluip $ pip install --user colour-science The detailed installation procedure for the secondary dependencies is described in the Installation Guide. Colour is also available for Anaconda from Continuum Analytics via conda-forge:oiup $ conda install -c conda-forge colour-science Tutorial The static tutorial provides an introduction to Colour. An interactive version is available via Google Colab.oui How-To The Google Colab How-To guide for Colour shows various techniques to solve specific problems and highlights some interesting use cases. Contributing If you would like to contribute to Colour, please refer to the following Contributing guide.oi Colour by Colour Developers Copyright 2013 Colour Developers – colour-developers@colour-science.org This software is released under terms of New BSD License: https://opensource.org/licenses/BSD-3-Clause https://github.com/colour-science/colourolip sf Popularity alternative: An alternative citation-based measure reflecting the current impact of an article (this was the basic popularity measured provided by BIP4COVID19 until version 26). This is based on the RAM6 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). RAM alleviates this problem using an approach known as "time-awareness". This is why it is more suitable to capture the current "hype" of an article. This measure was calculated using the PaperRanking (https://github.com/diwis/PaperRanking) library4.sfbsdf Social Media Attention: The number of tweets related to this article. Relevant data were collected from the COVID-19-TweetIDs dataset. In this version, tweets between 23/6/22-29/6/22 have been considered from the previous dataset.ftgujyol We provide five CSV files, all containing the same information, however each having its entries ordered by a different impact measure. All CSV files are tab separated and have the same columns (PubMed_id, PdfMC_id, DOI, influence_score, popularity_alt_score, popularity score, influence_alt score, tweets count).yjytik Rich offline experience, periodic background sync, push notification functionality, network requests control, improved performance via requests caching are only a few of the functionalities provided by the Service Worker (SW ) API. This new technology, supported by all major browsers, can significantly improve users’ experience by providing the publisher with the technical foundations that would normally require a native application. Albeit the capabilities of this new technique and its important role in the ecosystem of Progressive Web Apps (PWAs), it is still unclear what is their actual purpose on the web, and how publishers leverage the provided functionality in their web applications. In this study, we shed light in the real world deployment of SWs, by conducting the first large scale analysis of the prevalence of SWs in the wild.huiuop We see that SWs are becoming more and more popular, with the adoption increased by 26% only within the last 5 months. Surprisingly, besides their fruitful capabilities, we see that SWs are being mostly used for In-Page Push Advertising, in 65.08% of the SWs that connect with 3rd parties. We highlight that this is a relatively new way for advertisers to bypass ad-blockers and render ads on the user’s displays natively.

The 2022–23 Tour de Ski will be the 17th edition of the Tour de Ski and part of the 2022–23 FIS Cross-Country World Cup. The World Cup stage event will begin in Val Müstair, Switzerland on 31 December 2022 and conclude with the Final Climb stage in Val di Fiemme, Italy, on 8 January 2023. ✅✅Watch Free 2022–23 Tour de Ski Live ✅✅Watch Free 2022–23 Tour de Ski Live Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking (https://github.com/diwis/zdhPaperRanking) library4. The washers dataset features 70 defective parts. The gears and screws datasets feature 35 defective, 35 intact and several hundred unannotated parts. Some defects, such as notches and holes, are visible in most images (illuminations) with intensity and texture variations among them, while others, such as scratches, are only visible in a few.fghgj We split the datasets into train and test sets. The train sets contain 32 samples, and the test set 38 samples. Each sample comprises 108 images (each captured under a different illumination angle), an automatically extracted foreground segmentation mask, and a hand-labeled defect segmentation mask.fghgfj This dataset is challenging mainly because: each raw sample consists of 108 gray-scale images of resolution 512×512 and therefore takes 27MB of space; the metallic surfaces produce many specular reflections that sometimes saturate the camera sensors; the annotations are not very precise because the exact extent of defect contours is always subjective; the defects are very sparse also in the spatial dimensions: they cover only about 0.2% of the total image area in gears, 0.8% in screws, and 1.4% in washers; this creates an unbalanced dataset with a highly skewed class representation. gfhj The dataset is organized as follows: each sample resides in the Test, Train, or Unannotated directory; each sample has its own directory which contains the individual images, the foreground, and defect segmentation masks; each image is stored in 8-bit greyscale png format and has a resolution of 512 x 512 pixels; Image file names are formatted using three string fields separated with the underscore character: prefix_sampleNr_illuminationNr.png, where the prefix is e.g. washer, the sampleNr might be a three-digit number 001, and the illuminationNr is formed of 3 digits, first corresponding to the elevation index (1 - highest angle, 9 - lowest angle), and the additional two corresponding to the azimuth index (01-12). Each dataset contains light_vectors.csv, which contains the illumination angles (in lexicographic order of the illuminationNr), and light_intensities.csv that contains the numbers corresponding to the light intensity on the scale from 0 to 127. Please, be aware, that the azimuth angles were not calibrated and might be a few degrees misaligned.fdhfgj These data have been cleaned and integrated with data from COVID-19-TweetIDs and from other sources (e.g., PMC). The result was dataset of 500,314 unique articles along with relevant metadata (e.g., the underlying citation network). We utilized this dataset to produce, for each article, the values of the following impact measures:sdgfdfhfggh Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking (https://github.com/diwifss/PaperRanking) library4.sddfghfggd Influence_alt: Citation-based measure reflecting the total impact of an article. This is the Citation Count of each article, calculated based on the citation network between the articles contained in the BIP4COVID19 dataset.sddggf safs Popularity: Citation-based measure reflecting the current impact of an article. This is based on the AttRank5 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). AttRank alleviates this problem incorporating an attention-based mechanism, akin to a time-restricted version of preferential attachment, to explicitly capture a researcher's preference to read papers which received a lot of attention recently. This is why it is more suitable to capture the current "hype" of an article.asdfujsgdg Colour Science for Python Colour is an open-source Python package providing a comprehensive number of algorithms and datasets for colour science. sdg It is freely available under the New BSD License terms.uiuol Colour is an affiliated project of NumFOCUS, a 501(c)(3) nonprofit in the United States Draft Release Notes The draft release notes of the develop branch are available at this url.uiu Sponsors We are grateful 💖 for the support of our sponsors. If you'd like to join them, please consider becoming a sponsor on OpenCollective.uiu Features Colour features a rich dataset and collection of objects, please see the features in the documentation for more information.iu User Guid Installation Colour and its primary dependencies can be easily installed from the Python Package Index by issuing this command in a shell:oluip $ pip install --user colour-science The detailed installation procedure for the secondary dependencies is described in the Installation Guide. Colour is also available for Anaconda from Continuum Analytics via conda-forge:oiup $ conda install -c conda-forge colour-science Tutorial The static tutorial provides an introduction to Colour. An interactive version is available via Google Colab.oui How-To The Google Colab How-To guide for Colour shows various techniques to solve specific problems and highlights some interesting use cases. Contributing If you would like to contribute to Colour, please refer to the following Contributing guide.oi Colour by Colour Developers Copyright 2013 Colour Developers – colour-developers@colour-science.org This software is released under terms of New BSD License: https://opensource.org/licenses/BSD-3-Clause https://github.com/colour-science/colourolip sf Popularity alternative: An alternative citation-based measure reflecting the current impact of an article (this was the basic popularity measured provided by BIP4COVID19 until version 26). This is based on the RAM6 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). RAM alleviates this problem using an approach known as "time-awareness". This is why it is more suitable to capture the current "hype" of an article. This measure was calculated using the PaperRanking (https://github.com/diwis/PaperRanking) library4.sfbsdf Social Media Attention: The number of tweets related to this article. Relevant data were collected from the COVID-19-TweetIDs dataset. In this version, tweets between 23/6/22-29/6/22 have been considered from the previous dataset.ftgujyol We provide five CSV files, all containing the same information, however each having its entries ordered by a different impact measure. All CSV files are tab separated and have the same columns (PubMed_id, PdfMC_id, DOI, influence_score, popularity_alt_score, popularity score, influence_alt score, tweets count).yjytik Rich offline experience, periodic background sync, push notification functionality, network requests control, improved performance via requests caching are only a few of the functionalities provided by the Service Worker (SW ) API. This new technology, supported by all major browsers, can significantly improve users’ experience by providing the publisher with the technical foundations that would normally require a native application. Albeit the capabilities of this new technique and its important role in the ecosystem of Progressive Web Apps (PWAs), it is still unclear what is their actual purpose on the web, and how publishers leverage the provided functionality in their web applications. In this study, we shed light in the real world deployment of SWs, by conducting the first large scale analysis of the prevalence of SWs in the wild.huiuop We see that SWs are becoming more and more popular, with the adoption increased by 26% only within the last 5 months. Surprisingly, besides their fruitful capabilities, we see that SWs are being mostly used for In-Page Push Advertising, in 65.08% of the SWs that connect with 3rd parties. We highlight that this is a relatively new way for advertisers to bypass ad-blockers and render ads on the user’s displays natively. Preview

Cage Warriors 148: Team Europe vs Team USA | LIVE on UFC Fight Pass. Main Card – 1:00pm (PT) | 4:00pm (ET) | 9:00pm (GMT) | 10:00pm (CEST). ✅✅Watch Free Cage Warriors 148: Team Europe vs Team USA Live Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking (https://github.com/diwis/zdhPaperRanking) library4. The washers dataset features 70 defective parts. The gears and screws datasets feature 35 defective, 35 intact and several hundred unannotated parts. Some defects, such as notches and holes, are visible in most images (illuminations) with intensity and texture variations among them, while others, such as scratches, are only visible in a few.fghgj We split the datasets into train and test sets. The train sets contain 32 samples, and the test set 38 samples. Each sample comprises 108 images (each captured under a different illumination angle), an automatically extracted foreground segmentation mask, and a hand-labeled defect segmentation mask.fghgfj This dataset is challenging mainly because: each raw sample consists of 108 gray-scale images of resolution 512×512 and therefore takes 27MB of space; the metallic surfaces produce many specular reflections that sometimes saturate the camera sensors; the annotations are not very precise because the exact extent of defect contours is always subjective; the defects are very sparse also in the spatial dimensions: they cover only about 0.2% of the total image area in gears, 0.8% in screws, and 1.4% in washers; this creates an unbalanced dataset with a highly skewed class representation. gfhj The dataset is organized as follows: each sample resides in the Test, Train, or Unannotated directory; each sample has its own directory which contains the individual images, the foreground, and defect segmentation masks; each image is stored in 8-bit greyscale png format and has a resolution of 512 x 512 pixels; Image file names are formatted using three string fields separated with the underscore character: prefix_sampleNr_illuminationNr.png, where the prefix is e.g. washer, the sampleNr might be a three-digit number 001, and the illuminationNr is formed of 3 digits, first corresponding to the elevation index (1 - highest angle, 9 - lowest angle), and the additional two corresponding to the azimuth index (01-12). Each dataset contains light_vectors.csv, which contains the illumination angles (in lexicographic order of the illuminationNr), and light_intensities.csv that contains the numbers corresponding to the light intensity on the scale from 0 to 127. Please, be aware, that the azimuth angles were not calibrated and might be a few degrees misaligned.fdhfgj These data have been cleaned and integrated with data from COVID-19-TweetIDs and from other sources (e.g., PMC). The result was dataset of 500,314 unique articles along with relevant metadata (e.g., the underlying citation network). We utilized this dataset to produce, for each article, the values of the following impact measures:sdgfdfhfggh Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking (https://github.com/diwifss/PaperRanking) library4.sddfghfggd Influence_alt: Citation-based measure reflecting the total impact of an article. This is the Citation Count of each article, calculated based on the citation network between the articles contained in the BIP4COVID19 dataset.sddggf safs Popularity: Citation-based measure reflecting the current impact of an article. This is based on the AttRank5 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). AttRank alleviates this problem incorporating an attention-based mechanism, akin to a time-restricted version of preferential attachment, to explicitly capture a researcher's preference to read papers which received a lot of attention recently. This is why it is more suitable to capture the current "hype" of an article.asdfujsgdg Colour Science for Python Colour is an open-source Python package providing a comprehensive number of algorithms and datasets for colour science. sdg It is freely available under the New BSD License terms.uiuol Colour is an affiliated project of NumFOCUS, a 501(c)(3) nonprofit in the United States Draft Release Notes The draft release notes of the develop branch are available at this url.uiu Sponsors We are grateful 💖 for the support of our sponsors. If you'd like to join them, please consider becoming a sponsor on OpenCollective.uiu Features Colour features a rich dataset and collection of objects, please see the features in the documentation for more information.iu User Guid Installation Colour and its primary dependencies can be easily installed from the Python Package Index by issuing this command in a shell:oluip $ pip install --user colour-science The detailed installation procedure for the secondary dependencies is described in the Installation Guide. Colour is also available for Anaconda from Continuum Analytics via conda-forge:oiup $ conda install -c conda-forge colour-science Tutorial The static tutorial provides an introduction to Colour. An interactive version is available via Google Colab.oui How-To The Google Colab How-To guide for Colour shows various techniques to solve specific problems and highlights some interesting use cases. Contributing If you would like to contribute to Colour, please refer to the following Contributing guide.oi Colour by Colour Developers Copyright 2013 Colour Developers – colour-developers@colour-science.org This software is released under terms of New BSD License: https://opensource.org/licenses/BSD-3-Clause https://github.com/colour-science/colourolip sf Popularity alternative: An alternative citation-based measure reflecting the current impact of an article (this was the basic popularity measured provided by BIP4COVID19 until version 26). This is based on the RAM6 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). RAM alleviates this problem using an approach known as "time-awareness". This is why it is more suitable to capture the current "hype" of an article. This measure was calculated using the PaperRanking (https://github.com/diwis/PaperRanking) library4.sfbsdf Social Media Attention: The number of tweets related to this article. Relevant data were collected from the COVID-19-TweetIDs dataset. In this version, tweets between 23/6/22-29/6/22 have been considered from the previous dataset.ftgujyol We provide five CSV files, all containing the same information, however each having its entries ordered by a different impact measure. All CSV files are tab separated and have the same columns (PubMed_id, PdfMC_id, DOI, influence_score, popularity_alt_score, popularity score, influence_alt score, tweets count).yjytik Rich offline experience, periodic background sync, push notification functionality, network requests control, improved performance via requests caching are only a few of the functionalities provided by the Service Worker (SW ) API. This new technology, supported by all major browsers, can significantly improve users’ experience by providing the publisher with the technical foundations that would normally require a native application. Albeit the capabilities of this new technique and its important role in the ecosystem of Progressive Web Apps (PWAs), it is still unclear what is their actual purpose on the web, and how publishers leverage the provided functionality in their web applications. In this study, we shed light in the real world deployment of SWs, by conducting the first large scale analysis of the prevalence of SWs in the wild.huiuop We see that SWs are becoming more and more popular, with the adoption increased by 26% only within the last 5 months. Surprisingly, besides their fruitful capabilities, we see that SWs are being mostly used for In-Page Push Advertising, in 65.08% of the SWs that connect with 3rd parties. We highlight that this is a relatively new way for advertisers to bypass ad-blockers and render ads on the user’s displays natively.

The 2022 Music City Bowl is a college football bowl game that is scheduled to be played on December 31, 2022, at Nissan Stadium in Nashville, Tennessee. ✅✅Watch Free 2022 Music City Bowl Live Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking (https://github.com/diwis/zdhPaperRanking) library4. The washers dataset features 70 defective parts. The gears and screws datasets feature 35 defective, 35 intact and several hundred unannotated parts. Some defects, such as notches and holes, are visible in most images (illuminations) with intensity and texture variations among them, while others, such as scratches, are only visible in a few.fghgj We split the datasets into train and test sets. The train sets contain 32 samples, and the test set 38 samples. Each sample comprises 108 images (each captured under a different illumination angle), an automatically extracted foreground segmentation mask, and a hand-labeled defect segmentation mask.fghgfj This dataset is challenging mainly because: each raw sample consists of 108 gray-scale images of resolution 512×512 and therefore takes 27MB of space; the metallic surfaces produce many specular reflections that sometimes saturate the camera sensors; the annotations are not very precise because the exact extent of defect contours is always subjective; the defects are very sparse also in the spatial dimensions: they cover only about 0.2% of the total image area in gears, 0.8% in screws, and 1.4% in washers; this creates an unbalanced dataset with a highly skewed class representation. gfhj The dataset is organized as follows: each sample resides in the Test, Train, or Unannotated directory; each sample has its own directory which contains the individual images, the foreground, and defect segmentation masks; each image is stored in 8-bit greyscale png format and has a resolution of 512 x 512 pixels; Image file names are formatted using three string fields separated with the underscore character: prefix_sampleNr_illuminationNr.png, where the prefix is e.g. washer, the sampleNr might be a three-digit number 001, and the illuminationNr is formed of 3 digits, first corresponding to the elevation index (1 - highest angle, 9 - lowest angle), and the additional two corresponding to the azimuth index (01-12). Each dataset contains light_vectors.csv, which contains the illumination angles (in lexicographic order of the illuminationNr), and light_intensities.csv that contains the numbers corresponding to the light intensity on the scale from 0 to 127. Please, be aware, that the azimuth angles were not calibrated and might be a few degrees misaligned.fdhfgj These data have been cleaned and integrated with data from COVID-19-TweetIDs and from other sources (e.g., PMC). The result was dataset of 500,314 unique articles along with relevant metadata (e.g., the underlying citation network). We utilized this dataset to produce, for each article, the values of the following impact measures:sdgfdfhfggh Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking (https://github.com/diwifss/PaperRanking) library4.sddfghfggd Influence_alt: Citation-based measure reflecting the total impact of an article. This is the Citation Count of each article, calculated based on the citation network between the articles contained in the BIP4COVID19 dataset.sddggf safs Popularity: Citation-based measure reflecting the current impact of an article. This is based on the AttRank5 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). AttRank alleviates this problem incorporating an attention-based mechanism, akin to a time-restricted version of preferential attachment, to explicitly capture a researcher's preference to read papers which received a lot of attention recently. This is why it is more suitable to capture the current "hype" of an article.asdfujsgdg Colour Science for Python Colour is an open-source Python package providing a comprehensive number of algorithms and datasets for colour science. sdg It is freely available under the New BSD License terms.uiuol Colour is an affiliated project of NumFOCUS, a 501(c)(3) nonprofit in the United States Draft Release Notes The draft release notes of the develop branch are available at this url.uiu Sponsors We are grateful 💖 for the support of our sponsors. If you'd like to join them, please consider becoming a sponsor on OpenCollective.uiu Features Colour features a rich dataset and collection of objects, please see the features in the documentation for more information.iu User Guid Installation Colour and its primary dependencies can be easily installed from the Python Package Index by issuing this command in a shell:oluip $ pip install --user colour-science The detailed installation procedure for the secondary dependencies is described in the Installation Guide. Colour is also available for Anaconda from Continuum Analytics via conda-forge:oiup $ conda install -c conda-forge colour-science Tutorial The static tutorial provides an introduction to Colour. An interactive version is available via Google Colab.oui How-To The Google Colab How-To guide for Colour shows various techniques to solve specific problems and highlights some interesting use cases. Contributing If you would like to contribute to Colour, please refer to the following Contributing guide.oi Colour by Colour Developers Copyright 2013 Colour Developers – colour-developers@colour-science.org This software is released under terms of New BSD License: https://opensource.org/licenses/BSD-3-Clause https://github.com/colour-science/colourolip sf Popularity alternative: An alternative citation-based measure reflecting the current impact of an article (this was the basic popularity measured provided by BIP4COVID19 until version 26). This is based on the RAM6 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). RAM alleviates this problem using an approach known as "time-awareness". This is why it is more suitable to capture the current "hype" of an article. This measure was calculated using the PaperRanking (https://github.com/diwis/PaperRanking) library4.sfbsdf Social Media Attention: The number of tweets related to this article. Relevant data were collected from the COVID-19-TweetIDs dataset. In this version, tweets between 23/6/22-29/6/22 have been considered from the previous dataset.ftgujyol We provide five CSV files, all containing the same information, however each having its entries ordered by a different impact measure. All CSV files are tab separated and have the same columns (PubMed_id, PdfMC_id, DOI, influence_score, popularity_alt_score, popularity score, influence_alt score, tweets count).yjytik Rich offline experience, periodic background sync, push notification functionality, network requests control, improved performance via requests caching are only a few of the functionalities provided by the Service Worker (SW ) API. This new technology, supported by all major browsers, can significantly improve users’ experience by providing the publisher with the technical foundations that would normally require a native application. Albeit the capabilities of this new technique and its important role in the ecosystem of Progressive Web Apps (PWAs), it is still unclear what is their actual purpose on the web, and how publishers leverage the provided functionality in their web applications. In this study, we shed light in the real world deployment of SWs, by conducting the first large scale analysis of the prevalence of SWs in the wild.huiuop We see that SWs are becoming more and more popular, with the adoption increased by 26% only within the last 5 months. Surprisingly, besides their fruitful capabilities, we see that SWs are being mostly used for In-Page Push Advertising, in 65.08% of the SWs that connect with 3rd parties. We highlight that this is a relatively new way for advertisers to bypass ad-blockers and render ads on the user’s displays natively.

The 2022–23 Tour de Ski will be the 17th edition of the Tour de Ski and part of the 2022–23 FIS Cross-Country World Cup. The World Cup stage event will begin in Val Müstair, Switzerland on 31 December 2022 and conclude with the Final Climb stage in Val di Fiemme, Italy, on 8 January 2023. ✅✅Watch Free 2022–23 Tour de Ski Live ✅✅Watch Free 2022–23 Tour de Ski Live Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking (https://github.com/diwis/zdhPaperRanking) library4. The washers dataset features 70 defective parts. The gears and screws datasets feature 35 defective, 35 intact and several hundred unannotated parts. Some defects, such as notches and holes, are visible in most images (illuminations) with intensity and texture variations among them, while others, such as scratches, are only visible in a few.fghgj We split the datasets into train and test sets. The train sets contain 32 samples, and the test set 38 samples. Each sample comprises 108 images (each captured under a different illumination angle), an automatically extracted foreground segmentation mask, and a hand-labeled defect segmentation mask.fghgfj This dataset is challenging mainly because: each raw sample consists of 108 gray-scale images of resolution 512×512 and therefore takes 27MB of space; the metallic surfaces produce many specular reflections that sometimes saturate the camera sensors; the annotations are not very precise because the exact extent of defect contours is always subjective; the defects are very sparse also in the spatial dimensions: they cover only about 0.2% of the total image area in gears, 0.8% in screws, and 1.4% in washers; this creates an unbalanced dataset with a highly skewed class representation. gfhj The dataset is organized as follows: each sample resides in the Test, Train, or Unannotated directory; each sample has its own directory which contains the individual images, the foreground, and defect segmentation masks; each image is stored in 8-bit greyscale png format and has a resolution of 512 x 512 pixels; Image file names are formatted using three string fields separated with the underscore character: prefix_sampleNr_illuminationNr.png, where the prefix is e.g. washer, the sampleNr might be a three-digit number 001, and the illuminationNr is formed of 3 digits, first corresponding to the elevation index (1 - highest angle, 9 - lowest angle), and the additional two corresponding to the azimuth index (01-12). Each dataset contains light_vectors.csv, which contains the illumination angles (in lexicographic order of the illuminationNr), and light_intensities.csv that contains the numbers corresponding to the light intensity on the scale from 0 to 127. Please, be aware, that the azimuth angles were not calibrated and might be a few degrees misaligned.fdhfgj These data have been cleaned and integrated with data from COVID-19-TweetIDs and from other sources (e.g., PMC). The result was dataset of 500,314 unique articles along with relevant metadata (e.g., the underlying citation network). We utilized this dataset to produce, for each article, the values of the following impact measures:sdgfdfhfggh Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking (https://github.com/diwifss/PaperRanking) library4.sddfghfggd Influence_alt: Citation-based measure reflecting the total impact of an article. This is the Citation Count of each article, calculated based on the citation network between the articles contained in the BIP4COVID19 dataset.sddggf safs Popularity: Citation-based measure reflecting the current impact of an article. This is based on the AttRank5 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). AttRank alleviates this problem incorporating an attention-based mechanism, akin to a time-restricted version of preferential attachment, to explicitly capture a researcher's preference to read papers which received a lot of attention recently. This is why it is more suitable to capture the current "hype" of an article.asdfujsgdg Colour Science for Python Colour is an open-source Python package providing a comprehensive number of algorithms and datasets for colour science. sdg It is freely available under the New BSD License terms.uiuol Colour is an affiliated project of NumFOCUS, a 501(c)(3) nonprofit in the United States Draft Release Notes The draft release notes of the develop branch are available at this url.uiu Sponsors We are grateful 💖 for the support of our sponsors. If you'd like to join them, please consider becoming a sponsor on OpenCollective.uiu Features Colour features a rich dataset and collection of objects, please see the features in the documentation for more information.iu User Guid Installation Colour and its primary dependencies can be easily installed from the Python Package Index by issuing this command in a shell:oluip $ pip install --user colour-science The detailed installation procedure for the secondary dependencies is described in the Installation Guide. Colour is also available for Anaconda from Continuum Analytics via conda-forge:oiup $ conda install -c conda-forge colour-science Tutorial The static tutorial provides an introduction to Colour. An interactive version is available via Google Colab.oui How-To The Google Colab How-To guide for Colour shows various techniques to solve specific problems and highlights some interesting use cases. Contributing If you would like to contribute to Colour, please refer to the following Contributing guide.oi Colour by Colour Developers Copyright 2013 Colour Developers – colour-developers@colour-science.org This software is released under terms of New BSD License: https://opensource.org/licenses/BSD-3-Clause https://github.com/colour-science/colourolip sf Popularity alternative: An alternative citation-based measure reflecting the current impact of an article (this was the basic popularity measured provided by BIP4COVID19 until version 26). This is based on the RAM6 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). RAM alleviates this problem using an approach known as "time-awareness". This is why it is more suitable to capture the current "hype" of an article. This measure was calculated using the PaperRanking (https://github.com/diwis/PaperRanking) library4.sfbsdf Social Media Attention: The number of tweets related to this article. Relevant data were collected from the COVID-19-TweetIDs dataset. In this version, tweets between 23/6/22-29/6/22 have been considered from the previous dataset.ftgujyol We provide five CSV files, all containing the same information, however each having its entries ordered by a different impact measure. All CSV files are tab separated and have the same columns (PubMed_id, PdfMC_id, DOI, influence_score, popularity_alt_score, popularity score, influence_alt score, tweets count).yjytik Rich offline experience, periodic background sync, push notification functionality, network requests control, improved performance via requests caching are only a few of the functionalities provided by the Service Worker (SW ) API. This new technology, supported by all major browsers, can significantly improve users’ experience by providing the publisher with the technical foundations that would normally require a native application. Albeit the capabilities of this new technique and its important role in the ecosystem of Progressive Web Apps (PWAs), it is still unclear what is their actual purpose on the web, and how publishers leverage the provided functionality in their web applications. In this study, we shed light in the real world deployment of SWs, by conducting the first large scale analysis of the prevalence of SWs in the wild.huiuop We see that SWs are becoming more and more popular, with the adoption increased by 26% only within the last 5 months. Surprisingly, besides their fruitful capabilities, we see that SWs are being mostly used for In-Page Push Advertising, in 65.08% of the SWs that connect with 3rd parties. We highlight that this is a relatively new way for advertisers to bypass ad-blockers and render ads on the user’s displays natively. Preview

The 2022 Sugar Bowl is a college football bowl game that is scheduled to be played on December 31, 2022, at Caesars Superdome in New Orleans, Louisiana. The 89th annual Sugar Bowl, the game will feature Kansas State from the Big 12 Conference and Alabama from the Southeastern Conference. Alabama vs. Kansas State Live Stream ✅✅Watch Free 2022 Sugar Bowl Live Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking (https://github.com/diwis/zdhPaperRanking) library4. The washers dataset features 70 defective parts. The gears and screws datasets feature 35 defective, 35 intact and several hundred unannotated parts. Some defects, such as notches and holes, are visible in most images (illuminations) with intensity and texture variations among them, while others, such as scratches, are only visible in a few.fghgj We split the datasets into train and test sets. The train sets contain 32 samples, and the test set 38 samples. Each sample comprises 108 images (each captured under a different illumination angle), an automatically extracted foreground segmentation mask, and a hand-labeled defect segmentation mask.fghgfj This dataset is challenging mainly because: each raw sample consists of 108 gray-scale images of resolution 512×512 and therefore takes 27MB of space; the metallic surfaces produce many specular reflections that sometimes saturate the camera sensors; the annotations are not very precise because the exact extent of defect contours is always subjective; the defects are very sparse also in the spatial dimensions: they cover only about 0.2% of the total image area in gears, 0.8% in screws, and 1.4% in washers; this creates an unbalanced dataset with a highly skewed class representation. gfhj The dataset is organized as follows: each sample resides in the Test, Train, or Unannotated directory; each sample has its own directory which contains the individual images, the foreground, and defect segmentation masks; each image is stored in 8-bit greyscale png format and has a resolution of 512 x 512 pixels; Image file names are formatted using three string fields separated with the underscore character: prefix_sampleNr_illuminationNr.png, where the prefix is e.g. washer, the sampleNr might be a three-digit number 001, and the illuminationNr is formed of 3 digits, first corresponding to the elevation index (1 - highest angle, 9 - lowest angle), and the additional two corresponding to the azimuth index (01-12). Each dataset contains light_vectors.csv, which contains the illumination angles (in lexicographic order of the illuminationNr), and light_intensities.csv that contains the numbers corresponding to the light intensity on the scale from 0 to 127. Please, be aware, that the azimuth angles were not calibrated and might be a few degrees misaligned.fdhfgj These data have been cleaned and integrated with data from COVID-19-TweetIDs and from other sources (e.g., PMC). The result was dataset of 500,314 unique articles along with relevant metadata (e.g., the underlying citation network). We utilized this dataset to produce, for each article, the values of the following impact measures:sdgfdfhfggh Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking (https://github.com/diwifss/PaperRanking) library4.sddfghfggd Influence_alt: Citation-based measure reflecting the total impact of an article. This is the Citation Count of each article, calculated based on the citation network between the articles contained in the BIP4COVID19 dataset.sddggf safs Popularity: Citation-based measure reflecting the current impact of an article. This is based on the AttRank5 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). AttRank alleviates this problem incorporating an attention-based mechanism, akin to a time-restricted version of preferential attachment, to explicitly capture a researcher's preference to read papers which received a lot of attention recently. This is why it is more suitable to capture the current "hype" of an article.asdfujsgdg Colour Science for Python Colour is an open-source Python package providing a comprehensive number of algorithms and datasets for colour science. sdg It is freely available under the New BSD License terms.uiuol Colour is an affiliated project of NumFOCUS, a 501(c)(3) nonprofit in the United States Draft Release Notes The draft release notes of the develop branch are available at this url.uiu Sponsors We are grateful 💖 for the support of our sponsors. If you'd like to join them, please consider becoming a sponsor on OpenCollective.uiu Features Colour features a rich dataset and collection of objects, please see the features in the documentation for more information.iu User Guid Installation Colour and its primary dependencies can be easily installed from the Python Package Index by issuing this command in a shell:oluip $ pip install --user colour-science The detailed installation procedure for the secondary dependencies is described in the Installation Guide. Colour is also available for Anaconda from Continuum Analytics via conda-forge:oiup $ conda install -c conda-forge colour-science Tutorial The static tutorial provides an introduction to Colour. An interactive version is available via Google Colab.oui How-To The Google Colab How-To guide for Colour shows various techniques to solve specific problems and highlights some interesting use cases. Contributing If you would like to contribute to Colour, please refer to the following Contributing guide.oi Colour by Colour Developers Copyright 2013 Colour Developers – colour-developers@colour-science.org This software is released under terms of New BSD License: https://opensource.org/licenses/BSD-3-Clause https://github.com/colour-science/colourolip sf Popularity alternative: An alternative citation-based measure reflecting the current impact of an article (this was the basic popularity measured provided by BIP4COVID19 until version 26). This is based on the RAM6 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). RAM alleviates this problem using an approach known as "time-awareness". This is why it is more suitable to capture the current "hype" of an article. This measure was calculated using the PaperRanking (https://github.com/diwis/PaperRanking) library4.sfbsdf Social Media Attention: The number of tweets related to this article. Relevant data were collected from the COVID-19-TweetIDs dataset. In this version, tweets between 23/6/22-29/6/22 have been considered from the previous dataset.ftgujyol We provide five CSV files, all containing the same information, however each having its entries ordered by a different impact measure. All CSV files are tab separated and have the same columns (PubMed_id, PdfMC_id, DOI, influence_score, popularity_alt_score, popularity score, influence_alt score, tweets count).yjytik Rich offline experience, periodic background sync, push notification functionality, network requests control, improved performance via requests caching are only a few of the functionalities provided by the Service Worker (SW ) API. This new technology, supported by all major browsers, can significantly improve users’ experience by providing the publisher with the technical foundations that would normally require a native application. Albeit the capabilities of this new technique and its important role in the ecosystem of Progressive Web Apps (PWAs), it is still unclear what is their actual purpose on the web, and how publishers leverage the provided functionality in their web applications. In this study, we shed light in the real world deployment of SWs, by conducting the first large scale analysis of the prevalence of SWs in the wild.huiuop We see that SWs are becoming more and more popular, with the adoption increased by 26% only within the last 5 months. Surprisingly, besides their fruitful capabilities, we see that SWs are being mostly used for In-Page Push Advertising, in 65.08% of the SWs that connect with 3rd parties. We highlight that this is a relatively new way for advertisers to bypass ad-blockers and render ads on the user’s displays natively.

Cage Warriors 148: Team Europe vs Team USA | LIVE on UFC Fight Pass. Main Card – 1:00pm (PT) | 4:00pm (ET) | 9:00pm (GMT) | 10:00pm (CEST). ✅✅Watch Free Cage Warriors 148: Team Europe vs Team USA Live Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking (https://github.com/diwis/zdhPaperRanking) library4. The washers dataset features 70 defective parts. The gears and screws datasets feature 35 defective, 35 intact and several hundred unannotated parts. Some defects, such as notches and holes, are visible in most images (illuminations) with intensity and texture variations among them, while others, such as scratches, are only visible in a few.fghgj We split the datasets into train and test sets. The train sets contain 32 samples, and the test set 38 samples. Each sample comprises 108 images (each captured under a different illumination angle), an automatically extracted foreground segmentation mask, and a hand-labeled defect segmentation mask.fghgfj This dataset is challenging mainly because: each raw sample consists of 108 gray-scale images of resolution 512×512 and therefore takes 27MB of space; the metallic surfaces produce many specular reflections that sometimes saturate the camera sensors; the annotations are not very precise because the exact extent of defect contours is always subjective; the defects are very sparse also in the spatial dimensions: they cover only about 0.2% of the total image area in gears, 0.8% in screws, and 1.4% in washers; this creates an unbalanced dataset with a highly skewed class representation. gfhj The dataset is organized as follows: each sample resides in the Test, Train, or Unannotated directory; each sample has its own directory which contains the individual images, the foreground, and defect segmentation masks; each image is stored in 8-bit greyscale png format and has a resolution of 512 x 512 pixels; Image file names are formatted using three string fields separated with the underscore character: prefix_sampleNr_illuminationNr.png, where the prefix is e.g. washer, the sampleNr might be a three-digit number 001, and the illuminationNr is formed of 3 digits, first corresponding to the elevation index (1 - highest angle, 9 - lowest angle), and the additional two corresponding to the azimuth index (01-12). Each dataset contains light_vectors.csv, which contains the illumination angles (in lexicographic order of the illuminationNr), and light_intensities.csv that contains the numbers corresponding to the light intensity on the scale from 0 to 127. Please, be aware, that the azimuth angles were not calibrated and might be a few degrees misaligned.fdhfgj These data have been cleaned and integrated with data from COVID-19-TweetIDs and from other sources (e.g., PMC). The result was dataset of 500,314 unique articles along with relevant metadata (e.g., the underlying citation network). We utilized this dataset to produce, for each article, the values of the following impact measures:sdgfdfhfggh Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking (https://github.com/diwifss/PaperRanking) library4.sddfghfggd Influence_alt: Citation-based measure reflecting the total impact of an article. This is the Citation Count of each article, calculated based on the citation network between the articles contained in the BIP4COVID19 dataset.sddggf safs Popularity: Citation-based measure reflecting the current impact of an article. This is based on the AttRank5 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). AttRank alleviates this problem incorporating an attention-based mechanism, akin to a time-restricted version of preferential attachment, to explicitly capture a researcher's preference to read papers which received a lot of attention recently. This is why it is more suitable to capture the current "hype" of an article.asdfujsgdg Colour Science for Python Colour is an open-source Python package providing a comprehensive number of algorithms and datasets for colour science. sdg It is freely available under the New BSD License terms.uiuol Colour is an affiliated project of NumFOCUS, a 501(c)(3) nonprofit in the United States Draft Release Notes The draft release notes of the develop branch are available at this url.uiu Sponsors We are grateful 💖 for the support of our sponsors. If you'd like to join them, please consider becoming a sponsor on OpenCollective.uiu Features Colour features a rich dataset and collection of objects, please see the features in the documentation for more information.iu User Guid Installation Colour and its primary dependencies can be easily installed from the Python Package Index by issuing this command in a shell:oluip $ pip install --user colour-science The detailed installation procedure for the secondary dependencies is described in the Installation Guide. Colour is also available for Anaconda from Continuum Analytics via conda-forge:oiup $ conda install -c conda-forge colour-science Tutorial The static tutorial provides an introduction to Colour. An interactive version is available via Google Colab.oui How-To The Google Colab How-To guide for Colour shows various techniques to solve specific problems and highlights some interesting use cases. Contributing If you would like to contribute to Colour, please refer to the following Contributing guide.oi Colour by Colour Developers Copyright 2013 Colour Developers – colour-developers@colour-science.org This software is released under terms of New BSD License: https://opensource.org/licenses/BSD-3-Clause https://github.com/colour-science/colourolip sf Popularity alternative: An alternative citation-based measure reflecting the current impact of an article (this was the basic popularity measured provided by BIP4COVID19 until version 26). This is based on the RAM6 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). RAM alleviates this problem using an approach known as "time-awareness". This is why it is more suitable to capture the current "hype" of an article. This measure was calculated using the PaperRanking (https://github.com/diwis/PaperRanking) library4.sfbsdf Social Media Attention: The number of tweets related to this article. Relevant data were collected from the COVID-19-TweetIDs dataset. In this version, tweets between 23/6/22-29/6/22 have been considered from the previous dataset.ftgujyol We provide five CSV files, all containing the same information, however each having its entries ordered by a different impact measure. All CSV files are tab separated and have the same columns (PubMed_id, PdfMC_id, DOI, influence_score, popularity_alt_score, popularity score, influence_alt score, tweets count).yjytik Rich offline experience, periodic background sync, push notification functionality, network requests control, improved performance via requests caching are only a few of the functionalities provided by the Service Worker (SW ) API. This new technology, supported by all major browsers, can significantly improve users’ experience by providing the publisher with the technical foundations that would normally require a native application. Albeit the capabilities of this new technique and its important role in the ecosystem of Progressive Web Apps (PWAs), it is still unclear what is their actual purpose on the web, and how publishers leverage the provided functionality in their web applications. In this study, we shed light in the real world deployment of SWs, by conducting the first large scale analysis of the prevalence of SWs in the wild.huiuop We see that SWs are becoming more and more popular, with the adoption increased by 26% only within the last 5 months. Surprisingly, besides their fruitful capabilities, we see that SWs are being mostly used for In-Page Push Advertising, in 65.08% of the SWs that connect with 3rd parties. We highlight that this is a relatively new way for advertisers to bypass ad-blockers and render ads on the user’s displays natively.

The 2022 Music City Bowl is a college football bowl game that is scheduled to be played on December 31, 2022, at Nissan Stadium in Nashville, Tennessee. The 24th annual Music City Bowl, the game will feature Iowa from the Big Ten Conference and Kentucky from the Southeastern Conference. ✅✅Watch Free 2022 Music City Bowl Live Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking (https://github.com/diwis/zdhPaperRanking) library4. The washers dataset features 70 defective parts. The gears and screws datasets feature 35 defective, 35 intact and several hundred unannotated parts. Some defects, such as notches and holes, are visible in most images (illuminations) with intensity and texture variations among them, while others, such as scratches, are only visible in a few.fghgj We split the datasets into train and test sets. The train sets contain 32 samples, and the test set 38 samples. Each sample comprises 108 images (each captured under a different illumination angle), an automatically extracted foreground segmentation mask, and a hand-labeled defect segmentation mask.fghgfj This dataset is challenging mainly because: each raw sample consists of 108 gray-scale images of resolution 512×512 and therefore takes 27MB of space; the metallic surfaces produce many specular reflections that sometimes saturate the camera sensors; the annotations are not very precise because the exact extent of defect contours is always subjective; the defects are very sparse also in the spatial dimensions: they cover only about 0.2% of the total image area in gears, 0.8% in screws, and 1.4% in washers; this creates an unbalanced dataset with a highly skewed class representation. gfhj The dataset is organized as follows: each sample resides in the Test, Train, or Unannotated directory; each sample has its own directory which contains the individual images, the foreground, and defect segmentation masks; each image is stored in 8-bit greyscale png format and has a resolution of 512 x 512 pixels; Image file names are formatted using three string fields separated with the underscore character: prefix_sampleNr_illuminationNr.png, where the prefix is e.g. washer, the sampleNr might be a three-digit number 001, and the illuminationNr is formed of 3 digits, first corresponding to the elevation index (1 - highest angle, 9 - lowest angle), and the additional two corresponding to the azimuth index (01-12). Each dataset contains light_vectors.csv, which contains the illumination angles (in lexicographic order of the illuminationNr), and light_intensities.csv that contains the numbers corresponding to the light intensity on the scale from 0 to 127. Please, be aware, that the azimuth angles were not calibrated and might be a few degrees misaligned.fdhfgj These data have been cleaned and integrated with data from COVID-19-TweetIDs and from other sources (e.g., PMC). The result was dataset of 500,314 unique articles along with relevant metadata (e.g., the underlying citation network). We utilized this dataset to produce, for each article, the values of the following impact measures:sdgfdfhfggh Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking (https://github.com/diwifss/PaperRanking) library4.sddfghfggd Influence_alt: Citation-based measure reflecting the total impact of an article. This is the Citation Count of each article, calculated based on the citation network between the articles contained in the BIP4COVID19 dataset.sddggf safs Popularity: Citation-based measure reflecting the current impact of an article. This is based on the AttRank5 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). AttRank alleviates this problem incorporating an attention-based mechanism, akin to a time-restricted version of preferential attachment, to explicitly capture a researcher's preference to read papers which received a lot of attention recently. This is why it is more suitable to capture the current "hype" of an article.asdfujsgdg Colour Science for Python Colour is an open-source Python package providing a comprehensive number of algorithms and datasets for colour science. sdg It is freely available under the New BSD License terms.uiuol Colour is an affiliated project of NumFOCUS, a 501(c)(3) nonprofit in the United States Draft Release Notes The draft release notes of the develop branch are available at this url.uiu Sponsors We are grateful 💖 for the support of our sponsors. If you'd like to join them, please consider becoming a sponsor on OpenCollective.uiu Features Colour features a rich dataset and collection of objects, please see the features in the documentation for more information.iu User Guid Installation Colour and its primary dependencies can be easily installed from the Python Package Index by issuing this command in a shell:oluip $ pip install --user colour-science The detailed installation procedure for the secondary dependencies is described in the Installation Guide. Colour is also available for Anaconda from Continuum Analytics via conda-forge:oiup $ conda install -c conda-forge colour-science Tutorial The static tutorial provides an introduction to Colour. An interactive version is available via Google Colab.oui How-To The Google Colab How-To guide for Colour shows various techniques to solve specific problems and highlights some interesting use cases. Contributing If you would like to contribute to Colour, please refer to the following Contributing guide.oi Colour by Colour Developers Copyright 2013 Colour Developers – colour-developers@colour-science.org This software is released under terms of New BSD License: https://opensource.org/licenses/BSD-3-Clause https://github.com/colour-science/colourolip sf Popularity alternative: An alternative citation-based measure reflecting the current impact of an article (this was the basic popularity measured provided by BIP4COVID19 until version 26). This is based on the RAM6 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). RAM alleviates this problem using an approach known as "time-awareness". This is why it is more suitable to capture the current "hype" of an article. This measure was calculated using the PaperRanking (https://github.com/diwis/PaperRanking) library4.sfbsdf Social Media Attention: The number of tweets related to this article. Relevant data were collected from the COVID-19-TweetIDs dataset. In this version, tweets between 23/6/22-29/6/22 have been considered from the previous dataset.ftgujyol We provide five CSV files, all containing the same information, however each having its entries ordered by a different impact measure. All CSV files are tab separated and have the same columns (PubMed_id, PdfMC_id, DOI, influence_score, popularity_alt_score, popularity score, influence_alt score, tweets count).yjytik Rich offline experience, periodic background sync, push notification functionality, network requests control, improved performance via requests caching are only a few of the functionalities provided by the Service Worker (SW ) API. This new technology, supported by all major browsers, can significantly improve users’ experience by providing the publisher with the technical foundations that would normally require a native application. Albeit the capabilities of this new technique and its important role in the ecosystem of Progressive Web Apps (PWAs), it is still unclear what is their actual purpose on the web, and how publishers leverage the provided functionality in their web applications. In this study, we shed light in the real world deployment of SWs, by conducting the first large scale analysis of the prevalence of SWs in the wild.huiuop We see that SWs are becoming more and more popular, with the adoption increased by 26% only within the last 5 months. Surprisingly, besides their fruitful capabilities, we see that SWs are being mostly used for In-Page Push Advertising, in 65.08% of the SWs that connect with 3rd parties. We highlight that this is a relatively new way for advertisers to bypass ad-blockers and render ads on the user’s displays natively.

Stream live action from the United Cup (29 - 08 January 2023). Watch all 119 ATP matches on your favourite device. ✅✅ Wacth Free Tennis United Cup 2022 Live The 2023 United Cup will be the first edition of the United Cup, an international outdoor hard court mixed-gender team tennis tournament held by the Association of Tennis Professionals (ATP) and the Women's Tennis Association (WTA). Serving as the opener for the 2023 ATP Tour and the 2023 WTA Tour, it will be held from 29 December 2022 to 8 January 2023 at three venues in the Australian cities of Brisbane, Perth, and Sydney. It will also be the first mixed-gender team event to offer both ATP rankings and WTA rankings points to its players: a player will be able to win a maximum of 500 points Lift net/blanket net (çökeltme dalyanı in Turkish), is an artisanal fishing method established on the shores of the Aegean Sea in Türkiye, in which fish that migrate along the coast to feed or breed are caught. In this study, it was aimed to define the lift net fishery and to determine the socio-demographic and socio-economic characteristics and views of fishers on problems. The data were collected through surveys and field observations from April to May 2022 in three stations (Karaburun, Urla and Foça). Twenty-four fishers of the 31 existing lift net fishers were reached and the survey consisting of 41 questions was filled in face-to-face and telephone interviews. There was no significant difference between the stations in terms of age, lift net fishing experience, number of employees, annual income, total annual expense, annual profit, fishing day per year, and duration of fishing operation (p>0.05). There were four target species (mullet species, sea bass, sand steenbras and bluefish) in lift net fishing. 14,855 kg of mullet species, 2,390 kg of sea bass, 1,200 kg of sand steenbras and 450 kg of bluefish were caught annually. The annual income of lift net fishers varied between 637 US dollars ($) and 5,242 $, and the mean income was 1,979 ± 1,151 $/year. The mean income (165 $/month) was lower than the minimum wage in 2021 (212 $/month). 26.9% of the fishers were dissatisfied with their income and 69.1% engaged in additional activities such as in agriculture-livestock, gillnet-longline fishery, construction worker, seller in a market, waitstaff, and solid waste collector. The heirs of 15.4% of the fishers will not continue fishing. The biggest problem was SCUBA or skin divers near the lift net. Other reported problems were commercial (gillnet and longline) and recreational fishing near the lift net, the anchoring of private boats near the lift net, the decrease in fish stocks, illegal fishing and marine pollution. The number of lift nets has decreased over the years. The continuity of this artisanal fishing method is considered to be important for the socio-economic sustainability.

USA vs Czech Republic live match starts on 29 Dec 2022 at 01:00 UTC at Ken Rosewall Arena, Sydney, Australia. USA v Czech Republic is part of United Cup,.. USA host Czech Republic in a United Cup Teams game, certain to entertain all Tennis fans. This match will take place on 29/12/2022 at 01:00 UTC. Stream live action from the United Cup (29 - 08 January 2023). Watch all 119 ATP matches on your favourite device. ✅✅ Wacth Free Tennis United Cup 2022 Live The 2023 United Cup will be the first edition of the United Cup, an international outdoor hard court mixed-gender team tennis tournament held by the Association of Tennis Professionals (ATP) and the Women's Tennis Association (WTA). Serving as the opener for the 2023 ATP Tour and the 2023 WTA Tour, it will be held from 29 December 2022 to 8 January 2023 at three venues in the Australian cities of Brisbane, Perth, and Sydney. It will also be the first mixed-gender team event to offer both ATP rankings and WTA rankings points to its players: a player will be able to win a maximum of 500 points Lift net/blanket net (çökeltme dalyanı in Turkish), is an artisanal fishing method established on the shores of the Aegean Sea in Türkiye, in which fish that migrate along the coast to feed or breed are caught. In this study, it was aimed to define the lift net fishery and to determine the socio-demographic and socio-economic characteristics and views of fishers on problems. The data were collected through surveys and field observations from April to May 2022 in three stations (Karaburun, Urla and Foça). Twenty-four fishers of the 31 existing lift net fishers were reached and the survey consisting of 41 questions was filled in face-to-face and telephone interviews. There was no significant difference between the stations in terms of age, lift net fishing experience, number of employees, annual income, total annual expense, annual profit, fishing day per year, and duration of fishing operation (p>0.05). There were four target species (mullet species, sea bass, sand steenbras and bluefish) in lift net fishing. 14,855 kg of mullet species, 2,390 kg of sea bass, 1,200 kg of sand steenbras and 450 kg of bluefish were caught annually. The annual income of lift net fishers varied between 637 US dollars ($) and 5,242 $, and the mean income was 1,979 ± 1,151 $/year. The mean income (165 $/month) was lower than the minimum wage in 2021 (212 $/month). 26.9% of the fishers were dissatisfied with their income and 69.1% engaged in additional activities such as in agriculture-livestock, gillnet-longline fishery, construction worker, seller in a market, waitstaff, and solid waste collector. The heirs of 15.4% of the fishers will not continue fishing. The biggest problem was SCUBA or skin divers near the lift net. Other reported problems were commercial (gillnet and longline) and recreational fishing near the lift net, the anchoring of private boats near the lift net, the decrease in fish stocks, illegal fishing and marine pollution. The number of lift nets has decreased over the years. The continuity of this artisanal fishing method is considered to be important for the socio-economic sustainability.

USA vs Czech Republic live match starts on 29 Dec 2022 at 01:00 UTC at Ken Rosewall Arena, Sydney, Australia. USA v Czech Republic is part of United Cup,.. USA host Czech Republic in a United Cup Teams game, certain to entertain all Tennis fans. This match will take place on 29/12/2022 at 01:00 UTC. Stream live action from the United Cup (29 - 08 January 2023). Watch all 119 ATP matches on your favourite device. ✅✅ Wacth Free Tennis United Cup 2022 Live The 2023 United Cup will be the first edition of the United Cup, an international outdoor hard court mixed-gender team tennis tournament held by the Association of Tennis Professionals (ATP) and the Women's Tennis Association (WTA). Serving as the opener for the 2023 ATP Tour and the 2023 WTA Tour, it will be held from 29 December 2022 to 8 January 2023 at three venues in the Australian cities of Brisbane, Perth, and Sydney. It will also be the first mixed-gender team event to offer both ATP rankings and WTA rankings points to its players: a player will be able to win a maximum of 500 points Lift net/blanket net (çökeltme dalyanı in Turkish), is an artisanal fishing method established on the shores of the Aegean Sea in Türkiye, in which fish that migrate along the coast to feed or breed are caught. In this study, it was aimed to define the lift net fishery and to determine the socio-demographic and socio-economic characteristics and views of fishers on problems. The data were collected through surveys and field observations from April to May 2022 in three stations (Karaburun, Urla and Foça). Twenty-four fishers of the 31 existing lift net fishers were reached and the survey consisting of 41 questions was filled in face-to-face and telephone interviews. There was no significant difference between the stations in terms of age, lift net fishing experience, number of employees, annual income, total annual expense, annual profit, fishing day per year, and duration of fishing operation (p>0.05). There were four target species (mullet species, sea bass, sand steenbras and bluefish) in lift net fishing. 14,855 kg of mullet species, 2,390 kg of sea bass, 1,200 kg of sand steenbras and 450 kg of bluefish were caught annually. The annual income of lift net fishers varied between 637 US dollars ($) and 5,242 $, and the mean income was 1,979 ± 1,151 $/year. The mean income (165 $/month) was lower than the minimum wage in 2021 (212 $/month). 26.9% of the fishers were dissatisfied with their income and 69.1% engaged in additional activities such as in agriculture-livestock, gillnet-longline fishery, construction worker, seller in a market, waitstaff, and solid waste collector. The heirs of 15.4% of the fishers will not continue fishing. The biggest problem was SCUBA or skin divers near the lift net. Other reported problems were commercial (gillnet and longline) and recreational fishing near the lift net, the anchoring of private boats near the lift net, the decrease in fish stocks, illegal fishing and marine pollution. The number of lift nets has decreased over the years. The continuity of this artisanal fishing method is considered to be important for the socio-economic sustainability.

Australia vs Great Britain live match starts on 29 Dec 2022 at 08:00 UTC at Ken Rosewall Arena, Sydney, Australia. Australia v Great Britain is part of Learn How to Watch Australia vs Great Britain Live Stream Online on 29 December 2022, See Match Results and Teams. Stream live action from the United Cup (29 - 08 January 2023). Watch all 119 ATP matches on your favourite device. ✅✅ Wacth Free Tennis United Cup 2022 Live The 2023 United Cup will be the first edition of the United Cup, an international outdoor hard court mixed-gender team tennis tournament held by the Association of Tennis Professionals (ATP) and the Women's Tennis Association (WTA). Serving as the opener for the 2023 ATP Tour and the 2023 WTA Tour, it will be held from 29 December 2022 to 8 January 2023 at three venues in the Australian cities of Brisbane, Perth, and Sydney. It will also be the first mixed-gender team event to offer both ATP rankings and WTA rankings points to its players: a player will be able to win a maximum of 500 points Lift net/blanket net (çökeltme dalyanı in Turkish), is an artisanal fishing method established on the shores of the Aegean Sea in Türkiye, in which fish that migrate along the coast to feed or breed are caught. In this study, it was aimed to define the lift net fishery and to determine the socio-demographic and socio-economic characteristics and views of fishers on problems. The data were collected through surveys and field observations from April to May 2022 in three stations (Karaburun, Urla and Foça). Twenty-four fishers of the 31 existing lift net fishers were reached and the survey consisting of 41 questions was filled in face-to-face and telephone interviews. There was no significant difference between the stations in terms of age, lift net fishing experience, number of employees, annual income, total annual expense, annual profit, fishing day per year, and duration of fishing operation (p>0.05). There were four target species (mullet species, sea bass, sand steenbras and bluefish) in lift net fishing. 14,855 kg of mullet species, 2,390 kg of sea bass, 1,200 kg of sand steenbras and 450 kg of bluefish were caught annually. The annual income of lift net fishers varied between 637 US dollars ($) and 5,242 $, and the mean income was 1,979 ± 1,151 $/year. The mean income (165 $/month) was lower than the minimum wage in 2021 (212 $/month). 26.9% of the fishers were dissatisfied with their income and 69.1% engaged in additional activities such as in agriculture-livestock, gillnet-longline fishery, construction worker, seller in a market, waitstaff, and solid waste collector. The heirs of 15.4% of the fishers will not continue fishing. The biggest problem was SCUBA or skin divers near the lift net. Other reported problems were commercial (gillnet and longline) and recreational fishing near the lift net, the anchoring of private boats near the lift net, the decrease in fish stocks, illegal fishing and marine pollution. The number of lift nets has decreased over the years. The continuity of this artisanal fishing method is considered to be important for the socio-economic sustainability.

Taylor Fritz and Jiri Lehecka will clash against each other in the Round Robin of the United Cup for the 1st time in their career. They are scheduled to compete on Thursday at 12:00 pm on Ken Rosewall Arena. In this post, we analyze their head to head performance and prediction. Stream live action from the United Cup (29 - 08 January 2023). Watch all 119 ATP matches on your favourite device. ✅✅ Wacth Free Tennis United Cup 2022 Live The 2023 United Cup will be the first edition of the United Cup, an international outdoor hard court mixed-gender team tennis tournament held by the Association of Tennis Professionals (ATP) and the Women's Tennis Association (WTA). Serving as the opener for the 2023 ATP Tour and the 2023 WTA Tour, it will be held from 29 December 2022 to 8 January 2023 at three venues in the Australian cities of Brisbane, Perth, and Sydney. It will also be the first mixed-gender team event to offer both ATP rankings and WTA rankings points to its players: a player will be able to win a maximum of 500 points Lift net/blanket net (çökeltme dalyanı in Turkish), is an artisanal fishing method established on the shores of the Aegean Sea in Türkiye, in which fish that migrate along the coast to feed or breed are caught. In this study, it was aimed to define the lift net fishery and to determine the socio-demographic and socio-economic characteristics and views of fishers on problems. The data were collected through surveys and field observations from April to May 2022 in three stations (Karaburun, Urla and Foça). Twenty-four fishers of the 31 existing lift net fishers were reached and the survey consisting of 41 questions was filled in face-to-face and telephone interviews. There was no significant difference between the stations in terms of age, lift net fishing experience, number of employees, annual income, total annual expense, annual profit, fishing day per year, and duration of fishing operation (p>0.05). There were four target species (mullet species, sea bass, sand steenbras and bluefish) in lift net fishing. 14,855 kg of mullet species, 2,390 kg of sea bass, 1,200 kg of sand steenbras and 450 kg of bluefish were caught annually. The annual income of lift net fishers varied between 637 US dollars ($) and 5,242 $, and the mean income was 1,979 ± 1,151 $/year. The mean income (165 $/month) was lower than the minimum wage in 2021 (212 $/month). 26.9% of the fishers were dissatisfied with their income and 69.1% engaged in additional activities such as in agriculture-livestock, gillnet-longline fishery, construction worker, seller in a market, waitstaff, and solid waste collector. The heirs of 15.4% of the fishers will not continue fishing. The biggest problem was SCUBA or skin divers near the lift net. Other reported problems were commercial (gillnet and longline) and recreational fishing near the lift net, the anchoring of private boats near the lift net, the decrease in fish stocks, illegal fishing and marine pollution. The number of lift nets has decreased over the years. The continuity of this artisanal fishing method is considered to be important for the socio-economic sustainability.

PDC World Darts Championship 2023: Full schedule, how to watch and latest news. live stream, watch online, TV ✅✅Watch Free PDC World Darts Championship 2023 Live The 2023 PDC World Darts Championship is the 30th World Championship organised by the Professional Darts Corporation since it separated from the British Darts Organisation. The event is taking place at the Alexandra Palace in London from 15 December 2022 to 3 January 2023. The PDC World Darts Championship is well underway in London, with Rob Cross, Joe Cullen and Jonny Clayton amongst those who have booked their place in the next round. Peter Wright and Michael Smith, last year’s finalists, are among those to have already sealed their place in the tournament when it resumes at Alexandra Palace after Christmas, as the world’s best compete for the biggest prize in At 3pm, Andoo Comanche (John Winning Jr) was still in the lead with 179 nautical miles to go to make the finish line in Hobart. It was 80 nautical miles east of St Helens Point and, like its main rivals, LawConnect, Black Jack and Hamilton Island Wild Oats, was well east of the rhumb line. Andoo Comanche was still in the box seat, but all four 100-footers were in contention to finish first in the 628 nautical mile race, organised by the Cruising Yacht Club of Australia. Andoo Comanche’s navigator, Justin Shaffer, said it was hard to predict a finish time, but added that they might reach the Iron Pot at the mouth of the Derwent River at about 10pm. his study is an ex post facto which aims to examine the following: (1) the relationship between self-regulation and physics learning outcomes in students (2) the relationship between time management and physics learning outcomes in students (3) the relationship between self-regulation and time management with physics learning outcomes in students. The study design used is correlational. The population of this study were all students of class XI which totalled 85 students divided into 3 classes. The results showed that; 1) based on Correlation Coefficient = 0.013 there is a very weak relationship between self regulation with physics learning outcomes and obtained = 0.4659 = 3.9, which indicates there is no significant relationship between self regulation with physics learning outcomes in students 2) based on self regulation and time management with physics learning outcomes in students. The results showed that; 1) based on Correlation Coefficient = 0.013 there is a very weak relationship between self regulation with physics learning outcomes and obtained = 0.4659 = 3.9, which indicates there is no significant relationship between self regulation with physics learning outcomes in students because the value obtained at from 0.05. 2) based on Correlation Coefficient = 0.074 there is a very weak relationship between time management and physics learning outcomes and obtained = 0.4659 = 3.9, which indicates there is no significant relationship between time management and physics learning outcomes in students because the value obtained at 0.05. 3) There is no significant relationship between self-regulation and time management with the learning outcomes of students with sig value. 0.281 and obtained = 0.0024 = 3.1. Because the value obtained at from 0.05. The research implications of the results of this study are expected to be tried again in depth with a larger sample in order to provide more relevant information about the relationship between self regulation and time management with physics learning outcomes for further research.

LIVESTREAMs World Junior Ice Hockey Championships 2023 Live Stream. How to Watch World Junior Hockey Championship 2023 live Online. World Juniors live streams: How to watch 2023 IIHF World U20 Championship without cable ✅✅Watch Free World Junior Ice Hockey Championships 2023 Live The 2023 World Junior Ice Hockey Championships is the 47th edition of the IIHF World Junior Championship, which began on December 26, 2022 and is scheduled to end on January 5, 2023 in Halifax, Nova Scotia and Moncton, New Brunswick, Canada. This is the 18th time Canada is hosting the WJIHC. his study is an ex post facto which aims to examine the following: (1) the relationship between self-regulation and physics learning outcomes in students (2) the relationship between time management and physics learning outcomes in students (3) the relationship between self-regulation and time management with physics learning outcomes in students. The study design used is correlational. The population of this study were all students of class XI which totalled 85 students divided into 3 classes. The results showed that; 1) based on Correlation Coefficient = 0.013 there is a very weak relationship between self regulation with physics learning outcomes and obtained = 0.4659 = 3.9, which indicates there is no significant relationship between self regulation with physics learning outcomes in students 2) based on self regulation and time management with physics learning outcomes in students. The results showed that; 1) based on Correlation Coefficient = 0.013 there is a very weak relationship between self regulation with physics learning outcomes and obtained = 0.4659 = 3.9, which indicates there is no significant relationship between self regulation with physics learning outcomes in students because the value obtained at from 0.05. 2) based on Correlation Coefficient = 0.074 there is a very weak relationship between time management and physics learning outcomes and obtained = 0.4659 = 3.9, which indicates there is no significant relationship between time management and physics learning outcomes in students because the value obtained at 0.05. 3) There is no significant relationship between self-regulation and time management with the learning outcomes of students with sig value. 0.281 and obtained = 0.0024 = 3.1. Because the value obtained at from 0.05. The research implications of the results of this study are expected to be tried again in depth with a larger sample in order to provide more relevant information about the relationship between self regulation and time management with physics learning outcomes for further research.

The maxis are approaching the finish line in the 2022 Rolex Sydney Hobart Yacht Race and are expected to arrive in the early hours of Wednesday 28 December. ✅✅Watch Free Rolex Sydney Hobart Yacht Race 2022 Live The 2022 Rolex Sydney Hobart Yacht Race fleet is revelling in the downwind run to Tasmania thanks to the north-to-north easterly winds of Monday’s action-packed start prevailing. By mid-afternoon today, the four maxis were continuing to make their charge across Bass Strait in their bid for Line Honours victory in the early hours of tomorrow morning and outside the race record of time of 1 day, 9 hours, 15 minutes, 24 seconds, set by LDV Comanche in 2017. At 3pm, Andoo Comanche (John Winning Jr) was still in the lead with 179 nautical miles to go to make the finish line in Hobart. It was 80 nautical miles east of St Helens Point and, like its main rivals, LawConnect, Black Jack and Hamilton Island Wild Oats, was well east of the rhumb line. Andoo Comanche was still in the box seat, but all four 100-footers were in contention to finish first in the 628 nautical mile race, organised by the Cruising Yacht Club of Australia. Andoo Comanche’s navigator, Justin Shaffer, said it was hard to predict a finish time, but added that they might reach the Iron Pot at the mouth of the Derwent River at about 10pm. his study is an ex post facto which aims to examine the following: (1) the relationship between self-regulation and physics learning outcomes in students (2) the relationship between time management and physics learning outcomes in students (3) the relationship between self-regulation and time management with physics learning outcomes in students. The study design used is correlational. The population of this study were all students of class XI which totalled 85 students divided into 3 classes. The results showed that; 1) based on Correlation Coefficient = 0.013 there is a very weak relationship between self regulation with physics learning outcomes and obtained = 0.4659 = 3.9, which indicates there is no significant relationship between self regulation with physics learning outcomes in students 2) based on self regulation and time management with physics learning outcomes in students. The results showed that; 1) based on Correlation Coefficient = 0.013 there is a very weak relationship between self regulation with physics learning outcomes and obtained = 0.4659 = 3.9, which indicates there is no significant relationship between self regulation with physics learning outcomes in students because the value obtained at from 0.05. 2) based on Correlation Coefficient = 0.074 there is a very weak relationship between time management and physics learning outcomes and obtained = 0.4659 = 3.9, which indicates there is no significant relationship between time management and physics learning outcomes in students because the value obtained at 0.05. 3) There is no significant relationship between self-regulation and time management with the learning outcomes of students with sig value. 0.281 and obtained = 0.0024 = 3.1. Because the value obtained at from 0.05. The research implications of the results of this study are expected to be tried again in depth with a larger sample in order to provide more relevant information about the relationship between self regulation and time management with physics learning outcomes for further research.