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This article discusses a project that aims to create a database of potentially relevant papers on anxiety, substance use, and depressive disorders. The project involves a systematic review process, including a broad search, data cleaning, active learning using a shallow classifier, and a quality assessment procedure. The researchers have made all the scripts, data files, and output files available for reproducibility. It is important to note that this preprint has not yet undergone peer review. [Extracted from the article]

Byline: DANIELLE WALKERInstitutional investors need no reminding of the importance of picking the right active equity manager, as they seek out stocks that can deliver alpha. But a research paper [...]

This article discusses a project that aims to create a database of potentially relevant papers on anxiety, substance use, and depressive disorders. The project involves a systematic review process, including a broad search, data cleaning, active learning using a shallow classifier, and a quality assessment procedure. The researchers have made all the scripts, data files, and output files available for reproducibility. It is important to note that this preprint has not yet undergone peer review. [Extracted from the article]

Byline: Pragya Srivastava 80% adult Indians now have bank accounts - and India could achieve this tremendous growth due to Prime Minister Narendra Modi's Jan-Dhan Yojana and Aadhaar. But the [...]

A personal narrative is presented which explores the author's experience of conducting an attendee survey when he served as a program chair of a Very Large Data Bases (VLDB) conference in 2014.

The International Federation of Societies of Cosmetic Chemists (IFSCC) has relaunched Kosmet, its online academic paper database. According to IFSCC's newly appointed President, Dr Peter Kang, the revamp will simplify [...]

A study conducted by researchers at Sichuan University in Chengdu, China, aimed to identify the most influential publications on antibiotic adjuvants and analyze the hotspots and research trends in this field. The researchers retrieved original articles and reviews related to antibiotic adjuvants from the Web of Science Core Collection database and selected the top 100 highly cited publications. The analysis revealed that the top 100 cited publications spanned the years 1977-2020 and originated from 39 countries, with the United States leading in production. The study provides a comprehensive understanding of the characteristics and frontiers in the field of antibiotic adjuvants. [Extracted from the article]

Keywords: Charlottesville; State:Virginia; United States; North and Central America; Arthroplasty; Health and Medicine; Information Technology; Knee Arthroplasty; Orthopedics; Surgery EN Charlottesville State:Virginia United States North and Central America Arthroplasty Health and Medicine Information Technology Knee Arthroplasty Orthopedics Surgery 1129 1129 1 06/12/23 20230613 NES 230613 2023 JUN 12 (NewsRx) -- By a News Reporter-Staff News Editor at Medical Devices & Surgical Technology Week -- New research on Surgery - Arthroplasty is the subject of a report. Keywords for this news article include: Charlottesville, Virginia, United States, North and Central America, Arthroplasty, Health and Medicine, Information Technology, Knee Arthroplasty, Orthopedics, Surgery, University of Virginia. [Extracted from the article]

The article discusses the potential creation of a human drug metabolism database (hDMdb) by the IUPAC's Drug Discovery and Development Subcommittee. The database would serve as a tool for scientists involved in drug discovery and toxicology, providing information on the biological disposition of new compounds, the value of preclinical models for predicting drug metabolism, and the development of methods to account for chemical structures. The article also mentions the possibility of incorporating machine learning and AI into drug metabolism research. The authors are seeking input from the scientific community on the need for such a database and other resources that could be developed in this field. They are also considering the creation of a glossary or tutorial on the metabolism of medium and large molecule therapeutics. The authors request assistance from interested parties to contribute to this non-profit undertaking, with the goal of issuing a white paper in early 2025. [Extracted from the article]

The Economic Offences Unit (EOU) of the Bihar Police is warning candidates for the upcoming constable recruitment test to be cautious of cyber fraudsters. Last year's exam was cancelled due to leaked answer keys on social media, causing a nine-month delay. The EOU has implemented measures to prevent fraudulent activities, including a monitoring plan and specific contact information for reporting suspicious activity. The public is also encouraged to report any instances of leaked question papers or answer sheets on social media and to report cyber fraud through fake calls on the helpline number 1930. [Extracted from the article]

Purpose: This paper aims to analyze the performance of artificial neural networks with filling methods in predicting the minimum fluidization velocity of different biomass types for bioenergy applications. Design/methodology/approach: An extensive literature review was performed to create an efficient database for training purposes. The database consisted of experimental values of the minimum fluidization velocity, physical properties of the biomass particles (density, size and sphericity) and characteristics of the fluidization (monocomponent experiments or binary mixture). The neural models developed were divided into eight different cases, in which the main difference between them was the filling method type (K-nearest neighbors [KNN] or linear interpolation) and the number of input neurons. The results of the neural models were compared to the classical correlations proposed by the literature and empirical equations derived from multiple regression analysis. Findings: The performance of a given filling method depended on the characteristics and size of the database. The KNN method was superior for lower available data for training and specific fluidization experiments, like monocomponent or binary mixture. The linear interpolation method was superior for a wider and larger database, including monocomponent and binary mixture. The performance of the neural model was comparable with the predictions of the most well-known correlations from the literature. Originality/value: Techniques of machine learning, such as filling methods, were used to improve the performance of the neural models. Besides the typical comparisons with conventional correlations, comparisons with three main equations derived from multiple regression analysis were reported and discussed. [ABSTRACT FROM AUTHOR]

Fingerprints have been used to solve crimes for over 100 years. Fingerprints are unique to each individual and are formed by the patterns of ridges and valleys on the tips of our fingers. There are three main fingerprint patterns: loops, arches, and whorls. In the 1880s, a Scottish doctor named Henry Faulds suggested using fingerprints to identify criminals, and a system of measuring fingerprints was later developed by Sir Edward Henry. Fingerprints used to be recorded on paper, but now they are scanned electronically. Fingerprints can be found using a magnifying glass, fine powder, or ultraviolet light. In the UK, fingerprints are stored in a database called IDent1. Facial recognition technology is another form of biometrics used to identify people, but it raises concerns about privacy. Other forensic methods used to solve crimes include DNA analysis, footwear marks, and fiber analysis. [Extracted from the article]

SupplyPro has introduced its UStockit inventory shelf tag to prevent inventory stockouts, according to a press release. Electronic shelf labels use electronic ink to display a price and can connect to a computer database. The labelling technology makes changing prices... [ABSTRACT FROM AUTHOR]

The article offers information on increasing trends of genealogy with the onset of the Covid-19 pandemic. It mentions that Genealogy depends heavily on newspaper research to peel back the many layers of what makes up a life. It discusses that Newspapers.com has claimed to be the largest online newspaper archive.

The global increase in the incidence of wounds is concerning and fuels the search for new treatment options. The use of traditional medicinal plants in wound healing represents an appreciated available therapeutic possibility. This work introduces the VOLKSMED database, which contains plants and other materials used in Austrian folk medicine, either as monographs or mixtures. This work focuses on the monographs of the database. Concerning wound healing, Hypericum sp., Arnica montana, Calendula officinalis, Plantago sp., and Malva sp. are the most commonly used plants. The focus of this paper is set on selected lesser-known plants (Abies alba, Anthyllis vulneraria, Brassica sp., Gentiana sp., Larix decidua, Picea abies, Sambucus sp., Sanicula europaea) and their status quo in literature concerning wound healing. A systematic search using the databases SciFinder, SCOPUS, and PubMed yielded substantial evidence for the wound healing potential of Brassica sp., Gentiana sp., the Pinaceae A. abies, L. decidua, and P. abies , as well as Sambucus nigra. In vivo and clinical studies substantiate their use in Austrian folk medicine. According to the literature, especially A. vulneraria, Sambucus racemosa, and S. europaea would be worth investigating in-depth since data concerning their wound healing effects – even though scarce – are convincing. In conclusion, the VOLKSMED database contains promising opportunities for further treatment options in the field of wound healing. Future research should consider the listed plants to support their traditional use in Austrian folk medicine and possibly promote the implementation of old knowledge in modern medicine. [ABSTRACT FROM AUTHOR]

A recent study conducted by researchers at Western University in London, Canada, aimed to identify and analyze the various factors that contribute to the longevity of dental restorations. The study reviewed papers published between 1980 and 2024 and found that tooth-related, patient-related, and dentist-related factors all play a significant role in the success of dental restorations. Factors such as the number of surfaces restored, tooth location, patient age, medical conditions, and dentist experience were found to impact restoration survival. The study suggests that strategies to improve restoration outcomes should consider these multifactorial contributors, and emphasizes the importance of continuing professional education, patient guidance, material selection, and individualized treatment in reducing failure rates and improving the lifespan of restorations. [Extracted from the article]

To date, a large number of research papers have been written on malware classification, identification, classification into different families, and the distinction between malware and goodware. These works have been based on captured malware samples and have attempted to analyse malware and goodware using various techniques like the analysis of malware using malware visualization. These works usually convert malware samples capturing the malware structure into image structures which are then subject to image processing. In this paper, we propose an unconventional and novel approach to malware visualization based on its dynamical analysis, subsequent complex network conversion and fractal geometry, e.g. Julia sets visualization. Very interesting images being subsequently used to classify as malware and goodware. The classification is done by deep learning network. The results of the presented experiments of fractal conversion and subsequent classification are based on a database of 6,589,997 goodware, 827,853 potentially unwanted applications and 4,174,203 malware samples provided by ESET. 1 1 https://www.eset.com. This paper aims to show a new direction in visualizing malware using fractal geometry and possibilities in analysis and classification. [Display omitted] • Introduction of a new method for malware visualization based on fractal geometry. • Show interesting results when visualy comparing classified samples using fractal geometry. • Test novel malware image classification using deep learning. • To point out and define new research directions in visual malware analysis opened by our method. [ABSTRACT FROM AUTHOR]

This paper reports on the 28th Italian Symposium on Advanced Database Systems (SEBD 2020), held online as a virtual conference from the 21st to the 24th of June 2020. The topics that were addressed in this edition of the conference were organized in the sessions: ontologies and data integration, anomaly detection and dependencies, text analysis and search, deep learning, noSQL data, trajectories and diffusion, health and medicine, context and ranking, social and knowledge graphs, multimedia content analysis, security issues, and data mining. [ABSTRACT FROM AUTHOR]

Objective. Describe the quality of life of health personnel, the work environment, and interactions between employees and their work environment during the pandemic. Methods. A scoping review was conducted. The electronic databases PubMed, Cumulative Index of Nursing and Allied Literature Complete, and Google Scholar were used, as well as the repositories of the World Health Organization and the Centers for Disease Control and Prevention. Primary, secondary, and grey literature studies published between December 2019 and March 2021 in Spanish, English, and Portuguese were included. Methodological quality was assessed using the Authority, Accuracy, Coverage, Objectivity, Date and Importance (AACODS) checklist; a tool for the measurement of multiple systematic reviews (AMSTAR); and the Critical Appraisal Checklist for Text and Opinion Papers. A thematic analysis was carried out based on the quality-of-life and well-being model. Results. Of a total of 208 articles, 11 were included. The quality of life of health personnel during the COVID-19 pandemic was affected by the characteristics of health personnel, the work environment, and interactions between employees and their work environment. Problems related to psychosocial and occupational factors were observed. Discussion. The quality of life of health personnel was characterized by stigmatization, stress, anxiety, and fatigue. Organizational management and the implementation of psychological interventions appear to affect interactions between employees and their work environment, and improve their quality of life. [ABSTRACT FROM AUTHOR]

The article reminisces about the Radio Amateur Callbook Magazine, which served as the primary resource for accessing ham radio operators' information before the internet, highlighting its unique cover designs, extensive content, and nostalgic value.

This paper studies undefined behavior arising in systems programming languages such as C/C++. Undefined behavior bugs lead to unpredictable and subtle systems behavior, and their effects can be further amplified by compiler optimizations. Undefined behavior bugs are present in many systems, including the Linux kernel and the Postgres database. The consequences range from incorrect functionality to missing security checks. This paper proposes a formal and practical approach, which finds undefined behavior bugs by finding “unstable code” in terms of optimizations that leverage undefined behavior. Using this approach, we introduce a new static checker called Stack that precisely identifies undefined behavior bugs. Applying Stack to widely used systems has uncovered 161 new bugs that have been confirmed and fixed by developers. [ABSTRACT FROM AUTHOR]

The article discusses the introduction of a new app called ACRS Cloud, which aims to bring fully digital traceability to steel certification in Australia. The app is part of the National Traceability Pledge, which promotes traceability and digitalization of building product information in the construction supply chain. The ACRS Cloud app allows users to instantly verify the authenticity and conformance of steel products through QR codes and provides access to vital product information. It aligns with international standards and improves accessibility and verification processes in the construction industry. [Extracted from the article]

Purpose: Accurate evaluation of the consequences of new technologies in various industries is of great significance. So, it will be essential to examine the impact of this technology on the banking industry, representing how to create, deliver and gain value in this industry. This study aims to investigate whether blockchain can affect the business intelligence efficiency of banks. This study also aims to examine the impact of security, fraud reduction and privacy of blockchain, equal and anonymous access to the blockchain, decentralization and sustainability of blockchain, accountability and transparency of blockchain, quality, speed and efficiency of blockchain on business intelligence efficiency. Design/methodology/approach: Technological changes are creating new challenges and opportunities for various industries. The inability of organizations to adapt to these changes may even lead to their deletion from the market. Blockchain is one of the most critical technologies in recent years. One of the sectors that will undergo significant changes in blockchain technology is the banking industry. According to the reviewed literature in this study, a comprehensive model has been proposed to examine the impact of security, fraud reduction and privacy of blockchain, equal and anonymous access to the blockchain, the decentralization and sustainability of blockchain, accountability and transparency of blockchain and quality, speed and efficiency of blockchain on business intelligence efficiency. A survey method was used to collect data from banks of the Nanjing city. The partial least square technique was used for data analysis. Findings: The results showed that the fit of the proposed model was very good. Also, all assumptions except one were confirmed. It means that security, fraud reduction and privacy of blockchain factor have a remarkable and positive impact on all aspects of business intelligence efficiency, namely information technology, employees, competitors and customers. Also, equal and anonymous access to the blockchain factor has a positive and significant effect on all aspects of business intelligence efficiency. The decentralization and sustainability of blockchain factors have an impact on business intelligence efficiency. Also, blockchain's accountability and transparency as a fourth factor have a positive and significant impact on all aspects of business intelligence efficiency. Finally, the last factor (quality, speed and efficiency of blockchain) has a positive and significant effect on information technology, employees and customers' dimensions. But, it does not affect the competitors' dimension, and this hypothesis has not been confirmed. Practical implications: This paper offers valuable insight for business intelligence practitioners into how blockchain technology has the potential to disrupt existing business intelligence provisions. Originality/value: This paper is one of the first studies to examine the impact of blockchain on IT dimension, organizational employees' dimension, customer dimension and competitors' dimension. It lays a firm foundation for future research. [ABSTRACT FROM AUTHOR]

The article focuses on a paper concerning programming platforms capable of performing higher-level abstractions for machine learning over data and are capable of performing modern hardware optimizations. It states that in a machine learning setting the database methods can be very effective and revealed weight vectors and feature matrices often involved repeated floating-point computations.

This paper presents a design of strong physical unclonable function (PUF) exploiting the sneak paths in the resistive X-point array. The entanglement of the sneak paths in the X-point array greatly enhances the entropy of the physical system, thereby increasing the space of challenge-response pairs. To eliminate the undesired collision or diffuseness in X-point PUF with “analog” resistance distribution and “digital” resistance distribution is employed in this paper. The effect of design parameters and non-ideal properties in X-point array on the performance of X-point PUF is systematically investigated by Simulation Program with Integrated Circuit Emphasis (SPICE) simulation. The simulation results show that—1) the PUF’s performance presents strong dependence on the percent of cells in the on-state, thus should be carefully optimized for the robustness against the reference current variation of the sense amplifier; 2) the interconnect resistance decreases the column current thus the reference current should scale down with the scaling of technology node; 3) larger on/off ratio is desired to achieve low power consumption and high robustness against reference current variation; and 4) the device-to-device variation might degrade the performance of X-point PUF, which can be mitigated with write-verify programming scheme in the PUF construction phase. In addition, the proposed X-point PUF presents no correlation between challenges and responses, and strong security against the possible SPICE modeling attack and machine learning attack. Compared with the conventional Arbiter PUF, the X-point PUF has benefits in smaller area, lower energy, and enhanced security. [ABSTRACT FROM AUTHOR]

A new app called ACRS Cloud has been introduced in Australia to bring fully digital traceability to steel certification. The app aims to address the need for trustworthiness and transparency in the construction sector, particularly in ensuring the traceability of steel products. ACRS Cloud provides instant verification of steel products through QR codes and allows users to check the authenticity and certification details of steel products. The app aligns with international standards and supports best practices for sustainable steel. [Extracted from the article]

The global increase in the incidence of wounds is concerning and fuels the search for new treatment options. The use of traditional medicinal plants in wound healing represents an appreciated available therapeutic possibility. This work introduces the VOLKSMED database, which contains plants and other materials used in Austrian folk medicine, either as monographs or mixtures. This work focuses on the monographs of the database. Concerning wound healing, Hypericum sp., Arnica montana, Calendula officinalis, Plantago sp., and Malva sp. are the most commonly used plants. The focus of this paper is set on selected lesser-known plants (Abies alba, Anthyllis vulneraria, Brassica sp., Gentiana sp., Larix decidua, Picea abies, Sambucus sp., Sanicula europaea) and their status quo in literature concerning wound healing. A systematic search using the databases SciFinder, SCOPUS, and PubMed yielded substantial evidence for the wound healing potential of Brassica sp., Gentiana sp., the Pinaceae A. abies, L. decidua, and P. abies , as well as Sambucus nigra. In vivo and clinical studies substantiate their use in Austrian folk medicine. According to the literature, especially A. vulneraria, Sambucus racemosa, and S. europaea would be worth investigating in-depth since data concerning their wound healing effects – even though scarce – are convincing. In conclusion, the VOLKSMED database contains promising opportunities for further treatment options in the field of wound healing. Future research should consider the listed plants to support their traditional use in Austrian folk medicine and possibly promote the implementation of old knowledge in modern medicine. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to predict dynamic responses of aileron and spoiler control surfaces in subsonic flight via the use of surrogate models. The prepared reduced order models prove useful when quick estimations for a large number of variations are required. Design/methodology/approach: The linear frequency domain (LFD) method was used for the simulation study. Each surrogate contained a database of 100 control surface dynamic responses over a spectrum of 200 harmonics computed with LFD. To interpolate new results, the DLR surrogate modelling toolbox, SMARTy, was used. The database's samples were prepared in a Halton sequence, making interpolation reliable. The surrogate's parameter space was the Mach number, Reynold's number, angle of attack, control surface deflection angle and the control surface chord length. Findings: The LFD method proved effective for the mentioned purpose: the surrogates were accurate, up to 15% of relative error, in reproducing dynamic responses of aileron and spoiler deflections at low speed, within the limitations of flow field linearity, as well as surrogate prediction capability. The restrictions of the surrogate, and the reasoning thereof, are also presented in detail in the study. Future load alleviation studies are a potential of the findings here. Originality/value: LFD is an innovative technique for load prediction and alleviation studies. This paper provides a reference for engineers wishing to use the method for the two mentioned control surfaces, or the like. [ABSTRACT FROM AUTHOR]

The article discusses the importance of preventing ransomware attacks by designing software that is resilient against common cyberattacks. It emphasizes that software manufacturers have the ability to build products that are secure enough to prevent compromises. The article also highlights the need for businesses to push software manufacturers to prioritize security. It suggests that preventing vulnerabilities at the design stage is more cost-effective than dealing with the aftermath of a hack. Additionally, the article encourages customers to demand better security from software vendors and provides guidance on assessing vulnerabilities and procuring secure software. [Extracted from the article]

Copyright of Nuova Secondaria is the property of Edizioni Studium S.r.l and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A recent study conducted by researchers at the State University of Rio Grande do Norte analyzed the 100 most cited articles on Molar-Incisor Hypomineralization (MIH), an enamel defect that affects permanent molars and incisors. The study aimed to understand the trends and characteristics of research in this field. The findings revealed that the majority of the studies were published in the 2010s and focused on epidemiology. Europe had the highest contribution to the list of most cited articles, while Australia had the most papers included. The study concluded that analyzing the most cited articles provided valuable insights into the global scenario of MIH research. [Extracted from the article]

This paper presents a wireless power and downlink data transfer system for medical implants operating over a single $10~MHz$ inductive link. The system is based on a Carrier Width Modulation (CWM) scheme for high-speed communication and efficient power delivery using a novel modulator circuit design. Unlike conventional modulation techniques, the data rate of the proposed CWM is not limited by the quality factors of the primary and secondary coils. The functionality of the new modulation method is proven using a hybrid implementation comprising a custom-integrated demodulator circuit and board-level discrete components. The proposed Wireless Power and Data Transfer (WPDT) system is also capable of operating under a wide range of data rates. It allows a maximum data rate of $3.33~Mb/s$ for a maximum power delivery of $6.1~mW$ at $1~cm$ coils separation distance. The system can recover more power, reaching $55~mW$ at $100~kb/s$. Due to the system genericity, an operator can select the best compromise between power and data rates in accordance to application or current need, without reconfiguring the receiver. Another advantage of this modulation technique is the simple implementation and the ultra-low power consumption of the CWM demodulator despite its high-speed demodulation. [ABSTRACT FROM AUTHOR]

Nowadays, video quality assessment (VQA) is essential to video compression technology applied to video transmission and storage. However, small-scale video quality databases with imbalanced samples and low-level feature representations for distorted videos impede the development of VQA methods. In this paper, we propose a full-reference (FR) VQA metric integrating transfer learning with a convolutional neural network (CNN). First, we imitate the feature-based transfer learning framework to transfer the distorted images as the related domain, which enriches the distorted samples. Second, to extract high-level spatiotemporal features of the distorted videos, a six-layer CNN with the acknowledged learning ability is pretrained and finetuned by the common features of the distorted image blocks (IBs) and video blocks (VBs), respectively. Notably, the labels of the distorted IBs and VBs are predicted by the classic FR metrics. Finally, based on saliency maps and the entropy function, we conduct a pooling stage to obtain the quality scores of the distorted videos by weighting the block-level scores predicted by the trained CNN. In particular, we introduce a preprocessing and a postprocessing to reduce the impact of inaccurate labels predicted by the FR-VQA metric. Due to feature learning in the proposed framework, two kinds of experimental schemes including train-test iterative procedures on one database and tests on one database with training other databases are carried out. The experimental results demonstrate that the proposed method has high expansibility and is on a par with some state-of-the-art VQA metrics on two widely used VQA databases with various compression distortions. [ABSTRACT FROM AUTHOR]

In early stages, patients with bipolar disorder are often diagnosed as having unipolar depression in mood disorder diagnosis. Because the long-term monitoring is limited by the delayed detection of mood disorder, an accurate and one-time diagnosis is desirable to avoid delay in appropriate treatment due to misdiagnosis. In this paper, an elicitation-based approach is proposed for realizing a one-time diagnosis by using responses elicited from patients by having them watch six emotion-eliciting videos. After watching each video clip, the conversations, including patient facial expressions and speech responses, between the participant and the clinician conducting the interview were recorded. Next, the hierarchical spectral clustering algorithm was employed to adapt the facial expression and speech response features by using the extended Cohn–Kanade and eNTERFACE databases. A denoizing autoencoder was further applied to extract the bottleneck features of the adapted data. Then, the facial and speech bottleneck features were input into support vector machines to obtain speech emotion profiles (EPs) and the modulation spectrum (MS) of the facial action unit sequence for each elicited response. Finally, a cell-coupled long short-term memory (LSTM) network with an $L$ -skip fusion mechanism was proposed to model the temporal information of all elicited responses and to loosely fuse the EPs and the MS for conducting mood disorder detection. The experimental results revealed that the cell-coupled LSTM with the $L$ -skip fusion mechanism has promising advantages and efficacy for mood disorder detection. [ABSTRACT FROM AUTHOR]

A recent study conducted by researchers at Free University Berlin highlights the issue of "indexjacking," which involves the infiltration of hijacked journals into international indexing databases, particularly Scopus. The study identified at least 67 hijacked journals that have entered Scopus since 2013, with 41 still compromising the data of legitimate journals as of September 2023. This poses a challenge to scientific integrity and research evaluation, as unreliable papers that have not undergone peer review are legitimized and imported into other databases. The presence of cloned versions of approved journals further complicates the verification of authenticity. [Extracted from the article]

The article focuses on the database consisting of information of archive documents on the American Civil Liberties Union who defended the civil rights of the U.S. citizens.

Face sketch–photo synthesis plays an important role in law enforcement and digital entertainment. Most of the existing methods only use pixel intensities as the feature. Since face images can be described using features from multiple aspects, this paper presents a novel multiple representations-based face sketch–photo-synthesis method that adaptively combines multiple representations to represent an image patch. In particular, it combines multiple features from face images processed using multiple filters and deploys Markov networks to exploit the interacting relationships between the neighboring image patches. The proposed framework could be solved using an alternating optimization strategy and it normally converges in only five outer iterations in the experiments. Our experimental results on the Chinese University of Hong Kong (CUHK) face sketch database, celebrity photos, CUHK Face Sketch FERET Database, IIIT-D Viewed Sketch Database, and forensic sketches demonstrate the effectiveness of our method for face sketch–photo synthesis. In addition, cross-database and database-dependent style-synthesis evaluations demonstrate the generalizability of this novel method and suggest promising solutions for face identification in forensic science. [ABSTRACT FROM PUBLISHER]

Researchers at Sharda University in Greater Noida, India have identified three potential dual inhibitors for thymic stromal lymphopoietin (TSLP) and interleukin-33 (IL-33) proteins, which play important roles in allergic diseases. The study used structure-based virtual screening and molecular dynamics simulations to evaluate the binding affinity, stability, and safety of these compounds. The identified compounds, ZINC01105767, ZINC08764679, and ZINC33833100, show promising potential as anti-allergy agents and could lead to the development of more effective therapies for allergic diseases. Further investigations and experimental validations are needed to confirm these findings. [Extracted from the article]

Researchers from Gansu University of Chinese Medicine conducted a study to explore the current status, research hotspots, and trends in global uveitis research. They analyzed relevant literature from various databases and used software to visualize the data. The study found that research teams for uveitis have been formed globally, with the United States, the United Kingdom, and Germany being the top three countries in terms of publications. The research hotspots mainly focus on exploring the pathogenesis and different treatment methods for uveitis, with a shift towards molecular biology-related research topics. The researchers concluded that more scholars should dedicate themselves to uveitis-related research to make breakthroughs and progress in the field. They also emphasized the importance of large-scale and multicenter clinical studies to provide high-quality research evidence. [Extracted from the article]

Recently, many learning-based methods have explored logic learning task in the assumption that the training set and testing set are from the consistent distribution, achieving good performance. But, in most cases, this assumption does not hold. In this paper, we explore this topic on the open-set logic reasoning task where the digit length and the sequence length of the training set and testing set are from inconsistent distributions. To address this issue, inspired by multigranulation studies in granular computing, we propose a granulation logic reasoning machine, namely GLRM. In this method, this open-set task is granulated into a series of sub-tasks from two dimensions: the digit length and the sequence length, and then these sub-tasks are conquered one by one. Finally, the results of the sub-tasks are organized into the final result. The effectiveness of GLRM is demonstrated by experiments on the open-set Fashion-Logic data set and the open-set Fashion-Logic task proposed in this paper. This study provides a novel view for solving open-set logic reasoning tasks and promotes the research of data-driven logic learning. [ABSTRACT FROM AUTHOR]

Copyright of Zeitschrift für Arbeitswissenschaft is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Element matching approaches based on Optimal Combination Algorithms (OCAs) are methods that try to calibrate element mismatches in data converters by rearranging the mapping relations between circuit elements and control logics. This work for the first time proposes a universal method and its corresponding Figure-of-Merit to evaluate the performance of various OCA approaches. It adopts the quantile of integral nonlinearity as the criterion and is consequently compatible with most of matching OCAs. Dynamic performance and the corresponding effective-number-of-bits (ENOB) limited by mismatches are also investigated. A critical feature of the proposed method is its robustness to the disturbance of element standard deviation as long as the standard deviation is in a reasonable region. Merits of our method have been explained and proved mathematically, and also been verified numerically by data extracted from previously reported works and their reproduction by authors, which together make this proposal more convincing and rigorous. Due to those merits, our proposed method could also help designers to specify a feasible standard deviation for circuit elements when they are designing data converters with OCAs. [ABSTRACT FROM AUTHOR]

Heterogeneous face recognition (HFR) is the process of matching face images captured from different sources. HFR plays an important role in security scenarios. However, HFR remains a challenging problem due to the considerable discrepancies (i.e., shape, style, and color) between cross-modality images. Conventional HFR methods utilize only the information involved in heterogeneous face images, which is not effective because of the substantial differences between heterogeneous face images. To better address this issue, this paper proposes a data augmentation-based joint learning (DA-JL) approach. The proposed method mutually transforms the cross-modality differences by incorporating synthesized images into the learning process. The aggregated data augments the intraclass scale, which provides more discriminative information. However, this method also reduces the interclass diversity (i.e., discriminative information). We develop the DA-JL model to balance this dilemma. Finally, we obtain the similarity score between heterogeneous face image pairs through the log-likelihood ratio. Extensive experiments on a viewed sketch database, forensic sketch database, near-infrared image database, thermal-infrared image database, low-resolution photo database, and image with occlusion database illustrate that the proposed method achieves superior performance in comparison with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

This paper proposes a holistic approach that addresses both the message mapping in memory banks and the pipeline-related data hazards in low-density parity-check (LDPC) decoders. We consider a layered hardware architecture using single read/single write port memory banks. The throughput of such an architecture is limited by memory access conflicts, due to improper message mapping in the memory banks, and by pipeline data hazards, due to delayed update effect. We solve these issues by: 1) a residue-based layered scheduling that reduces the pipeline related hazards and 2) off-line algorithms for optimizing the message mapping in memory banks and the message read access scheduling. Our estimates for different LDPC codes indicate that the hardware usage efficiency of our layered decoder is improved by 3%–49% when only the off-line algorithms are employed and by 16%–57% when both the residue-based layered architecture and the off-line algorithms are used. [ABSTRACT FROM AUTHOR]

Dimensionality reduction (DR) aims at faithfully and meaningfully representing high-dimensional (HD) data into a low-dimensional (LD) space. Recently developed neighbor embedding DR methods lead to outstanding performances, thanks to their ability to foil the curse of dimensionality. Unfortunately, they cannot be directly employed on incomplete data sets, which become ubiquitous in machine learning. Discarding samples with missing features prevents their LD coordinates computation and deteriorates the complete samples treatment. Common missing data imputation schemes are not appropriate in the nonlinear DR context either. Indeed, even if they model the data distribution in the feature space, they can, at best, enable the application of a DR scheme on the expected data set. In practice, one would, instead, like to obtain the LD embedding with the closest cost function value on average with respect to the complete data case. As the state-of-the-art DR techniques are nonlinear, the latter embedding results from minimizing the expected cost function on the incomplete database, not from considering the expected data set. This paper addresses these limitations by developing a general methodology for nonlinear DR with missing data, being directly applicable with any DR scheme optimizing some criterion. In order to model the feature dependences, an HD extension of Gaussian mixture models is first fitted on the incomplete data set. It is afterward employed under the multiple imputation paradigms to obtain a single relevant LD embedding, thus minimizing the cost function expectation. Extensive experiments demonstrate the superiority of the suggested framework over alternative approaches. [ABSTRACT FROM AUTHOR]

Image recognition based on convolutional neural networks (CNNs) has recently been shown to deliver the state-of-the-art performance in various areas of computer vision and image processing. Nevertheless, applying a deep CNN to no-reference image quality assessment (NR-IQA) remains a challenging task due to critical obstacles, i.e., the lack of a training database. In this paper, we propose a CNN-based NR-IQA framework that can effectively solve this problem. The proposed method—deep image quality assessor (DIQA)—separates the training of NR-IQA into two stages: 1) an objective distortion part and 2) a human visual system-related part. In the first stage, the CNN learns to predict the objective error map, and then the model learns to predict subjective score in the second stage. To complement the inaccuracy of the objective error map prediction on the homogeneous region, we also propose a reliability map. Two simple handcrafted features were additionally employed to further enhance the accuracy. In addition, we propose a way to visualize perceptual error maps to analyze what was learned by the deep CNN model. In the experiments, the DIQA yielded the state-of-the-art accuracy on the various databases. [ABSTRACT FROM AUTHOR]

The article discusses the website NumisMaster that has the digital database which gives information about coins and paper money of the United States and the world with live updates and listing of images for many of the entries, issuing country, denomination, dates, and values.