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We present the first study of the Public Register of Licensed Persons and Registered Institutions maintained by the Hong Kong Securities and Futures Commission (SFC) through the lens of complex network analysis. This dataset, spanning 21 years with daily granularity, provides a unique view of the evolving social network between licensed professionals and their affiliated firms in Hong Kong's financial sector. Leveraging large language models, we classify firms (e.g., asset managers, banks) and infer the likely nationality and gender of employees based on their names. This application enhances the dataset by adding rich demographic and organizational context, enabling more precise network analysis. Our preliminary findings reveal key structural features, offering new insights into the dynamics of Hong Kong's financial landscape. We release the structured dataset to enable further research, establishing a foundation for future studies that may inform recruitment strategies, policy-making, and risk management in the financial industry., Comment: Complex Networks 2024

Enriching datasets with demographic information, such as gender, race, and age from names, is a critical task in fields like healthcare, public policy, and social sciences. Such demographic insights allow for more precise and effective engagement with target populations. Despite previous efforts employing hidden Markov models and recurrent neural networks to predict demographics from names, significant limitations persist: the lack of large-scale, well-curated, unbiased, publicly available datasets, and the lack of an approach robust across datasets. This scarcity has hindered the development of traditional supervised learning approaches. In this paper, we demonstrate that the zero-shot capabilities of Large Language Models (LLMs) can perform as well as, if not better than, bespoke models trained on specialized data. We apply these LLMs to a variety of datasets, including a real-life, unlabelled dataset of licensed financial professionals in Hong Kong, and critically assess the inherent demographic biases in these models. Our work not only advances the state-of-the-art in demographic enrichment but also opens avenues for future research in mitigating biases in LLMs., Comment: 8 pages, 7 Tables, 5 Figures

Newsela contracted with LearnPlatform by Instructure (LearnPlatform), a third-party edtech research company, to examine the relationship between Newsela ELA usage and outcomes for students. LearnPlatform designed the study to satisfy Level II requirements (Moderate Evidence) according to the Every Student Succeeds Act (ESSA). The study included 2,464 6th-8th grade students (1,232 students who consistently used Newsela ELA (i.e., twice a week on average) and 1,232 students who did not use it) across 162 elementary and middle schools in a large, urban district in California. An average student not using Newsela ELA would be expected to score 4 percentile points higher on the Smarter Balanced Assessment Consortium ELA assessment (SBAC) had they used Newsela ELA. Similarly, an average Hispanic/Latino student not using Newsela ELA would be expected to score 5 percentile points higher on the SBAC had they used Newsela ELA. These findings were statistically significant at the p = 0.05 level. This study satisfies ESSA evidence requirements for Level II (Moderate Evidence) given the positive, statistically significant findings. [This report was prepared for Newsela by LearnPlatform.]

High Performance Research Desktops are used by HPC centers and research computing organizations to lower the barrier of entry to HPC systems. These Linux desktops are deployed alongside HPC systems, leveraging the investments in HPC compute and storage infrastructure. By serving as a gateway to HPC systems they provide users with an environment to perform setup and infrastructure tasks related to the actual HPC work. Such tasks can take significant amounts of time, are vital to the successful use of HPC systems, and can benefit from a graphical desktop environment. In addition to serving as a gateway to HPC systems, High Performance Research Desktops are also used to run interactive graphical applications like MATLAB, RStudio or VMD. This paper defines the concept of High Performance Research Desktops and summarizes use cases from Indiana University, Lund University and Technical University of Denmark, which have implemented and operated such a system for more than 10 years. Based on these use cases, possible future directions are presented.

Text-to-video diffusion models enable the generation of high-quality videos that follow text instructions, making it easy to create diverse and individual content. However, existing approaches mostly focus on high-quality short video generation (typically 16 or 24 frames), ending up with hard-cuts when naively extended to the case of long video synthesis. To overcome these limitations, we introduce StreamingT2V, an autoregressive approach for long video generation of 80, 240, 600, 1200 or more frames with smooth transitions. The key components are:(i) a short-term memory block called conditional attention module (CAM), which conditions the current generation on the features extracted from the previous chunk via an attentional mechanism, leading to consistent chunk transitions, (ii) a long-term memory block called appearance preservation module, which extracts high-level scene and object features from the first video chunk to prevent the model from forgetting the initial scene, and (iii) a randomized blending approach that enables to apply a video enhancer autoregressively for infinitely long videos without inconsistencies between chunks. Experiments show that StreamingT2V generates high motion amount. In contrast, all competing image-to-video methods are prone to video stagnation when applied naively in an autoregressive manner. Thus, we propose with StreamingT2V a high-quality seamless text-to-long video generator that outperforms competitors with consistency and motion. Our code will be available at: https://github.com/Picsart-AI-Research/StreamingT2V, Comment: https://github.com/Picsart-AI-Research/StreamingT2V

As a broader set of applications from simulations to data analysis and machine learning require more parallel computational capability, the demand for interactive and urgent high performance computing (HPC) continues to increase. This paper overviews the progress made so far and elucidates the challenges and opportunities for greater integration of interactive and urgent HPC policies, techniques, and technologies into HPC ecosystems., Comment: 10 pages, submitted to IEEE CiSE journal

Conventional video matting outputs one alpha matte for all instances appearing in a video frame so that individual instances are not distinguished. While video instance segmentation provides time-consistent instance masks, results are unsatisfactory for matting applications, especially due to applied binarization. To remedy this deficiency, we propose Video Instance Matting~(VIM), that is, estimating alpha mattes of each instance at each frame of a video sequence. To tackle this challenging problem, we present MSG-VIM, a Mask Sequence Guided Video Instance Matting neural network, as a novel baseline model for VIM. MSG-VIM leverages a mixture of mask augmentations to make predictions robust to inaccurate and inconsistent mask guidance. It incorporates temporal mask and temporal feature guidance to improve the temporal consistency of alpha matte predictions. Furthermore, we build a new benchmark for VIM, called VIM50, which comprises 50 video clips with multiple human instances as foreground objects. To evaluate performances on the VIM task, we introduce a suitable metric called Video Instance-aware Matting Quality~(VIMQ). Our proposed model MSG-VIM sets a strong baseline on the VIM50 benchmark and outperforms existing methods by a large margin. The project is open-sourced at https://github.com/SHI-Labs/VIM.

Ensuring diagnostic performance of AI models before clinical use is key to the safe and successful adoption of these technologies. Studies reporting AI applied to digital pathology images for diagnostic purposes have rapidly increased in number in recent years. The aim of this work is to provide an overview of the diagnostic accuracy of AI in digital pathology images from all areas of pathology. This systematic review and meta-analysis included diagnostic accuracy studies using any type of artificial intelligence applied to whole slide images (WSIs) in any disease type. The reference standard was diagnosis through histopathological assessment and / or immunohistochemistry. Searches were conducted in PubMed, EMBASE and CENTRAL in June 2022. We identified 2976 studies, of which 100 were included in the review and 48 in the full meta-analysis. Risk of bias and concerns of applicability were assessed using the QUADAS-2 tool. Data extraction was conducted by two investigators and meta-analysis was performed using a bivariate random effects model. 100 studies were identified for inclusion, equating to over 152,000 whole slide images (WSIs) and representing many disease types. Of these, 48 studies were included in the meta-analysis. These studies reported a mean sensitivity of 96.3% (CI 94.1-97.7) and mean specificity of 93.3% (CI 90.5-95.4) for AI. There was substantial heterogeneity in study design and all 100 studies identified for inclusion had at least one area at high or unclear risk of bias. This review provides a broad overview of AI performance across applications in whole slide imaging. However, there is huge variability in study design and available performance data, with details around the conduct of the study and make up of the datasets frequently missing. Overall, AI offers good accuracy when applied to WSIs but requires more rigorous evaluation of its performance., Comment: 26 pages, 5 figures, 8 tables + Supplementary materials

Neuroscience research has expanded dramatically over the past 30 years by advancing standardization and tool development to support rigor and transparency. Consequently, the complexity of the data pipeline has also increased, hindering access to FAIR (Findable, Accessible, Interoperabile, and Reusable) data analysis to portions of the worldwide research community. brainlife.io was developed to reduce these burdens and democratize modern neuroscience research across institutions and career levels. Using community software and hardware infrastructure, the platform provides open-source data standardization, management, visualization, and processing and simplifies the data pipeline. brainlife.io automatically tracks the provenance history of thousands of data objects, supporting simplicity, efficiency, and transparency in neuroscience research. Here brainlife.io's technology and data services are described and evaluated for validity, reliability, reproducibility, replicability, and scientific utility. Using data from 4 modalities and 3,200 participants, we demonstrate that brainlife.io's services produce outputs that adhere to best practices in modern neuroscience research.

Recent text-to-video generation approaches rely on computationally heavy training and require large-scale video datasets. In this paper, we introduce a new task of zero-shot text-to-video generation and propose a low-cost approach (without any training or optimization) by leveraging the power of existing text-to-image synthesis methods (e.g., Stable Diffusion), making them suitable for the video domain. Our key modifications include (i) enriching the latent codes of the generated frames with motion dynamics to keep the global scene and the background time consistent; and (ii) reprogramming frame-level self-attention using a new cross-frame attention of each frame on the first frame, to preserve the context, appearance, and identity of the foreground object. Experiments show that this leads to low overhead, yet high-quality and remarkably consistent video generation. Moreover, our approach is not limited to text-to-video synthesis but is also applicable to other tasks such as conditional and content-specialized video generation, and Video Instruct-Pix2Pix, i.e., instruction-guided video editing. As experiments show, our method performs comparably or sometimes better than recent approaches, despite not being trained on additional video data. Our code will be open sourced at: https://github.com/Picsart-AI-Research/Text2Video-Zero ., Comment: The project is available at: https://github.com/Picsart-AI-Research/Text2Video-Zero

We discuss the inadequacy of covariances/correlations and other measures in L2 as relative distance metrics under some conditions. We propose a computationally simple heuristic to transform a map based on standard principal component analysis (PCA) (when the variables are asymptotically Gaussian) into an entropy-based map where distances are based on mutual information (MI). Rescaling Principal Component based distances using MI allows a representation of relative statistical associations when, as in genetics, it is applied on bit measurements between individuals' genomic mutual information. This entropy rescaled PCA, while preserving order relationships (along a dimension), changes the relative distances to make them linear to information. We show the effect on the entire world population and some subsamples, which leads to significant differences with the results of current research.

Exploration of bias has significant impact on the transparency and applicability of deep learning pipelines in medical settings, yet is so far woefully understudied. In this paper, we consider two separate groups for which training data is only available at differing image resolutions. For group H, available images and labels are at the preferred high resolution while for group L only deprecated lower resolution data exist. We analyse how this resolution-bias in the data distribution propagates to systematically biased predictions for group L at higher resolutions. Our results demonstrate that single-resolution training settings result in significant loss of volumetric group differences that translate to erroneous segmentations as measured by DSC and subsequent classification failures on the low resolution group. We further explore how training data across resolutions can be used to combat this systematic bias. Specifically, we investigate the effect of image resampling, scale augmentation and resolution independence and demonstrate that biases can effectively be reduced with multi-resolution approaches.

On Guåhan (Guam), the large percentages of students enrolling in non-credit-bearing courses at Kulehon Kumunidåt Guåhan (Guam Community College) and Unibetsedåt Guåhan (University of Guam) have raised concerns about college readiness and early college success. Without adequate research on predictors of college readiness and early success among students on Guåhan, educators and other stakeholders find it difficult to identify and support students at risk of being underprepared for college. This study examined which student characteristics predicted college readiness and early college success among students who graduated from Guåhan high schools and enrolled at Kulehon Kumunidåt Guåhan or Unibetsedåt Guåhan between 2012 and 2015. Students' college readiness and early college success were assessed using three indicators: enrolling in only credit-bearing math and English courses during the first year of college, earning all credits attempted during the first semester of college, and persisting to a second year of college. About 23 percent of students met all three indicators and were thus classified as demonstrating college readiness and early college success. The percentages of students who met each individual indicator varied: 30 percent enrolled in only credit-bearing math and English courses, 43 percent earned all the credits they attempted, and 74 percent persisted to a second year. Various student characteristics predicted meeting all three indicators and each individual indicator. Graduates of John F. Kennedy High School and male students were the most likely to meet all three indicators and were the most likely to enroll in only credit-bearing math and English courses. Completing a high-level math course during high school positively predicted meeting the composite indicator of college readiness and early college success and of enrolling in only credit-bearing math and English courses and earning all credits attempted. A higher cumulative high school grade point average also positively predicted meeting all three indicators and each individual indicator. Kulehon Kumunidåt Guåhan enrollees were more likely than Unibetsedåt Guåhan enrollees to earn all credits attempted during their first semester. [For the full report, see ED611775; for the appendixes, see ED611777; for the study snapshot, see ED611779.]

On Guåhan (Guam), the large percentages of students enrolling in non-credit-bearing courses at Kulehon Kumunidåt Guåhan (Guam Community College) and Unibetsedåt Guåhan (University of Guam) have raised concerns about college readiness and early college success. Without adequate research on predictors of college readiness and early success among students on Guåhan, educators and other stakeholders find it difficult to identify and support students at risk of being underprepared for college. This study examined which student characteristics predicted college readiness and early college success among students who graduated from Guåhan high schools and enrolled at Kulehon Kumunidåt Guåhan or Unibetsedåt Guåhan between 2012 and 2015. Students' college readiness and early college success were assessed using three indicators: enrolling in only credit-bearing math and English courses during the first year of college, earning all credits attempted during the first semester of college, and persisting to a second year of college. About 23 percent of students met all three indicators and were thus classified as demonstrating college readiness and early college success. The percentages of students who met each individual indicator varied: 30 percent enrolled in only credit-bearing math and English courses, 43 percent earned all the credits they attempted, and 74 percent persisted to a second year. Various student characteristics predicted meeting all three indicators and each individual indicator. Graduates of John F. Kennedy High School and male students were the most likely to meet all three indicators and were the most likely to enroll in only credit-bearing math and English courses. Completing a high-level math course during high school positively predicted meeting the composite indicator of college readiness and early college success and of enrolling in only credit-bearing math and English courses and earning all credits attempted. A higher cumulative high school grade point average also positively predicted meeting all three indicators and each individual indicator. Kulehon Kumunidåt Guåhan enrollees were more likely than Unibetsedåt Guåhan enrollees to earn all credits attempted during their first semester. [For the appendixes, see ED611777; for the study brief, see ED611778; for the study snapshot, see ED611779.]

In the Republic of the Marshall Islands, college readiness and early college success are major concerns. More than 75 percent of a recent cohort of incoming students at the College of the Marshall Islands placed into developmental courses, which suggests that students might not be academically prepared to take postsecondary coursework. A lack of research on predictors of college readiness and early college success for Marshallese students makes it difficult to develop and implement targeted interventions. This study examined academic preparation characteristics and the college readiness and early college success of students who graduated from Republic of the Marshall Islands public high schools and enrolled at the College of the Marshall Islands between 2015 and 2017. It also examined the relationships between student demographic and preparation characteristics and college readiness and early college success. College readiness and early college success were defined as achieving all three of the following indicators: placing into only credit-bearing math and English courses, earning all credits attempted during the first year of college, and persisting to a second year of college. About 3 percent of students met all three indicators; 5 percent placed into only credit-bearing math and English courses at the College of the Marshall Islands, 19 percent earned all credits attempted during their first year of college, and 53 percent persisted to a second year of college. Several student characteristics were related to college readiness and early college success. Female students were less likely than male students to persist to a second year of college. Students with a higher cumulative high school grade point average were more likely than other students to earn all credits attempted during their first year of college and to persist to a second year of college. [For the appendixes, see ED611201. For the study brief, see ED611202. For the study snapshot, see ED611204.]

In the Republic of the Marshall Islands, college readiness and early college success are major concerns. More than 75 percent of a recent cohort of incoming students at the College of the Marshall Islands placed into developmental courses, which suggests that students might not be academically prepared to take postsecondary coursework. A lack of research on predictors of college readiness and early college success for Marshallese students makes it difficult to develop and implement targeted interventions. This study examined academic preparation characteristics and the college readiness and early college success of students who graduated from Republic of the Marshall Islands public high schools and enrolled at the College of the Marshall Islands between 2015 and 2017. It also examined the relationships between student demographic and preparation characteristics and college readiness and early college success. College readiness and early college success were defined as achieving all three of the following indicators: placing into only credit-bearing math and English courses, earning all credits attempted during the first year of college, and persisting to a second year of college. About 3 percent of students met all three indicators; 5 percent placed into only credit-bearing math and English courses at the College of the Marshall Islands, 19 percent earned all credits attempted during their first year of college, and 53 percent persisted to a second year of college. Several student characteristics were related to college readiness and early college success. Female students were less likely than male students to persist to a second year of college. Students with a higher cumulative high school grade point average were more likely than other students to earn all credits attempted during their first year of college and to persist to a second year of college. [For the full report, see ED611200. For the appendixes, see ED611201. For the study snapshot, see ED611204.]

This article proposes and documents a machine-learning framework and tutorial for classifying images using mobile phones. Compared to computers, the performance of deep learning model performance degrades when deployed on a mobile phone and requires a systematic approach to find a model that performs optimally on both computers and mobile phones. By following the proposed pipeline, which consists of various computational tools, simple procedural recipes, and technical considerations, one can bring the power of deep learning medical image classification to mobile devices, potentially unlocking new domains of applications. The pipeline is demonstrated on four different publicly available datasets: COVID X-rays, COVID CT scans, leaves, and colorectal cancer. We used two application development frameworks: TensorFlow Lite (real-time testing) and Flutter (digital image testing) to test the proposed pipeline. We found that transferring deep learning models to a mobile phone is limited by hardware and classification accuracy drops. To address this issue, we proposed this pipeline to find an optimized model for mobile phones. Finally, we discuss additional applications and computational concerns related to deploying deep-learning models on phones, including real-time analysis and image preprocessing. We believe the associated documentation and code can help physicians and medical experts develop medical image classification applications for distribution., Comment: 20 pages