ethicalabs/Research-Intent-Collab huggingface dataset:
gFaaS: Enabling Generic Functions in Serverless Computing

With the advent of AWS Lambda in 2014, Serverless Computing, particularly Function-as-a-Service (FaaS), has witnessed growing popularity across various application domains. FaaS enables an application to be decomposed into fine-grained functions that are executed on a FaaS platform. It offers several advantages such as no infrastructure management, a pay-per-use billing policy, and on-demand fine-grained autoscaling. However, despite its advantages, developers today encounter various challenges while adopting FaaS solutions that reduce productivity. These include FaaS platform lock-in, support for diverse function deployment parameters, and diverse interfaces for interacting with FaaS platforms. To address these challenges, we present gFaaS, a novel framework that facilitates the holistic development and management of functions across diverse FaaS platforms. Our framework enables the development of generic functions in multiple programming languages that can be seamlessly deployed across different platforms without modifications. Results from our experiments demonstrate that gFaaS functions perform similarly to native platform-specific functions across various scenarios. A video demonstrating the functioning of gFaaS is available from https://youtu.be/STbb6ykJFf0.
Marine Ranching Construction and Management in East China Sea: Programs for Sustainable Fishery and Aquaculture

Marine ranching, which is considered a sustainable fishery mode that has advantages for the ecosystem approach to fishery, the ecosystem approach to aquaculture, and capture-based aquaculture, is rapidly growing in China. The development of marine ranching requires integrating different theoretical frameworks, methodological approaches for conceptual exploring, and models and management of ecosystem frameworks. We reviewed the definition of marine ranching, the history of marine ranching construction in China, and the techniques, principles, and cases of marine ranching construction and management in the East China Sea (ECS). We highlight four major developments in marine ranching in the ECS: (1) marine ranching site selection and design, (2) habitat restoration and construction technologies, (3) stock enhancement and the behavioral control of fishery resources, and (4) marine ranching management. We conclude that this step-wise procedure for marine ranching construction and management could have comprehensive benefits in terms of ecology, the economy, and society. Finally, a synthesis of the existing problems in ECS marine ranching construction, along with future challenges and directions, are outlined.
A query interface for clinical research with Chinese electronic health record using Natural Language Processing

Abstract         Background: The recruitment of clinical trials is a challenging task. Traditionally, the time-consuming task is accomplished manually or assisted by form-based tools. Electronic health record (EHR) contains comprehensive information which can speed up the process. The development of natural language processing (NLP) makes it possible to reduce manual effort. To our knowledge, there are almost no query interfaces based on NLP techniques for Chinese EHR.Methods: A query interface based on NLP was developed. Firstly, we collected and annotated eligibility criteria(EC) from the Chinese Clinical Trial Registry. Then, an information extraction(IE) system was developed to parse them including named entity recognition(NER), relation classification(RC), and concept matching(CM). Next, the extracted information was post-processed(PP) to generate formal queries. Finally, we built a natural language query interface based on the system.Results: 4691 stroke-related EC were collected for annotation. 91.23% F1 score was achieved for the NER task. For the RC task, 92.75% F1 score was achieved. For the CM task, 90.78% accuracy was achieved and an evaluation result showed 42.42% of entities from NER results can be matched with EHR. And a natural language query interface has been implemented and applied in clinical research about stroke. Conclusions: We build a query interface for Chinese EHR based on NLP techniques. The proposed information extraction pipeline can support medical professionals to reduce the information gap with minimal human effort when interacting with Chinese EHR.
The Tallinn manual on the international law applicable to cyber warfare : a brief introduction on its treatment of 𝘫𝘶𝘴 𝘢𝘥 𝘣𝘦𝘭𝘭𝘶𝘮 norms

peer-reviewed
Critical percolation threshold restricts late-summer Arctic sea ice melt pond coverage

During the summer, vast regions of Arctic sea ice are covered by meltwater ponds that significantly lower the ice reflectivity and accelerate melting. Ponds develop over the melt season through an initial rapid growth stage followed by drainage through macroscopic holes. Recent analysis of melt pond photographs indicates that late-summer ponds exist near the critical percolation threshold, a special pond coverage fraction below which the ponds are largely disconnected and above which they are highly connected. Here, we show that the percolation threshold, a statistical property of ice topography, constrains pond evolution due to pond drainage through macroscopic holes. We show that it sets the approximate upper limit and scales pond coverage throughout its evolution after the beginning of drainage. Furthermore, we show that the rescaled pond coverage during drainage is a universal function of a single non-dimensional parameter, $η$, roughly interpreted as the number of drainage holes per characteristic area of the surface. This universal curve allows us to formulate an equation for pond coverage time-evolution during and after pond drainage that captures the dependence on environmental parameters and is supported by observations on undeformed first-year ice. This equation reveals that pond coverage is highly sensitive to environmental parameters, suggesting that modeling uncertainties could be reduced by more directly parameterizing the ponds' natural parameter, $η$. Our work uncovers previously unrecognized constraints on melt pond physics and places ponds within a broader context of phase transitions and critical phenomena. Therefore, it holds promise for improving ice-albedo parameterizations in large-scale models.
Deep generative models for ultra-high granularity particle physics detector simulation

Die Simulation von Detektorantworten mit ultrahoher Granularität in der Teilchenphysik stellt eine kritische, jedoch rechenintensive Aufgabe dar. Diese Arbeit zielt darauf ab, diese Herausforderungen zu bewältigen, indem sie sich auf den Pixel-Vertex-Detektor (PXD) im Belle II-Experiment konzentriert, der über 7,5 Millionen Pixelkanäle verfügt – den höchstaufgelösten Detektorsimulationsdatensatz, der jemals mit tiefen generativen Modellen analysiert wurde. Als erste Beitrag führe ich das Intra-Event-Aware Generative Adversarial Network (IEA-GAN) ein, ein Modell, das relationales Denken und selbstüberwachtes Lernen integriert, um ein „Ereignis“ im Detektor zu simulieren. Diese Studie etabliert PXD-Daten als feinkörnigen Datensatz und unterstreicht die Bedeutung der intra-eventuellen Korrelation für nachgelagerte physikalische Analysen. IEA-GAN emuliert PXD-Signaturen auf eine geometriebewusste und korrelierte Weise.  Aufbauend darauf führt diese Arbeit YonedaVAE ein, ein fortschrittliches generatives Modell, das das offene Problem der Out-of-Distribution (OOD) Simulation von realen Daten angeht. Inspiriert durch die Kategorientheorie verwendet YonedaVAE ein lernbares Yoneda-Embedding, um die Gesamtheit eines Ereignisses anhand seiner Sensorbeziehungen zu erfassen, und formuliert eine formale Sprache für intra-eventuelles relationales Denken. Dies wird ergänzt durch einen selbstdestillierten Mengengenerator und einen adaptiven Top-q Abtastmechanismus, der es dem Modell ermöglicht, Punktewolken mit variablen intra- und inter-eventuellen Kardinalitäten weit über die Kardinalität der Trainingsdaten hinaus zu sampeln. Eine variable intra-event Kardinalität wurde zuvor noch nicht erreicht und ist von entscheidender Bedeutung, wenn man sich mit unregelmäßigen Detektorgeometrien und Treffermustern durch den Detektor beschäftigt.  Diese Studie präsentiert die ersten Ergebnisse für ein generatives Modell, das auf realen Daten in der Teilchenphysik mit ultrahoher Granularität trainiert wurde. Sie zeigt, dass das YonedaVAE-Modell, trainiert auf frühzeitigen Hintergrunddaten eines Experiments, eine vernünftige Simulation eines späteren Experiments mit fast doppelter Leuchtkraft erreichen kann, während es gleichzeitig eine signifikante Speicherentlastung bietet. Bemerkenswert ist, dass YonedaVAE diese Extrapolation ohne vorherige Exposition gegenüber Hochleuchtkraftdaten erreicht, was seine Robustheit und Generalisierbarkeit unterstreicht. Insgesamt reduzieren diese Modelle nicht nur erheblich den Rechenaufwand, sondern erreichen auch eine noch nie dagewesene Präzision in Detektorsimulationen mit ultrahoher Granularität. Dies eröffnet neue Wege sowohl für die Recheneffizienz als auch für die Genauigkeit in der Teilchenphysik.
Root Cause Analysis: Implications for Investigative Work

ABSTRACT                   Root Cause Analysis (RCA) is a powerful tool for any type of investigative work. This article provides an overview of RCA, presents an in-depth walkthrough of an RCA process, and outlines the currently available RCA software and tools. A solid understanding of RCA, how to implement RCA, and the benefits it can bring to an organization will improve any investigative process, provide continuity amidst frequent personnel turnover, and build confidence in the quality of the solutions implemented.
Applications of natural language processing in radiology: A systematic review

Recent advances in performance of natural language processing (NLP) techniques have spurred wider use and more sophisticated applications of NLP in radiology. This study systematically reviews the trends and applications of NLP in radiology within the last five years.A search of three databases of peer-reviewed journal articles and conference papers from January 1, 2016 to April 21, 2021 resulted in a total of 228 publications included in the review. Manuscripts were analyzed by several factors, including clinical application, study setting, NLP technique, and performance.Of the 228 included publications, there was an overall increase in number of studies published with an increase in use of machine learning models. NLP models showed high performance: >50% of publications reported F1 > 0.91. There was variable sample size across the studies with a median of 3708 data points, most commonly radiology reports. 145 studies utilized data from a single academic center. Applications were classified as clinical (n = 87), technical (n = 66), quality improvement (n = 61), research (n = 9), and education (n = 5).There has been a continued increase in number of studies involving NLP in radiology. Newer NLP techniques, including word embedding, deep learning, and transformers, are being applied and show improved performance. There has been growth in the interpretative and non-interpretative use of NLP techniques in radiology and has great capacity to improve patient care and delivery. Although the performance and breadth of NLP applications is impressive, there is an overall lack of high-level evidence for actual clinical application of published tools.NLP applications in radiology has been increasing studied and more accurate in the last 5 years. More direct clinical application and portability of the NLP pipelines is need to reach the technology's full potential.
Surveillance Technology and State Security in Africa: Pegasus, Cellebrite, and Privacy Rights: A South Sudan Case Study

This article examines Surveillance Technology and State Security in Africa: Pegasus, Cellebrite, and Privacy Rights: A South Sudan Case Study with a focused emphasis on South Sudan within the field of Political Science. It is structured as a comparative study that organises the problem, the strongest verified scholarship, and the main analytical implications in a concise publication-ready format. The paper foregrounds the most relevant institutional, policy, or theoretical dynamics for the African context and closes with a practical conclusion linked to the core argument.
Mathematical Model of Optimal Empty Rail Car Distribution at Railway Transport Nodes

At present there are two trends in the market of rail freight transportation in Russia: freight owners put forward higher demands to the transportation quality (promptness of delivery) in an effort to reduce storage costs by means of reducing the size of freight shipment; the structure of railcar traffic volume of the railways of Russia is getting more complex due to the reduction of the average shipment size and due to the transfer of railcar fleet ownership to a large number of operating companies. These trends significantly complicate operational management supervision of railway stations and transport nodes. Application of typical data from the information system about the railcar location at the transportation node is not enough for the dispatchers to make the best decision concerning the car traffic management. The article is concerned with the description and development of the mathematical model of empty railcar distribution for loading at the railway transport node; this model will take into account the requirements of railcar owners in terms of their cars application, the operating work level of railroad stations of the transportation node and the possibility of adding the groups of empty railcars to the transfer trains, clean-up trains and industrial railway trains operating on a tight schedule. The developed model and the software package were implemented in the information system of the industrial railway of the major metallurgical enterprise - OJSC "Magnitogorsk Metallurgical Works", which processes up to two thousand of railcars belonging to different owners. This model made it possible to reduce the labour intensity of dispatcher operation planning the empty railcar distribution for loading and reduce the total time the railcars spend in the enterprise railway system.
Relationships Between Psychological Distress and Affective, Behavioral, and Cognitive Experiences of Stuttering

Purpose:             People who stutter (PWS) are vulnerable to the development of various psychopathological symptoms, although prevalence data are mixed and less clarity exists about factors that potentially influence their occurrence. The current study sought to shed light on the prevalence of self-reported psychopathology in PWS and aimed to identify relationships between affective, behavioral, and cognitive (ABC) experiences of stuttering and psychological distress (PD).                                   Method:                            Forty-four PWS were administered the Behavior Assessment Battery (BAB) for Adults who Stutter and the Brief Symptom Inventory-18. The prevalence of clinically significant PD was calculated via BSI-18 global severity index               t               -score cutoffs. Regression analyses examined relationships between ABC variables of stuttering and PD.                                                Results:             Participants' BAB scores approximated normative values, while the PD score distribution was similar to that of a nonclinical sample. Nine percent of participants met thresholds for clinically significant PD. All ABC correlates of stuttering significantly and positively correlated with PD scores, capturing considerable amounts of shared variance.                                   Conclusions:             Levels of PD in PWS approximate those of the general community, highlighting the existence of psychologically distressed subgroups of PWS. Speech situation-specific anxiety had the strongest relationship to PD, followed closely by one's report of situation-specific speech disruption. To a lesser but still significant extent, PWS' frequency to which they engage in various avoidance/escape behaviors, as well as their communication attitude, predicted levels of psychopathology. These data inform diagnostic and clinical decision making, drawing attention to factors that should be attended to in treatment.
How Is Scientific Collaboration Organized in U.S. Interoperability and Health Information Exchange Research?

Abstract  Interoperability and Health Information Exchange (HIE) are central to current efforts to improve care coordination, support public health, and enable secondary use of clinical data in the United States. While substantial attention has been devoted to standards, regulation, and implementation, less is known about how the research community itself has organized collaboration around these topics. Examining patterns of scientific collaboration provides insight into how knowledge production in this area has evolved alongside policy initiatives and data infrastructure development. We conducted a bibliometric and social network analysis of 159 peer-reviewed articles indexed in MEDLINE/PubMed, retrieved using a MeSH-based search strategy focused on health information technology, electronic health records, telemedicine, interoperability, and information exchange in the U.S. context. Records were deduplicated using DOI, PMID, and normalized titles, and author names were standardized to reduce fragmentation due to name variants. Based on these data, we constructed weighted and binary co-authorship networks in which nodes represent authors and edges represent shared authorship ties. Network measures were computed in UCINET, with degree centrality used to identify highly collaborative authors and betweenness centrality used to identify authors occupying intermediary positions between groups. The co-authorship network exhibits a modular structure composed of  several dense clusters connected by a relatively small set of central actors. Authors with high degree centrality tend to be involved in multi-institutional empirical studies, often drawing on large datasets and addressing questions at the intersection of clinical practice and health policy. In contrast, authors with high betweenness centrality occupy positions that link different thematic and institutional domains, including measurement of interoperability, studies of HIE implementation, regulatory analysis, and public health applications. These positions reflect roles centered on connecting research communities rather than on publication volume alone. Overall, the structure of collaboration observed in  this corpus reflects the institutional and practical context in which interoperability and HIE research has developed in the United States. Mapping these patterns provides an empirical view of how the field has been organized and how different strands of expertise have converged around shared data, policy, and implementation challenges.   Keywords: Health Information Exchange;  Artificial Intelligence in Healthcare; Machine Learning; Health Data Governance, Interoperability; Scientific Collaboration; Social Network Analysis; Medical Informatics; United States.
Empowering Mechanistic Interpretability of Deep Neural Networks with Knowledge Graphs

Although neural models have achieved remarkable performance, they still encounter doubts due to the intransparency. To this end, model prediction explanation is attracting more and more attentions. However, current methods rarely incorporate external knowledge and still suffer from three limitations: (1) Neglecting concept completeness. Merely selecting concepts may not sufficient for prediction. (2) Lacking concept fusion. Failure to merge semantically-equivalent concepts. (3) Difficult in manipulating model behavior. Lack of verification for explanation on original model. To address these issues, we propose a novel knowledge-aware neuron interpretation framework to explain model predictions for image scene classification. Specifically, for concept completeness, we present core concepts of a scene based on knowledge graph, ConceptNet, to gauge the completeness of concepts. Our method, incorporating complete concepts, effectively provides better prediction explanations compared to baselines. Furthermore, for concept fusion, we introduce a knowledge graph-based method known as Concept Filtering, which produces over 23% point gain on neuron behaviors for neuron interpretation. At last, we propose Model Manipulation, which aims to study whether the core concepts based on ConceptNet could be employed to manipulate model behavior. The results show that core concepts can effectively improve the performance of original model by over 26%.
Application of Low-Cost Air Quality Monitoring Sensor to Assess the Exposure of Ambient Air Pollution Due to PM2.5 and PM10

Recent high ambient concentration of particulate matter (PM), such as air pollutants, has its multimodal effects on the environment and human health especially for the least developing countries because of the high cost associated with monitoring it. The aim of this study is to introduce a low-cost air quality monitoring (AQM) sensor to assess human exposure to support the health impact assessment and environmental policy formulation. A low-cost AQM sensor using light scattering technology, AirVisual (AV) pro was used in four sites in Dhaka city of Bangladesh in a period of five and a half months spreading from Feb 2019 to Aug 2019. Validation of the equipment was done comparing it with two equivalent equipment-HIM-6000 portable air quality monitor and BAM-1020 continuous particulate monitor. The correlations between the AV pro and BAM-1020 were found fairly strong (R2 = 0.77) at the US Embassy in Dhaka. The correlations between the AV pro and HIM-6000 were found also very strong (R2 = 0.91) at Mirpur Cantonment. This study defines the four heterogeneous environments in Mirpur–12, Mirpur Cantonment, Baridhara, and Darus Salam to conduct extensive statistical analysis of spatial, seasonal, diurnal, and temporal variations of air pollution. The results showed that Mirpur–12 to be the most polluted zone (mean PM2.5 = 122.62 µg/m3, mean PM10 = 186.84 µg/m3) after application of low-cost AQM sensor. It also revealed changes in PM concentrations in terms of time, meteorological condition, and place. Maximum PM concentration occurs when the traffic volume is high in the morning and evening and reduces from pre-monsoon to monsoon. This paper also highlights the utility and vulnerability of a low-cost monitoring sensor like AV pro to measure the PM concentration. With the above analysis, further studies are needed to prove the relationship between health impact assessment and human exposure to PM2.5 and PM10 concentration via low-cost sensors.
Permanent Establishments In The Age Of Autonomous Artificial Intelligence: Why The Existing International Tax Framework Fails To Capture Ai-Generated Income Table Of Contents

The existing permanent establishment framework under Article 5 of the OECD Model Tax Convention is structurally incapable of capturing AI-generated income: when autonomous AI systems deliver professional services across dozens of jurisdictions without human intervention, the three-prong test fails at every step, producing systematic undertaxation of source countries. This Comment proposes the Autonomous AI Nexus—a targeted modification to Article 5 that replaces physical presence with three functional equivalents: a deployment test, an economic effects test, and an autonomous activity attribution rule. A case study and stress test demonstrate the framework's precision and its resilience to formalistic avoidance.