ethicalabs/Research-Intent-Collab huggingface dataset:
Neural Collision Detection for Multi-arm Laparoscopy Surgical Robots Through Learning-from-Simulation

This study presents an integrated framework for enhancing the safety and operational efficiency of robotic arms in laparoscopic surgery by addressing key challenges in collision detection and minimum distance estimation. By combining analytical modeling, real-time simulation, and machine learning, the framework offers a robust solution for ensuring safe robotic operations. An analytical model was developed to estimate the minimum distances between robotic arms based on their joint configurations, offering precise theoretical calculations that serve as both a validation tool and a benchmark. To complement this, a 3D simulation environment was created to model two 7-DOF Kinova robotic arms, generating a diverse dataset of configurations for collision detection and distance estimation. Using these insights, a deep neural network model was trained with joint actuators of robot arms and relative positions as inputs, achieving a mean absolute error of 282.2 mm and an R-squared value of 0.85. The close alignment between predicted and actual distances highlights the network's accuracy and its ability to generalize spatial relationships. This work demonstrates the effectiveness of combining analytical precision with machine learning algorithms to enhance the precision and reliability of robotic systems.
Annotating Cognates and Etymological Origin in Turkic Languages

Turkic languages exhibit extensive and diverse etymological relationships among lexical items. These relationships make the Turkic languages promising for exploring automated translation lexicon induction by leveraging cognate and other etymological information. However, due to the extent and diversity of the types of relationships between words, it is not clear how to annotate such information. In this paper, we present a methodology for annotating cognates and etymological origin in Turkic languages. Our method strives to balance the amount of research effort the annotator expends with the utility of the annotations for supporting research on improving automated translation lexicon induction.
Sensing Apps and Public Data Sets for Digital Phenotyping of Mental Health: Systematic Review

Background                     Mental disorders are normally diagnosed exclusively on the basis of symptoms, which are identified from patients’ interviews and self-reported experiences. To make mental health diagnoses and monitoring more objective, different solutions have been proposed such as digital phenotyping of mental health (DPMH), which can expand the ability to identify and monitor health conditions based on the interactions of people with digital technologies.                                                           Objective                     This article aims to identify and characterize the sensing applications and public data sets for DPMH from a technical perspective.                                                           Methods                     We performed a systematic review of scientific literature and data sets. We searched 8 digital libraries and 20 data set repositories to find results that met the selection criteria. We conducted a data extraction process from the selected articles and data sets. For this purpose, a form was designed to extract relevant information, thus enabling us to answer the research questions and identify open issues and research trends.                                                           Results                     A total of 31 sensing apps and 8 data sets were identified and reviewed. Sensing apps explore different context data sources (eg, positioning, inertial, ambient) to support DPMH studies. These apps are designed to analyze and process collected data to classify (n=11) and predict (n=6) mental states/disorders, and also to investigate existing correlations between context data and mental states/disorders (n=6). Moreover, general-purpose sensing apps are developed to focus only on contextual data collection (n=9). The reviewed data sets contain context data that model different aspects of human behavior, such as sociability, mood, physical activity, sleep, with some also being multimodal.                                                           Conclusions                     This systematic review provides in-depth analysis regarding solutions for DPMH. Results show growth in proposals for DPMH sensing apps in recent years, as opposed to a scarcity of public data sets. The review shows that there are features that can be measured on smart devices that can act as proxies for mental status and well-being; however, it should be noted that the combined evidence for high-quality features for mental states remains limited. DPMH presents a great perspective for future research, mainly to reach the needed maturity for applications in clinical settings.
Serverless Computing and Cloud Function-based Applications

Serverless computing is a growing industry trend with corresponding rise in interest by scholars and tinkerers. Increasingly, open source and academic system prototypes are being proposed especially in relation with cloud, edge and fog computing among other distributed computing specialisations. Due to the strict separation between elastically scalable stateless microservices bound to stateful backend services prevalent in this computing paradigm, the resulting applications are inherently distributed with favourable characteristics such as elastic scalability and disposability. Still, software application developers are confronted with a multitude of different methods and tools to build, test and deploy their function-based applications in today’s serverless ecosystems. The logical next step is therefore a methodical development approach with key enablers based on a classification of languages, tools, systems, system behaviours, patterns, pitfalls, application architectures, compositions and cloud services around the serverless application development process.
Challenges in Deep Learning-Based Small Organ Segmentation A Benchmarking Perspective for Medical Research with Limited Datasets

Accurate segmentation of carotid artery structures in histopathological images is vital for cardiovascular disease research. This study systematically evaluates ten deep learning segmentation models including classical architectures, modern CNNs, a Vision Transformer, and foundation models, on a limited dataset of nine cardiovascular histology images. We conducted ablation studies on data augmentation, input resolution, and random seed stability to quantify sources of variance. Evaluation on an independent generalization dataset ($N=153$) under distribution shift reveals that foundation models maintain performance while classical architectures fail, and that rankings change substantially between in-distribution and out-of-distribution settings. Training on the second dataset at varying sample sizes reveals dataset-specific ranking hierarchies confirming that model rankings are not generalizable across datasets. Despite rigorous Bayesian hyperparameter optimization, model performance remains highly sensitive to data splits. The bootstrap analysis reveals substantially overlapping confidence intervals among top models, with differences driven more by statistical noise than algorithmic superiority. This instability exposes limitations of standard benchmarking in low-data clinical settings and challenges assumptions that performance rankings reflect clinical utility. We advocate for uncertainty-aware evaluation in low-data clinical research scenarios from two perspectives. First, the scenario is not niche and is rather widely spread; and second, it enables pursuing or discontinuing research tracks with limited datasets from incipient stages of observations.
Modulation of High Energy Cosmic Rays in the Heliosphere

No description supplied
OpenFWI: Large-Scale Multi-Structural Benchmark Datasets for Seismic Full Waveform Inversion

Full waveform inversion (FWI) is widely used in geophysics to reconstruct high-resolution velocity maps from seismic data. The recent success of data-driven FWI methods results in a rapidly increasing demand for open datasets to serve the geophysics community. We present OpenFWI, a collection of large-scale multi-structural benchmark datasets, to facilitate diversified, rigorous, and reproducible research on FWI. In particular, OpenFWI consists of 12 datasets (2.1TB in total) synthesized from multiple sources. It encompasses diverse domains in geophysics (interface, fault, CO2 reservoir, etc.), covers different geological subsurface structures (flat, curve, etc.), and contains various amounts of data samples (2K - 67K). It also includes a dataset for 3D FWI. Moreover, we use OpenFWI to perform benchmarking over four deep learning methods, covering both supervised and unsupervised learning regimes. Along with the benchmarks, we implement additional experiments, including physics-driven methods, complexity analysis, generalization study, uncertainty quantification, and so on, to sharpen our understanding of datasets and methods. The studies either provide valuable insights into the datasets and the performance, or uncover their current limitations. We hope OpenFWI supports prospective research on FWI and inspires future open-source efforts on AI for science. All datasets and related information can be accessed through our website at https://openfwi-lanl.github.io/
The Influence of Pop Songs Referencing Anxiety, Depression, and Suicidal Ideation on College Students’ Mental Health Empathy, Stigma, and Behavioral Intentions

U.S. college students' general mental health is rapidly declining, and scholars have observed significant increases in references to depression, suicide, and mental health metaphors in popular music. Given that college students are spending more time than ever before listening to popular music, this survey aimed to assess some of the potential implications. College students (N = 253) completed an online survey about their exposure to contemporary pop songs that reference mental health difficulties such as anxiety, depression, and suicidal ideation. Both college students' increased perceived personal connection (PPC) with the songs referencing mental health difficulties and parasocial relationships (PSR) with their performing artists were associated with increased mental health empathy. This increased mental health empathy mediated outcomes including reduced mental health stigma, increased support for public mental health resources, and increased willingness to support others struggling with their mental health. Neither PPC with the song nor PSR with the song's artist was associated with personal willingness to seek mental health help. However, post hoc analyses revealed that college students who reported stronger PPC to these songs also reported that they use music to "deal with their own mental health issues." This study's findings suggest that pop music artists who open up about mental health difficulties may have potential as novel message sources in communication campaigns designed to improve mental health outcomes among college students.
A systematic review of natural language processing applied to radiology reports

AbstractBackgroundNatural language processing (NLP) has a significant role in advancing healthcare and has been found to be key in extracting structured information from radiology reports. Understanding recent developments in NLP application to radiology is of significance but recent reviews on this are limited. This study systematically assesses and quantifies recent literature in NLP applied to radiology reports.MethodsWe conduct an automated literature search yielding 4836 results using automated filtering, metadata enriching steps and citation search combined with manual review. Our analysis is based on 21 variables including radiology characteristics, NLP methodology, performance, study, and clinical application characteristics.ResultsWe present a comprehensive analysis of the 164 publications retrieved with publications in 2019 almost triple those in 2015. Each publication is categorised into one of 6 clinical application categories. Deep learning use increases in the period but conventional machine learning approaches are still prevalent. Deep learning remains challenged when data is scarce and there is little evidence of adoption into clinical practice. Despite 17% of studies reporting greater than 0.85 F1 scores, it is hard to comparatively evaluate these approaches given that most of them use different datasets. Only 14 studies made their data and 15 their code available with 10 externally validating results.ConclusionsAutomated understanding of clinical narratives of the radiology reports has the potential to enhance the healthcare process and we show that research in this field continues to grow. Reproducibility and explainability of models are important if the domain is to move applications into clinical use. More could be done to share code enabling validation of methods on different institutional data and to reduce heterogeneity in reporting of study properties allowing inter-study comparisons. Our results have significance for researchers in the field providing a systematic synthesis of existing work to build on, identify gaps, opportunities for collaboration and avoid duplication.
Properties of sandy soil and allelochemical compounds of heath forest in Rantau Abang, Terengganu, Malaysia: Implication for ecological sustainability and biodiversity conservation

Kerangas or heath forests are found on podzolised siliceous sands (spodosols), where gradually decomposing organic matter occurs on the soil’s surface. Changes in the extent of podzol development, soil consistency, and poor nutrient contents signify the unique properties of sandy soil in the heath forest at Rantau Abang, Terengganu, Malaysia. Unfortunately, the sandy soil’s chemical and physical properties are in critical condition due to human activities such as cutting and burning, impacting the sustainability of this ecosystem and its ground cover of shrubs, sparse grass and sedge. Therefore, it is crucial to study the properties of sandy soils in heath forests before their extinction. This analyzed the physical and chemical attributes of heath forest sandy soils regarding heavy metal toxicity, pH, concentrations of carbon, nitrogen, available phosphorus, cation exchange capacity (CEC), allelochemical compounds, total phenolic content in the soil, soil colour, and soil texture. The solid was found to be nutrient-deprived due to its extreme acidity and toxicity. The results showed that allelochemical matters were higher in plants but not in the soil attributes and the water bodies, along with a highly active response in young plant tissues due to their sources of phenolic content. Remarkably, the overall phenolic content was observed to be considerably high in the semi-mature phase of vegetation.
What Influences the Adoption Timing of Accounting Practices? An Investigation of the Voluntary Adoption of Stand-Alone Corporate Social Responsibility Reports in China

Escalating societal concern about corporate social responsibility (CSR) issues has led regulators around the globe to publish CSR reporting guidelines, with China being a prominent actor in this field. Prior studies tend to predominantly focus on the Western institutional context and study the adoption of CSR reporting as a binary decision. In contrast, anchored in institutional theory, the theoretical aim of this thesis is to study factors influencing the timing of voluntary adoption of stand-alone CSR reports. Empirically, we focus on China’s socialist market economy as its hybridity enables a reconsideration of how adoption timing of accounting practices is influenced by institutional factors – both market-based and political. Using a sample of Chinese publicly listed firms from 2010 – 2020, we find that market factors and political factors coexist in China and interpenetrate each other to influence the adoption timing of stand-alone CSR reports. Firms with superior CSR performance, state ownership, and top executives serving on political councils are more likely to adopt stand-alone CSR reports earlier. Furthermore, although the relationship between CSR performance and the adoption timing is less pronounced for firms vulnerable to state influence, it remains positive and significant for these firms. Overall, our findings suggest that in the absence of hard laws and regulations, listed firms pursue both economic and symbolic resources, responding to both coercive institutional pressure as well as isomorphic pressure manifested as state signalling of priorities, a pressure that we label as state-sponsored isomorphism. The findings of this study have practical implications for investors, policymakers, and standard setters.
Corporate reporting on social responsibility under martial law

Today, it is important to create a demand for social responsibility, reporting on the contri­bution of business to the achievement of sustai­nable development goals to create jobs, improve communications between government, business and society, address economic, environmental and social issues, and build an economy that works for people. Businesses should report on their corporate social responsibility in counte­ring military aggression and maintaining peace and security. The purpose of the article is to highlight the peculiarities of preparing and pub­lishing corporate social responsibility reports in Ukraine and the EU. The study uses general scientific and special methods: induction, deduc-tion, comparative analysis, synthesis, generali­zation, grouping, abstraction, and the logical method. Reporting on corporate social respon­sibility allows demonstrating how a business affects the natural environment and society and what contribution it makes to achieving sustai­nable development goals. It is established that there is no single standard, guideline or frame­work for the preparation and presentation of corporate social responsibility reporting; a num­ber of regulatory documents at the international, European, regional and national levels have been adopted to date, but cooperation continues to harmonize existing standards and approaches and promote them among a wide range of users. In order to strengthen European integration, Ukraine needs to establish a national system of regulation, incentives, certification, monitoring and evaluation of corporate social respon­si­bility, verification and verification of corporate social responsibility reporting, and adoption of the best European experience.
Comment on "Measuring propagation speed of Coulomb fields" [R. de Sangro {\it et al.} Eur. Phys. J.C (2015) 75:137]

Some remarks are made on the theoretical interpretation of recent experimental results on the propagation speed of electromagnetic force fields.
An Improvement to Collaborative Filtering for Recommender Systems

Collaborative filtering recommenders utilize a database of user preferences to make personal product suggestions, and have achieved widespread successes in various e-commerce applications nowadays. Inverse user frequency is one of most well known approaches to improve the accuracy of the standard collaborative filtering recommender. In this paper, we propose a statistical attribute distance method that uses the similarity in statistics of users' ratings to calculate the user correlation instead of using the statistics of users that rate for similar items. Form our experiment results we suggest the statistical attribute distance outperforms inverse user frequency in recommendation accuracy and scalability
Optimal approximations of the Fokker-Planck-Kolmogorov equation: projection, maximum likelihood eigenfunctions and Galerkin methods

We study optimal finite dimensional approximations of the generally infinite-dimensional Fokker-Planck-Kolmogorov (FPK) equation, finding the curve in a given finite-dimensional family that best approximates the exact solution evolution. For a first local approximation we assign a manifold structure to the family and a metric. We then project the vector field of the partial differential equation (PDE) onto the tangent space of the chosen family, thus obtaining an ordinary differential equation for the family parameter. A second global approximation will be based on projecting directly the exact solution from its infinite dimensional space to the chosen family using the nonlinear metric projection. This will result in matching expectations with respect to the exact and approximating densities for particular functions associated with the chosen family, but this will require knowledge of the exact solution of FPK. A first way around this is a localized version of the metric projection based on the assumed density approximation. While the localization will remove global optimality, we will show that the somewhat arbitrary assumed density approximation is equivalent to the mathematically rigorous vector field projection. More interestingly we study the case where the approximating family is defined based on a number of eigenfunctions of the exact equation. In this case we show that the local vector field projection provides also the globally optimal approximation in metric projection, and for some families this coincides with a Galerkin method.
Evidence synthesis evaluating body weight gain among people treating HIV with antiretroviral therapy - a systematic literature review and network meta-analysis

This systematic review aimed to compare body weight gain associated outcomes over time between dolutegravir (DTG)-based antiretroviral (ART) regimens to other ART regimens, to compare tenofovir alafenamide (TAF)-based regimens, and to evaluate the associated prognostic factors.Systematic searches of MEDLINE, Embase, and CENTRAL for RCTs and observational studies comparing ART regimens were conducted on 13 September 2021. Outcomes of interest included: change in body weight, body mass index (BMI), waist circumference; and risk of hyperglycaemia and diabetes. Network meta-analyses were conducted at 12, 24, 48, 96 and 144 weeks using two networks differentiated by 3rd agents and backbone agents.The review identified 113 publications reporting on 73 studies. DTG-based regimens led to statistically higher weight gains than efavirenz-based regimens at all time points (mean difference: 1·99 kg at 96 weeks; 95% credible interval: 0·85-3·09) and was higher over time than low-dose efavirenz-, elvitegravir-, and rilpivirine-based regimens. They were comparable to raltegravir-, bictegravir- and atazanavir-based regimens. For backbones, TAF led to higher weight gain relative to tenofovir disoproxil fumarate (TDF), abacavir, and zidovudine. Prognostic factor analysis showed both low CD4 cell count and high HIV RNA viral load at baseline were consistently associated with higher weight gain, while sex was an effect modifier to African origins.DTG-based regimens lead to larger average weight gains than some other ART regimens and TAF leads to larger average weight gains than all other backbone antiretrovirals. Further research is needed to better understand long-term outcomes and their relationship to other metabolic outcomes.The WHO Global HIV, Hepatitis and Sexually Transmitted Infections Programmes.