ethicalabs/Research-Intent-Collab huggingface dataset:
Health Insurance Coverage and Access to Care Among US-Born and Foreign-Born Sexual Minorities

Sexual minorities and immigrants face unique challenges to accessing health care in the United States. This study used data on nonelderly adults (n = 100,667) from the 2013-2016 National Health Interview Survey. Unadjusted prevalence estimates and multivariable logistic regression models (with and without interactions between immigration and sexual minority status) were used to compare health insurance coverage and access to care by immigration and sexual minority status. We did not find any differences in uninsurance, having a usual source of care, and a recent office visit by sexual orientation for US-born and foreign-born adults. However, compared to their heterosexual counterparts, US-born and foreign-born sexual minorities were more likely to have an emergency room visit in the prior year and report financial-related barriers to medical care, dental care, prescription medications, and mental health care. Foreign-born sexual minorities were more likely to have an emergency room visit and unmet mental health care needs due to cost compared to other subpopulations after controlling for sociodemographic characteristics. Broadening the knowledge and scope of research on sexual minority immigrants can inform targeted health policy approaches with the goal of achieving health equity for sexual minority immigrants.
Subgraph Gaussian Embedding Contrast for Self-supervised Graph Representation Learning

Graph Representation Learning (GRL) is a fundamental task in machine learning, aiming to encode high-dimensional graph-structured data into low-dimensional vectors. Self-Supervised Learning (SSL) methods are widely used in GRL because they can avoid expensive human annotation. In this work, we propose a novel Subgraph Gaussian Embedding Contrast (SubGEC) method. Our approach introduces a subgraph Gaussian embedding module, which adaptively maps subgraphs to a structured Gaussian space, ensuring the preservation of input subgraph characteristics while generating subgraphs with a controlled distribution. We then employ optimal transport distances, more precisely the Wasserstein and Gromov-Wasserstein distances, to effectively measure the similarity between subgraphs, enhancing the robustness of the contrastive learning process. Extensive experiments across multiple benchmarks demonstrate that \method~outperforms or presents competitive performance against state-of-the-art approaches. Our findings provide insights into the design of SSL methods for GRL, emphasizing the importance of the distribution of the generated contrastive pairs.
Analisis dan Perancangan Human Resource Information System (HRIS) pada Universitas Tanri Abeng

Abstrakâ€”Pengelolaan data pada divisi sumber daya manusia merupakan perihal yang penting dalam setiap instansi, karena dapat mengukur kinerja pegawai. Setelah dilakukan analisis terhadap sistem yang sedang berjalan di Universitas Tanri Abeng, maka dikembangkan sistem informasi yang bernama HRIS. HRIS merupakan sistem informasi berbasis web yang dikembangkan menggunakan metode Waterfall dan menggunakan perancangan UML (Unified Modelling Language). Sistem yang dikembangkan dapat membantu manager HRD dalam mengelola dan mengolah data karyawan, tenaga pendidik, dan tenaga kependidikan seperti data profil, cuti dan izin, kehadiran, sampai dengan perhitungan pendapatan. HRIS telah melakukan tahapan pengujian oleh end user yaitu manajer HRD dan pengujian black box
Optimizing Energy Consumption and Latency in IoT Through Edge Computing in Air&#x2013;Ground Integrated Network With Deep Reinforcement Learning

With the increasing computational demands of Internet of Things (IoT) applications, air-ground integrated networks (AGIN), leveraging the capabilities of Unmanned Aerial Vehicles (UAVs) and High-Altitude Platform (HAP), provides an essential solution to these challenges. In this paper, we propose a framework that facilitates local computing at IoT devices and offers the flexibility to offload tasks to aerial platforms when necessary. Specifically, we formulate a multi-objective optimization model aiming at simultaneously minimizing energy consumption and reducing task latency by adjusting control variables such as transmit power, offloading decisions, and UAV placement in a distributed network of IoT devices. Our proposed framework employs Deep Deterministic Policy Gradient (DDPG) techniques to dynamically optimize network operations, allowing for efficient real-time adjustments to network conditions and task demands. The performance of the proposed algorithm is compared to traditional algorithms, including the Whale Optimization Algorithm (WOA), Gradient Search with Barrier, and Bayesian Optimization (BO). Simulation results show that this approach significantly minimizes energy consumption and latency, outperforming conventional optimization methods. Additionally, scalability tests confirm that our framework can efficiently integrate an increasing number of IoT devices and UAVs.
The Rise of Citizen Science in Health and Biomedical Research

Citizen science models of public participation in scientific research represent a growing area of opportunity for health and biomedical research, as well as new impetus for more collaborative forms of engagement in large-scale research. However, this also surfaces a variety of ethical issues that both fall outside of and build upon the standard human subjects concerns in bioethics. This article provides background on citizen science, examples of current projects in the field, and discussion of established and emerging ethical issues for citizen science in health and biomedical research.
Quantile Regression for Qualifying Match of GEFCom2017 Probabilistic Load Forecasting

We present a simple quantile regression-based forecasting method that was applied in a probabilistic load forecasting framework of the Global Energy Forecasting Competition 2017 (GEFCom2017). The hourly load data is log transformed and split into a long-term trend component and a remainder term. The key forecasting element is the quantile regression approach for the remainder term that takes into account weekly and annual seasonalities such as their interactions. Temperature information is only used to stabilize the forecast of the long-term trend component. Public holidays information is ignored. Still, the forecasting method placed second in the open data track and fourth in the definite data track with our forecasting method, which is remarkable given simplicity of the model. The method also outperforms the Vanilla benchmark consistently.
Active Amplification of the Terrestrial Albedo to Mitigate Climate Change: An Exploratory Study

This study explores the potential to enhance the reflectance of solar insolation by the human settlement and grassland components of the Earth's terrestrial surface as a climate change mitigation measure. Preliminary estimates derived using a static radiative transfer model indicate that such efforts could amplify the planetary albedo enough to offset the current global annual average level of radiative forcing caused by anthropogenic greenhouse gases by as much as 30 percent or 0.76 W/m2. Terrestrial albedo amplification may thus extend, by about 25 years, the time available to advance the development and use of low-emission energy conversion technologies which ultimately remain essential to mitigate long-term climate change. However, additional study is needed to confirm the estimates reported here and to assess the economic and environmental impacts of active land-surface albedo amplification as a climate change mitigation measure.
Making transparency real? Accounting and popular participation in corruption control

Abstract   Accounting, broadly defined, has been the focus of high hopes and expectations for centuries. Classic works of art from The Netherlands, for example, depict the settling of accounts as not only a personal but a civic virtue—as a critical factor in the nation's development and prosperity. In that light it is not surprising that today's anti-corruption groups often see accounting and accountants as obvious parts of the reform repertoire, and as more or less inevitably helping produce better government. But the early successes of accounting had as much to do with the interests and interactions of a small class of elites as with any inherent redemptive qualities of the techniques themselves. A similar logic applies to the contemporary anti-corruption uses of accounting: because the techniques themselves can serve and conceal all manner of interests and agendas, their effectiveness in controlling corruption will depend upon support from active and sustainable reform constituencies. Skilled and rigorous accounting techniques  can  become a powerful anti-corruption force, but active support from elites and citizens alike must demand that they be dedicated to that agenda.
QUALITY ASSURANCE OF AIR QUALITY MONITORING WITH LOW COST ELECTROCHEMICAL AIR POLLUTION SENSORS

Prednosti malih senzora za praćenje kvalitete zraka u odnosu na konvencionalne (referentne) metode su definitivno njihova cijena i dimenzije. Zbog toga, mali senzori omogućuju praćenje kvalitete zraka na puno više lokacija. Međutim, postavlja se pitanje mogu li oni osigurati istu ili barem približno jednaku kvalitetu podataka kao i postaje koje prate kvalitetu zraka referentnim metodama. Kako bi se osigurala što bolja kvaliteta podataka mali senzor je potrebno postaviti na referentnu postaju. Postavljenjem senzora na referentnu postaju dobivaju se informacije potrebne za izračunavanje varijabli skaliranja, nagiba i pomaka. Skaliranje osigurava povećanu razinu točnosti na temelju lokalnih okolišnih uvjeta i načina na koji senzori reagiraju na okruženje. Varijable skaliranja izračunavaju se za određeno vremensko razdoblje. Nakon velike promjene u okolišnim uvjetima, npr. promjena prosječne temperature za 10 °C ili više, potrebno je ponovno izračunati varijable skaliranja. Primjenom tako izračunatih varijabli za nagib i pomak mogu se dobiti najbolji mogući rezultati tijekom istog perioda. Postupak skaliranja senzora može se podijeliti u četiri osnovna koraka. Prvi korak je regresijska usporedba malog senzora s referentnom metodom. Regresijska analiza može se izvršiti pomoću programa Microsoft Excel, MathCad ili sličnih matematičkih programa. U drugom koraku uklanjaju se potencijalno pogrešni podaci i pronalazi se jednadžba pravca. U trećem koraku regresijskom analizom dobivene varijable skaliranja, nagib i pomak primjenjuju se na cijeli skup podataka sa senzora, uključivo i pogrešne podatke. U četvrtom koraku vrijednosti varijabli skaliranja, nagiba i pomaka učitavaju se u online aplikaciju. Senzor koji je prošao ovakav postupak skaliranja može se dalje koristiti za validaciju ostalih senzora u mreži senzora. Iako se u trenutnom stupnju razvoja malim senzorima i dalje ne može dobiti ista kvaliteta podataka kao mjerenjima referentnim metodama, ovim postupkom skaliranja dobivaju se podaci koji se za pojedine plinove znatno ne razlikuju od podataka dobivenih mjerenjem referentnim metodama.
Uncertainty Modeling with Imprecise Statistical Reasoning and the Precautionary Principle in Decision Making

A number of unconventional formal approaches to decision making have been developed to provide mathematical foundations for rational choices under both aleatory and epistemic uncertainty. They challenge a central assumption of the Bayesian theory, that uncertainty should always be gauged by a single (additive) measure, and values should always be gauged by a precise utility function [3].
Med7: A transferable clinical natural language processing model for electronic health records

16 pages, 1 figure, 15 tables
Cosmic ray modulation in the outer heliosphere: Predictions for cosmic ray intensities up to the heliopause along Voyager 1 and 2 trajectories

Abstract   Time dependent cosmic ray modulation in the outer heliosphere is calculated and results are compared to Voyager 1 and 2 observations using a two-dimensional time-dependent cosmic ray transport model. We predict possible future 133–242 MeV proton observations along the Voyager 1 and 2 spacecraft trajectories. Recent theoretical advances in cosmic ray transport parameters are introduced in order to provide a time-dependence for the assumed transport parameters used in the model. This leads to results that are in general compatible with the spacecraft observations in the inner and outer heliosphere over multiple solar cycles. However, for the outer heliosphere, we find that the Voyager 1 and 2 spacecraft observations cannot be fitted with an identical set of parameters along both trajectories. This indicates a possible asymmetric heliosphere or a symmetric heliosphere but with different diffusion parameters in the northern and southern hemispheres, respectively. Furthermore, results indicate that Voyager 2 observations are still under the influence of solar cycle related changes because of the large modulation volume between the heliopause and spacecraft location in contrast to Voyager 1 which shows a steady increase in cosmic ray intensities.
Real-Time Streaming Attribution: Sub-Second Event Processing with Apache Kafka and Flink

Marketing attribution has historically been a batch process — events accumulate, models run overnight, and decisions are made on yesterday's data. This paper presents a streaming attribution architecture built on Apache Kafka and Apache Flink that processes attribution events in under 100ms. The framework integrates stateful stream processing, windowed aggregation, and real-time Shapley calculation to enable millisecond-latency attribution decisions for live bidding and budget optimization. Provides infrastructure foundation for Robinson (2026a) through (2026f).
On the discriminability of self-supervised representation learning

Self-supervised learning (SSL) has recently shown notable success in various visual tasks. However, in terms of discriminability, SSL is still not on par with supervised learning (SL). This paper identifies a key issue, the ``crowding problem," where features from different classes are not well-separated, and there is high intra-class variance. In contrast, SL ensures clear class separation. Our analysis reveals that SSL objectives do not adequately constrain the relationships between samples and their augmentations, leading to poorer performance in complex tasks. We further establish a theoretical framework that connects SSL objectives to cross-entropy risk bounds, explaining how reducing intra-class variance and increasing inter-class separation can improve generalization. To address this, we propose the Dynamic Semantic Adjuster (DSA), a learnable regulator that enhances feature aggregation and separation while being robust to outliers. Comprehensive experiments conducted on diverse benchmark datasets validate that DSA leads to substantial gains in SSL performance, narrowing the performance gap with SL.
The Atacama Cosmology Telescope: A Measurement of the Cosmic Microwave Background Power Spectrum at 148 and 218 GHz from the 2008 Southern Survey

We present measurements of the cosmic microwave background (CMB) power spectrum made by the Atacama Cosmology Telescope at 148 GHz and 218 GHz, as well as the cross-frequency spectrum between the two channels. Our results clearly show the second through t
Ethical Considerations for Planetary Protection in Space Exploration: A Workshop

With the recognition of an increasing potential for discovery of extraterrestrial life, a diverse set of researchers have noted a need to examine the foundational ethical principles that should frame our collective space activities as we explore outer space. A COSPAR Workshop on Ethical Considerations for Planetary Protection in Space Exploration was convened at Princeton University on June 8–10, 2010, to examine whether planetary protection measures and practices should be extended to protect planetary environments within an ethical framework that goes beyond “science protection”                     per se                     . The workshop had been in development prior to a 2006 NRC report on preventing the forward contamination of Mars, although it responded directly to one of the recommendations of that report and to several peer-reviewed papers as well. The workshop focused on the implications and responsibilities engendered when exploring outer space while avoiding harmful impacts on planetary bodies. Over 3 days, workshop participants developed a set of recommendations addressing the need for a revised policy framework to address “harmful contamination” beyond biological contamination, noting that it is important to maintain the current COSPAR planetary protection policy for scientific exploration and activities. The attendees agreed that there is need for further study of the ethical considerations used on Earth and the examination of management options and governmental mechanisms useful for establishing an environmental stewardship framework that incorporates both scientific input and enforcement. Scientists need to undertake public dialogue to communicate widely about these future policy deliberations and to ensure public involvement in decision making. A number of incremental steps have been taken since the workshop to implement some of these recommendations. Key Words: Planetary protection—Extraterrestrial life—Life in extreme environments—Environment—Habitability. Astrobiology 12, 1017–1023.
The synthesis of scientific shreds of evidence: a critical appraisal on systematic review and meta-analysis methodology

Synthesising results across studies to recognise the causes of variation in outcomes and to reach an overall understanding of a problem is a crucial part of the scientific method. Until in recent times, the results of scientific findings have been summarised in narrative reviews where the summary of transparent and objective results have become increasingly difficult. Systematic reviews and meta-analyses, conducted by subsequent strict protocols to guarantee reproducibility and decrease bias, have become more common in the synthesis of evidence.
Open Knowledge Maps: Discovery for an Open Science

This presentation was given as part of COAR Webinar and Discussion Series on 25/11/2019.
The reaction coordinate mapping in quantum thermodynamics

We present an overview of the reaction coordinate approach to handling strong system-reservoir interactions in quantum thermodynamics. This technique is based on incorporating a collective degree of freedom of the reservoir (the reaction coordinate) into an enlarged system Hamiltonian (the supersystem), which is then treated explicitly. The remaining residual reservoir degrees of freedom are traced out in the usual perturbative manner. The resulting description accurately accounts for strong system-reservoir coupling and/or non-Markovian effects over a wide range of parameters, including regimes in which there is a substantial generation of system-reservoir correlations. We discuss applications to both discrete stroke and continuously operating heat engines, as well as perspectives for additional developments. In particular, we find narrow regimes where strong coupling is not detrimental to the performance of continuously operating heat engines.
The Tallinn Manual on the International Law Applicable to Cyber Warfare: A Commentary on Chapter II—The Use of Force

This paper provides a commentary on the jus ad bellum rules that apply to cyber war, as contained in Chapter II of the ‘Tallinn Manual’. More specifically, it critically examines the application to cyber attacks of the rule prohibiting the threat or use of force, the scope of self-defence against on-going or imminent cyber attacks, but it also deals with the question of attribution, the use of force by and against non-state actors as well as with the issue of non-intervention.
SPIROS: Streamlined, Precise, Intuitive, and Rapid Optical Simulator for particle physics detectors

Abstract                   This paper presents SPIROS (Streamlined, Precise, Intuitive,   and Rapid Optical Simulator), a dedicated optical simulation tool   developed for the design and analysis of particle physics   detectors. Unlike general-purpose frameworks such as GEANT4, SPIROS   offers a lightweight and user-friendly interface optimized   specifically for optical processes, including scintillation,   Cherenkov emission, and photon transport with reflection,   refraction, scattering, absorption, and detection. Detector   geometries can be directly imported from 3D CAD models, and all   configurations including materials, surfaces, sources, and sensors   are specified via a single human-readable input file. Validation   against GEANT4 shows excellent agreement in photon generation and   propagation behaviors, while benchmark tests demonstrate that SPIROS   runs more than two times faster for typical detector   configurations. The software has already been applied to multiple   neutrino experiments, including T2K, NINJA, and AXEL, for detector   design, performance studies, and optimization. SPIROS is open-source   and freely available at https://github.com/tkikawa/spiros.
Rainbow DQN for Intrusion Detection: A Unified Deep Reinforcement Learning Approach Across Benchmark Datasets

This paper presents Rainbow DQN for Intrusion Detection: A Unified Deep Reinforcement Learning Approach Across Benchmark Datasets, designed to address the increasing complexity of cyber threats. By incorporating advanced reinforcement learning components such as Double DQN, Dueling Networks, Prioritized Experience Replay, N-step Learning, Distributional RL, and Noisy Nets, the proposed model enhances both accuracy and stability in intrusion detection. The approach was rigorously evaluated on three benchmark datasets: CIC-IDS2017, KDD CUP 99, and UNSW-NB15. Experimental results demonstrated remarkable performance, achieving 99.8%, 98.4%, and 97.9% accuracy, respectively, with high precision, recall, and F1-scores across diverse attack categories. Compared with baseline DQ-IDS models, Rainbow DQN consistently improved detection capabilities and reduced false positives. These results highlight the robustness, adaptability, and generalizability of Rainbow DQN, making it a promising solution for next-generation intrusion detection systems in dynamic and heterogeneous network environments.
Avoiding Opportunity Cost Neglect in Cost-Effectiveness Analysis for Health Technology Assessment

Despite being a fundamental tenet of economic analysis there is a lack of clarity regarding the relevance of opportunity costs to cost-effectiveness analysis for health technology assessment. We argue that this is due, in part, to the importance of the decision context in understanding the nature of opportunity costs. Taking the example of the National Institute of Health and Care Excellence (NICE) on behalf of the National Health Service (NHS) in England and Wales, we explore the implications of existing discrepancies between policy thresholds and emerging empirical evidence of health opportunity costs. In particular, we consider analysts communicating the results of cost-effectiveness analysis, and recommend that analysts provide analysis according to both the policy threshold and the latest empirical evidence until the discrepancies are better understood or resolved. A number of conceptually related, but distinct, issues are discussed and clarified.