ethicalabs/Research-Intent-Collab huggingface dataset:
When repeats drive the vocabulary: a Byte-Pair Encoding analysis of T2T primate genomes

The emergence of telomere-to-telomere (T2T) genome assemblies has opened new avenues for comparative genomics, yet effective tokenization strategies for genomic sequences remain underexplored. In this pilot study, we apply Byte Pair Encoding (BPE) to nine T2T primate genomes including three human assemblies by training independent BPE tokenizers with a fixed vocabulary of 512,000 tokens using our custom tool, dnaBPE. Our analysis reveals that only 11,569 tokens are shared across all assemblies, while nearly 991,854 tokens are unique to a single genome, indicating a rapid decline in shared vocabulary with increasing assembly comparisons. Moreover, phylogenetic trees derived from token overlap failed to recapitulate established primate relationships, a discrepancy attributed to the disproportionate influence of species-specific high-copy repetitive elements. These findings underscore the dual nature of BPE tokenization: while it effectively compresses repetitive sequences, its sensitivity to high-copy elements limits its utility as a universal tool for comparative genomics. We discuss potential hybrid strategies and repeat-masking approaches to refine genomic tokenization, emphasizing the need for domain-specific adaptations in the development of large-scale genomic language models. The dnaBPE tool used in this study is open-source and available at https://github.com/aglabx/dnaBPE.
Improving Fairness in Criminal Justice Algorithmic Risk Assessments Using Optimal Transport and Conformal Prediction Sets

In the United States and elsewhere, risk assessment algorithms are being used to help inform criminal justice decision-makers. A common intent is to forecast an offender’s “future dangerousness.” Such algorithms have been correctly criticized for potential unfairness, and there is an active cottage industry trying to make repairs. In this paper, we use counterfactual reasoning to consider the prospects for improved fairness when members of a disadvantaged class are treated by a risk algorithm as if they are members of an advantaged class. We combine a machine learning classifier trained in a novel manner with an optimal transport adjustment for the relevant joint probability distributions, which together provide a constructive response to claims of bias-in-bias-out. A key distinction is made between fairness claims that are empirically testable and fairness claims that are not. We then use confusion tables and conformal prediction sets to evaluate achieved fairness for estimated risk. Our data are a random sample of 300,000 offenders at their arraignments for a large metropolitan area in the United States during which decisions to release or detain are made. We show that substantial improvement in fairness can be achieved consistently with a Pareto improvement for legally protected classes.
Research on Personality Rights Infringement Risks and Governance Strategies in the AIGC (Artificial Intelligence-Generated Content) Era

Personality rights constitute the primary and fundamental civil rights of natural persons. As China's AIGC industry continues to expand, the potential legal risks associated with personality rights violations have become increasingly evident. Specifically, AIGC has restructured traditional personality elements and infringement scenarios, leading to increased infringement frequency, greater difficulty in identifying violations, heightened challenges in rights protection, and complex liability determinations. However, China's current infringement governance framework remains underdeveloped, facing three critical issues: legislative application dilemmas, regulatory gaps, and industry ecosystem irregularities. To address these challenges, China urgently needs to refine its governance approach through four key measures: clarifying the boundaries of personality rights systems, improving existing liability attribution models, establishing inclusive yet prudent regulatory frameworks, and formulating compliance guidelines for AIGC enterprises. This comprehensive strategy aims to build an integrated governance system encompassing legislation, law enforcement, and industry practices, thereby ensuring effective protection of personality rights.
Impact of Nurse-Patient Ratios on Quality of Care and Patient Safety in Hospital Settings

Background and Purpose:  Nurse–patient ratios are a critical determinant of hospital care outcomes, influencing both the quality of care delivered and patient safety. Increasing concerns about understaffing and workload stress necessitate an in-depth understanding of how staffing levels shape nursing practices, patient experiences, and overall healthcare outcomes. This study aimed to explore the perceived impact of nurse–patient ratios on quality of care and patient safety in hospital settings.  Methods:A qualitative research design was employed using semi-structured interviews with registered nurses and focus group discussions with hospital staff. Participants were purposively sampled from various departments in tertiary care hospitals. Data were thematically analyzed to capture patterns and perspectives regarding staffing challenges, workload distribution, and patient safety implications.  Key Findings:  The analysis revealed four major themes: (1) increased workload leading to compromised patient monitoring, (2) decreased nurse–patient interaction negatively affecting patient satisfaction, (3) heightened stress and burnout among nurses, and (4) increased risk of medication errors and adverse events. Participants emphasized that adequate staffing not only improved safety and outcomes but also enhanced teamwork and professional satisfaction.  Conclusion:Findings highlight that nurse–patient ratios significantly influence the quality and safety of care in hospital settings. Addressing staffing shortages is essential to reduce risks, improve patient experiences, and promote a supportive work environment for nurses. Policy reforms and organizational strategies should prioritize optimal staffing to safeguard patient health outcomes.
DYNAMICAL EVOLUTION OF THE YOUNG STARS IN THE GALACTIC CENTER:<i>N</i>-BODY SIMULATIONS OF THE S-STARS

We use N-body simulations to study the evolution of the orbital eccentricities of stars deposited near (&lt;0.05 pc) the Milky Way massive black hole (MBH), starting from initial conditions motivated by two competing models for their origin: formation in a disk followed by inward migration; and exchange interactions involving a binary star. The first model predicts modest eccentricities, lower than those observed in the S-star cluster, while the second model predicts higher eccentricities than observed. The N-body simulations include a dense cluster of 10 M_sun stellar black holes (SBHs), expected to accumulate near the MBH by mass segregation. Perturbations from the SBHs tend to randomize the stellar orbits, partially erasing the dynamical signatures of their origin. The eccentricities of the initially highly eccentric stars evolve, in 20 Myr (the S-star lifespan), to a distribution that is consistent at the ~95 % level with the observed eccentricity distribution. In contrast, the eccentricities of the initially more circular orbits fail to evolve to the observed values in 20 Myr, arguing against the disk migration scenario. We find that 20 % - 30 % of the S-stars are tidally disrupted by the MBH over their lifetimes, and that the S-stars are not likely to be ejected as hypervelocity stars outside the central 0.05 pc by close encounters with stellar black holes.
Fast error-controlling MOID computation for confocal elliptic orbits

We present an algorithm to compute the minimum orbital intersection distance (MOID), or global minimum of the distance between the points lying on two Keplerian ellipses. This is achieved by finding all stationary points of the distance function, based on solving an algebraic polynomial equation of $16$th degree. The algorithm tracks numerical errors appearing on the way, and treats carefully nearly degenerate cases, including practical cases with almost circular and almost coplanar orbits. Benchmarks confirm its high numeric reliability and accuracy, and that regardless of its error--controlling overheads, this algorithm pretends to be one of the fastest MOID computation methods available to date, so it may be useful in processing large catalogs.
On the ratification by the Kyrgyz Republic of the Rome Statute of the International Criminal Court

The article analyzes the problems of ratification by the Kyrgyz Republic of the Rome Statute of the International Criminal Court related to armed border conflict, concludes that the development of Kyrgyzstan's international legal policy in the field of ratification of the ICC statute should continue in conjunction with membership and coordination of activities within such regional international organizations as the SCO and CSTO.
Tracking scientific evidence based decision making in government policy responses to plastic pollution

Single-use plastics (SUPs) are major contributors to local and global plastic pollution. There has been a recent increase in studies examining policy and legislative tools to reduce plastic pollution, particularly SUPs (e.g., plastic bags, microbeads, straws, plastic cutlery). Although early government policy responses targeted plastic bags, there has also been an increase in policies targeting other SUPs (e.g., microbeads, straws, plastic cutlery). Although there has been an increase in the number of national legislative strategies to address plastic pollution, many have lacked scientific evidence-based data to inform these policies and fewer still have used evidence to monitor the effectiveness of these plastic pollution intervention policies. This presentation provides an overview of recent trends in local, regional, national and international strategies and policies to reduce SUPs, and presents opportunities for continuous monitoring to assess effectiveness of these policies and to make readjustments of these policies based on the evidence. This presentation also discusses scientific evidence that should be used to inform the ongoing Global Plastics Treaty negotiations to reduce plastic pollution.  Also see: https://micro2024.sciencesconf.org/557954/document
Evidence of a causal link between health outcomes, insurance coverage, and a policy to expand access: experimental data from children in the Philippines

In this paper, we present evidence on the health effects of a health insurance intervention targeted to poor children using data from a randomized policy experiment known as the Quality Improvement Demonstration Study. Among study participants, using a difference-in-difference regression model, we estimated a 9-12 and 4-9 percentage point reduction in the likelihood of wasting and having an infection, respectively, as measured by a common biomarker C-reactive Protein. Interestingly, these benefits were not apparent at the time of discharge; the beneficial health effects were manifest several weeks after release from the hospital.
Aligning Algorithmic Risk Assessments with Criminal Justice Values

Abstract                   Federal and state criminal justice systems use algorithmic risk assessment tools extensively. Much of the existing scholarship on this topic engages in normative and technical analyses of these tools, or seeks to identify best practices for tool design and use. Far less work has been done on how courts and other criminal justice actors perceive and utilize these tools on the ground. This is an important gap. Judges’ and other criminal justice actors’ attitudes toward, and implementation of, algorithmic risk assessment tools profoundly affect how these tools impact defendants, incarceration rates, and the broader criminal justice system. Those who would understand, and potentially seek to improve, the courts’ use of these tools would benefit from more information on how judges actually think about and employ them. This article begins to fill in this picture. The authors surveyed Ohio Courts of Common Pleas judges and staff, and interviewed judges and other key stakeholders, to learn how they view and use algorithmic risk assessment tools. The article describes how Ohio Common Pleas Courts implement algorithmic risk assessment tools and how judges view and utilize the tools and the risk scores they generate. It then compares Ohio practice in this area to the best practices identified in the literature and, on this basis, recommends how the Ohio Courts of Common Pleas—and, by implication, other state and federal court systems—can better align their use of algorithmic risk assessment tools with core criminal justice values.
A machine learning methodology for the analysis of workplace accidents

This article proposes a methodology for the analysis of the causes and types of workplace accidents (in this paper we focus specifically on floor-level falls). The approach is based on machine learning techniques: Bayesian networks trained using different algorithms (with and without a priori information), classification trees, support vector machines and extreme learning machines. The results obtained using the different techniques are compared in terms of explanatory capacity and predictive potential, both factors facilitating the development of risk prevention measures. Bayesian networks are revealed to be the best all-round technique for this type of study, as they combine a powerful interpretative capacity with a predictive capacity that is comparable to that of the best available techniques. Moreover, the Bayesian networks force experts to apply a scientific approach to the construction and progressive enrichment of their models and also enable the basis to be laid for an accident prevention policy that is solidly grounded. Furthermore, the procedure enables better variable definition, better structuring of the data capture, coding, and quality control processes.
Addressing Ascertainment Bias in the Study of Cardiovascular Disease Burden in Opioid Use Disorders - Application of Natural Language Processing of Electronic Health Records

© 2022 Jade Huang Singleton
Synthesis of Diazacyclic and Triazacyclic Small-Molecule Libraries Using Vicinal Chiral Diamines Generated from Modified Short Peptides and Their Application for Drug Discovery

Small-molecule probes are powerful tools for studying biological systems and can serve as lead compounds for developing new therapeutics. Especially, nitrogen heterocycles are of considerable importance in the pharmaceutical field. These compounds are found in numerous bioactive structures. Their synthesis often requires several steps or the use of functionalized starting materials. This review describes the use of vicinal diamines generated from modified short peptides to access substituted diaza- and triazacyclic compounds. Small-molecule diaza- and triazacyclic compounds with different substitution patterns and embedded in various molecular frameworks constitute important structure classes in the search for bioactivity. The compounds are designed to follow known drug likeness rules, including “Lipinski’s Rule of Five”. The screening of diazacyclic and traizacyclic libraries has shown the utility of these classes of compounds for the de novo identification of highly active compounds, including antimalarials, antimicrobial compounds, antifibrotic compounds, potent analgesics, and antitumor agents. Examples of the synthesis of diazacyclic and triazacyclic small-molecule libraries from vicinal chiral polyamines generated from modified short peptides and their application for the identification of highly active compounds are described.
Scalable GitOps Models for Multi-Cloud Infrastructure as Code Deployment

As enterprises embrace multi-cloud strategies to enhance agility, reduce vendor lock-in, and meet regulatory requirements, the need for scalable and reliable infrastructure management becomes critical. GitOps a paradigm that leverages Git as the single source of truth for declarative infrastructure offers a transformative approach to manage Infrastructure as Code (IaC) across heterogeneous cloud environments. This paper explores scalable GitOps models tailored for multi-cloud deployments, highlighting key architectural patterns, toolchains, and workflows that enable secure, auditable, and automated infrastructure operations. The study analyzes the strengths and limitations of leading GitOps tools such as ArgoCD and Flux in coordinating cross-cloud configurations and reconcile loops. The study also examines strategies for repository structuring, modularization of IaC, policy-as-code integration, and dynamic secrets management to support enterprise-scale deployments. The study propose a reference architecture that addresses the challenges of scalability, compliance, and resilience in multi-cloud GitOps workflows. My findings demonstrate that, when correctly implemented, GitOps can serve as a powerful operational model for achieving continuous delivery and governance in complex cloud-native ecosystems.
Limitations With California Medicaid Data for Palliative and End of Life Care Quality Measures

In 2014 the California legislature passed Senate Bill 1004 (SB 1004) that was designed to expand access to specialty palliative care for individuals served by California's Medicaid (known as Medi-Cal) Managed Care Plans (MCPs). The California Department of Health Care Services (DHCS) operationalized the legislation by developing minimum requirements for palliative care programs that all MCPs must meet or exceed.7 Quality and utilization data specific to California's Medicaid population are needed for stakeholders to identify care deficiencies and disparities, describe the end of life experience and utilization patterns of MCP members, compare these patterns to Medicare beneficiaries or other populations, and set appropriate targets to help monitor progress. We evaluated the feasibility of using Medicaid claims data and encounter data either by partnering with MCPs or by obtaining statewide data from DHCS to measure the quality of palliative care and end of life care. In a concurrent but separate effort, we analyzed administrative data supplied by three MCPs as part of a prospective pilot of standards for home-based palliative care in California, including care delivered to Medicaid beneficiaries under SB 1004. Beyond the practical challenges of allowing time for data access and approvals, both projects revealed several limitations to using administrative data to assess quality of palliative and end of life care for a Medicaid population. We describe these challenges that undermined our confidence in analysis results and propose solutions to measuring the quality of palliative and end of life care for Medicaid patients and suggested next steps.
A Scalable Hybrid Recommender System Integrating Semantic Content, Collaborative Filtering and User Clustering

Recommender systems must address heterogeneous user contexts, ranging from cold-start users to individuals with extensive interaction histories, while ensuring scalability and diversity of results. This paper presents a scalable hybrid recommender system that integrates semantic content modeling, collaborative filtering, and user clustering within a unified architecture. Semantic embeddings derived from procedure titles and descriptions are combined with matrix factorization techniques through a dynamically weighted hybrid scoring function. To enhance personalization while preserving structural privacy, users are grouped via K-Means clustering, enabling cluster-level optimization instead of individual modeling. The proposed system is evaluated using an adapted conversion curve metric and diversity index on a large-scale dataset of administrative procedures. Experimental results demonstrate that the hybrid semantic–collaborative approach improves recommendation accuracy over standalone models, while clustering enhances robustness and computational efficiency. A cost-performance analysis further shows that clustered matrix factorization achieves an effective trade-off between precision, diversity, and deployment scalability. These findings suggest that hybrid architectures combining semantic representations and collaborative filtering with cluster-based personalization constitute a practical and scalable solution for real-world recommendation systems.
Enhancing Privacy Protection on Android Devices through Federated Learning and Differential Privacy

Abstract. The rapid expansion of Android devices has brought about significantconvenience and connectivity improvements, but it has also escalated the risk ofdata breaches and unauthorized access to sensitive information. This researchpresents a thorough investigation into the sources of data leakage within theAndroid ecosystem, evaluates existing detection methodologies. Through theanalysis of real-world data breaches, critical vulnerabilities are identified forandroid devices.The study evaluates the effectiveness of current detection tools and theirlimitations, revealing that while static and dynamic analysis techniques arevaluable, they are often resource-intensive and prone to false positives. Toaddress these challenges, a novel hybrid model integrating Federated Learning(FL) and Differential Privacy (DP) is proposed. This DP+FL model ensuresdata remains on user devices and adds noise to local model updates to maintainprivacy.Experimental results demonstrate that the DP+FL model significantlyoutperforms traditional FL and DP methods, achieving an accuracy of 95%,precision of 94%, recall of 95%, and F1 score of 94%. These findings indicatethat the DP+FL model not only enhances privacy protection but also maintainshigh model performance.
Long-period comets as a tracer of the Oort cloud structure

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High Throughput Screening for Small Molecule Enhancers of the Interferon Signaling Pathway to Drive Next-Generation Antiviral Drug Discovery

Most of current strategies for antiviral therapeutics target the virus specifically and directly, but an alternative approach to drug discovery might be to enhance the immune response to a broad range of viruses. Based on clinical observation in humans and successful genetic strategies in experimental models, we reasoned that an improved interferon (IFN) signaling system might better protect against viral infection. Here we aimed to identify small molecular weight compounds that might mimic this beneficial effect and improve antiviral defense. Accordingly, we developed a cell-based high-throughput screening (HTS) assay to identify small molecules that enhance the IFN signaling pathway components. The assay is based on a phenotypic screen for increased IFN-stimulated response element (ISRE) activity in a fully automated and robust format (Z'>0.7). Application of this assay system to a library of 2240 compounds (including 2160 already approved or approvable drugs) led to the identification of 64 compounds with significant ISRE activity. From these, we chose the anthracycline antibiotic, idarubicin, for further validation and mechanism based on activity in the sub-µM range. We found that idarubicin action to increase ISRE activity was manifest by other members of this drug class and was independent of cytotoxic or topoisomerase inhibitory effects as well as endogenous IFN signaling or production. We also observed that this compound conferred a consequent increase in IFN-stimulated gene (ISG) expression and a significant antiviral effect using a similar dose-range in a cell-culture system inoculated with encephalomyocarditis virus (EMCV). The antiviral effect was also found at compound concentrations below the ones observed for cytotoxicity. Taken together, our results provide proof of concept for using activators of components of the IFN signaling pathway to improve IFN efficacy and antiviral immune defense as well as a validated HTS approach to identify small molecules that might achieve this therapeutic benefit.