ethicalabs/Research-Intent-Collab huggingface dataset:
The Reluctance of Member States in Implementation of the African Court of Human and Peoples’s Rights Decisions: Experience from Rwanda and Tanzania

It is trite understanding by almost all human rights lawyers that under International Human Rights Law, once a state ratifies a treaty and becomes party to the said international instrument; such a State is under obligations to promote; respect, protect and fulfil human rights. However, reports show that only one State has fully complied with the Court’s decision; some partially but many States against which judgments have been pronounced have not shown any endeavour to heed to the court’s decisions which is centrally to Article 30 of the Protocol. This study focuses on the reluctance of member States to the Protocol as among the factors that impedes implementation and compliance with the decisions and judgements of the Court as exemplified by Tanzania and Rwanda. Objectively, the research was centred on assessing the extent to which member States of the ACHPR comply with international legal commitments recalling the decisions and judgments of the Court with reference to Tanzania and Rwanda. The study employed qualitative research approach whereby 21 interview from the Office of Attorney General, Judiciary, Tanganyika Law Society, Legal and Human Rights Centre, Ministry of Justice and Constitutional Affairs, Legislature, and Law Reform Commission was conducted to collect primary. The data collected was subsequently analyzed qualitatively through narrative data analysis approach where the research question was affirmatively concluded. The findings underscore the necessity of member States to change their habit of being reluctant on implementation and compliance with the Court’s decisions and judgment in order to fulfil the legal accountability and enhance justice within the region. The study recommends that in order to effectively implement Court’s orders, member states must refrain from reluctance on heeding to what the Court has decided.
Trade agreements, intellectual property and access to essential medicines: What future role for the right to health?

The Global Governance of HIV/AIDS explores the implications of high international intellectual property standards for access to essential medicines in developing countries. With a focus on HIV/AIDS governance, the volume provides a timely analysis of the international legal and political landscape, the relationship between human rights and intellectual property, and emerging issues in global health policy. It concludes with concrete strategies on how to improve access to HIV/AIDS medicines.
Seeing is Believing: 3D-Printed Boundary Objects for the Additive Manufacturing Twin Transition

This article describes the use of 3D-printed demonstrators as boundary objects in intra-firm collaboration processes aimed at implementing additive manufacturing (AM). AM is a disruptive digital technology with large potential environmental benefits. Demonstrators are common early in the learning process to familiarize organisations with AM, and it’s important to understand both their benefits and the limitations and risks involved.  The results suggest that 3D-printed demonstrators can be effective in establishing a common minimum level of knowledge across diverse professions within the same organisation. Having physical objects available makes conversations about AM more engaging and legitimizes the idea of utilising AM as realistic. However, the fabrication of demonstrators can be a lengthy and costly process, and the result may not be a good representation of the final design. Negative experiences in such initial trials may turn managers away from AM prematurely. Many of the benefits of demonstrators can be obtained by producing low-cost miniatures and/or polymer parts that will mitigate the risks.
Error mitigation enables PET radiomic cancer characterization on quantum computers

Abstract                 Background                 Cancer is a leading cause of death worldwide. While routine diagnosis of cancer is performed mainly with biopsy sampling, it is suboptimal to accurately characterize tumor heterogeneity. Positron emission tomography (PET)-driven radiomic research has demonstrated promising results when predicting clinical endpoints. This study aimed to investigate the added value of quantum machine learning both in simulator and in real quantum computers utilizing error mitigation techniques to predict clinical endpoints in various PET cancer patients.                                Methods                 Previously published PET radiomics datasets including 11C-MET PET glioma, 68GA-PSMA-11 PET prostate and lung 18F-FDG PET with 3-year survival, low-vs-high Gleason risk and 2-year survival as clinical endpoints respectively were utilized in this study. Redundancy reduction with 0.7, 0.8, and 0.9 Spearman rank thresholds (SRT), followed by selecting 8 and 16 features from all cohorts, was performed, resulting in 18 dataset variants. Quantum advantage was estimated by Geometric Difference (GDQ) score in each dataset variant. Five classic machine learning (CML) and their quantum versions (QML) were trained and tested in simulator environments across the dataset variants. Quantum circuit optimization and error mitigation were performed, followed by training and testing selected QML methods on the 21-qubit IonQ Aria quantum computer. Predictive performances were estimated by test balanced accuracy (BACC) values.                                Results                 On average, QML outperformed CML in simulator environments with 16-features (BACC 70% and 69%, respectively), while with 8-features, CML outperformed QML with + 1%. The highest average QML advantage was + 4%. The GDQ scores were ≤ 1.0 in all the 8-feature cases, while they were &gt; 1.0 when QML outperformed CML in 9 out of 11 cases. The test BACC of selected QML methods and datasets in the IonQ device without error mitigation (EM) were 69.94% BACC, while EM increased test BACC to 75.66% (76.77% in noiseless simulators).                                Conclusions                 We demonstrated that with error mitigation, quantum advantage can be achieved in real existing quantum computers when predicting clinical endpoints in clinically relevant PET cancer cohorts. Quantum advantage can already be achieved in simulator environments in these cohorts when relying on QML.
From Prototype to Production: A Practitioner's Framework for Governing AI Agent Deployments in Regulated Industries

&lt;div&gt;  Organizations across the United States are racing to deploy AI agent systems into compliance-and document-intensive workflows-building code review, clinical documentation, benefits adjudication, procurement processing-with remarkably little structured governance. The consequences are already visible: ECRI has named insufficient AI governance the No. 2 patient safety threat for 2025, and only 12% of U.S. hospitals have formal AI governance frameworks despite the FDA authorizing over 1,000 AI/ML-enabled medical devices. National policy frameworks like the NIST AI Risk Management Framework define what good governance should look like in principle, but a critical translation layer is missing: the practitioner-level mechanisms that organizations can actually deploy.&amp;nbsp; &lt;/div&gt; &lt;div&gt;  &amp;nbsp; &amp;nbsp; &amp;nbsp;This paper presents a four-pillar framework for AI deployment governancegrounding techniques, evaluation frameworks, reference architectures, and releasereadiness gates-integrated through human-in-the-loop (HITL) governance as a structural design principle. The framework is not theoretical. It is derived from the author's direct experience building and deploying governance mechanisms across national-scale government analytics systems (82 cities, 12,000 users), regulated construction compliance automation, consumer-scale AI platforms (40M+ users), and enterprise AI enablement. Key elements of the framework have been independently adopted by teams beyond the author's direct involvement, confirming its transferability. The paper maps the framework to NIST AI RMF functions and current federal policy directives, and provides implementation guidance for organizations at varying levels of governance maturity. &lt;/div&gt;
Risk Concentration and Diversification: Second-Order Properties

The quantification of diversification benefits due to risk aggregation plays a prominent role in the (regulatory) capital management of large firms within the financial industry. However, the complexity of today's risk landscape makes a quantifiable reduction of risk concentration a challenging task. In the present paper we discuss some of the issues that may arise. The theory of second-order regular variation and second-order subexponentiality provides the ideal methodological framework to derive second-order approximations for the risk concentration and the diversification benefit.
Rethinking Business Responsibility in a Global Context

This book examines topical issues in global corporate social responsibility (CSR) from both scholarly and practical perspectives. It offers a variety of viewpoints and cases from countries around the globe and combines them with current academic knowledge.Intended for students, academics, and managers wishing to keep abreast of the challenges and opportunities for corporations operating in our ever-more-complex globalized world, this book provides fresh insights into responsible business conduct.Includes recent examples of responsible business conduct from around the worldCovers continents/countries that have been underrepresented so far in CSR research Challenges the mainstream management thinking on the role of business in societyIncorporates perspectives on up-to-date international policy agendas such as the United Nation's Sustainable Development Goals
The roles of climate mitigation, sustainable land use and area-based conservation in curbing future biodiversity loss

Abstract                 Biodiversity loss is a pressing global challenge. Protected Areas (PAs) are crucial measures to curb species and ecosystem declines, yet their long-term ecological effectiveness amid future climate and land use changes remains uncertain. Here, we assess future biodiversity changes under different climate, land use, and PA expansion scenarios for 13908 terrestrial vertebrate species. Three PA scenarios (no explicit PA, current 17% coverage, and a 30% target) are investigated alongside two climate and socioeconomic scenarios; the “sustainability” (SSP1-RCP2.6) and the “inequality” scenario (SSP4-RCP6.0). The results show that sustainable land use is the biggest lever in preventing future biodiversity decline, with a projected avoided species richness loss of 7.9-8.6% by 2080, compared to avoided loss of PA expansion (0.9-4.5%). PAs provide an additional but smaller contribution to avoided species richness loss, particularly under the “inequality” scenario, showing the need for PA expansion under unsustainable climate and land use policies. Birds and mammals benefit most from PA expansion. We conclude that expanding PAs supports biodiversity but integrating them with broader climate and land use policies is crucial.
Right to Privacy and Surveillance

Journal article: Right to Privacy and Surveillance
Concept-Based Explainable AI: Interpreting Deep Learning Models through Human-Readable Concepts in Financial Applications

In high-stakes domains like finance, the interpretability of deep learning models is crucial. Concept-Based Explainable AI (XAI) has emerged as a promising approach to bridge the gap between complex neural networks and human understanding by explaining model decisions in terms of human-readable concepts rather than low-level featuresxaiworldconference.com. This paper provides a comprehensive overview of concept-based XAI techniques and their application in finance, including credit risk scoring, fraud detection, and portfolio management. We survey relevant literature – with particular emphasis on recent work by Chaudhari and colleagues – and discuss how concepts (e.g., “credit history quality” or “transaction anomaly patterns”) can be used to interpret deep models. We describe methodologies such as Testing with Concept Activation Vectors (TCAV)arxiv.org and concept bottleneck modelsproceedings.mlr.press, and propose a conceptual framework for integrating domain-specific concepts into financial deep learning models. Experiments drawing on existing studies and hypothetical simulations demonstrate that concept-based explanations can illuminate model reasoning without significantly sacrificing predictive performance. For instance, concept-enhanced fraud detection models maintain high accuracy while providing clear explanations for flagged transactions, thus improving user trust and meeting regulatory requirements for transparencyjrtcse.comarxiv.org. We present a case study with results showing improved fraud detection (F1-score 0.82 vs 0.60) when using enriched data and XAI techniquesjrtcse.com. We also include a conceptual diagram of a concept-based model pipeline and a performance comparison table. Our contribution is a detailed synthesis of concept-based XAI in finance, highlighting theoretical underpinnings, practical implementation considerations, and the potential for these methods to foster more transparent and accountable AI systems in financial services.
A mono-material Nernst thermopile with hermaphroditic legs

A large transverse thermoelectric response, known as anomalous Nernst effect (ANE) has been recently observed in several topological magnets. Building a thermopile employing this effect has been the subject of several recent propositions. Here, we design and build a thermopile with an array of tilted adjacent crystals of Mn$_3$Sn. The design employs a single material and replaces pairs of P and N thermocouples of the traditional design with hermaphroditic legs. The design exploits the large lag angle between the applied field and the magnetization, which we attribute to the interruption of magnetic octupoles at the edge of $xy$-plane. Eliminating extrinsic contacts between legs will boost the efficiency, simplify the process and pave the way for a new generation of thermopiles.
Introduction to Alternative Dispute Resolution: A Comparison between Arbitration and Mediation

Historically, legal disputes have been resolved either by litigation or by arbitration. Mediation (a form of ADR) is a new way to settle commercial disputes. Litigation is quite unlike mediation, but some consider that arbitration is a form of ADR and similar to mediation. In fact the two are fundamentally different. The purpose of this paper is to describe these differences and to set out some supplementary information about mediation, its use and effectiveness. The main body of this paper has been designed so that you can dip into any section or point of interest, or alternatively read the whole narrative. Arbitration (and litigation) procedures in England and Wales are in many ways excellent and legal process is arguably indispensable. However, and these are broad generalisations, legal process has deficiencies which can be remedied in suitable cases by the use of mediation.
Information Fusion for Anomaly Detection with the Dendritic Cell Algorithm

Dendritic cells are antigen presenting cells that provide a vital link between the innate and adaptive immune system, providing the initial detection of pathogenic invaders. Research into this family of cells has revealed that they perform information fusion which directs immune responses. We have derived a Dendritic Cell Algorithm based on the functionality of these cells, by modelling the biological signals and differentiation pathways to build a control mechanism for an artificial immune system. We present algorithmic details in addition to experimental results, when the algorithm was applied to anomaly detection for the detection of port scans. The results show the Dendritic Cell Algorithm is sucessful at detecting port scans.
AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting

We introduce AutoGluon-TimeSeries - an open-source AutoML library for probabilistic time series forecasting. Focused on ease of use and robustness, AutoGluon-TimeSeries enables users to generate accurate point and quantile forecasts with just 3 lines of Python code. Built on the design philosophy of AutoGluon, AutoGluon-TimeSeries leverages ensembles of diverse forecasting models to deliver high accuracy within a short training time. AutoGluon-TimeSeries combines both conventional statistical models, machine-learning based forecasting approaches, and ensembling techniques. In our evaluation on 29 benchmark datasets, AutoGluon-TimeSeries demonstrates strong empirical performance, outperforming a range of forecasting methods in terms of both point and quantile forecast accuracy, and often even improving upon the best-in-hindsight combination of prior methods.
Verification, Validation and Testing in Software Engineering

A Sample of Contents: Faultbased Testing Integrating Usability, Semiotic, and Software Engineering into a Method for Evaluating User Interfaces Test-case mutation Static Type Systems: From Specification to Implementation.
The low-extinction afterglow in the solar-metallicity host galaxy of<i>γ</i>-ray burst 110918A

Metallicity is theoretically thought to be a fundamental driver in gamma-ray burst (GRB) explosions and energetics, but is still, even after more than a decade of extensive studies, not fully understood. This is largely related to two phenomena: a dust-extinction bias, that prevented high-mass and thus likely high-metallicity GRB hosts to be detected in the first place, and a lack of efficient instrumentation, that limited spectroscopic studies including metallicity measurements to the low-redshift end of the GRB host population. The subject of this work is the very energetic GRB 110918A, for which we measure a redshift of z=0.984. GRB 110918A gave rise to a luminous afterglow with an intrinsic spectral slope of b=0.70, which probed a sight-line with little extinction (A_V=0.16 mag) typical of the established distributions of afterglow properties. Photometric and spectroscopic follow-up observations of the galaxy hosting GRB 110918A, including optical/NIR photometry with GROND and spectroscopy with VLT/X-shooter, however, reveal an all but average GRB host in comparison to the z~1 galaxies selected through similar afterglows to date. It has a large spatial extent with a half-light radius of ~10 kpc, the highest stellar mass for z&lt;1.9 (log(M_*/M_sol) = 10.68+-0.16), and an Halpha-based star formation rate of 41 M_sol/yr. We measure a gas-phase extinction of ~1.8 mag through the Balmer decrement and one of the largest host-integrated metallicities ever of around solar (12 + log(O/H) = 8.93+/-0.13). This presents one of the very few robust metallicity measurements of GRB hosts at z~1, and establishes that GRB hosts at z~1 can also be very metal rich. It conclusively rules out a metallicity cut-off in GRB host galaxies and argues against an anti-correlation between metallicity and energy release in GRBs.
How Collusion Perpetuates Racial Discrimination in Societies that Ostensibly Promote Equal Opportunity

AbstractIt is shown here that injustices due to racial discrimination are best identified in light of the deleterious effects they have upon their victims, rather than the beliefs and attitudes of their perpetrators. For among participants who cooperate clandestinely to bring about racial injustice there may be broad disagreement about what it is they are doing collectively, and why; or they may disagree in principle about whether what they are doing is morally right. I employ the notion of ‘nomotropic’ behaviour to replace the oversimplified notion of ‘rule-following’ in order to explain how duplicity and hypocrisy fall shy of being regarded as irrational in social climates where implicit norms reinforce racial privileging while explicit norms denounce it. Further, examining the ‘collective utility’ of dogmatic beliefs and norms comprising part of the social architecture that covertly reinforces racial injustice (while ostensibly deriding it) may help to explain why it often seems to make so little diffe...
ADME, Pharmacokinetic Scaling, Pharmacodynamic and Prediction of Human Dose and Regimen of Novel Antiviral Drugs

The search for new drugs is an extremely time-consuming and expensive endeavour. Much of that time and money go into generating predictive human pharmacokinetic profiles from preclinical efficacy and safety animal data. These pharmacokinetic profiles are used to prioritize or minimize the attrition at later stages of the drug discovery process. In the area of antiviral drug research, these pharmacokinetic profiles are equally important for the optimization, estimation of half-life, determination of effective dose, and dosing regimen, in humans. In this article we have highlighted three important aspects of these profiles. First, the impact of plasma protein binding on two primary pharmacokinetic parameters—volume of distribution and clearance. Second, interdependence of primary parameters on unbound fraction of the drug. Third, the ability to extrapolate human pharmacokinetic parameters and concentration time profiles from animal profiles.
Coastal vulnerability assessment of the French Mediterranean coasts to storms flooding and erosion phenomena, and morphodynamic modeling under climate change projections

Storm events on sandy coastal environments already weakened by a sediment deficit can generate a strong erosion of the beach-dune system, accentuating the phenomena of marine flooding and damage to infrastructures. In order to assess this risk of coastal flooding under stormy conditions and taking into account climate change and the new IPCC projection (IPCC, 2021), modeling approaches nowadays provide a robust tool. In this study, a modeling chain is used to simulate present-day and future coastal hazards on the Mediterranean French coastline, taking into account the complex interaction of hydrodynamic and morphodynamic phenomena.
Machine learning force field molecular dynamics simulation of SEI formation on lithium metal

Abstract                 Lithium metal batteries represent a cornerstone technology for next-generation energy storage systems owing to their exceptional energy density. However, the extreme reactivity of lithium metal drives continuous parasitic reactions with electrolytes, causing interfacial instability and dendritic growth that severely hinder practical deployment. Formation of a protective solid electrolyte interphase (SEI) through electrolyte reduction is essential for stabilizing the lithium metal surface, yet the underlying mechanisms remain poorly understood due to the scarcity of experimental techniques capable of accurately probing highly reducing interfacial environments. Here we investigate the formation processes of SEI on lithium metal at the atomistic scale using molecular dynamics simulations based on a machine learning force field with first-principles accuracy, covering unprecedented spatial–temporal scales including chemical reactions as rare events. Furthermore, an automatic reaction identification program developed in this work enables systematic extraction of key kinetic information such as the identification, sequence, and probability of elementary reactions, as well as the formation rates of decomposition products. Typical experimental observations on the characteristic SEI structure, consisting of an inner oxide-rich layer and an outer fluorinated layer, were successfully reproduced. This simulation methodology provides an effective predictive framework for the initial stage of SEI formation and offers guiding principles for the rational design of stable lithium metal batteries.