ethicalabs/Research-Intent-Collab huggingface dataset:
Self-Supervised Feature Representation for SAR Image Target Classification Using Contrastive Learning

Nowadays, the developed deep neural networks (DNNs) have been widely applied to synthetic aperture radar (SAR) image interpretation, such as target classification and recognition, which can automatically learn high-level semantic features in data-driven and task-driven manners. For the supervised learning methods, abundant labeled samples are required to avoid the over-fitting of designed networks, which is usually difficult for SAR image applications. To address these issues, a novel two-stage algorithm based on contrastive learning (CL) is proposed for SAR image target classification. In the pretraining stage, to extract self-supervised representations (SSRs) from an unlabeled train set, a convolutional neural network (CNN)-based encoder is first pretrained using a contrasting strategy. This encoder can convert SAR images into a discriminative embedding space. Meanwhile, the optimal encoder can be determined using a linear evaluation protocol, which can indirectly confirm the transferability of prelearned SSRs to downstream tasks. Therefore, in the fine-tuning stage, a SAR target classifier can be adequately trained using a few labeled SSRs in a supervised manner, which benefits from the powerful pretrained encoder. Numerical experiments are carried out on the shared MSTAR dataset to demonstrate that the model based on the proposed self-supervised feature learning algorithm is superior to the conventional supervised methods under labeled data constraints. In addition, knowledge transfer experiments are also conducted on the openSARship dataset, showing that the encoder pretrained from the MSTAR dataset can support the classifier training with high efficiency and precision. These results demonstrate the excellent training convergence and classification performance of the proposed algorithm.
The Unity of Consciousness and the Practical Ethics of Neural Organoid Research

Abstract           This article investigates a critical yet underexplored structural aspect of consciousness in the context of the practical ethics of neural organoid research: the unity of consciousness. We advocate for the necessity of the unified field, which has garnered substantial support from both philosophical and empirical standpoints, although the debate remains unresolved. We highlight the brainstem as a potential source of the unified conscious field, a structure already under scrutiny in neural organoid research in relation to conditions such as Parkinson’s disease and post-COVID-19 syndromes. We argue that if unity is a necessary feature of consciousness, consciousness is contingent upon a specific biological system without which consciousness cannot arise, thereby narrowing the range of neural organoids of ethical concern. Furthermore, the transplantation of neural organoids into animals has emerged as a practical concern, with ethical implications varying based on the necessity of the unity of consciousness. We argue that transplantation evades a significant ethical dilemma if unity is necessary and if the organoids to be transplanted lack the neural basis for the unified field of consciousness.
The Chandra Cygnus OB2 Legacy Survey: Design and X-ray Point Source Catalog

The Cygnus OB2 association is the largest concentration of young and massive stars within 2 kpc of the Sun, including an estimated 65 O-type stars and hundreds of OB stars. The Chandra Cygnus OB2 Legacy Survey is a large imaging program undertaken with the Advanced CCD Imaging Spectrometer onboard the Chandra X-ray Observatory. The survey has imaged the central 0.5 deg^2 of the Cyg OB2 association with an effective exposure of 120ks and an outer 0.35 deg^2 area with an exposure of 60ks. Here we describe the survey design and observations, the data reduction and source detection, and present a catalog of 8,000 X-ray point sources. The survey design employs a grid of 36 heavily (~50%) overlapping pointings, a method that overcomes Chandra's low off-axis sensitivity and produces a highly uniform exposure over the inner 0.5 deg^2. The full X-ray catalog is described here and is made available online.
Protecting the protectors: Whistleblowing and retaliation in the compliance arena

AbstractIn the last decade, we learned of massive scandals at some of the world's largest companies. In each of those cases, compliance officers were charged with ensuring that the company adhered to legal and regulatory requirements and their own internal codes of conduct, and yet, these companies were not protected from their own bad actors. Compliance functions have grown in importance, while, at the same time, it has become increasingly difficult to hire and retain qualified personnel for compliance roles. We posit that a key issue facing compliance personnel—one that could be improved with legislative attention—is the failure of the law to protect compliance officers from retaliation when they blow the whistle by reporting unlawful or unacceptable conduct to superiors inside the organization. In essence, when compliance officers do their jobs and alert the company to possible violations of law or take issue with the company's handling of a potential legal violation, these officers are vulnerable to retaliation and can be terminated, demoted, and the like without legal consequence. The very employees that organizations hire to protect them are themselves unprotected. In this article, we consider compliance officers in three areas: Equal Employment Opportunity (EEO), securities fraud and financial regulation, and anti‐money laundering. In two out of the three areas, we find compliance officers uniquely exposed to lawful retaliation, while the third area provides a far more protective environment and offers a path forward for the other two. In both the EEO sector and the securities fraud sector, we highlight the common law doctrines and statutory interpretations that have created this situation for compliance officers. In contrast, the Anti‐Money Laundering Act of 2020 (AMLA) provides exceptional protection for whistleblower compliance officers in this sector, and as a result, we propose using the AMLA as model legislation for proposed changes in the other two domains. The plight of compliance officer whistleblowers is complicated by courts that have intentionally and unintentionally narrowed protections without contemplating the broader implications of their actions. We propose that Congress respond to these narrowing doctrines so that compliance officers can effectively do their jobs and protect their organizations from legal liability and scandals, with the assurance of protection against retaliation as they perform this essential function.
Synergistic Integration of Human Capital, Transportation Optimization, and Digitalization in Logistics Efficiency

This study examines the synergistic integration of human capital, transportation optimization, and digitalization in enhancing logistics efficiency. Employing a mixed-methods approach, quantitative data were collected from 150 logistics firms across Indonesia through structured questionnaires and analyzed using structural equation modeling. Qualitative interviews with industry experts complemented the findings, offering contextual insights into operational challenges. The results indicate that human capital competencies significantly influence transportation optimization strategies, while digitalization acts as a critical mediator that amplifies the impact of both factors on logistics performance. Specifically, investment in employee training and skill development improves route planning and resource allocation, whereas advanced digital platforms facilitate real-time tracking, data analytics, and process automation. The integrated model explains 68% of the variance in logistics efficiency, highlighting the importance of coordinated investment across human, physical, and technological resources. Implications for policymakers and practitioners include prioritizing workforce development and digital infrastructure to achieve sustainable competitive advantage in the logistics sector.
The environmental rule of law, intergenerational equity and climate litigation in Latin America

International law, including international courts and tribunals, is undergoing important changes in their legal culture in the face of the challenges that climate change is set to pose. With this, special emphasis is placed on the transformation associated with the “Environmental rule of law,” a concept that still has significant margin for discussion, and where its new applications will form part of the fundamental debates in this field. In view of this fact, this text will focus on the installation of this legal structure in the climate debate through the principle of intergenerational equity and the extrapolation of the latter to contemporary climate disputes, especially those intergenerational disputes in the Global South and Latin America, which seek to transform and give new horizons to the international system.
Information disclosure in corporate social responsibility reports. The case of Lithuanian companies

Though the phenomenon of corporate social responsibility (CSR) is closely scrutinized in studies of both Lithuanian and foreign scholars, the problem is the quality of social information disclosed in social responsibility reports. The objective of this article is to analyse the quality of disclosed information in CRS reports of Lithuanian companies. The characteristics of quality of information were comparability, reliability, objectivity and sustainability. The research demonstrated that in Lithuania, CSR reports provide unreliable information, which is only partly comparable and objective, however, relatively sustainable.
Revolutionizing DevOps: AI-Driven Test Automation as a Catalyst for Efficiency and Quality in CI/CD Pipelines

The relentless demand for accelerated software delivery and the increasing complexity of modern applications have positionedDevOps, particularly Continuous Integration/Continuous Deployment (CI/CD) pipelines, as essential methodologies. However,the testing phase remains a persistent bottleneck within these pipelines. This paper comprehensively explores the transformativeintegration of Artificial Intelligence (AI) into test automation to overcome these limitations and enhance CI/CD workflows.We present a detailed analysis of how AI capabilities— leveraging machine learning, pattern recognition, and predictiveanalytics—are revolutionizing key testing domains. These include AI-powered test case generation for improved coverage,intelligent bug prediction and anomaly detection for proactive defect identification, autonomous and optimized regressiontesting, self-healing tests for reduced maintenance, and predictive analytics for overall CI/CD pipeline optimization. The paperexamines specific AI tools and techniques enabling these advancements, such as Diffblue Cover, Mabl, Testim.io, Dynatrace,and SonarQube (AI- augmented).A critical comparative analysis contrasts traditional DevOps practices with AI-enhanced approaches, demonstrating significantimprovements across vital metrics: AI-enhanced DevOps achieves up to 40% faster deployment cycles, 25% fewer post-releasedefects, 30% lower operational costs, enhanced scalability, and more proactive, data-driven decision- making.Despite the compelling benefits, the adoption of AI-driven test automation presents challenges, including initial setupcomplexity, stringent data dependency and quality requirements, model interpretability ("black box") concerns, skill gaps, andpotential ethical biases. The paper addresses these obstacles, emphasizing the need for robust data governance, Explainable AI(XAI), continuous upskilling, and ethical frameworks.The paper concludes that AI-driven test automation is fundamentally transforming software engineering by breaking testingbottlenecks, enabling unprecedented levels of speed, reliability, and efficiency in CI/CD pipelines. While challenges exist,strategic implementation paves the way for more resilient, adaptive, and intelligent DevOps ecosystems, promising sustainedinnovation and long-term value.Future advancements point towards deeper learning, sophisticated anomaly detection, andincreasingly autonomous pipelines.
Derivation of the Lorentz Force Law, the Magnetic Field Concept and the Faraday-Lenz Law using an Invariant Formulation of the Lorentz Transformation

It is demonstrated how the right hand sides of the Lorentz Transformation equations may be written, in a Lorentz invariant manner, as 4--vector scalar products. This implies the existence of invariant length intervals analogous to invariant proper time intervals. This formalism, making essential use of the 4-vector electromagnetic potential concept, provides a short derivation of the Lorentz force law of classical electrodynamics, the conventional definition of the magnetic field, in terms of spatial derivatives of the 4--vector potential and the Faraday-Lenz Law. An important distinction between the physical meanings of the space-time and energy-momentum 4--vectors is pointed out.
Inverse Kinematics Simulation for a Surgical Robot Using CATIA

Robotic arm with six degrees of freedom can be successfully used to do a surgical task through a small incision called (RCM point) on the patient's body. Inverse Kinematics modeling and simulating of a 6 DOF surgical robot is developed in this paper. The mathematical model equations are built using geometric approach and the Denavit-Hartenberg convention. The 3D model of the robot is created by CATIA5 to simulate the motion of the robot in surgical environments. The inverse kinematics equations model is validated through the simulating model. Result confirms that the proposed robot mechanism is applicable for minimally invasive surgery applications.
Integrated Pest Management and Biological Control in High Tunnel Production

Crops grown in high tunnels are just as susceptible to pests and diseases as those grown under greenhouse and field conditions. Crops that lend themselves economically to this type of production system are edible and/or minor crops. Therefore, labeled pesticides for these crops are limited and sometimes nonexistent. However, there is a wide range of integrated pest management (IPM) strategies available to high tunnel producers. These strategies include biological control, which is often left out of traditional IPM programs when labeled pesticides are available. High tunnel production is very conducive to the inclusion of biological controls and allows for a truly IPM system. This article provides a selective overview of common arthropod pests and diseases encountered in high tunnels, as well as strategies that have potential for becoming best management practices in high tunnels with additional research.
Bayesian policy gradient and actor-critic algorithms

Policy gradient methods are reinforcement learning algorithms that adapt a parameterized policy by following a performance gradient estimate. Conventional policy gradient methods use Monte-Carlo techniques to estimate the gradient, which tend to have high variance, requiring many samples and resulting in slow convergence. We first propose a Bayesian framework for policy gradient, based on modeling the policy gradient as a Gaussian process. This reduces the number of samples needed to obtain accurate gradient estimates. Moreover, estimates of the natural gradient and a measure of the uncertainty in the gradient estimates, namely, the gradient covariance, are provided at little extra cost. Since the proposed framework considers system trajectories as its basic observable unit, it does not require the dynamics within trajectories to be of any particular form, and can be extended to partially observable problems. On the downside, it cannot exploit the Markov property when the system is Markovian. To address this, we supplement our Bayesian policy gradient framework with a new actor-critic learning model in which a Bayesian class of non-parametric critics, based on Gaussian process temporal difference learning, is used. Such critics model the action-value function as a Gaussian process, allowing Bayes rule to be used to compute the posterior distribution over action-value functions, conditioned on the observed data. Appropriate choices of the policy parameterization and of the prior covariance (kernel) between action-values yield closed-form expressions for the posterior of the gradient of the expected return with respect to the policy parameters. We perform detailed experimental comparisons of the proposed Bayesian policy gradient and actor-critic algorithms with classic Monte-Carlo based policy gradient methods, on a number of reinforcement learning problems.
Examination of the effectiveness and limitation of United Nation Security Council, its role in promoting peace and security in international law

The most effective way to diminish human suffering and the massive economic costs of conflicts and their aftermath is to prevent conflicts in the first place. The United Nations plays an important role in conflict prevention, using diplomacy, good offices and mediation. Among the tools the Organization uses to bring peace are special envoys and political missions in the field. Peacekeeping has proven to be one of the most effective tools available to the UN to assist countries to navigate the difficult path from conflict to peace. Today's multidimensional peacekeeping operations are called upon not only to maintain peace and security but also to facilitate political processes, protect civilians, assist in the disarmament, demobilization and reintegration of former combatants; support constitutional processes and the organization of elections, protect and promote human rights and assist in restoring the rule of law and extending legitimate state authority. Peacekeeping operations get their mandates from the UN Security Council; their troops and police are contributed by Members States; and they are managed by the Department of Peace Operations and supported by the Department of Operational Support at UN Headquarters in New York.&nbsp; &nbsp;
An investigation of the lived experiences of non-offending family members of men who download Child Sexual Abuse Material (CSAM)

Non-offending family members are often viewed as a valuable protective resource for offenders, supporting post-accusation/conviction adjustment and reducing risk of re-offending. Yet, from the perspective of non-offending relatives, there is a limited understanding about how they actually cope after their family member is accused and/or convicted of a sexual offence, and the extent to which they are supported. The aim of the present research was to examine the lived experiences of relatives of sex offenders in Ireland and the United Kingdom. A qualitative research design was adopted, and data analysed via reflexive thematic analysis. Fifteen individuals self-selected for interview following a recruitment campaign utilising four gatekeeper agencies across Ireland and the United Kingdom. All participants identified as family members of individuals that had been accused and/or convicted of an online, non-contact sexual crime and interviews resulted in a very rich source of data for analysis. Five key themes were identified, Shattered Worldview, The Injured Self, Contamination by Association, Navigation through Secondary Stigma and Fragile Future. A number of sub-themes were also identified within each of the five themes which served to provide further clarification about the lived experiences of relatives of sex offenders. The analysis highlighted how non-offending family members experienced secondary stigma with consequences that impacted every aspect of their lives. These findings are discussed in relation to the limited available literature along with research implications with suggestions about future directions as well as recommendations for both policy and practice.
Are peer reviews of grant proposals reliable? An analysis of Economic and Social Research Council (ESRC) funding applications

Peer-review is widely used throughout academia, most notably in the publication of journal articles and the allocation of research grants. Yet peer-review has been subject to much criticism, including being slow, unreliable, subjective and potentially prone to bias. This paper contributes to this literature by investigating the consistency of peer-reviews and the impact they have upon a high-stakes outcome (whether a research grant is funded). Analysing data from 4,000 social science grant proposals and 15,000 reviews, this paper illustrates how the peer-review scores assigned by different reviewers have only low levels of consistency (a correlation between reviewer scores of only 0.2). Reviews provided by ‘nominated reviewers’ (i.e. reviewers selected by the grant applicant) appear to be overly generous and do not correlate with the evaluations provided by independent reviewers. Yet a positive review from a nominated reviewer is strongly linked to whether a grant is awarded. Finally, a single negative peer-review is shown to reduce the chances of a proposal being funding from around 55% to around 25% (even when it has otherwise been rated highly).
Bounds for the tracking error of first-order online optimization methods

This paper investigates online algorithms for smooth time-varying optimization problems, focusing first on methods with constant step-size, momentum, and extrapolation-length. Assuming strong convexity, precise results for the tracking iterate error (the limit supremum of the norm of the difference between the optimal solution and the iterates) for online gradient descent are derived. The paper then considers a general first-order framework, where a universal lower bound on the tracking iterate error is established. Furthermore, a method using "long-steps" is proposed and shown to achieve the lower bound up to a fixed constant. This method is then compared with online gradient descent for specific examples. Finally, the paper analyzes the effect of regularization when the cost is not strongly convex. With regularization, it is possible to achieve a non-regret bound. The paper ends by testing the accelerated and regularized methods on synthetic time-varying least-squares and logistic regression problems, respectively.
Location of Siegel capture polynomials in parameter spaces

A cubic polynomial $f$ with a periodic Siegel disk containing an eventual image of a critical point is said to be a \emph{Siegel capture polynomial}. If the Siegel disk is invariant, we call $f$ a \emph{IS-capture polynomial} (or just an IS-capture; IS stands for Invariant Siegel). We study the location of IS-capture polynomials in the parameter space of all cubic polynomials and show that any IS-capture is on the boundary of a unique hyperbolic component determined by the rational lamination of the map. We also relate IS-captures to the cubic Principal Hyperbolic Domain and its closure (by definition, the \emph{cubic Principal Hyperbolic Domain} consists of cubic hyperbolic polynomials with Jordan curve Julia sets) and prove that, in the slice of cubic polynomials given by a fixed multiplier at one of the fixed points, the closure of the cubic principal hyperbolic domain might possibly only have bounded complementary domains $U$ such that (1) critical points of $f\in U$ are distinct and belong to $J(f)$, and (2) $J(f)$ has positive Lebesgue measure and carries an invariant line field.