ethicalabs/Research-Intent-Collab huggingface dataset:
An Examination of The Legal Framework for Corporate Social Responsibility and Its Effectiveness in Promoting Ethical Business Practices

Businesses that make positive contributions to society and the environment are often lauded as having "Corporate Social Responsibility" (CSR). CSR promotes businesses' incorporation of moral and ethical principles. The corporation, its shareholders, and the public at large all stand to gain from this development. Corporate social responsibility (CSR) projects and programmes are those that are initiated by a company's board of directors on the recommendation of the CSR committee. When people learn about the good a company is doing in the world, they tend to form a more favourable opinion of it. CSR boosts a company's profile in the media, which is useful because positive press is contagious. CSR raises brand value by increasing consumer loyalty via increased corporate participation in their communities. Corporate social responsibility (CSR) activities help businesses stand out from the crowd and expand their sphere of impact.
The Right to Privacy in the Era of Mass Surveillance in India: A Constitutional Perspective

The expansion of digital technologies has significantly transformed state surveillance practices in India, raising complex constitutional questions concerning privacy, liberty, and democratic governance. This paper examines the right to privacy in the context of mass surveillance from a constitutional perspective. It analyses the evolution of privacy jurisprudence in India, culminating in its recognition as a fundamental right by the Supreme Court in Justice K.S. Puttaswamy (Retd.) v. Union of India. The study explores the nature and scope of mass surveillance, its impact on fundamental rights beyond privacy, and the limitations of the existing legal framework. It argues that unchecked surveillance poses risks to dignity, autonomy, and democratic participation. The paper concludes by emphasising the need for a rights-based surveillance framework grounded in constitutional principles, robust oversight, and accountability to ensure that security objectives do not erode fundamental freedoms.
Conducting experimental research in marginalised populations: clinical and methodological implications from a mixed-methods randomised controlled trial in Kenya

Experimental studies to test interventions for people living with HIV in low- and middle-income countries are essential to ensure appropriate and effective clinical care. The implications of study participation on outcome data in such populations have been discussed theoretically, but rarely empirically examined. We aimed to explore the effects of participating in a randomised controlled trial conducted in an HIV clinic in Mombasa, Kenya. We report qualitative data from the Treatment Outcomes in Palliative Care trial, which evaluated the impact of a nurse-led palliative care intervention for HIV positive adults on antiretroviral therapy compared to standard care. Participants in both arms attended five monthly quantitative data collection appointments. Post-trial exit, 10 control and 20 intervention patients participated in semi-structured qualitative interviews, analysed using thematic analysis. We found benefit attributed to the compassion of the research team, social support, communication, completion of patient reported outcome measures (PROMs) and material support (transport reimbursement). Being treated with compassion and receiving social support enabled participants to build positive relationships with the research team, which improved mental health and well-being. Open and non-judgmental communication made participants feel accepted. Participants described how repeated completion of the PROMs was a prompt for reflection, through which they began to help themselves and self-care. Participant reimbursements relieved financial hardship and enabled them to fulfil their social responsibilities, enhancing self-worth. These findings emphasise the importance of compassion, support and effective communication in the clinical encounter, particularly in stigmatised and isolated populations, and the potential of the integration of simple PROMs to improve patient outcomes. Participation in research has unexpected positive benefits for participants, which should be taken into account when designing research in similar populations. Researchers should be aware of the effects of financial reimbursement and contact with researchers in isolated and impoverished communities.
Time series aggregation for optimization: One-size-fits-all?

One of the fundamental problems of using optimization models that use different time series as data input, is the trade-off between model accuracy and computational tractability. To overcome the computational intractability of these full optimization models, the dimension of input data and model size is commonly reduced through time series aggregation (TSA) methods. However, traditional TSA methods often apply a one-size-fits-all approach based on the common belief that the clusters that best approximate the input data also lead to the aggregated model that best approximates the full model, while the metric that really matters - the resulting output error in optimization results - is not well addressed. In this paper, we plan to challenge this belief and show that output-error based TSA methods with theoretical underpinnings have unprecedented potential of computational efficiency and accuracy.
Align-SLM: Textless Spoken Language Models with Reinforcement Learning from AI Feedback

While textless Spoken Language Models (SLMs) have shown potential in end-to-end speech-to-speech modeling, they still lag behind text-based Large Language Models (LLMs) in terms of semantic coherence and relevance. This work introduces the Align-SLM framework, which leverages preference optimization inspired by Reinforcement Learning with AI Feedback (RLAIF) to enhance the semantic understanding of SLMs. Our approach generates multiple speech continuations from a given prompt and uses semantic metrics to create preference data for Direct Preference Optimization (DPO). We evaluate the framework using ZeroSpeech 2021 benchmarks for lexical and syntactic modeling, the spoken version of the StoryCloze dataset for semantic coherence, and other speech generation metrics, including the GPT4-o score and human evaluation. Experimental results show that our method achieves state-of-the-art performance for SLMs on most benchmarks, highlighting the importance of preference optimization to improve the semantics of SLMs.
Design of an optical half-adder based on two-dimensional photonic crystals using feedforward neural networks

The optical half-adder is one of the important components in the design of optical digital circuits. In this study, an optical half-adder based on two-dimensional photonic crystals with a square lattice has been designed, optimized, and simulated. A feedforward neural network model is used to achieve the optimal state by selecting the defect rod dimension and phase of the input laser to optimize the performance of the optical device. The results show that the proposed structure has an appropriate power difference in both high and low logic states, which results in fewer errors in detecting logical values at the outputs. Another feature of this structure is the use of a minimal number of defect rods and the absence of ring resonators, which leads to a reduction in size and propagation delay time of light in the structure. Moreover, due to its aforementioned features and small size, this structure is suitable for use in integrated optical circuits.
A Novel Design for an Autonomous Mobile Agricultural Fruit Harvesting Robot

This paper focuses its attention on the mechanical design of autonomous agricultural robots used for the purpose of harvesting fruits in plantations. The paper analysis the present mechanical designs, identifies the research gaps and proposes a novel design of a fruit plucking robot. A design consisting of a four wheeled mobile base, a manipulator and an end-effector has been proposed. An open-chain rotary-rotary-prismatic (RRP) manipulator attached to the mobile base and having two additional active rotary joints at the end-effector at the free-end has been designed. A hose has been provided to transfer fruits from the tree to the basket. The chassis of the mobile base is equipped with four solid rubber power (motorized) wheels controlled by skid steering method and a detachable basket for collecting fruits. A counterweight mechanism has been designed to provide static stability. The paper addresses the issues present in the current mechanical designs with a brief discussion on the system architecture.
A Fast Gridded Method for the Estimation of the Power Spectrum of the Cosmic Microwave Background from Interferometer Data with Application to the Cosmic Background Imager

We describe an algorithm for the extraction of the angular power spectrum of an intensity field, such as the cosmic microwave background (CMB), from interferometer data. This new method, based on the gridding of interferometer visibilities in the aperture plane followed by a maximum likelihood solution for band powers, is much faster than direct likelihood analysis of the visibilities and deals with foreground radio sources, multiple pointings, and differencing. The gridded aperture-plane estimators are also used to construct Wiener-filtered images using the signal and noise covariance matrices used in the likelihood analysis. Results are shown for simulated data. The method has been used to determine the power spectrum of the CMB from observations with the Cosmic Background Imager, and the results are given in companion papers. Subject headings: cosmic microwave background — methods: data analysis
Why Can't We Predict RNA Structure At Atomic Resolution?

No existing algorithm can start with arbitrary RNA sequences and return the precise, three-dimensional structures that ensures their biological function. This chapter outlines current algorithms for automated RNA structure prediction (including our own FARNA-FARFAR), highlights their successes, and dissects their limitations, using a tetraloop and the sarcin/ricin motif as examples. The barriers to future advances are considered in light of three particular challenges: improving computational sampling, reducing reliance on experimentally solved structures, and avoiding coarse-grained representations of atomic-level interactions. To help meet these challenges and better understand the current state of the field, we propose an ongoing community-wide CASP-style experiment for evaluating the performance of current structure prediction algorithms.
Optimizing service mesh performance and security trade-offs in Kubernetes with Istio and Linkerd

Service mesh technologies have emerged as critical components in Kubernetes environments, offering essential capabilities for managing and securing microservice communication. This article presents an empirical comparison between Istio and Linkerd, examining their architectural differences and performance characteristics under various security configurations. The investigation establishes baseline metrics for each service meshes and measures the impact of progressively enabling security features including mutual TLS encryption and authorization policies. Through controlled laboratory testing and production environment data, the comparison reveals distinct trade-offs between security posture and performance overhead. Istio provides comprehensive security features at the cost of increased resource consumption, while Linkerd delivers efficient performance with a more streamlined security model. The article offers optimization strategies for enterprise deployments, including configuration techniques for balancing security and performance, scaling considerations for large environments, and workload-specific tuning recommendations. A decision framework guides implementation choices based on specific security requirements, ultimately providing architects and DevOps professionals with actionable insights for maximizing both security and performance in Kubernetes service mesh deployments.
Aromatics and Cyclic Molecules in Molecular Clouds: A New Dimension of Interstellar Organic Chemistry

Astrochemistry lies at the nexus of astronomy, chemistry, and molecular physics. On the basis of precise laboratory data, a rich collection of more than 200 familiar and exotic molecules have been identified in the interstellar medium, the vast majority by their unique rotational fingerprint. Despite this large body of work, there is scant evidence in the radio band for the basic building blocks of chemistry on earth -- five and six-membered rings -- despite long standing and sustained efforts during the past 50 years. In contrast, a peculiar structural motif, highly unsaturated carbon in a chain-like arrangement, is instead quite common in space. The recent astronomical detection of cyanobenzene, the simplest aromatic nitrile, in the dark molecular cloud TMC-1, and soon afterwards in additional pre-stellar, and possibly protostellar sources, establishes that aromatic chemistry is likely widespread in the earliest stages of star formation. The subsequent discovery of cyanocyclopentadienes and even cyanonapthlenes in TMC-1 provides further evidence that organic molecules of considerable complexity are readily synthesized in regions with high visual extinction but where the low temperature and pressure are remarkably low. This review focuses on laboratory efforts now underway to understand the rich transition region between linear and planar carbon structures using microwave spectroscopy. We present key features, advantages, and disadvantages of current detection methods, a discussion of the types of molecules found in space and in the laboratory, and approaches under development to identify entirely new species in complex mixtures. Studies focusing on the cyanation of hydrocarbons and the formation of benzene from acyclic precursors are highlighted, as is the role that isotopic studies might play in elucidating the chemical pathways to ring formation.
Audio visual speech recognition

A method and apparatus for indicating at least some of a sequence of spoken phonemes in which detected sounds are analyzed to determine a group of phonemes to which a phoneme belongs, the lipshape is optically detected and the respective signals correlated by a computer to indicate the detected phoneme.
Ispitivanje vjerojatnosti nesreća na cjevovodima za prirodni plin metodom integracije analize stabla kvarova i What-if scenarija

Prirodni plin danas se smatra najvećim izvorom energije potrebne stambenim područjima i industriji. Pogled na tehnologije kojima se Iran koristi otkriva da su plinovodi najbolji i najsigurniji način za distribuciju prirodnog plina u zemlji. Ovaj izvor energije, koji se ponekad naziva čistim gorivom, ima stratešku ulogu u opskrbi potrebne energije. S druge strane, opskrba gorivom mora biti stalna, ali i sigurna. Stoga se zaključuje da su potrebne odgovarajuće mjere za upravljanje rizicima od curenja, požara i eksplozija na ovakvim postrojenjima. Za postizanje toga cilja najpotrebnije je ustanoviti uzroke nesreća i ispitati pouzdanost. Nadalje, neizbježno se mora procijeniti vjerojatnost događanja nesreće na cjevovodima. Jedan od ključnih koraka u procesu vrednovanja rizika jest procjena vjerojatnosti mogućih nesreća. Članak se koristi FTA (fault tree analysis) metodologijom za proučavanje načina procjene vjerojatnosti nesreća na cjevovodima za prirodni plin. Nadalje, ponuđena je i nova metoda temeljena na pridruživanju težina stablu kvarova, a ‘what-if’ (što-ako) scenarij služi za utvrđivanje opasnosti pri transportu prirodnog plina od njegova glavnog izvora. Na osnovi rezultata studije predlaže se da se vjerojatnost događanja nesreća na cjevovodima za prirodni plin kontrolira tehničkim i sustavnim pristupom kojim se utvrđuju i kontroliraju uzroci tih nesreća.
Effects of Continuous Medicaid Coverage in 2020–2023 on Children's Health Insurance Coverage, Access to Care, Health Services Use by Type, and Health Status

ABSTRACT                                        Objective                     To examine the effects of continuous Medicaid coverage in 2020–2023 under the Families First Coronavirus Response Act (FFCRA) on children's health insurance coverage, access to care, likelihood of using healthcare services by type, and health status.                                                           Study Setting and Design                     A difference‐in‐differences event study compares outcomes pre and post FFCRA between states without pre‐FFCRA continuity provisions (treatment group) and those that required 12‐month continuous coverage (control group).                                                           Data Sources and Analytical Sample                     The main sample includes 122,901–126,117 children (depending on outcome) aged 1–17 years with family income below 300% of federal poverty level from the 2016–2023 National Survey of Children's Health.                                                           Primary Findings                     After FFCRA, public coverage increased in treatment states in 2020, 2021, and 2022 by 4.1 (95% CI: 0.004, 8.3), 4.7 (95% CI, 0.4, 9.0), and 5.4 (95% CI: 2.0, 8.7) percentage points, respectively, relative to control states. Privately purchased coverage declined in 2020 by 3.5 (95% CI: −5.3, −1.7) percentage points. The likelihood of having a usual place for sick care increased by 3.6 (95% CI: 0.5, 6.8) percentage points in 2021, and the likelihood of unmet care needs decreased by 1.7 (95% CI: −2.8, −0.7) and 2.4 (95% CI: −3.8, −1.0) percentage points in 2021 and 2022. The likelihood of excellent/very good health increased by 2.5 (95% CI: 0.4, 4.5), 3.8 (95% CI: 0.7, 6.8), and 2.7 (95% CI: 0.4, 5.0) percentage points in 2020, 2021, and 2023, respectively. There were no changes in the likelihood of medical, preventive, mental health, specialist, and emergency department visits and hospital admissions.                                                           Conclusions                     Medicaid continuity under the FFCRA increased the children's public coverage rate. Despite potential switching from private coverage, there is evidence for reductions in unmet care needs and improved health status. Findings provide insights into potential effects of recent federal requirements that all states provide 12‐month Medicaid continuity for children.
The conservation of marine biodiversity in the Mediterranean Sea through marine protected areas : a lost cause?

The world’s largest semi-enclosed sea, the Mediterranean Sea, with its exceptional biodiversity value but also its major strategic significance has been a great source of food and wealth for the 22 surrounding states since ancient times. However, during the last years, the Mediterranean Sea has been adversely affected by many factors, e.g. climate change, overexploitation, marine litter, and an increase in invasive alien species. As a consequence, the region experiences the highest rates of biodiversity loss in the world, with 40 per cent of its marine species in decline. This threatens the ecological resilience of the whole region but also the states which depend on it. This dissertation examines the current legal but also policy framework concerning the establishment and management of Marine Protected Areas (MPAs)- a tool for the conservation of marine biodiversity which has recently attracted the attention of the international community- while identifying legal gaps and implementation inadequacies on behalf of Mediterranean states. Lastly, the recent paths that have been followed by states in order to conserve successfully the marine biodiversity through an effectively implemented network of marine protected areas in the Mediterranean Sea are analyzed.
Erratum: The lateral trigger probability function for the ultra-high energy cosmic ray showers detected by the Pierre Auger Observatory (Astroparticle Physics (2011) 35 (266-276))

info:eu-repo/semantics/published
Efficient Architecture Search by Network Transformation

Techniques for automatically designing deep neural network architectures such as reinforcement learning based approaches have recently shown promising results. However, their success is based on vast computational resources (e.g. hundreds of GPUs), making them difficult to be widely used. A noticeable limitation is that they still design and train each network from scratch during the exploration of the architecture space, which is highly inefficient. In this paper, we propose a new framework toward efficient architecture search by exploring the architecture space based on the current network and reusing its weights. We employ a reinforcement learning agent as the meta-controller, whose action is to grow the network depth or layer width with function-preserving transformations. As such, the previously validated networks can be reused for further exploration, thus saves a large amount of computational cost. We apply our method to explore the architecture space of the plain convolutional neural networks (no skip-connections, branching etc.) on image benchmark datasets (CIFAR-10, SVHN) with restricted computational resources (5 GPUs). Our method can design highly competitive networks that outperform existing networks using the same design scheme. On CIFAR-10, our model without skip-connections achieves 4.23\% test error rate, exceeding a vast majority of modern architectures and approaching DenseNet. Furthermore, by applying our method to explore the DenseNet architecture space, we are able to achieve more accurate networks with fewer parameters.
Communication Coaching in Cardiology (CCC): A study protocol and methodological challenges and solutions of a randomized controlled trial in outpatient cardiology clinics

Given the role of effective communication in improving patient adherence and satisfaction, high quality patient-clinician communication is critical. Building on previous communication interventions in oncology and pediatrics, we developed a tailored communication coaching intervention to improve empathic communication quality and patient-centered care. In this randomized controlled trial, cardiologists record their patient encounters for review by a communication coach who provides tailored feedback. We are recruiting 40 cardiologists and 400 patients, or 4 patients per cardiologist in the Pre-intervention phase and 6 patients per cardiologists in the Post-intervention phase, from outpatient cardiology clinics within the Duke Health System. The primary goal of the trial is to determine the efficacy of the clinician communication coaching versus usual care in the post-intervention phase (240 patient encounters). In this paper, we describe the development of the communication coaching intervention. We also describe the details of the methods and outcomes of the ongoing trial. Finally, we discuss the challenges, solutions, and lessons learned during the start-up phase of the study.
Learning an Optimized Deep Neural Network for Link Prediction on Knowledge Graphs

Recent years have seen the emergence of graph-based Knowledge Bases build upon Semantic Web technologies, known as Knowledge Graphs. Effectively learning from these complex relational structures remains a challenge yet to be overcome.Knowledge Graphs For this purpose, we are investigating the effectiveness of Link Prediction through means of Deep Learning an Artificial Neural Network, as well to optimize both learning method and model through Bayesian Hyper-parameter optimization. Moreover, during evaluation, special attention will be given to the usefulness of made