ethicalabs/Research-Intent-Collab huggingface dataset:
Validation of deep learning models for cuffless blood pressure estimation on a large benchmarking dataset

Objective: This study aims to evaluate the effectiveness of deep learning techniques in estimating cuffless blood pressure (BP) across a diverse patient population in intensive care units (ICUs).Methods: A comprehensive ICU benchmarking dataset encompassing 2,154 patients with a wide demographic range (18-97 years old) and varied cardiovascular status was employed to validate several deep learning models in predicting continuous BP waveforms. Three methods were developed to enhance the model's generalizability to this heterogeneous dataset. Ten-fold subject-independent cross-validation was performed and the model performance was assessed through mean absolute error (MAE), Pearson’s correlation coefficient (PCC), and compared with significance analysis.Results: The UTransBPNet_Demo_In model, which incorporated demographic and physiological signals as inputs, achieved a PCC of 0.89 and a MAE of 10.38 mmHg in predicting arterial BP waveforms, demonstrating the highest tracking capability among all models. Notably, the performance of UTransBPNet_Demo_In remained robust across variations in demographic and cardiovascular status.Conclusion: The UTransBPNet_Demo_In model demonstrates robust predictive capabilities across a broad spectrum of demographics and cardiovascular conditions. Although the performance still needs further improvement, this study offers a benchmark in the field of cuffless BP monitoring in critical care settings for future studies.
A Framework for Gateway-Agnostic API Management in Distributed Enterprise Systems

Enterprise systems increasingly rely on APIs to connect applications, partners, and cloud services. While API gateways play a central role in security, traffic control, and observability, many organizations become tightly coupled to a specific gateway vendor or platform. This coupling creates long-term risks related to migration, scalability, cost, and architectural flexibility. This paper presents a gateway-agnostic API management framework designed to decouple API design, governance, and runtime policies from underlying gateway implementations. The framework introduces a layered architecture that separates API contracts, policy definitions, and operational concerns from gateway-specific features. We describe the core components of the framework, explain how it integrates with existing enterprise systems, and evaluate its benefits using a realistic enterprise deployment scenario. The results show that a gateway-agnostic approach improves portability, reduces vendor lock-in, and simplifies long-term API governance without sacrificing security or performance [1][6].
Modeling, optimization and experimental assessment of continuous L‐(−)‐carnitine production by <i>Escherichia coli</i> cultures

AbstractIn a previous paper Cánovas et al. (Biotechnol Bioeng 2002;77:764–775) presented a model for L‐(−)‐carnitine production using Escherichia coli O44 K74, in a cell‐recycle bioreactor for the biotransformation of crotonobetaine into L‐carnitine. In this work we optimize this biotechnological setup and experimentally verify the predicted optimal parameter profiles. Provided with a reliable and robust S‐system description of the cell–bioreactor combined system, we applied the Indirect Optimization Method described by Torres et al. (Biotechnol Bioeng 1997;55(5):758–772; Food Technol Biotechnol 1998;36(3):177–184). This optimization approach provides different parameter value profiles, all of which are compatible with the cell physiology and the bioreactor operating conditions, that yield increased rates of L‐(−)‐carnitine production. Three parameters were seen to be of critical importance for maximizing L‐(−)‐carnitine production: the dilution rate, the initial crotonobetaine concentration, and the carnitine dehydratase activity. When the first two were changed in the experimental setup, there was a 74% increase in the L‐(−)‐carnitine production rate, performance that was in close agreement with the predictions of the model. In accordance with the optimized solution, a further improvement (90% increase in the L‐(−)‐carnitine production rate) could be attained by over‐expressing up to 5 times the carnitine dehydratase basal activity. Thus the optimization approach shown herein provides experimental evidence of a new strategy which demonstrates the possible variables that can be subjected to modifications compatible with the cell physiology and bioreactor operating conditions, and which are able to yield increased rates of L‐(−)‐carnitine production.  © 2002 Wiley Periodicals, Inc. Biotechnol Bioeng 80: 794–805, 2002.
Russian Federation: Indigenous Peoples and Land Rights

Within the course of the past decades, many achievements have been made with reference to indigenous rights standards, primarily through indigenous engagement and dedication within global society. After 50 years of active participation in the global arena, indigenous rights movements continue to gain momentum transforming into one of “the most visible civil society grouping across the UN” (Morgan, 2011,p.2). As a result of adoption of international standards and guidelines in addition to the establishment of institutions that specifically target the concerns of indigenous people, today indigenous peoples are more mobilized than any other time. With the notable exception (among the Arctic states) of the Russian Federation where despite a rather promising beginning of professional indigenous activism in the early 2000s, Russian indigenous groups saw even further division — yet more separate paths in contrast to international indigenous development (Eckert, 2012). While the protection of indigenous peoples’ rights and interests is becoming an important global goal and the essential sphere of international cooperation, domestically there are still some fundamental imbalances in power, rights and inclusion of indigenous peoples in decision-making process.This article is an attempt to raise fundamental questions about the nature of contemporary Russian policy towards its indigenous population and shed light upon the various characteristics that have come to define Russia’s response to indigenous problematics.
Profiling and Automated Decision Making in the Present and New EU Data Protection Frameworks

The digital world of the 21st century is increasingly the world of automatic decision making. In such a world, an ever larger number of tasks are relegated to computers which gather and process data as well as suggest or make decisions silently and with little supervision. This situation has been made possible by a transfer of a staggering portion of our daily lives from the offline world to the Internet. It is a truism that automation would be impossible without our willing participation on the Internet. We freely take part in social networks, post on blogs, and send our emails. On the other hand, it is equally true that we are increasingly monitored by the state, by profit‐maximizing corporations and by our fellow citizens and that these methods of monitoring are becoming smarter. Vast amounts of data which have become available and which we contribute, form what we today call “big data”.1 This is then harvested for connections and correlations and profiles created that can be used for commercial and other purposes. We fear this world but are also dependant on it. The creation of these profiles and their usage is an uncharted territory for the social sciences as much as it is a strange territory for the regulators.
Strategies and Process of Trade Secret Misappropriation Mitigation: The Case of <scp>UK</scp>

ABSTRACTTrade Secrets (TS) are vital for innovation‐driven companies, and their loss can inflict significant financial and reputational damage, particularly in the absence of established policies to mitigate misappropriation in the UK. The current academic literature lacks detailed strategies for firms to effectively address these threats. This paper aims to bridge the research gap by conducting interviews with industry professionals and analyzing UK legal cases related to TS misappropriation. By integrating findings within the Dynamic Capabilities (DC) theoretical framework, we develop a more comprehensive approach to mitigating TS misappropriation, thereby enhancing the detail and relevance of DC approaches in the TS context. This research, therefore, not only offers practical strategies for companies in the UK to prevent future breaches but also advances academic understanding of the process of TS misappropriation‐mitigation and outlines potential directions for further inquiry in this area.
Influence of Workload on Primary Care Nurses’ Health and Burnout, Patients’ Safety, and Quality of Care: Integrative Review

The increase in the demand for care has not led to a proportional growth in the number of primary care nurses. This imbalance is related to the decrease in the quality of care and patient safety, and also to the impact on the health of the professional group. The objective of this study is to identify relationships between overload, illness of the nurse, professional exhaustion, quality and safety in the services; and differentiate study methods and instruments for measuring the phenomena. For this, a comprehensive and structured review of the literature following the scoping review model is performed. The studies on which this review is based allow us to recognize that the scope of this phenomenon is global. The review includes 45 studies that show that there is a high pressure of care for Primary Care nursing, who suffer many alterations of their health due to burnout syndrome and that this situation contributes to the impairment of the quality of care and patient safety. However, for future lines, new evidence is needed to determine the degree of relationship between the high pressure suffered by Primary Care nursing and the attainment of health goals for professionals and patients.
Physical Principles of Quantum Biology

This technical monograph provides a comprehensive overview of the field of quantum biology. It approaches quantum biology from a physical perspective with core quantum mechanical concepts presented foremost to provide a theoretical foundation for the field. An extensive body of research is covered to clarify the significance of quantum biology as a scientific field, outlining the field's long-standing importance in the historical development of quantum theory. This lays the essential groundwork to enable further advances in nanomedicine and biotechnology. Written for academics, biological science researchers, physicists, biochemists, medical technologists, and students of quantum mechanics, this text brings clarity to fundamental advances being made in the emerging science of quantum biology.
Deep MRI Reconstruction: Unrolled Optimization Algorithms Meet Neural Networks

Image reconstruction from undersampled k-space data has been playing an important role for fast MRI. Recently, deep learning has demonstrated tremendous success in various fields and also shown potential to significantly speed up MR reconstruction with reduced measurements. This article gives an overview of deep learning-based image reconstruction methods for MRI. Three types of deep learning-based approaches are reviewed, the data-driven, model-driven and integrated approaches. The main structure of each network in three approaches is explained and the analysis of common parts of reviewed networks and differences in-between are highlighted. Based on the review, a number of signal processing issues are discussed for maximizing the potential of deep reconstruction for fast MRI. the discussion may facilitate further development of optimal network and performance analysis from a theoretical point of view.
Assessing and managing the risks of COVID-19 in the workplace: Applying industrial hygiene (IH)/occupational and environmental health and safety (OEHS) frameworks

As businesses attempt to reopen to varying degrees amid the current coronavirus disease (COVID-19) pandemic, industrial hygiene (IH) and occupational and environmental health and safety (OEHS) professionals have been challenged with assessing and managing the risks of COVID-19 in the workplace. In general, the available IH/OEHS tools were designed to control hazards originating in the workplace; however, attempts to tailor them specifically to the control of infectious disease outbreaks have been limited. This analysis evaluated the IH decision-making framework (Anticipate, Recognize, Evaluate, Control, and Confirm (“ARECC”)) as it relates to biological hazards, in general, and to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), specifically. Available IH/OEHS risk assessment and risk management tools (e.g. control banding and the hierarchy of controls) are important components of the ARECC framework. These conceptual models, however, were primarily developed for controlling chemical hazards and must be adapted to the unique characteristics of highly infectious and virulent pathogens, such as SARS-CoV-2. This assessment provides an overview of the key considerations for developing occupational infection control plans, selecting the best available controls, and applying other emerging tools (e.g. quantitative microbial risk assessment), with the ultimate goal of facilitating risk management decisions during the current global pandemic.
Independent Policy Gradient Methods for Competitive Reinforcement Learning

Appeared at NeurIPS 2020
Leveraging Pre-Trained Neural Networks to Enhance Machine Learning with Variational Quantum Circuits

Abstract         Quantum Machine Learning (QML) offers tremendous potential but is currently limited by the availability of qubits. We introduce an innovative approach that utilizes pre-trained neural networks to enhance Variational Quantum Circuits (VQC). This technique effectively separates approximation error from qubit count and removes the need for restrictive conditions, making QML more viable for real-world applications. Our method significantly improves parameter optimization for VQC while delivering notable gains in representation and generalization capabilities, as evidenced by rigorous theoretical analysis and extensive empirical testing on quantum dot classification tasks. Moreover, our results extend to applications such as human genome analysis, demonstrating the broad applicability of our approach. By addressing the constraints of current quantum hardware, our work paves the way for a new era of advanced QML applications, unlocking the full potential of quantum computing in fields such as machine learning, materials science, medicine, mimetics, and various interdisciplinary areas.
Hybrid Alignment Training for Large Language Models

Alignment training is crucial for enabling large language models (LLMs) to cater to human intentions and preferences. It is typically performed based on two stages with different objectives: instruction-following alignment and human-preference alignment. However, aligning LLMs with these objectives in sequence suffers from an inherent problem: the objectives may conflict, and the LLMs cannot guarantee to simultaneously align with the instructions and human preferences well. To response to these, in this work, we propose a Hybrid Alignment Training (Hbat) approach, based on alternating alignment and modified elastic weight consolidation methods. The basic idea is to alternate between different objectives during alignment training, so that better collaboration can be achieved between the two alignment tasks.We experiment with Hbat on summarization and dialogue tasks. Experimental results show that the proposed \textsc{Hbat} can significantly outperform all baselines. Notably, Hbat yields consistent performance gains over the traditional two-stage alignment training when using both proximal policy optimization and direct preference optimization.
Learning Abstract Information Theory On Visual Data: An Integrated Course On Wavelet Based Image Compression

Comment: 9 pages
AI Agent-Driven Framework for Automated Product Knowledge Graph Construction in E-Commerce

The rapid expansion of e-commerce platforms generates vast amounts of unstructured product data, creating significant challenges for information retrieval, recommendation systems, and data analytics. Knowledge Graphs (KGs) offer a structured, interpretable format to organize such data, yet constructing product-specific KGs remains a complex and manual process. This paper introduces a fully automated, AI agent-driven framework for constructing product knowledge graphs directly from unstructured product descriptions. Leveraging Large Language Models (LLMs), our method operates in three stages using dedicated agents: ontology creation and expansion, ontology refinement, and knowledge graph population. This agent-based approach ensures semantic coherence, scalability, and high-quality output without relying on predefined schemas or handcrafted extraction rules. We evaluate the system on a real-world dataset of air conditioner product descriptions, demonstrating strong performance in both ontology generation and KG population. The framework achieves over 97\% property coverage and minimal redundancy, validating its effectiveness and practical applicability. Our work highlights the potential of LLMs to automate structured knowledge extraction in retail, providing a scalable path toward intelligent product data integration and utilization.
Antimicrobial Resistance in Humans, Animals, Water and Household Environs in Rural Andean Peru: Exploring Dissemination Pathways through the One Health Lens

Antimicrobial resistance (AMR) is a global public health threat, especially for low and middle-income countries (LMIC) where the threat has not been fully identified. Our study aims to describe E. coli AMR in rural communities to expand our knowledge on AMR bacterial contamination. Specifically, we aim to identify and describe potential dissemination routes of AMR-carrying bacteria in humans (children’s stools), community water sources (reservoirs and household sources), household environments (yard soil) and domestic animals of subsistence farmers in rural Andean areas. Our cross-sectional study was conducted in rural households in the region of Cajamarca, Peru. A total of 266 samples were collected. Thirty-four point six percent of reservoir water and 45% of household water source samples were positive for thermotolerant coliforms. Of the reservoir water samples, 92.8% were positive for E. coli, and 30.8% displayed resistance to at least one antibiotic, with the highest resistance to tetracycline. E. coli was found in 57.1% of the household water sources, 18.6% of these isolates were multidrug-resistant, and displayed the highest resistance to tetracycline (31.3%). Among samples from the children’s drinking water source, 32.5% were positive for thermotolerant coliforms, and 57.1% of them were E. coli. One third of E. coli isolates were multidrug-resistant and displayed the highest AMR to tetracycline (41.6%) and ampicillin (25%). Thermotolerant coliforms were found in all the soil samples, 43.3% of the isolates were positive for E. coli, 34.3% of the E. coli isolates displayed AMR to at least one antibiotic, and displayed the highest AMR to tetracycline (25.7%). We determined thermotolerant coliforms in 97.5% of the child feces samples; 45.3% of them were E. coli, 15.9% displayed multidrug resistance, and displayed the highest resistance to ampicillin (34.1%). We identified thermotolerant coliforms in 67.5% of the animal feces samples. Of those, 38.7% were E. coli, and 37.7% were resistant to at least one antibiotic. For all the samples, the prevalence of resistance to at least one antibiotic in the E. coli and Klebsiella spp. isolates was almost 43% and the prevalence of MDR in the same isolates was nearly 9%, yet the latter nearly doubled (15.9%) in children’s stools. Our results provide preliminary evidence for critical pathways and the interconnectedness of animal, human and environmental transmission but molecular analysis is needed to track dissemination routes properly.
ARTIFICIAL INTELLIGENCE, ETHICS AND TECHNOLOGY: a contribution of philosophy of technology to the discussion of ethics in artificial intelligence

Os avanços da inteligência artificial e suas consequências trazem possibilidades de realizações positivas para a humanidade, mas também trazem ameaças. Tal potencial ambíguo exige um exame dessa tecnologia, a quem ela serve e que poder ela representa, abrangendo da ética à política, já que as mudanças trazidas implicam a necessidade de se investigar qual tipo de desenvolvimento da IA pode proporcionar um progresso sustentável e positivo para o indivíduo e a sociedade. Nesta relação do ser humano com máquinas que apresentam vasto conhecimento, capacidade de aprendizado e raciocínio, surge uma pressão sobre o ser humano para que encontre uma forma bem-sucedida de compartilhar o mundo com a inteligência artificial. Esse trabalho traz reflexões sobre ética e filosofia da tecnologia, na visão dos filósofos Mark Coeckelbergh e Andrew Feenberg, na busca de fundamentar uma resposta a essa questão
11 TeraFLOPs per second photonic convolutional accelerator for deep learning optical neural networks

Convolutional neural networks (CNNs), inspired by biological visual cortex systems, are a powerful category of artificial neural networks that can extract the hierarchical features of raw data to greatly reduce the network parametric complexity and enhance the predicting accuracy. They are of significant interest for machine learning tasks such as computer vision, speech recognition, playing board games and medical diagnosis. Optical neural networks offer the promise of dramatically accelerating computing speed to overcome the inherent bandwidth bottleneck of electronics. Here, we demonstrate a universal optical vector convolutional accelerator operating beyond 10 TeraFLOPS (floating point operations per second), generating convolutions of images of 250,000 pixels with 8 bit resolution for 10 kernels simultaneously, enough for facial image recognition. We then use the same hardware to sequentially form a deep optical CNN with ten output neurons, achieving successful recognition of full 10 digits with 900 pixel handwritten digit images with 88% accuracy. Our results are based on simultaneously interleaving temporal, wavelength and spatial dimensions enabled by an integrated microcomb source. This approach is scalable and trainable to much more complex networks for demanding applications such as unmanned vehicle and real-time video recognition.