ethicalabs/Research-Intent-Collab huggingface dataset:
The ethics of AI-assisted digital phenotyping in adolescent mental health: a framework for informed consent and trust

Abstract                   The rapid advancement of digital technologies, particularly AI-assisted digital phenotyping, offers transformative potential for adolescent mental health care. Digital phenotyping facilitates continuous monitoring of behavior and physiological states through data collected from digital devices, providing critical insights into mental health conditions such as depression, addiction, and anxiety. Nonetheless, digital phenotyping itself gives rise to substantial ethical concerns pertaining to privacy, surveillance, and the medicalization of normal behavior, which are further compounded by the integration of AI technologies. The present paper explores the ethical complexities of obtaining adolescent consent for digital phenotyping, emphasizing the importance of trust between adolescents, healthcare providers, and AI technologies. The paper proposes a theoretical framework for understanding adolescent consent with respect to their evolving identity and agency. This framework is grounded in relational autonomy, emphasizing the importance of considering adolescent consent within the network of relationships that shape their development. The paper contends that informed consent processes should prioritize adolescent autonomy, ensuring transparency, accountability, and trustworthiness in AI systems. This paper proposes a comprehensive framework for ensuring ethically sound consent that extends beyond procedural considerations to address the structural power dynamics inherent in the technological monitoring of adolescent behavior.
The impacts of water vapour and co-pollutants on the performance of electrochemical gas sensors used for air quality monitoring

The analytical performance of low cost air pollution sensors under real-world conditions is a key factor that will influence their future uses and adoption. In this study five different electrochemical gas sensors (O3, SO2, CO, NO, NO2) are tested for their performance when challenged with cross interferences of water vapour and other gaseous co-pollutants. These experiments were conducted under both controlled laboratory conditions and during ambient air monitoring in urban background air at a site in York, UK. Signal outputs for O3, SO2 and CO showed a positive linear dependence on relative humidity (RH). The output for the NO sensor showed a negative correlation. The output for the NO2 sensor showed no trend with RH. Potential co-pollutants (O3, SO2, CO, NO2, NO and CO2) were introduced under controlled conditions using gas standards and delivered to each sensor in series along with variable RH. A matrix of cross-interference sensitivities were established which could be used to correct sensor signals. Interference-corrected sensor responses were compared against reference observations over an 18-day period. Once cross interferences had been removed the corrected 5 min averaging data for O3, CO, NO and NO2 sensors showed good agreement with the reference techniques with r2 values of 0.89, 0.76, 0.72, and 0.69, respectively. The SO2 sensor could not be evaluated in ambient air since ambient SO2 was below the sensor limit of detection.
Bridging Data Management and Knowledge Discovery in the Life Sciences

In this work we present an application for integrating and analyzing life science data using a biomedical data warehouse system and tools developed in-house enabling knowledge discovery tasks. Knowledge discovery is known as a process where different steps have to be coupled in order to solve a specified question. In order to create such a combination of steps, a data miner using our in-house developed knowledge discovery tool KD3 is able to assemble functional objects to a data mining workflow. The generated workflows can easily be used for ulterior purposes by only adding new data and parameterizing the functional objects in the process. Workflows guide the performance of data integration and aggregation tasks, which were defined and implemented using a public available open source tool. To prove the concept of our application, intelligent query models were designed and tested for the identification of genotype-phenotype correlations in Marfan Syndrome. It could be shown that by using our application, a data miner can easily develop new knowledge discovery algorithms that may later be used to retrieve medical relevant information by clinical researchers.
sj-docx-1-inq-10.1177_00469580221092856 – Supplemental material for Effects of Virginia’s 2019 Medicaid Expansion on Health Insurance Coverage, Access to Care, and Health Status

Supplemental material, sj-docx-1-inq-10.1177_00469580221092856 for Effects of Virginia’s 2019 Medicaid Expansion on Health Insurance Coverage, Access to Care, and Health Status by Wei Lyu and George L. Wehby in INQUIRY: The Journal of Health Care Organization, Provision, and Financing
Demonstration of teleportation-based error correction in the IBM quantum computer

26 pages, 5 figures
Byzantine-Resilient SGD in High Dimensions on Heterogeneous Data

We study distributed stochastic gradient descent (SGD) in the master-worker architecture under Byzantine attacks. We consider the heterogeneous data model, where different workers may have different local datasets, and we do not make any probabilistic assumptions on data generation. At the core of our algorithm, we use the polynomial-time outlier-filtering procedure for robust mean estimation proposed by Steinhardt et al. (ITCS 2018) to filter-out corrupt gradients. In order to be able to apply their filtering procedure in our {\em heterogeneous} data setting where workers compute {\em stochastic} gradients, we derive a new matrix concentration result, which may be of independent interest.   We provide convergence analyses for smooth strongly-convex and non-convex objectives. We derive our results under the bounded variance assumption on local stochastic gradients and a {\em deterministic} condition on datasets, namely, gradient dissimilarity; and for both these quantities, we provide concrete bounds in the statistical heterogeneous data model. We give a trade-off between the mini-batch size for stochastic gradients and the approximation error. Our algorithm can tolerate up to $\frac{1}{4}$ fraction Byzantine workers. It can find approximate optimal parameters in the strongly-convex setting exponentially fast and reach to an approximate stationary point in the non-convex setting with a linear speed, thus, matching the convergence rates of vanilla SGD in the Byzantine-free setting.   We also propose and analyze a Byzantine-resilient SGD algorithm with gradient compression, where workers send $k$ random coordinates of their gradients. Under mild conditions, we show a $\frac{d}{k}$-factor saving in communication bits as well as decoding complexity over our compression-free algorithm without affecting its convergence rate (order-wise) and the approximation error.
Quantum Computing Error Correction Solved Using Chaotic Numbers

Abstract                 Quantum Mechanics and Computation has a major problem calledthe measurement problem [7] [19]. This has given physicists a very hardtime over the years when I first looked into the problem my approach wassimple find a new number system that can go with the uncertainty of aQuantum particle the paper deals with the mathematics of uncertaintywhich has solved 2 millenium prize problems [4], [5] and quantum measurementproblem very efficiently. We divide chaos into two parts lowchaos and high chaos then we find the desired value [19] inside the intersectionof both. This helps us find something in a ℵ3 &gt;&gt;&gt; ∞ this takesthe problems around us to the next level if we are able to control a chaosthen we can achieve pretty much anything. This paper is inspired by [22]
READINESS OF NURSING STUDENTS FOR CLINICAL PRACTICE IN TERMS OF PATIENT SAFETY AND QUALITY OF CARE

Ensuring nursing students' readiness for clinical practice is crucial for maintaining patient safety and upholding quality care standards within healthcare settings, as it directly impacts their ability to perform essential nursing tasks competently and confidently. Adequate preparation through rigorous education and hands-on training ensures that nursing students can effectively manage patient care responsibilities, reducing the likelihood of errors and improving overall patient outcomes. This study investigated the readiness of nursing students for clinical practice, focusing specifically on their preparedness concerning patient safety and the delivery of quality care. Through a comprehensive analysis, the research evaluated various factors influencing nursing students' readiness, including their theoretical knowledge, clinical skills proficiency, communication abilities, and awareness of patient safety protocols. Utilizing a mixed-methods approach, data were collected from nursing students undergoing clinical training in diverse healthcare settings. Findings revealed both strengths and areas for improvement in nursing students' readiness for clinical practice, shedding light on potential interventions to enhance their preparedness. The study underscored the importance of integrating patient safety and quality of care principles into nursing education curricula to better equip students for the challenges of clinical practice. Furthermore, it emphasized the need for collaborative efforts between educational institutions and clinical settings to provide comprehensive training and support for nursing students' transition to professional practice. The insights gained from this research contributed to the ongoing discourse on nursing education and practice, offering valuable implications for curriculum development, clinical training programs, and healthcare policy.
FIRB SQUARE PROJECT: NANO-STRUCTURED SENSORS FOR THE DETECTION OF THE POLLUTING IC ENGINE EXHAUST GASES AND FOR INDOOR AIR QUALITY MONITORING

Oral communication - ISSN:
Role of Treasury Single Account in Enhancing Transparency, Good Governance and Curbing Corrupt Practices in the Nigerian Public Service

The Treasury Single Account (TSA) was introduced by the global international financial institutions specifically the World and IMF to help countries block leakages and other wastages that are associated with corrupt practices in the management of the public funds by the public servants. The TSA was embraced by the Nigerian Government with the same objective of blocking leakages and fostering transparency in public services. This has been behind the background of the allegations of corrupt practices, mismanagement, leakages, waste and squandering of public resources by the public servants who dubiously maintained several accounts for one ministry or agency. This study investigated the role played by TSA in the promotion of transparency, good governance, and preventing corrupt practices. The study used documented sources for assembling data involving the existing body of literature after thoroughly identifying the clear gap for contribution to knowledge. The sources obtained were discussed using empiricism where statistical data were analysed on the impact of TSA on preventing leakages and corrupt practices. The study discovered that the introduction of TSA in the Nigerian public service was a good reform and a success in minimising dubious transactions, corrupt practices, and the operation of suspicious redundant accounts by Ministries, Departments, and Agencies (MDAs) of Government. The study therefore, recommends that there is a need for full automation and digitalisation of governmental processes in Nigeria to reflect in other sectors of governance which can help in preventing leakages, malpractices, corrupt practices, and misgovernance.
Risk Assessment and Safety Management in Underground Coal Excavation: A Case Study Analysis

This study investigates safety management and risk assessment practices in underground coal excavation, using Mine-A in Chandrapur, Maharashtra, as a representative case study. The primary objectives are to examine various risk assessment techniques, evaluate and analyze past excavation accidents, strengthen mine safety through hazard recognition and risk evaluation, and apply systematic risk assessment methodologies for effective risk control. A mixed-methods approach was adopted, integrating both quantitative and qualitative techniques. Historical accident data from 2014 to 2022 was analyzed alongside qualitative tools such as HAZOP, FTA, and ETA. The study covered ten major hazard categories and assessed worker exposure across multiple operational roles within Mine-A, which follows the Board and Pillar mining method and is classified as Degree-I in terms of gassiness. Results identified fire and inundation as high-risk hazards requiring immediate intervention. Medium-risk areas included ventilation and strata control systems. Accident data revealed support personnel and general workers as the most frequently affected groups. Additionally, national safety data from 2020 to 2022 indicated a consistent decline in fatal accidents and injuries, validating the effectiveness of structured risk assessment approaches. This study confirms their broader applicability in enhancing coal mine safety performance in India.
Time, knowledge &amp; space sharing : Science &amp; Discovery Centre - Lund Science Village

The buildings face into the landscape &amp; influence the urban fabric around where the sculptural interplay of the envelope &amp; ground transforms onto its glass facades. Here time, ideas, space &amp; knowledge is shared by creating environments where the participants can inform &amp; be re-informed by the adaptiveness of the architecture surrounded. The dynamic of the spaces generates creative thinking &amp; increases the social interaction &amp; sharing throughout the transformational sequences giving various spatial experiences.
Numerical Modeling of Galactic Cosmic Ray Modulation in the Heliosphere

Submitted to Lobachevskii Journal of Mathematics
Erratum: “A Measurement of the Cosmic Microwave Background B-Mode Polarization Power Spectrum at Sub-degree Scales with POLARBEAR” (2014, ApJ, 794, 171)

Not Available
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

We present Fashion-MNIST, a new dataset comprising of 28x28 grayscale images of 70,000 fashion products from 10 categories, with 7,000 images per category. The training set has 60,000 images and the test set has 10,000 images. Fashion-MNIST is intended to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms, as it shares the same image size, data format and the structure of training and testing splits. The dataset is freely available at https://github.com/zalandoresearch/fashion-mnist
On the Borel summability of formal solutions of certain higher-order linear ordinary differential equations

We consider a class of $n^{\text{th}}$-order linear ordinary differential equations with a large parameter $u$. Analytic solutions of these equations can be described by (divergent) formal series in descending powers of $u$. We demonstrate that, given mild conditions on the potential functions of the equation, the formal solutions are Borel summable with respect to the parameter $u$ in large, unbounded domains of the independent variable. We establish that the formal series expansions serve as asymptotic expansions, uniform with respect to the independent variable, for the Borel re-summed exact solutions. Additionally, we show that the exact solutions can be expressed using factorial series in the parameter, and these expansions converge in half-planes, uniformly with respect to the independent variable. To illustrate our theory, we apply it to an $n^{\text{th}}$-order Airy-type equation.
Molecular Dynamics Simulations: Force Fields, Sampling Techniques and Drug Design Applications

Molecular dynamics (MD) simulations have emerged as a cornerstone of modern computer-aided drug design (CADD), enabling the atomistic exploration of biomolecular behavior in physiologically relevant environments. Unlike static docking or rigid modeling, MD captures the temporal evolution of molecular systems through Newtonian mechanics, providing mechanistic insights into protein flexibility, ligand binding, conformational transitions, and thermodynamic stability. The chapter elucidates the theoretical and computational principles underlying MD, emphasizing the role of force fields, integration algorithms, and ensemble sampling strategies. It compares classical force fields such as AMBER, CHARMM, OPLS, and GROMOS with newer polarizable and coarse-grained models that balance computational efficiency and physical realism. Advanced sampling techniques including replica exchange, metadynamics, and accelerated MD are reviewed for their capacity to overcome energy barriers and improve conformational coverage. Through recent case studies, the chapter demonstrates how MD refines docking poses, predicts binding affinities via free-energy methods, and identifies resistance mutations. Emerging paradigms in AI-assisted, GPU-accelerated, and hybrid quantum–classical simulations are discussed in the context of next-generation drug discovery workflows. Collectively, the chapter integrates methodological rigor with translational relevance, positioning MD as a dynamic bridge between theoretical models and experimental validation.
Nuevos desafíos del Tribunal Permanente de los Pueblos en el siglo XXI: las luchas por la hegemonía en la creación del derecho penal internacional; Novos desafios do Tribunal Permanente dos Povos no século XXI: as lutas pela hegemonia na criação do direito penal internacional; New challenges of the Permanent Peoples' Court in the 21st century: the struggles for hegemony in the creation of international criminal law

Resumen: Surgido como reacción ante los horrores del nazismo, el derecho penal internacional poco a poco se fue transformando en una herramienta de legitimación de las intervenciones neocolonialistas. Es en dicho contexto de cambios (propio del siglo XXI) en el que aparece como relevante recuperar la experiencia de los Tribunales de Opinión (y en particular del Tribunal Permanente de los Pueblos) como posibilidad de revalorizar a la facultad de juzgar y a la necesidad de autonomía de los pueblos frente a las nuevas ofensivas de las potencias hegemónicas. Resumo: Surgiu como uma reação aos horrores do nazismo, o direito penal internacional gradualmente se tornou uma ferramenta de legitimação para intervenções neocolonialistas. É neste contexto de mudanças (típicas do século XXI) que parece relevante recuperar a experiência dos Tribunais de Opinião (e em particular do Tribunal Permanente dos Povos) como uma possibilidade de reavaliar o poder de julgar e a necessidade de autonomia do povo contra as novas ofensivas das potências hegemônicas. Abstract: Emerged as a reaction to the horrors of Nazism, international criminal law gradually became a legitimation tool for neo-colonialist interventions. It is in this context of changes (typical of the 21st century) in which it appears as relevant to recover the experience of the Courts of Opinion (and in particular of the Permanent Peoples Court) as a possibility of revaluing the power to judge and the need to autonomy of the people against the new offensives of the hegemonic powers.
Ecole: A Gym-like Library for Machine Learning in Combinatorial Optimization Solvers

We present Ecole, a new library to simplify machine learning research for combinatorial optimization. Ecole exposes several key decision tasks arising in general-purpose combinatorial optimization solvers as control problems over Markov decision processes. Its interface mimics the popular OpenAI Gym library and is both extensible and intuitive to use. We aim at making this library a standardized platform that will lower the bar of entry and accelerate innovation in the field. Documentation and code can be found at https://www.ecole.ai.
Spatial conservation prioritization to conserve biodiversity in response to sea level rise and land use change in the Matanzas River Basin, Northeast Florida

Abstract   Sea level rise and land use change are likely to be some of the most fundamental and important challenges for biodiversity conservation in low-lying coastal areas in the 21st century. To protect biodiversity in coastal areas, there is an urgent need to identify conservation priorities in response to sea level rise and land use change. In this study, an integrated modeling process using a geomorphological model, species habitat models, and conservation prioritization is developed to identify conservation priorities in the face of sea level rise and land use change. We present a case study in the Matanzas River Basin of Northeast Florida that utilizes this integrated modeling approach with data for 38 focal species. We incorporate species-specific connectivity requirements in the analysis and compare the conservation priorities with existing conservation datasets including current conservation areas and the Florida Ecological Greenways Network (FEGN). Results show that current reserves are not adequate to protect some of the most important conservation priorities in response to sea level rise but the updated FEGN does serve as a good foundation to inform future conservation decisions relevant to sea level rise. To protect the top 10% conservation priorities, approximately 11,700, 10,900 and 15,200 acres of additional land will need to be acquired for the 0.5, 1.0, and 2.5 m sea level rise scenarios respectively.
Artificial intelligence for diagnosis and prognosis in neuroimaging for dementia; a systematic review

Abstract                                    Introduction                   Recent developments in artificial intelligence (AI) and neuroimaging offer new opportunities for improving diagnosis and prognosis of dementia. To synthesise the available literature, we performed a systematic review.                                                     Methods                   We systematically reviewed primary research publications up to January 2021, using AI for neuroimaging to predict diagnosis and/or prognosis in cognitive neurodegenerative diseases. After initial screening, data from each study was extracted, including: demographic information, AI methods, neuroimaging features, and results.                                                     Results                   We found 2709 reports, with 252 eligible papers remaining following screening. Most studies relied on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset (n=178) with no other individual dataset used more than 5 times. Algorithmic classifiers, such as support vector machine (SVM), were the most commonly used AI method (47%) followed by discriminative (32%) and generative (11%) classifiers. Structural MRI was used in 71% of studies with a wide range of accuracies for the diagnosis of neurodegenerative diseases and predicting prognosis. Lower accuracy was found in studies using a multi-class classifier or an external cohort as the validation group. There was improvement in accuracy when neuroimaging modalities were combined, e.g. PET and structural MRI. Only 17 papers studied non-Alzheimer’s disease dementias.                                                     Conclusion                   The use of AI with neuroimaging for diagnosis and prognosis in dementia is a rapidly emerging field. We make a number of recommendations addressing the definition of key clinical questions, heterogeneity of AI methods, and the availability of appropriate and representative data. We anticipate that addressing these issues will enable the field to move towards meaningful clinical translation.