ethicalabs/Research-Intent-Collab huggingface dataset:
ReXrank: A Public Leaderboard for AI-Powered Radiology Report Generation

AI-driven models have demonstrated significant potential in automating radiology report generation for chest X-rays. However, there is no standardized benchmark for objectively evaluating their performance. To address this, we present ReXrank, https://rexrank.ai, a public leaderboard and challenge for assessing AI-powered radiology report generation. Our framework incorporates ReXGradient, the largest test dataset consisting of 10,000 studies, and three public datasets (MIMIC-CXR, IU-Xray, CheXpert Plus) for report generation assessment. ReXrank employs 8 evaluation metrics and separately assesses models capable of generating only findings sections and those providing both findings and impressions sections. By providing this standardized evaluation framework, ReXrank enables meaningful comparisons of model performance and offers crucial insights into their robustness across diverse clinical settings. Beyond its current focus on chest X-rays, ReXrank's framework sets the stage for comprehensive evaluation of automated reporting across the full spectrum of medical imaging.
DIRECT MEASUREMENT OF THE ANGULAR POWER SPECTRUM OF COSMIC MICROWAVE BACKGROUND TEMPERATURE ANISOTROPIES IN THE<i>WMAP</i>DATA

Angular power spectrum of the cosmic microwave background (CMB) temperature anisotropies is one of the most important on characteristics of the Universe such as its geometry and total density. Using flat sky approximation and Fourier analysis, we estimate the angular power spectrum from an ensemble of least foreground-contaminated square patches from WMAP W and V frequency band map. This method circumvents the issue of foreground cleaning and that of breaking orthogonality in spherical harmonic analysis due to masking out the bright Galactic plane region, thereby rendering a direct measurement of the angular power spectrum. We test and confirm Gaussian statistical characteristic of the selected patches, from which the first and second acoustic peak of the power spectrum are reproduced, and the third peak is clearly visible albeit with some noise residual at the tail.
The modulation of cosmic rays in the high latitude heliosphere: A computer simulation

Modulation models based on the numerical solution of Parker’s transport equation for galactic cosmic rays in the heliosphere make clear predictions about modulation in the high latitude heliosphere. However, for these predictions certain assumptions have to be made, for example, what the heliospheric magnetic field (HMF) looks like above the solar poles and what the spatial dependence of the diffusion coefficients are. For this presentation the general predictions of a standard drift model for the modulation of cosmic rays in the high latitude heliosphere, in particular predictions for the Ulysses trajectory, are discussed and critically reviewed. Preliminary results from Ulysses show a significant increase in the solar wind speed towards higher latitudes. The effects of this strong latitudinal dependence together with different modifications of the HMF at these high latitudes on the apparently too large diffusion and drifts predicted by current models are also shown.
Persistent Homology and Discrete Fourier Transform

This book proposes contributions to various problems in the field of topological analysis of musical data: the objects studied are scores represented symbolically by MIDI files, and the tools used are the discrete Fourier transform and persistent homology. The manuscript is divided into three parts: the first two are devoted to the study of the aforementioned mathematical objects and the implementation of the model. More precisely, the notion of DFT introduced by Lewin is generalized to the case of dimension two, by making explicit the passage of a musical bar from a piece to a subset of Z/tZ×Z/pZ, which leads naturally to a notion of metric on the set of musical bars by their Fourier coefficients. This construction gives rise to a point cloud, to which the filtered Vietoris-Rips complex is associated, and consequently a family of barcodes given by persistent homology. This approach also makes it possible to generalize classical results such as Lewin's lemma and Babitt's Hexachord theorem. The last part of this book is devoted to musical applications of the model: the first experiment consists in extracting barcodes from artificially constructed scores, such as scales or chords. This study leads naturally to song harmonization process, which reduces a song to its melody and chord grid, thus defining the notions of graph and complexity of a piece. Persistent homology also lends itself to the problem of automatic classification of musical style, which will be treated here under the prism of symbolic descriptors given by statistics calculated directly on barcodes. Finally, the last application proposes a encoding of musical bars based on the Hausdorff distance, which leads to the study of musical textures.The book is addressed to graduate students and researchers in mathematical music theory and music information research, but also at researchers in other fields, such as applied mathematicians and topologists, who want to learn more about mathematical music theory or music information research.
On the Efficiency of Privacy Attacks in Federated Learning

Recent studies have revealed severe privacy risks in federated learning, represented by Gradient Leakage Attacks. However, existing studies mainly aim at increasing the privacy attack success rate and overlook the high computation costs for recovering private data, making the privacy attack impractical in real applications. In this study, we examine privacy attacks from the perspective of efficiency and propose a framework for improving the Efficiency of Privacy Attacks in Federated Learning (EPAFL). We make three novel contributions. First, we systematically evaluate the computational costs for representative privacy attacks in federated learning, which exhibits a high potential to optimize efficiency. Second, we propose three early-stopping techniques to effectively reduce the computational costs of these privacy attacks. Third, we perform experiments on benchmark datasets and show that our proposed method can significantly reduce computational costs and maintain comparable attack success rates for state-of-the-art privacy attacks in federated learning. We provide the codes on GitHub at https://github.com/mlsysx/EPAFL.
Literature Review of the Working Environment of Certified Registered Nurse Anesthetists’ and its Relationship with Patient Safety, Quality of Care, and Job Satisfaction

Although there is vast amount of literature addressing the working conditions of registered nurses, little is known about the work environment of Certified Registered Nurse Anesthetists (CRNAs). The purpose of this literature review was to synthesize existing evidence on working conditions of CRNAs and, to the extent possible, examine how they influence patient safety, quality of care, and work satisfaction.   Primary sources used for the literature review were CINAHL and PubMed.   By utilizing combinations of Mesh Terms related to different aspects of CRNA work environment (communication, decision making, collaboration, and patient safety), fourteen articles were selected for analysis. The review of literatures revealed two overarching themes: (1) there were discrepancies in attitudes toward collaboration between CRNAs and anesthesiologists, and (2) CRNAs work in an intense environment that can lead to decreased job satisfaction and decreased patient safety. It must be noted that there was a limited research related to CRNA-physician collaboration, which limited clear ways to look into such aspect of the OR work environment. Future research should focus on utilizing strategies such as OR debriefings to mitigate the discrepancies in teamwork perceptions, implementing standardized hand-off process to improve patient safety, and instituting interventions such as zero tolerance policies and workshops to minimize incivility in the OR environment.
A Trust-Centered Explainable Deep-Learning Framework for Acute Lymphoblastic Leukemia Detection Using Multi-Model Fusion and Interpretability Scoring

Currently, advances in healthcare technologies are transforming medical diagnostics, particularly for data-driven disease detection. Acute lymphoblastic leukemia is a common and life-threatening blood cancer, especially prevalent in children. Although existing artificial intelligence-based diagnostic models have demonstrated promising accuracy, their black-box nature limits transparency, explainability, and clinical trust. Moreover, most explainable artificial intelligence approaches for leukemia diagnosis rely primarily on qualitative visual explanations and lack a unified quantitative mechanism to measure trust. This study presents a novel, trust-centered explainable deep-learning framework for automated acute lymphoblastic leukemia detection using 153 publicly available microscopic blood smear images and multiple transfer-learning models. Among the evaluated architectures, the fine-tuned EfficientNetB4 achieved high diagnostic performance, attaining an accuracy of 98.31%, and was selected as the base model of the proposed framework. Beyond diagnostic accuracy, this work introduces a novel unified interpretability score that quantitatively assesses model trustworthiness by integrating diagnostic performance, explanation robustness (evaluated using deletion and stability metrics), and clinician trust feedback into a single reliability measure. This quantitative trust formulation represents a distinct advancement over existing studies based on explainable artificial intelligence, which typically rely on isolated or qualitative explainability assessments. The framework further enhances transparency through visual and textual explanations generated using standard post hoc explainable methods and advanced fusion heatmaps, while human-centric evaluation is conducted using two clinician-based trust scales. Overall, this study provides a unified and transparent framework that jointly evaluates accuracy, explainability, and clinician trust, representing a step towards the clinical adoption of trustworthy artificial intelligence driven leukemia diagnostic systems.
Optimizing Supply Chain Management: The Impact of Machine Learning on Inventory and Logistics

Optimizing supply chain management through machine learning can significantly enhance inventory and logistics operations. This study presents a sophisticated machine learning framework designed to integrate demand forecasting with optimization algorithms to effectively manage inventory levels and logistics processes. The framework employs a Long Short-Term Memory (LSTM) model for demand forecasting, leveraging its capability to handle time-series data and capture complex patterns. By analyzing comprehensive historical sales data and various supply chain metrics, the model achieves a remarkable demand prediction accuracy of 92.5%. The study also incorporates optimization techniques, specifically Genetic Algorithms (GA) combined with Simulated Annealing (SA), to fine-tune inventory policies. This hybrid approach ensures that inventory holding costs are minimized while maintaining high service levels. The framework was implemented in a real-world retail supply chain, where it demonstrated substantial improvements. Notably, it reduced inventory holding costs by 18% and enhanced order fulfillment rates by 22%, reflecting its effectiveness in balancing cost efficiency with service quality. In addition to the technical aspects, the study addresses practical challenges such as data preprocessing, system integration, and continuous model retraining to adapt to changing market conditions. The results emphasize the profound influence of machine learning on enhancing the efficiency of supply chains, demonstrating its capacity to generate substantial cost savings and enhance service quality. This study focuses on the practical applications and advantages of advanced machine learning approaches in optimizing supply chain management, which will facilitate future advancements in this crucial field.
Additional file 2: of Toward the precision breast cancer survival prediction utilizing combined whole genome-wide expression and somatic mutation analysis

The Kaplanâ Meier estimator for an independent patient dataset. The survival curve base on 64 of 118 hazard-ratio-selected genes for METABRIC breast cancer patient dataset. (PDF 119Â kb)
AI-Augmented Creativity: Evaluating the Role  of Generative Models in Music Composition

Artificial Intelligence (AI) has become an increasingly influential force in the domain of creative expression, with generative models now playing a significant role in music composition. These models, particularly those built on deep learning architectures, are capable of producing melodies, harmonies, rhythms, and full musical pieces in a variety of styles. While AI's contributions to music composition   raise questions about authorship, originality, and artistic value, they also open new pathways for human-machine collaboration and creative augmentation. This paper explores the foundational technologies behind AI-generated music, including recurrent neural networks, transformers, and variation auto encoders. It investigates the use of generative models in both autonomous composition and cocreative systems. Use cases in film scoring, commercial music production, and educational tools are analyzed. Realworld applications and collaborations between musicians and AI systems are reviewed to understand the strengths and limitations of current technologies. Ethical and philosophical surrounding creativity, considerations intellectual property, and cultural representation are discussed. Finally, the paper considers the challenges of musical coherence, expressivity, and human-AI interaction, and outlines future directions in multimodal creativity, personalized composition, and real-time improvisation. AI-augmented music composition stands as a dynamic intersection of technology and art, expanding the boundaries of what it means to create music.
“Pop-Up Systems”—Innovative Sport and Exercise-Oriented Offerings for Promoting Physical Activity in All-Day Schools

In all-day schools and schools with extended education, children have the opportunity to spend the whole day at school. Full-day attendance at school requires a reorientation of extended time. Therefore, it is important to consider how children spend their time between lessons and identify conducive opportunities. As part of the project “Sport in the School Environment”, multiple mobile pop-up facilities were installed at several all-day schools in Zurich, Switzerland, for a period of 3–6 weeks. These facilities included “Parkour”, “Pumptrack”, “Skatepark”, and “Streetsoccer”. The aim was to find out whether these installations offer an opportunity for physical activity (PA). At the same time, social aspects of the students using these facilities as part of their all-day school attendance were observed. The following questions were investigated: How, by whom, and in which contexts are the installations used? The use of the facilities was evaluated content-analytically and descriptively. It was shown that the facilities are used intensively and in very different ways. The main focus of the pop-up facilities is on the use of the facilities and the creation of opportunities for children and young people to exercise and meet up with each other.
Overcoming Catastrophic Forgetting by XAI

Explaining the behaviors of deep neural networks, usually considered as black boxes, is critical especially when they are now being adopted over diverse aspects of human life. Taking the advantages of interpretable machine learning (interpretable ML), this work proposes a novel tool called Catastrophic Forgetting Dissector (or CFD) to explain catastrophic forgetting in continual learning settings. We also introduce a new method called Critical Freezing based on the observations of our tool. Experiments on ResNet articulate how catastrophic forgetting happens, particularly showing which components of this famous network are forgetting. Our new continual learning algorithm defeats various recent techniques by a significant margin, proving the capability of the investigation. Critical freezing not only attacks catastrophic forgetting but also exposes explainability.
Probing X-Ray Continua with Accretion-Disk Wind in Black Hole X-Ray Binaries

Abstract                High energy coronal radiation in Black Hole X-Ray Binary sources is often absorbed by accretion disk winds and the resulting absorption signatures heavily depend on its ionizing spectral energy distribution (SED). Considering multiple ion absorbers in the wind (e.g. Fe xxv/xxvi, S xvi, Si xiv, Ca xx and Mg xii), we find that the relative strength of absorption among these ions in general sensitively depends on the shape of the SED. We calculate a series of absorption line spectra from radiative transfer process for 9 distinct SED. Furthermore, we simulate 50 ks XRISM/Resolve spectra for each SED. We then fit the spectra by two-parameter wind model (SED and wind density normalization) to examine whether or not absorption features imprinted by the wind can be uniquely mapped to the ionizing SED.
Lessons Learned from Designing an Aeronautical Reconnaissance Coverage Geographic Information System Dashboard in the Geographic Information System-Naïve Environment to Fit the Spatial Epidemiology Study

Background Since patient access to healthcare is controlled by geographic location, patient records routinely include street addresses, postal codes, or other location elements. By linking spatial, numeric, and categorical health data through a single interface, the aeronautical reconnaissance coverage geographic information system (ArcGIS) dashboard adds the dimension of location and the quality of health information needed at the point of care. Its functions may be used to answer questions that would be difficult to answer without integrating raw patient data with location intelligence technology. However, the provision of geographic information system (GIS) functionalities at the point of care has not been thoroughly investigated. Aim This paper evaluates the ArcGIS dashboard functionalities through a case study of disruption in care for international patient populations due to the coronavirus disease-2019 (COVID-19) pandemic based on their home locations at the tertiary hospital in the U. S. Since the data used in this case study comes from the hospital health data repository, constructing the dashboard afforded an opportunity to explore the provision of GIS functionalities in the GIS–naïve hospital environment. Methods The model used the loose-coupling approach where two components (electronic health records and GIS data) were integrated and visualized through dashboard linking operations and programming languages. The process resulted in three data-driven dashboard elements: area map, attribute table, and statistical plots, enabling simultaneous exploration of various properties of spatial data. Conclusion This paper underpins the potential benefits of the provision of GIS functionalities in the electronic medical records to improve health outcomes, lower costs, and advance spatial epidemiology research. Additionally, it evaluates dashboard scaling limitations and solutions for overcoming the implementation barriers and proposes the future integration of the GIS dashboard with electronic health records by using the health level seven (HL7) fast healthcare interoperability resources (FHIR).
Linear Perturbations of the Scalar Temporal Field Ontology and the Origin of the Cosmic Microwave Background Angular Power Spectrum

This work derives and implements the linear perturbation sector of the Scalar Temporal Field Ontology (STFO), treating time as a dynamical scalar field. The temporal perturbations are shown to source metric fluctuations and reproduce the observed cosmic microwave background angular power spectrum while allowing a high late-time expansion rate. Numerical integration yields a high-precision fit to Planck 2018 data at H0=73.2H_0 = 73.2H0=73.2 km/s/Mpc, providing a first-principles resolution of the Hubble tension.
Legislative protection of minority rights in the language sphere: historical experience of Czechoslovakia and contemporary practice in Ukraine

A comprehensive analysis of the legislative protection of minority language rights in the First Czechoslovak Republic and contemporary Ukraine has been carried out in the article. The relevance of the study is determined by the need to combine the principle of the functioning of the state language with guarantees of linguistic identity of national minorities in accordance with European human rights standards.  The main legal foundations for regulating minority language rights in interwar Czechoslovakia have been determined, including constitutional provisions, the Language Law of 1920, and international obligations concerning minority protection. It has been clarified that the legislation of the First Czechoslovak Republic contained progressive provisions regarding the use of minority languages; however, their practical implementation was complicated by administrative and socio-political factors. Modern Ukrainian legislation in the field of minority language rights has been analyzed, including the Law of Ukraine “On Ensuring the Functioning of the Ukrainian Language as the State Language,” the Law “On National Minorities (Communities) of Ukraine,” and relevant by-laws aimed at implementing language rights in practice. The main directions of state policy on supporting linguistic diversity and integrating international standards into the national legal system have been identified.  A comparative analysis of the historical experience of Czechoslovakia and contemporary Ukrainian practice has been conducted. Common and distinctive features in the legal regulation of minority language rights have been determined. It has been established that the effectiveness of the implementation of legislative guarantees largely depends on institutional mechanisms, staffing capacity, and resource provision.  It is concluded that improvement of the mechanisms for implementing minority language rights in Ukraine requires a systematic approach aimed at balancing state language policy with guarantees of linguistic identity and cultural development of national minorities in accordance with international standards.
CMFM-Contrastive Masked Feature Modeling for Self-Supervised Representation Learning of High-Resolution Remote Sensing Images

CMFM is a self-supervised learning framework that combines masked autoencoders and contrastive learning, specifically designed for remote sensing image semantic segmentation tasks. Based on CNN and Vision Transformer architecture, this project achieves excellent performance in remote sensing image analysis through pretraining and downstream task fine-tuning.
Heliospheric Energetic Particles and Galactic Cosmic Ray Modulation

The paper presents an overview of the SH 'Solar and Heliospheric cosmic rays' session of the 24th European Cosmic Ray Symposium (ECRS), Kiel, Germany, 2014. It covers the topics of Solar Energetic Particle (SEP) origin, acceleration and transport at the Sun and in the interplanetary medium, also from the aspect of multi-spacecraft observations, as well as the Galactic Cosmic Ray (GCR) short- and long-term variations and the Jovian electron variations in the heliosphere. Relevant instruments and methods presented are also covered by this review. The paper is written from a personal perspective, emphasizing those results that the author found most interesting.