IntroductionIntelligent room systems are experiencing a surge in demand within the Healthcare 4.0 ecosystem. The integration of Federated Learning (FL) and Data-Centric AI has led to substantial enhancements in the predictive capabilities of machine learning models while maintaining data privacy. However, centralized aggregation in FL remains a single point of failure and is vulnerable to […]
Construction of patient trajectories to model clinical trial outcomes: application to myasthenia gravis
IntroductionAccurate prediction of patient outcomes in clinical trials is crucial for the timely assessment of treatment efficacy. This study proposes a novel approach to predict patient response using longitudinal clinical data.MethodsWe construct temporal trajectories from longitudinal data and extrapolate these trajectories to forecast individual patient outcomes. Additionally, we assess when new patients align with established […]
Effectiveness of the mobile application Holidaily in reducing work-related rumination when returning to work after vacation: a randomized controlled trial
BackgroundVacations reliably improve indicators of mental health, largely by providing relief from work-related stress. Low levels of work-related rumination, a key transdiagnostic factor linked to burnout and depression, are considered prerequisites for successful recovery both during vacations and in daily working life. However, such benefits are typically short-lived, with a rapid “fade-out” upon return to […]
Effectiveness of digital and mobile-based interventions on sleep quality among nurses: a systematic review and meta-analysis
BackgroundNurses frequently endure diminished sleep quality, sleeplessness, and psychological distress due to high-intensity shifts and persistent work pressure. Digital health interventions are increasingly utilised to enhance sleep behaviour; however, systematic information about their real benefits on the nursing population remains insufficient.ObjectiveTo assess the efficacy of digital and mobile interventions on sleep and associated psychological consequences […]
EAD-Net: Emotion-Aware Talking Head Generation with Spatial Refinement and Temporal Coherence
arXiv:2604.23325v1 Announce Type: cross Abstract: Emotionally talking head video generation aims to generate expressive portrait videos with accurate lip synchronization and emotional facial expressions. Current methods rely on simple emotional labels, leading to insufficient semantic information. While introducing high-level semantics enhances expressiveness, it easily causes lip-sync degradation. Furthermore, mainstream generation methods struggle to balance computational […]
Evaluating CUDA Tile for AI Workloads on Hopper and Blackwell GPUs
arXiv:2604.23466v1 Announce Type: cross Abstract: NVIDIA’s CUDA Tile (CuTile) introduces a Python-based, tile-centric abstraction for GPU kernel development that aims to simplify programming while retaining Tensor Core and Tensor Memory Accelerator (TMA) efficiency on modern GPUs. We present the first independent, cross-architecture evaluation of CuTile against established approaches such as cuBLAS, Triton, WMMA, and raw […]
Mixture of Heterogeneous Grouped Experts for Language Modeling
arXiv:2604.23108v1 Announce Type: cross Abstract: Large Language Models (LLMs) based on Mixture-of-Experts (MoE) are pivotal in industrial applications for their ability to scale performance efficiently. However, standard MoEs enforce uniform expert sizes,creating a rigidity that fails to align computational costs with varying token-level complexity. While heterogeneous expert architectures attempt to address this by diversifying expert […]
Training Machine Learning Models on Encrypted Data: A Privacy-Preserving Framework using Homomorphic Encryption
arXiv:2604.23245v1 Announce Type: cross Abstract: The use of Machine Learning (ML) for data-driven decision-making often relies on access to sensitive datasets, which introduces privacy challenges. Traditional encryption methods protect data at rest or in transit but fail to secure it during processing, exposing it to unauthorized access. Homomorphic encryption emerges as a transformative solution, enabling […]
Institutions for the Post-Scarcity of Judgment
arXiv:2604.22966v1 Announce Type: cross Abstract: Each major technological revolution inverts a particular scarcity and rebuilds institutions around the shift. The near-consensus diagnosis of the AI revolution holds that AI collapses the cost of prediction while judgment remains scarce. This Opinion argues the inversion has now flipped: competent-looking judgment (selecting, ranking, attributing, certifying) is produced at […]
On the Existence of an Inverse Solution for Preference-Based Reductions in Argumentation
arXiv:2604.22958v1 Announce Type: new Abstract: Preference-based argumentation frameworks (PAFs) extend Dung’s approach to abstract argumentation (AAFs) by encoding preferences over arguments. Such preferences control the transformation of attacks into defeats, and different approaches to doing so result in different reductions from a PAF to an AAF. In this paper we consider a PAF inverse problem […]
Inverting Foundation Models of Brain Function with Simulation-Based Inference
arXiv:2604.23865v1 Announce Type: cross Abstract: Foundation models of brain activity promise a new frontier for in silico neuroscience by emulating neural responses to complex stimuli across tasks and modalities. A natural next step is to ask whether these models can also be used in reverse. Can we recover a stimulus or its properties from synthetic […]
CyberCane: Neuro-Symbolic RAG for Privacy-Preserving Phishing Detection with Formal Ontology Reasoning
arXiv:2604.23563v1 Announce Type: cross Abstract: Privacy-critical domains require phishing detection systems that satisfy contradictory constraints: near-zero false positives to prevent workflow disruption, transparent explanations for non-expert staff, strict regulatory compliance prohibiting sensitive data exposure to external APIs, and robustness against AI-generated attacks. Existing rule-based systems are brittle to novel campaigns, while LLM-based detectors violate privacy […]