arXiv:2504.21344v3 Announce Type: replace-cross Abstract: Machine learning models have utilized semantic features, deep features, or both to assess lung nodule malignancy. However, their reliance on manual annotation during inference, limited interpretability, and sensitivity to imaging variations hinder their application in real-world clinical settings. Thus, this research aims to integrate semantic features derived from radiologists’ assessments […]
Detecting active L’evy particles using differential dynamic microscopy
arXiv:2511.00775v1 Announce Type: cross Abstract: Detecting L’evy flights of cells has been a challenging problem in experiments. The challenge lies in accessing data in spatiotemporal scales across orders of magnitude, which is necessary for reliably extracting a power-law scaling. Differential dynamic microscopy has been shown to be a powerful method that allows one to acquire […]
Theoretical morphology of a cichlid according to the approach of Systemic Morphometry
arXiv:2511.00310v1 Announce Type: new Abstract: We analyzed the body structure of the Blackstripe Cichlid Vieja fenestrata (G”unther, 1860), a species with highly phenotypic variability, by the Systemics Morphometrics Methodology, previously proposed by one of the authors. From this perspective and considering the properties of its bauplan, we describe the expected morphometrics variability of this species. […]
Deciphering Scientific Collaboration in Biomedical LLM Research: Dynamics, Institutional Participation, and Resource Disparities
arXiv:2511.00818v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly transforming biomedical discovery and clinical innovation, yet their impact extends far beyond algorithmic revolution-LLMs are restructuring how scientific collaboration occurs, who participates, and how resources shape innovation. Despite this profound transformation, how this rapid technological shift is reshaping the structure and equity of scientific […]
Balancing Caregiving and Self-Care: Exploring Mental Health Needs of Alzheimer’s and Dementia Caregivers
arXiv:2506.14196v2 Announce Type: replace-cross Abstract: Alzheimer’s Disease and Related Dementias (AD/ADRD) are progressive neurodegenerative conditions that impair memory, thought processes, and functioning. Family caregivers of individuals with AD/ADRD face significant mental health challenges due to long-term caregiving responsibilities. Yet, current support systems often overlook the evolving nature of their mental wellbeing needs. Our study examines […]
MULTI-Bench: A Multi-Turn Interactive Benchmark for Assessing Emotional Intelligence ability of Spoken Dialogue Models
arXiv:2511.00850v1 Announce Type: cross Abstract: Spoken Dialogue Models (SDMs) have advanced rapidly, yet their ability to sustain genuinely interactive multi-turn conversations remains underexplored, as most benchmarks focus on single-turn exchanges. We introduce Multi-Bench, the first benchmark explicitly designed to evaluate SDMs in multi-turn interactive dialogue with an emphasis on emotional intelligence. Multi-Bench employs a hierarchical […]
Better Call CLAUSE: A Discrepancy Benchmark for Auditing LLMs Legal Reasoning Capabilities
arXiv:2511.00340v1 Announce Type: new Abstract: The rapid integration of large language models (LLMs) into high-stakes legal work has exposed a critical gap: no benchmark exists to systematically stress-test their reliability against the nuanced, adversarial, and often subtle flaws present in real-world contracts. To address this, we introduce CLAUSE, a first-of-its-kind benchmark designed to evaluate the […]
Learning with Category-Equivariant Representations for Human Activity Recognition
arXiv:2511.00900v1 Announce Type: cross Abstract: Human activity recognition is challenging because sensor signals shift with context, motion, and environment; effective models must therefore remain stable as the world around them changes. We introduce a categorical symmetry-aware learning framework that captures how signals vary over time, scale, and sensor hierarchy. We build these factors into the […]
RL Fine-Tuning Heals OOD Forgetting in SFT
arXiv:2509.12235v2 Announce Type: replace-cross Abstract: The two-stage fine-tuning paradigm of Supervised Fine-Tuning (SFT) followed by Reinforcement Learning (RL) has empirically shown better reasoning performance than one-stage SFT for the post-training of Large Language Models (LLMs). However, the evolution and mechanism behind the synergy of SFT and RL are still under-explored and inconclusive. In our study, […]
Modeling Microenvironment Trajectories on Spatial Transcriptomics with NicheFlow
arXiv:2511.00977v1 Announce Type: cross Abstract: Understanding the evolution of cellular microenvironments in spatiotemporal data is essential for deciphering tissue development and disease progression. While experimental techniques like spatial transcriptomics now enable high-resolution mapping of tissue organization across space and time, current methods that model cellular evolution operate at the single-cell level, overlooking the coordinated development […]