Contrastive Analysis
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Diff-CA: Separating Common and Salient Factors with Diffusion Models
arXiv:2606.06120v1 Announce Type: new Abstract: Contrastive Analysis aims to separate factors that are common between two data distributions from those that are salient to only one of them. Existing contrastive methods are based on generative models (e.g., VAEs or GANs) that often suffer from limited reconstruction and image quality, which hampers effective latent factor separation and limits their applicability to high-fidelity image generation and edition. We propose a novel conditioning...
Interpolatory Approximations of PMU Data: Dimension Reduction and Pilot Selection
arXiv:2510.20116v2 Announce Type: replace Abstract: This work investigates the reduction of phasor measurement unit (PMU) data through low-rank matrix approximations. To reconstruct a PMU data matrix from fewer measurements, we propose the framework of interpolatory matrix decompositions (IDs). In contrast to methods relying on principal component analysis or singular value decomposition, IDs recover the complete data matrix using only a few of its rows (PMU datastreams) and/or a few of its...
Encoding neuronal shape in the stochastic dynamics of branching processes
Cell shape critically influences function, yet how complex and reproducible morphologies emerge from stochastic cellular dynamics remains unclear. Here, we investigate dendritic morphogenesis of two classes of Drosophila mechanosensory neurons with contrasting architectures, combining in vivo live imaging, quantitative analysis, cytoskeletal perturbations, and computational modeling. We show that despite sharing similar local stochastic branching rules, the two classes exhibit divergent...
CoughSense: Five-Class Respiratory Disease Classification via Whisper Encoder Fine-Tuning and Dual-Encoder Cross-Attention Fusion with Balanced Contrastive Learning
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Transcriptomic profiling of the human habenula reveals a shared molecular architecture across mood disorders
The habenula is a critical regulator of monoaminergic and reward circuitry and is increasingly implicated in the neurobiology of mood disorders. Preclinical studies demonstrate that habenula hyperactivity drives depressive-like behaviours and can be reversed by interventions such as ketamine and deep brain stimulation. However, the molecular architecture of the human habenula remains largely unexplored.
AI Outperforms Law Professors in Stanford Law Study
A groundbreaking study led by Stanford Law School Professor Julian Nyarko reveals that law professors overwhelmingly prefer AI-generated answers to student questions over responses written by their fellow instructors—a finding that could reshape how legal education is delivered. The study, titled “Law Professors Prefer AI Over Peer Answers,” was conducted with 16 law professors across U.S. law schools and tested whether large language models could serve as effective tutors for contract law...
Have I Solved This Before? Retrieving Similar Segmentation Problems for Evolutionary Learning
Announce Type: new Abstract: Reliable integration and solid configuration of monitoring systems constitute a fundamental prerequisites for achieving high efficiency and productivity in contemporary manufacturing environments. Design decisions on sensor type and system architecture have to be made at an early stage and under comparably high uncertainty. This work investigates a research direction that deviates from the traditional monitoring-system development process by shifting the...
HEIST: A Graph Foundation Model for Spatial Transcriptomics and Proteomics Data
arXiv:2506.11152v4 Announce Type: replace-cross Abstract: Single-cell transcriptomics and proteomics have become a great source for data-driven insights into biology, enabling the use of advanced deep learning methods to understand cellular heterogeneity and gene expression at the single-cell level. With the advent of spatial-omics data, we have the promise of characterizing cells within their tissue context as it provides both spatial coordinates and intra-cellular transcriptional or...
Variable jackpot individuals provide most alleles for repeated, rapid adaptation to freshwater by anadromous Threespine Stickleback
The Threespine Stickleback has become a key model organism to study evolutionary biology. Experimental introductions of anadromous stickleback into freshwater habitats lacking this species allow analysis of the process of adaptation to freshwater forward-in-time in contrast to retrospective inference using naturally colonized populations. We examined the population genomic dynamics during early stages of adaptation in three replicate lakes that were experimentally founded, each using ~3000...
Unsupervised Skill Discovery for Agentic Data Analysis
arXiv:2606.06416v1 Announce Type: new Abstract: Inference-time skill augmentation provides a lightweight way to improve data-analytic agents by injecting reusable procedural knowledge without updating model parameters. However, discovering effective skills for data analysis remains challenging, as reliable supervision is expensive and success criteria vary across analytical formats. This raises the key question of how to discover reusable data-analysis skills from unlabeled exploration alone.