COVID-19

GermRL: Alleviating The Germline Bias In Autoregressive Antibody Language Models Through Reinforcement Learning

Antibodies are powerful therapeutics whose antigen specificity arises from sequence diversity shaped during development. Recently, language models trained on large antibody repertoire datasets have enabled the generation and screening of novel candidates, but these models retain a strong germline bias. As AI adoption increases in therapeutic workflows, it is crucial to develop models that harness ...

AGZArank: Investigating epitope-conditioned antibody binder ranking with structure-derived synthetic supervision

Computational antibody design methods can generate large libraries of candidate binders for a target epitope, but prioritizing which candidates to test experimentally remains a major bottleneck. Existing scoring approaches, including physics-based affinity estimators, structure-prediction-derived confidence measures, and inverse-folding likelihood models, provide useful proxy signals but are not e...

TMO: ASYMMETRIC CROSS-MODAL ATTENTION FOR LEARNINGCELL-STATE-DEPENDENT REGULATORY LAGS FROM SINGLE-CELL MULTIOMIC DATA

Abstract Background: Single-cell multi-omics technologies simultaneously measure chromatin accessibility (ATAC) and gene expression (RNA), providing a...

Is It You or Your Environment? A Bayesian Inference Framework for Genomically-Anchored Personalized Physiological Interpretation

Personalized health AI systems face a fundamental cold-start problem: machine learning models for physiological interpretation require weeks of indivi...

Active Learning-Guided Peptide Design for Modulating Condensate Properties upon Recruitment

The physical properties of biomolecular condensates, which form through phase separation, are central to their organisation and function and are incre...

Topological Deep Learning Identifies Polygenic Variant Clusters Across Familial Multimorbid Disorders

Whole-genome sequencing comprehensively captures coding, non-coding and structural variation in families with suspected inherited disorders, yet its c...

AI-guided analysis of human pancreatic islet sociology reveals distinct cell compositional changes in type 1 diabetes

Human pancreatic islets exhibit greater anatomic and cellular heterogeneity than previously appreciated, raising fundamental questions about how their...

PF-Trans: Physics-Embedded Frequency-Aware Transformer for Spectral Reconstruction

Snapshot Broadband Filter Array (BFA) imaging provides high light throughput for spectral reconstruction but introduces severe spectral aliasing due t...

Do Vision-Language Models See or Guess? Measuring and Reducing Textual-Prior Reliance with a Phrasing-Controlled Benchmark

Vision-language models (VLMs) are increasingly deployed where answers must follow from what is in the image, yet they often answer from textual priors...

Vision-Assisted Foundation Model for Solving Multi-Task Vehicle Routing Problems

Multi-task vehicle routing problems play a critical role in enhancing efficiency across various industries and service sectors. These problems consist...

Is Fairness Truly Fair? Towards Reliable Lipschitz Fairness in Multi-Task Learning via Fixed-\texorpdfstring{$δ$}{delta} Alignment

Lipschitz-style individual fairness formalizes the idea that semantically similar examples should receive similar predictions, but its evaluation in m...

Exploring the Design Space of Reward Backpropagation for Flow Matching

Aligning text-to-image flow matching models with human preferences via direct reward backpropagation is sample-efficient but hampered by two well-know...

MOFA-VTON: More Fashion Possibilities with Fine-Grained Adaptations in Virtual Try-On

Virtual try-on aims to fit an in-shop clothing image onto a specific human body. An optimal virtual try-on method should provide diverse and flexible ...

Quality Is Not a Safety Proxy Under Quantization

Quantized checkpoints are often screened first with quality metrics and only later, if at all, with direct safety tests. This paper audits that shortc...

Multi-feature Classification to Improve Colorimetric Loop-Mediated Isothermal Amplification Fidelity

Loop-mediated isothermal amplification (LAMP) is a cost-effective and portable assay technique for performing nucleic acid-based diagnostics in the fi...

Reason Twice: Segmentation via Candidate Discovery and Comparative Reasoning

The rapid development of pretrained foundation models has enabled more general image segmentation. Multimodal large language models (MLLMs) have been ...

Echo-DM: Ultrasound Marker Removal via Conditional Latent Diffusion and Region-Aware Fusion

Clinical ultrasound images often contain artificial markers, such as measurement calipers and text, to assist diagnostic interpretation and comparison...

GD-MIL: Grade-Disentangled Multiple Instance Learning for Multimodal Biochemical Recurrence Prediction in Prostate Cancer

Biochemical recurrence (BCR) after radical prostatectomy is a critical endpoint in prostate cancer, yet risk stratification relies almost entirely on ...

End-to-End Optimization of Incoherent Imaging for Classification Under Detector-Limited Readout

End-to-end co-optimization of optical front-ends (e.g. metasurfaces) and neural network back-ends has been widely applied to imaging tasks, yet a form...

TrioPose: Native Triple-Stream Diffusion Transformers for Pose-Guided Text-to-Image Generation

Pose-guided text-to-image generation often suffers from limb distortions and feature crosstalk in complex multi-person scenarios. While existing UNet-...