Product Alert

EAQuant: Enhancing Post-Training Quantization for MoE Models via Expert-Aware Optimization

Mixture-of-Experts (MoE) models have emerged as a cornerstone of large-scale deep learning by efficiently distributing computation and enhancing performance. However, their unique architecture-characterized by sparse expert activation and dynamic routing mechanisms-introduces inherent complexities that challenge conventional quantization techniques. Existing post-training quantization (PTQ) meth...

Buy it Now, Track Me Later: Attacking User Privacy via Wi-Fi AP Online Auctions

Static and hard-coded layer-two network identifiers are well known to present security vulnerabilities and endanger user privacy. In this work, we introduce a new privacy attack against Wi-Fi access points listed on secondhand marketplaces. Specifically, we demonstrate the ability to remotely gather a large quantity of layer-two Wi-Fi identifiers by programmatically querying the eBay marketplace...

Beyond the First Read: AI-Assisted Perceptual Error Detection in Chest Radiography Accounting for Interobserver Variability

Chest radiography is widely used in diagnostic imaging. However, perceptual errors -- especially overlooked but visible abnormalities -- remain comm...

Inference-Time Gaze Refinement for Micro-Expression Recognition: Enhancing Event-Based Eye Tracking with Motion-Aware Post-Processing

Event-based eye tracking holds significant promise for fine-grained cognitive state inference, offering high temporal resolution and robustness to m...

Post Persona Alignment for Multi-Session Dialogue Generation

Multi-session persona-based dialogue generation presents challenges in maintaining long-term consistency and generating diverse, personalized respon...

Post-Training Quantization for Video Matting

Video matting is crucial for applications such as film production and virtual reality, yet deploying its computationally intensive models on resourc...

Fine-Tuning Large Audio-Language Models with LoRA for Precise Temporal Localization of Prolonged Exposure Therapy Elements

Prolonged Exposure (PE) therapy is an effective treatment for post-traumatic stress disorder (PTSD), but evaluating therapist fidelity remains labor...

Hierarchical Feature-level Reverse Propagation for Post-Training Neural Networks

End-to-end autonomous driving has emerged as a dominant paradigm, yet its highly entangled black-box models pose significant challenges in terms of ...

Flood-DamageSense: Multimodal Mamba with Multitask Learning for Building Flood Damage Assessment using SAR Remote Sensing Imagery

Most post-disaster damage classifiers succeed only when destructive forces leave clear spectral or structural signatures -- conditions rarely presen...

Statistically Valid Post-Deployment Monitoring Should Be Standard for AI-Based Digital Health

This position paper argues that post-deployment monitoring in clinical AI is underdeveloped and proposes statistically valid and label-efficient tes...

Reliably detecting model failures in deployment without labels

The distribution of data changes over time; models operating operating in dynamic environments need retraining. But knowing when to retrain, without...

Medical World Model: Generative Simulation of Tumor Evolution for Treatment Planning

Providing effective treatment and making informed clinical decisions are essential goals of modern medicine and clinical care. We are interested in ...

Attention Regulation Among Sleep-Deprived Air-Force Pilots.

Short sleep duration is associated with adverse physical and mental events. However, it is quite challenging to objectively quantify its impact on hum...

Drug-Drug interactions and special considerations in breast cancer patients treated with CDK4/6 inhibitors: A comprehensive review.

Cyclin-dependent kinase 4/6 inhibitors (CDK4/6i) have reshaped the treatment paradigm of hormone receptor positive (HR + )/HER2-negative breast cancer...

From Acquisition to Prognosis: The Role of AI in Cardiac Magnetic Resonance Imaging Evaluation of Ischemic Cardiomyopathy.

Acute and chronic ischemic cardiomyopathy (ICM) still represents a leading cause of morbidity and mortality. Cardiac magnetic resonance (CMR) imaging ...

CR-deal: Explainable Neural Network for circRNA-RBP Binding Site Recognition and Interpretation.

circRNAs are a type of single-stranded non-coding RNA molecules, and their unique feature is their closed circular structure. The interaction between ...

PregAN-NET: Addressing Class Imbalance with GANs in Interpretable Computational Framework for Predicting Safety Profile of Drugs Considering Adverse Reactions During Pregnancy.

Adverse Drug Reactions (ADRs) during pregnancy pose significant risks to both the mother and the fetus. Conventional approaches to predict ADR are ina...

Establishment of a machine learning-based prediction framework to assess trade-offs in decisions that affect post-HCT outcomes.

In this study, we propose a conceptual framework of decision support tools, built upon machine learning and multi-objective optimization, aimed at off...

BenchXAI: Comprehensive benchmarking of post-hoc explainable AI methods on multi-modal biomedical data.

The increasing digitalization of multi-modal data in medicine and novel artificial intelligence (AI) algorithms opens up a large number of opportuniti...

Attention to early stages: predicting acute kidney injury in a post cardiosurgical ICU setting using an inclusive time-to-event model.

BACKGROUND: Acute kidney injury (AKI) is a critical complication in intensive care units (ICUs) that is known to have multifaceted impacts. However, a...