Staffing & Scheduling

Optimizing Earth Observation Satellite Schedules under Unknown Operational Constraints: An Active Constraint Acquisition Approach

Earth Observation (EO) satellite scheduling (deciding which imaging tasks to perform and when) is a well-studied combinatorial optimization problem. Existing methods typically assume that the operational constraint model is fully specified in advance. In practice, however, constraints governing separation between observations, power budgets, and thermal limits are often embedded in engineering art...

Towards Successful Implementation of Automated Raveling Detection: Effects of Training Data Size, Illumination Difference, and Spatial Shift

Raveling, the loss of aggregates, is a major form of asphalt pavement surface distress, especially on highways. While research has shown that machine learning and deep learning-based methods yield promising results for raveling detection by classification on range images, their performance often degrades in large-scale deployments where more diverse inference data may originate from different runs...

Adaptive Data Dropout: Towards Self-Regulated Learning in Deep Neural Networks

Deep neural networks are typically trained by uniformly sampling large datasets across epochs, despite evidence that not all samples contribute equall...

Nationwide Prediction of Missed and Cancelled Appointments Using Real-World EHR Data

ObjectivesTo develop and evaluate predictive models for unused outpatient appointments (missed or cancelled) using a large national electronic health ...

Speech-preserving active noise control: a deep learning approach in reverberant environments

Traditional Active Noise Control (ANC) systems are mostly based on FxLMS algorithms, but such algorithms rely on linear assumptions and are often limi...

Efficient Transceiver Design for Aerial Image Transmission and Large-scale Scene Reconstruction

Large-scale three-dimensional (3D) scene reconstruction in low-altitude intelligent networks (LAIN) demands highly efficient wireless image transmissi...

Anthropogenic Regional Adaptation in Multimodal Vision-Language Model

While the field of vision-language (VL) has achieved remarkable success in integrating visual and textual information across multiple languages and do...

Not All Forgetting Is Equal: Architecture-Dependent Retention Dynamics in Fine-Tuned Image Classifiers

Fine-tuning pretrained image classifiers is standard practice, yet which individual samples are forgotten during this process, and whether forgetting ...

LangFlow: Continuous Diffusion Rivals Discrete in Language Modeling

Continuous diffusion models have achieved strong performance across domains such as images. However, in language modeling, prior continuous diffusion ...

WBCBench 2026: A Challenge for Robust White Blood Cell Classification Under Class Imbalance

We present WBCBench 2026, an ISBI challenge and benchmark for automated WBC classification designed to stress-test algorithms under three key difficul...

SHIFT: Steering Hidden Intermediates in Flow Transformers

Diffusion models have become leading approaches for high-fidelity image generation. Recent DiT-based diffusion models, in particular, achieve strong p...

Arbitration Failure, Not Perceptual Blindness: How Vision-Language Models Resolve Visual-Linguistic Conflicts

When a Vision-Language Model (VLM) sees a blue banana and answers "yellow", is the problem of perception or arbitration? We explore the question in te...

EGLOCE: Training-Free Energy-Guided Latent Optimization for Concept Erasure

As text-to-image diffusion models grow increasingly prevalent, the ability to remove specific concepts-mostly explicit content and many copyrighted ch...

FlowInOne:Unifying Multimodal Generation as Image-in, Image-out Flow Matching

Multimodal generation has long been dominated by text-driven pipelines where language dictates vision but cannot reason or create within it. We challe...

QNAS: A Neural Architecture Search Framework for Accurate and Efficient Quantum Neural Networks

Designing quantum neural networks (QNNs) that are both accurate and deployable on NISQ hardware is challenging. Handcrafted ansatze must balance expre...

CRISP: Rank-Guided Iterative Squeezing for Robust Medical Image Segmentation under Domain Shift

Distribution shift in medical imaging remains a central bottleneck for the clinical translation of medical AI. Failure to address it can lead to sever...

Adaptive Differential Privacy for Federated Medical Image Segmentation Across Diverse Modalities

Large volumes of medical data remain underutilized because centralizing distributed data is often infeasible due to strict privacy regulations and ins...

Attitudes and Perceptions Toward the Use of Artificial Intelligence Chatbots for Peer Review in Medical Journals: A Large-Scale, International Cross-Sectional Survey

Background: Artificial intelligence chatbots (AICs), as a form of generative artificial intelligence (AI), are increasingly being considered for use i...

Transfer Learning for Meta-analysis Under Covariate Shift

Randomized controlled trials often do not represent the populations where decisions are made, and covariate shift across studies can invalidate standa...

Light-ResKAN: A Parameter-Sharing Lightweight KAN with Gram Polynomials for Efficient SAR Image Recognition

Synthetic Aperture Radar (SAR) image recognition is vital for disaster monitoring, military reconnaissance, and ocean observation. However, large SAR ...