Staffing & Scheduling

Preserve Anything: Controllable Image Synthesis with Object Preservation

We introduce \textit{Preserve Anything}, a novel method for controlled image synthesis that addresses key limitations in object preservation and semantic consistency in text-to-image (T2I) generation. Existing approaches often fail (i) to preserve multiple objects with fidelity, (ii) maintain semantic alignment with prompts, or (iii) provide explicit control over scene composition. To overcome t...

SODA: Out-of-Distribution Detection in Domain-Shifted Point Clouds via Neighborhood Propagation

As point cloud data increases in prevalence in a variety of applications, the ability to detect out-of-distribution (OOD) point cloud objects becomes critical for ensuring model safety and reliability. However, this problem remains under-explored in existing research. Inspired by success in the image domain, we propose to exploit advances in 3D vision-language models (3D VLMs) for OOD detection ...

TADA: Improved Diffusion Sampling with Training-free Augmented Dynamics

Diffusion models have demonstrated exceptional capabilities in generating high-fidelity images but typically suffer from inefficient sampling. Many ...

Improving Diffusion-Based Image Editing Faithfulness via Guidance and Scheduling

Text-guided diffusion models have become essential for high-quality image synthesis, enabling dynamic image editing. In image editing, two crucial a...

Prediction of remaining surgery duration based on machine learning methods and laparoscopic annotation data.

OBJECTIVES: The operating room is a fast-paced and demanding environment. Among the various factors involved in its optimization, predicting surgery d...

Advances in Intelligent Hearing Aids: Deep Learning Approaches to Selective Noise Cancellation

The integration of artificial intelligence into hearing assistance marks a paradigm shift from traditional amplification-based systems to intelligen...

Machine learning-driven insights into retention mechanism in IAM chromatography of anticancer sulfonamides: Implications for biological efficacy.

Machine learning (ML) tools offer new opportunities in drug discovery, especially for enhancing our understanding of molecular interactions with biolo...

Cohort Discovery: A Survey on LLM-Assisted Clinical Trial Recruitment

Recent advances in LLMs have greatly improved general-domain NLP tasks. Yet, their adoption in critical domains, such as clinical trial recruitment,...

Aligning Evaluation with Clinical Priorities: Calibration, Label Shift, and Error Costs

Machine learning-based decision support systems are increasingly deployed in clinical settings, where probabilistic scoring functions are used to in...

Train Once, Forget Precisely: Anchored Optimization for Efficient Post-Hoc Unlearning

As machine learning systems increasingly rely on data subject to privacy regulation, selectively unlearning specific information from trained models...

Towards Reliable WMH Segmentation under Domain Shift: An Application Study using Maximum Entropy Regularization to Improve Uncertainty Estimation

Accurate segmentation of white matter hyperintensities (WMH) is crucial for clinical decision-making, particularly in the context of multiple sclero...

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 per...

Learning to Fuse: Modality-Aware Adaptive Scheduling for Robust Multimodal Foundation Models

Multimodal foundation models have achieved impressive progress across a wide range of vision-language tasks. However, existing approaches often adop...

Dynamic Modality Scheduling for Multimodal Large Models via Confidence, Uncertainty, and Semantic Consistency

Multimodal Large Models (MLLMs) have achieved remarkable progress in vision-language understanding and generation tasks. However, existing MLLMs typ...

OCT in dermatology: a process for determining whether a fully diversified dataset is needed for AI model-building.

Optical coherence tomography (OCT) has sufficient depth penetration for detection of skin pathologies, but its detection effectiveness can be aided by...

Less Conservative Adaptive Gain-scheduling Control for Continuous-time Systems with Polytopic Uncertainties

The synthesis of adaptive gain-scheduling controller is discussed for continuous-time linear models characterized by polytopic uncertainties. The pr...

Revisiting Clustering of Neural Bandits: Selective Reinitialization for Mitigating Loss of Plasticity

Clustering of Bandits (CB) methods enhance sequential decision-making by grouping bandits into clusters based on similarity and incorporating cluste...

Semantic Scheduling for LLM Inference

Conventional operating system scheduling algorithms are largely content-ignorant, making decisions based on factors such as latency or fairness with...

The Cambrian Explosion of Mixed-Precision Matrix Multiplication for Quantized Deep Learning Inference

Recent advances in deep learning (DL) have led to a shift from traditional 64-bit floating point (FP64) computations toward reduced-precision format...

DMAF-Net: An Effective Modality Rebalancing Framework for Incomplete Multi-Modal Medical Image Segmentation

Incomplete multi-modal medical image segmentation faces critical challenges from modality imbalance, including imbalanced modality missing rates and...