Staffing & Scheduling

High-Fidelity Differential-information Driven Binary Vision Transformer

The binarization of vision transformers (ViTs) offers a promising approach to addressing the trade-off between high computational/storage demands and the constraints of edge-device deployment. However, existing binary ViT methods often suffer from severe performance degradation or rely heavily on full-precision modules. To address these issues, we propose DIDB-ViT, a novel binary ViT that is hig...

AIRES: Accelerating Out-of-Core GCNs via Algorithm-System Co-Design

Graph convolutional networks (GCNs) are fundamental in various scientific applications, ranging from biomedical protein-protein interactions (PPI) to large-scale recommendation systems. An essential component for modeling graph structures in GCNs is sparse general matrix-matrix multiplication (SpGEMM). As the size of graph data continues to scale up, SpGEMMs are often conducted in an out-of-core...

Vision-Aided ISAC in Low-Altitude Economy Networks via De-Diffused Visual Priors

Emerging low-altitude economy networks (LAENets) require agile and privacy-preserving resource control under dynamic agent mobility and limited infr...

Learning Camera-Agnostic White-Balance Preferences

The image signal processor (ISP) pipeline in modern cameras consists of several modules that transform raw sensor data into visually pleasing images...

Review of Advanced Technologies and Circular Pathways for Food Waste Valorization.

Food waste (FW) valorization provides a sustainable solution to global waste challenges by enhancing resource efficiency and enabling a circular bioec...

HERCULES: Hardware accElerator foR stoChastic schedULing in hEterogeneous Systems

Efficient workload scheduling is a critical challenge in modern heterogeneous computing environments, particularly in high-performance computing (HP...

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration

Edge computing enables real-time data processing closer to its source, thus improving the latency and performance of edge-enabled AI applications. H...

Meaningful Data Erasure in the Presence of Dependencies

Data regulations like GDPR require systems to support data erasure but leave the definition of "erasure" open to interpretation. This ambiguity make...

Impact of Noisy Supervision in Foundation Model Learning.

Foundation models are usually pre-trained on large-scale datasets and then adapted to different downstream tasks through tuning. This pre-training and...

Approaches for Measuring and Predicting Fouling During Thermal Processing of Dairy Solutions.

Fouling during the thermal processing of dairy products remains a significant challenge, reducing operational efficiency, increasing energy consumptio...

Optimizing swine manure composting parameters with integrated CatBoost and XGBoost models: nitrogen loss mitigation and mechanism.

In this study, machine learning was used to optimize the aerobic composting process of swine manure to enhance nitrogen retention and compost maturity...

Laryngopharyngeal Reflux Prevalence and Predictors Among Doctors.

Laryngopharyngeal reflux (LPR) refers to the retrograde flow of stomach contents into the larynx due to an abnormality involving the upper oesophageal...

Deep implicit optimization enables robust learnable features for deformable image registration.

Deep Learning in Image Registration (DLIR) methods have been tremendously successful in image registration due to their speed and ability to incorpora...

Machine learning in biofluid mechanics: A review of recent developments.

This review paper comprehensively examines recent advancements in machine learning (ML) applications within biofluid mechanics, with a targeted focus ...

MHS U-Net: Multi-scale hybrid subtraction network for medical image segmentation.

Medical image segmentation plays a critical role in modern clinical diagnosis. However, existing methods face challenges such as insufficient feature ...

PBa-LLM: Privacy- and Bias-aware NLP using Named-Entity Recognition (NER)

The use of Natural Language Processing (NLP) in highstakes AI-based applications has increased significantly in recent years, especially since the e...

Navigating with Annealing Guidance Scale in Diffusion Space

Denoising diffusion models excel at generating high-quality images conditioned on text prompts, yet their effectiveness heavily relies on careful gu...

Agent.xpu: Efficient Scheduling of Agentic LLM Workloads on Heterogeneous SoC

The proliferation of agentic Large Language Models (LLMs) on personal devices introduces a new class of workloads characterized by a dichotomy of ob...

HASD: Hierarchical Adaption for pathology Slide-level Domain-shift

Domain shift is a critical problem for pathology AI as pathology data is heavily influenced by center-specific conditions. Current pathology domain ...

Blending Concepts with Text-to-Image Diffusion Models

Diffusion models have dramatically advanced text-to-image generation in recent years, translating abstract concepts into high-fidelity images with r...