Staffing & Scheduling

Empirical Analysis of the Impact of 5G Jitter on Time-Aware Shaper Scheduling in a 5G-TSN Network

Deterministic communications are essential for industrial automation, ensuring strict latency requirements and minimal jitter in packet transmission. Modern production lines, specializing in robotics, require higher flexibility and mobility, which drives the integration of Time-Sensitive Networking (TSN) and 5G networks in Industry 4.0. TSN achieves deterministic communications by using mechanis...

Adaptive Weighted Parameter Fusion with CLIP for Class-Incremental Learning

Class-incremental Learning (CIL) enables the model to incrementally absorb knowledge from new classes and build a generic classifier across all previously encountered classes. When the model optimizes with new classes, the knowledge of previous classes is inevitably erased, leading to catastrophic forgetting. Addressing this challenge requires making a trade-off between retaining old knowledge a...

Out-of-distribution evaluations of channel agnostic masked autoencoders in fluorescence microscopy

Developing computer vision for high-content screening is challenging due to various sources of distribution-shift caused by changes in experimental ...

Revisiting Automatic Data Curation for Vision Foundation Models in Digital Pathology

Vision foundation models (FMs) are accelerating the development of digital pathology algorithms and transforming biomedical research. These models l...

Balanced Direction from Multifarious Choices: Arithmetic Meta-Learning for Domain Generalization

Domain generalization is proposed to address distribution shift, arising from statistical disparities between training source and unseen target doma...

End-to-End Deep Learning for Real-Time Neuroimaging-Based Assessment of Bimanual Motor Skills

The real-time assessment of complex motor skills presents a challenge in fields such as surgical training and rehabilitation. Recent advancements in...

Seeing What Matters: Empowering CLIP with Patch Generation-to-Selection

The CLIP model has demonstrated significant advancements in aligning visual and language modalities through large-scale pre-training on image-text p...

Specifying What You Know or Not for Multi-Label Class-Incremental Learning

Existing class incremental learning is mainly designed for single-label classification task, which is ill-equipped for multi-label scenarios due to ...

Jasmine: Harnessing Diffusion Prior for Self-supervised Depth Estimation

In this paper, we propose Jasmine, the first Stable Diffusion (SD)-based self-supervised framework for monocular depth estimation, which effectively...

Am I eligible? Natural Language Inference for Clinical Trial Patient Recruitment: the Patient's Point of View

Recruiting patients to participate in clinical trials can be challenging and time-consuming. Usually, participation in a clinical trial is initiated...

Real-world validation of a multimodal LLM-powered pipeline for High-Accuracy Clinical Trial Patient Matching leveraging EHR data

Background: Patient recruitment in clinical trials is hindered by complex eligibility criteria and labor-intensive chart reviews. Prior research usi...

Robust Object Detection of Underwater Robot based on Domain Generalization

Object detection aims to obtain the location and the category of specific objects in a given image, which includes two tasks: classification and loc...

Involution and BSConv Multi-Depth Distillation Network for Lightweight Image Super-Resolution

Single Image Super-Resolution (SISR) aims to reconstruct high-resolution (HR) images from low-resolution (LR) inputs. Deep learning, especially Conv...

Revisiting Image Fusion for Multi-Illuminant White-Balance Correction

White balance (WB) correction in scenes with multiple illuminants remains a persistent challenge in computer vision. Recent methods explored fusion-...

CRCE: Coreference-Retention Concept Erasure in Text-to-Image Diffusion Models

Text-to-Image diffusion models can produce undesirable content that necessitates concept erasure. However, existing methods struggle with under-eras...

Shift, Scale and Rotation Invariant Multiple Object Detection using Balanced Joint Transform Correlator

The Polar Mellin Transform (PMT) is a well-known technique that converts images into shift, scale and rotation invariant signatures for object detec...

Fire and Smoke Datasets in 20 Years: An In-depth Review

Fire and smoke phenomena pose a significant threat to the natural environment, ecosystems, and global economy, as well as human lives and wildlife. ...

Container late-binding in unprivileged dHTC pilot systems on Kubernetes resources

The scientific and research community has benefited greatly from containerized distributed High Throughput Computing (dHTC), both by enabling elasti...

DehazeMamba: SAR-guided Optical Remote Sensing Image Dehazing with Adaptive State Space Model

Optical remote sensing image dehazing presents significant challenges due to its extensive spatial scale and highly non-uniform haze distribution, w...

Federated Learning with Domain Shift Eraser

Federated learning (FL) is emerging as a promising technique for collaborative learning without local data leaving their devices. However, clients' ...