The Proceedings of the ACM on Networking (PACMNET) series showcases top-tier research in emerging computer networks and their applications. We welcome submissions introducing new technologies, innovative experiments, creative applications of networking ...

Gait re-training is an effective approach to slow disease progression and alleviate pain in knee osteoarthritis (KOA) patients. Personalized gait re-training strategies, based on knee loading and muscle forces, have shown promise in improving ...

Recent advancements in large language models have spurred significant developments in Time Series Foundation Models (TSFMs). These models claim great promise in performing zero-shot forecasting without the need for specific training, leveraging the ...

Diffusion models have demonstrated remarkable efficacy in generating high-quality samples. Existing diffusion-based image restoration algorithms exploit pre-trained diffusion models to leverage data priors, yet they still preserve elements inherited from ...

Recent studies reveal that even highly biased dense networks can contain an invariant substructure with superior out-of-distribution (OOD) generalization. While existing works commonly seek these substructures using global sparsity constraints, the ...

Conventionally, federated learning aims to optimize a single objective, typically the utility. However, for a federated learning system to be trustworthy, it needs to simultaneously satisfy multiple objectives, such as maximizing model performance, ...

Sensing in low-light and dark environments has a wide range of applications. However, existing sensing technologies suffer several major challenges, such as excessive noise and low resolution. In this work, we propose Mozart - a new mobile sensing system ...

The prediction of stock prices is a highly sought-after topic in the data mining field. In recent decades, many promising methods have been proposed and widely adopted for stock price prediction. However, these methods have inherent limitations, such as ...

Data imputation is a crucial task due to the widespread occurrence of missing data. Many methods adopt a two-step approach: initially crafting a preliminary imputation (the "draft") and then refining it to produce the final missing data imputation result,...

Lane-level traffic prediction is crucial for refined smart city applications, yet the scarcity and quality issues of datasets hinder its development. To overcome these challenges, this study introduces a novel <u> M </u>ulti-<u> c </u>hannel <u> g </u>...

Social event detection refers to extracting relevant message clusters from social media data streams to represent specific events in the real world. Social event detection is important in numerous areas, such as opinion analysis, social safety, and ...

In medical imaging applications, particularly in cardiac and skeletal analysis, the anatomical structure detection is crucial for diagnosing cardiac disease and other disease. However, the domain gap between images acquired from different sources ...

COIN++ is a special variant of Implicit Neural Representation (INR), which encodes signals as modulations applied to the base INR network. It is becoming a promising method for applications in image compression. However, INR’s effectiveness is ...

Recently, learning-based underwater enhancement (UIE) methods have made considerable progress, significantly benefiting downstream tasks such as underwater semantic segmentation and underwater depth estimation. Most existing unsupervised UIE ...

Actionable Warning Identification (AWI) plays a crucial role in improving the usability of static code analyzers. With recent advances in Machine Learning (ML), various approaches have been proposed to incorporate ML techniques into AWI. These ML-based ...

Three-dimensional (3D) shape generation techniques leveraging deep learning have garnered significant interest from both computer vision and architectural design communities, promising to enrich the content in the virtual environment. However, research on ...

Utilizing potent representations of the large vision-language models (VLMs) to accomplish various downstream tasks has attracted increasing attention. Within this research field, soft prompt learning has become a representative approach for ...

Lymph node metastasis (LNM) classification is crucial for breast cancer staging. However, the process of identifying tiny metastatic cancer cells within gigapixel whole slide image (WSI) is tedious, time-consuming, and expensive. To address this ...

Knowledge transfer from a source to a target domain is vital for whole slide image classification, given the limited dataset size due to high annotation costs. However, domain shift and task discrepancy between datasets can impede this process. To ...

Survival prediction requires integrating Whole Slide Images (WSIs) and genomics, a task complicated by significant heterogeneity and complex inter- and intra-modal interactions between modalities. Previous methods used co-attention, fusing ...