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Unsupervised Cardiac Video Translation Via Motion Feature Guided Diffusion Model
Swakshar Deb, Nian Wu, Frederick H. Epstein, Miaomiao Zhang
https://arxiv.org/abs/2507.02003

Unsupervised Cardiac Video Translation Via Motion Feature Guided Diffusion Model
This paper presents a novel motion feature guided diffusion model for unpaired video-to-video translation (MFD-V2V), designed to synthesize dynamic, high-contrast cine cardiac magnetic resonance (CMR) from lower-contrast, artifact-prone displacement encoding with stimulated echoes (DENSE) CMR sequences. To achieve this, we first introduce a Latent Temporal Multi-Attention (LTMA) registration network that effectively learns more accurate and consistent cardiac motions from cine CMR image videos.…

Intelligent Channel Allocation for IEEE 802.11be Multi-Link Operation: When MAB Meets LLM
Shumin Lian, Jingwen Tong, Jun Zhang, Liqun Fu
https://arxiv.org/abs/2506.04594

Intelligent Channel Allocation for IEEE 802.11be Multi-Link Operation: When MAB Meets LLM
WiFi networks have achieved remarkable success in enabling seamless communication and data exchange worldwide. The IEEE 802.11be standard, known as WiFi 7, introduces Multi-Link Operation (MLO), a groundbreaking feature that enables devices to establish multiple simultaneous connections across different bands and channels. While MLO promises substantial improvements in network throughput and latency reduction, it presents significant challenges in channel allocation, particularly in dense netwo…

Show, Tell and Summarize: Dense Video Captioning Using Visual Cue Aided Sentence Summarization
Zhiwang Zhang, Dong Xu, Wanli Ouyang, Chuanqi Tan
https://arxiv.org/abs/2506.20567

Show, Tell and Summarize: Dense Video Captioning Using Visual Cue Aided Sentence Summarization
In this work, we propose a division-and-summarization (DaS) framework for dense video captioning. After partitioning each untrimmed long video as multiple event proposals, where each event proposal consists of a set of short video segments, we extract visual feature (e.g., C3D feature) from each segment and use the existing image/video captioning approach to generate one sentence description for this segment. Considering that the generated sentences contain rich semantic descriptions about the …

Domain-Adversarial Transfer Learning for Fault Root Cause Identification in Cloud Computing Systems
Bruce Fang, Danyi Gao
https://arxiv.org/abs/2507.02233 …

Domain-Adversarial Transfer Learning for Fault Root Cause Identification in Cloud Computing Systems
This paper addresses the challenge of fault root cause identification in cloud computing environments. The difficulty arises from complex system structures, dense service coupling, and limited fault information. To solve this problem, an intelligent identification algorithm based on transfer learning is proposed. The method introduces a shared feature extraction module and a domain adversarial mechanism to enable effective knowledge transfer from the source domain to the target domain. This imp…