A User Study (Extended Version)
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A User Study (Extended Version)

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. […]

A User Study (Extended Version)
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Text-to-Emotional-Image Generation based on Valence-Arousal Model

[Submitted on 10 Jan 2025 (v1), last revised 22 Jul 2025 (this version, v2)] View a PDF of the paper titled EmotiCrafter: Text-to-Emotional-Image Generation based on Valence-Arousal Model, by Shengqi Dang and 5 other authors View PDF HTML (experimental) Abstract:Recent research shows that emotions can enhance users’ cognition and influence information communication. While research on

A User Study (Extended Version)
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Interpretable Topic Extraction and Word Embedding Learning using row-stochastic DEDICOM

arXiv:2507.16695v1 Announce Type: cross Abstract: The DEDICOM algorithm provides a uniquely interpretable matrix factorization method for symmetric and asymmetric square matrices. We employ a new row-stochastic variation of DEDICOM on the pointwise mutual information matrices of text corpora to identify latent topic clusters within the vocabulary and simultaneously learn interpretable word embeddings. We introduce a

A User Study (Extended Version)
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[2212.14720] Learning from Data Streams: An Overview and Update

[Submitted on 30 Dec 2022 (v1), last revised 22 Jul 2025 (this version, v3)] View a PDF of the paper titled Learning from Data Streams: An Overview and Update, by Jesse Read and Indr\.e \v{Z}liobait\.e View PDF HTML (experimental) Abstract:The literature on machine learning in the context of data streams is vast and growing. However,

A User Study (Extended Version)
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Zebra-CoT: A Dataset for Interleaved Vision Language Reasoning

arXiv:2507.16746v1 Announce Type: cross Abstract: Humans often use visual aids, for example diagrams or sketches, when solving complex problems. Training multimodal models to do the same, known as Visual Chain of Thought (Visual CoT), is challenging due to: (1) poor off-the-shelf visual CoT performance, which hinders reinforcement learning, and (2) the lack of high-quality visual

A User Study (Extended Version)
AI

Plug-and-Play Frequency Band Substitution of Diffusion Features for Highly Controllable Text-Driven Image Translation

[Submitted on 2 Aug 2024 (v1), last revised 22 Jul 2025 (this version, v4)] View a PDF of the paper titled FBSDiff: Plug-and-Play Frequency Band Substitution of Diffusion Features for Highly Controllable Text-Driven Image Translation, by Xiang Gao and 1 other authors View PDF Abstract:Large-scale text-to-image diffusion models have been a revolutionary milestone in the

A User Study (Extended Version)
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Lightweight Restoration of Safety in Pruned Large Vision-Language Models

[Submitted on 22 May 2025 (v1), last revised 22 Jul 2025 (this version, v2)] View a PDF of the paper titled Hierarchical Safety Realignment: Lightweight Restoration of Safety in Pruned Large Vision-Language Models, by Yue Li and 5 other authors View PDF HTML (experimental) Abstract:With the increasing size of Large Vision-Language Models (LVLMs), network pruning

A User Study (Extended Version)
AI

Training and Evaluating Reasoning LLM for Semiconductor Display

[Submitted on 19 Jul 2025 (v1), last revised 22 Jul 2025 (this version, v2)] Authors:Xiaolin Yan, Yangxing Liu, Jiazhang Zheng, Chi Liu, Mingyu Du, Caisheng Chen, Haoyang Liu, Ming Ding, Yuan Li, Qiuping Liao, Linfeng Li, Zhili Mei, Siyu Wan, Li Li, Ruyi Zhong, Jiangling Yu, Xule Liu, Huihui Hu, Jiameng Yue, Ruohui Cheng, Qi

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