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Posts
Git常用命令实用指南
Published:
Git 是目前世界上最流行的分布式版本控制系统。它不仅能够高效地处理从小型到大型项目的各种版本管理需求，还提供了强大的分支管理、协作开发和历史追溯功能。本文将重点介绍 Git 的常用命令和实用技巧，帮助您在日常开发中更高效地使用 Git。
Hugging Face CLI (hf命令) 实用指南
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huggingface-cli 是 Hugging Face 官方提供的命令行工具，用于与 Hugging Face Hub 进行交互。随着其功能的不断增强，现在已更名为 hf 命令，提供了更简洁、强大的接口。本文将重点介绍 hf 命令的具体用法，包括如何使用 hf-mirror 镜像加速下载，以及其他实用的加速措施。
zip、unzip 和 tar 压缩解压实用指南
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zip、unzip 和 tar 是 Linux 系统中最常用的三个压缩解压工具。zip 和 unzip 专门用于处理 .zip 格式的压缩文件，其中 zip 用于创建压缩包，unzip 用于解压。而 tar 则主要用于打包多个文件和目录，并可以配合压缩算法（如 gzip、bzip2、xz）进行压缩。本文将重点介绍这三个工具的实用操作和常见场景，帮助您在日常工作中高效地处理各种压缩文件。
关于Tmux的常用操作
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tmux (Terminal Multiplexer) 是一个强大的终端复用器。它允许用户在单个终端窗口中创建、管理和切换多个独立的会话（Session）、窗口（Window）和面板（Pane）。tmux 的核心优势在于其会话持久化能力，即使终端关闭或网络断开，您在 tmux 中运行的任务也能继续在后台执行，随时可以重新连接并恢复工作。这对于远程开发、长时间运行任务以及提升终端工作效率至关重要。
portfolio
Portfolio item number 1
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Portfolio item number 2
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publications
Deep Learning Algorithm Based Remote Sensing Image Classification Research
Published in ICETCI 2023, 2023
This paper studies the performance ability of different deep learning models on WHDLD dataset.
Recommended citation: H. Zhu, X. Wang and R. Chen, "Deep Learning Algorithm Based Remote Sensing Image Classification Research," 2023 IEEE 3rd International Conference on Electronic Technology, Communication and Information (ICETCI), Changchun, China, 2023, pp. 1368-1373, doi: 10.1109/ICETCI57876.2023.10176407. https://ieeexplore.ieee.org/abstract/document/10176407
Prompt as a Double-Edged Sword: A Dynamic Equilibrium Gradient-Assigned Attack against Graph Prompt Learning
Published in KDD 2025, 2025
This paper proposes a dynamic equilibrium gradient-assigned attack against graph prompt learning, which can successfully attack the graph prompt learning model with a small amount of perturbations.
Recommended citation: Ju Jia, Jingxuan Yu, Di Wu, Cong Wu, Hengjie Zhu, and Lina Wang. 2025. Prompt as a Double-Edged Sword: A Dynamic Equilibrium Gradient-Assigned Attack against Graph Prompt Learning. In Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 (KDD 25). Association for Computing Machinery, New York, NY, USA, 1049–1060. https://doi.org/10.1145/3711896.3737091 https://dl.acm.org/doi/abs/10.1145/3711896.3737091
Discretization Is Not Always Better: Rethinking Deep Quantization for Asymmetric Image Retrieval
Published in AAAI 2026, 2026
This paper rethinks strict discretization for asymmetric image retrieval and proposes Deep Correlation Alignment Hashing (DCAH) with correlation alignment based quantization.
Recommended citation: Xinze Liu, Dayan Wu, Hengjie Zhu, Chenming Wu, and Pengwen Dai. 2026. Discretization Is Not Always Better: Rethinking Deep Quantization for Asymmetric Image Retrieval. Proceedings of the AAAI Conference on Artificial Intelligence, 40(9), 7323-7331. https://doi.org/10.1609/aaai.v40i9.37670 https://doi.org/10.1609/aaai.v40i9.37670
talks
Talk 1 on Relevant Topic in Your Field
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This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.