仓库/Skill 库

使用 vision-language model 处理视觉与文本数据，执行多模态推理与图像理解任务。Process visual and textual data with this vision-language model for multimodal reasoning and image understanding tasks.

综述论文《From Representation Learning to Foundation Models》的官方仓库。系统综述空间转录组学与病理学的多模态融合，提出三层分类法（Embedding、Model、Knowledge 层级）及 2018 至 2025 的演进路线图。Official repository for the survey "From Representation Learning to Foundation Models". A systematic review of multimodal fusion for Spatial Transcriptomics and Pathology, featuring a three-tier taxonomy (Embedding, Model, and Knowledge levels) and an evolutionary roadmap from 2018 to 2025.

🌐 通过 Geo-Llama 利用几何深度学习增强语言理解，结合 conformal manifolds 和递归等距变换提升 AI 模型性能。🌐 Enhance language understanding through geometric deep learning with Geo-Llama, leveraging conformal manifolds and recursive isometries for improved AI models.

用自然英语描述一个零件，即可获得完全可编辑的参数化 Fusion 360 模型。LLM 针对 28 个类型化 CAD 端点生成经过校验的 JSON feature 方案，通过 MCP server 与 Fusion 360 插件执行。附带 mock 后端，无需安装 CAD 即可运行。Describe a part in plain English, get a fully editable parametric Fusion 360 model. An LLM emits a validated JSON feature plan against 28 typed CAD endpoints, executed through an MCP server and a Fusion 360 add-in. Ships a mock backend so it runs with no CAD install.

面向 Linux VPS 服务器的自主式 AI 驱动 SOC 安全 agent 与实时威胁分类器。包含针对 48 种攻击类别微调的 DistilBERT Transformer、DDoS 异常检测器、系统资源监控器、带 HTML 邮件告警的中央告警引擎，以及 REST 健康诊断接口。Autonomous AI-powered SOC Security Agent & Real-Time Threat Classifier for Linux VPS servers. Features a fine-tuned DistilBERT transformer (48 attack classes), DDoS anomaly detector, system resource monitor , central alert engine with HTML email reporting, and REST health diagnostics.

RIFT — Race-state Inference From Telemetry。一项可复现研究实现，仅基于位置遥测重建具备事件感知的关联性比赛状态，涵盖路线进度、共享事件标识、检查点通过、物理顺序、前车关系、间距与间隔。RIFT — Race-state Inference From Telemetry. A reproducible research implementation for reconstructing occurrence-aware relational race state from positional telemetry alone, including route progress, shared occurrence identity, checkpoint crossings, physical order, car-ahead relations, gaps and intervals.

目标：将 State Space Models（SSM）/ Mamba 用于医学时间序列分析；总体研究问题：Mamba 模型能否提升基于步态信号检测帕金森病的分类模型性能: To use State Space Models (SSM)/ Mamba for medical time series analysis General research question: Can Mamba models improve the performance of classification models trained to detect Parkinson's disease using gait signals.