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Farming Tong's Projects

mgfn icon mgfn

MGFN: Multi-Graph Fusion Networks for Urban Region Embedding (MGFN, https://fanxlxmu.github.io/publication/ijcai22/) was accepted by IJCAI-2022.

minerproxy icon minerproxy

最稳定的ETH以太坊代理中转矿池程序,MinerProxy/矿池代理,支持TCP和SSL协议,支持自定义抽水,高性能高并发,支持web界面管理。

minerproxy-1 icon minerproxy-1

最稳定的ETH代理中转程序;纯中转模式无任何抽水;支持SSL、自有证书;包含自启动和进程守护!放开防火墙和连接数限制,一键搞定。 挂一个冒充原作者的垃圾:qq1694202骗子一个

minerproxy-2 icon minerproxy-2

MinerProxy(中文名:加密挖全球 ),采用IOCP超高性能的网络框架编写,具有稳定性高,传输速度快,并发强等优点。可快速实现一键搭建任意矿池(ETH/ETC/RVN/ERGO/AE/BTC等)的中转节点,支持SSL加密,支持SSL矿池,支持任意第三方挖矿软件(包括OS系统),支持协议模拟功能,内网无需客户端即可SSL加密传输至VPS解密,并同时支持5个矿池的转发,有精准清晰的矿机数据展示列表,可方便配置合理抽水,自动拦截抽水增加业主算力。在保持业主算力不受大的影响的情况下,给予VPS搭建者一定的运营费用。

minerproxy-3 icon minerproxy-3

本程序为正版原创,开发费恒定!无论你抽3%还是30%,甚至80%,都是0.3%开发费!无视CC攻击,不怕扫描攻击,内置TLS证书(SSL升级版),抽水设置范围支持0.1%-80%;无需繁琐设置,支持ETC!欢迎实测!

minerproxy-4 icon minerproxy-4

基于GoLang开发的高性能MinerProxy矿池代理工具,支持ETH/ETC,支持TCP和SSL(TLS)协议,支持显示开发者抽水份额/平均难度/抽水比例。

minerproxy303 icon minerproxy303

一键安装+开防火墙+改连接数+自启动守护 矿池代理,支持TCP和SSL协议,支持自定义抽水,高性能高并发,支持web界面管理。

minerproxy303-1 icon minerproxy303-1

本仓库仅原版303的一键安装脚本,顺便加开防火墙+改连接数+自启动守护 矿池代理,支持TCP和SSL协议,支持自定义抽水,高性能高并发,支持web界面管理。

mise icon mise

Multimodal Image Synthesis and Editing: A Survey [Under Submission of TPAMI]

ndr-code icon ndr-code

[NeurIPS 2022] Neural Surface Reconstruction of Dynamic Scenes with Monocular RGB-D Camera

netdev icon netdev

基于fastapi实现简单网络自动化后端服务

neuralpoints icon neuralpoints

【Code of CVPR 2022 paper】Neural Points: Point Cloud Representation with Neural Fields for Arbitrary Upsampling

newblog icon newblog

使用SpringCloudAlibaba系列技术,采用nacos作为服务注册与配置中心。整合SSM框架进行后端开发,使用Vue和axios进行前端开发。 使用oauth2进行服务之间的权限管理以及验证并实现单点登陆,使用ElasticSearch实现文章搜索,使用Redis结合SpringCache实现缓存。 使用Rabbitmq实现邮件发送,SpringAop结合Rabbitmq实现日志管理 使用ThreadPool和异步编排优化数据获取数据。 使用nginx进行部署,并对其进行速度优化。

nodenote icon nodenote

MindMap software designed by PyQt5 with rich text and python synaxhighlighter.

np-match icon np-match

A Pytorch implementation of ICML 2022 paper "NP-Match: When Neural Processes meet Semi-Supervised Learning"

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