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　　这道题教会我 们 一个道理靠谁也不如靠自己。 　　当时学长已经讲了，然而一脸懵逼，好吧，上网搜题解，二脸懵逼，于是自己动手，丰衣足食。自己推！ 　　首先就是建模了，这道题谁与谁之间建模已经十分明了，超级源点，超级汇点没跑，重点在权值，首先，依照题意，是“舍弃”，因此基本确认为最小割，那么最小割从哪里割，就是我们每个人都雇佣所赚的钱（由样例可知，实际上是2倍所赚的钱，以下省略“*2”，简称合作金），但先不必算雇佣金，在一开始先只考虑赚钱，那么先说最简单的，两个人都雇佣，那么我们所赚的钱为两人合作金-雇佣两人所花的钱，那么我们割掉的边的流量就应当是雇佣金了，于是乎，连向终点的流量get，即为雇佣金。 　　于是我们happy的继续推，如果这两个人我都不雇佣，那么我损失的就是雇佣他俩所赚的钱了，而我割的边的流量就是合作金，因为两人均摊，因此不必*2。 　　最后，也就是个人认为比较复杂的就是一个雇佣一个不雇佣的情况，那么比起两人一起合作所产生的合作金我少的就是两倍合作金+敌对公司让我减少的一倍合作金+我所雇佣的那个人的雇佣金，由之前我们可知我割掉雇佣的那个人与汇点的时候已减少雇佣金，我割掉不雇佣的人与源点的时候以减去一倍合作金，于是乎，剩下的就是两倍合作金了，这就是两人之间的流量，记得开双向哈。 　　总的来说就是这样，由简到难，由已知边的流量去推未知边的流量，找准总值为关键

People-Detection-and-Tracking [Github 原文档] @Bobby Chen 记得留下小星星 TownCentre 行人流量计数 Demo - [ Bilibili ] Requirement - 依赖项 OpenCV NumPy sklean Pillow tensorflow-gpu 1.10.0 项目算法 It uses: * YOLOv3 to detect objects on each of the video frames. - 目标检测算法 * Deep_SORT to track those objects over different frames. - 目标追踪算法 DeepSORT 是在 SORT 目标追踪基础上的改进版本，加入了行人重识别数据集上训练的深度学习模型，在实时目标追踪过程中，提取目标的表观特征进行最近邻匹配，提高了目标追踪的鲁棒性。 This repository contains code for Simple Online and Realtime Tracking with a Deep Association Metric (Deep SORT). We extend the original SORT algorithm to integrate appearance information based

In 2019 ICPC subregions structure was changed a little. Now for each subregion, we need to choose the best place for the subregion finals. To make things fair we want to choose a city in such a way that all teams will spend the same amount of time to get there. To make things simpler we say that all teams will use trains to get to the finals. The railroad system can be represented as a tree where each city is a vertex and some pairs of cities are connected by a railroad. It takes exactly one hour to get from one city to another if they are directly connected. You are given a description of the

优先队列重载有三种写法： 第一种： struct node { int val, deep; friend bool operator < (node a, node b) { if(a.val == b.val) { return a.deep > b.deep; } return a.val > b.val; } }; 其中，当满足下列自己填写的条件时，队列就会按照从小到大来排序。 第二种： struct node { int val, deep; bool operator < (const node &a) const { if(val == a.val) { return deep > a.deep; } return val > a.val; } }; 第三种： struct node { int val, deep; }; bool operator < (const node &a, const node &b) { if(a.val == b.val) { return a.deep > b.deep; } return a.val > b.val; } 上面两种都是放结构体里面，第三种时放结构体外面。 来源： https://www.cnblogs.com/buhuiflydepig/p/11330996.html

Deep Learning at NIPS2012 by Kevin Duh on Dec 17, 2012 I went to NIPS2012 to learn about recent advances in Deep Learning (see [1] for conference pre-proceedings and [2] for a well-attended workshop program by Bengio, Bergstra, & Le). There were an amazing number of papers on this topic this year. To summarize, the area is developing extremely fast, and what we knew last year may no longer be "true" this year. For example, I had thought that the unsupervised training objective and the auto-encoder/RBM building blocks are the key ingredients of Deep Learning. However, some have called this into