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题目大意 对一个初始矩阵进行置换操作,已知经K次置换后得到的矩阵为,求一组可能的. 样例解释 这里只选取第二组样例进行解释. 4 2 3 4 1 2 2 3 4 1 初始矩阵为,根据Sample Output得知置换操作为,第一次置换后得到矩阵,第二次置换后得到矩阵(和给出的一致),因此是一组可能的. 解题思路 考察一个特定的置换操作(我们暂且称这种矩阵为“置换矩阵”),我们将其写成另外一种形式,这样写的好处就是我们能方便的看出有2个循环节,而且我们会发现不管置换多少次,都是循环节内的数相互动了…
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Graph Reconstruction Time Limit: 2 Seconds      Memory Limit: 65536 KB      Special Judge Let there be a simple graph with N vertices but we just know the degree of each vertex. Is it possible to reconstruct the graph only by these information? A sim…
题目链接 Graph Reconstruction 题意 给你无向图每个点的度数, 问是否存在唯一解, 存在输出唯一解, 多解输出两个, 无解输出IMPOSSIBLE 思路 这里用到了 Havel-Hakimi定理, 实际上就是按照度数构图的一种贪心策略. 这样能判断出来一个解或无解, 多解的情况, 只要在比较构图时最后面两个点的当前度数一样, 那么选其一都是可行的, 但是选其一的情况有保证了另外一个肯定没有连上, 所以能找到其它解. 代码 #include <bits/stdc++.h> #…
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ICLR 2013 International Conference on Learning Representations May 02 - 04, 2013, Scottsdale, Arizona, USA ICLR 2013 Workshop Track Accepted for Oral Presentation Zero-Shot Learning Through Cross-Modal Transfer Richard Socher, Milind Ganjoo, Hamsa Sr…
Contents [hide] 1 Description 1.1 Task specific mailing list 2 Data 2.1 MajorMiner Tag Dataset 2.2 Mood Tag Dataset 3 Evaluation 3.1 Binary (Classification) Evaluation 3.2 Affinity (Ranking) Evaluation 3.3 Ranking and significance testing 3.4 Runtime…
论文信息 论文标题:Graph Auto-Encoder via Neighborhood Wasserstein Reconstruction论文作者:Shaked Brody, Uri Alon, Eran Yahav论文来源:2022,ICLR论文地址:download 论文代码:download 1 Abstract 图神经网络(GNNs)近年来引起了广泛的研究关注,主要是在半监督学习的背景下.当任务不可知的表示是首选或监督完全不可用时,自动编码器框架与无监督GNN训练的自然图重建目标一…
题目链接 http://acm.hdu.edu.cn/showproblem.php?pid=4800 Problem Description A role-playing game (RPG and sometimes roleplaying game) is a game in which players assume the roles of characters in a fictional setting. Players take responsibility for acting…
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