investigating专题

UVa 11361 Investigating Div-Sum Property

这道题居然提交了十次才过....期间小问题不断。思路的话基本是《训练指南》里面来的,不过有几个小问题需要注意一下。第一,当K在大于100的情况下,就直接输出0就可以了。因为a,b不超过2^31,可以估算出a,b最多十位十进制数,那么每位最大为9,所以各个数字之和是不可能超过100的,那么个数字之和为模K为0的条件是永远不可能到达的。       还有一点是,当剩余数字d=0时,当且

UVa 11361 Investigating Div-Sum Property / 数位DP

先上代码 以后再说 #include <cstdio>#include <cstring>const int maxn = 110;int dp[maxn][maxn][maxn];int ok(int x, int k){if(x < 10)return x / k;int a = x;int b = 1;int l = 0;while(a){l++;a /= 10;b *= 10;

Malware Forensics: Investigating and Analyzing Malicious Code

版权声明:原创作品,允许转载,转载时请务必以超链接形式标明文章原始出版、作者信息和本声明。否则将追究法律责任。 http://blog.csdn.net/topmvp - topmvp Malware Forensics: Investigating and Analyzing Malicious Code covers the emerging and evolving field of

UVa 11361 Investigating Div-Sum Property (数位DP)

UVa 11361 Investigating Div-Sum Property 题目大意: 给定a,b,k三个正整数,统计在[a,b]之间的整数n中,有多少n自身是k的倍数,且n的各个数字(十进制)之和也是k的倍数.( 1⩽a⩽b⩽231 1\leqslant a\leqslant b\leqslant 2^{31}) 题目分析: 这是一道典型的数位DP题. n非常大,若是直接枚举的话

实习点滴(7)--《Investigating LSTM for Punctuation Prediction》论文笔记

原文地址:http://lxie.nwpu-aslp.org/papers/2016ISCSLP-XKT.pdf         本文是利用BiLstm(双向Lstm)+CRF模型,对词组间的标点符号进行预测。         作者首先强调了,递归神经网络(RNN)及其变体在各种序列标签的任务已经显示出优越的性能,例如词性(POS)标签,分块和命名实体识别,韵律边界预测和语言理

【胶囊网络-CapsNet】Investigating Capsule Networks with Dynamic Routing for Text Classification

Investigating Capsule Networks with Dynamic Routing for Text Classification 发表在EMNLP 2018 文章目录 ModelN-gram convolutional layerprimary capsule layerChild-Parent Relationships Dynamic Routingconv

Investigating Neural Network based Query-by-Example Keyword Spotting Approach for Personalized Wake-

Investigating Neural Network based Query-by-Example Keyword Spotting Approach for Personalized Wake-up Word Detection in Mandarin Chinese 基于神经网络的示例查询关键词识别方法在普通话个性化唤醒词检测中的研究 Abstract 我们使用示例查询关键字查找(Q

论文解读: 2023-Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval

Title: Investigating the Factual Knowledge Boundary of Large Language Modelswith Retrieval Augmentation URL: https://arxiv.org/abs/2307.11019 Code: https://github.com/RUCAIBox/LLM-Knowledge-Boundary/t