click-through prediction triangle interest
[Codeforces] CF1551C Interesting Story
CF1551C Interesting Story 题目传送门 题意 给定 \(n\) 个仅由 \(\texttt{a,b,c,d,e}\) 组成的单词 （\(n \le 2\times 10^5\)），从其中选出尽可能多的单词，使得存在某个字母在这些单词中出现的次数比其他所有字母的出现次数之和还要 ......
【WALT】predict_and_update_buckets() 与 update_task_pred_demand() 代码详解
@目录【WALT】predict_and_update_buckets() 与 update_task_pred_demand() 代码详解代码展示代码逻辑⑴ 根据 runtime 给出桶的下标⑵ 根据桶的下标预测 pred_demand1. 如果任务刚被创建，直接结束2. 根据下标 bidx 和数 ......
610. Triangle Judgement（Case when）
参考：https://blog.csdn.net/wh_07/article/details/103292280 思路就是使用CASE语句，但是我对这个不太熟悉，一起来学习一下吧。 CASE 语句是在 SQL 中用于实现条件逻辑的一种强大工具。它允许在查询中根据不同的条件执行不同的操作。CASE 语 ......
Predict potential miRNA-disease associations based on bounded nuclear norm regularization
Predict potential miRNA-disease associations based on bounded nuclear norm regularization 2023/12/8 16:00:57 Predicting potential miRNA-disease associ ......
[Codeforces] CF1538F Interesting Function
CF1538F Interesting Function 题目传送门 题意 给定两个正整数 \(l, r\)（\(l < r\)），将 \(l\) 不断加 \(1\) 直到 \(l = r\)，求出这一过程中 \(l\) 发生变化的位数总数。 位数变化指： \(l=909\)，将 \(l+1\) 后 ......
LandBench 1.0: a benchmark dataset and evaluation metrics for data-driven land surface variables prediction
李老师对于landbench的，基准模型进行的论文。 里面对于变量，数据集的描述，写论文可以用。 题目： “LandBench 1.0: a benchmark dataset and evaluation metrics for data-driven land surface variables ......
Predicting Drug-Target Interactions. drug-target interactions prediction
2023 [j22] Junjun Zhang, Minzhu Xie:Graph regularized non-negative matrix factorization with L2,1 norm regularization terms for drug-target interactio ......
论文精读：STMGCN利用时空多图卷积网络进行移动边缘计算驱动船舶轨迹预测（STMGCN: Mobile Edge Computing-Empowered Vessel Trajectory Prediction Using Spatio-Temporal Multigraph Convolutional Network）
《STMGCN: Mobile Edge Computing-Empowered Vessel Trajectory Prediction Using Spatio-Temporal Multigraph Convolutional Network》 论文链接：https://doi.org/10. ......
论文精读：基于具有时空感知的稀疏多图卷积混合网络的大数据驱动船舶轨迹预测（Big data driven trajectory prediction based on sparse multi-graph convolutional hybrid network withspatio-temporal awareness）
论文精读：基于具有时空感知的稀疏多图卷积混合网络的大数据驱动船舶轨迹预测 《Big data driven vessel trajectory prediction based on sparse multi-graph convolutional hybrid network with spati ......
CF1842E Tenzing and Triangle 题解
题意不多赘述。 思路 如果两个所选的三角形有重合部分的话，那么这种情况肯定是不会出现的。因为如果把这两个三角形合成一个大三角形的话，不仅覆盖面积会增大，而且花费的代价还不会多。 于是我们可以想到用 dp 来解决，设 \(dp_{i}\) 表示删完横坐标为 \(0\) 到 \(i\) 中的点的最小代价 ......
A Novel Approach Based on Bipartite Network Recommendation and KATZ Model to Predict Potential Micro-Disease Associations
A Novel Approach Based on Bipartite Network Recommendation and KATZ Model to Predict Potential Micro-Disease Associations Shiru Li 1, Minzhu Xie 1, Xi ......
Drug response prediction using graph representation learning and Laplacian feature selection
Drug response prediction using graph representation learning and Laplacian feature selection Minzhu Xie 1 2, Xiaowen Lei 3, Jianchen Zhong 3, Jianxing ......
Predict potential miRNA-disease associations based on bounded nuclear norm regularization
Predict potential miRNA-disease associations based on bounded nuclear norm regularization Yidong Rao 1, Minzhu Xie 1, Hao Wang 1 Affiliations expand P ......
Predicting gene expression from histone modifications with self-attention based neural networks and transfer learning
Predicting gene expression from histone modifications with self-attention based neural networks and transfer learning Yuchi Chen 1, Minzhu Xie 1, Jie ......
Graph regularized non-negative matrix factorization with prior knowledge consistency constraint for drug-target interactions prediction
Graph regularized non-negative matrix factorization with prior knowledge consistency constraint for drug-target interactions prediction Junjun Zhang 1 ......
LPI-IBWA: Predicting lncRNA-protein interactions based on an improved Bi-Random walk algorithm
LPI-IBWA: Predicting lncRNA-protein interactions based on an improved Bi-Random walk algorithm Minzhu Xie 1, Ruijie Xie 2, Hao Wang 3 Affiliations exp ......
Graph regularized non-negative matrix factorization with [Formula: see text] norm regularization terms for drug-target interactions prediction
Graph regularized non-negative matrix factorization with [Formula: see text] norm regularization terms for drug-target interactions prediction Junjun ......
LDAEXC: LncRNA-Disease Associations Prediction with Deep Autoencoder and XGBoost Classifier.
LDAEXC: LncRNA-Disease Associations Prediction with Deep Autoencoder and XGBoost Classifier. 作者: Lu Cuihong; Xie Minzhu 作者背景: College of Information S ......
B4185. LPI-IBWA:Predicting lncRNA-protein Interactions Based on Improved Bi-Random Walk Algorithm
B4185. LPI-IBWA:Predicting lncRNA-protein Interactions Based on Improved Bi-Random Walk Algorithm Minzhu Xie1, Hao Wang1 and Ruijie Xi1 1Hunan Normal ......
论文：Predicting Optical Water Quality Indicators from Remote Sensing Using Machine Learning Algorithms in Tropical Highlands of Ethiopia
水刊，中科院都没有收录。不属于sci。 吃一堑长一智，以后先看属于哪个期刊的。总是忘记。 期刊：Hydrology 浪费时间，啥也没有，没有创新点，就一点点的对比工作量。 “Predicting Optical Water Quality Indicators from Remote Sensing ......
论文：Predicting the performance of green stormwater infrastructure using multivariate long short-term memory (LSTM) neural network
题目“Predicting the performance of green stormwater infrastructure using multivariate long short-term memory (LSTM) neural network” (Al Mehedi 等, 2023, ......
论文：Multistep ahead prediction of temperature and humidity in solar greenhouse based on FAM-LSTM model
Multistep ahead prediction of temperature and humidity in solar greenhouse based on FAM-LSTM model 基于 FAM-LSTM 模型的日光温室温湿度多步提前预测 题目：“Multistep ahead pr ......
UVA11275 3D Triangles 题解
Link UVA11275 3D Triangles Question 给你三维空间中的两个三角形，请判断它们是否有公共点。 Solution 如果在三维空间中相交，那么，肯定有一个三角形的某一条边穿过了另外一个三角形 Code #include<bits/stdc++.h> using names ......
HTML 中用 js 画出谢尔宾斯基三角形 Sierpinski triangle ( chaos 画法)
谢尔宾斯基三角形（英语：Sierpinski triangle）是一种分形，由波兰数学家谢尔宾斯基在1915年提出。它是自相似集的例子。它的豪斯多夫维是log(3)/log(2) ≈ 1.585。 随机的绘画方法 先定三点ABC使其构成一个没有边的等边三角形 然后在三角形内随机定一个点P 然后在AB ......
Paper Reading: A hybrid deep forest-based method for predicting synergistic drug combinations
为了解决联合用药数据的不平衡、高维、样本数量有限的问题，本文首先构建了一个由药物的物理、化学和生物特性组成的特征集，包括了丰富的生物学信息。特征空间的每个维度都有特定的含义，便于进行可解释性分析，找出预测过程中的关键特征。针对这种不平衡的高维中型数据集，提出了一种改进的基于 Deep Forest ... ......
Linkless Link Prediction via Relational Distillation
目录概符号说明LLP代码 Guo Z., Shiao W., Zhang S., Liu Y., Chawla N. V., Shah N. and Zhao T. Linkless link prediction via relational distillation. ICML, 2023. 概 ......
城市时空预测的统一数据管理和综合性能评估 [实验、分析和基准]《Unified Data Management and Comprehensive Performance Evaluation for Urban Spatial-Temporal Prediction [Experiment, Analysis & Benchmark]》
2023年11月1日，还有两个月，2023年就要结束了，希望在结束之前我能有所收获和进步，冲呀，老咸鱼。 摘要 解决了访问和利用不同来源、不同格式存储的不同城市时空数据集，以及确定有效的模型结构和组件。 1.为城市时空大数据设计的统一存储格式“原子文件”，并在40个不同的数据集上验证了其有效性，简化 ......
CF1025F Disjoint Triangles
虽然我不懂计算几何，但是两个三角形互相进入，感觉很涩啊！ —— By 【】 考虑两个互不相交的三角形，寻找一个方式能够不重不漏地统计它们。 容易发现两条不交的线段 \(A_1A_2,B_1B_2\) 之间，必然存在一条直线将 \(A_1A_2,B_1B_2\) 分在直线两端，且与 \(A_1A_2, ......
ST-SSL: 用于交通流量预测的时空自监督学习《Spatio-Temporal Self-Supervised Learning for Traffic Flow Prediction》(交通流量预测、自监督)
2023年10月23日，继续论文，好困，想发疯。 论文：Spatio-Temporal Self-Supervised Learning for Traffic Flow Prediction Github：https://github.com/Echo-Ji/ST-SSL AAAI 2023的论文 ......