As the computer industry changes from single-processor to multiprocessor architectures, this revolution requires a fundamental change in how programs are written. To leverage the performance and power of multiprocessor programming, also known as multicore programming, you need to learn the new principles, algorithms, and tools presented in this book. It includes fully-developed Java examples detailing data structures, synchronization techniques, transactional memory, and more. Prof. Maurice Herlihy, who coined the phrase "transactional memory," is on the faculty of Brown University. He is the recipient of the 2003 Dijkstra Prize in distributed computing. Prof. Nir Shavit is on the faculty of Tel-Aviv University and a member of the technical staff at Sun Microsystems Laboratories. In 2004 they shared the Gödel Prize, the highest award in theoretical computer science. * THE book on multicore programming, the new paradigm of computer science * Written by the world's most revered experts in multiprocessor programming and performance * Includes examples, models, exercises, PowerPoint slides, and sample Java programs

Book.(2001)Multi-Objective_Optimization Using Evolutionary Algorithms使用进化算法解决多目标问题，英语原版经典，绝版图书

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多传感器信号融合 书和配套源码 方便学习 老外写的书 思路清晰

This book equips readers to handle complex multi-view data representation, centered around several major visual applications, sharing many tips and insights through a unified learning framework. This framework is able to model most existing multi-view learning and domain adaptation, enriching readers’ understanding from their similarity, and differences based on data organization and problem settings, as well as the research goal. A comprehensive review exhaustively provides the key recent research on multi-view data analysis, i.e., multi-view clustering, multi-view classification, zero-shot learning, and domain adaption. More practical challenges in multi-view data analysis are discussed including incomplete, unbalanced and large-scale multi-view learning. Learning Representation for Multi-View Data Analysis covers a wide range of applications in the research fields of big data, human-centered computing, pattern recognition, digital marketing, web mining, and computer vision.

Saliency Detection with Multi-Scale Superpixels对应的源码

学习多智能体建模的论文，可供研究智能体建模应用等参考。。

本资源是一个以python/unittest/airtest为基础的自动测试框架。可以支持多部手机/模拟器设备进行同步运行测试用例脚本，并根据不同设备，生成独立的Html报告。

vicsek模型的matlab仿真，有动态作图的展示。研究速度一致性对多智能体协同的影响。