The eld of arti cial intelligence (AI) and the law is on the cusp of a revolution that began with text analytic programs like IBM’s Watson and Debater and the open-source informa- tion management architectures on which they are based. Today, new legal applications are beginning to appear, and this book – designed to explain computational processes to non-programmers – describes how they will change the practice of law, speci cally by connecting computational models of legal reasoning directly with legal text, generat- ing arguments for and against particular outcomes, predicting outcomes, and explaining these predictions with reasons that legal professionals will be able to evaluate for them- selves. These legal apps will support conceptual legal information retrieval and enable cognitive computing, enabling a collaboration between humans and computers in which each performs the kinds of intelligent activities that they can do best. Anyone interested in how AI is changing the practice of law should read this illuminating work. Dr. Kevin D. Ashley is a Professor of Law and Intelligent Systems at the University of Pittsburgh, Senior Scientist, Learning Research and Development Center, and Adjunct Professor of Computer Science. He received a B.A. from Princeton University, a JD from Harvard Law School, and Ph.D. in computer science from the University of Mas- sachusetts. A visiting scientist at the IBM Thomas J. Watson Research Center, NSF Presidential Young Investigator, and Fellow of the American Association for Arti cial Intelligence, he is co-Editor-in-Chief of Arti cial Intelligence and Law and teaches in the University of Bologna Erasmus Mundus doctoral program in Law, Science, and Technology.

Noxim-NoC模拟器 欢迎来到Noxim，这是由卡塔尼亚大学（意大利）开发的片上网络模拟器。 Noxim仿真器是使用SystemC（一种基于C ++的系统描述语言）开发的，可以根据GPL许可条款进行下载。 如果您在研究中使用Noxim，我们将在以下No No贡献的出版物中给予以下引用： V. Catania，A。Mineo，S。Monteleone，M。Palesi和D. Patti，“通过在线选择缓冲器和关闭接收器来提高无线芯片网络架构的能效”，2016年第13届IEEE年度消费者通信和网络会议（ CCNC），内华达州拉斯维加斯，2016年，第668-673页，doi：10.1109 / CCNC.2016.7444860。 V. Catania，A。Mineo，S。Monteleone，M。Palesi和D. Patti，“无线芯片上无线网络架构中的节能收发器”，2016年欧

cst4-game: 天下大计 剧本在application/static/scripts中，ending.js为结局列表，stage1.js为目前的剧本。 图片放在application/static/image中。 主要的代码是application/static/app.js和application/static/scripting.js，前者负责逻辑，后者负责解析剧本。 网站只有一个网页，在application/templates/index.html中。 运行方法 pip install flask # install Flask (in Python 3.6) npm install -g bower # install bower bower install # download static files for the webpages pyth

Pavia University高光谱数据和地面验证数据，MATLAB版本

上海大学高级论文LaTeX模板 这是我罚款论文撰写期间所写的一个LaTeX学位论文模板，欢迎大家使用。文件。本模板预编译的PDF： : 。如果您从GitHub下载源代码比较慢，也可以从这里下载： 。 参考范文： 不熟悉的同学可以参考我的制服论文： LaTeX源代码： : ; 预编译的PDF： ： ； 背面广告链接： ://cn.overleaf.com/read/ppwrbgqjbgjq。 如有问题，请到项目主页留言： ://kaizhao.net/shu-thesis。

ParaView_Tutorial-Indiana University.pdf

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written by Aharon Ben-Tal Laurent El Ghaoui Arkadi Nemirovski Copyright © 2009 by Princeton University Press PART I. ROBUST LINEAR OPTIMIZATION 1 Chapter 1. Uncertain Linear Optimization Problems and their Robust Counterparts 3 1.1 Data Uncertainty in Linear Optimization 3 1.2 Uncertain Linear Problems and their Robust Counterparts 7 1.3 Tractability of Robust Counterparts 16 1.4 Non-Affine Perturbations 23 1.5 Exercises 25 1.6 Notes and Remarks 25 Chapter 2. Robust Counterpart Approximations of Scalar Chance Constraints 27 2.1 How to Specify an Uncertainty Set 27 2.2 Chance Constraints and their Safe Tractable Approximations 28 2.3 Safe Tractable Approximations of Scalar Chance Constraints: Basic Examples 31 2.4 Extensions 44 2.5 Exercises 60 2.6 Notes and Remarks 64 Chapter 3. Globalized Robust Counterparts of Uncertain LO Problems 67 3.1 Globalized Robust Counterpart — Motivation and Definition 67 3.2 Computational Tractability of GRC 69 3.3 Example: Synthesis of Antenna Arrays 70 3.4 Exercises 79 3.5 Notes and Remarks 79 Chapter 4. More on Safe Tractable Approximations of Scalar Chance Constraints 81 4.1 Robust Counterpart Representation of a Safe Convex Approximation to a Scalar Chance Constraint 81 4.2 Bernstein Approximation of a Chance Constraint 83 4.3 From Bernstein Approximation to Conditional Value at Risk and Back 90 4.4 Majorization 105 4.5 Beyond the Case of Independent Linear Perturbations 109 4.6 Exercises 136 4.7 Notes and Remarks 145 PART II. ROBUST CONIC OPTIMIZATION 147 Chapter 5. Uncertain Conic Optimization: The Concepts 149 5.1 Uncertain Conic Optimization: Preliminaries 149 5.2 Robust Counterpart of Uncertain Conic Problem: Tractability 151 5.3 Safe Tractable Approximations of RCs of Uncertain Conic Inequalities 153 5.4 Exercises 156 5.5 Notes and Remarks 157 Chapter 6. Uncertain Conic Quadratic Problems with Tractable RCs 159 6.1 A Generic Solvable Case: Scenario Uncertainty 159 6.2 Solvable Case I: Simple Interval Uncertainty 160 6.3 Solv

CS231n-2017年Spring 这些是我对斯坦福大学的CS231n 2017年Spring课程的解决方案。 我完成了所有作业，以提高自己的Python技能以及对深度学习的理解。 已完成 作业1 作业2（PyTorch和Tensorflow） 作业3（PyTorch和Tensorflow） 未来的工作 额外的信用任务 问题 如果您有任何疑问，我们将很乐意为您解答，只需将其发布为问题，然后我会尽可能答复。

ccs5破解， CCSv5-China-University-Site_License.lic