Sage for Abstract Algebra a Supplement to Abstract Algebra, Theory and Applications
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Practice Tips for Open Source Licensing Adam Kubelka
Santa Clara High Technology Law Journal Volume 22 | Issue 4 Article 4 2006 No Free Beer - Practice Tips for Open Source Licensing Adam Kubelka Matthew aF wcett Follow this and additional works at: http://digitalcommons.law.scu.edu/chtlj Part of the Law Commons Recommended Citation Adam Kubelka and Matthew Fawcett, No Free Beer - Practice Tips for Open Source Licensing, 22 Santa Clara High Tech. L.J. 797 (2005). Available at: http://digitalcommons.law.scu.edu/chtlj/vol22/iss4/4 This Article is brought to you for free and open access by the Journals at Santa Clara Law Digital Commons. It has been accepted for inclusion in Santa Clara High Technology Law Journal by an authorized administrator of Santa Clara Law Digital Commons. For more information, please contact [email protected]. ARTICLE NO FREE BEER - PRACTICE TIPS FOR OPEN SOURCE LICENSING Adam Kubelkat Matthew Fawcetttt I. INTRODUCTION Open source software is big business. According to research conducted by Optaros, Inc., and InformationWeek magazine, 87 percent of the 512 companies surveyed use open source software, with companies earning over $1 billion in annual revenue saving an average of $3.3 million by using open source software in 2004.1 Open source is not just staying in computer rooms either-it is increasingly grabbing intellectual property headlines and entering mainstream news on issues like the following: i. A $5 billion dollar legal dispute between SCO Group Inc. (SCO) and International Business Machines Corp. t Adam Kubelka is Corporate Counsel at JDS Uniphase Corporation, where he advises the company on matters related to the commercialization of its products. -
An Introduction to Software Licensing
An Introduction to Software Licensing James Willenbring Software Engineering and Research Department Center for Computing Research Sandia National Laboratories David Bernholdt Oak Ridge National Laboratory Please open the Q&A Google Doc so that I can ask you Michael Heroux some questions! Sandia National Laboratories http://bit.ly/IDEAS-licensing ATPESC 2019 Q Center, St. Charles, IL (USA) (And you’re welcome to ask See slide 2 for 8 August 2019 license details me questions too) exascaleproject.org Disclaimers, license, citation, and acknowledgements Disclaimers • This is not legal advice (TINLA). Consult with true experts before making any consequential decisions • Copyright laws differ by country. Some info may be US-centric License and Citation • This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). • Requested citation: James Willenbring, David Bernholdt and Michael Heroux, An Introduction to Software Licensing, tutorial, in Argonne Training Program on Extreme-Scale Computing (ATPESC) 2019. • An earlier presentation is archived at https://ideas-productivity.org/events/hpc-best-practices-webinars/#webinar024 Acknowledgements • This work was supported by the U.S. Department of Energy Office of Science, Office of Advanced Scientific Computing Research (ASCR), and by the Exascale Computing Project (17-SC-20-SC), a collaborative effort of the U.S. Department of Energy Office of Science and the National Nuclear Security Administration. • This work was performed in part at the Oak Ridge National Laboratory, which is managed by UT-Battelle, LLC for the U.S. Department of Energy under Contract No. DE-AC05-00OR22725. • This work was performed in part at Sandia National Laboratories. -
Randomized Algorithms
Chapter 9 Randomized Algorithms randomized The theme of this chapter is madendrizo algorithms. These are algorithms that make use of randomness in their computation. You might know of quicksort, which is efficient on average when it uses a random pivot, but can be bad for any pivot that is selected without randomness. Analyzing randomized algorithms can be difficult, so you might wonder why randomiza- tion in algorithms is so important and worth the extra effort. Well it turns out that for certain problems randomized algorithms are simpler or faster than algorithms that do not use random- ness. The problem of primality testing (PT), which is to determine if an integer is prime, is a good example. In the late 70s Miller and Rabin developed a famous and simple random- ized algorithm for the problem that only requires polynomial work. For over 20 years it was not known whether the problem could be solved in polynomial work without randomization. Eventually a polynomial time algorithm was developed, but it is much more complicated and computationally more costly than the randomized version. Hence in practice everyone still uses the randomized version. There are many other problems in which a randomized solution is simpler or cheaper than the best non-randomized solution. In this chapter, after covering the prerequisite background, we will consider some such problems. The first we will consider is the following simple prob- lem: Question: How many comparisons do we need to find the top two largest numbers in a sequence of n distinct numbers? Without the help of randomization, there is a trivial algorithm for finding the top two largest numbers in a sequence that requires about 2n − 3 comparisons. -
** OPEN SOURCE LIBRARIES USED in Tv.Verizon.Com/Watch
** OPEN SOURCE LIBRARIES USED IN tv.verizon.com/watch ------------------------------------------------------------ 02/27/2019 tv.verizon.com/watch uses Node.js 6.4 on the server side and React.js on the client- side. Both are Javascript frameworks. Below are the licenses and a list of the JS libraries being used. ** NODE.JS 6.4 ------------------------------------------------------------ https://github.com/nodejs/node/blob/master/LICENSE Node.js is licensed for use as follows: """ Copyright Node.js contributors. All rights reserved. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. """ This license applies to parts of Node.js originating from the https://github.com/joyent/node repository: """ Copyright Joyent, Inc. and other Node contributors. -
MA3K0 - High-Dimensional Probability
MA3K0 - High-Dimensional Probability Lecture Notes Stefan Adams i 2020, update 06.05.2020 Notes are in final version - proofreading not completed yet! Typos and errors will be updated on a regular basis Contents 1 Prelimaries on Probability Theory 1 1.1 Random variables . .1 1.2 Classical Inequalities . .4 1.3 Limit Theorems . .6 2 Concentration inequalities for independent random variables 8 2.1 Why concentration inequalities . .8 2.2 Hoeffding’s Inequality . 10 2.3 Chernoff’s Inequality . 13 2.4 Sub-Gaussian random variables . 15 2.5 Sub-Exponential random variables . 22 3 Random vectors in High Dimensions 27 3.1 Concentration of the Euclidean norm . 27 3.2 Covariance matrices and Principal Component Analysis (PCA) . 30 3.3 Examples of High-Dimensional distributions . 32 3.4 Sub-Gaussian random variables in higher dimensions . 34 3.5 Application: Grothendieck’s inequality . 36 4 Random Matrices 36 4.1 Geometrics concepts . 36 4.2 Concentration of the operator norm of random matrices . 38 4.3 Application: Community Detection in Networks . 41 4.4 Application: Covariance Estimation and Clustering . 41 5 Concentration of measure - general case 43 5.1 Concentration by entropic techniques . 43 5.2 Concentration via Isoperimetric Inequalities . 50 5.3 Some matrix calculus and covariance estimation . 54 6 Basic tools in high-dimensional probability 57 6.1 Decoupling . 57 6.2 Concentration for Anisotropic random vectors . 62 6.3 Symmetrisation . 63 7 Random Processes 64 ii Preface Introduction We discuss an elegant argument that showcases the -
Open Source Software Notice
Open Source Software Notice This document describes open source software contained in LG Smart TV SDK. Introduction This chapter describes open source software contained in LG Smart TV SDK. Terms and Conditions of the Applicable Open Source Licenses Please be informed that the open source software is subject to the terms and conditions of the applicable open source licenses, which are described in this chapter. | 1 Contents Introduction............................................................................................................................................................................................. 4 Open Source Software Contained in LG Smart TV SDK ........................................................... 4 Revision History ........................................................................................................................ 5 Terms and Conditions of the Applicable Open Source Licenses..................................................................................... 6 GNU Lesser General Public License ......................................................................................... 6 GNU Lesser General Public License ....................................................................................... 11 Mozilla Public License 1.1 (MPL 1.1) ....................................................................................... 13 Common Public License Version v 1.0 .................................................................................... 18 Eclipse Public License Version -
Are Projects Hosted on Sourceforge.Net Representative of the Population of Free/Open Source Software Projects?
Are Projects Hosted on Sourceforge.net Representative of the Population of Free/Open Source Software Projects? Bob English February 5, 2008 Introduction We are researchers studying Free/Open Source Software (FOSS) projects . Because some of our work uses data gathered from Sourceforge.net (SF), a web site that hosts FOSS projects, we consider it important to understand how representative SF is of the entire population of FOSS projects. While SF is probably hosts the greatest number of FOSS projects, there are many other similar hosting sites. In addition, many FOSS projects maintain their own web sites and other project infrastructure on their own servers. Based on performing an Internet search and literature review (see Appendix for search procedures), it appears that no one knows how many Free/Open Source Software Projects exist in the world or how many people are working on them. Because the population of FOSS projects and developers is unknown, it is not surprising that we were not able to find any empirical analysis to assess how representative SF is of this unknown population. Although an extensive empirical analysis has not been performed, many researchers have considered the question or related questions. The next section of this paper reviews some of the FOSS literature that discusses the representativeness of SF. The third section of the paper describes some ideas for empirically exploring the representativeness of SF. The paper closes with some arguments for believing SF is the best choice for those interested in studying the population of FOSS projects. Literature Review In one of the earliest references to the representativeness of SF, Madley, Freeh and Tynan (2002) mention that they assumed that SF was representative because of its popularity and because of the number of projects hosted there, although they note that this assumption “needs to be confirmed” (p. -
Open Source Acknowledgements
This document acknowledges certain third‐parties whose software is used in Esri products. GENERAL ACKNOWLEDGEMENTS Portions of this work are: Copyright ©2007‐2011 Geodata International Ltd. All rights reserved. Copyright ©1998‐2008 Leica Geospatial Imaging, LLC. All rights reserved. Copyright ©1995‐2003 LizardTech Inc. All rights reserved. MrSID is protected by the U.S. Patent No. 5,710,835. Foreign Patents Pending. Copyright ©1996‐2011 Microsoft Corporation. All rights reserved. Based in part on the work of the Independent JPEG Group. OPEN SOURCE ACKNOWLEDGEMENTS 7‐Zip 7‐Zip © 1999‐2010 Igor Pavlov. Licenses for files are: 1) 7z.dll: GNU LGPL + unRAR restriction 2) All other files: GNU LGPL The GNU LGPL + unRAR restriction means that you must follow both GNU LGPL rules and unRAR restriction rules. Note: You can use 7‐Zip on any computer, including a computer in a commercial organization. You don't need to register or pay for 7‐Zip. GNU LGPL information ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation; either version 2.1 of the License, or (at your option) any later version. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You can receive a copy of the GNU Lesser General Public License from http://www.gnu.org/ See Common Open Source Licenses below for copy of LGPL 2.1 License. -
HIGHER MOMENTS of BANACH SPACE VALUED RANDOM VARIABLES 1. Introduction Let X Be a Random Variable with Values in a Banach Space
HIGHER MOMENTS OF BANACH SPACE VALUED RANDOM VARIABLES SVANTE JANSON AND STEN KAIJSER Abstract. We define the k:th moment of a Banach space valued ran- dom variable as the expectation of its k:th tensor power; thus the mo- ment (if it exists) is an element of a tensor power of the original Banach space. We study both the projective and injective tensor products, and their relation. Moreover, in order to be general and flexible, we study three different types of expectations: Bochner integrals, Pettis integrals and Dunford integrals. One of the problems studied is whether two random variables with the same injective moments (of a given order) necessarily have the same projective moments; this is of interest in applications. We show that this holds if the Banach space has the approximation property, but not in general. Several sections are devoted to results in special Banach spaces, in- cluding Hilbert spaces, CpKq and Dr0; 1s. The latter space is non- separable, which complicates the arguments, and we prove various pre- liminary results on e.g. measurability in Dr0; 1s that we need. One of the main motivations of this paper is the application to Zolotarev metrics and their use in the contraction method. This is sketched in an appendix. 1. Introduction Let X be a random variable with values in a Banach space B. To avoid measurability problems, we assume for most of this section for simplicity that B is separable and X Borel measurable; see Section 3 for measurability in the general case. Moreover, for definiteness, we consider real Banach spaces only; the complex case is similar. -
Differential Calculus and Sage
Differential Calculus and Sage William Granville and David Joyner Contents ii CONTENTS 0 Preface ix 1 Variables and functions 1 1.1 Variablesandconstants . 1 1.2 Intervalofavariable ........................ 2 1.3 Continuous variation . 2 1.4 Functions .............................. 2 1.5 Notation of functions . 3 1.6 Independent and dependent variables . 5 1.7 Thedomainofafunction . 6 1.8 Exercises .............................. 7 2 Theory of limits 9 2.1 Limitofavariable.......................... 9 2.2 Division by zero excluded . 12 2.3 Infinitesimals ............................ 12 2.4 The concept of infinity ( )..................... 13 2.5 Limiting value of a function∞ . 14 2.6 Continuous and discontinuous functions . 14 2.7 Continuity and discontinuity of functions illustrated by their graphs 18 2.8 Fundamental theorems on limits . 26 2.9 Speciallimitingvalues . 31 sin x 2.10 Show that limx 0 = 1 ..................... 32 → x 2.11 The number e ............................ 34 2.12 Expressions assuming the form ∞ ................. 36 2.13Exercises ..............................∞ 37 iii CONTENTS 3 Differentiation 41 3.1 Introduction............................. 41 3.2 Increments.............................. 42 3.3 Comparison of increments . 43 3.4 Derivative of a function of one variable . 44 3.5 Symbols for derivatives . 45 3.6 Differentiable functions . 46 3.7 General rule for differentiation . 46 3.8 Exercises .............................. 50 3.9 Applications of the derivative to geometry . 52 3.10Exercises .............................. 55 4 Rules for differentiating standard elementary forms 59 4.1 Importance of General Rule . 59 4.2 Differentiation of a constant . 63 4.3 Differentiation of a variable with respect to itself . ...... 63 4.4 Differentiation of a sum . 64 4.5 Differentiation of the product of a constant and a function . -
Free As in Freedom (2.0): Richard Stallman and the Free Software Revolution
Free as in Freedom (2.0): Richard Stallman and the Free Software Revolution Sam Williams Second edition revisions by Richard M. Stallman i This is Free as in Freedom 2.0: Richard Stallman and the Free Soft- ware Revolution, a revision of Free as in Freedom: Richard Stallman's Crusade for Free Software. Copyright c 2002, 2010 Sam Williams Copyright c 2010 Richard M. Stallman Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.3 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts. A copy of the license is included in the section entitled \GNU Free Documentation License." Published by the Free Software Foundation 51 Franklin St., Fifth Floor Boston, MA 02110-1335 USA ISBN: 9780983159216 The cover photograph of Richard Stallman is by Peter Hinely. The PDP-10 photograph in Chapter 7 is by Rodney Brooks. The photo- graph of St. IGNUcius in Chapter 8 is by Stian Eikeland. Contents Foreword by Richard M. Stallmanv Preface by Sam Williams vii 1 For Want of a Printer1 2 2001: A Hacker's Odyssey 13 3 A Portrait of the Hacker as a Young Man 25 4 Impeach God 37 5 Puddle of Freedom 59 6 The Emacs Commune 77 7 A Stark Moral Choice 89 8 St. Ignucius 109 9 The GNU General Public License 123 10 GNU/Linux 145 iii iv CONTENTS 11 Open Source 159 12 A Brief Journey through Hacker Hell 175 13 Continuing the Fight 181 Epilogue from Sam Williams: Crushing Loneliness 193 Appendix A { Hack, Hackers, and Hacking 209 Appendix B { GNU Free Documentation License 217 Foreword by Richard M. -
Control Policy with Autocorrelated Noise in Reinforcement Learning for Robotics
International Journal of Machine Learning and Computing, Vol. 5, No. 2, April 2015 Control Policy with Autocorrelated Noise in Reinforcement Learning for Robotics Paweł Wawrzyński primitives by means of RL algorithms based on MDP Abstract—Direct application of reinforcement learning in framework, but it does not alleviate the problem of robot robotics rises the issue of discontinuity of control signal. jerking. The current paper is intended to fill this gap. Consecutive actions are selected independently on random, In this paper, control policy is introduced that may undergo which often makes them excessively far from one another. Such reinforcement learning and have the following properties: control is hardly ever appropriate in robots, it may even lead to their destruction. This paper considers a control policy in which It is applicable to robot control optimization. consecutive actions are modified by autocorrelated noise. That It does not lead to robot jerking. policy generally solves the aforementioned problems and it is readily applicable in robots. In the experimental study it is It can be optimized by any RL algorithm that is designed applied to three robotic learning control tasks: Cart-Pole to optimize a classical stochastic control policy. SwingUp, Half-Cheetah, and a walking humanoid. The policy introduced here is based on a deterministic transformation of state combined with a random element in Index Terms—Machine learning, reinforcement learning, actorcritics, robotics. the form of a specific stochastic process, namely the moving average. The paper is organized as follows. In Section II the I. INTRODUCTION problem of our interest is defined. Section III presents the main contribution of this paper i.e., a stochastic control policy Reinforcement learning (RL) addresses the problem of an that prevents robot jerking while learning.