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Data Preparation & Descriptive Statistics
Data Preparation & Descriptive Statistics (ver. 2.4) Oscar Torres-Reyna Data Consultant [email protected] PU/DSS/OTR http://dss.princeton.edu/training/ Basic definitions… For statistical analysis we think of data as a collection of different pieces of information or facts. These pieces of information are called variables. A variable is an identifiable piece of data containing one or more values. Those values can take the form of a number or text (which could be converted into number) In the table below variables var1 thru var5 are a collection of seven values, ‘id’ is the identifier for each observation. This dataset has information for seven cases (in this case people, but could also be states, countries, etc) grouped into five variables. id var1 var2 var3 var4 var5 1 7.3 32.27 0.1 Yes Male 2 8.28 40.68 0.56 No Female 3 3.35 5.62 0.55 Yes Female 4 4.08 62.8 0.83 Yes Male 5 9.09 22.76 0.26 No Female 6 8.15 90.85 0.23 Yes Female 7 7.59 54.94 0.42 Yes Male PU/DSS/OTR Data structure… For data analysis your data should have variables as columns and observations as rows. The first row should have the column headings. Make sure your dataset has at least one identifier (for example, individual id, family id, etc.) id var1 var2 var3 var4 var5 First row should have the variable names 1 7.3 32.27 0.1 Yes Male 2 8.28 40.68 0.56 No Female Cross-sectional data 3 3.35 5.62 0.55 Yes Female 4 4.08 62.8 0.83 Yes Male 5 9.09 22.76 0.26 No Female 6 8.15 90.85 0.23 Yes Female 7 7.59 54.94 0.42 Yes Male id year var1 var2 var3 1 2000 7 74.03 0.55 Group 1 1 2001 2 4.6 0.44 At least one identifier 1 2002 2 25.56 0.77 2 2000 7 59.52 0.05 Cross-sectional time series data Group 2 2 2001 2 16.95 0.94 or panel data 2 2002 9 1.2 0.08 3 2000 9 85.85 0.5 Group 3 3 2001 3 98.85 0.32 3 2002 3 69.2 0.76 PU/DSS/OTR NOTE: See: http://www.statistics.com/resources/glossary/c/crossdat.php Data format (ASCII)… ASCII (American Standard Code for Information Interchange). -
Administrator's Guide
Trend Micro Incorporated reserves the right to make changes to this document and to the product described herein without notice. Before installing and using the product, review the readme files, release notes, and/or the latest version of the applicable documentation, which are available from the Trend Micro website at: http://docs.trendmicro.com/en-us/enterprise/scanmail-for-microsoft- exchange.aspx Trend Micro, the Trend Micro t-ball logo, Apex Central, eManager, and ScanMail are trademarks or registered trademarks of Trend Micro Incorporated. All other product or company names may be trademarks or registered trademarks of their owners. Copyright © 2020. Trend Micro Incorporated. All rights reserved. Document Part No.: SMEM149028/200709 Release Date: November 2020 Protected by U.S. Patent No.: 5,951,698 This documentation introduces the main features of the product and/or provides installation instructions for a production environment. Read through the documentation before installing or using the product. Detailed information about how to use specific features within the product may be available at the Trend Micro Online Help Center and/or the Trend Micro Knowledge Base. Trend Micro always seeks to improve its documentation. If you have questions, comments, or suggestions about this or any Trend Micro document, please contact us at [email protected]. Evaluate this documentation on the following site: https://www.trendmicro.com/download/documentation/rating.asp Privacy and Personal Data Collection Disclosure Certain features available in Trend Micro products collect and send feedback regarding product usage and detection information to Trend Micro. Some of this data is considered personal in certain jurisdictions and under certain regulations. -
ACS – the Archival Cytometry Standard
http://flowcyt.sf.net/acs/latest.pdf ACS – the Archival Cytometry Standard Archival Cytometry Standard ACS International Society for Advancement of Cytometry Candidate Recommendation DRAFT Document Status The Archival Cytometry Standard (ACS) has undergone several revisions since its initial development in June 2007. The current proposal is an ISAC Candidate Recommendation Draft. It is assumed, however not guaranteed, that significant features and design aspects will remain unchanged for the final version of the Recommendation. This specification has been formally tested to comply with the W3C XML schema version 1.0 specification but no position is taken with respect to whether a particular software implementing this specification performs according to medical or other valid regulations. The work may be used under the terms of the Creative Commons Attribution-ShareAlike 3.0 Unported license. You are free to share (copy, distribute and transmit), and adapt the work under the conditions specified at http://creativecommons.org/licenses/by-sa/3.0/legalcode. Disclaimer of Liability The International Society for Advancement of Cytometry (ISAC) disclaims liability for any injury, harm, or other damage of any nature whatsoever, to persons or property, whether direct, indirect, consequential or compensatory, directly or indirectly resulting from publication, use of, or reliance on this Specification, and users of this Specification, as a condition of use, forever release ISAC from such liability and waive all claims against ISAC that may in any manner arise out of such liability. ISAC further disclaims all warranties, whether express, implied or statutory, and makes no assurances as to the accuracy or completeness of any information published in the Specification. -
Full Document
R&D Centre for Mobile Applications (RDC) FEE, Dept of Telecommunications Engineering Czech Technical University in Prague RDC Technical Report TR-13-4 Internship report Evaluation of Compressibility of the Output of the Information-Concealing Algorithm Julien Mamelli, [email protected] 2nd year student at the Ecole´ des Mines d'Al`es (N^ımes,France) Internship supervisor: Luk´aˇsKencl, [email protected] August 2013 Abstract Compression is a key element to exchange files over the Internet. By generating re- dundancies, the concealing algorithm proposed by Kencl and Loebl [?], appears at first glance to be particularly designed to be combined with a compression scheme [?]. Is the output of the concealing algorithm actually compressible? We have tried 16 compression techniques on 1 120 files, and the result is that we have not found a solution which could advantageously use repetitions of the concealing method. Acknowledgments I would like to express my gratitude to my supervisor, Dr Luk´aˇsKencl, for his guidance and expertise throughout the course of this work. I would like to thank Prof. Robert Beˇst´akand Mr Pierre Runtz, for giving me the opportunity to carry out my internship at the Czech Technical University in Prague. I would also like to thank all the members of the Research and Development Center for Mobile Applications as well as my colleagues for the assistance they have given me during this period. 1 Contents 1 Introduction 3 2 Related Work 4 2.1 Information concealing method . 4 2.2 Archive formats . 5 2.3 Compression algorithms . 5 2.3.1 Lempel-Ziv algorithm . -
Lossless Data Compression with Transformer
Under review as a conference paper at ICLR 2020 LOSSLESS DATA COMPRESSION WITH TRANSFORMER Anonymous authors Paper under double-blind review ABSTRACT Transformers have replaced long-short term memory and other recurrent neural networks variants in sequence modeling. It achieves state-of-the-art performance on a wide range of tasks related to natural language processing, including lan- guage modeling, machine translation, and sentence representation. Lossless com- pression is another problem that can benefit from better sequence models. It is closely related to the problem of online learning of language models. But, despite this ressemblance, it is an area where purely neural network based methods have not yet reached the compression ratio of state-of-the-art algorithms. In this paper, we propose a Transformer based lossless compression method that match the best compression ratio for text. Our approach is purely based on neural networks and does not rely on hand-crafted features as other lossless compression algorithms. We also provide a thorough study of the impact of the different components of the Transformer and its training on the compression ratio. 1 INTRODUCTION Lossless compression is a class of compression algorithms that allows for the perfect reconstruc- tion of the original data. In the last decades, statistical methods for lossless compression have been dominated by PAQ-type approaches (Mahoney, 2005). The structure of these approaches is similar to the Prediction by Partial Matching (PPM) of Cleary & Witten (1984) and are composed of two separated parts: a predictor and an entropy encoding. Entropy coding scheme like arithmetic cod- ing (Rissanen & Langdon, 1979) are optimal and most of the compression gains are coming from improving the predictor. -
PKZIP MVS User's Guide
PKZIP for MVS MVS/ESA, OS/390, & z/OS User’s Guide PKMU-V5R5000 PKWARE, Inc. PKWARE, Inc. 9009 Springboro Pike Miamisburg, Ohio 45342 Sales: 937-847-2374 Support: 937-847-2687 Fax: 937-847-2375 Web Site: http://www.pkzip.com Sales - E-Mail: [email protected] Support - http://www.pkzip.com/support 5.5 Edition (2003) PKZIP for MVS™, PKZIP for OS/400™, PKZIP for VSE™, PKZIP for UNIX™, and PKZIP for Windows™ are just a few of the many members in the PKZIP® family. PKWARE, Inc. would like to thank all the individuals and companies -- including our customers, resellers, distributors, and technology partners -- who have helped make PKZIP® the industry standard for Trusted ZIP solutions. PKZIP® enables our customers to efficiently and securely transmit and store information across systems of all sizes, ranging from desktops to mainframes. This edition applies to the following PKWARE of Ohio, Inc. licensed program: PKZIP for MVS™ (Version 5, Release 5, 2003) PKZIP(R) is a registered trademark of PKWARE(R) Inc. Other product names mentioned in this manual may be a trademark or registered trademarks of their respective companies and are hereby acknowledged. Any reference to licensed programs or other material, belonging to any company, is not intended to state or imply that such programs or material are available or may be used. The copyright in this work is owned by PKWARE of Ohio, Inc., and the document is issued in confidence for the purpose only for which it is supplied. It must not be reproduced in whole or in part or used for tendering purposes except under an agreement or with the consent in writing of PKWARE of Ohio, Inc., and then only on condition that this notice is included in any such reproduction. -
Encryption Introduction to Using 7-Zip
IT Services Training Guide Encryption Introduction to using 7-Zip It Services Training Team The University of Manchester email: [email protected] www.itservices.manchester.ac.uk/trainingcourses/coursesforstaff Version: 5.3 Training Guide Introduction to Using 7-Zip Page 2 IT Services Training Introduction to Using 7-Zip Table of Contents Contents Introduction ......................................................................................................................... 4 Compress/encrypt individual files ....................................................................................... 5 Email compressed/encrypted files ....................................................................................... 8 Decrypt an encrypted file ..................................................................................................... 9 Create a self-extracting encrypted file .............................................................................. 10 Decrypt/un-zip a file .......................................................................................................... 14 APPENDIX A Downloading and installing 7-Zip ................................................................. 15 Help and Further Reference ............................................................................................... 18 Page 3 Training Guide Introduction to Using 7-Zip Introduction 7-Zip is an application that allows you to: Compress a file – for example a file that is 5MB can be compressed to 3MB Secure the -
Implementing Compression on Distributed Time Series Database
Implementing compression on distributed time series database Michael Burman School of Science Thesis submitted for examination for the degree of Master of Science in Technology. Espoo 05.11.2017 Supervisor Prof. Kari Smolander Advisor Mgr. Jiri Kremser Aalto University, P.O. BOX 11000, 00076 AALTO www.aalto.fi Abstract of the master’s thesis Author Michael Burman Title Implementing compression on distributed time series database Degree programme Major Computer Science Code of major SCI3042 Supervisor Prof. Kari Smolander Advisor Mgr. Jiri Kremser Date 05.11.2017 Number of pages 70+4 Language English Abstract Rise of microservices and distributed applications in containerized deployments are putting increasing amount of burden to the monitoring systems. They push the storage requirements to provide suitable performance for large queries. In this paper we present the changes we made to our distributed time series database, Hawkular-Metrics, and how it stores data more effectively in the Cassandra. We show that using our methods provides significant space savings ranging from 50 to 95% reduction in storage usage, while reducing the query times by over 90% compared to the nominal approach when using Cassandra. We also provide our unique algorithm modified from Gorilla compression algorithm that we use in our solution, which provides almost three times the throughput in compression with equal compression ratio. Keywords timeseries compression performance storage Aalto-yliopisto, PL 11000, 00076 AALTO www.aalto.fi Diplomityön tiivistelmä Tekijä Michael Burman Työn nimi Pakkausmenetelmät hajautetussa aikasarjatietokannassa Koulutusohjelma Pääaine Computer Science Pääaineen koodi SCI3042 Työn valvoja ja ohjaaja Prof. Kari Smolander Päivämäärä 05.11.2017 Sivumäärä 70+4 Kieli Englanti Tiivistelmä Hajautettujen järjestelmien yleistyminen on aiheuttanut valvontajärjestelmissä tiedon määrän kasvua, sillä aikasarjojen määrä on kasvanut ja niihin talletetaan useammin tietoa. -
Pack, Encrypt, Authenticate Document Revision: 2021 05 02
PEA Pack, Encrypt, Authenticate Document revision: 2021 05 02 Author: Giorgio Tani Translation: Giorgio Tani This document refers to: PEA file format specification version 1 revision 3 (1.3); PEA file format specification version 2.0; PEA 1.01 executable implementation; Present documentation is released under GNU GFDL License. PEA executable implementation is released under GNU LGPL License; please note that all units provided by the Author are released under LGPL, while Wolfgang Ehrhardt’s crypto library units used in PEA are released under zlib/libpng License. PEA file format and PCOMPRESS specifications are hereby released under PUBLIC DOMAIN: the Author neither has, nor is aware of, any patents or pending patents relevant to this technology and do not intend to apply for any patents covering it. As far as the Author knows, PEA file format in all of it’s parts is free and unencumbered for all uses. Pea is on PeaZip project official site: https://peazip.github.io , https://peazip.org , and https://peazip.sourceforge.io For more information about the licenses: GNU GFDL License, see http://www.gnu.org/licenses/fdl.txt GNU LGPL License, see http://www.gnu.org/licenses/lgpl.txt 1 Content: Section 1: PEA file format ..3 Description ..3 PEA 1.3 file format details ..5 Differences between 1.3 and older revisions ..5 PEA 2.0 file format details ..7 PEA file format’s and implementation’s limitations ..8 PCOMPRESS compression scheme ..9 Algorithms used in PEA format ..9 PEA security model .10 Cryptanalysis of PEA format .12 Data recovery from -
Improved Neural Network Based General-Purpose Lossless Compression Mohit Goyal, Kedar Tatwawadi, Shubham Chandak, Idoia Ochoa
JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015 1 DZip: improved neural network based general-purpose lossless compression Mohit Goyal, Kedar Tatwawadi, Shubham Chandak, Idoia Ochoa Abstract—We consider lossless compression based on statistical [4], [5] and generative modeling [6]). Neural network based data modeling followed by prediction-based encoding, where an models can typically learn highly complex patterns in the data accurate statistical model for the input data leads to substantial much better than traditional finite context and Markov models, improvements in compression. We propose DZip, a general- purpose compressor for sequential data that exploits the well- leading to significantly lower prediction error (measured as known modeling capabilities of neural networks (NNs) for pre- log-loss or perplexity [4]). This has led to the development of diction, followed by arithmetic coding. DZip uses a novel hybrid several compressors using neural networks as predictors [7]– architecture based on adaptive and semi-adaptive training. Unlike [9], including the recently proposed LSTM-Compress [10], most NN based compressors, DZip does not require additional NNCP [11] and DecMac [12]. Most of the previous works, training data and is not restricted to specific data types. The proposed compressor outperforms general-purpose compressors however, have been tailored for compression of certain data such as Gzip (29% size reduction on average) and 7zip (12% size types (e.g., text [12] [13] or images [14], [15]), where the reduction on average) on a variety of real datasets, achieves near- prediction model is trained in a supervised framework on optimal compression on synthetic datasets, and performs close to separate training data or the model architecture is tuned for specialized compressors for large sequence lengths, without any the specific data type. -
Chapter 2 HISTORY and DEVELOPMENT of MILITARY LASERS
History and Development of Military Lasers Chapter 2 HISTORY AND DEVELOPMENT OF MILITARY LASERS JACK B. KELLER, JR* INTRODUCTION INVENTING THE LASER MILITARIZING THE LASER SEARCHING FOR HIGH-ENERGY LASER WEAPONS SEARCHING FOR LOW-ENERGY LASER WEAPONS RETURNING TO HIGHER ENERGIES SUMMARY *Lieutenant Colonel, US Army (Retired); formerly, Foreign Science Information Officer, US Army Medical Research Detachment-Walter Reed Army Institute of Research, 7965 Dave Erwin Drive, Brooks City-Base, Texas 78235 25 Biomedical Implications of Military Laser Exposure INTRODUCTION This chapter will examine the history of the laser, Military advantage is greatest when details are con- from theory to demonstration, for its impact upon the US cealed from real or potential adversaries (eg, through military. In the field of military science, there was early classification). Classification can remain in place long recognition that lasers can be visually and cutaneously after a program is aborted, if warranted to conceal hazardous to military personnel—hazards documented technological details or pathways not obvious or easily in detail elsewhere in this volume—and that such hazards deduced but that may be relevant to future develop- must be mitigated to ensure military personnel safety ments. Thus, many details regarding developmental and mission success. At odds with this recognition was military laser systems cannot be made public; their the desire to harness the laser’s potential application to a descriptions here are necessarily vague. wide spectrum of military tasks. This chapter focuses on Once fielded, system details usually, but not always, the history and development of laser systems that, when become public. Laser systems identified here represent used, necessitate highly specialized biomedical research various evolutionary states of the art in laser technol- as described throughout this volume. -
Windows X32 Cross Compile Guide
Guide for cross compiling Trezarcoin for windows. By Iwens Fortis Tested on Ubuntu 16.04.2 LTS. • Start on ubuntu your terminal (search for terminal with ubuntu dash top icon left, or press windows key to open dash). • First we need to install the dependencies, for this execute the following cmd’s stated below in the terminal window. The commands to execute are in the grey textboxes. The commands will ask for a password which and u have to confirm some commands with y from yes. Tip transfer the pdf to ubuntu for easy copy and paste. • First update the apt library. sudo apt-get update • Install needed dependencies to install mxe to cross compile Trezarcoin. sudo apt-get install p7zip-full autoconf automake autopoint bash bison bzip2 cmake flex gettext git g++ gperf intltool \ libffi-dev libtool libltdl-dev libssl-dev libxml-parser-perl make openssl patch perl pkg-config python ruby scons sed unzip \ wget xz-utils libtool-bin libgdk-pixbuf2.0-dev g++-multilib libc6-dev-i386 upx -y • We need to get latest mxe from github and compile libraries needed. git clone https://github.com/mxe/mxe.git cd mxe make MXE_TARGETS="i686-w64-mingw32.static" boost make MXE_TARGETS="i686-w64-mingw32.static" qttools make MXE_TARGETS="i686-w64-mingw32.static" miniupnpc • Next we need to compile the recommended Berkeley DB our self cd ~ wget 'http://download.oracle.com/berkeley-db/db-4.8.30.NC.tar.gz' tar zxvf db-4.8.30.NC.tar.gz cd db-4.8.30.NC • Now we will create a bash script for this execute the following commands to create the bash script.