1 The Electric Sheep Screen-Saver: A Case Study in Aesthetic Evolution Scott Draves Spotworks, San Francisco CA, USA Abstract. Electric Sheep is a distributed screen-saver that harnesses idle computers into a render farm with the purpose of animating and evolving artificial life-forms known as sheep. The votes of the users form the basis for the fitness function for a genetic algorithm on a space of fractal animations. Users also may design sheep by hand for inclusion in the gene pool. This paper describes the system and its algorithms, and reports statistics from 11 weeks of operation. The data indicate that Electric Sheep functions more as an amplifier of its human collaborators creativity rather than as a traditional genetic algorithm that optimizes a fitness function. 1 Introduction Electric Sheep   was inspired by  and has a similar design. Both are distributed systems with client/server architecture where the client is a screen-saver installable on an ordinary PC. Electric Sheep distributes the rendering of fractal animations. Each animation is 128 frames long and is known as a sheep. Besides rendering frames, the client also downloads completed sheep and displays them to the user. The user may vote for the currently displayed sheep by pressing the up arrow key. Each sheep is specified by a genetic code comprised of about 160 floating-point numbers. The codes are generated by the server according to a genetic algorithm where the fitness is determined by the collective votes of the users. This is a form of aesthetic evolution, a concept first realized by Karl Sims  and analyzed by Alan Dorin . This is how Electric Sheep worked until March 2004, when a new source of genomes appeared: Apophysis . Apophysis is a traditional, stand-alone Windows GUI to the sheep genetic code primarily intended for still-image design, but useful for key-frame animation. Besides a traditional direct manipulation interface where the user drags sliders and types numbers into labeled fields, it includes a Sims-style mutation explorer. In March, Townsend and Draves connected this application to the Electric Sheep server. A simple menu command causes the current genome to be posted to the server, rendered, and distributed to all active clients. If the resulting animation receives votes it may reproduce and interbreed with the artificially evolved population. Not surprisingly, these posted genomes proved much more popular than the purely random ones. And as they are subject to mutation and crossover, the genetic algorithm creates variants of them. One can compare the total amount of quality animation to the amount that was human designed. This ratio is the creative amplification factor of the system, as discussed in Section 6.1.
2 The rest of this paper is structured as follows: Section 2 describes the architecture and implementation of Electric Sheep. Section 3 briefly explains the concept and artistic goals of the project. Section 4 surveys the genetic code on which all sheep rendering and evolution is based, and Section 5 explains the genetic operators and the specifics of the evolutionary algorithm. Section 6 reports empirical results of running this system for over 11 weeks during which time more than 6000 sheep were born. Section 7 puts this work in context of past research, and Section 8 concludes. Fig. 1. Sheep 15875, on the top-left, was born on August 16 and died 24 hours later after receiving one vote. It was one of 42 siblings. It was reincarnated on October 28 as sheep 29140, received a peak rating of 29, lived 7 days, and had 26 children, 8 of which appear to its right. Below are five generations of sheep in order starting on the left. Their numbers are 1751, 1903, 2313, 2772, and The last is a result of mutation, the previous three of crossover, the first was posted by etomchek. 2 Architecture and Implementation Electric Sheep has a client/server architecture. The client initiates all communication between them, and if no client were running the server would not run at all. The client has three main threads. One thread downloads sheep animations from the server to a local disk cache. It downloads those with highest rating first. The default size of the cache is 300Mbytes (enough for 65 animations) but the user may change it. Another thread reads the sheep from the cache and displays them in a continuous sequence on the screen. The third thread contacts the server, receives a genome specifying a frame to render, renders the frame, then uploads the resulting JPEG file. The server maintains several collections of sheep. Sheep are numbered as they are created and are identified by this sequence number. Freshly conceived genomes start out in the render queue. When all the frames of a sheep have been uploaded, they are compressed into MPEG and deleted, and the sheep is made available for download and
3 voting. Sheep average 4.6Mbytes each. Eventually the sheep dies (Section 5.2 explains when) and the MPEG file is deleted. All these sheep are referred to collectively as a generation. Each time the server is reset the database is wiped, all sheep are deleted from the server and from all client caches, the generation number is incremented, and evolution starts fresh. The sheep that are the subject of this paper are members of generation 165. The server is implemented with two machines in separate colocation facilities. Both are commodity Linux x86 servers running Apache. One runs the evolutionary algorithm, collects frames and votes, compresses frames, and sends genomes to clients for rendering. The other only serves the completed MPEGs. The first server receives 221Kbit/s from the clients and transmits 263Kbit/s to them (measured average of July to October 2004). The MPEG server s bandwidth allocation has varied from 15 to 20Mbit/s, and it uses all of it. The MPEG server is currently the bottleneck in the system. A future version of Electric Sheep will use a P2P network to distribute this bandwidth load much as the computation load already is. The client runs on Linux, OSX, and Windows. It uses only the HTTP protocol on port 80 and it supports proxies. However, it does require a broadband, always-on connection to the internet. When the server is not reachable the client s sheep display still works but no new sheep appear. All the code is open source and is licensed under the GPL (General Public License). The fractal flame utilities are written in C and the server is written in Perl. The clients are written in C, C++, and Objective-C. 3 Concept and Motivation Electric Sheep is an attention vortex. It illustrates the process by which the longer and closer one studies something, the more detail and structure appears. Electric sheep investigates the role of experiencers in creating the experience. If nobody ran the client, there would be nothing to see. The sheep system exhibits increasing returns on each of its levels: As more clients join, more computational muscle becomes available, and the resolution of the graphics may be increased, either by making the sheep longer, larger, or sharper. The more people who participate, the better the graphics look. These adjustments are made manually on the server or with new client releases. Likewise, as developers focus more of their attention on the source code, the client and server themselves become more efficient, grow new features, and are ported into new habitats. The project gains momentum, and attracts more developers. And as more users vote for their favorite sheep, the evolutionary algorithm more quickly distills randomness into eye candy. The votes tell the server which sheep are receiving the most attention. Those sheep are elaborated, expanding the variety and detail of those parts of the fractal space that are most interesting.
4 There is a deeper motivation however: I believe the free flow of code is an increasingly important social and artistic force. The proliferation of powerful computers with high-bandwidth network connections forms the substrate of an expanding universe. The electric sheep and we their shepherds are colonizing this new frontier. 4 The Genetic Code Each image produced by Electric Sheep is a fractal flame , a generalization and refinement of the Iterated Function System (IFS) category of fractals . The genetic code used by Electric Sheep is just the parameter set for these fractals. It consists of about 160 floating-point numbers. A classic IFS consists of a recursive set-equation on the plane: n 1 S = F i (S) i=0 The solution S is a subset of the plane (and hence a two-tone image). The F i are a small collection of n affine transforms of the plane. A fractal flame is based on the same recursive equation, but the transforms may be non-linear and the solution algorithm produces a full-color image. The transforms are linear blends of a set of 18 basis functions known as variations. The variations are composed with an affine matrix, like in classic IFS. So each transform F i is: F i (x,y) = v i j V j (a i x+b i y+c i,d i x+e i y+ f i ) j where v i j are the 18 blending coefficients for F i, and a i through f i are 6 affine matrix coefficients. The V j are the variations, here is a partial list: V 0 (x,y) = (x,y) V 3 (x,y) = (r cos(θ + r),r sin(θ + r)) V 1 (x,y) = (sinx,siny) V 4 (x,y) = (r cos(2θ),r sin(2θ)) V 2 (x,y) = (x/r 2,y/r 2 ) V 5 (x,y) = (θ/π,r 1) where r and θ are the polar coordinates for the point (x,y) in rectangular coordinates. V 0 is the identity function so this space of non-linear functions is a superset of the space of linear functions. See  for the complete list. There are 3 additional parameters for density, color, and symmetry, not covered here. Together these 27 (18 for v i j plus 6 for a i to f i plus 3 is 27 total) parameters make up one transform, and are roughly equivalent to a gene in biological genetics. The order of the transforms in the genome does not effect the solution image. Many transforms have visually identifiable effects on the solution, for example particular shapes, structures, angles, or locations. Normally there are up to 6 transforms in the function system, making for 162 (6 27) floating-point numbers in the genome. Note however that most sheep have most variational coeffients set to zero, which reduces the effective dimensionality of the space.
5 4.1 Animation and Transitions The previous section described how a single image rather than an animation is defined by the genome. To create animations, Electric Sheep rotates over time the 2 2 matrix part (a i, b i, d i, and e i ) of each of the transforms. After a full circle, the solution image returns to the first frame, so sheep animations loop smoothly. Sheep are 128 frames long, and by default are played back at 23 frames per second making them 5.5 seconds long. The client does not just cut from one looping animation to another. It displays a continuously morphing sequence. To do this the system renders transitions between sheep in addition to the sheep themselves. The transitions are genetic crossfades based on pair-wise linear interpolation, but using a spline to maintain C 1 continuity with the endpoints. Transitions are also 128 frames long. For each sheep created, three transitions are also created: one from another random flock member to the new sheep, one from the new sheep to a random flock member, and another one between two other random members. Most of the rendering effort is spent on transitions. 5 The Genetic Algorithm There are three parts of the genetic algorithm: the rating system that collects the votes and computes the fitness of individual sheep, the genetic operators used to create new genomes, and the main loop that controls which live and die. As already mentioned, users can vote for a sheep they like by pressing the up arrow key. If the sheep is alive its rating is incremented. Pressing the down arrow key decrements the rating. Votes for dead sheep are discarded. Users may also vote for or against a sheep by pressing buttons on its web page. The ratings decay over time. Each day the ratings are divided by four with integer arithmetic rounding down. 5.1 Genetic Operators There are four sources of genomes for new sheep: random, mutation, crossover and posts from Apophysis. The parents for mutation and crossover operators are randomly picked from the current population weighted by rating. The probability of being selected is proportional to the rating. Sheep that have received no votes have rating zero and so cannot be selected. random The affine matrix coefficients are chosen with uniform distribution from [-1, 1]. The variational coefficients are set to zero except for one variation chosen at random that is set to one. crossover The crossover operation has two methods chosen equally often. One method creates a genome by alternating transforms (genes) from the parents. The other method does pair-wise linear interpolation between the two parent genomes where the blend factor is chosen uniformly from [0, 1]. mutation The mutation operator has several different methods: randomizing just the variational coefficients, randomizing just the matrix coefficients of one transform,
6 adding noise (-10 decibels, or numbers from [-0.1, 0.1]) to all the matrix coefficients, changing just the colors, and adding symmetry. When applying these three automatic operators, the server renders a low-resolution frame and tests if the image is too dark or too bright. The operator is iterated until the resulting genome passes. For random genomes, 43% are rejected (in a test run 177 tries were required to get 100 passing genomes). post Human designers may post genomes to the server with Apophysis. The designer is required to submit a password with the genome, but its value is shared on a public list and is common knowledge there. The genome is checked for syntactic correctness, but the image it creates is not tested. The server has a queue of sheep and transitions that are currently being rendered. Posted genomes go into this queue. When the queue is left with fewer than 12 sheep, it is filled with genomes derived with one of the three automatic operators. 1/4 of these genomes are random and have no ancestor. The remaining 3/4 are divided equally between mutation and crossover. 5.2 The Main Loop The server maintains a single flock of sheep and continuously updates their ratings, creates new sheep, and kills off old ones. The server has 510MB of disk space for storing sheep animations, enough for 28 sheep and 83 transitions. Each time a sheep is born, the sheep with the lowest rating is killed to make room. If several sheep are tied for worst, then the oldest is taken (usually several sheep have received no votes and are tied with a rating of zero). Killing a sheep removes the animation file from the server, but not from clients who may have allocated more disk space to their caches. The other records, including the peak rating, parentage, genome, 16 thumbnails, and the first frame are kept. This archive may be browsed on the server either sorted by peak rating, or as extended family trees. This on-line or steady-state approach contrasts with the more traditional genetic algorithm s off-line main loop that divides the population into generations and alternates between rating all the individuals in a generation and then deriving the next generation from the ratings. Note that Electric Sheep does have generations, but it means something else, see Section 2. 6 Empirical Results Three main datasets are analyzed below. The voting and posting data are from the web server log files from August 7th to November 4th 2004, 90 days later. Another dataset has daily aggregate usage reports from the server June 18th to October 31st (139 days). The primary dataset was collected from the server s database starting May 13th until October 13th, 153 days later. May 13th is when version 2.5 became operational. Previous versions of the server did not keep a record of the sheep: when they died they were completely deleted from the server. And though there are large collections of sheep collected by clients from before May 13th, they are not complete and they lack fields for ratings and parentage.
7 During this time the server was subject to performance optimization but the basic algorithm remained fixed with one exception: until July 13th there was no limit on how many votes a user could make. An increasing incidence of users voting many times in rapid succession instigated a limit of 10 votes per user per day. The numbers below are from after that change. The data we have collected are a starting point to understanding the system and its behavior. However, they are somewhat confounded: IP addresses are equated with users, but because of the prevalence of Network Address Translation (NAT), several or many users may appear as the same IP address. Worse, some computers are assigned an IP address dynamically, so one user may appear under many addresses. The client uses the ratings to prioritize downloading. Now that the server is busy enough that some clients cannot download all the sheep, this causes a snowball effect where a high rating itself causes more votes. The audience is fickle: sheep with identical genomes regularly receive completely different ratings (See Figure 1). Presumably the audience becomes fatigued by repeated exposure to variations of a successful genome, and stops voting for them. Even once popular sheep reintroduced much later do not necessarily fare well. Designers may vote for their own sheep, post many similar sheep, or post alterations of the results of automatic evolution. The administrator occasionally kills sheep, explicitly directs mating, mutation, reincarnation, and votes without limit. There are many ways of measuring the number of users. Figure 2 shows four of them, and a graph of the rate of new users trying the system. The daily downloaders linear growth rate is 9 users per day, far fewer than the hundreds of new addresses per day. When Electric Sheep is first installed and run it takes quite some time (often about ten minutes but according to some reports hours or even days) to download and display the first sheep. Perhaps many people decide the software is broken and remove it. Or it could result from miscounting dynamic IP addresses. There were on average 166 downloads of the client installers per day from the homepage during the first 8 days of December. But the installers are distributed on CD-ROM and mirrored on high-volume sites such as nonags.com, debian.org, and freebsd.org from which no statistics are available. These secondary sites also lack preview graphics and explanation so users from them may be likely to remove it. Figure 3 shows the distribution of how many clients had a given number of days of activity (at least one attempted download) clients were active half the days or more, and 65 were active every day. Using data collected from November 2 to November , there were 626 votes made, 76% with the arrow keys on client, and 24% with the web site. The average number of valid votes per day is 111. During the 90 day period votes were received from a total of 1682 different client IP addresses. The average number of posts per day is 10.8 from a total of 64 different client IP addresses.
8 hourly uploaders daily uploaders weekly uploaders daily downloaders /11 08/11 09/11 10/ /10 10/11 Fig. 2. On the left is a graph of number of users over time. Downloaders refers to clients downloading sheep, uploaders refers to clients uploading rendered frames. The dip from 9/2 to 9/19 coincides with server outages. On the right is a graph of the number of new client addresses over time. The total number of unique downloaders is Fig. 3. On the top-left is a histogram of lengths of lineages, and on the top-right is a histogram of the ratings of the sheep. On bottom-left is a histogram of the sum of ratings of all descendents. Children of two parents contribute half of their sum to each parent. On the bottom-right is a histogram of number of days of activity out of 90 total possible by each IP address clients were active half the days or more, and 65 were active every day (the upturn on the far right).
9 6.1 Amplification of Creativity In a system with human-computer collaboration, the creative amplification is the ratio of total content divided by the human-created content. If we compare the posted genomes with their evolved descendents we can measure how much creative amplification Electric Sheep provides. In the primary dataset there were 21% hand-designed, posted sheep and 79% evolved sheep. If the sum is weighted by rating, then we get 48% to 52%, for an amplification factor of 2.08 (1+52/48). One could say the genetic algorithm is doubling the output of the human posters. Of the 79% evolved sheep, 42% of them result from the totally random genetic operator. Their fraction of total ratings is only 3.8%. There are some caveats to this metric. For example, if the genetic algorithm just copied the posted genomes, it might receive some votes for its creativity. Or if it ignored the posted genomes and evolved on its own, it would receive some votes but they would not represent amplification. Figure 3 shows the distribution of lengths of lineages of the sheep. The lineage length of a sheep is the maximum number of generations of children that issue from it. Sheep with no children are assigned one, and sheep with children are assigned one plus the maximum of the lineage lengths of those children. Instead of fitness increasing along lineage, we find it dying out: the rating of the average parent is 6.7 but the average maximum rating of direct siblings is only 3.8. The decay in ratings may result from the audience losing interest in a lineage because it fails to change fast enough, rather than a decay of absolute quality of those sheep. The viewpoint of watching the screensaver and seeing sheep sequentially is different from the viewpoint of browsing the archive and comparing all the sheep. Neither can be called definitive. Genetic algorithms normally run for many tens to hundreds or thousands of generations. In contrast, the lineages (number of generations) of the sheep are very short: the longest is Related Research There are now many distributed screen-savers. Most are scientific climateprediction.net), cryptographic (distributed.net) or mathematical (zetagrid.net), rather than graphical or artistic. The project  has a evolutionary algorithm for evolving locomotion in electro-mechanical assemblages. The aesthetic selection used by Electric Sheep is inspired by Karl Sims . His supercomputer is replaced by internet-connected commodity PCs. The sheep voting community is much larger but much less focused than his user-base. Sims used Lisp expressions on pixel coordinates, but also included a primitive for IFS. The International Genetic Art IV project  uses a web-based Java client to evolve images following the technique of Sims. Since the images are rendered on the clients, the computation is distributed, but users do not share votes or the gene pool. Its previous incarnation, International Genetic Art II, ran on a single server with a web interface so
10 the computation was not distributed but the voting and gene pool were shared by all users. 8 Summary and Conclusion Electric Sheep is a distributed screen-saver for animating and evolving fractal flames, a kind of iterated function system. The animations are shared among the clients and displayed in parallel with rendering. The evolution is guided by the will of the audience. The genetic code is made of geometric transforms, each one containing 6 coefficients for an affine transform of the plane and 18 coefficients for blending the variational functions. The genetic algorithm employed does not converge on an optimal solution, but follows the attention of the users. And while the genetic algorithm is not competitive with the human designs, it does serve to effectively elaborate and amplify human designs. Acknowledgements Many thanks to: The anonymous reviewers, Tamara Munzner, and Matthew Stone for providing feedback on drafts of this paper, Dean Gaudet for help with performance optimization and server hosting, Tristan Horn for hosting the high-bandwidth server, Matt Reda and Nick Long for porting the client to OSX and Windows, respectively. And to all those who sent in patches, reported bugs, voted for their favorite sheep, or just ran the software once. References 1. David Anderson et al: An Experiment in Public-Resource Computing. Communications of the ACM 45 (2002) Michael Barnsley: Fractals Everywhere. Academic Press, San Diego, Alan Dorin: Aesthetic Fitness and Artificial Evolution for the Selection of Imagery from The Mythical Infinite Library. Advances in Artificial Life, Proc. 6th European Conference on Artificial Life, (2001) Scott Draves: The Fractal Flame Algorithm. Forthcoming, available from 5. Scott Draves: The Interpretation of Dreams. YLEM Journal 23;6 (2003) Scott Draves: The Electric Sheep web site athttp://electricsheep.org. 7. Hod Lipson and Jordan B. Pollack: Automatic Design and Manufacture of Robotic Lifeforms, Nature 406 (6799), 2000 August 31, John Mount, Scott Reilly, Michael Witbrock: International Genetic Art IV. Published on the web athttp://mzlabs.com/gart. 9. Karl Sims: Artificial Evolution for Computer Graphics. Proceedings of SIGGRAPH 1991, ACM Press, New York. 10. Mark Townsend: Apophysis v2 software distributed fromhttp://apophysis.org
BeamYourScreen User Guide Mac Version Table of Contents Registration 3 Download & Installation 4 Start a Session 5 Join a Session 6 Features 7 Participant List 7 Switch Presenter 8 Remote Control 8 Whiteboard
Mikogo User Guide Linux Version Table of Contents Registration 3 Downloading & Running the Application 4 Start a Session 5 Join a Session 6 Features 7 Participant List 7 Switch Presenter 8 Remote Control
Architectural Overview CatDV Pro Workgroup Server Square Box Systems Ltd May 2003 The CatDV Pro client application is a standalone desktop application, providing video logging and media cataloging capability
Comodo LoginPro Software Version 1.5 User Guide Guide Version 1.5.030513 Comodo Security Solutions 1255 Broad Street STE 100 Clifton, NJ 07013 Table of Contents 1.Introduction to Comodo LoginPro... 3 1.1.System
Load Balancing Backtracking, branch & bound and alpha-beta pruning: how to assign work to idle processes without much communication? Additionally for alpha-beta pruning: implementing the young-brothers-wait
AXIGEN Mail Server Reporting Service Usage and Configuration The article describes in full details how to properly configure and use the AXIGEN reporting service, as well as the steps for integrating it
Common Core Unit Summary Grades 6 to 8 Grade 8: Unit 1: Congruence and Similarity- 8G1-8G5 rotations reflections and translations,( RRT=congruence) understand congruence of 2 d figures after RRT Dilations
1358 CHAPTER 33 Logging and Debugging Customizing the Event Log The properties of an event log can be configured. In Event Viewer, the properties of a log are defined by general characteristics: log path,
Robot Task-Level Programming Language and Simulation M. Samaka Abstract This paper presents the development of a software application for Off-line robot task programming and simulation. Such application
The Center for Astrophysical Thermonuclear Flashes VisIt Visualization Tool Randy Hudson firstname.lastname@example.org Argonne National Laboratory Flash Center, University of Chicago An Advanced Simulation and Computing
Parallel Analysis and Visualization on Cray Compute Node Linux David Pugmire, Oak Ridge National Laboratory and Hank Childs, Lawrence Livermore National Laboratory and Sean Ahern, Oak Ridge National Laboratory
www.ijcsi.org 413 A Robust Method for Solving Transcendental Equations Md. Golam Moazzam, Amita Chakraborty and Md. Al-Amin Bhuiyan Department of Computer Science and Engineering, Jahangirnagar University,
SIDN Server Measurements Yuri Schaeffer 1, NLnet Labs NLnet Labs document 2010-003 July 19, 2010 1 Introduction For future capacity planning SIDN would like to have an insight on the required resources
Chapter 340 Principal Components Regression Introduction is a technique for analyzing multiple regression data that suffer from multicollinearity. When multicollinearity occurs, least squares estimates
Mikogo User Guide Windows Version Table of Contents Registration 3 Download & Installation 4 Start a Session 4 Join a Session 5 Features 6 Participant List 6 Switch Presenter 7 Remote Control 7 Whiteboard
Allworx Queuing and Automated Call Distribution Guide (Release 7.2.3.x) No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic,
Citrix EdgeSight for Load Testing User s Guide Citrx EdgeSight for Load Testing 2.7 Copyright Use of the product documented in this guide is subject to your prior acceptance of the End User License Agreement.
ImageVis3D Mobile This software can be used to display and interact with different kinds of datasets - such as volumes or meshes - on mobile devices, which currently includes iphone and ipad. A selection
Are Image Quality Metrics Adequate to Evaluate the Quality of Geometric Objects? Bernice E. Rogowitz and Holly E. Rushmeier IBM TJ Watson Research Center, P.O. Box 704, Yorktown Heights, NY USA ABSTRACT
Allworx Queuing and Automated Call Distribution Guide (Release 7.1.0.x) No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic,
Original brief explanation I installed the Shoutcast server onto a desktop and made some minor configuration changes, such as setting the passwords and the maximum number of listeners. This was quite easy
Performance Workload Design The goal of this paper is to show the basic principles involved in designing a workload for performance and scalability testing. We will understand how to achieve these principles
CS 147: Computer Systems Performance Analysis CS 147: Computer Systems Performance Analysis 1 / 39 Overview Overview Overview What is a Workload? Instruction Workloads Synthetic Workloads Exercisers and
Introduction to Computer Graphics Torsten Möller TASC 8021 778-782-2215 email@example.com www.cs.sfu.ca/~torsten Today What is computer graphics? Contents of this course Syllabus Overview of course topics
Shape, Space, and Measurement- Primary A student shall apply concepts of shape, space, and measurement to solve problems involving two- and three-dimensional shapes by demonstrating an understanding of:
A Short Introduction to Computer Graphics Frédo Durand MIT Laboratory for Computer Science 1 Introduction Chapter I: Basics Although computer graphics is a vast field that encompasses almost any graphical
Thnkwell s Homeschool Precalculus Course Lesson Plan: 36 weeks Welcome to Thinkwell s Homeschool Precalculus! We re thrilled that you ve decided to make us part of your homeschool curriculum. This lesson
Wrist Audio Player Link Soft for Macintosh User s Guide Trademarks Macintosh and Mac OS are registered trademarks of Apple Computer Inc. All other product, service and company names mentioned herein may
B2.53-R3: COMPUTER GRAPHICS NOTE: 1. There are TWO PARTS in this Module/Paper. PART ONE contains FOUR questions and PART TWO contains FIVE questions. 2. PART ONE is to be answered in the TEAR-OFF ANSWER
Hadoop Technology for Flow Analysis of the Internet Traffic Rakshitha Kiran P PG Scholar, Dept. of C.S, Shree Devi Institute of Technology, Mangalore, Karnataka, India ABSTRACT: Flow analysis of the internet
The main imovie window is divided into six major parts. 1. Project Drag clips to the project area to create a timeline 2. Preview Window Displays a preview of your video 3. Toolbar Contains a variety of
CHAPTER 4 CURVES 4.1 Introduction In order to understand the significance of curves, we should look into the types of model representations that are used in geometric modeling. Curves play a very significant
Trend Micro Incorporated reserves the right to make changes to this document and to the products described herein without notice. Before installing and using the software, please review the readme files,
4D WebSTAR 5.1: Performance Advantages CJ Holmes, Director of Engineering, 4D WebSTAR OVERVIEW This white paper will discuss a variety of performance benefits of 4D WebSTAR 5.1 when compared to other Web
Chapter 4 The Scientific Data Mining Process When I use a word, Humpty Dumpty said, in rather a scornful tone, it means just what I choose it to mean neither more nor less. Lewis Carroll [87, p. 214] In
LCMON Network Traffic Analysis Adam Black Centre for Advanced Internet Architectures, Technical Report 79A Swinburne University of Technology Melbourne, Australia firstname.lastname@example.org Abstract The Swinburne
The Union-Find Problem Kruskal s algorithm for finding an MST presented us with a problem in data-structure design. As we looked at each edge, cheapest first, we had to determine whether its two endpoints
NMS300 Network Management System User Manual June 2013 202-11289-01 350 East Plumeria Drive San Jose, CA 95134 USA Support Thank you for purchasing this NETGEAR product. After installing your device, locate
Name: Block: A. Introduction 1. Animation simulation of movement created by rapidly displaying images or frames. Relies on persistence of vision the way our eyes retain images for a split second longer
SQL databases An introduction AMP: Apache, mysql, PHP This installations installs the Apache webserver, the PHP scripting language, and the mysql database on your computer: Apache: runs in the background
WebSphere Commerce V7 Feature Pack 2 Pricing tool 2011 IBM Corporation This presentation provides an overview of the Pricing tool of the WebSphere Commerce V7.0 feature pack 2. PricingTool.ppt Page 1 of
Remote Access and Control of the Programmer/Controller Version 1.0 9/07/05 Remote Access and Control... 3 Introduction... 3 Installing Remote Access Viewer... 4 System Requirements... 4 Activate Java console...
WebEx Network Bandwidth White Paper WebEx Communications Inc. - 1 - Copyright WebEx Communications, Inc. reserves the right to make changes in the information contained in this publication without prior
Sequences and Series Overview Number of instruction days: 4 6 (1 day = 53 minutes) Content to Be Learned Write arithmetic and geometric sequences both recursively and with an explicit formula, use them
47 6 Scalar, Stochastic, Discrete Dynamic Systems Consider modeling a population of sand-hill cranes in year n by the first-order, deterministic recurrence equation y(n + 1) = Ry(n) where R = 1 + r = 1
433-659 DISTRIBUTED COMPUTING PROJECT, CSSE DEPT., UNIVERSITY OF MELBOURNE CloudAnalyst: A CloudSim-based Tool for Modelling and Analysis of Large Scale Cloud Computing Environments MEDC Project Report
CHAPTER 14 This chapter describes how to monitor the health and activities of the system. It covers these topics: About Logged Information, page 14-121 Event Logging, page 14-122 Monitoring Performance,
For Mac OS X Software version 4.1.7 Version 2.2 Disclaimer This document is compiled with the greatest possible care. However, errors might have been introduced caused by human mistakes or by other means.
Version 1.0 January 2011 Xerox Phaser 3635MFP 2011 Xerox Corporation. XEROX and XEROX and Design are trademarks of Xerox Corporation in the United States and/or other countries. Changes are periodically
Chapter 5 Clustering & Visualization Clustering in high-dimensional databases is an important problem and there are a number of different clustering paradigms which are applicable to high-dimensional data.
PRINT CONFIGURATION Red Flag Server5 has improved the designs of the printer configuration tool to facilitate you to conduct print configuration and print tasks management in a more convenient and familiar
First Semester Development 1A On completion of this subject students will be able to apply basic programming and problem solving skills in a 3 rd generation object-oriented programming language (such as
Graphic Design Active Layer- When you create multi layers for your images the active layer, or the only one that will be affected by your actions, is the one with a blue background in your layers palette.
Chapter 552 Gamma Distribution Fitting Introduction This module fits the gamma probability distributions to a complete or censored set of individual or grouped data values. It outputs various statistics
Network Probe User Guide Network Probe User Guide Table of Contents 1. Introduction...1 2. Installation...2 Windows installation...2 Linux installation...3 Mac installation...4 License key...5 Deployment...5
2008 AGI-Information Management Consultants May be used for personal purporses only or by libraries associated to dandelon.com network. Computing Concepts with Java Essentials 3rd Edition Cay Horstmann
Chapter 449 Introduction This algorithm provides for clustering in the multiple regression setting in which you have a dependent variable Y and one or more independent variables, the X s. The algorithm
Paul Pocatilu 1 and Ctlin Boja 2 1) 2) The Bucharest Academy of Economic Studies, Romania E-mail: email@example.com E-mail: firstname.lastname@example.org Abstract The educational process is a complex service which
Eucalyptus 3.4.2 User Console Guide 2014-02-23 Eucalyptus Systems Eucalyptus Contents 2 Contents User Console Overview...4 Install the Eucalyptus User Console...5 Install on Centos / RHEL 6.3...5 Configure
DEPLOYMENT GUIDE Deploying the BIG-IP LTM with the Cacti Open Source Network Monitoring System Version 1.0 Deploying F5 with Cacti Open Source Network Monitoring System Welcome to the F5 and Cacti deployment
School of Computing FACULTY OF ENGINEERING MEng, BSc Applied Computer Science Year 1 COMP1212 Computer Processor Effective programming depends on understanding not only how to give a machine instructions
Presenter ICT Training Updated: May 2001 Job Aid Prepared by Luc Gelinas Introduction to Microsoftw Presenter 4.0 In this workshop you will learn how to use Presnter 4.0 to create dynamic Web pages and
Chapter 1 Animation "Animation can explain whatever the mind of man can conceive. This facility makes it the most versatile and explicit means of communication yet devised for quick mass appreciation."
Mikogo User Guide Linux Version Table of Contents Registration 3 Downloading & Running the Application 3 Enter Your Account Details 4 Start a Session 5 Join a Session 6 Features 7 Participant List 7 Switch
The K 2 System: Lisp at the Core of the ISP Business Espen J. Vestre Nextra AS 1 Introduction The Nextra Group, a subsidiary of Telenor (the norwegian Telecom), is Norway s largest ISP, with over 400.000
Video Tracking Software User s Manual Version 1.0 Triangle BioSystems International 2224 Page Rd. Suite 108 Durham, NC 27703 Phone: (919) 361-2663 Fax: (919) 544-3061 www.trianglebiosystems.com Table of
Ricoh HotSpot Printer/MFP Whitepaper Version 4_r4 Table of Contents Introduction... 3 What is a HotSpot Printer?... 3 Understanding the HotSpot System Architecture... 4 Reliability of HotSpot Service...
Adobe Training Services Exam Guide ACE: After Effects CS6 Adobe Training Services provides this exam guide to help prepare partners, customers, and consultants who are actively seeking accreditation as
ANIMATION a system for animation scene and contents creation, retrieval and display Peter L. Stanchev Kettering University ABSTRACT There is an increasing interest in the computer animation. The most of
The Role and uses of Peer-to-Peer in file-sharing Computer Communication & Distributed Systems EDA 390 Jenny Bengtsson Prarthanaa Khokar email@example.com firstname.lastname@example.org Gothenburg, May
3. Interpolation Closing the Gaps of Discretization... Beyond Polynomials Closing the Gaps of Discretization... Beyond Polynomials, December 19, 2012 1 3.3. Polynomial Splines Idea of Polynomial Splines
ABB solar inverters User s manual ABB Remote monitoring portal List of related manuals Title ABB Remote monitoring portal User s manual NETA-01 Ethernet adapter module User s manual Code (English) 3AUA0000098904
Avigilon Control Center Web Client User Guide Version: 4.12 Enterprise OLH-WEBCLIENT-E-E-Rev2 Copyright 2013 Avigilon. All rights reserved. The information presented is subject to change without notice.
1 CHAPTER 1 INTRODUCTION Exploration is a process of discovery. In the database exploration process, an analyst executes a sequence of transformations over a collection of data structures to discover useful
Introduction to Hadoop HDFS and Ecosystems ANSHUL MITTAL Slides credits: Cloudera Academic Partners Program & Prof. De Liu, MSBA 6330 Harvesting Big Data Topics The goal of this presentation is to give
Computer science Case study: CGI For use in May 2016 and November 2016 Instructions to candidates ycase study booklet required for higher level paper 3. 6 pages International Baccalaureate Organization
Muse Server Sizing 18 June 2012 Document Version 0.0.1.9 Muse 22.214.171.124 Notice No part of this publication may be reproduced stored in a retrieval system, or transmitted, in any form or by any means, without
School of Computing FACULTY OF ENGINEERING MEng, BSc Computer Science with Artificial Intelligence Year 1 COMP1212 Computer Processor Effective programming depends on understanding not only how to give
Firewall Security: Policies, Testing and Performance Evaluation Michael R. Lyu and Lorrien K. Y. Lau Department of Computer Science and Engineering The Chinese University of Hong Kong, Shatin, HK email@example.com,
Spyglass Portal Manual v.20140214 Spyglass Portal Manual... 1 Getting Started... 2 Pre- mission... 3 As a user, I want to create and download a mission for the ESP... 3 Define default setting (phasecfg.rb)...
Process A Whitepaper Copyright 2006. Technologies Pvt. Ltd. All Rights Reserved. is a registered trademark of, Inc. All other trademarks are owned by the respective owners. Proprietary Table of Contents