A New Kind of Science
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Jet Development in Leading Log QCD.Pdf
17 CALT-68-740 DoE RESEARCH AND DEVELOPMENT REPORT Jet Development in Leading Log QCD * by STEPHEN WOLFRAM California Institute of Technology, Pasadena. California 91125 ABSTRACT A simple picture of jet development in QCD is described. Various appli- cations are treated, including transverse spreading of jets, hadroproduced y* pT distributions, lepton energy spectra from heavy quark decays, soft parton multiplicities and hadron cluster formation. *Work supported in part by the U.S. Department of Energy under Contract No. DE-AC-03-79ER0068 and by a Feynman Fellowship. 18 According to QCD, high-energy e+- e annihilation into hadrons is initiated by the production from the decaying virtual photon of a quark and an antiquark, ,- +- each with invariant masses up to the c.m. energy vs in the original e e collision. The q and q then travel outwards radiating gluons which serve to spread their energy and color into a jet of finite angle. After a time ~ 1/IS, the rate of gluon emissions presumably decreases roughly inversely with time, except for the logarithmic rise associated with the effective coupling con 2 stant, (a (t) ~ l/log(t/A ), where It is the invariant mass of the radiating s . quark). Finally, when emissions have degraded the energies of the partons produced until their invariant masses fall below some critical ~ (probably c a few times h), the system of quarks and gluons begins to condense into the observed hadrons. The probability for a gluon to be emitted at times of 0(--)1 is small IS and may be est~mated from the leading terms of a perturbation series in a (s). -
PARTON and HADRON PRODUCTION in E+E- ANNIHILATION Stephen Wolfram California Institute of Technology, Pasadena, California 91125
549 * PARTON AND HADRON PRODUCTION IN e+e- ANNIHILATION Stephen Wolfram California Institute of Technology, Pasadena, California 91125 Ab stract: The production of showers of partons in e+e- annihilation final states is described according to QCD , and the formation of hadrons is dis cussed. Resume : On y decrit la production d'averses d� partons dans la theorie QCD qui forment l'etat final de !'annihilation e+ e , ainsi que leur transform ation en hadrons. *Work supported in part by the U.S. Department of Energy under contract no. DE-AC-03-79ER0068. SSt1 Introduction In these notes , I discuss some attemp ts e ri the cl!evellope1!1t of - to d s c be hadron final states in e e annihilation events ll>Sing. QCD. few featm11res A (barely visible at available+ energies} of this deve l"'P"1'ent amenab lle· are lt<J a precise and formal analysis in QCD by means of pe·rturbatirnc• the<J ry. lfor the mos t part, however, existing are quite inadle<!i""' lte! theoretical mett:l�ods one must therefore simply try to identify dl-iimam;t physical p.l:ten<0melilla tt:he to be expected from QCD, and make estll!att:es of dne:i.r effects , vi.th the hop·e that results so obtained will provide a good appro�::illliatimn to eventllLal'c ex act calculations . In so far as such estllma�es r necessa�y� pre�ise �r..uan- a e titative tests of QCD are precluded . On the ott:her handl , if QC!Ji is ass11l!med correct, then existing experimental data to inve•stigate its be may \JJ:SE•«f havior in regions not yet explored by theoreticalbe llJleaums . -
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a platform for all that we know savas parastatidis http://savas.me savasp transition from web to apps increasing focus on information (& knowledge) rise of personal digital assistants importance of near-real time processing http://aptito.com/blog/wp-content/uploads/2012/05/smartphone-apps.jpg today... storing computing computers are huge amounts great tools for of data managing indexing example google and microsoft both have copies of the entire web (and more) for indexing purposes tomorrow... storing computing computers are huge amounts great tools for of data managing indexing acquisition discovery aggregation organization we would like computers to of the world’s information also help with the automatic correlation analysis and knowledge interpretation inference data information knowledge intelligence wisdom expert systems watson freebase wolframalpha rdbms google now web indexing data is symbols (bits, numbers, characters) information adds meaning to data through the introduction of relationship - it answers questions such as “who”, “what”, “where”, and “when” knowledge is a description of how the world works - it’s the application of data and information in order to answer “how” questions G. Bellinger, D. Castro, and A. Mills, “Data, Information, Knowledge, and Wisdom,” Inform. pp. 1–4, 2004 web – the data platform web – the information platform web – the knowledge platform foundation for new experiences “wisdom is not a product of schooling but of the lifelong attempt to acquire it” representative examples wolframalpha watson source: -
Ontologies and Languages for Representing Mathematical Knowledge on the Semantic Web
Ontologies and Languages for Representing Mathematical Knowledge on the Semantic Web Editor(s): Aldo Gangemi, ISTC-CNR Rome, Italy Solicited review(s): Claudio Sacerdoti Coen, University of Bologna, Italy; Alexandre Passant, DERI, National University of Galway, Ireland; Aldo Gangemi, ISTC-CNR Rome, Italy Christoph Lange data vocabularies and domain knowledge from pure and ap- plied mathematics. FB 3 (Mathematics and Computer Science), Many fields of mathematics have not yet been imple- University of Bremen, Germany mented as proper Semantic Web ontologies; however, we Computer Science, Jacobs University Bremen, show that MathML and OpenMath, the standard XML-based exchange languages for mathematical knowledge, can be Germany fully integrated with RDF representations in order to con- E-mail: [email protected] tribute existing mathematical knowledge to the Web of Data. We conclude with a roadmap for getting the mathematical Web of Data started: what datasets to publish, how to inter- link them, and how to take advantage of these new connec- tions. Abstract. Mathematics is a ubiquitous foundation of sci- Keywords: mathematics, mathematical knowledge manage- ence, technology, and engineering. Specific areas of mathe- ment, ontologies, knowledge representation, formalization, matics, such as numeric and symbolic computation or logics, linked data, XML enjoy considerable software support. Working mathemati- cians have recently started to adopt Web 2.0 environments, such as blogs and wikis, but these systems lack machine sup- 1. Introduction: Mathematics on the Web – State port for knowledge organization and reuse, and they are dis- of the Art and Challenges connected from tools such as computer algebra systems or interactive proof assistants. -
The Problem of Distributed Consensus: a Survey Stephen Wolfram*
The Problem of Distributed Consensus: A Survey Stephen Wolfram* A survey is given of approaches to the problem of distributed consensus, focusing particularly on methods based on cellular automata and related systems. A variety of new results are given, as well as a history of the field and an extensive bibliography. Distributed consensus is of current relevance in a new generation of blockchain-related systems. In preparation for a conference entitled “Distributed Consensus with Cellular Automata and Related Systems” that we’re organizing with NKN (= “New Kind of Network”) I decided to explore the problem of distributed consensus using methods from A New Kind of Science (yes, NKN “rhymes” with NKS) as well as from the Wolfram Physics Project. A Simple Example Consider a collection of “nodes”, each one of two possible colors. We want to determine the majority or “consensus” color of the nodes, i.e. which color is the more common among the nodes. A version of this document with immediately executable code is available at writings.stephenwolfram.com/2021/05/the-problem-of-distributed-consensus Originally published May 17, 2021 *Email: [email protected] 2 | Stephen Wolfram One obvious method to find this “majority” color is just sequentially to visit each node, and tally up all the colors. But it’s potentially much more efficient if we can use a distributed algorithm, where we’re running computations in parallel across the various nodes. One possible algorithm works as follows. First connect each node to some number of neighbors. For now, we’ll just pick the neighbors according to the spatial layout of the nodes: The algorithm works in a sequence of steps, at each step updating the color of each node to be whatever the “majority color” of its neighbors is. -
Datatone: Managing Ambiguity in Natural Language Interfaces for Data Visualization Tong Gao1, Mira Dontcheva2, Eytan Adar1, Zhicheng Liu2, Karrie Karahalios3
DataTone: Managing Ambiguity in Natural Language Interfaces for Data Visualization Tong Gao1, Mira Dontcheva2, Eytan Adar1, Zhicheng Liu2, Karrie Karahalios3 1University of Michigan, 2Adobe Research 3University of Illinois, Ann Arbor, MI San Francisco, CA Urbana Champaign, IL fgaotong,[email protected] fmirad,[email protected] [email protected] ABSTRACT to be both flexible and easy to use. General purpose spread- Answering questions with data is a difficult and time- sheet tools, such as Microsoft Excel, focus largely on offer- consuming process. Visual dashboards and templates make ing rich data transformation operations. Visualizations are it easy to get started, but asking more sophisticated questions merely output to the calculations in the spreadsheet. Asking often requires learning a tool designed for expert analysts. a “visual question” requires users to translate their questions Natural language interaction allows users to ask questions di- into operations on the spreadsheet rather than operations on rectly in complex programs without having to learn how to the visualization. In contrast, visual analysis tools, such as use an interface. However, natural language is often ambigu- Tableau,1 creates visualizations automatically based on vari- ous. In this work we propose a mixed-initiative approach to ables of interest, allowing users to ask questions interactively managing ambiguity in natural language interfaces for data through the visualizations. However, because these tools are visualization. We model ambiguity throughout the process of often intended for domain specialists, they have complex in- turning a natural language query into a visualization and use terfaces and a steep learning curve. algorithmic disambiguation coupled with interactive ambigu- Natural language interaction offers a compelling complement ity widgets. -
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Mel says, “This is swell! But it’s not ideal—it’s a free, grainy PDF.” Attain your ideals! Purchase a nicer, printable PDF of this issue. Or nicest of all, subscribe to the paper version of the Annals of Improbable Research (six issues per year, delivered to your doorstep!). To purchase pretty PDFs, or to subscribe to splendid paper, go to http://www.improbable.com/magazine/ ANNALS OF Special Issue THE 2009 IG® NOBEL PRIZES Panda poo spinoff, Tequila-based diamonds, 11> Chernobyl-inspired bra/mask… NOVEMBER|DECEMBER 2009 (volume 15, number 6) $6.50 US|$9.50 CAN 027447088921 The journal of record for inflated research and personalities Annals of © 2009 Annals of Improbable Research Improbable Research ISSN 1079-5146 print / 1935-6862 online AIR, P.O. Box 380853, Cambridge, MA 02238, USA “Improbable Research” and “Ig” and the tumbled thinker logo are all reg. U.S. Pat. & Tm. Off. 617-491-4437 FAX: 617-661-0927 www.improbable.com [email protected] EDITORIAL: [email protected] The journal of record for inflated research and personalities Co-founders Commutative Editor VP, Human Resources Circulation (Counter-clockwise) Marc Abrahams Stanley Eigen Robin Abrahams James Mahoney Alexander Kohn Northeastern U. Research Researchers Webmaster Editor Associative Editor Kristine Danowski, Julia Lunetta Marc Abrahams Mark Dionne Martin Gardiner, Tom Gill, [email protected] Mary Kroner, Wendy Mattson, General Factotum (web) [email protected] Dissociative Editor Katherine Meusey, Srinivasan Jesse Eppers Rose Fox Rajagopalan, Tom Roberts, Admin Tom Ulrich Technical Eminence Grise Lisa Birk Psychology Editor Dave Feldman Robin Abrahams Design and Art European Bureau Geri Sullivan Art Director emerita Kees Moeliker, Bureau Chief Contributing Editors PROmote Communications Peaco Todd Rotterdam Otto Didact, Stephen Drew, Ernest Lois Malone Webmaster emerita [email protected] Ersatz, Emil Filterbag, Karen Rich & Famous Graphics Steve Farrar, Edinburgh Desk Chief Hopkin, Alice Kaswell, Nick Kim, Amy Gorin Erwin J.O. -
Cellular Automation
John von Neumann Cellular automation A cellular automaton is a discrete model studied in computability theory, mathematics, physics, complexity science, theoretical biology and microstructure modeling. Cellular automata are also called cellular spaces, tessellation automata, homogeneous structures, cellular structures, tessellation structures, and iterative arrays.[2] A cellular automaton consists of a regular grid of cells, each in one of a finite number of states, such as on and off (in contrast to a coupled map lattice). The grid can be in any finite number of dimensions. For each cell, a set of cells called its neighborhood is defined relative to the specified cell. An initial state (time t = 0) is selected by assigning a state for each cell. A new generation is created (advancing t by 1), according to some fixed rule (generally, a mathematical function) that determines the new state of each cell in terms of the current state of the cell and the states of the cells in its neighborhood. Typically, the rule for updating the state of cells is the same for each cell and does not change over time, and is applied to the whole grid simultaneously, though exceptions are known, such as the stochastic cellular automaton and asynchronous cellular automaton. The concept was originally discovered in the 1940s by Stanislaw Ulam and John von Neumann while they were contemporaries at Los Alamos National Laboratory. While studied by some throughout the 1950s and 1960s, it was not until the 1970s and Conway's Game of Life, a two-dimensional cellular automaton, that interest in the subject expanded beyond academia. -
Downloadable PDF of the 77500-Word Manuscript
From left: W. Daniel Hillis, Neil Gershenfeld, Frank Wilczek, David Chalmers, Robert Axelrod, Tom Griffiths, Caroline Jones, Peter Galison, Alison Gopnik, John Brockman, George Dyson, Freeman Dyson, Seth Lloyd, Rod Brooks, Stephen Wolfram, Ian McEwan. In absentia: Andy Clark, George Church, Daniel Kahneman, Alex "Sandy" Pentland (Click to expand photo) INTRODUCTION by Venki Ramakrishnan The field of machine learning and AI is changing at such a rapid pace that we cannot foresee what new technical breakthroughs lie ahead, where the technology will lead us or the ways in which it will completely transform society. So it is appropriate to take a regular look at the landscape to see where we are, what lies ahead, where we should be going and, just as importantly, what we should be avoiding as a society. We want to bring a mix of people with deep expertise in the technology as well as broad 1 thinkers from a variety of disciplines to make regular critical assessments of the state and future of AI. —Venki Ramakrishnan, President of the Royal Society and Nobel Laureate in Chemistry, 2009, is Group Leader & Former Deputy Director, MRC Laboratory of Molecular Biology; Author, Gene Machine: The Race to Decipher the Secrets of the Ribosome. [ED. NOTE: In recent months, Edge has published the fifteen individual talks and discussions from its two-and-a-half-day Possible Minds Conference held in Morris, CT, an update from the field following on from the publication of the group-authored book Possible Minds: Twenty-Five Ways of Looking at AI. As a special event for the long Thanksgiving weekend, we are pleased to publish the complete conference—10 hours plus of audio and video, as well as this downloadable PDF of the 77,500-word manuscript. -
WOLFRAM EDUCATION SOLUTIONS MATHEMATICA® TECHNOLOGIES for TEACHING and RESEARCH About Wolfram Research
WOLFRAM EDUCATION SOLUTIONS MATHEMATICA® TECHNOLOGIES FOR TEACHING AND RESEARCH About Wolfram Research For over two decades, Wolfram Research has been dedicated to developing tools that inspire exploration and innovation. As we work toward our goal to make the world’s data computable, we have expanded our portfolio to include a variety of products and technologies that, when combined, provide a true campuswide solution. At the center is Mathematica—our ever-advancing core product that has become the ultimate application for computation, visualization, and development. With millions of dedicated users throughout the technical and educational communities, Mathematica is used for everything from teaching simple concepts in the classroom to doing serious research using some of the world’s largest clusters. Wolfram’s commitment to education spans from elementary education to research universities. Through our free educational resources, STEM teacher workshops, and on-campus technical talks, we interact with educators whose feedback we rely on to develop tools that support their changing needs. Just as Mathematica revolutionized technical computing 20 years ago, our ongoing development of Mathematica technology and continued dedication to education are transforming the composition of tomorrow’s classroom. With more added all the time, Wolfram educational resources bolster pedagogy and support technology for classrooms and campuses everywhere. Favorites among educators include: Wolfram|Alpha®, the Wolfram Demonstrations Project™, MathWorld™, -
The Wolfram Physics Project: the First Two Weeks—Stephen Wolfram Writings ≡
6/21/2020 The Wolfram Physics Project: The First Two Weeks—Stephen Wolfram Writings ≡ RECENT | CATEGORIES | The Wolfram Physics Project: The First Two Weeks April 29, 2020 Project Announcement: Finally We May Have a Path to the Fundamental Theory of Physics… and It’s Beautiful Website: Wolfram Physics Project First, Thank You! We launched the Wolfram Physics Project two weeks ago, on April 14. And, in a word, wow! People might think that interest in fundamental science has waned. But the thousands of messages we’ve received tell a very different story. People really care! They’re excited. They’re enjoying understanding what we’ve figured out. They’re appreciating the elegance of it. They want to support the project. They want to get involved. It’s tremendously encouraging—and motivating. I wanted this project to be something for the world—and something lots of people could participate in. And it’s working. Our livestreams—even very technical ones—have been exceptionally popular. We’ve had lots of physicists, mathematicians, computer scientists and others asking questions, making suggestions and offering help. We’ve had lots of students and others who tell us how eager they are to get into doing research on the project. And we’ve had lots of people who just want to tell us they appreciate what we’re doing. So, thank you! https://writings.stephenwolfram.com/2020/04/the-wolfram-physics-project-the-first-two-weeks/ 1/35 6/21/2020 The Wolfram Physics Project: The First Two Weeks—Stephen Wolfram Writings Real-Time Science Science is usually done behind closed doors. -
The Story About Stephen Wolfram
The Physicist Turned a Successful Entrepreneur THE STORY ABOUT STEPHEN WOLFRAM by Gennaro Cuofano FourWeekMBA.com Wolfram Alpha: The Physicist Turned a Successful Entrepreneur - The Four-Week MBA Wolfram Alpha: The Physicist Turned a Successful Entrepreneur Why Wolfram Alpha? Among my passions, I love to check financial data about companies I like. Recently I was looking for a quick way to get reliable financial information for comparative analyses. At the same time, I was looking for shortcuts to perform that analysis. I was researching for myself. I thought why waste so much time on scraping financial reports? While surfing the web, I was looking for a solution and a term popped to my eyes “computational engine.” Wolfram Alpha! That search opened me a universe I wasn’t aware of. Yet that universe wasn’t only about a fantastic tool I learned to use in several ways. I found out the most amazing entrepreneurial story. That is how I jumped in and researched as much as I could about this topic! Why is this story so remarkable? Imagine a kid that as many others, is struggling at arithmetic. Imagine that same kid at 12 years old building a physics dictionary and by the age of 14 drafting three books about particle physics. A few years later at 23, that kid, now a man gets awarded as a prodigious physicist. We could stop this story here, and it would be already one of the most incredible stories you’ll ever hear. Yet this is only the beginning. In fact, what if I told you, that same person turned into a successful entrepreneur, which built several companies and a whole new science from scratch! (Let’s save some details for later) That isn’t only the story of Wolfram Alpha, a tool that I learned to use and cherish.