Computa癢nal Thinking
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CRN What It Was Doing and Why It Was Cognitive Systems Vision Doing It, and to Recover from Mental Continued on Page 8 Expanding the Pipeline
COMPUTING RESEARCH NEWS Computing Research Association, Celebrating 30 Years of Service to the Computing Research Community November 2002 Vol. 14/No. 5 DARPA’s New Cognitive Systems Vision By Ron Brachman and IPTO’s goal is to create a new to cope with systems both keep Zachary Lemnios generation of cognitive systems. growing. In order to make our systems more reliable, more secure, The impact of the Defense Mired in Moore’s Law? and more understandable, and to Advanced Research Projects Agency One benefit of such cognitive continue making substantial contri- (DARPA) on computing over the systems would be their help in butions to society, we need to do past 40 years has been profound. Led extracting us from a corner into something dramatically different. by the visionary J.C.R. Licklider and which our success seems to have his innovative successors in the painted us. The research that has The Promise of Cognitive Information Processing Techniques helped the industry follow Moore’s Systems Office (IPTO), DARPA initiated “Law” has created processors that are IPTO is attacking this problem by work that ultimately put personal remarkably fast and small, and data driving a fundamental change in computers on millions of desktops storage capabilities that are vast and computing systems. By giving systems Ron Brachman and made the global Internet a cheap. Unfortunately, these incred- more cognitive capabilities, we reality. In fact, the original IPTO, ible developments have cut two ways. believe we can make them more which lasted from 1962 to 1985, was While today’s computers are more responsible for their own behavior in large part responsible for estab- powerful than ever, we have been and maintenance. -
The Conference Program Booklet
Austin Convention Center Conference Austin, TX Program http://sc15.supercomputing.org/ Conference Dates: Exhibition Dates: The International Conference for High Performance Nov. 15 - 20, 2015 Nov. 16 - 19, 2015 Computing, Networking, Storage and Analysis Sponsors: SC15.supercomputing.org SC15 • Austin, Texas The International Conference for High Performance Computing, Networking, Storage and Analysis Sponsors: 3 Table of Contents Welcome from the Chair ................................. 4 Papers ............................................................... 68 General Information ........................................ 5 Posters Research Posters……………………………………..88 Registration and Conference Store Hours ....... 5 ACM Student Research Competition ........ 114 Exhibit Hall Hours ............................................. 5 Posters SC15 Information Booth/Hours ....................... 5 Scientific Visualization/ .................................... 120 Data Analytics Showcase SC16 Preview Booth/Hours ............................. 5 Student Programs Social Events ..................................................... 5 Experiencing HPC for Undergraduates ...... 122 Registration Pass Access .................................. 7 Mentor-Protégé Program .......................... 123 Student Cluster Competition Kickoff ......... 123 SCinet ............................................................... 8 Student-Postdoc Job & ............................. 123 Convention Center Maps ................................. 12 Opportunity Fair Daily Schedules -
Curriculum Vitae
Massachusetts Institute of Technology School of Engineering Faculty Personnel Record Date: April 1, 2020 Full Name: Charles E. Leiserson Department: Electrical Engineering and Computer Science 1. Date of Birth November 10, 1953 2. Citizenship U.S.A. 3. Education School Degree Date Yale University B. S. (cum laude) May 1975 Carnegie-Mellon University Ph.D. Dec. 1981 4. Title of Thesis for Most Advanced Degree Area-Efficient VLSI Computation 5. Principal Fields of Interest Analysis of algorithms Caching Compilers and runtime systems Computer chess Computer-aided design Computer network architecture Digital hardware and computing machinery Distance education and interaction Fast artificial intelligence Leadership skills for engineering and science faculty Multicore computing Parallel algorithms, architectures, and languages Parallel and distributed computing Performance engineering Scalable computing systems Software performance engineering Supercomputing Theoretical computer science MIT School of Engineering Faculty Personnel Record — Charles E. Leiserson 2 6. Non-MIT Experience Position Date Founder, Chairman of the Board, and Chief Technology Officer, Cilk Arts, 2006 – 2009 Burlington, Massachusetts Director of System Architecture, Akamai Technologies, Cambridge, 1999 – 2001 Massachusetts Shaw Visiting Professor, National University of Singapore, Republic of 1995 – 1996 Singapore Network Architect for Connection Machine Model CM-5 Supercomputer, 1989 – 1990 Thinking Machines Programmer, Computervision Corporation, Bedford, Massachusetts 1975 -
Typical Stability
Typical Stability Raef Bassily∗ Yoav Freundy Abstract In this paper, we introduce a notion of algorithmic stability called typical stability. When our goal is to release real-valued queries (statistics) computed over a dataset, this notion does not require the queries to be of bounded sensitivity – a condition that is generally assumed under differential privacy [DMNS06, Dwo06] when used as a notion of algorithmic stability [DFH+15b, DFH+15c, BNS+16] – nor does it require the samples in the dataset to be independent – a condition that is usually assumed when generalization-error guarantees are sought. Instead, typical stability requires the output of the query, when computed on a dataset drawn from the underlying distribution, to be concentrated around its expected value with respect to that distribution. Typical stability can also be motivated as an alternative definition for database privacy. Like differential privacy, this notion enjoys several important properties including robustness to post-processing and adaptive composition. However, privacy is guaranteed only for a given family of distributions over the dataset. We also discuss the implications of typical stability on the generalization error (i.e., the difference between the value of the query computed on the dataset and the expected value of the query with respect to the true data distribution). We show that typical stability can control generalization error in adaptive data analysis even when the samples in the dataset are not necessarily independent and when queries to be computed are not necessarily of bounded- sensitivity as long as the results of the queries over the dataset (i.e., the computed statistics) follow a distribution with a “light” tail. -
A Complete Bibliography of Publications in the Journal of Computer and System Sciences
A Complete Bibliography of Publications in the Journal of Computer and System Sciences Nelson H. F. Beebe University of Utah Department of Mathematics, 110 LCB 155 S 1400 E RM 233 Salt Lake City, UT 84112-0090 USA Tel: +1 801 581 5254 FAX: +1 801 581 4148 E-mail: [email protected], [email protected], [email protected] (Internet) WWW URL: http://www.math.utah.edu/~beebe/ 26 May 2021 Version 1.04 Title word cross-reference (s; t) [1475]. 0(n) [1160]. 1 [270]. 1 − L [1371, 924]. 12n2 [450]. 2 [3525, 3184, 2191, 1048, 3402, 1500, 3364, 2034, 2993, 834, 2473, 3101]. f2; 3g [2843]. #09111 [1814]. #AM01053M 2pn 1=3 − 2 O(n ) [1862, 1877, 1904]. #better 5X8 els [1856]. 2 [2353]. 2 [445]. 2 [1977]. 3 #better 5X8 els.pdf [1856]. #BIS [3184]. [1920, 2242, 2827, 2374, 1961, 2825, 3240, · #BIS-hardness #CSP 1071, 1133]. 43 [3620]. 4 Log2N [655]. 5=4 [3184]. ∗ 0 #econdirectM [1612]. 8 [2998]. =? [1433]. [858]. [2696, 2908, 2500, 3508]. 1 ∗ NP [1893]. #HA03022M [1860, 1875]. [1625, 3418, 1085]. [1523]. [3194]. NP[O(log n)] p ⊆ PH #HA05062N [1876]. #HA05062O [2235]. [995]. 2 [2235]. [1849, 1861]. #HA06043M [1501]. 2 [3418]. H [1565]. a [426, 289]. [1892, 1871, 1889]. #HA08111M [1846]. A[P (x); 2x; x + 1] [500]. Ax = λBx [377]. b · #P [1195, 3598, 1261, 1264]. [3253]. β [3444]. [3418]. d #praise 5X8 els.pdf [1832]. [2527, 1362, 3256, 3563]. δ [3553]. `p [2154]. #sciencejobs N [1802]. [1551, 2617, 1864, 1693]. g [3312]. h [1019]. K [1320, 2756, 191, 3494, 1300, 1546, 3286, #P [1373]. -
An Efficient Boosting Algorithm for Combining Preferences Raj
An Efficient Boosting Algorithm for Combining Preferences by Raj Dharmarajan Iyer Jr. Submitted to the Department of Electrical Engineering and Computer Science in partial fulfillment of the requirements for the degree of Master of Science at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY September 1999 © Massachusetts Institute of Technology 1999. All rights reserved. A uth or .............. ..... ..... .................................. Department of E'ectrical ngineering and Computer Science August 24, 1999 C ertified by .. ................. ...... .. .............. David R. Karger Associate Professor Thesis Supervisor Accepted by............... ........ Arthur C. Smith Chairman, Departmental Committe Graduate Students MACHU OF TEC Lo NOV LIBRARIES An Efficient Boosting Algorithm for Combining Preferences by Raj Dharmarajan Iyer Jr. Submitted to the Department of Electrical Engineering and Computer Science on August 24, 1999, in partial fulfillment of the requirements for the degree of Master of Science Abstract The problem of combining preferences arises in several applications, such as combining the results of different search engines. This work describes an efficient algorithm for combining multiple preferences. We first give a formal framework for the problem. We then describe and analyze a new boosting algorithm for combining preferences called RankBoost. We also describe an efficient implementation of the algorithm for certain natural cases. We discuss two experiments we carried out to assess the performance of RankBoost. In the first experi- ment, we used the algorithm to combine different WWW search strategies, each of which is a query expansion for a given domain. For this task, we compare the performance of Rank- Boost to the individual search strategies. The second experiment is a collaborative-filtering task for making movie recommendations. -
A Temporal Logic Approach to Information-Flow Control
R S V E I T I A N S U S S A I R S A V I E N A Temporal Logic Approach to Information-flow Control Thesis for obtaining the title of Doctor of Natural Science of the Faculty of Natural Science and Technology I of Saarland University by Markus N. Rabe Saarbrücken February, 2016 Dean of the Faculty Prof. Dr. Markus Bläser Day of Colloquium January 28, 2016 Chair of the Committee Prof. Dr. Dr. h.c. Reinhard Wilhelm Reviewers Prof. Bernd Finkbeiner, Ph.D. Prof. David Basin, Ph.D. Prof. Sanjit A. Seshia, Ph.D. Academic Assistant Dr. Swen Jacobs i Abstract Information leaks and other violations of information security pose a severe threat to individuals, companies, and even countries. The mechanisms by which attackers threaten information security are diverse and to show their absence thus proved to be a challenging problem. Information-flow control is a principled approach to prevent security incidents in programs and other tech- nical systems. In information-flow control we define information-flow proper- ties, which are sufficient conditions for when the system is secure in a particu- lar attack scenario. By defining the information-flow property only based on what parts of the executions of the system a potential attacker can observe or control, we obtain security guarantees that are independent of implementa- tion details and thus easy to understand. There are several methods available to enforce (or verify) information-flow properties once defined. We focus on static enforcement methods, which automatically determine whether a given system satisfies a given information-flow property for all possible inputs to the system. -
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T G¨ P 2012 C N Deadline: December 31, 2011 The Gödel Prize for outstanding papers in the area of theoretical computer sci- ence is sponsored jointly by the European Association for Theoretical Computer Science (EATCS) and the Association for Computing Machinery, Special Inter- est Group on Algorithms and Computation Theory (ACM-SIGACT). The award is presented annually, with the presentation taking place alternately at the Inter- national Colloquium on Automata, Languages, and Programming (ICALP) and the ACM Symposium on Theory of Computing (STOC). The 20th prize will be awarded at the 39th International Colloquium on Automata, Languages, and Pro- gramming to be held at the University of Warwick, UK, in July 2012. The Prize is named in honor of Kurt Gödel in recognition of his major contribu- tions to mathematical logic and of his interest, discovered in a letter he wrote to John von Neumann shortly before von Neumann’s death, in what has become the famous P versus NP question. The Prize includes an award of USD 5000. AWARD COMMITTEE: The winner of the Prize is selected by a committee of six members. The EATCS President and the SIGACT Chair each appoint three members to the committee, to serve staggered three-year terms. The committee is chaired alternately by representatives of EATCS and SIGACT. The 2012 Award Committee consists of Sanjeev Arora (Princeton University), Josep Díaz (Uni- versitat Politècnica de Catalunya), Giuseppe Italiano (Università a˘ di Roma Tor Vergata), Mogens Nielsen (University of Aarhus), Daniel Spielman (Yale Univer- sity), and Eli Upfal (Brown University). ELIGIBILITY: The rule for the 2011 Prize is given below and supersedes any di fferent interpretation of the parametric rule to be found on websites on both SIGACT and EATCS. -
Theory and Algorithms for Modern Problems in Machine Learning and an Analysis of Markets
Theory and Algorithms for Modern Problems in Machine Learning and an Analysis of Markets by Ashish Rastogi A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy Department of Computer Science Courant Institute of Mathematical Sciences New York University May 2008 Richard Cole—Advisor Mehryar Mohri—Advisor °c Ashish Rastogi All Rights Reserved, 2008 To the most wonderful parents in the whole world, Mrs. Asha Rastogi and Mr. Shyam Lal Rastogi iv Acknowledgements First and foremost, I would like to thank my advisors, Professor Richard Cole and Professor Mehryar Mohri, for their unwavering support, guidance and constant encouragement. They have been inspiring mentors and much of what lies in the following pages can be credited to them. Working under their supervision has been one of the most enriching experiences of my life. I would also like to thank Professor Joel Spencer, Professor Arun Sun- dararajan, Professor Subhash Khot and Dr. Corinna Cortes for agreeing to serve as members on my thesis committee. Professor Spencer’s class on Random Graphs remains one of the most stim- ulating courses I undertook as a graduate student. Internships at Google through the summers of 2005, 2006 and 2007 were some of the most enjoy- able periods of my graduate school life. Many thanks are due to Dr. Corinna Cortes for providing me with the opportunity to work on several challenging problems at Google. Research initiated during these internships culminated in the development of ideas that form the bulk of this thesis. I would also like to thank my peers from the graduate school. -
40Th ACM Symposium on Theory of Computing (STOC 2008) Saturday
40th ACM Symposium on Theory of Computing (STOC 2008) Saturday, May 17, 2008 7:00pm – 10:00pm Registration (Conference Centre) 8:00pm – 10:00pm Reception (Palm Court) Sunday, May 18, 2008 8:00am – 5:00pm Registration (Conference Centre) 8:10am – 8:35am Breakfast (Conference Centre) Session 1A Session 1B (Theatre) (Saanich Room) Chair: Venkat Guruswami Chair: David Shmoys (Cornell (University of Washington and University) Institute for Advanced Study) 8:35am - 8:55am Parallel Repetition in Projection The Complexity of Temporal Games and a Concentration Bound Constraint Satisfaction Problems Anup Rao Manuel Bodirsky, Jan Kara 9:00am - 9:20am SDP Gaps and UGC Hardness for An Effective Ergodic Theorem Multiway Cut, 0-Extension and and Some Applications Metric Labeling Satyadev Nandakumar Rajeskar Manokaran, Joseph (Seffi) Naor, Prasad Raghavendra, Roy Schwartz 9:25am – 9:45am Unique Games on Expanding Algorithms for Subset Selection Constraint Graphs are Easy in Linear Regression Sanjeev Arora, Subhash A. Khot, Abhimanyu Das, David Kempe Alexandra Kolla, David Steurer, Madhur Tulsiani, Nisheeth Vishnoi 9:45am - 10:10am Break Session 2 (Theatre) Chair: Joan Feigenbaum (Yale University) 10:10am - 11:10am Rethinking Internet Routing Invited talk by Jennifer Rexford (Princeton University) 11:10am - 11:20am Break Session 3A Session 3B (Theatre) (Saanich Room) Chair: Xiaotie Deng (City Chair: Anupam Gupta (Carnegie University of Hong Kong) Mellon University) The Pattern Matrix Method for 11:20am – 11:40am Interdomain Routing and Games Lower Bounds on Quantum Hagay Levin, Michael Schapira, Communication Aviv Zohar Alexander A. Sherstov 11:45am – 12:05pm Optimal approximation for the Classical Interaction Cannot Submodular Welfare Problem in Replace a Quantum Message the value oracle model Dmitry Gavinsky Jan Vondrak 12:10pm – 12:30pm Optimal Mechanism Design and Span-program-based quantum Money Burning algorithm for evaluating formulas Jason Hartline, Tim Ben W. -
Computational Thinking
0 Computational Thinking Jeannette M. Wing President’s Professor of Computer Science and Department Head Computer Science Department Carnegie Mellon University Microsoft Asia Faculty Summit 26 October 2012 Tianjin, China My Grand Vision • Computational thinking will be a fundamental skill used by everyone in the world by the middle of the 21st Century. – Just like reading, writing, and arithmetic. – Incestuous: Computing and computers will enable the spread of computational thinking. – In research: scientists, engineers, …, historians, artists – In education: K-12 students and teachers, undergrads, … J.M. Wing, “Computational Thinking,” CACM Viewpoint, March 2006, pp. 33-35. Paper off http://www.cs.cmu.edu/~wing/ Computational Thinking 2 Jeannette M. Wing Computing is the Automation of Abstractions Abstractions 1. Machine 2. Human Automation 3. Network [Machine + Human] Computational Thinking focuses on the process of abstraction - choosing the right abstractions - operating in terms of multiple layers of abstraction simultaneously as in - defining the relationships the between layers Mathematics guided by the following concerns… Computational Thinking 3 Jeannette M. Wing Measures of a “Good” Abstraction in C.T. as in • Efficiency Engineering – How fast? – How much space? NEW – How much power? • Correctness – Does it do the right thing? • Does the program compute the right answer? – Does it do anything? • Does the program eventually produce an answer? [Halting Problem] • -ilities – Simplicity and elegance – Scalability – Usability – Modifiability – Maintainability – Cost – … Computational Thinking 4 Jeannette M. Wing Computational Thinking, Philosophically • Complements and combines mathematical and engineering thinking – C.T. draws on math as its foundations • But we are constrained by the physics of the underlying machine – C.T. -
Research News
Computing Research News COMPUTING RESEARCH ASSOCIATION, CELEBRATING 40 YEARS OF SERVICE TO THE COMPUTING RESEARCH COMMUNITY APRIL 2014 Vol. 26 / No. 4 Announcements 2 2014 CRA Board Election Results 3 Visions 2025: Interacting with the Computers All Around Us 4 Conference at Snowbird 4 CERP Infographic 5 Postdoc Best Practices Award Recipients Announced 6 University-Industry Partnership to Advance Machine Learning 7 Expanding the Pipeline: 1st CRA-W/CDC Broadening Participation in Visualization (BPViz) Workshop 8 CCC Workshop Report: Multidisciplinary Research for Online Education 10 Highlights of the CISE Fiscal Year 2015 Budget Request 11 Time to Degree in Computing 13 CRA Board Members 16 CRA Board Officers 16 CRA Staff 16 Professional Opportunities 17 COMPUTING RESEARCH NEWS, APRIL 2014 Vol. 26 / No. 4 Announcements Rabin and Klawe Named 2014 Women of Vision by the Richard Tapia receives Anita Borg Institute 2014 Vannevar Bush Award CRA, CRA-W and CDC congratulate Richard Tapia for receiving the 2014 Vannevar Bush Award. National Science Board has announced that mathematician Photo credit – Rice University Richard Tapia, a leader in mentoring minorities in science, engineering Dr. Tal Rabin Dr. Maria Klawe Richard Tapia and mathematics fields, is the 2014 recipient Congratulations to both Tal Rabin and Maria Klawe. Klawe is of its Vannevar Bush Award. Tapia is also a previous president of Harvey Mudd College and was a founding co- recipient of CRA’s A. Nico Habermann Award, and the chair of the highly successful CRA-W Committee. She will be Richard Tapia Celebration of Diversity in Computing a plenary speaker at the 2014 Conference at Snowbird.