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Dc Josephson Current Between an Isotropic and a D-Wave Or Extended S-Wave Partially Gapped Charge Density Wave Superconductor
Chapter 12 dc Josephson Current Between an Isotropic and a d-Wave or Extended s-Wave Partially Gapped Charge Density Wave Superconductor Alexander M. Gabovich, Suan Li Mai, Henryk Szymczak and Alexander I. Voitenko Additional information is available at the end of the chapter http://dx.doi.org/10.5772/46073 1. Introduction The discovery and further development of superconductivity is extremely interesting because of its pragmatic (practical) and purely academic reasons. At the same time, the superconductivity science is very remarkable as an important object for the study in the framework of the history and methodology of science, since all the details are well documented and well-known to the community because of numerous interviews by participants including main heroes of the research and the fierce race for higher critical temperatures of the superconducting transition, Tc. Moreover, the whole science has well-documented dates, starting from the epoch-making discovery of the superconducting transition by Heike Kamerlingh-Onnes in 1911 [1–7], although minor details of this and, unfortunately, certain subsequent discoveries in the field were obscured [8–11]. As an illustrative example of a senseless dispute on the priority, one can mention the controversy between the recognition of Bardeen-Cooper-Schrieffer (BCS) [12] and Bogoliubov [13] theories. If one looks beyond superconductivity, it is easy to find quite a number of controversies in different fields of science [14, 15]. Recent attempts [16–18] to contest and discredit the Nobel Committee decision on the discovery of graphene by Andre Geim and Kostya Novoselov [19, 20] are very typical. The reasons of a widespread disagreement concerning various scientific discoveries consist in a continuity of scientific research process and a tense competition between different groups, as happened at liquefying helium and other cryogenic gases [9, 21–24] and was reproduced in the course of studying graphite films [25, 26]. -
Efficient Algorithms with Asymmetric Read and Write Costs
Efficient Algorithms with Asymmetric Read and Write Costs Guy E. Blelloch1, Jeremy T. Fineman2, Phillip B. Gibbons1, Yan Gu1, and Julian Shun3 1 Carnegie Mellon University 2 Georgetown University 3 University of California, Berkeley Abstract In several emerging technologies for computer memory (main memory), the cost of reading is significantly cheaper than the cost of writing. Such asymmetry in memory costs poses a fun- damentally different model from the RAM for algorithm design. In this paper we study lower and upper bounds for various problems under such asymmetric read and write costs. We con- sider both the case in which all but O(1) memory has asymmetric cost, and the case of a small cache of symmetric memory. We model both cases using the (M, ω)-ARAM, in which there is a small (symmetric) memory of size M and a large unbounded (asymmetric) memory, both random access, and where reading from the large memory has unit cost, but writing has cost ω 1. For FFT and sorting networks we show a lower bound cost of Ω(ωn logωM n), which indicates that it is not possible to achieve asymptotic improvements with cheaper reads when ω is bounded by a polynomial in M. Moreover, there is an asymptotic gap (of min(ω, log n)/ log(ωM)) between the cost of sorting networks and comparison sorting in the model. This contrasts with the RAM, and most other models, in which the asymptotic costs are the same. We also show a lower bound for computations on an n × n diamond DAG of Ω(ωn2/M) cost, which indicates no asymptotic improvement is achievable with fast reads. -
Computational Learning Theory: New Models and Algorithms
Computational Learning Theory: New Models and Algorithms by Robert Hal Sloan S.M. EECS, Massachusetts Institute of Technology (1986) B.S. Mathematics, Yale University (1983) Submitted to the Department- of Electrical Engineering and Computer Science in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY June 1989 @ Robert Hal Sloan, 1989. All rights reserved The author hereby grants to MIT permission to reproduce and to distribute copies of this thesis document in whole or in part. Signature of Author Department of Electrical Engineering and Computer Science May 23, 1989 Certified by Ronald L. Rivest Professor of Computer Science Thesis Supervisor Accepted by Arthur C. Smith Chairman, Departmental Committee on Graduate Students Abstract In the past several years, there has been a surge of interest in computational learning theory-the formal (as opposed to empirical) study of learning algorithms. One major cause for this interest was the model of probably approximately correct learning, or pac learning, introduced by Valiant in 1984. This thesis begins by presenting a new learning algorithm for a particular problem within that model: learning submodules of the free Z-module Zk. We prove that this algorithm achieves probable approximate correctness, and indeed, that it is within a log log factor of optimal in a related, but more stringent model of learning, on-line mistake bounded learning. We then proceed to examine the influence of noisy data on pac learning algorithms in general. Previously it has been shown that it is possible to tolerate large amounts of random classification noise, but only a very small amount of a very malicious sort of noise. -
MODELING and ANALYSIS of MOBILE TELEPHONY PROTOCOLS by Chunyu Tang a DISSERTATION Submitted to the Faculty of the Stevens Instit
MODELING AND ANALYSIS OF MOBILE TELEPHONY PROTOCOLS by Chunyu Tang A DISSERTATION Submitted to the Faculty of the Stevens Institute of Technology in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Chunyu Tang, Candidate ADVISORY COMMITTEE David A. Naumann, Chairman Date Yingying Chen Date Daniel Duchamp Date Susanne Wetzel Date STEVENS INSTITUTE OF TECHNOLOGY Castle Point on Hudson Hoboken, NJ 07030 2013 c 2013, Chunyu Tang. All rights reserved. iii MODELING AND ANALYSIS OF MOBILE TELEPHONY PROTOCOLS ABSTRACT The GSM (2G), UMTS (3G), and LTE (4G) mobile telephony protocols are all in active use, giving rise to a number of interoperation situations. This poses serious challenges in ensuring authentication and other security properties. Analyzing the security of all possible interoperation scenarios by hand is, at best, tedious under- taking. Model checking techniques provide an effective way to automatically find vulnerabilities in or to prove the security properties of security protocols. Although the specifications address the interoperation cases between GSM and UMTS and the switching and mapping of established security context between LTE and previous technologies, there is not a comprehensive specification of which are the possible interoperation cases. Nor is there comprehensive specification of the procedures to establish security context (authentication and short-term keys) in the various interoperation scenarios. We systematically enumerate the cases, classifying them as allowed, disallowed, or uncertain with rationale based on detailed analysis of the specifications. We identify the authentication and key agreement procedure for each of the possible cases. We formally model the pure GSM, UMTS, LTE authentication protocols, as well as all the interoperation scenarios; we analyze their security, in the symbolic model of cryptography, using the tool ProVerif. -
Department of Computer Science
i cl i ck ! MAGAZINE click MAGAZINE 2014, VOLUME II FIVE DECADES AS A DEPARTMENT. THOUSANDS OF REMARKABLE GRADUATES. 50COUNTLESS INNOVATIONS. Department of Computer Science click! Magazine is produced twice yearly for the friends of got your CS swag? CS @ ILLINOIS to showcase the innovations of our faculty and Commemorative 50-10 Anniversary students, the accomplishments of our alumni, and to inspire our t-shirts are available! partners and peers in the field of computer science. Department Head: Editorial Board: Rob A. Rutenbar Tom Moone Colin Robertson Associate Department Heads: Rob A. Rutenbar shop now! my.cs.illinois.edu/buy Gerald DeJong Michelle Wellens Jeff Erickson David Forsyth Writers: David Cunningham CS Alumni Advisory Board: Elizabeth Innes Alex R. Bratton (BS CE ’93) Mike Koon Ira R. Cohen (BS CS ’81) Rick Kubetz Vilas S. Dhar (BS CS ’04, BS LAS BioE ’04) Leanne Lucas William M. Dunn (BS CS ‘86, MS ‘87) Tom Moone Mary Jane Irwin (MS CS ’75, PhD ’77) Michelle Rice Jennifer A. Mozen (MS CS ’97) Colin Robertson Daniel L. Peterson (BS CS ’05) Laura Schmitt Peter L. Tannenwald (BS LAS Math & CS ’85) Michelle Wellens Jill C. Zmaczinsky (BS CS ’00) Design: Contact us: SURFACE 51 [email protected] 217-333-3426 Machines take me by surprise with great frequency. Alan Turing 2 CS @ ILLINOIS Department of Computer Science College of Engineering, College of Liberal Arts & Sciences University of Illinois at Urbana-Champaign shop now! my.cs.illinois.edu/buy click i MAGAZINE 2014, VOLUME II 2 Letter from the Head 4 ALUMNI NEWS 4 Alumni -
Lipics-ISAAC-2020-42.Pdf (0.5
Multiparty Selection Ke Chen Department of Computer Science, University of Wisconsin–Milwaukee, WI, USA [email protected] Adrian Dumitrescu Department of Computer Science, University of Wisconsin–Milwaukee, WI, USA [email protected] Abstract Given a sequence A of n numbers and an integer (target) parameter 1 ≤ i ≤ n, the (exact) selection problem is that of finding the i-th smallest element in A. An element is said to be (i, j)-mediocre if it is neither among the top i nor among the bottom j elements of S. The approximate selection problem is that of finding an (i, j)-mediocre element for some given i, j; as such, this variant allows the algorithm to return any element in a prescribed range. In the first part, we revisit the selection problem in the two-party model introduced by Andrew Yao (1979) and then extend our study of exact selection to the multiparty model. In the second part, we deduce some communication complexity benefits that arise in approximate selection. In particular, we present a deterministic protocol for finding an approximate median among k players. 2012 ACM Subject Classification Theory of computation Keywords and phrases approximate selection, mediocre element, comparison algorithm, i-th order statistic, tournaments, quantiles, communication complexity Digital Object Identifier 10.4230/LIPIcs.ISAAC.2020.42 1 Introduction Given a sequence A of n numbers and an integer (selection) parameter 1 ≤ i ≤ n, the selection problem asks to find the i-th smallest element in A. If the n elements are distinct, the i-th smallest is larger than i − 1 elements of A and smaller than the other n − i elements of A. -
Four Results of Jon Kleinberg a Talk for St.Petersburg Mathematical Society
Four Results of Jon Kleinberg A Talk for St.Petersburg Mathematical Society Yury Lifshits Steklov Institute of Mathematics at St.Petersburg May 2007 1 / 43 2 Hubs and Authorities 3 Nearest Neighbors: Faster Than Brute Force 4 Navigation in a Small World 5 Bursty Structure in Streams Outline 1 Nevanlinna Prize for Jon Kleinberg History of Nevanlinna Prize Who is Jon Kleinberg 2 / 43 3 Nearest Neighbors: Faster Than Brute Force 4 Navigation in a Small World 5 Bursty Structure in Streams Outline 1 Nevanlinna Prize for Jon Kleinberg History of Nevanlinna Prize Who is Jon Kleinberg 2 Hubs and Authorities 2 / 43 4 Navigation in a Small World 5 Bursty Structure in Streams Outline 1 Nevanlinna Prize for Jon Kleinberg History of Nevanlinna Prize Who is Jon Kleinberg 2 Hubs and Authorities 3 Nearest Neighbors: Faster Than Brute Force 2 / 43 5 Bursty Structure in Streams Outline 1 Nevanlinna Prize for Jon Kleinberg History of Nevanlinna Prize Who is Jon Kleinberg 2 Hubs and Authorities 3 Nearest Neighbors: Faster Than Brute Force 4 Navigation in a Small World 2 / 43 Outline 1 Nevanlinna Prize for Jon Kleinberg History of Nevanlinna Prize Who is Jon Kleinberg 2 Hubs and Authorities 3 Nearest Neighbors: Faster Than Brute Force 4 Navigation in a Small World 5 Bursty Structure in Streams 2 / 43 Part I History of Nevanlinna Prize Career of Jon Kleinberg 3 / 43 Nevanlinna Prize The Rolf Nevanlinna Prize is awarded once every 4 years at the International Congress of Mathematicians, for outstanding contributions in Mathematical Aspects of Information Sciences including: 1 All mathematical aspects of computer science, including complexity theory, logic of programming languages, analysis of algorithms, cryptography, computer vision, pattern recognition, information processing and modelling of intelligence. -
Navigability of Small World Networks
Navigability of Small World Networks Pierre Fraigniaud CNRS and University Paris Sud http://www.lri.fr/~pierre Introduction Interaction Networks • Communication networks – Internet – Ad hoc and sensor networks • Societal networks – The Web – P2P networks (the unstructured ones) • Social network – Acquaintance – Mail exchanges • Biology (Interactome network), linguistics, etc. Dec. 19, 2006 HiPC'06 3 Common statistical properties • Low density • “Small world” properties: – Average distance between two nodes is small, typically O(log n) – The probability p that two distinct neighbors u1 and u2 of a same node v are neighbors is large. p = clustering coefficient • “Scale free” properties: – Heavy tailed probability distributions (e.g., of the degrees) Dec. 19, 2006 HiPC'06 4 Gaussian vs. Heavy tail Example : human sizes Example : salaries µ Dec. 19, 2006 HiPC'06 5 Power law loglog ppk prob{prob{ X=kX=k }} ≈≈ kk-α loglog kk Dec. 19, 2006 HiPC'06 6 Random graphs vs. Interaction networks • Random graphs: prob{e exists} ≈ log(n)/n – low clustering coefficient – Gaussian distribution of the degrees • Interaction networks – High clustering coefficient – Heavy tailed distribution of the degrees Dec. 19, 2006 HiPC'06 7 New problematic • Why these networks share these properties? • What model for – Performance analysis of these networks – Algorithm design for these networks • Impact of the measures? • This lecture addresses navigability Dec. 19, 2006 HiPC'06 8 Navigability Milgram Experiment • Source person s (e.g., in Wichita) • Target person t (e.g., in Cambridge) – Name, professional occupation, city of living, etc. • Letter transmitted via a chain of individuals related on a personal basis • Result: “six degrees of separation” Dec. -
Facts and Figures 2013
Facts and Figures 201 3 Contents The University 2 World ranking 4 Academic pedigree 6 Areas of impact 8 Research power 10 Spin-outs 12 Income 14 Students 16 Graduate careers 18 Alumni 20 Faculties and Schools 22 Staff 24 Estates investment 26 Visitor attractions 28 Widening participation 30 At a glance 32 1 The University of Manchester Our Strategic Vision 2020 states our mission: “By 2020, The University of Manchester will be one of the top 25 research universities in the world, where all students enjoy a rewarding educational and wider experience; known worldwide as a place where the highest academic values and educational innovation are cherished; where research prospers and makes a real difference; and where the fruits of scholarship resonate throughout society.” Our core goals 1 World-class research 2 Outstanding learning and student experience 3 Social responsibility 2 3 World ranking The quality of our teaching and the impact of our research are the cornerstones of our success. 5 The Shanghai Jiao Tong University UK Academic Ranking of World ranking Universities assesses the best teaching and research universities, and in 2012 we were ranked 40th in the world. 7 World European UK European Year Ranking Ranking Ranking ranking 2012 40 7 5 2010 44 9 5 2005 53 12 6 2004* 78* 24* 9* 40 Source: 2012 Shanghai Jiao Tong University World Academic Ranking of World Universities ranking *2004 ranking refers to the Victoria University of Manchester prior to the merger with UMIST. 4 5 Academic pedigree Nobel laureates 1900 JJ Thomson , Physics (1906) We attract the highest calibre researchers and Ernest Rutherford , Chemistry (1908) teachers, boasting 25 Nobel Prize winners among 1910 William Lawrence Bragg , Physics (1915) current and former staff and students. -
M1757 Facts and Figures 2017.Indd
FACTS AND FIGURES 2017 CONTENTS 2 The University 20 Alumni 4 World ranking 22 Faculties and Schools 6 Academic pedigree 24 Staff 8 World-class research 26 Income 10 Innovation 28 Campus investment 12 Global challenges, 30 Making a diff erence Manchester solutions 32 Widening participation 14 Students 34 Public attractions 16 Stellify 36 At a glance 18 Graduate careers 1 THE UNIVERSITY 1 OF MANCHESTER WORLD-CLASS RESEARCH Our Manchester 2020 strategic plan states our mission: “By 2020 The University of Manchester will be a world-leading university recognised globally OUR COREOUTSTANDING GOALS for the excellence of its research, outstanding LEARNING AND 3 learning and student experience, and its STUDENT EXPERIENCE social, economic and cultural impact.” SOCIAL 2 RESPONSIBILITY 2 3 WORLD RANKING The quality of our teaching and the impact of our research are the cornerstones of our success. We have risen from 78th in 2004* to 35th in 2016 in the Academic Ranking of World Universities (ARWU). League table World ranking European ranking UK ranking ARWU 35 7 5 QS 29 9 7 Times Higher 35 7 5 55 15 8 Education WORLD EUROPE UK *2004 ranking refers to the Victoria University of Manchester prior to the merger with UMIST. 4 5 John Cockcroft John Richard Hicks Economic Sciences Robert Robinson Physics (1951) Joseph E Stiglitz ACADEMIC PEDIGREE (1972) Economic Sciences (2001) Chemistry (1947) We attract the highest calibre researchers and Arthur Lewis teachers, with 25 Nobel Prize winners among CTR Wilson Walter Norman Economic Sciences Physics (1927) Arthur Harden (1979) Andre Geim our current and former staff and students. -
Intermission 2017
If people don’t have a good sense of humour, they are usually not very good scientists either. Andre Geim (Nobel Prize, 2010) You will always be lucky if you know how to make friends with strange cats. --ancient proverb Change is inevitable, except from a vending machine. Eagles may soar, but weasels don’t get sucked into jet engines. Chance favors the prepared mind. Louis Pasteur To escape criticism—do nothing, say nothing, be nothing. Elbert Hubbard Imagination is more important than knowledge. For knowledge is limited to all we know and understand, while imagination embraces the entire world, and all there ever will be to know and understand. Albert Einstein The only one who really likes change is a wet baby. The First Rule of Holes: When you are in one, stop digging. Never confuse motion with action. Ernest Hemingway Basic research is what I am doing when I don’t know what I am doing. Wernher von Braun Beer is a sign that God loves us and wants us to be happy. Benjamin Franklin From error to error, one discovers truth. Sigmund Freud You can’t always get what you want. But if you try sometime You just might find You get what you need. Mick Jagger Why do we park in driveways and drive on parkways?? Genius is 1 % inspiration and 99 % perspiration. Albert Einstein We must become the change we want to see. Gandhi The only difference between a rut and a grave is depth. Time flies like an arrow. Fruit flies like a banana. -
A Memorable Trip Abhisekh Sankaran Research Scholar, IIT Bombay
A Memorable Trip Abhisekh Sankaran Research Scholar, IIT Bombay It was my first trip to the US. It had not yet sunk in that I had been chosen by ACM India as one of two Ph.D. students from India to attend the big ACM Turing Centenary Celebration in San Francisco until I saw the familiar face of Stephen Cook enter a room in the hotel a short distance from mine; later, Moshe Vardi recognized me from his trip to IITB during FSTTCS, 2011. I recognized Nitin Saurabh from IMSc Chennai, the other student chosen by ACM-India; 11 ACM SIG©s had sponsored students and there were about 75 from all over the world. Registration started at 8am on 15th June, along with breakfast. Collecting my ©Student Scholar© badge and stuffing in some food, I entered a large hall with several hundred seats, a brightly lit podium with a large screen in the middle flanked by two others. The program began with a video giving a brief biography of Alan Turing from his boyhood to the dynamic young man who was to change the world forever. There were inaugural speeches by John White, CEO of ACM, and Vint Cerf, the 2004 Turing Award winner and incoming ACM President. The MC for the event, Paul Saffo, took over and the panel discussions and speeches commenced. A live Twitter feed made it possible for people in the audience and elsewhere to post questions/comments which were actually taken up in the discussions. Of the many sessions that took place in the next two days, I will describe three that I found most interesting.