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Juris Hartmanis

Bio: Juris Hartmanis is an academic researcher from Cornell University. The author has contributed to research in topics: Structural complexity theory & Computational complexity theory. The author has an hindex of 46, co-authored 171 publications receiving 10705 citations. Previous affiliations of Juris Hartmanis include National Research Council & General Electric.


Papers
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01 Jan 2013
TL;DR: Constraining Delimited Control with Contracts, Behavioral Polymorphism and Parametricity in Session-Based Communication, and Taming Concurrency Structural Lock Correlation with Ownership Types.
Abstract: Refinement Types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209 Niki Vazou, Patrick M. Rondon, and Ranjit Jhala Constraining Delimited Control with Contracts . . . . . . . . . . . . . . . . . . . . . . 229 Asumu Takikawa, T. Stephen Strickland, and Sam Tobin-Hochstadt Session V: Shared-Memory Concurrency and Verification Verifying Concurrent Memory Reclamation Algorithms with Grace . . . . . 249 Alexey Gotsman, Noam Rinetzky, and Hongseok Yang Interleaving and Lock-Step Semantics for Analysis and Verification of GPU Kernels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 270 Peter Collingbourne, Alastair F. Donaldson, Jeroen Ketema, and Shaz Qadeer Verifying Concurrent Programs against Sequential Specifications . . . . . . . 290 Ahmed Bouajjani, Michael Emmi, Constantin Enea, and Jad Hamza Session VI: Process Calculi On Distributability in Process Calculi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 310 Kirstin Peters, Uwe Nestmann, and Ursula Goltz Behavioral Polymorphism and Parametricity in Session-Based Communication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 330 Lúıs Caires, Jorge A. Pérez, Frank Pfenning, and Bernardo Toninho Higher-Order Processes, Functions, and Sessions: A Monadic Integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 350 Bernardo Toninho, Lúıs Caires, and Frank Pfenning Concurrent Flexible Reversibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 370 Ivan Lanese, Michael Lienhardt, Claudio Antares Mezzina, Alan Schmitt, and Jean-Bernard Stefani Session VII: Taming Concurrency Structural Lock Correlation with Ownership Types . . . . . . . . . . . . . . . . . . . 391 Yi Lu, John Potter, and Jingling Xue Table of

8 citations

Proceedings ArticleDOI
13 Oct 1975
TL;DR: It is shown that the tape bounded complexity classes of languages over single letter alphabets are closed under complementation and that every infinite sla language recognizable on less than log n tape has infinitely many different regular subsets.
Abstract: In this note we show that the tape bounded complexity classes of languages over single letter alphabets are closed under complementation. We then use this result to show that there exists an infinite hierarchy of tape bounded complexity classes of sla languages between log n and log log n tape bounds. We also show that every infinite sla language recognizable on less than log n tape has infinitely many different regular subsets, and, therefore, the set of primes in unary notation, P, requires exactly log n tape for its recognition and every infinite subset of P requires at least log n tape.

8 citations

Proceedings ArticleDOI
04 May 1977
TL;DR: These diagonalization methods show that the Gap Theorem for resource bounded computations does not hold for complexity classes consisting only of languages accepted by Turing machines for which it can be formally proven that they run in the required time bound.
Abstract: In this paper we study diagonal processes over time-bounded computations of one-tape Turing machines by diagonalizing only over those machines for which there exist formal proofs that they operate in the given time bound. This replaces the traditional “clock” in resource bounded diagonalization by formal proofs about running times and establishes close relations between properties of proof systems and existence of sharp time bounds for one-tape Turing machine complexity classes. Furthermore, these diagonalization methods show that the Gap Theorem for resource bounded computations does not hold for complexity classes consisting only of languages accepted by Turing machines for which it can be formally proven that they run in the required time bound.

8 citations

BookDOI
01 Jan 1989
TL;DR: Book Descriptive and computational complexity by N. Immerman Complexity issues in cryptography by A. L. Selman Interactive proof systems by S. Goldwasser.
Abstract: Overview of computational complexity theory by J. Hartmanis The isomorphism conjecture and sparse sets by S. R. Mahaney Restricted relativizations of complexity classes by R. V. Book Descriptive and computational complexity by N. Immerman Complexity issues in cryptography by A. L. Selman Interactive proof systems by S. Goldwasser.

8 citations

Journal ArticleDOI
TL;DR: From this group theoretic characterization of linear permutation automata, a complete characterization of all homomorphisms of a linear automaton which yield linearly realizable image automata is derived as well as several results about the structure of linear automata.

8 citations


Cited by
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Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

13,246 citations

Book
01 Jan 1974
TL;DR: This text introduces the basic data structures and programming techniques often used in efficient algorithms, and covers use of lists, push-down stacks, queues, trees, and graphs.
Abstract: From the Publisher: With this text, you gain an understanding of the fundamental concepts of algorithms, the very heart of computer science. It introduces the basic data structures and programming techniques often used in efficient algorithms. Covers use of lists, push-down stacks, queues, trees, and graphs. Later chapters go into sorting, searching and graphing algorithms, the string-matching algorithms, and the Schonhage-Strassen integer-multiplication algorithm. Provides numerous graded exercises at the end of each chapter. 0201000296B04062001

9,262 citations

Journal ArticleDOI
TL;DR: In this paper, the authors considered factoring integers and finding discrete logarithms on a quantum computer and gave an efficient randomized algorithm for these two problems, which takes a number of steps polynomial in the input size of the integer to be factored.
Abstract: A digital computer is generally believed to be an efficient universal computing device; that is, it is believed able to simulate any physical computing device with an increase in computation time by at most a polynomial factor. This may not be true when quantum mechanics is taken into consideration. This paper considers factoring integers and finding discrete logarithms, two problems which are generally thought to be hard on a classical computer and which have been used as the basis of several proposed cryptosystems. Efficient randomized algorithms are given for these two problems on a hypothetical quantum computer. These algorithms take a number of steps polynomial in the input size, e.g., the number of digits of the integer to be factored.

7,427 citations

Book
25 Apr 2008
TL;DR: Principles of Model Checking offers a comprehensive introduction to model checking that is not only a text suitable for classroom use but also a valuable reference for researchers and practitioners in the field.
Abstract: Our growing dependence on increasingly complex computer and software systems necessitates the development of formalisms, techniques, and tools for assessing functional properties of these systems. One such technique that has emerged in the last twenty years is model checking, which systematically (and automatically) checks whether a model of a given system satisfies a desired property such as deadlock freedom, invariants, and request-response properties. This automated technique for verification and debugging has developed into a mature and widely used approach with many applications. Principles of Model Checking offers a comprehensive introduction to model checking that is not only a text suitable for classroom use but also a valuable reference for researchers and practitioners in the field. The book begins with the basic principles for modeling concurrent and communicating systems, introduces different classes of properties (including safety and liveness), presents the notion of fairness, and provides automata-based algorithms for these properties. It introduces the temporal logics LTL and CTL, compares them, and covers algorithms for verifying these logics, discussing real-time systems as well as systems subject to random phenomena. Separate chapters treat such efficiency-improving techniques as abstraction and symbolic manipulation. The book includes an extensive set of examples (most of which run through several chapters) and a complete set of basic results accompanied by detailed proofs. Each chapter concludes with a summary, bibliographic notes, and an extensive list of exercises of both practical and theoretical nature.

4,905 citations

Journal ArticleDOI
Gerard J. Holzmann1
01 May 1997
TL;DR: An overview of the design and structure of the verifier, its theoretical foundation, and an overview of significant practical applications are given.
Abstract: SPIN is an efficient verification system for models of distributed software systems. It has been used to detect design errors in applications ranging from high-level descriptions of distributed algorithms to detailed code for controlling telephone exchanges. The paper gives an overview of the design and structure of the verifier, reviews its theoretical foundation, and gives an overview of significant practical applications.

4,159 citations