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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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Journal ArticleDOI
TL;DR: It is shown that any set complete in CSL, PTAPE, etc. must be dense and therefore, for example, cannot be over a single letter alphabet.

52 citations

Journal ArticleDOI
TL;DR: The essence of various proofs of undecidability are abstracted and wide classes of properties and general conditions on families of languages such that these proofs of Undecidable hold are found.

48 citations

Journal ArticleDOI
TL;DR: In scientific work, the recognition by one’s peers is one of the greatest rewards and an official recognition by the scientific community, as Richard Stearns and I are honored by the 1993 ACM Turing Award is very satisfying and deeply appreciated.
Abstract: In scientific work, the recognition by one’s peers is one of the greatest rewards. In particular, an official recognition by the scientific community, as Richard Stearns and I are honored by the 1993 ACM Turing Award, is very satisfying and deeply appreciated. Science is a great intellectual adventure and one of humankind’s greatest achievements. Furthermore, a research career can be an exciting, rewarding and ennobling activity, particularly so if one is fortunate to participate in the creation of a completely new and very important science, as many scientists are. My road to computer science was not a direct one. Actually it looks more like a random walk, in retrospect, with the right intellectual stops to prepare me for work in computer science. I was born in Latvia, which lost its independence during World War II and from which we had to flee because of heavy fighting. After the war as a D.P. (displaced person) in Germany, I finished a superb Latvian high school in a D.P. camp staffed by elite refugee academics who conveyed their enthusiasm for knowledge, scholarship, and particularly for science. I studied physics at the Philips University in Marburg and waited for a chance to emigrate to the United States. This chance came after about two-and-a-half years of studies. In the U. S., our sponsors were in Kansas City, and, after arriving there, I proceeded to the University of Kansas City (now part of the University of Missouri system). My two-plus years of study were judged to be the equivalent of a bachelor’s degree, and I was accepted for graduate work and very generously awarded a fellowship. Since there was no graduate program in physics, I was advised (or told) to study mathematics, which had a graduate program. A year later I emerged with a master’s degree in mathematics and with a far better appreciation of the power and beauty of mathematics. The California Institute of Technology accepted me for graduate work and from my record decided that I looked like “an applied mathematician” (which is probably what you get if you mix two years of European physics with a year of Kansas City mathematics, though I had never taken a course in applied mathematics). Since there was at that time no program in applied mathematics at Cal Tech, I was advised I would be perfectly happy studying pure mathematics.

47 citations

Journal ArticleDOI
TL;DR: A basic technique is used which is easy to apply to problems of this type and yields an explicit construction of the sequential machine which recognizes the sequences in R, if R \t' is a regular set.
Abstract: This paper is concerned with the problem of determining whether a set of sequences R \t', obtained by some given rule from a regular set of sequences R , is again a regular set. A number of such problems are solved in this paper and a basic technique is used which is easy to apply to problems of this type. This technique yields an explicit construction of the sequential machine which recognizes the sequences in R \t', if R \t' is a regular set. Among other things it is shown that: (i) the derivative of a regular set R with respect to any set of sequences W is a regular set, although a regular expression designating this set cannot in general be computed; (ii) the set of sequences obtained from a regular set R by removing “arbitrary halves” of the sequences in R and the set of these removed “halves” are both regular sets; (iii) the set of sequences, obtained from a regular set R by making no more than k changes in any m consecutive digits in sequences from R , is regular; (iv) the set of sequences which can be concatenated in one and only one way from sequences in R is regular.

46 citations

Book ChapterDOI
18 Jul 1983
TL;DR: It is shown that the deterministic computation time for sets in NP can depend on their density if and only if there is a collapse or partial collapse of the corresponding higher nondeterministic and deterministic time bonded complexity classes.
Abstract: In this paper we study the computational complexity of sets of different densities in NP. We show that the deterministic computation time for sets in NP can depend on their density if and only if there is a collapse or partial collapse of the corresponding higher nondeterministic and deterministic time bonded complexity classes. We show also that for NP sets of different densities there exist complete sets of the corresponding density under polynomial time Turing reductions. Finally, we show that these results can be interpreted as results about the complexity of theorem proving and proof presentation in axiomatized mathematical systems. This interpretation relates fundamental questions about the complexity of our intellectual tools to basic structural problems about P, NP, CoNP, and PSPACE, discussed in this paper.

45 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