Showing papers by "Juris Hartmanis published in 1995"

Computer Science Today: Recent Trends and Developments

Abstract: A quantum jump in computer science.- Artificial life and real world computing.- Recurrent neural networks.- Scalable computing.- Efficient use of parallel & distributed systems: From theory to practice.- Experimental validation of models of parallel computation.- Quo vadetis, parallel machine models?.- Templates for linear algebra problems.- The ART behind IDEAS.- Algorithmic number theory and its relationship to computational complexity.- Edge-coloring algorithms.- Towards a computational theory of genome rearrangements.- Algebraic topology and distributed computing a primer.- Differential BDDs.- Algorithmic techniques for geometric optimization.- All the needles in a haystack: Can exhaustive search overcome combinatorial chaos?.- Fundamental limitations on search algorithms: Evolutionary computing in perspective.- Mathematical system models as a basis of software engineering.- Formulations and formalisms in software architecture.- The Oz Programming Model.- Standard Generalized Markup Language: Mathematical and philosophical issues.- Avoiding the undefined by underspecification.- Towards a theory of recursive structures.- Chu spaces and their interpretation as concurrent objects.- Abstracting unification: A key step in the design of logic program analyses.- Programming Satan's computer.- Petri Net models of distributed algorithms.- Symmetry and induction in model checking.- Alternating automata and program verification.- Reasoning about actions and change with ramification.- Trends in active vision.- Computational machine learning in theory and praxis.- Fuzzy sets as a tool for modeling.- Information retrieval and informative reasoning.- Database transaction models.- Multimedia authoring tools: State of the art and research challenges.- Computational models for distributed multimedia applications.- Hypermedia systems as internet tools.

On the Weight of Computations.

Abstract: One of the Grand Challenges to computer science is to understand what is and is not feasibly computable. Recursive function theory clarified what is and is not effectively computable and in the process extended our understanding of Goedel incompleteness results about the limits of the power of formal mathematical methods. Since computing is universal and encompasses the power of mathematics, the understanding of the limits of the feasibly computable could give a deeper understanding of the limits of rational intellectual processes and insights into the power and limits of scientific theories. The search for what is and is not feasibly computable has two distinct aspects. The first is to determine (estimate) how much and what kind of computing power will be available in the foreseeable future. The other problem is to determine what kind of problems can be solved with these available computing resources. The first problem is a technological assessment of the existing and potential computing technologies to estimate what kind and how much computing work our machines will be able to render. The other problem leads us to the central questions of complexity theory: what is the intrinsic complexity of important classes of problems we wish to solve. Clearly, the P=NP=PSPACE? problems are among the best known in this area. In all these considerations, the exponential function seems to give a crude upper limit for the feasibly computable. May it be time, memory or weight requirements, if they grow exponentially, then the computations are not feasible. We do not know what computations require exponential amounts of resources, nor do all instances of problems in exponential complexity classes require exponential resources. Even if the exact solutions require exponential resources, good approximations to the solution may not. But if indeed the problem requires exponential resources, then it is clearly not feasibly solvable already for moderate size instances of the problem.

Efficient Graph Rewriting and Its Implementation

Abstract: Graph rewriting systems - The basic concepts- UBS-Graph rewriting systems - matching subgraphs in constant time- Programmed attributed graph rewrite systems - An advanced modelling formalism- The abstract machine for graph rewriting - supporting a fast implementation- A graphical implementation of functional languages - a case study in UBS-graph rewriting systems- Conclusions

On computational complexity and the nature of computer science

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.

Response to the essay “On computational complexity and the nature of computer science”

Abstract: I am impressed by the twelve essays in response to my Turing Award paper: they show great variety and originality, with interesting observations and insights about computer science. They call for new directions and some express concern for the future of our science. I hope that these essays as well as my paper will stimulate further reflection on the nature of computer science. I cannot agree with the statement, “Debates about ‘What is CS?’ can be counterproductive.” I am convinced that a better understanding of the nature of computer science will lead to more shared values in the community, better understanding of computer science by other scientists, and even to better research. At the same time, I like the eloquent rejection of the question, “Are we scientists or engineers?” I am sorry that time and space do not permit me to give proper attention to the ideas and comments in all the essays and that my response will touch on a few topics only. There is no doubt in my mind that computer science is emerging as a major science with rich intellectual achievements and great potential importance to many of our intellectual, scientific, and commercial activities. Not only that, computer science is forging new research paradigms for a new type of science that is likely to influence and guide other intellectual developments. I do not believe that computer scientists should feel apologetic in any way about what has been achieved. Quite the contrary. Still, in some essays one can detect a longing that computer science be more like physics and the view that if it does not fit the mold of the physical sciences, tlhe fault is that of computer science or its immaturity. I disagree with these views very strongly indeed. Let us not try to force this new science into any preconceived mold. Let us observe this fascinating intellectual development and try to understand it in the broad perspective of other sciences and be proud of it. I fully agree with the call for computer science to get more involved with interdisciplinary work, the encouragement of theoreticians “to move much closer to practice” and the view that “there is progressively greater need for theoretical study of approaches prior to experimentation. Appropriate models and techniques are vital.” I like the formulation of “effective computer science.” I will not argue whether we should call computer science “a new species among the sciences” or “a new species of engineering.” It is only

Exploitation of Fine-Grain Parallelism

Abstract: Kinds of parallelism.- Architectures for fine-grain parallelism.- VLIW machines.- Constraints on VLIW architectures.- Architectural support for exploitation of fine-grain parallelism.- Constraints for instruction scheduling.- Instruction-scheduling methods.- Developing instruction-scheduling methods.- Tools for instruction scheduling.- The machine model.- The horizontal instruction-scheduler.- Resource management.- Exceptions.- Vertical instruction-scheduling.- Conclusion.

On the Computing Paradigm and Computational Complexity

Abstract: Computational complexity theory is the study of the quantitative laws that govern computing. Since the computing paradigm is universal and pervasive, the quantitative laws of computational complexity apply to all information processing from numerical computations and simulation to logical reasoning and formal theorem proving, as well as processes of rational reasoning.