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Explicit Cyclic Substitutions
, 1993
"... In this paper we consider rewrite systems that describe the lambdacalculus enriched with recursive and nonrecursive local definitions by generalizing the `explicit substitutions' used by Abadi, Cardelli, Curien, and Lévy [1] to describe sharing in lambdaterms. This leads to `explicit cyclic subst ..."
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Cited by 25 (2 self)
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In this paper we consider rewrite systems that describe the lambdacalculus enriched with recursive and nonrecursive local definitions by generalizing the `explicit substitutions' used by Abadi, Cardelli, Curien, and Lévy [1] to describe sharing in lambdaterms. This leads to `explicit cyclic substitutions' that can describe the mutual sharing of local recursive definitions. We demonstrate how this may be used to describe standard binding constructions (let and letrec)  directly using substitution and fixed point induction as well as using `smallstep' rewriting semantics where substitution is interleaved with the mechanics of the following betareductions. With this we hope to contribute to the synthesis of denotational and operational specifications of sharing and recursion.
Efficient Lossless Compression of Trees and Graphs
 In IEEE Data Compression Conference (DCC
, 1996
"... In this paper, we study the problem of compressing a data structure (e.g. tree, undirected and directed graphs) in an efficient way while keeping a similar structure in the compressed form. To date, there has been no proven optimal algorithm for this problem. We use the idea of building LZW tree in ..."
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Cited by 7 (0 self)
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In this paper, we study the problem of compressing a data structure (e.g. tree, undirected and directed graphs) in an efficient way while keeping a similar structure in the compressed form. To date, there has been no proven optimal algorithm for this problem. We use the idea of building LZW tree in LZW compression to compress a binary tree generated by a stationary ergodic source in an optimal manner. We also extend our tree compression algorithm to compress undirected and directed acyclic graphs.
Graphbased Operational Semantics of a Lazy Functional Language
 Term Graph Rewriting: Theory and Practice, chapter 22
"... Machine [Car83], the Gmachine [Joh84, BPJR88], Clean [BvEvLP87, KSvEP91], and DACTL [Ken88, GKS89]. All of these can be perceived as notations for algorithms that describe how graphs may be used to model the evaluation of the implemented language. Such notations have the advantage that they are dir ..."
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Cited by 1 (1 self)
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Machine [Car83], the Gmachine [Joh84, BPJR88], Clean [BvEvLP87, KSvEP91], and DACTL [Ken88, GKS89]. All of these can be perceived as notations for algorithms that describe how graphs may be used to model the evaluation of the implemented language. Such notations have the advantage that they are directly executable. However, they are often not very readable since all the details of the implementation have to be present in the specification. On the other hand, the idea has also been used in attempts to build models of functional languages and to prove properties of such models directly. Again starting from [Wad71] such attempts are naturally focused on models of the calculusa 2 We will use the traditional word "redex" (reducible expression) rather than some abbreviation of "reducible subgraph" even though we will always refer to the latter. GRAPHBASED OPERATIONAL SEMANTICS OF A LAZY FUNCTIONAL LANGUAGE 239 rather large are of research, so we will just mention [Sta78] and [Lam90...
Using Learning and Difficulty of Prediction to Decrease Computation: A Fast Sort and Priority Queue on Entropy Bounded Inputs ∗
"... There is an upsurge in interest in the Markov model and also more general stationary ergodic stochastic distributions in theoretical computer science community recently, (e.g. see [Vitter,Krishnan,FOCS91], [Karlin,Philips,Raghavan,FOCS92] [Raghavan92]) for use of Markov models for online algorithms ..."
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There is an upsurge in interest in the Markov model and also more general stationary ergodic stochastic distributions in theoretical computer science community recently, (e.g. see [Vitter,Krishnan,FOCS91], [Karlin,Philips,Raghavan,FOCS92] [Raghavan92]) for use of Markov models for online algorithms e.g., cashing and prefetching). Their results used the fact that compressible sources are predictable (and vise versa), and show that online algorithms can improve their performance by prediction. Actual page access sequences are in fact somewhat compressible, so their predictive methods can be of benefit. This paper investigates the interesting idea of decreasing computation by using learning in the opposite way, namely to determine the difficulty of prediction. That is, we will approximately learn the input distribution, and then improve the performance of the computation when the input is not too predictable, rather than the reverse. To our knowledge, this is first case of a computational problem where we do not assume any particular fixed input distribution and yet computation is decreased when the input is less predictable, rather than the reverse. We concentrate our investigation on a basic computational problem: sorting and a basic data structure problem: maintaining a priority queue. We present the first known case of sorting and priority queue algorithms whose complexity depends on the binary entropy H ≤ 1 of input keys where assume that input keys are generated from an unknown but arbitrary stationary ergodic source. This is, we assume that each of the input keys can be each arbitrarily long, but have entropy H. Note that H