Showing papers in "Journal of Computer and System Sciences in 2002"

TL;DR: A negative answer is given by proving the NP-completeness of the problem whether for each constant k it can be determined in polynomial time if a query has query-width at most k, and the new concept of hypertree decomposition of a query and the corresponding notion ofhypertree-width is introduced.

TL;DR: In this article, a new method for proving lower bounds on quantum query algorithms was proposed, where instead of a classical adversary that runs the algorithm with on input and then modifies the input, they use a quantum adaption.

TL;DR: In this paper, the authors studied on-line learning in the linear regression framework and proposed adaptive tunings for generalized linear regression and Weighted Majority over a finite set of experts.

TL;DR: It is shown that division lies in the complexity class FOM + POW obtained by augmenting FOM with a predicate for powering modulo small primes, and that the predicate POW itself lies in FOM.

TL;DR: In this article, the authors consider the problem of modeling the flow of information through a distributed network, studying the sparsity of the information flow, and studying the relationships involving time.

TL;DR: Improved the speed by combining the algorithm with the algorithm of Bader et al. that computes reversal distances for permutations in linear time and improved the construction by fixing some problems with the construction.

TL;DR: An approach for the rule-based transformation of hierarchically structured hypergraphs, which extends the well-known double-pushout approach from flat to hierarchical graphs and makes rules more expressive by introducing variables which allow to copy and remove hierarchical subgraphs in a single rule application.

TL;DR: New phylogenetic analyses of a subset of the Campanulaceae family are conducted, confirming various conjectures about the relationships among members of the subset and confirming that inversion can be viewed as the principal mechanism of evolution for their chloroplast genome.

TL;DR: Preliminary tests on both simulated and real data demonstrate that the algorithms are very efficient and able to locate useful regions in sequences, including GC-rich regions in a genomic DNA sequence and computing length-constrained ungapped local alignment.

TL;DR: This paper presents algorithms for tracking (approximate) join and self-join sizes in limited storage, in the presence of insertions and deletions to the data set(s), and proposes schemes based on maintaining a small signature of each relation independently, such that join sizes can be quickly and accurately estimated between any pair of relations using only these signatures.

TL;DR: It is shown that computing the consensus string, and approximating its probability within any constant factor, is NP-hard, and that the same holds for the closely related labeling problem for class hidden Markov models.

TL;DR: If the languages defined by M are effectively semilinear, then so are the languagesdefined by Mc, and, hence, their emptiness problem is decidable, and this work proves a surprising undecidability result for commutation of languages.

TL;DR: In this paper, the authors give explicit constructions of extractors which work for a source of any minentropy on strings of length n. These extractors can extract any constant fraction of the min-entropy using O(log 2 n).

TL;DR: It is proved that LAP CS(NESTED, NESTED) is NP-hard, which answers an open question in Evans (1999), and a polynomial-time approximation scheme for LAPCS(Nested, NestED) with an additional c-diagonal restriction is presented.

TL;DR: This work considers the complexity of problems related to two-dimensional texts (2D-texts) described succinctly, and gives efficient algorithms for the related problems of randomized equality testing and testing for a given occurrence of an uncompressed pattern.

TL;DR: This paper gives optimal upper bounds for the number of states of nondeterministic and deterministic finite automata equivalent to unary context-free grammars in Chomsky normal form and proves a log log n lower bound for the workspace used by one-way auxiliary pushdown automata in order to accept nonregular unary languages.

TL;DR: This work studies linear regression and density estimation problems and shows how the leave-one-out loss can be used to prove instantaneous loss bounds for these cases.

TL;DR: In this paper, the authors developed a framework for quantifying the difference between two datasets in terms of the models they induce, i.e., whether the underlying datasets have statistically significant differences in their characteristics.

TL;DR: It is shown that under the same weak learning assumption used for decision tree learning there exists a greedy BP-growth algorithm whose training error is guaranteed to decline as 2−b`|T|, where |T| is the size of the branching program and b is a constant determined by the weak learning hypothesis.

TL;DR: The key idea is to reduce the problem to a series of subproblems involving genomes containing at most two copies of each gene, and to describe an algorithm, based on Hannenhalli and Pevzner graph and result, that is exact when certain conditions are verified.

TL;DR: Results hold for attributed tree transducers, for controlled EDT0L systems, and for YIELD mappings (which proves properness of the IO-hierarchy).

TL;DR: Context-free languages of sub-exponential growth were studied and it was shown that context- free languages of intermediate growth were nonexistent.

TL;DR: This work solves the problem whether LTL (linear temporal logic) is expressively complete with respect to the first order theory for finite and infinite traces and for the simplest temporal logic, which is based only on next and until modalities.

TL;DR: This paper addresses the problem of classifying information by enforcing explicit data classification as well as inference and association constraints, and forms an approach to the solution of this problem which is linear in simple cases, and quadratic in the general case.

TL;DR: The principles of queries that allow for incomplete answers are investigated, and a polynomial algorithm for evaluating a special type of queries with filter constraints is described, and the complexity of evaluating other queries for several kinds of constraints is assessed.

TL;DR: New lower bounds for nearest neighbor search in the Hamming cube are proved for randomized, two-sided error, algorithms in Yao's cell probe model in the form of a tradeoff among the number of cells, the size of a cell, and the search time.

TL;DR: A robust O(n4) worst-case algorithm that can provably tolerate an absolute error of O(δ/n) (where δ is the minimum inter-site distance) while giving a unique reconstruction of the DNA molecule from the digest is given.

TL;DR: In this article, the authors model reinforcement learning as the problem of learning to control a partially observable Markov decision process (POMDP) and focus on gradient ascent approaches to this problem.

TL;DR: Refinements and modules for typed graph transformation systems to support the software specification development in both dimensions: modules for the horizontal structuring of a specification, i.e., its composition from feasible parts, and refinements for the development over time.

TL;DR: An abstract model of exact learning via queries that can be instantiated to all the query learning models currently in use, while being closer to them than previous unifying attempts is introduced.