2320487
On Synchronization Languages
New constructs for synchronization termed synchronization expressions (SEs) have been developed as high-level language constructs for parallel programming languages [8, 9]. Statements that are constrained by certain synchronization requirements are tagged, and synchronization requests are specified as expressions of statement tags. In this paper, we introduce a new family of languages named synchronization languages which we use to give a precise semantic description for SEs. Under this description, relations such as equivalence and inclusion between SEs can be easily understood and tested. In practice, it also provides us with a systematic way for the implementation as well as the simplification of SEs in parallel programming languages. We show that each synchronization language is closed under the following rewriting rules: (1) asbs → bsas, (2) atbt → btat, (3) asbt → btas, (4) atasbtbs → btbsatas and also h(atasbtbs) → h(btbsatas) for any morphism h that satisfies certain conditions which will be specified in the paper. We conjecture that closure under the above rewriting rules is a sufficient condition for a regular st-language to be a synchronization language. Several other properties of synchronization languages are also studied.
27445105
Minimal cover-automata for finite languages
Abstract A cover-automaton A of a finite language L⊆Σ ∗ is a finite deterministic automaton (DFA) that accepts all words in L and possibly other words that are longer than any word in L . A minimal deterministic finite cover automaton (DFCA) of a finite language L usually has a smaller size than a minimal DFA that accept L . Thus, cover automata can be used to reduce the size of the representations of finite languages in practice. In this paper, we describe an efficient algorithm that, for a given DFA accepting a finite language, constructs a minimal deterministic finite cover-automaton of the language. We also give algorithms for the boolean operations on deterministic cover automata, i.e., on the finite languages they represent.
14075162
Synchronization Expressions: Characterization Results and Implementation
Synchronization expressions are defined as restricted regular expressions that specify synchronization constraints between parallel processes and their semantics is defined using the synchronization languages. In this paper we survey results on synchronization languages, in particular, various approaches to obtain a characterization of this language family using closure under a set of rewriting rules. Also, we discuss the use and implementation of synchronization expressions in a programming language designed for a parallel or distributed computing environment.
546963
Decision Problems for Patterns
We settle an open problem, the inclusion problem for pattern languages. This is the first known case where inclusion is undecidable for generative devices having a trivially decidable equivalence problem. The study of patterns goes back to the seminal work of Thue and is important also, for instance, in recent work concerning inductive inference and learning. Our results concern both erasing and nonerasing patterns.
28358466
On Synchronization in P Systems
The P systems were recently introduced as distributed parallel computing models of a biochemical type. Multisets of objects are placed in a hierarchical structure of membranes and they evolve according to given rules, which are applied in a synchronous manner: at each step, all objects which can evolve, from all membranes, must evolve. We consider here the case when this restriction is removed. As expected, unsynchronized systems (even using catalysts) are weaker than the synchronized ones, providing that no priority relation among rules is considered. The power of P systems is not diminished when a priority is used and, moreover, the catalysts can change their states, among two possible states for each catalyst.
7865019
Follow automata
We give two new algorithms for constructing small nondeterministic finite automata (NFA) from regular expressions. The first constructs NFAs with e-transitions (eNFA) which are smaller than all the other eNFAs obtained by similar constructions. Their size is at most 3/2 |α| + 5/2, where α is the regular expression. This is very close to optimal since we prove also the lower bound 4/3; |α| + 5/2. The second constructs NFAs. It uses e-elimination in the eNFAs we just introduced and builds a quotient of the well-known position automaton w.r.t. the equivalence given by the follow relation; therefore giving the name of follow automaton. The new automaton uses optimally the information from the positions of a regular expression. We compare the follow automaton with the best constructions to date and show that it has important advantages over those.
329765
Unsupervised segmentation of color images
A novel technique for unsupervised learning in feature space is presented. The feature space is considered as composed of two distinct sources, "mode" and "valley", in the point of view of information theory. An entropy-based thresholding is taken to distinguish the discretized cells in the feature space. The cells labeled as "mode" are then chained to form mode areas. Thereafter a modified Akaike's information criterion is proposed to solve the cluster validity problem. After all the parameters are estimated, a labeling algorithm is developed based on the majority game theory. The method is applied to color image segmentation. The segmentation process is completely autonomous.
2852882
Hierarchy-regularized latent semantic indexing
Organizing textual documents into a hierarchical taxonomy is a common practice in knowledge management. Beside textual features, the hierarchical structure of directories reflect additional and important knowledge annotated by experts. It is generally desired to incorporate this information into text mining processes. In this paper, we propose hierarchy-regularized latent semantic indexing, which encodes the hierarchy into a similarity graph of documents and then formulates an optimization problem mapping each document into a low dimensional vector space. The new feature space preserves the intrinsic structure of the original taxonomy and thus provides a meaningful basis for various learning tasks like visualization and classification. Our approach employs the information about class proximity and class specificity, and can naturally cope with multi-labeled documents. Our empirical studies show very encouraging results on two real-world data sets, the new Reuters (RCVI) benchmark and the Swissprot protein database.
14436503
Implementing a Model Checker for LEGO
Interactive theorem proving provides a general approach to modelling and verification of both hardware and software systems but requires significant human efforts to deal with many tedious proofs. To be effectively used in practice, we need some automatic tools such as model checkers to deal with those tedious proofs. In this paper, we formalise a verification system of both CCS and an imperative language in the proof development system LEGO which can be used to verify both finite and infinite problems. Then a model checker, LegoMC, is implemented to generate LEGO proof terms for finite-state problems automatically. Therefore people can use LEGO to verify a general problem with some of its finite sub-problems verified by LegoMC. On the other hand, this integration extends the power of model checking to verify more complicated and infinite models as well.
38593151
Performance Comparison of a Deterministic and a Stochastic Method for Image Classification
In this paper, we compare the performances of two methods that we proposed for image classification. The first method is a stochastic approach which uses a Markov random field modelization. An optimization technique based on the game theory is used for the maximization of the a posteriori probability of the classes. The second method is a deterministic one which uses a feed-forward neural network. A novel learning method called projection learning is employed to train the neural network. The performances of these two methods are compared through their applications to image classification. The advantages and disadvantages of both methods, as well as possibilities to improve them are discussed.
946526
A Probabilistic Clustering-Projection Model for Discrete Data
For discrete co-occurrence data like documents and words, calculating optimal projections and clustering are two different but related tasks. The goal of projection is to find a low-dimensional latent space for words, and clustering aims at grouping documents based on their feature representations. In general projection and clustering are studied independently, but they both represent the intrinsic structure of data and should reinforce each other. In this paper we introduce a probabilistic clustering-projection (PCP) model for discrete data, where they are both represented in a unified framework. Clustering is seen to be performed in the projected space, and projection explicitly considers clustering structure. Iterating the two operations turns out to be exactly the variational EM algorithm under Bayesian model inference, and thus is guaranteed to improve the data likelihood. The model is evaluated on two text data sets, both showing very encouraging results.