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.
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.
16103350
State Complexity of Regular Languages
State complexity is a descriptive complexity measure for regular languages. We investigate the problems related to the state complexity of regular languages and their operations. In particular, we compare the state complexity results on regular languages with those on nite languages.
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.
206058077
Synchronization Expressions with Extended Join Operation
Synchronization expressions are high level constructs used for specifying minimal synchronization constraints of parallel processes. Their semantics is defined by the corresponding synchronization language. The original definition of synchronization expressions [12, 13] forces the languages to be closed under rewriting rules that, for instance, do not preserve regularity. Here we propose an extension of the syntactic definition of synchronization expressions, and an appropriate modification of their semantics. The extended definition has the advantage that it allows us to eliminate the less well motivated transformations (rewriting rules) describing properties of synchronization languages. We show that the modified rewriting rules preserve regularity of the languages. Also, we obtain a characterization of finite synchronization languages as the family consisting of languages satisfying the start-termination property and closed under three types of simple rewriting rules.
5267471
Fault-Tolerant Servers for Anycast Communication
Plenty of research has been done for any cast service, but few research touches the fault-tolerant problem based on the best of our knowledge. In this paper, we propose and analyse a fault-tolerant model, called twin server model, for anycast communication to provide reliable and continuous anycast services. We select a twin server in an anycast group for a given anycast server, the primary server. If the twin server suspects that its primary server is dead, it will take the unfinished job(s) of its primary server. We propose two algorithms: the server failure detecting algorithm and the server failure broadcasting algorithm. We then analyse the performance change when a primary server fails using queue theory and obtain some interesting conclusions. At the end, we summary the paper and present the future work.
11104953
Multi-Output Regularized Feature Projection
Dimensionality reduction by feature projection is widely used in pattern recognition, information retrieval, and statistics. When there are some outputs available (e.g., regression values or classification results), it is often beneficial to consider supervised projection, which is based not only on the inputs, but also on the target values. While this applies to a single-output setting, we are more interested in applications with multiple outputs, where several tasks need to be learned simultaneously. In this paper, we introduce a novel projection approach called multi-output regularized feature projection (MORP), which preserves the information of input features and, meanwhile, captures the correlations between inputs/outputs and (if applicable) between multiple outputs. This is done by introducing a latent variable model on the joint input-output space and minimizing the reconstruction errors for both inputs and outputs. It turns out that the mappings can be found by solving a generalized eigenvalue problem and are ready to extend to nonlinear mappings. Prediction accuracy can be greatly improved by using the new features since the structure of outputs is explored. We validate our approach in two applications. In the first setting, we predict users' preferences for a set of paintings. The second is concerned with image and text categorization where each image (or document) may belong to multiple categories. The proposed algorithm produces very encouraging results in both settings
13332239
A Hierarchical Markov Random Field Model for Figure-Ground Segregation
To segregate overlapping objects into depth layers requires the integration of local occlusion cues distributed over the entire image into a global percept. We propose to model this process using hierarchical Markov random field (HMRF), and suggest a broader view that clique potentials in MRF models can be used to encode any local decision rules. A topology-dependent multiscale hierarchy is used to introduce long range interaction. The operations within each level are identical across the hierarchy. The clique parameters that encode the relative importance of these decision rules are estimated using an optimization technique called learning from rehearsals based on 2-object training samples. We find that this model generalizes successfully to 5-object test images, and that depth segregation can be completed within two traversals across the hierarchy. This computational framework therefore provides an interesting platform for us to investigate the interaction of local decision rules and global representations, as well as to reason about the rationales underlying some of recent psychological and neurophysiological findings related to figure-ground segregation.
1394662
Dirichlet Enhanced Latent Semantic Analysis
This paper describes nonparametric Bayesian treatments for analyzing records containing occurrences of items. The introduced model retains the strength of previous approaches that explore the latent factors of each record (e.g. topics of documents), and further uncovers the clustering structure of records, which reflects the statistical dependencies of the latent factors. The nonparametric model induced by a Dirichlet process (DP) flexibly adapts model complexity to reveal the clustering structure of the data. To avoid the problems of dealing with infinite dimensions, we further replace the DP prior by a simpler alternative, namely Dirichlet-multinomial allocation (DMA), which maintains the main modelling properties of the DP. Instead of relying on Markov chain Monte Carlo (MCMC) for inference, this paper applies efficient variational inference based on DMA. The proposed approach yields encouraging empirical results on both a toy problem and text data. The results show that the proposed algorithm uncovers not only the latent factors, but also the clustering structure.
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.