26444044
Efficient Implementation of Regular Languages Using Reversed Alternating Finite Automata
Alternating finite automata (AFA) provide a natural and succinct way to denote regular languages. We introduce a bit-wise representation of reversed AFA (r-AFA) transition functions and describe an efficient implementation method for r-AFA and their operations using this representation. Experiments have shown that this implementation is much more efficient than, for instance, the Grail DFA implementation in both space and time.
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.
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.
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.
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.
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.
14165952
Efficient Night Gait Recognition Based on Template Matching
Gait is a useful biometric which can be used to recognize people at a distance when other biometrics are incapable. However, most work on gait recognition has been visible spectrum-oriented over the past decade, ignoring recognition at night which is in reality demand-imperative. This paper deals with the problem of night gait recognition via thermal infrared imagery. First of all, human detection is accomplished, based on the Gaussian mixture modeling of the background. Then, human silhouettes are extracted on the basis of preceding detection results. Moreover, a new gait representation called HTI is proposed to characterize gait signatures for recognition. An infrared night gait database was built to provide a foundation for night gait recognition. Experimental results on two gait datasets show the effectiveness of this method
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.
268086
Collaborative ordinal regression
Ordinal regression has become an effective way of learning user preferences, but most research focuses on single regression problems. In this paper we introduce collaborative ordinal regression, where multiple ordinal regression tasks are handled simultaneously. Rather than modeling each task individually, we explore the dependency between ranking functions through a hierarchical Bayesian model and assign a common Gaussian Process (GP) prior to all individual functions. Empirical studies show that our collaborative model outperforms the individual counterpart in preference learning applications.