10900126
Measures of Nondeterminism for Pushdown Automata
D. Vermeir and W. Savitch (Fund. Inform.4 (1981), 401-418) introduced two measures of nondeterminism for pushdown automata and showed interestingly that the second measure, which we refer to as the depth measure, yields an infinite hierarchy of language families between the deterministic context-free and general context-free languages. However, the proof given in op. cit. for this hierarchy theorem was incorrect. In this paper, using a pumping result for deterministic context-free languages we give a new proof for the strictness of the depth hierarchy. We introduce the monadic depth measure which is also shown to give rise to an infinite hierarchy of language families. Furthermore, we show that the monadic hierarchy is shifted by at most one level from the unrestricted depth hierarchy.
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.
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.
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.
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.
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.
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.
1058977
Block-based web search
Multiple-topic and varying-length of web pages are two negative factors significantly affecting the performance of web search. In this paper, we explore the use of page segmentation algorithms to partition web pages into blocks and investigate how to take advantage of block-level evidence to improve retrieval performance in the web context. Because of the special characteristics of web pages, different page segmentation method will have different impact on web search performance. We compare four types of methods, including fixed-length page segmentation, DOM-based page segmentation, vision-based page segmentation, and a combined method which integrates both semantic and fixed-length properties. Experiments on block-level query expansion and retrieval are performed. Among the four approaches, the combined method achieves the best performance for web search. Our experimental results also show that such a semantic partitioning of web pages effectively deals with the problem of multiple drifting topics and mixed lengths, and thus has great potential to boost up the performance of current web search engines.
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.
2791772
Segmentation induced by scale invariance
Perceptual organization is scale-invariant. In turn, a segmentation that separates features consistently at all scales is the desired one that reveals the underlying structural organization of an image. Addressing cross-scale correspondence with interior pixels, we develop this intuition into a general segmenter that handles texture and illusory contours through edges entirely without any explicit characterization of texture or curvilinearity. Experimental results demonstrate that our method not only performs on par with either texture segmentation or boundary completion methods on their specialized examples, but also works well on a variety of real images.
2476670
Soft Clustering on Graphs
We propose a simple clustering framework on graphs encoding pairwise data similarities. Unlike usual similarity-based methods, the approach softly assigns data to clusters in a probabilistic way. More importantly, a hierarchical clustering is naturally derived in this framework to gradually merge lower-level clusters into higher-level ones. A random walk analysis indicates that the algorithm exposes clustering structures in various resolutions, i.e., a higher level statistically models a longer-term diffusion on graphs and thus discovers a more global clustering structure. Finally we provide very encouraging experimental results.