Estimating Maximum Likelihood Parameters for Stochastic Context-Free Graph Grammars

This paper presents a new algorithm in computer vision for the recognition of hand gestures. In the proposed system, Kinect sensor is used to track and segment hand in the clutter background and feature extracted by finger and an angle between them. Classify the hand posture using multi-class support vector machine. The hand gesture is recognised by stochastic context free grammar (SCFG). Stochastic context free grammar uses syntactic structure analysis and by this method, recognises hand gestures by set of production rules which consists of a combination of hand postures. The proposed algorithm is able to recognise various hand postures in real time with more than 97% accuracy.

We deal with the question as to whether there exists a polynomial time algorithm for computing the most probable parse tree of a sentence generated by a data-oriented parsing (DOP) model. (Scha, 1990; Bod, 1992, 1993a). Therefore we describe DOP as a stochastic tree-substitution grammar (STSG). In STSG, a tree can be generated by exponentially many derivations involving different elementary trees. The probability of a tree is equal to the sum of the probabilities of all its derivations.We show that in STSG, in contrast with stochastic context-free grammar, the Viterbi algorithm cannot be used for computing a most probable tree of a string. We propose a simple modification of Viterbi which allows by means of a select-random search to estimate the most probable tree of a string in polynomial time.Experiments with DOP on ATIS show that only in 68% of the cases, the most probable derivation of a string generates the most probable tree of that string. Therefore, the parse accuracy obtained by the most probable trees (96%) is dramatically higher than the parse accuracy obtained by the most probable derivations (65%).It is still an open question whether the most probable tree of a string can be deterministically computed in polynomial time.

The aim of syntactic tracking is to classify spatio-temporal patterns of a target's motion using natural language processing models. In this paper, we generalize earlier work by considering a constrained stochastic context free grammar (CSCFG) for modeling patterns confined to a roadmap. The constrained grammar facilitates modeling specific directions and road names in a roadmap. We present a novel particle filtering algorithm that exploits the CSCFG model for estimating the target's patterns. This meta-level algorithm operates in conjunction with a base-level tracking algorithm. Extensive numerical results using simulated ground moving target indicator (GMTI) radar measurements show substantial improvement in target tracking accuracy.

In Bayesian statistical inference, an unknown probability distribution is estimated from a sample using a statistical model and a prior. In general, a pair of a model and a prior may or may not be appropriate for unknown distribution; some evaluation procedures of the estimated result are necessary. If a statistical model is regular and the likelihood function can be approximated by some Gaussian function, then AIC and BIC can be applied to such evaluation processes. However, if a statistical model contains hierarchical structure or latent variables, then regularity condition is not satisfied. The information criteria WAIC and WBIC are devised so as to estimate the generalization loss and the free energy, respectively, even if the posterior distribution is far from any normal distribution and even if the unknown true distribution is not realizable by a statistical model. In this paper, we introduce mathematical foundation and computing methods of WAIC and WBIC in a normal mixture which is a typically singular statistical model, and discuss their properties in statistical inference. Also, we study the case that samples are not independently and identically distributed, for example, they are conditional independent or exchangeable.

Structure detection and segmentation of documents using 2D stochastic context-free grammars

Conformational entropy for atomic-level, three dimensional biomolecules is known experimentally to play an important role in protein-ligand discrimination, yet reliable computation of entropy remains a difficult problem. Here we describe the first two accurate and efficient algorithms to compute the conformational entropy for RNA secondary structures, with respect to the Turner energy model, where free energy parameters are determined from UV absorption experiments. An algorithm to compute the derivational entropy for RNA secondary structures had previously been introduced, using stochastic context free grammars (SCFGs). However, the numerical value of derivational entropy depends heavily on the chosen context free grammar and on the training set used to estimate rule probabilities. Using data from the Rfam database, we determine that both of our thermodynamic methods, which agree in numerical value, are substantially faster than the SCFG method. Thermodynamic structural entropy is much smaller than derivational entropy, and the correlation between length-normalized thermodynamic entropy and derivational entropy is moderately weak to poor. In applications, we plot the structural entropy as a function of temperature for known thermoswitches, such as the repression of heat shock gene expression (ROSE) element, we determine that the correlation between hammerhead ribozyme cleavage activity and total free energy is improved by including an additional free energy term arising from conformational entropy, and we plot the structural entropy of windows of the HIV-1 genome. Our software RNAentropy can compute structural entropy for any user-specified temperature, and supports both the Turner’99 and Turner’04 energy parameters. It follows that RNAentropy is state-of-the-art software to compute RNA secondary structure conformational entropy. Source code is available at https://github.com/clotelab/RNAentropy/; a full web server is available at http://bioinformatics.bc.edu/clotelab/RNAentropy, including source code and ancillary programs.

Graph Operations, Graph Transformations and Monadic Second-Order Logic A survey

Graph Operations, Graph Transformations and Monadic Second-Order Logic:: A survey

Inference of high-dimensional grammars is discussed. Specifically, techniques for inferring tree grammars are briefly presented. The problem of inferring a stochastic grammar to model the behavior of an information source is also introduced and techniques for carrying out the inference process are presented for a class of stochastic finite-state and context-free grammars. The possible practical application of these methods is illustrated by examples.

The petroleum industry uses high level dynamic simulations applied to geocellular models to guide forecasts of oil, gas and water production. Uncertainty in model choice and input variable selection is often addressed through large numbers of computationally slow Monte Carlo simulations designed around physics based models. Here, an alternate approach is proposed, which uses a relatively small amount of data and a reduced number of simulations of the high level physics model to train a fast (to evaluate) proxy or surrogate model based on a Polynomial Chaos Expansion. We give details of the theory and incorporated techniques, which significantly increase flexibility. Input variables (e.g. cell-by-cell variations in porosity and permeability) are sampled from unknown probability distributions and sensitivity analysis is based on low level proxy models. The theory is tested by developing proxy models to predict total gas production from a five-spot well configuration in the Hermitage area that taps into the Walloon Coal Measures of the Surat Basin in Queensland. Synthetic training data is simulated using commercial dynamic simulation software based on a high level physics model.

In this paper, a new structure for cooperative learning automata called extended learning automata (eDLA) is introduced. Based on the new structure, an iterative randomized heuristic algorithm using sampling is proposed for finding an optimal subgraph in a stochastic edge-weighted graph. Stochastic graphs are graphs in which the weights of edges have an unknown probability distribution. The proposed algorithm uses an eDLA to find a policy that leads to a subgraph that satisfy some restrictions such as minimum or maximum weight (length). At each stage of the proposed algorithm, the eDLA determines which edges should be sampled. The proposed eDLA-based sampling method may reduce unnecessary samples and hence decrease the time required for finding an optimal subgraph. It is shown that the proposed method converges to an optimal solution, the probability of which can be made arbitrarily close to 1 by using a sufficiently small learning parameter. A new variance-aware threshold value is also proposed that can significantly improve the convergence rate of the proposed eDLA-based algorithm. It is further shown that our algorithm is competitive in terms of the quality of the solution.

Frequent Itemsets (FIs) mining is a fundamental primitive in data mining. It requires to identify all itemsets appearing in at least a fraction θ of a transactional dataset D. Often though, the ultimate goal of mining D is not an analysis of the dataset per se, but the understanding of the underlying process that generated it. Specifically, in many applications D is a collection of samples obtained from an unknown probability distribution π on transactions, and by extracting the FIs in D one attempts to infer itemsets that are frequently (i.e., with probability at least θ) generated by π, which we call the True Frequent Itemsets (TFIs). Due to the inherently stochastic nature of the generative process, the set of FIs is only a rough approximation of the set of TFIs, as it often contains a huge number of false positives, i.e., spurious itemsets that are not among the TFIs. In this work we design and analyze an algorithm to identify a threshold such that the collection of itemsets with frequency at least in contains only TFIs with probability at least 1 – δ, for some user-specified δ. Our method uses results from statistical learning theory involving the (empirical) VC-dimension of the problem at hand. This allows us to identify almost all the TFIs without including any false positive. We also experimentally compare our method with the direct mining of at frequency θ and with techniques based on widely-used standard bounds (i.e., the Chernoff bounds) of the binomial distribution, and show that our algorithm outperforms these methods and achieves even better results than what is guaranteed by the theoretical analysis.

This article focuses on the stability issue of switched network control systems (SNCSs) under deception attacks described by a Bernoulli process with unknown probability distribution. The false information in deception attacks is unknown but bounded and may be state dependent or state independent. By means of the input-to-state stability (ISS) tool and the convex combination method, an improved lemma is first developed for SNCSs, which facilitates the derivations of our results. After that, some attack-independent sufficient conditions for the ISS of SNCSs are obtained for mode-dependent average dwell time switching and stochastic switching, respectively. Different from existing results, the concerned switching contributes to the stability of SNCSs, which benefits the ISS performance of SNCSs even though the unknown deception attacks cause all subsystems to be non-ISS. The proposed results provide an effective solution with strong robustness to deal with unknown deception attacks or denial-of-service attacks.

We present a sequential framework for change detection. This framework allows us to use multiple images from reference and mission passes of a scene of interest in order to improve detection performance. It includes a change statistic that is easily updated when additional data becomes available. Detection performance using this statistic is predictable when the reference and image data are drawn from known distributions. We verify our performance prediction by simulation. Additionally, we show that detection performance improves with additional measurements on a set of synthetic aperture radar images and a set of visible images with unknown probability distributions.

Effective website fingerprinting attack based on the first packet direction only