Abstract

In this paper we present a novel approach to robust visual servoing. This method removes the feature tracking step from a typical visual servoing algorithm. We do not need correspondences of the features for deriving the control signal. This is achieved by modeling the image features as a Mixture of Gaussians in the current as well as desired images. Using Lyapunov theory, a control signal is derived to minimize a distance function between the two Gaussian mixtures. The distance function is given in a closed form, and its gradient can be efficiently computed and used to control the system. For simplicity, we first consider the 2D motion case. Then, the general case is presented by introducing the depth distribution of the features to control the six degrees of freedom. Experiments are conducted within a simulation framework to validate our proposed method.

Abstract

Video completion algorithms have concentrated on obtaining visually consistent solutions to fill-in the missing portions, without any emphasis on the physical correctness of the video. Resulting solutions thus use texture or image structure based cues and are limited in the situations they can handle. In this paper we take a model based signal processing approach to video completion [1]. Completion of the video is then defined as satisfying the given model by detecting and removing the error (selected parts of the video to be replaced). Given a probabilistic model, video completion then becomes an unsupervised learning algorithm with the input video giving a “noisy” version. Dense completion is the automatic inferencing of the “noise-less” or “true” video from the input. This approach finds a solution that satisfies visual coherence and is applicable to a wide variety of scenarios. We demonstrate the efficacy of our approach and its wide applicability using two scenarios.

Abstract

Fourier domain methods have had a long association with geometric vision. In this paper, we introduce Fourier domain methods into the field of visual servoing for the first time. We show how different properties of Fourier transforms may be used to address specific issues in traditional visual servoing methods, giving rise to algorithms that are more flexible. Specifically, we demonstrate how Fourier analysis may be used to obtain straight camera paths in the Cartesian space, do path following and correspondenceless visual servoing. Most importantly, by introducing Fourier techniques, we set a framework into which robust Fourier based geometry processing algorithms may be incorporated to address the various issues in servoing.

Abstract

Text classification techniques such as Bayesian classifiers have been proved to be giving as good or better results than much more sophisticated approaches to induction. These algorithms depend on the conditional independence of the attributes given the class and term based statistical representations of documents. However, one of the main shortcomings of such methods is that they largely disregard lexical semantics and, as a consequence, are not sufficiently robust with respect to variations in word usage. In this paper we investigate the use of word sense disambiguation and other lexical semantic based techniques to achieve consistent improvement with Bayesian classifiers. Finally, we discuss the test cases experimented with Rainbow-a standard Bayesian classifier.

Abstract

In this paper we consider the problem of extracting the spe-cial properties of any given record in a dataset. We are interested in determining what makes a given record unique or different from the ma-jority of the records in a dataset. In the real world, records typically represent objects or people and it is often worthwhile to know what spe-cial properties are present in each object or person, so that we can make the best use of them. This problem has not been considered earlier inthe research literature. We approach this problem using ideas from clus-tering, attribute oriented induction (AOI) and frequent itemset mining.Most of the time consuming work is done in a preprocessing stage and the online computation of the uniqueness of a given record is instantaneous

Abstract

Motivation: Various types of functional RNAs-tRNA, Ribosomes, Introns, Aptamers, Ribozymes Diverse and Dynamic molecules Recent availability of RNA crystal structures has facilitated study and analysis

Abstract

Schistosoma mansoni, a trematode parasite causes schistosomiasis, a tropical disease affecting more than 200 million people. It lives in an aerobic environment and therefore has effective redox mechanisms for its survival as well as to evade reactive oxygen species from its host. Although, the host performs these activities by two different systems: thioredoxin and glutaredoxin separately, the parasite has only one unique multifunctional enzyme, thioredoxin glutathione reductase (TGR) for both the activities which is its main bottleneck. Therefore this emerges as a promising target for schistosomiasis. TGR is a homodimer of 65kDa sub units. each consisting 598 amino acids with a selenocysteine residue in the C-terminus. It shows an unprecedented fusion of two proteins: glutaredoxin (Grx) and thioredoxin reductase (TR). Although many biochemical studies have been reported for TGR, the three dimensional structural information is still missing. Herein, we present some of our efforts towards development of a homology model for S. mansoni TGR (SmTGR) using Modeller9.1. A BLAST search with SmTGR sequence revealed about 60% homology with different mammalian TRs and the initial 100 residues showed close similarity with Grx. Therefore, homology modeling was carried out using both TR and Grx templates. The developed models were dimerized and validated by Procheck analysis. The validated structure was then minimized by MD simulations using heated simulated annealing method.

Abstract

The need for computational characterization of DNA structure in the context of DNA bending/binding, B ->A transitions in protein-DNA complexes and also in the context of strand orientation especially in DNA triplexes and quadruplexes have given rise to several fine grained parameters, such as backbone conformations, sugar pucker and chi angle, local strand orientation, base pair and base pair step parameters backbone base inclination etc. With their unpaired bases, extensive non helical regions, non-canonical base pairs and tertiary interactions, RNA molecules host a large variety of recurrent structural and functional motifs and parameters such as Zp, defined in the context of double helical DNA base pairs and base pair steps and effectively used for A and B DNA differentiations, can not be used for computationally driven automated mining of complex RNA motifs. Though several algorithms have been proposed based on backbone conformational parameters and though algorithms for computational detection of base pairs and analysis of base pair parameters, are available, there are no reports of motif mining studies involving parameters which, as with Zp, simultaneously involve backbone conformation as well as base pairing geometry. In this work, we propose a novel set of parameters, Omega Pseudo-Torsions and Omega Distances, which capture backbone geometries at base(i) - base(j) interaction points and which can be used to computationally characterize structural features both in RNA and DNA. Pseudo torsion angles: Omega eta: P(i)-C4'(j)-P(j); Omega theta: P(i+1)-C4'(j)-P(j+1) Omega 1: P(i)-C4'(i)-C4'(j)-P(j+1); Omega 2: P(i+1)-C4'(i)-C4'(j)-P(j) Pseudo bond distance: Omega distance: C4'(i)-C4'(j) Where 'i' and 'j' are bases interacting through hydrogen bonds. We report the results of our benchmarking studies of Omega parameters with standard DNA structure classification parameters and of our preliminary studies on their effectiveness in classifying local geometries of RNA strands in different base pairing (canonical and non-canonical) contexts.

Abstract

DNA structures and their variations are important in several contexts such as chromatin remodeling and transcriptional initiation and regulation etc. Theneed for computational characterization of DNA structures associated with DNA bending/binding, B ? A transitions in protein-DNA complexes, molecular dynamics trajectory analysis and also in the context of strand orientation, especially in DNA triplexes and quadruplexes, have given rise to several fine grained parameters, such as backbone conformations, sugar pucker and ? angle, local strand orientation, base pair and base pair step parameters, backbone base inclination etc. With their unpaired bases, extensive non helical regions, non-canonical base pairs and tertiary interactions, RNA molecules host a large variety of recurrent structural and functional motifs and parameters such as zp, defined in the context of double helical DNA base pairs and base pair steps and effectively used for A and B DNA differentiations, can not be used for computationally driven automated mining of complex RNA motifs. Thus for RNA structures, though several motif mining algorithms based on backbone conformational parameters and involving computational detection and analysis of base pairs and their parameters are available, there are no reports of studies involving parameters which simultaneously involve backbone conformation as well as base pairing geometry. In this work, we propose a novel set of parameters, Omega Torsions and Omega Distances, which captures backbone geometries at base(i) ? base(j) interaction points and which can be used to computationally characterize structural features both in RNA and DNA. Pseudo torsion angles: Omega ?: P(i)-C4'(i)-C4'(j)-P(j); Omega ?: P(i+1)-C4'(i)-C4'(j)-P(j+1) Omega 1: P(i)-C4'(i)-C4'(j)-P(j+1); Omega 2: P(i+1)-C4'(i)-C4'(j)-P(j) Pseudo bond distance: Omega distance: C4'(i)-C4'(j) Where 'i' and 'j' are bases interacting through hydrogen bonds. We report the results of our benchmarking studies of Omega parameters, as applied to protein DNA complexes including e.g. TATA box-TBP complexes and nucleosomal DNA, with standard DNA structure classification parameters, such as zp, and of our preliminary studies on their effectiveness in classifying local geometries of RNA strands in different base pairing contexts. We conclude that omega parameters can potentially lead to the development of a single set of parameters which can be used to characterize both DNA as well as RNA structures.

Abstract

Projection of a Markov process with constant rates of transition to a small number of observable aggregated states can result in complex kinetics with memory. Here, we define the entropy production along a single sequence of aggregated states and show that it obeys detailed and integral fluctuation theorems. More importantly, we prove that projection shifts the distribution of entropy production over the ensemble of paths for a nonequilibrium process toward one characteristic of a system at equilibrium. This statement represents an analog of the second law of thermodynamics for path-dependent entropies and thus a new form of constraint of irreversible systems. Numeric examples are presented to illustrate these ideas.