Abstract

: Density functional B3LYP method was used to investigate the preference of intra and intermolecular cyclizations of linear tripeptides containing tetrahydrofuran amino acids. Two distinct model pathways were conceived for the cyclization reaction and all possible transition states and intermediates were located. Analysis of the energetics indicate intermolecular cyclization being favored by both thermodynamic and kinetic control. Geometric and NBO analyses were performed to explain the trends obtained along both the reaction pathways. Conceptual density functional theory based reactive indices also show that reaction pathways leading to intermolecular cyclization of the tripeptides are relatively more facile compared to intramolecular cyclization.

Abstract

Theoretical justication for preferential heterochiral cyclic trimerization of 5- (aminoethyl)-2-furancarboxylic acid (AEFC) is attempted using density functional theory (DFT) calculations. Results from explicit solvent assisted reaction pathways indicate greater stability of heterochiral cyclic tripeptides over their homochiral counterparts, contrary to ndings from gas phase and implicit solvent phase results. Pathways explored at M06/6- 31G(d,p) and MP2/6-31G(d,p) levels of theory show kinetic preference for heterochiral cy- clization. Analysis of optimized geometries reveals existence of strong hydrogen bonding interactions in the solvated heterochiral tripeptides. Thus, the ability of the cyclic tripep- tides to form strong noncovalent interactions increases with conversion of stereochemistry at one of its chiral centers from homo to heterochiral conformation. The resulting change in molecular symmetry facilitates the interacting sites to reorient such that the peptide can in- teract with a nucleophile from both the faces. This is further substantiated by computed IR spectra, NBO and AIM data. Additionally, justication for the stability of heterochiral cyclic tripeptides comes from molecular electrostatic potential and electron density surfaces. These studies show clearly that for the kind of systems presented here, gas phase or implicit sol- vent phase studies are inadequate in explaining realistic situations. Calculations with solvent molecules, even if a few only, are necessary to substantiate experimental observations.

Abstract

Optimal control theory in combination with time-dependent quantum dynamics is employed to design laser pulses which can perform selective vibrational and rotational excitations in a heteronuclear diatomic system. We have applied the conjugate gradient method for the constrained optimization of a suitably designed functional incorporating the desired objectives and constraints. Laser pulses designed for several excitation processes of the HF molecule were able to achieve predefined dynamical goals with almost 100% yield.

Abstract

Theoretical insights into homo versus heterochiral cyclic trimerization of 5-(aminoethyl)-2-furancarboxylic acid (AEFC) are provided based on density functional theory (DFT) calculations[1]. A comparison of pathways leading to cyclic trimerization explored at M06/6-31G(d,p) and B3LYP/6-31G(d,p) levels of theory indicates signicance of the M06 functional in determining the barrier heights. Kinetic preference for heterochiral cyclization is clearly observed from results obtained at M06/6-31G(d,p) level of theory. Structure analysis of geometries reveals existence of strong hydrogen bonding interactions in the solvated heterochiral tripeptides. Further support comes from IR spectra, NBO and AIM calculations, molecular electrostatic potential and electron density surfaces. Our study shows that for the kind of systems, calculations with solvent molecules, even if a few only, are sucient to substantiate experimental observations. Further study on anion binding anity of the peptides has revealed that while both homo and heterochiral cyclic tripeptides show equal electronic energies of interaction for a uoride ion in gas phase, the homochiral cyclic tripeptide shows relatively larger electronic energy of interaction for chloride and bromide ions ( 4 kcal/mol) compared to that shown by heterochiral cyclic tripeptide. In solvent phase, the peptides possess anity for uoride and chloride ions only. While, heterochiral cyclic peptide shows slightly larger interaction for uoride ion, anity of homochiral peptide for chloride ion is slightly larger than its heterochiral counterpart. Though the heterochiral cyclic tripeptide of AEFC is a stable product with high yield, anion recognition propensities are comparable to that of homochiral cyclic tripeptide.

Abstract

t Interaction between metal nanoparticles and biomolecules is important from the view point of developing and designing biosensors. Studies on proline tagged with gold nanoclusters are reported here using density functional theory (DFT) calculations for its structural, electronic and bonding properties. Geometries of the complexes are optimized using the PBE1PBE functional and mixed basis set, i. e., 6-311++G for the amino acid and SDD for the gold clusters. Equilibrium configurations are analyzed in terms of interaction energies, molecular orbitals and charge density. The complexes associated with cluster composed of an odd number of Au atoms show higher stability. Marked decrease in the HOMO-LUMO gaps is observed on complexation. Major components of interaction between the two moieties are: the anchoring N-Au and O-Au bond; and the non covalent interactions between Au and N-H or O-H bonds. The electron affinities and vertical ionization potentials for all complexes are calculated. They show an increased value of electron affinity and ionization potential on complexation. Natural bond orbital (NBO) analysis reveals a charge transfer between the donor (proline) and acceptor (gold cluster). The results indicate that the nature of interaction between the two moieties is partially covalent. Our results will be useful for further experimental studies and may be important for future application

Abstract

In this paper we present a novel recognition approach that results in a 15% decrease in word error rate on heavily degraded Indian language document images. OCRs have considerably good performance on good quality documents, but fail easily in presence of degradations. Also, classical OCR approaches perform poorly over complex scripts such as those for Indian languages. We address these issues by proposing to recognize character n-gram images, which are basically groupings of consecutive character/component segments. Our approach is unique, since we use the character n-grams as a primitive for recognition rather than for postprocessing. By exploiting the additional context present in the character n-gram images, we enable better disambiguation between confusing characters in the recognition phase. The labels obtained from recognizing the constituent n-grams are then fused to obtain a label for the word that emitted them. Our method is inherently robust to degradations such as cuts and merges which are common in digital libraries of scanned documents. We also present a reliable and scalable scheme for recognizing character n-gram images. Tests on English and Malayalam document images show considerable improvement in recognition in the case of heavily degraded documents.

Abstract

This paper presents a Bag of Visual Words (BoVW) based approach to retrieve similar word images from a large database, efficiently and accurately. We show that a text retrieval system can be adapted to build a word image retrieval solution. This helps in achieving scalability. We demonstrate the method on more than 1 Million word images with a sub-second retrieval time. We validate the method on four Indian languages, and report a mean average precision of more than 0.75. We represent the word images as histogram of visual words present in the image. Visual words are quantized representation of local regions, and for this work, SIFT descriptors at interest points are used as feature vectors. To address the lack of spatial structure in the BoVW representation, we re-rank the retrieved list. This significantly improves the performance.

Abstract

We describe a method for real time video retrieval where the task is to match the 2D human pose of a query. A user can form a query by (i) interactively controlling a stickman on a web based GUI, (ii) uploading an image of the desired pose, or (iii) using the Kinect and acting out the query himself. The method is scalable and is applied to a dataset of 18 films totaling more than three million frames. The real time performance is achieved by searching for approximate nearest neighbors to the query using a random forest of K-D trees. Apart from the query modalities, we introduce two other areas of novelty. First, we show that pose retrieval can proceed using a low dimensional representation. Second, we show that the precision of the results can be improved substantially by combining the outputs of independent human pose estimation algorithms. The performance of the system is assessed quantitatively over a range of pose queries.

Abstract

Scene text recognition has gained significant attention from the computer vision community in recent years. Recognizing such text is a challenging problem, even more so than the recognition of scanned documents. In this work, we focus on the problem of recognizing text extracted from street images. We present a framework that exploits both bottom-up and top-down cues. The bottom-up cues are derived from individual character detections from the image. We build a Conditional Random Field model on these detections to jointly model the strength of the detections and the interactions between them. We impose top-down cues obtained from a lexicon-based prior, i.e. language statistics, on the model. The optimal word represented by the text image is obtained by minimizing the energy function corresponding to the random field model. We show significant improvements in accuracies on two challenging public datasets, namely Street View Text (over 15%) and ICDAR 2003 (nearly 10%).

Abstract

We investigate the fine grained object categorization problem of determining the breed of animal from an image. To this end we introduce a new annotated dataset of pets covering 37 different breeds of cats and dogs. The visual problem is very challenging as these animals, particularly cats, are very deformable and there can be quite subtle differences between the breeds. We make a number of contributions: first, we introduce a model to classify a pet breed automatically from an image. The model combines shape, captured by a deformable part model detecting the pet face, and appearance, captured by a bag-of-words model that describes the pet fur. Fitting the model involves automatically segmenting the animal in the image. Second, we compare two classification approaches: a hierarchical one, in which a pet is first assigned to the cat or dog family and then to a breed, and a flat one, in which the breed is obtained directly. We also investigate a number of animal and image orientated spatial layouts. These models are very good: they beat all previously published results on the challenging ASIRRA test (cat vs dog discrimination). When applied to the task of discriminating the 37 different breeds of pets, the models obtain an average accuracy of about 59%, a very encouraging result considering the difficulty of the problem.