Femtosecond Coherent Vibrational Dynamics of Anabaena Sensory Rhodopsin

The photo-induced isomerization of retinal protonated Schiff base (RPSB) inside the protein pocket is one of the fastest (<ps) and most stereo-selective photochemical reactions in nature. The ground state structure of the RPSB and its surrounding protein constructions are thought to be the two most crucial factors to drive this reaction. The investigation of each factor individually was the main goal of this thesis. Anabaena Sensory Rhodopsin (ASR), a recently discovered microbial retinal protein, serves as an ideal system for this study as it binds two structural isomers (all-trans: AT and 13-cis: 13C) of the RPSB within the same protein constructions in its photocycle. In the present work, the photo-induced dynamics of the RPSB in ASR has been explored with the help of time resolved coherent vibrational spectroscopic methods, which monitor the photo-induced sub-ps structural changes of the RPSB. These studies have helped to shed light on the intricate relationship between electronic and vibrational dynamics of the RPSB. In the first half of this thesis, a comparative study showed both electronic and vibrational dynamics are widely distinct for the AT and 13C isomers of the RPSB in ASR. In particular, the 13C isomer exhibited more than five folds faster dynamics than the AT isomer. One possible molecular origin behind this dynamical difference was found by comparing the ground state Raman spectra of the two isomers. It depicted an increase in the amplitude of hydrogen-out-of-plane (HOOP) modes for the 13C isomer, which is usually considered to be an evidence of distortion in the ground state structure for the retinal system. The ground state pre-distortion has been reported as a potential element for the acceleration of the isomerization reaction for the 13C isomer, in analogy with the cis isomers of visual rhodopsin and bacteriorhodopsin. The second half of this work explored the role of the part of protein helix inside the retinal pocket as well as that far away from the pocket. In particular, the replacement of the amino acid residues in vicinity of the RPSB by point mutation caused an acceleration of the reaction rate for the AT isomer, but it had only a minor effect for the 13C isomer of the RPSB. Furthermore, the truncation of the part of the protein, embedded into the cytoplasmic region, affected the formation of the primary photoproduct. All these experimental results lead to two major conclusions of this thesis: (i) the protein constructions govern the retinal isomerization dynamics and (ii) the same protein cage exerts differential interactions on two structural isomers of the RPSB

Signature Verification Approach using Fusion of Hybrid Texture Features

In this paper, a writer-dependent signature verification method is proposed. Two different types of texture features, namely Wavelet and Local Quantized Patterns (LQP) features, are employed to extract two kinds of transform and statistical based information from signature images. For each writer two separate one-class support vector machines (SVMs) corresponding to each set of LQP and Wavelet features are trained to obtain two different authenticity scores for a given signature. Finally, a score level classifier fusion method is used to integrate the scores obtained from the two one-class SVMs to achieve the verification score. In the proposed method only genuine signatures are used to train the one-class SVMs. The proposed signature verification method has been tested using four different publicly available datasets and the results demonstrate the generality of the proposed method. The proposed system outperforms other existing systems in the literature.Comment: Neural Computing and Applicatio

Enhancing Document Information Analysis with Multi-Task Pre-training: A Robust Approach for Information Extraction in Visually-Rich Documents

This paper introduces a deep learning model tailored for document information analysis, emphasizing document classification, entity relation extraction, and document visual question answering. The proposed model leverages transformer-based models to encode all the information present in a document image, including textual, visual, and layout information. The model is pre-trained and subsequently fine-tuned for various document image analysis tasks. The proposed model incorporates three additional tasks during the pre-training phase, including reading order identification of different layout segments in a document image, layout segments categorization as per PubLayNet, and generation of the text sequence within a given layout segment (text block). The model also incorporates a collective pre-training scheme where losses of all the tasks under consideration, including pre-training and fine-tuning tasks with all datasets, are considered. Additional encoder and decoder blocks are added to the RoBERTa network to generate results for all tasks. The proposed model achieved impressive results across all tasks, with an accuracy of 95.87% on the RVL-CDIP dataset for document classification, F1 scores of 0.9306, 0.9804, 0.9794, and 0.8742 on the FUNSD, CORD, SROIE, and Kleister-NDA datasets respectively for entity relation extraction, and an ANLS score of 0.8468 on the DocVQA dataset for visual question answering. The results highlight the effectiveness of the proposed model in understanding and interpreting complex document layouts and content, making it a promising tool for document analysis tasks