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

Enhancing Small Object Encoding in Deep Neural Networks: Introducing Fast&Focused-Net with Volume-wise Dot Product Layer

In this paper, we introduce Fast&Focused-Net, a novel deep neural network architecture tailored for efficiently encoding small objects into fixed-length feature vectors. Contrary to conventional Convolutional Neural Networks (CNNs), Fast&Focused-Net employs a series of our newly proposed layer, the Volume-wise Dot Product (VDP) layer, designed to address several inherent limitations of CNNs. Specifically, CNNs often exhibit a smaller effective receptive field than their theoretical counterparts, limiting their vision span. Additionally, the initial layers in CNNs produce low-dimensional feature vectors, presenting a bottleneck for subsequent learning. Lastly, the computational overhead of CNNs, particularly in capturing diverse image regions by parameter sharing, is significantly high. The VDP layer, at the heart of Fast&Focused-Net, aims to remedy these issues by efficiently covering the entire image patch information with reduced computational demand. Experimental results demonstrate the prowess of Fast&Focused-Net in a variety of applications. For small object classification tasks, our network outperformed state-of-the-art methods on datasets such as CIFAR-10, CIFAR-100, STL-10, SVHN-Cropped, and Fashion-MNIST. In the context of larger image classification, when combined with a transformer encoder (ViT), Fast&Focused-Net produced competitive results for OpenImages V6, ImageNet-1K, and Places365 datasets. Moreover, the same combination showcased unparalleled performance in text recognition tasks across SVT, IC15, SVTP, and HOST datasets. This paper presents the architecture, the underlying motivation, and extensive empirical evidence suggesting that Fast&Focused-Net is a promising direction for efficient and focused deep learning

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