Abstract

Protein allostery is a fundamental biological mechanism that plays a pivotal role in regulating various cellular processes. It allows proteins to change their shape and function in response to specific signals or ligands. In the context of nucleotide excision repair (NER), proteins involved in the repair process, such as the Rad4/XPC protein, undergo conformational changes when they bind to damaged DNA, which begin the lesion excision process by locating the lesion site, which is followed by the recruitment of other repair factors and facilitates the removal of damaged DNA segments. Essentially, protein allostery ensures that the repair machinery is only activated when and where it is needed, contributing to the precision and efficiency of DNA repair processes in cells. Molecular dynamics (MD) simulations enable the investigation of the conformational changes that facilitate complex, selective, and biologically significant processes involving proteins binding to a variety of substrates. Hence, the present work uses MD to study two processes that proteins participate in: (a) transcription modulation through mediator allostery and (b) locating UV-lesions present in DNA for their ultimate excision and repair. F-helices in CAP are able to ultimately recognise and bind to DNA for transcription when they undergo reorientation after cAMP, and a mediator has situated itself into the binding pockets of CAP. The present study uses a simulation-based approach to investigate the mechanism of cAMP-induced changes in the conformation and energetics of F-helices observed during the allosteric regulation of CAP by cAMP. The free energy profiles obtained by two-dimensional umbrella sampling of CAP and cAMPbound CAP provide a detailed picture of the elasticity modifications observed in the DNA-binding domain of CAP when cAMP is appropriately situated. Residue-wise interaction energy maps w.r.t. CAP residues under the different conformations of CAP, cAMP-bound CAP, and cAMP-bound DNAcomplexed CAP are created, which ultimately offer clues on the microscopic origin of the inter-subunit cooperativity and dimer stability of CAP. UV radiation-induced DNA damage has adverse effects on genome integrity and cellular function. The most prevalent DNA lesion is the cyclobutane pyrimidine dimer (CPD), implicated in a variety of genetic skin-related diseases and cancers in humans. Rad4/XPC is a damage-sensing protein that recognises and helps repair CPD lesions with high affinity. This binding efficiency of Rad4 depends on how efficiently the BHD2 and BHD3 domains have been associated with the CPD-containing lesion site of DNA. The present thesis investigates the mechanism, energetics, dynamics, and molecular basis for this Rad4-DNA association using CPD-containing perfectly matched DNA. This key molecular event that occurs in NER is studied using suitable reaction coordinates, and the resultant free energy surface when compared with the same of TTT/TTT mismatched DNA reveals that Rad4 has a higher tendency to stay in the associated conformation with CPD-containing DNA than TTT/TTT mismatched DNA, hence having a higher lesion-recognition efficiency on the former than the latter.

Abstract

Remote coordination between humans and robots has been of much interest over the past many years. In this thesis, we perform an empirical study regarding the comfort level of users in teleoperating a robotic manipulator to perform operations such as pick and place and stacking. We claim that such daily life operations while easy to perform by humans, are difficult to remotely operate using a manipulator due to their limited sensory perception. The problem is challenging because it requires optimum placement of cameras and an intuitive interface for the user to view and control the manipulator simultaneously. Hence, in our work, instead of limiting the control options, we give users the freedom to choose between controlling the manipulator using Cartesian Control and Joint Control, Joystick interface and also zoom in and out the various camera views for accurate visibility for pick and place and stacking tasks from a remote location. We validate our results by conducting the user trials on users with age between 21 and 63 from multiple countries. We observe that 71% of the users prefer using Cartesian space as the reference control, 57% feel the need for additional viewpoints for better visibility in first experiment which reduced to 29% in third experiment with different positioning of the same cameras, and more than 90% do not require the data from a distance sensor for guidance.

Abstract

Reinforcement Learning (RL) has witnessed remarkable advancements in both algorithms and engineering, enabling a wide range of exciting applications. Multi-agent Reinforcement Learning (MARL) in particular has made strides of progress enabling multiple learning entities to interact in effective manner. Few of the challenges that still remain are learning under sparse rewards, achieving social generalization by adapting to changing behaviours of other agents and reproducibility. This thesis tackles two important challenges in MARL: (1) learning in multi-agent sparse reward environments and (2) reproducibility with social generalization. In the first part, we address the issue of learning a reliable critic in multi-agent sparse reward scenarios. The exponential growth of the joint action space with the number of agents, coupled with reward sparsity and environmental noise, poses significant hurdles for accurate learning. To mitigate these challenges, we propose regularizing the critic with spectral normalization (SN). Our experiments demonstrate that the regularized critic exhibits improved robustness, enabling faster learning even in complex multi-agent scenarios. These findings highlight the importance of critic regularization for stable learning. In the second part, we introduce marl-jax, a powerful software package for MARL that focuses on training and evaluating social generalization of agents. Built on DeepMind’s JAX ecosystem and leveraging their RL framework, marl-jax supports cooperative and competitive environments with multiple agents acting simultaneously. It provides an intuitive command-line interface for training agent populations and evaluating their generalization capabilities. Researchers interested in exploring social generalization in MARL can leverage marl-jax as a reliable baseline. In conclusion, this thesis addresses two crucial challenges in RL: learning in multi-agent sparse reward scenarios and reproducibility for social generalization in MARL. By introducing spectral normalization as a regularization technique and providing the marl-jax software package, this research contributes to enhancing stability, robustness, social generalization and reproducibility in RL.

Abstract

Relational interpreters, interpreters written for programming languages as mathematical relations, have been studied for at least over a decade. However, while relational interpreters are mostly written in miniKanren, a relational language where programs are mathematical relations, the interpreted language tends to be non-relational. This dissertation presents metaKanren, a purely relational miniKanren-like language, and relational interpreter implemented as a deep embedding for metaKanren. We also construct a relational interpreter for a minimal relational language as a shallow embedding and point out the limitation of a shallow embedding, further emphasizing the need for a deep embedding. We show how one can perform program synthesis of miniKanren programs using our relational interpreter for metaKanren. We also show how to extend metaKanren and its relational interpreter to handle datatype constraints. Finally, we also show how metaKanren can be made fully metacircular, and precisely point out the problems in building a metacircular interpreter for miniKanren. This dissertation argues that it is feasible and useful to build a metacircular relational interpreter for miniKanren

Abstract

Research over several decades has demonstrated modest and moderately consistent relationships between personality and music preference, but has primarily relied on self-reported data and lab-based listening experiments. Recently, music preference research has begun to take advantage of online listening data from platforms such as Spotify and Last.fm in order to more directly link listening behavior to individual differences. This study extends this new line of research by investigating the associations between naturally occurring music listening behaviors and personality traits, utilizing listening data acquired from Last.fm. We examined social-tagging data by extracting tags related to musical genre and emotion from frequently listened tracks of each participant and clustered them into broader categories to study their association with OCEAN traits. We further evaluated preferences in terms of co-occurring genre and emotion tags and analyzed listening patterns over time to better understand natural patterns of listening as they relate to personality. Our results corroborated previous research and revealed several novel associations that could be used to provide more accurate and highly customized music recommendations, tailored to the user’s personality. Then, we comment on existing music preference frameworks and their relevance to naturally occurring music listening behavior. Finally, we compare and analyze the emotions derived from semantic tags and acoustic features, shedding light on the interplay between subjective and objective measures of emotions in music listening and how they relate to personality.

Abstract

“Your brain is constantly rewiring itself, adapting with each new experience. It’s a dynamic, ever-changing network of connections, shaped by the world around you.” - David Eagleman This thesis investigates the reorganization of brain networks during the aging process, particularly in the transition from young to middle-aged to older adults. The study utilizes the CAMCAN dataset, a comprehensive cross-sectional dataset with multimodal data, including pre-processed resting-state fMRI (rs-fMRI) data. The aim is to understand how brain networks evolve with age and identify key brain regions and network properties that undergo changes. Data-driven statistical and graph-theoretic measures are employed to study modular segregation and integration in the brain. The results reveal characteristic nodes forming stable cores and flexible peripheries in both young and old age groups. Notably, regions within the Default Mode network (DMN) show a negative correlation with modularity in the old age group, while regions from the Limbic, SensoriMotor (SMN), and Salience networks display a positive correlation. Machine learning models based on flexibility scores further validate the relevance of these regions, providing promising insights for future investigations. Additionally, the study uncovers age-related changes in brain connectivity and network properties. Modularity increases with age, indicating greater functional specialization in the aging brain, accompanied by a decrease in flexibility, suggesting reduced adaptability to changing cognitive demands. The negative correlation between flexibility and modularity across all age groups implies that as the brain becomes less modular, it becomes more flexible in its organization. Certain brain regions show significant connectivity alterations, with increased participation coefficient in some frontal and temporal regions and decreased participation coefficient in several frontal and parietal regions. Hemispheric differences indicate that age-related connectivity changes may vary between hemispheres. Furthermore, the complexity of the relationship between cognitive abilities, task performance, and brain network dynamics is highlighted. The absence of strong correlations between task scores and network measures at the nodal level, along with the weak correlation between Cattell scores and global flexibility, underscore the multifaceted nature of these associations. Age alone cannot fully account for the observed dynamics, suggesting the involvement of other factors in shaping the relationship between cognition and brain network measures. However, this study has limitations. The use of cross-sectional data hinders the exploration of individual changes over time, and longitudinal data would provide more robust insights. The dataset’s relatively small size and the categorization of age into discrete groups may limit the generalizability of the findings. Moreover, relying solely on resting-state fMRI data may not fully capture the dynamic nature of brain function during various cognitive processes. Additionally, causal inferences should be made with caution, as the study is based on observational data, and other factors may influence the observed brain network changes. In conclusion, this thesis contributes to understanding age-related changes in brain networks and their impact on cognitive aging. The findings highlight the importance of specific brain regions in maintaining functional networks during aging and underscore the complexity of brain network dynamics. Addressing the limitations in future research will enhance our knowledge of brain aging and the interplay between brain networks, cognitive abilities, and behavior.

Abstract

Code-switching or code-mixing occurs when ”lexical items and/or grammatical features from two languages appear in one sentence”. With the rising popularity of social media platforms such as Twitter, Facebook, and Reddit, the volume of texts on these platforms has also grown significantly. Twitter alone has over 500 million text posts (tweets) per day. India, a country with over 300 million multilingual speakers, has over 23 million users on Twitter as of January 2022, and code-switching can be observed heavily on this social media platform. Code-mixed social media posts present unique challenges due to the mixing of languages, slang, and informal expressions. Extracting valuable information from code-mixed data enables a better understanding of user sentiments, preferences, and trends across different language communities. It helps identify named entities, detect events, and extract relevant information from multilingual conversations. By effectively extracting and analyzing code-mixed content, businesses, researchers, and language processing systems can gain valuable insights into diverse language patterns, linguistic phenomena, and cultural nuances, contributing to more accurate language processing, sentiment analysis, and targeted communication strategies. Extracting valuable information from social media content has numerous benefits that impact various domains. This has led to a growing interest in Information Extraction (IE) as an active research area in artificial intelligence. However, research efforts are often hindered by the lack of automated NLP tools to analyse massive amounts of code-mixed data, especially for resource-poor languages. There have been significant efforts towards understanding some languages, such as English, German, French, and Spanish, by creating annotated resources for various tasks, while some languages have not received the same focus, making them resource-poor. One such language is Kannada, which has over 58 million native and secondarylanguage (L2) speakers worldwide. Like in most places around the world, Kannada speakers tend to use code-mixed language, mixed with English, in informal settings including social media and texting platforms, but the same has not received much focus from researchers. In this thesis, we work towards information extraction from Kannada-English code-mixed data from social media. We work on the problems of Named Entity Recognition (NER) and Event Detection. Named Entity Recognition (NER) and Event Detection on social media code-mixed data are crucial for understanding and analyzing user-generated content in diverse languages. They enable the identification of named entities, such as names of people, organizations, and locations, as well as the detection of events, allowing for deeper insights into multilingual conversations, cross-cultural trends, and effective information retrieval from code-mixed social media posts. We have collected Kanglish code-mixed data from social media, Twitter, and curated annotated datasets for both NER and Event Detection tasks while mentioning the annotation guidelines for the same. We have analysed the challenges that are unique to Kannada-English code-mixed data and have provided annotation guidelines for the same. We have also proposed a few supervised approaches towards these tasks on our dataset with careful feature selection and critically analysed the results in hopes to promote more focus from the research community on such low-resource languages.

Abstract

Videos have become an integral part of our daily digital consumption. With the widespread adoption of mobile devices, internet connectivity, and social media platforms, the number of online users and consumers has risen exponentially in recent years. This has led to an unprecedented surge in video content consumption and creation, ranging from short-form content on TikTok to educational material on Coursera and entertainment videos on YouTube. Consequently, there is an urgent need to study videos as a modality in Computer Vision, as it can enable a multitude of applications across various domains, including virtual reality, education, and entertainment. By understanding the intricacies of video content, we can unlock its potential and leverage its benefits to enhance user experiences and create innovative solutions. Producing video content at scale can be challenging due to various practical issues. The recording process can take several hours of practice, and setting up the right studio and camera equipment can be time-consuming and expensive. Moreover, recording requires manual effort, and any mistakes made during the shoot can be difficult to rectify or modify, often requiring the entire video to be re-shot. In this thesis, we aim to ask the question “Can synthetically generated videos take the place of real videos?” as automatic content creation can significantly scale digital media production and ease the process of content creation that can aid several applications. A form of human-centric representation that is becoming increasingly popular in the research community is the ability to generate talking-head videos automatically. Talking-head generation refers to the ability to generate realistic videos of a person speaking, where the generated video can be of a person that may not exist in reality or may exhibit significantly different characteristics than the original person. Recent deep learning approaches can synthesize synthetic talking-head videos at tremendous scale and quality, with diverse content and styles, that are visually indistinguishable from real videos. Therefore, it is imperative to study the process of generating talking-head videos as these videos can be used for a variety of applications, such as video conferencing, movie-making, broadcasting news, vlogging, and language learning among others. Consider a digital avatar reading news from a text transcript being broadcasted on news. In this vein, this thesis aims to explore two prominent use cases of generating synthetic talkingheads automatically - the first one towards generating large-scale synthetic content to aid people in lipreading at scale. The second use case is for automating the task of actor-double face-swapping in the moviemaking industry. We study and elucidate the challenges and limitations of the existing approaches, propose solutions based on synthetic talking head generation, and show the superiority of our methods through extensive experimental evaluation and user studies. In the first task, we address the challenges associated with learning to lipread. Lipreading is a primary mode of communication for people suffering from some form of hearing loss. Therefore, learning to lipread is an important aspect for hard-of-hearing people. However, learning to lipread is not an easy task and finding resources to improve one’s lipreading skills can be challenging. Existing lipreading training websites that provide basic online resources to improve lipreading skills, are unfortunately, limited by real-world variations in the talking faces, cover only a limited vocabulary, and are available in a few select languages and accents. This leaves the vast majority of users without access to adequate lipreading training resources. To address this challenge, we propose an end-to-end pipeline to develop an online lipreading training platform using state-of-the-art talking head video generator networks, textto-speech models, and computer vision techniques, to increase the amount of online content on the LRT platforms in an automated and cost-effective manner. We show that incorporating existing talking heading generator networks for the task of lipreading is not trivial, and requires careful adaptation. For instance, we develop an audio-video alignment module that aligns the speech utterance on the region with the mouth movements and adds silence around the aligned utterance. Such modifications are necessary to generate realistic-looking videos that don’t cause distress to the lipreaders. We also design carefully thought out lipreading training exercises, conduct extensive user studies, and perform statistical analysis to show the effectiveness of the generated content in replacing the manually recorded lipreading training videos. In the second problem, we address challenges in the entertainment industry. Body doubles play an indispensable role in the moviemaking industry. They take

Abstract

Word problem Solving is a popular NLP task that deals with solving mathematical problems described in natural language. Mathematical Word Problems cover problems over a large mathematical domain with various complexities ranging from Arithmetic and Algebraic to Geometry and Calculus. While most word problems are entirely textual, some word problems, like geometric word problems, may also have a visual component. Much research has been carried out to solve different genres of word problems with various complex- ity levels in recent years. However, most publicly available datasets and work are in English. Recently there has been a surge in word problem-solving in Chinese with the creation of large benchmark datasets. Labelled benchmark datasets for low-resource languages are very scarce. The first requirement for solving word problems, like any other problem, is data. To the best of our knowledge, no datasets are available for any Indian Languages for Word Problem Solving. Such limitations on data availability not only encouraged us to create a new dataset for an Indian Language but also made us explore techniques by which data for other Indian Languages can be created with ease. In this work, we present a diverse dataset containing 2336 Arithmetic word problems in Hindi built by manually crafting word problems and using word problems augmented from benchmark datasets of other languages. For augmentation, we used translation of word problems (of other languages in which Word Problem Solving data was developed) as a tool to generate diverse word problems. In this process, we studied the translated word problems, gathered the patterns of issues seen in these translations and defined the steps to eliminate the errors and improve the quality of the translated output for it to be suited to be a set of Hindi word problems that look as natural as word problems that are studied across India in Hindi medium schools. We also developed baseline systems for solving these word problems - a rule-based solver that uses verbs to identify operations for generating the answers to word problems and an end-to-end deep learning-based solver that generates equations for word problems. We also propose a new evaluation technique for word problem solvers taking equation equivalence into account. This will form the basis for future work for Word Problem Solving in Indian Languages, especially in Hindi.

Abstract

Autonomous driving requires accurate reasoning of the location of objects from raw sensor data. Recent end-to-end learning methods go from raw sensor data to a trajectory output via Bird’s Eye View (BEV) segmentation as an interpretable intermediate representation. Motion planning over cost maps generated via Birds Eye View (BEV) segmentation has emerged as a prominent approach in autonomous driving. However, current approaches have two critical gaps. First, the optimization process is simplistic and involves just evaluating a fixed set of trajectories over the cost map, which are not adapted based on their associated cost values. Second, the existing cost maps do not account for the uncertainty arising from noise in RGB images, BEV annotations. As a result, these approaches can struggle in challenging scenarios where there is abrupt cut-in, stopping, overtaking, and merging from neighboring vehicles. In this thesis, we propose UAP-BEV, a novel approach that models the noise in Spatio-Temporal BEV predictions to create uncertainty-aware occupancy grid maps. Using queries of the distance to the closest occupied cell, we obtain a sample estimate of the collision probability of the ego-vehicle. Subsequently, our approach uses gradient-free sampling-based optimization to compute low-cost trajectories over the cost map. Notably, the sampling distribution is adapted based on the optimal cost values of the sampled trajectories. By explicitly modeling probabilistic collision avoidance in the BEV space, our approach can outperform the cost-map-based baselines in collision avoidance, route completion, time to completion, and smoothness. To further validate our method, we also show results on the real-world dataset NuScenes, where we report collision avoidance and smoothness improvements. It also outperforms other uncertainty-based methods in conservatism.