Abstract

Gynecologic and breast cancers are among the leading causes of morbidity and mortality in women worldwide, posing a significant threat to womens health. It is estimated that roughly one in five women will be diagnosed with cancer during their lifetime, with approximately one in twelve succumbing to the disease. Their molecular and cellular heterogeneity significantly contributes to therapeutic resistance and variability in clinical outcomes. Deciphering these complexities at various resolution is essential for advancing precision oncology. This thesis aims to unravel the molecular landscape of these cancers by leveraging bulk omics, single-cell RNA-seq (scRNA-seq), and spatial transcriptomics datasets. Metabolic reprogramming is a hallmark of cancer and is characterized by significant heterogeneity, presenting challenges for treatment efficacy and patient outcomes. Understanding metabolic adaptation and its heterogeneity in tumor tissues may provide new insights and help in developing personalized therapeutic strategies. We investigated the metabolic alterations of endometrial cancer (EC) to understand the variations in metabolism within tumor samples. Integration of transcriptomics data of EC (RNA-Seq) and the human genome-scale metabolic network was performed to identify the metabolic subtypes of EC and uncover the underlying dysregulated metabolic pathways and reporter metabolites in each subtype. The relationship between metabolic subtypes and clinical variables was explored. Further, we correlated the metabolic changes occurring at the transcriptome level with the genomic alterations. Based on metabolic profile, EC patients were stratified into two subtypes (metabolic subtype-1 and subtype-2) that significantly correlated to patient survival, tumor stages, mutation, and copy number variations. We observed the co-activation of the pentose phosphate pathway, one-carbon metabolism, and genes involved in controlling estrogen levels in metabolic subtype-2, which is linked to poor survival. PNMT and ERBB2 are also upregulated in metabolic subtype-2 samples and present on the same chromosome locus 17q12, which is amplified. PTEN and TP53 mutations show mutually exclusive behavior between subtypes and display a difference in survival. This work identifies metabolic subtypes with distinct characteristics at the transcriptome and genome levels, highlighting the metabolic heterogeneity within EC.

Abstract

Recent efforts to embed ethical reasoning into AI and machine learning systems have focused on training deep learning models to generate moral judgments in response to situational inputs. Parallel developments in the criminal justice system illustrate the application of such technologies, where risk assessment tools are employed to predict defendants’ risk scores. These scores are then used by legal institutions to make decisions related to parole, policing, and sentencing. Together, these approaches reflect a growing trend towards integrating AI into moral decision-making and as a potential replacement for human decision-making. In our work, we critically evaluate moral decision-making in AI systems through a philosophical lens, with particular emphasis on virtue ethics. First, we evaluate Delphi (Jiang et. al., 2021 [1]), which attempts to use the Rawlsian decision procedure as a bottom-up approach to capture abstract moral principles from crowdsourcing moral opinions across a diverse demographic. We critically evaluate this model on the basis of the effectiveness of (a) the current bottom-up approach of incorporating Rawlsian decision procedure, and (b) the proposed hybridization of the former with a top-down approach leading to reflective equilibrium. We argue against (a) on the grounds that the system fails to satisfy the criteria for being a competent moral judge as outlined by Rawls (Rawls, 1971 [2]); further, we argue against (b) on the grounds that reflective equilibrium requires phronesis or practical wisdom, which cannot be embedded into AI systems. We critically evaluate (b) in the light of Irikefe’s neo-Aristotelian, agentbased account of reflective equilibrium (Irikefe, 2019 [3]) to argue against the possibility of AI-ML systems to incorporate phronesis in order to attain reflective equilibrium. We then evaluate COMPAS, a risk assessment software that predicts the risk of recidivism of defendants based on responses to a questionnaire. We investigate COMPAS from a virtue ethics perspective by (1) discussing how punishment in the criminal justice system can be implemented as a practice, as defined within the framework of MacIntyre’s virtue ethics (MacIntyre, 2007 [4]) and (2) analyzing why such a practice cannot be implemented by tools like COMPAS.

Abstract

A control system aims to regulate the behavior of a dynamical system to achieve a desired output by adjusting the input. The control system design specifications for dynamical systems are typically provided in terms of the desired transient response and steady-state response. Performance measures like Rise Time, Settling Time, Steady-State Error, Control Effort, etc., help us understand the effectiveness of the designed controller in real-time. In traditional approaches, based on these specifications, we select design methods such as Root Locus or Bode Plot analysis, and algorithms like ProportionalIntegral-Derivative (PID), Linear Quadratic Regulator (LQR), and Model Predictive Control (MPC). After tuning with the allowed constraints, we may end up with a control system that meets our specifications. However, mathematical models of many real-world systems are not fully known. Meeting design specifications for such unknown dynamical systems is a challenging task. Partial or approximate system models often lead to inaccuracies due to internal modeling errors that are not always avoidable or correctable. Therefore, there is a need to explore control strategies that do not depend on mathematical models. Model-free Reinforcement Learning (RL) methods have demonstrated strong performance in this context. Their performance was comparable to traditional control design methods with system knowledge, and sometimes even better. This is due to their ability to learn optimal policies through interaction with the environment and adaptability to changes in real-time. Techniques such as Deep Q-Network (DQN), Proximal Policy Optimization (PPO), and Twin Delay Deep Deterministic Policy Gradient (TD3) have shown significant potential in handling complex control tasks without explicit system models. This thesis presents a new approach for designing controllers for dynamical systems with unknown mathematical models using a model-free RL framework based on the TD3 algorithm. The proposed method enables an RL agent to learn and shape the error dynamics and overall system response in trajectory tracking, particularly in state regulation tasks, through a suitably designed reward function. The effectiveness of the approach is validated on first-order and second-order linear systems, as well as a nonlinear pendulum system. The resulting controller significantly influences both the transient and steady-state behavior of an unknown system while adhering to control and input constraints. By systematically modifying the reward parameters, we analyze how reward shaping during training affects test-time performance metrics. This analysis establishes the relationship between reward design and control performance, providing a means to tune reward parameters to meet desired specifications. Furthermore, we introduce a data-driven optimization framework using Particle Swarm Optimization (PSO) to identify sets of reward parameters that satisfy given performance requirements. The method achieves exponential convergence of tracking error dynamics in linear systems without requiring explicit knowledge of system models.

Abstract

We present an efficient framework for identity-preserving face swapping into scenic portrait sketches. First, we analyze latent identity representations via a StyleGAN-based face cartoonization pipeline. Our analysis reveals domain-dependent shifts in StyleGAN’s identity encodings when mapping photos to sketches, underscoring the need for cross-domain consistency. Second, we introduce Portrait Sketching StyleGAN (PS-StyleGAN), a novel GAN architecture with attentive affine-transform blocks. These blocks learn to modulate a pretrained StyleGAN’s style codes by joint content-and-style attention, thereby learning style-specific identity transformations. PS-StyleGAN requires only a few photo-sketch pairs (and short training) to learn each style, making it broadly adaptable. Thus, PS-StyleGAN produces editable, expressive portrait sketches that faithfully preserve the input identity. Third, we extend diffusion-based generative modeling by adapting InstantID for sketch synthesis. Our diffusion module integrates strong semantic identity embeddings and landmark guidance (inspired by InstantID’s IdentityNet) to steer high-fidelity portrait generation. This yields personalized sketches with markedly improved identity fidelity while requiring only zero-shot (tuning-free) personalization. Finally, motivated by recent diffusion-based face-swapping advances, we build a real-time end-to-end pipeline. By encoding facial identity and pose as conditioning inputs and optimizing the diffusion process for speed, our system can seamlessly embed user faces into tourist-style sketches on the fly. Extensive evaluations on standard benchmarks confirm that our methods significantly enhance identity preservation and visual quality compared to prior approaches, advancing the state of the art in generative portrait stylization and face swapping.

Abstract

River water quality is fundamentally influenced by hydrological factors such as discharge and water temperature, both of which are increasingly affected by climate variability and human-induced pressures. These variables, when considered independently, can provide a limited view of water quality risks. However, compound events, where low discharge coincides with high water temperatures, can pose significant ecological threats, particularly to sensitive aquatic species and overall river health. Existing studies often rely on univariate or deterministic models, which fail to capture the joint behaviour of these interconnected variables. There is also a noticeable gap in translating advanced statistical models into practical tools for river water quality risk management, especially in computing compound event probabilities and supporting basin-specific decision-making. This study addresses these gaps by developing a copula-based joint modelling framework to quantify compound low-flow and high-temperature risks across six diverse Indian rivers: Yamuna, Bhadra, Kaveri, Mahi, Sabarmati, and Vardha. The study begins by fitting appropriate univariate probability distributions to river discharge and water temperatures using diagnostic tools such as skewness, kurtosis, Q-Q plots, and information criteria to ensure statistically sound marginal modelling. Dependence structures between the variables are assessed using rank correlation metrics to identify rivers where joint modelling is both appropriate and necessary. An extensive set of 18 copula families, including symmetric, asymmetric, and tail-dependent models, is evaluated to capture complex interdependencies across river basins. The selected copulas are then applied to estimate joint probabilities, conditional probabilities, and return periods of compound events of discharge and water temperatures of various river gauging stations of India. The findings reveal clear differences in compound event frequencies and intensities across the studied rivers, underscoring the need for localized and river-specific water quality management strategies. The key objectives of this study include identifying the best-fit univariate distributions, selecting the most appropriate copula families for joint modelling, and constructing a flexible, transferable statistical framework that can support future applications, including multivariable risk assessments, climate change impact studies, and adaptive water resource management. By addressing the underutilization of joint probabilistic frameworks for Indian river systems and demonstrating their practical application, this study contributes to a more holistic understanding of compound water quality risks and provides valuable guidance for sustainable river management and policy development

Abstract

Human knowledge is predominantly encoded in natural language. Automatic understanding of natural language has been a most awaited goal of AI/ML. Among several challenges of natural language processing and understanding, Machine Translation (MT) stands to be a major task. MT is the process of converting the source text in one natural language to another natural language. With the advent of neural models, the field of neural MT (NMT) has made significant progress, yet the challenge of effectively evaluating the translation quality remains critical. This thesis focuses on the automatic evaluation of machine translation outputs, addressing both the limitations of existing metrics and proposing innovative approaches to enhance evaluation accuracy. Existing widely used metrics, such as BLEU and METEOR, are scrutinized for their reliance on n-gram overlap and limited linguistic insight, often failing to capture nuances of language such as fluency, adequacy, and semantic equivalence. To mitigate these shortcomings, this research explores advanced evaluation methods leveraging pre-trained multilingual embeddings and deep learning, which model linguistic phenomena more comprehensively. This thesis introduces several novel automatic evaluation approaches (unsupervised referencebased, unsupervised reference-free and supervised approaches) that integrates semantic similarity and contextual relevance by employing pre-trained language models. Through extensive experimentation, our metrics are benchmarked against the traditional ones, demonstrating superior performance in correlating with human judgments. Additionally, this work investigates the existing evaluation datasets and evaluation approaches, discusses their limitations, and also provides decision trees to help the researchers choose particular evaluation criteria or a metric based on available computation and linguistic resources. Ultimately, this thesis contributes to the field by offering robust methodologies for MT output evaluation, emphasizing the need for metrics that align more closely with human perceptions of translation quality. The proposed advancements pave the way for more reliable and insightful evaluation systems, fostering continued innovation and improvement in machine translation technologies.

Abstract

Power transmission infrastructure requires regular inspection and maintenance to ensure reliable operation, yet traditional methods involving human crews pose significant safety risks and operational costs, while autonomous aerial inspection systems face limitations in achieving closecontact inspection due to collision avoidance requirements and existing wire-traversing robots struggle with obstacle navigation and complex deployment procedures. This thesis presents WireFlie, a novel hybrid robotic system that addresses these challenges by integrating aerial mobility with contact-based wire traversal capabilities through a dual-arm underactuated mechanism mounted on a multi-rotor drone platform, enabling seamless transitions between flight and rolling locomotion modes via a sequential two-phase operation for safe wire engagement. The system employs computer vision for autonomous wire detection and obstacle classification, coupled with hierarchical control for precise wire approach and an obstacle avoidance module that automatically selects appropriate operational modes based on detected obstacle types, enabling dual-arm rolling over common obstacles, single-arm rolling for spacer configurations, and flight mode transitions for larger obstacles like transmission pylons. Experimental validation demonstrates the system’s effectiveness across multiple scenarios, confirming stable operation on inclined transmission lines, consistent autonomous wire engagement, and successful obstacle navigation through various configurations, with real-world validation on representative transmission line setups demonstrating the complete operational sequence from autonomous wire detection through obstacle navigation to controlled detachment. This research contributes to robotic infrastructure inspection by demonstrating the feasibility of hybrid aerial-ground locomotion for power line monitoring, establishing a foundation for next-generation transmission line inspection systems that combine the mobility advantages of aerial platforms with the precision requirements of contact-based inspection, ultimately enhancing power grid maintenance capabilities while improving operational safety and efficiency. Keywords: Transmission line inspection, underactuated mechanisms, hybrid locomotion, obstacle avoidance, autonomous control, robotic infrastructure monitoring

Abstract

Computational methods have profoundly impacted the field of chemistry by providing essen- tial tools to model and study complex chemical systems and processes. These methods make use of advanced algorithms, mathematical models, and implemented on high-performance com- puting to simulate the behavior of molecules, reactions, and materials at various scales, from atomistic to macroscopic levels. Computational techniques have become essential in modern chemical research by bridging the gap between theoretical chemistry and experimental obser- vations. One of the major applications of computational methods is to predict the properties and behavior of chemical systems. Quantum chemical calculations, such as Density Functional Theory (DFT) and ab-initio methods, aid scientists to analyze electronic structures, molec- ular geometries, and reaction mechanisms with high accuracy. These analysis are important for understanding fundamental chemical reactions like bond formation, energy transfer and catalysis, which are often critical to observe experimentally. Additionally, molecular dynamics (MD) simulations enable researchers to explore the time-dependent behavior of molecules, pro- viding comprehensive information about molecular interactions, diffusion, and conformational changes. This is vital in biochemistry, where MD simulations have helped to demonstrate protein folding, membrane dynamics and drug binding. Computational methods have also played a key role in material science, where they are used to design and optimize new material with desired properties, like polymers, catalysts and nano- materials. By predicting the performance of a materials before chemical synthesis, such methods save time and resources in their development process. The role of computational chemistry in drug discovery is indispensable. It enables virtual screening of huge chemical spaces, by pre- dicting the molecular interactions with biological targets, and optimization of drug candidates. This facilitates the identification of potential therapeutics and reduces the cost of experimen- tal validation. Techniques like quantum mechanics/molecular mechanics (QM/MM) combine the detailed accuracy of quantum mechanics with the cost efficiency of molecular mechanics, allowing the study of large biomolecules like enzymes with accuracy and computational feasibil- ity. The amalgamation of machine learning (ML) into computational chemistry has improved the predictive capabilities by analyzing large datasets to identify patterns and relationships in chemical properties. This synergy has allowed faster predictions in drug discovery and material sciences. Computational chemistry and its methods have revolutionized chemistry by provid-ing efficient, accurate and cost effective approaches to model chemical systems and processes. They have improved our understanding of molecular behavior, and facilitated the design of new materials and drugs. This work demonstrated in this thesis focuses on the application of the various computa- tional methods to the understanding of molecular properties of newly synthesized molecules and various metal clusters, and modeling various types of catalysis reactions. The use of gold nanoclusters (AuNC) in complex reactions involving large substrates and supporting agents is an active field of interest and molecular study of the catalytic role or AuNC in many reactions is still unexplored. The thesis lays key focus on the discussion of pure and copper doped 13 atom AuNC and prediction of their key molecular properties. It discusses the structure prop- erty relationship of the entire range of Aum Cun clusters where m+n = 13. The work also demonstrates the comparative catalytic activity of pure and doped Cu and Au rich 13 atom clusters in the aerobic oxidation of benzyl alcohol to benzaldehyde. The work then transitions to the study of various types of catalytic reactions such as photocatalysis of the cycloaddition of CO2 and epoxide by Zn-salen derivatives, kinetic vs thermodynamic controlled asymmet- ric Henry reaction in the synthesis of β-nitroalcohols catalysed by Baliospermum montanum, synthesis of Aryl ketones by activating toluene, and activation of esters to undergo annulative π-extension lactonization (APEX-LAC). The discussion concludes with directions to continue in future which will aid in demonstrating the key aspects across more dynamic and diverse molecular systems and chemical processes

Abstract

Coal mining has long served as a cornerstone of India’s industrial growth, energy security, and regional employment, powering not only factories and cities but also shaping the economic aspirations of mineral-rich states. However, behind its promise of development lies a complex terrain of ecological disruptions, social displacements, and uneven gains—making it imperative to critically examine the long-term impacts of coal extraction on landscapes and communities. This thesis undertakes an interdisciplinary analysis of the spatial, environmental, and socio-economic transformations induced by coal mining over the past five decades in two of India’s most prominent coalfields: North Karanpura in Jharkhand and the Ib Valley in Odisha. Leveraging remote sensing data and GIS-based change detection methods, the study maps and quantifies Land Use and Land Cover (LULC) changes across five time points. The results reveals an enormous and consistent expansion of open-cast mining zones—North Karanpura grew from 6.6 km² in 1973 to 49.6 km² in 2023, and Ib Valley from 0.12 km² in 1976 to 49.49 km² in 2024, accompanied by corresponding losses in dense vegetation, agricultural lands, and wetland ecosystems. Simultaneously, settlements increased manifold, reflecting rapid industrialisation and demographic shifts driven by labour migration and urban sprawl led to environmental degradation, including air and water pollution, biodiversity loss, and the depletion of potable water sources. The thesis also explores the human dimensions of these transformations, drawing upon census data, land acquisition records, and socio-demographic indicators to assess patterns of displacement, changes in literacy and employment, and the growing rural-urban divide. It highlights how industrial expansion has often excluded local and indigenous communities from its benefits, raising questions about justice, equity, and governance in resource management. By integrating geospatial evidence with socioeconomic narratives, the study not only documents the material footprint of coal mining but also critiques the institutional and policy mechanisms that have enabled its unchecked growth. Ultimately, the research calls for a more sustainable, inclusive, and spatially aware approach to development in India’s coal-bearing regions.

Abstract

Despite the success of the Standard Model (SM), the absence of new physics signals at the Large Hadron Collider (LHC) motivates the exploration of Beyond the SM (BSM) scenarios that deviate from minimal assumptions. Searches for TeV-scale Vectorlike Quarks (VLQs), a common feature in many BSM frameworks, are generally assumed to decay through SM bosons, leading to stringent, model-dependent mass limits. In this thesis, we discuss the phe- nomenology of next-to-minimal VLQ models where this assumption is relaxed, allowing for dominant decays into a new, SM-singlet scalar or pseudoscalar state (Φ). First, we present the theoretical motivations for such extensions, demonstrating their potential to address funda- mental issues like the strong CP problem and Higgs vacuum stability, and their natural emer- gence within frameworks such as Two-Higgs-Doublet and Composite Higgs models. Then, a bottom-up phenomenological framework for both singlet and doublet VLQs containing top and bottom partners is developed. By reinterpreting current experimental constraints from the LHC, we see that a vast, viable parameter space for these exotic decays remains open with- out requiring significant fine-tuning. Finally, to assess the discovery potential at future LHC runs, detailed collider analyses of VLQ pair-production are presented, leveraging a full LHC simulation chain. We use advanced machine learning techniques to distinguish signals from substantial SM backgrounds in several challenging final states. Specifically, this work eval- uates prospects for three signatures with different ML models: 1) monoleptonic final states from singlet 𝑇-quark pair-production using boosted decision trees; 2) monoleptonic signatures from singlet 𝐵-quark pair-production via mixed decays, analysed with a deep neural network model; and 3) fully hadronic final states from 𝐵-quark pair-production, where a novel graph neural network-based model is used to perform event-level classification. The results project a strong discovery potential for VLQs with masses extending well beyond current limits, pro- viding a concrete roadmap for future experimental searches at the High-Luminosity LHC. This thesis underscores the critical synergy between BSM phenomenology and sophisticated machine learning in uncovering new physics in complex collider environments.