Abstract

As text processing systems become increasingly central to human-computer interactions—such as voice assistants, chatbots, and search engines—it is vital that these technologies support natural, flexible, and inclusive modes of communication. Language usage is far from uniform and varies significantly across regions, cultures, and communities. A particularly widespread phenomenon in multilingual societies is code-mixing, where speakers fluidly incorporate linguistic units from two or more languages within a single utterance or conversational context. This form of language usage is especially common in countries like India, where multilingualism is the norm. However, contemporary NLP systems often struggle with such data because code-mixed data is under-represented in conventional sources of text data. Therefore, there is an urgent need to develop robust NLP pipelines that can handle code-mixed input effectively and equitably. This thesis addresses key challenges in processing code-mixed text by proposing comprehensive solutions for its analysis, benchmarking, and generation. Research presented in this thesis advances the field through several core contributions. Syntactic Code-Mixing Metric (SyMCoM): We propose, SyMCoM, a novel metric designed to quantify syntactic code-mixing by analyzing the source language associated with each part-of-speech (PoS) tag in a sentence. This metric offers a linguistically grounded alternative to existing language ID based metrics, enabling deeper insights into the structure of code-mixed text and facilitating more nuanced analysis of corpora and systems. Acceptability of Code-Mixed Text: We introduce CLINE, a large-scale dataset containing English – Hindi code-mixed sentences annotated with human judgments on their acceptability. Through empirical analysis, we show that existing code-mix metrics often fail to distinguish between acceptable and unacceptable code-mixing. Further, we demonstrate that multilingual language models like XLM-Roberta and Llama, when fine-tuned appropriately, can effectively learn to model these human acceptability judgments, offering a path forward for more human-aligned code-mix evaluation. A Cross-Lingual Task-Oriented Dialogue Dataset for Hindi and English-Hindi: We develop and release Hindi and English–Hindi versions of a multi-domain, task-oriented dialogue dataset. This dataset supports both natural language understanding (NLU) and generation (NLG) tasks and provides a valuable benchmark for evaluating multilingual and code-mixed capabilities of large language models in realistic, task-based scenarios.Model Merging Strategies for Code-Mixed Scenarios: To improve performance on code-mixed tasks, we explore novel strategies for adapting pre-trained models for code-mixed tasks. We also evaluate the possibility of combining monolingual and code-mixed data through model merging. Results of our study indicate that merging models offers an effective approach to adapting pre-trained models for code-mixed tasks, frequently yielding better performance than the traditional method of continued pre-training followed by fine-tuning. CodeMixToolkit: Finally, we present the CodeMixToolkit, a modular and extensible framework that standardizes the pipeline for working with code-mixed data. Toolkit offers tools for accessing and preprocessing data, model training, and evaluation, and supports multiple NLP tasks, with a focus on English–Hindi, but is extendable to other language pairs. This resource aims to accelerate research and development in the area by standardizing the pipeline in Code-Mixing research, lowering entry barriers, and promoting reproducibility. We conclude by discussing the limitations of our approaches, including challenges related to data availability, generalization across domains, and evaluation frameworks. We also outline future directions for advancing code-mixed NLP, such as improved representation learning, transfer learning techniques, and deeper linguistic analysis. This thesis presents a comprehensive framework for processing codemixed languages, enabling the development of NLP systems that are capable of processing code-mixed text, and better suited to the needs of multilingual users.

Abstract

Air pollution is a growing concern in India, specifically in the National Capital Region (NCR) which is one of the world’s most polluted regions. Despite the recognized problem, there is a lack of research focusing on how people actually perceive air pollution, the specific actions they take to protect their well-being from its impact, and how effective these personal strategies really are. This thesis is designed to address these knowledge gaps by examining public awareness of air pollution in India and the effectiveness of solutions used to improve Indoor Air Quality (IAQ) and further explores how urban residents respond to degraded air quality through a combination of personal behaviours, technological interventions, and adaptive strategies such as change in window opening and closing behaviour in response to pollution, change in day-to-day lifestyle choices and willingness to invest in cleaner air. As part of the study, ethnographic interviews were conducted in person to gather detailed socioeconomic perspectives. A mixed-methods approach was employed, involving interviews from 101 households in Delhi and it’s neighbouring cities like Ghaziabad and Noida. Both Qualitative and Quantitative methods were used to corroborate the statistical findings with descriptive accounts. The findings reveal widespread awareness of the Air Quality Index (88% are aware), but limited understanding of PM2.5 and its health implications (only 33% know about it). Air purifiers emerged as a common IAQ intervention, yet 40% of non-owners cited doubts about effectiveness or believe that IAQ in their house is sufficiently good. Cost and habitual behaviour to air purifiers were additional primary barriers. Among purifier owners, 75% reported perceived benefits, but many saw these as temporary or insufficient given the scale of outdoor pollution. Furthermore aspects like frequency of purifier use and regular maintenance were assessed as they impact the efficacy of solution. Over 35% of surveyed households already own purifiers indicating revealed Willingness to pay (WTP), while an additional 23% showed interest if costs were subsidized or if they were offered a different plan scheme once given enough information about Indoor Air Quality. Estimated WTP for clean air interventions ranges from 10,000–15,000 INR per household annually, depending on income and awareness levels. Similarly when asked about trust in government mitigation programs to address air pollution people, more than 60% people revealed that they don’t trust government to address these issues but around 58% of people still want to contribute a monthly amount to government to improve public transportation and other programs that addresses vehicular pollution. The study also assesses how people respond to pollution, and thus tries to bridge the gap between occupant behaviour and optimal natural ventilation practice. The thesis proposes a pollution-aware natural ventilation (NV) strategy, guided by PM2.5 and PM10 thresholds. To evaluate its effectiveness and compare it with common practices, this strategy was modelled using EnergyPlus simulations. The simulation results indicate that aligning natural ventilation (NV) schedules with real-time pollution data can reduce energy consumption by up to 10.5%, compared to typical occupant behaviour during pollution episodes, while maintaining acceptable indoor air quality. This approach promotes energy-efficient and occupant-centric ventilation practices even in high-pollution environments while making sure that IAQ is not compromised. The thesis concludes with recommending a multi-pronged approach: integrating sensor-based NV systems into residential buildings, scaling public awareness campaigns focused on PM2.5 literacy and IAQ awareness, and designing incentive programs to expand access to IAQ technologies including better usage practices of Air Purifiers. By combining interview-based behavioral insights, simulationbased optimization, and economic insights, this research offers actionable pathways for managing and improving Indoor Air Quality in polluted urban regions of India.

Abstract

Flexible pressure sensors based on conductive polymers and porous dielectric materials are gaining significant attention for applications in wearable devices, human-machine interaction, and soft robotics. In this work, we present the fabrication and characterization of flexible pressure sensors using polypyrrolecoated cotton textile (PCC) and bubble-induced PDMS foam. A highly compressible multilayer capacitive pressure sensor was developed using PDMS foam as the dielectric layer and PCC as the electrodes. The PDMS foam was prepared by mixing specific amounts of ethanol into PDMS, and the electrodes were attached using silicone adhesive. The fabricated sensor demonstrated low hysteresis, high repeatability, and good step response. Surface profiling and scanning electron microscopy were used to characterize the bubble structure. Applications such as mouse click detection and grasping objects with varying weights were demonstrated, highlighting the sensor’s versatility for free-form electronics. Additionally, an ultra-soft capacitive pressure sensor was developed using polyurethane foam with polypyrrole-coated surfaces as electrodes. Various concentrations of pyrrole and FeCl3 were used to optimize the sensor performance, achieving a high sensitivity of 1.01 kPa−1 at 25 kPa pressure. Finite element simulations were performed to study mechanical deformation and space charge variations under pressure. This sensor also offers customizable shapes and sizes for diverse applications. Further, a piezoresistive pressure sensor was fabricated using PCC synthesized via in-situ chemical oxidative polymerization. This sensor exhibited high sensitivity in the low-pressure range of 160 Pa to 16 kPa and a response time of 463 ms. It showed excellent repeatability over 1000 loading-unloading cycles, with a gauge factor of 0.32 for compressive strains up to 99.6%, enabled by the fibrous structure of cotton. Finally, a large-area flexible conductive cotton fabric (20 cm × 20 cm) was fabricated using in-situ polymerization of polypyrrole. The fabric maintained stable conductivity after multiple washing cycles, with resistance changes stabilizing after the fourth wash. This demonstrates its potential in interactive textiles and smart fabric application

Abstract

Over the past decade, football analytics has transformed how the game is played, understood, and managed, primarily in elite European contexts. Football analytics refers to the use of data-driven methods to evaluate tactics, player performance, recruitment, and decision-making across all levels of the sport. Yet little is known about how data practices take shape in low-resource environments, especially in the Global South. This thesis investigates the emergence of Indian football analytics as a decentralised, digitally scaffolded ecosystem, driven by informal learning, frugal innovation, and community-led mentorship. Drawing on digital ethnography, semi-structured interviews, and social media shadowing, this research traces how aspiring analysts build careers, collaborate, and gain legitimacy in the absence of formal training pathways. Participants describe a rich peer-to-peer learning culture enabled by opensource tools, public data, and social media platforms. Knowledge circulates through blogs, repositories, newsletters, and YouTube videos, creating a porous but potent apprenticeship network. The thesis identifies four key dynamics that shape this ecosystem: (1) informal learning and community mentorship as alternatives to institutional training, (2) institutional frictions that limit the uptake of data in football clubs, despite analyst expertise, (3) frugal innovation and grassroots tooling to bypass infrastructure gaps, and (4) social media as a hiring and visibility pipeline. Together, these themes reveal an ecosystem that enables participation while denying long-term stability, where analysts must continuously navigate precarious labour conditions, shifting legitimacy structures, and platform pressures. By centring the everyday practices and lived experiences of Indian football analysts, this study makes three core contributions. First, it offers an empirically grounded account of how decentralised analytics infrastructures operate in the Global South. Second, it highlights how frugality and informality can be generative forces in technological learning and labour. Third, it calls attention to the politics of visibility, credibility, and care in informal data ecosystems. This work contributes to ongoing conversations in HCI and ICTD around alternative infrastructures, peer learning, and the ethics of digital labour. It concludes by offering design-oriented suggestions to support more sustainable, equitable pathways for young data workers in sport.

Abstract

The dynamics exhibited by reaction networks is often a manifestation of their steady states. This dissertation show that there exists endotactic and strongly endotactic dynamical systems that are not weakly reversible and possess a family of infinitely many positive steady states. In addition, for some of these systems we prove that there exist no weakly reversible mass-action systems that are dynamically equivalent to mass-action systems generated by these networks. This extends a result by Boros, Craciun and Yu [1], who proved the existence of weakly reversible dynamical systems with infinitely many steady states. We also investigate the possibility that for any given reaction rate vector k associated with a network G, there exists another network G′ with a corresponding reaction rate vector that reproduces the massaction dynamics generated by (G,k). Our focus is on a particular class of networks for G, where the corresponding network G′ is weakly reversible. In particular, we show that strongly endotactic twodimensional networks with a two-dimensional stoichiometric subspace, as well as certain endotactic networks under additional conditions, exhibit this property. Furthermore, we show that being endotactic is necessary for the dynamics of a network to be included in the dynamics of a weakly reversible network. Additionally, we establish a strong connection between this family of networks and the locus in the space of rate constants in which the corresponding dynamics admits globally stable steady states.

Abstract

Memory allows us to store and recall past experiences, and understanding how this works is key to uncovering the mechanisms behind human cognition. This thesis investigates how temporal context — the background of experiences surrounding a memory — influences recognition in a continuous recognition task. Unlike traditional memory studies that rely heavily on free recall, this research uses the Natural Scenes Dataset (NSD) and functional MRI (fMRI) data to explore how the brain retrieves and uses temporal context. The Temporal Context Model (TCM) suggests that when we remember something, we also bring back the contextual backdrop of that memory. This study tests whether such context retrieval occurs in a continuous recognition task, where participants viewed thousands of images and indicated whether they had seen them before. The dataset’s randomized image presentation ensured minimal strategic recall, making it an excellent environment to examine authentic context retrieval effects. Results show a clear pattern: participants were more likely to correctly recognize an image if it followed another image seen in close temporal proximity. This suggests that when a memory is triggered, the brain retrieves not just the image but also the surrounding temporal context. The closer the images were during encoding, the stronger this effect — a concept known as the temporal gradient. Neural analysis further supports these findings. fMRI data reveal heightened neural similarity in the medial temporal lobe (MTL), particularly in the hippocampus and entorhinal cortex, when recognition was successful. These regions showed patterns consistent with temporal context reinstatement, confirming their central role in memory retrieval. This study contributes compelling evidence that temporal context retrieval is a fundamental aspect of episodic memory, even in scenarios without explicit recall strategies. The findings also provide insights into how the brain’s memory systems work together. Future research could build on these results by exploring how context retrieval operates across different memory tasks or using computational models to further refine the TCM. Ultimately, this study advances our understanding of how we remember our past, offering a clearer picture of the cognitive and neural foundations of memory

Abstract

Error-correcting codes are mathematical objects that are useful for mitigating errors due to noise, but they are also fundamental to other problems, such as designing and ensuring the privacy and efficiency of storage systems. Reed-Muller codes is one such family of codes that is widely used across these diverse applications, but these codes have limited flexibility in parameters like rate and length, which are required in many cases. In this thesis, we propose a new family of codes similar to Reed-Muller codes and study their properties and decoding. We study a family of subcodes of the m-dimensional product code C ⊗m (‘subproduct codes’) that have a recursive Plotkin-like structure, and which include Reed-Muller (RM) codes and Dual Berman codes as special cases. We denote the codes in this family as C ⊗[r,m] , where 0 ≤ r ≤ m is the ‘order’ of the code. These codes allow a ‘projection’ operation that can be exploited in iterative decoding, viz., the sum of two carefully chosen subvectors of any codeword in C ⊗[r,m] belongs to C ⊗[r−1,m−1] . Recursive subproduct codes provide a wide range of rates and block lengths compared to RM codes while possessing several of their structural properties, such as the Plotkin-like design, the projection property, and fast ML decoding of first-order codes. Our simulation results for first-order and secondorder codes, that are based on a belief propagation decoder and a local graph search algorithm, show instances of subproduct codes that perform either better than or within 0.5 dB of comparable RM codes and CRC-aided Polar codes

Abstract

Quadrotors are extensively utilized in applications such as surveillance, mapping, and delivery, owing to their high maneuverability and vertical takeoff and landing (VTOL) capabilities. The deployment of quadrotors in these critical tasks necessitates a high degree of safety and reliability. However, their inherent lack of rotor redundancy poses significant challenges in addressing motor faults, making fault tolerance a crucial concern in their design and operation. This thesis presents a simple method for stabilizing the quadrotor dynamics under complete loss of one actuator using two-stage optimal control. Detailed equilibrium analysis and subsequent selection of the operating point under actuator loss are provided, incorporating constraints on the maximum available thrust. Two distinct cases were considered for equilibrium selection: one in which two actuators have equal inputs, and another in which all actuator inputs are allowed to vary independently. It is shown that when all actuator inputs are allowed to vary independently, the system can track an additional state, thereby enabling attitude tracking. A detailed simulation study using a high-fidelity nonlinear model of the quadrotor is presented, showing the stability and performance of the closed-loop system under complete actuator loss, in the presence of external disturbances. To ensure better maneuverability, the quadrotor under fault may have to shift to multiple equilibrium states along the course of a single flight. This poses a challenge as designing a controller for every equilibrium state across the state space is a tedious task. To address this challenge, a controller capable of stabilizing the quadrotor under fault across multiple equilibrium states was designed by applying Linear Matrix Inequality (LMI) conditions. The controller was tested in simulation using a high-fidelity nonlinear model of the quadrotor. The proposed method not only ensures continued stable flight in fault scenarios but also lays the groundwork for resilient flight control design in safety-critical aerial robotics applications.

Abstract

In this work, we introduce and rigorously characterize a broad class of quantum operations termed quantum ergodic channels. These are completely positive, and trace-preserving (CPTP) maps that possess a unique fixed point toward which any initial quantum state asymptotically evolves. The existence of such a fixed point enables a systematic exploration of ergodicity in quantum dynamics, extending classical notions into the quantum regime. We construct and analyze Lindblad-type master equations governing the evolution under these ergodic channels in arbitrary finite-dimensional Hilbert spaces. These equations describe both Markovian and non-Markovian dynamics, depending on the time dependence and structure of the generator. In particular, the class of ergodic channels considered here encompasses both memoryless and memory-affected evolution, allowing us to explore their differences through established quantitative measures of non-Markovianity. A special focus is placed on the case where the fixed point of the channel is a passive state, i.e., a state from which no work can be extracted via unitary operations. In such settings, evolution under ergodic channels gives rise to non-trivial ergotropy dynamics. Ergotropy is a fundamental thermodynamic quantity that quantifies the maximum extractable work from a quantum state via unitary transformations, assuming no access to additional resources. We show that in the Markovian regime, where the dynamics lack memory, the ergotropy of the system decreases monotonically over time, consistent with the second law of thermodynamics in open systems. However, under non-Markovian dynamics, the situation changes significantly. We demonstrate that ergotropy can temporarily increase during evolution, indicating a backflow of useful energy from the environment into the system. This phenomenon—referred to as ergotropy backflow—is a direct manifestation of memory effects and points to the possibility of temporary reversals in the degradation of thermodynamic resources. Our analysis shows that this backflow is not only physically meaningful but also operationally relevant: it can serve as a dynamical witness or indicator of non-Markovianity. These findings offer a novel perspective on the interplay between non-Markovianity and thermodynamic behavior in open quantum systems. By grounding the discussion in welldefined resource-theoretic and thermodynamic terms, this study deepens our understanding of how memory effects influence the evolution of quantum resources such as ergotropy. In particular, it highlights the role of ergodic channels as a versatile theoretical framework for studying energy dynamics, dissipation, and resource recovery in realistic quantum settings—including potential applications in the design and analysis of quantum batteries. Overall, the results presented here enrich the theoretical framework of open quantum systems and pave the way for future investigations into the thermodynamic implications of quantum memory effects. They suggest practical strategies for identifying, characterizing, and potentially exploiting non-Markovian dynamics to enhance the performance of emerging quantum technologies.

Abstract

The fundamental question of whether advertising revenue compromises editorial independence has long been debated in media studies, yet empirical evidence has remained limited due to the difficulty of systematically analyzing large-scale newspaper content. This thesis investigates the relationship between advertising practices and editorial coverage in Indian print media, examining whether companies that advertise more receive more favorable or extensive news coverage. To conduct this investigation, we developed a scalable computational pipeline employing advanced image processing and Optical Character Recognition (OCR) techniques to systematically extract articles and advertisements from digital versions of print newspapers with high accuracy. This methodology enabled us to process and analyze content on a larger scale, overcoming traditional limitations in media content analysis. Our automated approach successfully parsed newspaper layouts, distinguished between editorial and advertising content, and extracted text with sufficient precision for quantitative analysis. Applying this pipeline to five prominent newspapers that span multiple regions and three languages—English, Hindi, and Telugu—we assembled a comprehensive dataset comprising more than 12,000 editions and several hundred thousand advertisements collected over six years. These newspapers collectively reach a readership exceeding 100 million people, making our analysis representative of India’s diverse print media landscape. The data set includes publications with varying editorial orientations, geographic focus, and readership demographics, providing robust coverage of the Indian print media ecosystem. Our analysis reveals several significant findings on contemporary print advertising practices and their potential influence on editorial content. Despite declining print circulation trends, advertising levels have remained remarkably consistent over the six-year study period, suggesting advertisers continue to value print media’s reach and credibility. We observe systematic patterns in ad placement, with corporate advertisements appearing disproportionately on prominent pages, while government advertisements contribute a substantial portion of overall advertising revenue. The data also reveal temporal patterns in advertising intensity, seasonal variations between different advertiser categories, and regional differences in advertising strategies. Most importantly, our regression analyses provide strong empirical evidence that advertising expenditure is correlated with editorial coverage patterns. Companies that advertise more heavily in a newspaper receive significantly more extensive coverage, and this coverage tends to be more favorable in sentiment compared to non-advertising entities. This relationship remains statistically robust for different model specifications, time periods, and advertiser popularity levels. The effect is particularly pronounced for corporate advertisers, suggesting that commercial relationships may indeed influence editorial decisions in ways that could compromise journalistic independence and potentially mislead readers about the true newsworthiness or merit of covered entities.