VictorJuiz/thesis huggingface dataset:
Addressing discontinuous root-finding for subsequent differentiability in machine learning, inverse problems, and control
Keywords: Engineering.

There are many physical processes that have inherent discontinuities in their mathematical formulations. This thesis is motivated by the specific case of collisions between two rigid or deformable bodies and the intrinsic nature of that discontinuity. The impulse response to a collision is discontinuous with the lack of any response when no collision occurs, which causes difficulties for numerical approaches that require differentiability which are typical in machine learning, inverse problems, and control. We theoretically and numerically demonstrate that the derivative of the collision time with respect to the parameters becomes infinite as one approaches the barrier separating colliding from not colliding, and use lifting to complexify the solution space so that solutions on the other side of the barrier are directly attainable as precise values. Subsequently, we mollify the barrier posed by the unbounded derivatives, so that one can tunnel back and forth in a smooth and reliable fashion facilitating the use of standard numerical approaches. Moreover, we illustrate that standard approaches fail in numerous ways mostly due to a lack of understanding of the mathematical nature of the problem (e.g. typical backpropagation utilizes many rules of differentiation, but ignores L'Hopital's rule)
Advancements in wireless communication : AI-driven error correcting code design and multiuser system optimization
Keywords: Engineering.
Electrical Engineering.

The rapid expansion of wireless technology in recent years has ushered in an era where an increasing number of devices are interconnected. These devices, from the Internet of Things sensors to industrial automation systems, place stringent demands on wireless networks in terms of data rates and latency. As the wireless landscape evolves towards 5G and next-generation networks, the need for efficient and reliable communication systems becomes paramount. This dissertation presents a comprehensive exploration of advancements in wireless communication design, focusing on two pivotal areas: AI-based polar code design and multi-user system optimization. The first part of the dissertation ventures into the realm of AI-based polar code design. Polar codes, known for their error correction capabilities, play a pivotal role in ensuring reliable data transmission. This part introduces two novel techniques that harness the power of reinforcement learning, transforming polar code design into maze-traversing game and graph evolution process, respectively. Both learning-based approaches successfully find polar codes that outperform the existing ones, and the graph-based method offers scalability and adaptability that breaks through bottlenecks seen in other learning-based methods for code design. The dissertation's second part turns its focus to multi-user system optimization within next-generation networks, particularly in cases where the number of users exceeds the wireless environment's capacity. This part conducts an in-depth analysis of optimization algorithms meticulously crafted to maximize system capacity. Additionally, it highlights the practical implementation of newly-developed, readily-available software solutions. In an age characterized by the intricate interplay of connected devices and users, these optimization techniques assume a central role in shaping the seamless, interconnected experiences that next-generation networks are expected to deliver. This dissertation's findings underscore the transformative potential of interdisciplinary research in wireless communication. By addressing the complex challenges posed by evolving wireless technologies, it paves the way for innovative solutions that can push network capacities to their limits.
Advancing ambient intelligence in healthcare : granularity, efficiency, and privacy
Keywords: Engineering.

Artificial Intelligence (AI) is revolutionizing the healthcare industry by providing cutting-edge solutions that can significantly enhance patient care and medical research. However, this wave of technological transformation has largely overlooked the physical environment of clinical care delivery. This dissertation illuminates the significant impact of integrating video activity recognition, powered by advanced machine learning and intelligent sensors, into ICU and residential care, demonstrating its potential to alleviate healthcare burdens and improve patient outcomes. We first detail how healthcare spaces can be equipped with ambient intelligence using smart sensors and machine learning algorithms. We then introduce three crucial technical foundations that enable this application. Firstly, we instantiate a novel task, benchmark, and model for discerning complex human activities, offering a hierarchical comprehension of actor roles, object attributes, and their relationships. Secondly, we unveil a suite of data and label-efficient algorithms tailored to address overcome the scarcity of annotated data, a challenge amplified by privacy constraints, proprietary considerations, and the substantial costs associated with domain-specific annotation. Finally, we embark on a comprehensive dialogue on ensuring trustworthy machine learning from ethical and privacy perspectives. We introduce a scalable, differentially private algorithm, specifically designed for large-scale video activity recognition tasks. To demonstrate the real-world impact of our ambient intelligence system, we conclude the dissertation by showcasing two clinical deployments - one within the hospital environment and one in daily living spaces.
Advancing generative models for real-world applications
Keywords: Engineering.

While generative models hold thrilling potential, their limited usability presents substantial challenges for their widespread adoption in real-world applications. Specifically, existing methods tend to amplify harmful societal biases ingrained in their training data and often fail to accurately reflect subjective user specifications such as style in the generated outputs. Furthermore, a notable performance gap exists when handling data distributions with unique structures, such as periodicity, restricting their applicability beyond image and text data. This dissertation considers all such facets to help construct safe, reliable generative AI systems for practical integration and deployment. First, we present a methodological framework for tackling the challenges of bias mitigation and controllability. Building on the classical approach of density ratio estimation (DRE), we develop techniques to correct a learned model distribution such that it exhibits characteristics that are more closely aligned with an alternative target distribution of interest. Together, these contributions not only yield a new theoretical framework for DRE, but also improve performance on a diverse set of downstream tasks such as domain adaptation, data augmentation, and mutual information estimation. Next, we present two real-world applications of these methods for societal applications. We demonstrate that: (a) our reweighted generative modeling framework successfully mitigates dataset bias, and (b) more controllable models can better customize AI-generated music to individual preferences and assist the creative process. Finally, we conclude by developing new learning algorithms that incorporate domain-specific inductive biases into generative models for wireless communications, as well as for discrete data distributions.
Advancing resource recovery following anaerobic secondary treatment of domestic wastewater
Keywords: Engineering.

Proper treatment of domestic wastewater is crucial for protecting human health and the environment. However, conventional wastewater treatment processes are often high-cost, energy-intensive, and insufficient for recovering resources. Furthermore, water infrastructure in the United States is nearing the end of its intended design lifespan, posing a key opportunity for reinvention. Anaerobic secondary treatment is a promising example of next-generation water infrastructure that prioritizes resource recovery through the production of methane energy. However, anaerobic secondary effluent requires further attention due to the presence of dissolved methane, sulfide, nitrogen, and phosphorus. This dissertation explores post-treatment of anaerobic secondary effluent to maximize resource recovery from domestic wastewater. Specifically, a life cycle assessment was performed to evaluate tradeoffs between physical/chemical processes and biological processes for dissolved methane, sulfide, nitrogen, and phosphorus removal. Additionally, a membrane-aerated biofilm reactor was tested to treat anaerobic secondary effluent with high concentrations of ammonium-nitrogen and sulfide. Lastly, the use of wastewater-derived struvite as a novel fire retardant was explored to improve the profitability of phosphorus-recovery technologies. These studies serve to direct future efforts in developing complete water treatment trains with anaerobic secondary treatment
Advancing RNA therapeutics : regulation, immunogenicity, and delivery
Keywords: Medicine.
Stem Cell Biology and Regenerative Medicine.

The Central Dogma of molecular biology formalized the significant role of RNA in regulating the flow of information from DNA to proteins. However, beyond its canonical function in translation, RNA carries a wide array of functions, serving as a molecular machinery that regulates gene expression during development and cellular differentiation. Furthermore, the remarkable success of RNA-based therapies in combating the COVID-19 pandemic has sparked greater interest in the translational potential of this remarkable molecule. The renewed enthusiasm in RNA medicine comes from its simple design, safe and relatively fast manufacturing, and, most importantly, its diverse molecular formats, capable of reaching formerly deemed 'undruggable' targets at the DNA, RNA, or protein levels. Furthermore, the large variety of RNA molecules, including the most recently described class, known as circular RNA, uncovers many new biological functions and therapeutic applications. Nevertheless, the development of RNA therapeutics still remains hampered by significant challenges. These encompass the rapid degradation of exogenous RNA by ubiquitous RNases, the difficulty of transporting negatively charged RNA through hydrophobic cytoplasmic membranes, and the potent immunogenicity of exogenous RNA, leading to cellular toxicity and hindering its translation into therapeutic proteins. My dissertation addresses these hurdles, exploring mechanisms of RNA delivery for vaccine development and cell reprogramming. Chapters delve into circular RNA uptake, immunogenicity, and biodistribution, proposing novel nanoparticle transporters for targeted mRNA and circular RNA delivery. The research aims to transform vaccine development and cell therapy by enhancing circRNA delivery.
Advancing the performance of multi-physics heart simulations
Keywords: Engineering.
Mechanical Engineering.

Cardiovascular diseases (CVDs) are the leading cause of death globally, accounting for an estimated 17.9 million deaths in 2019. CVDs are a group of disorders that affect the heart and blood vessels, and include coronary heart disease, stroke, heart failure, and arrhythmias. Computational models of the heart offer a powerful tool to understand the mechanisms of CVDs and develop new diagnostic and therapeu- tic strategies. Finite element method (FEM) is a widely used numerical method for solving complex engineering problems, and has been successfully applied to model the heart. In this thesis, we used FEM to develop computational models of the heart to understand disease mechanisms, study model parameters to understand their ef- fects on the simulation results, and develop a novel constitutive material model to improve accuracy of the simulations. We first studied the effects of viscoelastic model parameters on the simulation results of healthy heart model, in order to understand the trade-off between increased model complexity and increased accuracy of the sim- ulations. We also developed a patient-specific computational model of a diseased pediatric heart. We used this model to study the mechanisms playing role in the progress of this disease and to evaluate possible treatment options. Finally, we devel- oped a novel constitutive material model for the heart muscle. This model is more accurate than existing models, and can better capture the complex behavior of the heart muscle. Our work demonstrates the potential of computational modeling to improve our understanding of CVDs and develop new diagnostic and therapeutic strategies. Given the scale of the CVD epidemic, it is essential to continue to invest in research on computational modeling of the heart.
Adversity roadmap : improving psychological well-being and resilience through a values-affirmation approach
Keywords: Education.

In this dissertation, I created a psychological roadmap for navigating adversity by grounding people in their core values. Research trends and global events have underscored the enduring significance of well-being and mental health. Central to this study is the self-affirmation theory, which proposes that humans possess an intrinsic need to perceive themselves as morally and adaptively capable, even when confronted with challenges. My objective was to tailor values-affirmation-based interventions to specific contexts and target groups, with the aim of enhancing well-being and motivation-related outcomes in the general population (Study 1), elite athletes (Study 2), and college students (Study 3). Study 1 involved a three-stage longitudinal randomized experiment conducted during the initial months of the COVID-19 pandemic. The study evaluated the effectiveness of two versions of the values-affirmation intervention in mitigating pandemic-related psychological burdens, particularly the feelings of social disconnection and loneliness. Notably, the interventions proved most beneficial for men, who exhibited the sharpest decline in social belonging and mental health. In Study 2, I delved into the world of high-performance athletics. I identified two root causes of compromised psychological well-being among professional Dancesport athletes -- (1) high psychological stress, and (2) outcome-contingent self-worth. To address these challenges, I developed a novel "Resilience Training + Affirmation" (RTA) intervention. Results demonstrated promising advantages of RTA for athletes' mental health and self-efficacy. Furthermore, once the competitive stress had subsided, RTA bolstered athletes' sense of social belonging and reinforced their dance identities. The findings also indicated the RTA's potential to enhance resilience among Dancesport athletes, even in the face of unsatisfactory competition results, thereby mitigating the risks associated with outcome-dependent self-worth. Study 3 extended the insights from Study 1 to an educational context. By integrating established values-affirmation techniques with actionable plans, I aimed to assist students in establishing tangible links between their values and everyday experiences. I explore unexpected correlations between well-being outcomes and pertinent covariates, such as minority race, gender, social class, and first-generation status. My aspiration is that the discoveries outlined in this dissertation, along with future research endeavors, will pave the way for the development of finely tuned strategies capable of enhancing various facets of the human experience across diverse domains.
Aerospace vehicle design with Bayesian collaborative optimization
Keywords: Engineering.
Aeronautics and Astronautics.

Complex engineering design requires solving large optimization problems involving several disciplines. Collaborative Optimization is a two-level design optimization architecture that solves multidisciplinary problems as a series of single-disciplinary problems with little infrastructure overhead. It can be critical when practitioners do not have a fully coupled multidisciplinary optimizer but have access to multiple single-disciplinary optimizers. However, it comes at a steep computational cost because the limited information sharing between disciplines slows downs the progress of the optimization. Our work shows that this issue can be mitigated by exploiting the full extent of the available information. In the Collaborative Optimization framework, disciplines are repeatedly given a design point and tasked with finding the closest feasible design. These iterations are computationally expensive as they require each discipline to solve an optimization problem and report the result. To combine the information collected by all iterations, we propose to learn a predictive model of the distance to the feasible set of each discipline using datasets of feasible and infeasible design points. This requires modeling the signed distance function of each set, which is a challenging machine-learning task. Therefore, we introduce Householder networks: a new, lightweight neural network architecture that can learn distance functions more efficiently than conventional architectures. We then introduce our new method, called Bayesian Collaborative Optimization, which uses ensembles of Householder networks to represent probabilistic models of the disciplinary feasible sets. Following the Bayesian Optimization framework, these models are iteratively refined and used to find values of the design variables that improve the objective function while remaining feasible for every discipline. This method is shown to outperform previous Collaborative Optimization approaches on simple test problems. Finally, we introduce a new multi-disciplinary aircraft design problem. We optimize the airframe, propulsion system, and trajectory of an unmanned fixed-wing vehicle tasked with completing a half-marathon with a fixed battery. This problem tries to balance out realism, by including a diverse set of modeling and optimization approaches, with simplicity and low computational cost. The importance of trajectory optimization, which is efficiently solved by itself but hard to solve coupled with other disciplines, makes this problem different from those previously available. We hope that this problem will be useful to the research community as a test for multidisciplinary design optimization architectures. We have open-sourced the code that generated the results presented in this document. It can be accessed at the following links: https://github.com/jdebecdelievre/HouseholderNets.jl , https://github.com/jdebecdelievre/BayesianCollaborativeOptimization.jl , and https://github.com/jdebecdelievre/ModelAirRaces.jl.
Against productivity : unproductive writing as resistance in early Latin American fiction
Keywords: Humanities and Sciences.
Iberian and Latin American Cultures.

This dissertation examines Latin American novels from the 1840s through the 1920s in which characters use writing as a means to resist oppression. While acknowledging that in the period of nation-state consolidation, writing worked as a colonial imposition (Mignolo), a tool for neocolonial domination (Rama), and a means of social indoctrination (Sommer), I argue that certain writing practices of the time were rebellious and "unproductive" in that they worked against the economically productive interests of slavery-based and emerging capitalist societies. I show that Gertrudis Gómez de Avellaneda's Sab (Cuba, 1841), José de Alencar's The Guarani (Brazil, 1857), Joaquim Maria Machado de Assis's The Posthumous Memoirs of Brás Cubas (Brazil, 1881), and Teresa de la Parra's Iphigenia (Venezuela, 1924) depict disenfranchised subjects (clerks, homemakers, enslaved peoples, former Indigenous leaders) writing in minor genres (notes, letters, scribbles, diaries) to take a stance against racial, gendered, and social norms, as well as to escape the oppressive experiences of domestic, pauperized, and forced labor. Given the paucity of archival evidence documenting penning habits in the national consolidation era, novels serve as privileged sources to interrogate how nineteenth-century and early-twentieth-century Latin Americans conceived of the writing process and experienced its liberating power. By considering the freeing function that writing adopts in early national novels, my research shifts the conversation away from the binaries that have dominated the fields of literary, cultural, and historiographical studies of Latin American societies, including writing/orality, original/derivative, local/colonial, and foundational/afoundational. I move away from binaries by demonstrating that in nineteenth and early-twentieth-century Latin America, acts of writing were not mere colonial impositions of the Spanish and Portuguese Empires, but also practices that might have historically worked alongside oral traditions to turn colonial legacies and their successor neocolonialist orders against themselves. Following early novelists, writing may have served to oppose experiences of enslavement, migration, solipsism, and physical confinement and to creatively resist the expectation of "productivity" by socioeconomic systems
Algorithm-driven paradigms for the optimization and simulation of photonic devices
Keywords: Engineering.
Electrical Engineering.

The world has witnessed transformative advancements in photonics, encompassing a broad range of applications from AR/VR glasses to LiDAR technology employed in self-driving cars. These captivating applications heavily rely on the simulation (forward problem) and optimization (inverse problem) of photonic devices. However, both the forward and inverse problems present their own set of challenges: 1) For forward problems, how to rapidly and accurately compute electromagnetic field distributions within structured media, especially when evaluating large simulation domains and large batches of devices. 2) For inverse problems, how to efficiently identify the overall optimal device in a non-convex design space while incorporating physical and fabrication constraints. This thesis discusses how algorithms based on classical optimization and deep learning are able to overcome the aforementioned challenges and are establishing a new conceptual framework for freeform photonic engineering. In the first half of the thesis, we introduce how a design space reparameterization scheme is able to simplify the inverse design problem and can naturally enforce hard fabrication constraints in adjoint-based freeform topology optimizations. The combination of device capability, feature size constraints, and ease of manufacturability enabled by this methodology facilitate our design of robust, high performance metasurfaces such as metagratings, metalenses, and metapolarizers. In the second half of the thesis, we present a high accuracy and ultra-fast surrogate electromagnetic simulator for the forward problem that is based on physics-augmented deep learning. A central feature of this neural network surrogate simulator is the utilization of data and physics to model and exploit high-dimensional relationships between geometric structure and electromagnetic response within the constraints of Maxwell's equations. Finally, by combining the high-speed forward simulators with the reparameterization inverse design scheme, we showcase hybrid ultrafast freeform optimizers that are able to effectively search for local and global optima of dielectric nanostructures with a speed-up of three to four orders of magnitude. We anticipate that these algorithmic approaches to photonic engineering will empower practitioners to move away from asking \emph{how} to realize a photonic system and focus on \emph{what} system functionality is useful for an application.