VictorJuiz/thesis huggingface dataset:
Single-cell transcriptomic analysis of animals across broad evolutionary scales
Keywords: Bioengineering.

The newfound ability to characterize cellular heterogeneity at single-cell resolution has encouraged broad efforts to construct transcriptomic maps of cell types from whole organisms throughout the animal kingdom. However, the deeper we delve into the more uncharted territories across the tree of life, the more challenging the data analysis and interpretation become. My thesis aims to overcome this challenge along three primary axes. First, I developed the self-assembling manifold (SAM) algorithm, an iterative soft feature selection strategy to quantify gene relevance and improve dimensionality reduction. I demonstrated its advantages over other state-of-the-art methods with novel biological findings and rigorous quantitative benchmarking. This has allowed us to identify cell types in an unsupervised, robust, and sensitive fashion, and has thus facilitated the single-cell transcriptomic analysis of a wide variety of organisms. Second, to account for the fact that cell type relationships are inherently hierarchical, I integrated nonlinear dimensionality reduction techniques with SAM to improve the unsupervised detection of cell types and subtypes in hierarchically complex datasets. This approach was enabled by my scalable implementation of kernel Principal Component Analysis using implicit linear operators. Third, I built on SAM to develop SAMap, an algorithm, first of its type, for mapping single-cell atlas datasets across evolutionarily distant species. SAMap identifies homologous cell types with shared expression programs across distant species within phyla, even in complex examples where homologous tissues emerge from distinct germ layers. Comparing species across the animal kingdom, spanning mouse to sponge, SAMap reveals ancient cell type families that likely emerged early in animal evolution. Altogether, the computational methods and analyses described in my thesis provide a roadmap for cell type discovery across the tree of life and the reconstruction of cell type evolution
Social learning and communication about the self
Keywords: Psychology.

As humans, we are curious about what others think of us. Our understanding of what others think of us critically informs what we learn about the self and how we communicate about the self to others. Bridging rich phenomena from social psychology and recent advances in cognitive development, the overarching goal for this dissertation is to investigate the basic cognitive capacities that underlie how children learn and communicate about the self (i.e., their performance and competence). Chapters 2-3 test young children's ability to provide informative evidence about their competence and the boundary conditions on these communications. Chapter 4 investigates children's abilities to evaluate and seek out informative verbal feedback (praise). Taken together, this work highlights the flexibility of children's inferential and communicative capacities, and suggests their importance for children's developing self-concepts, motivation, and achievement
Soft matter approaches towards understanding cell type diversity
Keywords: Bioengineering.

From purely physical models to sequencing technology, soft matter science provides an expansive inventory of tools for gaining a deeper understanding of diverse cell types that assemble into tissues, organs, and organisms. My thesis uses soft matter approaches, both conceptual and technical, to analyze cell type diversity. First, I take a physics approach to characterize the organizational pattern of the brain structure in a basal organism, to show that cell type-specific interactions govern cellular packing in space. These findings extend the classical notion of particulate packing by including cell type diversity as a previously neglected orthogonal dimension in order to understand the packing phenomena in biological matter. Second, I developed a double emulsion droplet platform to encapsulate and phenotype single cells with high throughput and wide applicability. To use these emulsion droplets as picoreactors for measuring gene expression, I characterize and overcome several key challenges in performing small-volume reactions to significantly improve picoscale droplet-based PCR assays. This demonstration opens up a new path towards enriching for rare cell types through droplet-based gene expression screening in order to access otherwise concealed cell type-specific information
Software-defined hardware without sacrificing performance
Keywords: Electrical Engineering.

In recent years, field programmable gate arrays (FPGAs) have emerged as popular accelerators for certain kinds of compute workloads. FPGAs allow the programmer to define and implement digital circuits that are specialized for arbitrary computation. They exhibit performance-per-watt advantages over traditional instruction-based architectures, like CPUs and GPUs, and are more flexible than customized application-specific integrated circuits (ASICs). Traditionally, the best languages for programming FPGAs rely on the programmer to describe their circuit using register-transfer languages (RTLs), such as Verilog and VHDL. This level of abstraction is very cumbersome and makes it difficult for the programmer to quickly and easily design their application. It is usually rare for a programmer who is an expert in a field, such as machine learning, to also be an expert in writing RTL. This mismatch is the motivation for the work in this thesis. The work presented in this thesis envisions a new class of performance-oriented programmers who wish to design highly efficient applications on FPGAs without requiring intimate knowledge of RTLs. There are a variety of new high level languages aimed at bridging this gap between performance and abstraction. One approach is high level synthesis (HLS) tools, and allow the programmer to write code in languages like C with pragmas that help describe how it should map to hardware. While these languages can generate highly efficient hardware designs, their API can be too restrictive for programmers who cannot formulate their whole application within the domain. In this work, we introduce tools and optimizations that were built on top of Spatial, a high level language for programming FPGAs and other reconfigurable dataflow architectures (RDUs), such as Plasticine. We show how this tool can help the programmer achieve up to 22x better performance while improving resource utilization by a factor up to 2.8x on a variety of standard benchmarks. Then, we introduce a memory partitioning tool that is capable of quickly solving for highly efficient schemes. We show that this tool can be used to automatically improve LUT utilization by up to 86\%, BRAM utilization by up to 38\%, and almost always eliminate all DSPs from the memory partitioning logic on a variety of benchmarks, as compared to other state-of-the-art tools. Next, we show how these new components in Spatial can be used to design a data compression kernel that can be used to solve a real-world data compression problem. We explored a variety of different classes of machine learning kernels and swept the parameter space of each one to characterize each one on ML accuracy, latency, and resource utilization. Finally, we describe a series of enhancements added to the Spatial language and compiler that enable new kinds of applications and provide the possibility of using Spatial in more diverse environments
Solvent-free carbonate-promoted transformations for CO2 utilization
Keywords: Chemistry.

Utilization of captured greenhouse gas emissions for the synthesis of fuels and chemicals is one of many potential strategies to address the impacts of atmospheric greenhouse gas accumulation. In order for carbon capture and utilization to lower net emissions relative to conventional synthesis, the employed processes must account for several factors including minimization of the use of energy-intensive reagents. One promising general reaction scheme for carbon capture and utilization involves the use of carbon dioxide (CO2) for the synthesis of carboxylic acids, which are a diverse class of chemicals with many uses. Conventional carboxylation reactions involve the consumption of energy-intensive resources, however, and are thus not well suited as a strategy for emissions mitigation. To address this, prior work has demonstrated the use of carbonate salts in solvent-free reaction media to promote carboxylation reactions for CO2 utilization. Carbonates are abundant and regenerable reagents, but in conventional (solution-phase) organic reaction media are only weakly basic and therefore incapable of performing the necessary deprotonation step of the carboxylation reaction mechanism for most substrates of interest. Solvent-free carbonate-promoted reactions overcome this limitation for some substrates, but a better understanding of the physical properties of carbonate salts in solvent-free reaction media was required in order to apply this chemistry more generally. The work described in this dissertation involves investigation of the physical properties which enable reactions of carbonate salts with solid- and gas-phase substrates under solvent-free reaction conditions. For carboxylation of solid substrates, carbonate reactivity was found to rely on the formation of molten eutectic mixtures at intermediate temperatures. To extend the scope of carbonate reactivity beyond eutectic-forming systems and provide access to gas-phase substrates, dispersion of carbonate salts onto high surface area materials was investigated. Surface dispersion was found to result in the formation of a structurally disordered and mobile carbonate species with enhanced basicity relative to the bulk crystalline species. The improved understanding of carbonate reactivity that was obtained through these studies was used to investigate and improve upon various reactions of interest for CO2 utilization, including the formation of a monomer for polyester production from a waste biomass-derived substrate and a catalytic CO2 hydrogenation reaction that could be used to provide the feedstock for biological production of liquid fuels
The soteriology of the soul : the Shen Bumie discourse in early medieval Chinese buddhism
Keywords: Religious Studies.

This dissertation highlights the historical importance of the unique systems of Buddhist practice and thought that emerged in China during the period I propose to refer to as the "era of isolation." Although overlooked by previous studies, the fourth century CE is marked by a seven-decade hiatus (from ca. 310 to ca. 380) in the missionary and translation activities of foreign Buddhists in China, and during this "era of isolation, " the aspiring Buddhists of China independently developed unprecedented forms of religious practice and thought on the basis of the fragmentary information about Indian Buddhism available to them. By examining the initial formation of the scholastic discourse on the imperishability of the soul (shen bumie) during the era of isolation, the theoretical development of the discourse in the early fifth century by Lushan Huiyuan (334--416), Zheng Xianzhi (364--427), and Shi Sengwei (fl. late fourth and early fifth centuries), and the discourse's lasting influence on the Sinitic exegesis of the Indian Mahāyāna doctrine of innate Buddhahood transmitted in the mid-fifth century, I argue that the indigenous Buddhist practice and thought of the isolation period profoundly informed the historical trajectory of Buddhism in China
Sound speed estimation and phase-aberration correction for medical ultrasound imaging
Keywords: Electrical Engineering.

Pulse-echo ultrasound imaging is a medical imaging modality that constructs images based on the back-scatter of acoustic waves in tissue. Most ultrasound systems assume a spatially constant speed of sound when the time-of-flight of the acoustic wave is used to focus these back-scattered signals into medical images. However, the speed of sound in human tissue can vary spatially by tissue type and composition. The primary focus of this thesis is the estimation of a spatially-varying speed of sound in the tissue and using this speed of sound in order to optimally focus ultrasound signals into images, effectively correcting for phase aberrations resulting from an erroneous speed of sound during the focusing. We first develop a retrospective encoding and decoding scheme that enables us to reconstruct a multistatic synthetic aperture dataset from an arbitrary sequence of transmissions. The regularized inversion technique for the recovery of the multistatic dataset achieves a 99.36\% correlation between the multistatic dataset recovered from a walking-aperture focused-transmit sequence and the ground-truth. This technique is used to successfully demonstrate sound speed estimation and distributed aberration correction in abdominally-layered media. For sound speed estimation in layered media, the root mean-square error in simulations of two-layer media is 4.6 and 2.5 m/s at 4 and 8 MHz transmit frequency, respectively. These sound speed estimates are then used to optimally focus the multistatic synthetic aperture and achieve distributed aberration corrections. In phantom experiments, point target resolution improves from 0.58 to 0.26 and 0.27 mm, and lesion contrast improves from 17.7 to 23.5 and 25.9 dB, as a result of distributed aberration correction using the eikonal and wave-field correlation techniques, respectively. Then, we explore modeling and inversion techniques from ultrasound transmission tomography and seismic imaging, which we apply to pulse-echo ultrasound imaging. We demonstrate that shot-profile migration from seismic imaging can be used as a generalized synthetic aperture imaging technique for pulse-echo ultrasound imaging where it yields a 55\% improvement in point target resolution and 8.6 dB improvement in contrast over virtual source synthetic aperture. Shot-profile migration also lays the groundwork for an initial implementation of wave-equation migration velocity analysis (WEMVA) for pulse-echo ultrasound imaging. The complete investigation of WEMVA in pulse-echo ultrasound is left to future work
The sounds of Kabul : radio and the politics of popular culture in Afghanistan, 1960-79
Keywords: History.

The Sounds of Kabul is an in-depth analysis of radio and popular culture in modern Afghanistan. As the first historical study of Afghanistan to use archives of sound, this dissertation employs literature, poetry, and song to uncover thousands of years of shared textual history and cultural memory across Central and South Asia, and the broader Islamic World. Over the course of six chapters covering the introduction of radio broadcasting, transnational cultural collaborations in music and the performing arts, cassettes and the global circulation of sound, the biography of the iconic Afghan pop-star, Ahmad Zahir, who used music to challenge cultural norms and hegemonic social structures, acclaimed literature and poetry programs created by Afghan women, and the merger of radio and television that further popularized regional music, this interdisciplinary study places sound and mass media and their diverse interlocutors in direct conversation with broader social, cultural, and political developments unfolding primarily in the 1960s and 1970s. Accordingly, a wide array of elite and ordinary actors including musicians, artists, performers, and cultural producers surface in the following chapters and contribute to a new sonic approach that captures the visceral experience of daily life in the region. Resonant with Afghanistan's transregional pasts and transnational presents through a diaspora that is both global and multilingual, this dissertation draws on self-conducted oral histories and archives from around the world in multiple languages—Arabic, Farsi, Pashto, Tajiki, Urdu and English—housed in Afghanistan, India, Great Britain, France, Tajikistan and the United States. At the foundation of these archives are primary audio, visual, and textual sources, which, along with other untapped materials, broaden the methodological horizons of scholarship on Afghanistan and its surrounding regions, and elucidate what the study of radio and sound may teach us about a society's extraordinary past
The Soviet political police : establishment, training, and operations in the Soviet Republics, 1918-1953
Keywords: History.

This dissertation examines how the Soviet Union's domestic intelligence service, or political police, functioned across its republics from 1918 until Stalin's death in March 1953. It traces the establishment and evolution of the Soviet political police specifically in the Soviet Union's western republics—Estonia, Georgia, Latvia, Lithuania, Soviet Moldavia, and Ukraine—as well as Georgia to explain how the political elite in Moscow endeavored to employ the political police to pacify and transform multiple insubordinate populations into loyal Soviet citizens. Both an institutional and social history, this dissertation charts the development of the political police in the Soviet borderlands and the influence this institution exerted on both individual behavior and collective action, while also analyzing the experiences of the ordinary men and women who staffed the republican departments of the political police. This dissertation yields three major conclusions. First, in an effort to combat the image that the Soviets were a neo-colonial force solely bent on subjugating non-Russians, Moscow endeavored to form an intelligence service staffed by loyal, local nationals who would willingly police their own neighbors, friends, and family. Second, while the Communist Party leaders in Moscow sought to create a professional domestic intelligence service in the republics, they continually had to adjust their expectations as they encountered unforeseen challenges posed by local circumstances and their own ideology. Finally, Moscow had less control over the republican political police departments than historians have commonly assumed, and officers in the republics both influenced and shaped the implementation of Soviet policy at the local level. The result is a broader institutional and social history of the Soviet's domestic intelligence service that considers the complex nature of local-level interests and conflicts while providing a more nuanced view of how the political police contributed to the creation of the Soviet Union
Spatial and temporal electron beam manipulation with dielectric laser accelerators
Keywords: Electrical Engineering.

Particle accelerators are an indispensable tool of modern physics. However, high-energy particle accelerators tend to have sizes ranging from large buildings to small cities, which limits their utility. Dielectric Laser Accelerators (DLAs) use pulsed lasers to increase the "acceleration gradient" tenfold over conventional accelerators. This allows them to be made many times smaller, and to be nanofabricated at scale on silicon wafers. This dissertation will present the techniques developed for manipulation of electron beams in DLAs, including demonstrations of three key accelerator subunits: a laser-driven magnetic lens for beam confinement; a two-stage ballistic microbunching scheme; and a streak camera capable of attosecond resolution for beam diagnostics. By combining these subunits, a working prototype of a DLA-based injector is demonstrated for future on-chip high-energy beamlines. In the first chapter, the two-dimensional theory of DLA operation is derived in its full generality. Next, strong, laser-driven spatial focusing of an electron beam is demonstrated, sufficient to confine the electron beam within the DLA channel. Third, I investigate the manipulation of longitudinal beam structure by a ballistic microbunching scheme, and demonstrate attosecond-scale control over microbunch formation using a two-stage DLA. This allows demonstration of the net acceleration of a microbunched pulse train. Finally, by combining the two-stage longitudinal bunch manipulation techniques with the laser-driven DLA lens, I am able to demonstrate the creation and coherent acceleration of low energy spread microbunched pulse trains, which constitutes a fully functional prototype of a DLA injector
Speaker and group specificity in spoken word recognition
Keywords: Linguistics.

Spoken words vary phonetically along a number of dimensions, such as duration, pitch, and vowel quality. Much of this variation is associated with social factors like the dialect, age, or gender of the speaker -- a type of variation termed 'socio-indexical'. Traditional theories of speech perception have seen this socio-indexical variation as a source of noise that listeners must 'filter out', in order to match the noisy speech signal to abstract mental representations of words or phonemes. But more recent theories propose that socially salient variation can actually make speech perception easier for listeners, as long as the variation is congruent with their experience (exemplar theory: Goldinger, 1996; Johnson, 2006), because listeners maintain specific detailed memories of instances of language that they have experienced. Exemplar models make fairly narrow predictions about the interactions between specific memories and socio-indexical information -- specific memories are represented only at the surface phonetic level, so they should not affect deeper linguistic systems like semantics; and socio-indexical group recognition is a byproduct of word recognition, happening serially after words are recognized. Recent evidence, however, raises significant questions about these predictions. In this dissertation, I argue that while socio-indexical variation does facilitate word recognition, this facilitation is more widespread than expected. I begin by using several large collections of spoken conversations to determine which words are used more often by women and which more often by men. I then use laboratory experiments to establish that listeners' sensitivity to these group-specific word frequencies interacts with both phonology and semantics beyond the predictions of exemplar theory, suggesting the need for more complex spoken word recognition mechanisms
Stabilizing anderson mixing for accelerated optimization
Keywords: Computational and Mathematical Engineering.

The acceleration of existing algorithms has been a continual effort in the field of optimization, in which Nesterov's acceleration and (quasi-)Newton methods are two of the most representative examples. However, Nesterov's acceleration is specific to gradient methods. And while (quasi-)Newton methods have been commonly adopted to accelerate general iterative algorithms by viewing them as fixed-point iterations, they are typically memory consuming and require sophisticated and potentially expensive line-search procedures. To design widely applicable schemes for black-box, memory-efficient and line-search free acceleration, we resort to Anderson acceleration (AA), which dates back to the 1960s. AA belongs to the family of sequence acceleration methods, which achieve faster convergence through certain sequence transformations. Despite its elegance in implementation, popularity in chemistry and physics, and success in specific optimization problems, a systematic treatment of AA in optimization-related applications was lacking. In addition, although AA has been demonstrated to work well on a wide spectrum of (unaccelerated) algorithms with a small memory (typically 5--10) and essentially no line search in many cases, it also suffers from severe instability issues in general. Moreover, on the theory side, the global convergence theory was missing for AA. This dissertation fills the aforementioned gaps by taking a unified view of AA as either an extrapolation method or a generalized quasi-Newton method, and showing that unlike classical quasi-Newton methods, it is effective without a line search and requires less computation and memory per iteration so long as certain stabilization measures are adopted. Our discussions cover both convex and nonconvex settings, as well as both solvable and pathological scenarios