VictorJuiz/thesis huggingface dataset:
Cholinergic Modulation of Visual Perceptual Learning of Texture Discrimination

Acetylcholine is a neuromodulator implicated in cognitive processes such as attention and memory, as well as the regulation of sensory cortical plasticity. In animal models, acetylcholine boosts the amplitude of visually evoked cortical responses, especially to behaviorally pertinent stimuli. Perceptual learning is a well-studied manifestation of sensory cortical plasticity, and can be induced and subtly probed in humans with minimal invasiveness. Previous studies of the effects of pharmacological cholinergic enhancement on perceptual learning in humans have yielded mixed results across different tasks. In this dissertation, I present two experiments that combine behavioral pharmacology and repeated psychophysical training in healthy human subjects to assess the effects of increased cholinergic neurotransmission on a broadly used and thoroughly studied measure of visual perceptual learning: the texture discrimination task./nIn Chapter 2, I describe a study of the effects of transient cholinergic enhancement (a single drug dose administered at training’s onset) on perceptual learning induced by a brief course of texture discrimination task training. The results indicate that transient cholinergic enhancement neither increased nor decreased the extent or location selectivity of texture discrimination learning, and are consistent with several competing interpretations. One possibility is that the plasticity mechanisms mediating improved texture discrimination performance are impervious to cholinergic modulation. Conversely, it is also possible that cholinergic effects on texture discrimination learning would have emerged had our drug administration and/or training procedures been more substantial and/or prolonged./nI address this second possibility directly in the experiment detailed in Chapter 3. This study assesses the effects of sustained cholinergic enhancement (multiple drug doses administered throughout training) on perceptual learning during a course of texture discrimination training over several days. The results demonstrate that sustained cholinergic enhancement significantly increased the magnitude, but neither the location nor background orientation specificity, of texture discrimination learning compared to placebo. These results shed light on the interpretation of the findings discussed in Chapter 2, and demonstrate that the processes underlying improved performance on the texture discrimination task are indeed susceptible to cholinergic modulation. While sustained cholinergic enhancement significantly boosted texture discrimination learning’s magnitude, it also tended to reduce the background orientation specificity of this learning. This suggests that selective facilitation of plasticity for the neuronal population representing task-relevant stimuli is unlikely to be the primary biological mechanism through which cholinergic enhancement bolsters texture discrimination learning. Other possibilities consistent with the results include cholinergic effects on visual responsiveness, attention, and memory consolidation.

Advisor: Silver, Michael A
The functions of SIRT7 in the maintenance of neural stem cells and the regulation of the somatotroph axis

SIRT7 is a histone deacetylase that represses the expression of ribosomal proteins and mitochondrial ribosomal proteins to regulate the unfolded protein responses in the ER (UPRER) and the mitochondria (UPRmt). At the organismal level, SIRT7 deficiency results in the development of fatty livers and compromised maintenance of hematopoietic stem cells. Using a SIRT7 knockout mouse model, I found that SIRT7 deficiency also leads to reduced number of neural stem cells and reduced neurogenesis, suggesting that the role of SIRT7 in stem cell maintenance is conserved across tissues. I also revealed hepatic gene expression changes and metabolic characterization of SIRT7 knockout mice that are consistent with the repression of the somatotroph axis, uncovering a physiological response to loss of proteostasis.

Advisor: Chen, Danica
Scalable nanomanufacturing of semiconducting van der Waals monolayers

Van der Waals solids are a class of materials made up of loosely-bound, molecularly-thin layers. These layers can be readily detached from one another due to their weak interlayer van der Waals bonds, giving the materials their name. In 2004, the isolation of graphene, a single-atom-thick layer of carbon, from graphite, a bulk van der Waals material, prompted a surge of research interest in the nearly two-dimensional layers making up these materials. Their mechanical strength, stability in ambient conditions, flexibility, and near-transparency are intriguing for the production of flexible electronics. Meanwhile, their ultimate thinness opens an avenue for exploring extreme physics, offering the possibility of new kinds of devices, and making these materials intriguing to researchers in the short term. Graphene, the most famous, is a conductor. However, some van der Waals materials are semiconductors, which are necessary to produce electronic switches and light-emitting diodes (LEDs), while other van der Waals materials are insulators. There is considerable potential in combining these different van der Waals materials to produce so-called heterostructures, which can re-create existing functionality, such as light emission, or produce new devices with unique capabilities./nThe difficulty of manipulating atomically-thin semiconductors has prevented them from achieving commercial application. Existing processes for manipulating van der Waals semiconductors do not offer control over the shape or size of the single-layer features produced./nBy interrogating the role of stamp mechanics in the success of producing single layers, this thesis develops a method that enables the creation of van der Waals heterostructure arrays. After an introduction to the current state of the art in scalable manufacture of van der Waals monolayers and its tradeoffs in Chapter 1, Chapter 2 demonstrates a new process for creating patterned arrays of semiconducting monolayers from a bulk source. In order to measure the effectiveness of the method, this work introduces yield metrics relevant to nanomanufacturing of 2D material patterns. To further understand the role of the stamp in the process of exfoliation, Chapter 3 presents a finite element model bridging macroscale and nanoscale physics to understand the interaction between van der Waals layers and an elastic stamp. The results from this model carry implications for nanomanufacturing process design./nChapter 4 then concludes with discussion of the usefulness and limitations of the methods developed in this work, and considers future directions that will enable high-yield mass production of van der Waals heterostructures.

Advisor: Taylor, Hayden
Learning to Generalize via Self-Supervised Prediction

Generalization, i.e., the ability to adapt to novel scenarios, is the hallmark of human intelligence. While we have systems that excel at recognizing objects, cleaning floors, playing complex games and occasionally beating humans, they are incredibly specific in that they only perform the tasks they are trained for and are miserable at generalization. Could optimizing towards fixed external goals be hindering the generalization instead of aiding it? In this thesis, we present our initial efforts toward endowing artificial agents with a human-like ability to generalize in diverse scenarios. The main insight is to first allow the agent to learn general-purpose skills in a completely self-directed manner, without optimizing for any external goal./nTo be able to learn on its own, the claim is that an artificial agent must be embodied in the world, develop an understanding of its sensory input (e.g., image stream) and simultaneously learn to map this understanding to its motor outputs (e.g., torques) in an unsupervised manner. All these considerations lead to two fundamental questions: how to learn rich representations of the world similar to what humans learn?; and how to re-use such a representation of past knowledge to incrementally adapt and learn more about the world similar to how humans do? We believe prediction is the key to this answer. We propose generic mechanisms that employ prediction as a self-supervisory signal in allowing the agents to learn sensory representations as well as motor control. These two abilities equip an embodied agent with a basic set of general-purpose skills which are then later repurposed to perform complex tasks./nWe discuss how this framework can be instantiated to develop curiosity-driven agents (virtual as well as real) that can learn to play games, learn to walk, and learn to perform real-world object manipulation without any rewards or supervision. These self-directed robotic agents, after exploring the environment, can generalize to find their way in office environments, tie knots using rope, rearrange object configuration, and compose their skills in a modular fashion.

Advisor: Darrell, Trevor
Efros, Alexei A.
Probabilistic models and statistical inference in population genetics

Advances in sequencing and genotyping technologies have enabled data collection at unprecedented scales. This data deluge has driven demand for new, scalable methods and has allowed genomic approaches to be used to answer a broader set of questions. In this dissertation I analyze probabilistic models arising in population genetics, and I develop statistical techniques for extracting information from sequencing and genotyping data. I begin by analyzing Lambda and Xi coalescents, which arise as models of the genealogy of a sample from a population with large variance in the number of offspring per individual, such as marine species with sweepstakes-like reproduction or viruses undergoing continuous, strong selection. In particular, I show how to compute the expected value of a common summary statistic--the site frequency spectrum--under such models, and prove theorems about the identifiability of the model from the observed frequency spectrum. Such results suggest that it may be possible to learn about the underlying biological processes from observed site frequency spectra. I then present a method to find segments of Neanderthal ancestry in present-day humans, and use that method to learn about selective pressures on Neanderthal ancestry. I find that the observed patterns of Neanderthal ancestry are consistent with simple negative selection, as opposed to hybrid incompatibilities. Lastly, I develop a fast method to infer fine-scale recombination rates and apply it to 26 diverse human populations, elucidating the evolutionary dynamics and molecular modifiers of local recombination rates.

Advisor: Song, Yun S.
Item cluster-based assessment: Modeling and design

This three-paper dissertation explores item cluster-based assessments, first in general as it relates to modeling, and then, specific issues surrounding a particular item cluster-based assessment designed/nThere should be a reasonable analogy between the structure of a psychometric model and the cognitive theory that the assessment is based upon. Specifically, for item response theory (IRT) models in educational assessment scores, the structure of dependencies among items that are designed as item clusters (groups of items that share common stimulus material, etc) should be reflected in the model. This type of designed local item dependence (LID) can be modeled in many different ways. The literature on the existence of LID and models developed to account for this LID is somewhat extensive, though there is little work to unify and organize these different approaches. The first paper presents a general framework to guide modeling decisions for item cluster-based assessments by first formalizing some of the terminology used in the context of LID, providing an overview of methods for detecting LID, and discussing general modeling approaches for response data that is theorized to exhibit LID./nRecent pushes for increased rigor and focus on complex constructs (such as critical thinking) in K-16 education highlight a need to develop assessments that measure these complex constructs. The second paper explores these issues in the context of a particular complex constructs in statistics education, that of Linking Data to a Claim (LDC), Meta-Representation Competence (MRC), and Formal Inference (FoI). We present a multidimensional treatment and analysis of field test data for the Critical Reasoning for College-Readiness (CR4CR) Assessment, an item cluster-based assessment. We found that the LDC and FoI items as written can provide a mapping of student ability estimates to the construct map levels as defined, but that the MRC items do not. Further, as expected, we found moderately strong correlations among the three constructs./nThe third paper describes the design of selected response items based on open-ended counterparts for the CR4CR Assessment, and the empirical comparisons of these different formats. It is commonly thought that multiple choice (or selected response) items on tests do not provide useful information to educators regarding higher level thinking skills such as argumentation or critical thinking. However, there is also a need for diagnostic assessments to provide educators with timely feedback on student performance so that instruction can be adapted or interventions administered based upon student needs. We found that though existing literature suggests that selected response item types are easier, in general, than constructed response item types, this may not be the case for all constructs. We found that, for the LDC and FoI constructs, multi-select multiple choice items behaved similarly to their constructed response counterparts.

Advisor: Wilson, Mark
Local and Adaptive Image-to-Image Learning and Inference

Much of the recent progress on visual processing has been driven by deep learning and its bicameral heart of composition and end-to-end optimization. The machinery of convolutional networks is now ubiquitous. Its diffusion however was neither instantaneous nor effortless. To advance across the frontiers of vision, deep learning had to be equipped with the right structures: the true, intrinsic structures of the visual world./nThis thesis incorporates locality and scale structure into end-to-end learning for visual recognition. Locality structure is key for addressing image-to-image tasks that take image inputs and return image outputs. Scale structure is ubiquitous, and optimizing over it learns the degree of locality for the task and data. Alongside structure, this thesis examines adaptive computation to help cope with the variability of rich image-to-image prediction problems. These directions are studied through the lens of local recognition tasks that require inference of what and where./nFully convolutional networks decompose image-to-image learning and inference into local scopes. Factorizing these scopes into structured and free-form parts, and learning both, optimizes their size and shape to control the degree of locality. Adaptive computation across time, computing layers according to their rate of change, exploits temporal locality to improve the efficiency of video processing. Adaptive computation across tasks, extracting a latent representation of local supervision, transcends locality to non-locally guide and correct inference. Locality is the defining principle of our fully convolutional networks. Adaptivity equips our networks to more fully engage with the vastness and variety of vision.

Advisor: Darrell, Trevor
A Deliberative-Democratic Theory of Policy Analysis

This dissertation offers a normative account of policy analysis based on the idea that policy analysis must enable reasoned deliberation among equal citizens. It gives policy analysis a normative core, emphasizing the value of deliberation both as a means to govern on free and equal terms of social cooperation and as a means to make better public decisions. Policy analysis is one institution in a basic structure designed to make democratic deliberation possible./nPolicy analysis is situated within the institutional structure of democracy by defining three roles for unelected analysts: (1) informing public deliberation, (2) ensuring policy justification, and (3) serving as a symbol of democratic legitimacy. These roles require analysts to be neutral, but not in a technical or objective sense. Rather, analysts can and must be held accountable for looking outward to collect evidence on all minimally acceptable reasons that citizens and policy makers believe to be relevant. In so doing, policy analysis is standardized and impersonal, without unjustifiably limiting public deliberations to only quantifiable impacts./nThe dissertation charts a middle path between a purely quantitative approach to policy analysis and an argumentative approach, where all analysis is political argument. It does so through the mechanism of professional ethics, assigning analysts a duty to be comprehensively neutral. This standard acknowledges that analysis can never be value-free, but it does not abandon neutrality as a goal. Instead, it asks analysts to prepare citizens and policy makers to weigh controversial evidence and questions of equity. Taken together, the articles of this dissertation show how careful institutional design can enable an epistemic division of labor among experts, analysts, and citizens, while holding those professionals accountable for enabling deliberation on complex issues of public concern.

Advisor: Raphael, Steven
Urban Air Mobility: Deconstructing the Next Revolution in Urban Transportation - Feasibility, Capacity and Productivity

Owing to a century of innovation in aircraft design, for the first time in history, air transport presents a potential competitive alternative to road, for hub-to-door and door-to-door urban services. In this dissertation, we first study the feasibility of uncongested air transport, for moving people and goods in an urban area, based on three metrics - enroute travel time, fuel cost and carbon dioxide (CO2) emissions. We estimate the metrics from emission standards and operational assumptions on vehicles based on current market data and compare electric air travel of near future to predominantly gasoline road travel of today. For passenger movement, air is faster than road for all distances. It fares better on fuel cost and emissions for longer distances (specific transition distances are stated in the main text). For consolidated movement of goods, air is at par or better than road dependent on the type of aircraft used. Finally, for movement of unconsolidated goods, air far outperforms road on all three metrics./nTo enable the feasible air-based services, a typical metropolitan region's airspace needs to accommodate traffic orders of magnitude higher than the manned airspace of today, while staying uncongested to deliver the afore-mentioned benefits. Hence we also develop methods to study the urban airspace capacity. We use our methods to evaluate the airspace capacity for a specific use case of goods movement under 400 feet (low altitude airspace) and find that with today's technologies at least 10,000 free routed small Unmanned Aircraft Systems (sUAS) flights per day can be safely enabled in the San Francisco Bay area. Better onboard technologies would only improve this number. Furthermore, our methods can be extended to evaluate the metropolitan airspace capacity to accommodate other use cases including movement of passengers and goods in a much wider band of airspace./nFinally, we look at the energy efficiency, travel time and throughput trade-off between speed and direction control. We find that while maintaining a similar decent throughput, direction control is more energy efficient for enroute tactical resolution unless aircraft can be built with very high hover energy efficiency. However, speed control has a lower impact on travel time extension. Hovering capability additionally offers high flexibility for the type of operations that can be enabled in an urban airspace. Hence, the findings of this dissertation also have policy implications for the aircraft design industry for enabling Urban Air Mobility (UAM)./nIt is quite noteworthy that all our results are based on a road-friendly urban design. Changes in design that facilitate easier access to air-based hub-to-door and door-to-door services, would only make the case stronger for UAM as the next revolution in urban transportation.

Advisor: Sengupta, Raja
Hunger Artists: Appetite, Desire, and Self-Unmaking in Baudelaire, Colette, and Weil

This dissertation identifies and analyzes a tension between self-creation and decreation—the unmaking of self—within literary texts by three modern French authors: Charles Baudelaire, Colette, and Simone Weil. The tension I propose to focus on is symptomatic of a larger trend in modern art, literature, and thought. I will argue that each of these authors treats the body as a site of self-distancing and a surface on which subjectivity can be made and unmade through a manipulation of its space, shape, and dimensions. In particular, non-consummation, unfulfillment, and willful hunger, metaphorical or literal, figured as a reach toward non-being within life, marks an aesthetic, bodily language of plasticity in each that allows for an imagining of the self outside the bounds of the socially given. Each gestures toward the transcendence of bodily form, suggesting alternative acts of self-shaping within the conditions of modernity, directly engaging with the alienation, objectification, and deadening of the human senses, and the abjection of the body in the face of commodified existence and restrictive social norms. In taking distance from the body and its needs as material givens, each of these authors identifies non-being within life as a response to the conditions of subject formation in their world and as an opening onto other ways of being. Hunger and unfulfillment, the flattening and unfleshing of the body, non-consummation and self-consummation map the body as the location of self-undoing in the act of self- and world-making for each.

Advisor: Butler, Judith
Cascardi, Anthony
Spatial and Temporal Responses of Animals to Landscape Heterogeneity, Predation Risk, and Human Activity

The global expansion of human activity has had profound consequences for wildlife. Research has documented the effects of widespread habitat destruction and defaunation on species and ecosystems, but the more subtle pathways through which humans alter the natural world have largely escaped quantification. Much like apex predators, humans can instill strong fear in wild animals, which may adjust their activity to avoid contact with humans. In this dissertation, my collaborators and I examine pathways through which human disturbance, predation risk, and environmental heterogeneity influence animal behavior and distribution. We review the literature and synthesize theory to develop a novel framework for studying landscapes of fear, and we apply this framework in a global meta-analysis and field studies from California and Mozambique to understand how large mammals perceive and respond to spatial and temporal patterns of risk from humans and carnivores. We consider links between risk and response in complex systems with multiple predators or multiple prey species, and we explore ecology of fear dynamics in the context of seasonality, human disturbance, and restoration. Together, this work integrates disciplines of behavioral ecology, community ecology, and landscape ecology, applying predator-prey theory to understand the role of humans in ecological communities. By elucidating behavioral pathways linking human disturbance to wildlife community dynamics, this research contributes to our understanding of wildlife ecology in human-dominated landscapes and highlights mechanisms for human-wildlife coexistence.

Advisor: Brashares, Justin S