VictorJuiz/thesis huggingface dataset:
Circadian Arrhythmia Causes Reversible Disruption of Recall of Traumatic Memories

This project investigates how disruptions in circadian rhythms impact cued and contextual fear memories in Siberian hamsters (Phodopus sungorus). While studies in these hamsters show that the loss of circadian timing impairs spatial and recognition memory, it was unclear whether this impairment is permanent or reversible. To assess this, we employed a 4-day fear conditioning protocol followed one week later by a disruptive phase shift (DPS) protocol to eliminate circadian rhythms in these hamsters. They were then assessed 7 days and 42 days following the end of the protocol for memory of the fear conditioning.
Our results show that temporary loss of circadian rhythms reversibly blocks recall of previously formed traumatic memories. Contextual memories are wholly blocked while cued memories are partially suppressed. In the contextual memory test, all animals exhibited increased fear behavior after initial testing. While hamsters that remained entrained to a normal circadian rhythm showed increased fear behavior at all timepoints, hamsters that became permanently arrhythmic showed completely suppressed fear behavior (no significant difference compared to baseline) after becoming arrhythmic (p < 0.05). Animals that were arrhythmic for a period of time before re-entraining showed completely suppressed fear behavior (p < 0.05) while they were arrhythmic; after re-entraining, these animals showed increased fear behavior similar to the entrained animals.
In the cued test, all animals exhibited fear behavior at all timepoints tested, but animals demonstrated partially suppressed fear behavior when arrhythmic compared to when entrained (p < 0.05). This demonstrates that loss of rhythmicity likely is responsible for impairing recall of the traumatic fear conditioning. This work has clinical implications in disorders involving memory loss such as Alzheimer’s disease.

Keywords:  Department of Biology
Biology
Neurobiology
Memory
Learning
Circadian Rhythms
Siberian Hamsters
Thesis
Advisor: Heller H. Craig
Sapolsky Robert
Ruby Norman F.
Citizenship Amendment Act, 2019: Revisiting Indian Secularity

When the Citizenship Amendment Bill (CAB) was first introduced in the Indian parliament on December 2019, protests erupted all over India over the perceived non-secular nature of the proposed bill. Despite the outrage, India passed the legislation rendering it the official status of Citizenship Amendment Act of 2019.
The main beneficiaries of this act are illegal immigrants of certain minority religious faiths from neighboring Islamic countries, who fled to India to escape religious persecution in their own countries. This act accelerates their path to Indian citizenship thereby securing a safe life in India as citizens with freedom to practice their religion without any fear.
However, it is the first time that religion has featured prominently in an Indian legislation, thereby sparking unrest among indigenous population, especially more so because it excludes Muslims. Additionally, it possibly negates the efforts of the border states in North East India, that have been battling the issue of infiltration of illegal immigrants for decades.
As the protests continue in India despite the COVID19 crisis, this thesis aims to analyze this act in the context of the Indian constitution and secularity, the history and perspective of the people in the North Eastern states and Indian Muslims, and the impact its implementation could possibly have all across the country.

Keywords:  Public Policy Program
Public Policy
India
Citizenship Amendment Act
CAA
NRC
NPR
Indian Muslims
CAA protests
Assam
Detention centers
Secular
Indian Constitution
India-Pakistan Partition
1947
Thesis
Advisor: Jha Saumitra
Clinging to Power: British Economic Policy and Political Coercion in Egypt, 1930-1952

By the onset of the Great Depression, the British Empire was in decline. As a result, the British government began to seek new methods to continue exerting British power and influence in strategically important regions overseas without risking confrontation or revolt.
What were these “new methods” of control, and to what extent were they successful? My thesis studies the interplay of British economic and political power in Egypt between 1930 and 1952 and examines how the British used economic policy to maintain political influence in Egypt during this time. Egypt constitutes a unique case among the regions most crucial to British imperial strength due to the defensive importance of the Suez Canal and the strength and coherence of its nationalist movement, which offered a singular challenge to British imperial power.
By relying on qualitative analysis of British archival records, I find that the British employed economic policy in two ways: when political negotiation was not possible, and when the government needed to cover up the political damage of its actions. In the 1930s, economic and political relations between the two countries were favorable enough that the British were willing to offer economic concessions to achieve their political goals in Egypt. Yet during and following the Second World War, the British dropped the guise of goodwill and began to impose changes by force, hoping that economic compromises offered after the fact would forestall any serious backlash from the Egyptians over this political interference. However, these cover-up methods ultimately failed, setting the stage for Egyptian revolution in 1952.

Keywords: Program in International Relations
Egypt
Britain
economic policy
politics
political coercion
archival research
empire
Thesis
Advisor: Rakove Robert
Coarse-grained, semiflexible copolymer behavior with applications to heterochromatin

The properties of polymer materials can vary widely and depend on their organization and the identity and interactions of their monomers. By requiring many monomers to occupy adjacent spatial locations, the connectivity of a polymer amplifies relatively weak monomer interactions. In this thesis I use theoretical and computational techniques to predict the resulting phase behavior and structure. These techniques use the wormlike chain model to incorporate the structural rigidity of the polymer backbone and use coarse-graining to capture the interactions. A semiflexible polymer backbone coupled with a repulsive Flory-Huggins interaction between blocks of a random copolymer causes frustration in the microphase segregation of the monomer types. The resulting phase diagram and structures can be adjusted by varying the statistics of the random sequence and the polymer rigidity. When the interactions depend on the orientation rather than identity of the monomers, a liquid crystal phase forms. I calculate the Frank elastic constants for liquid crystal solutions of wormlike chains that interact via a Maier-Saupe type interaction. A particular focus is devoted to the application of polymer physics techniques to the biological system of chromatin. An HP1-H3K9me3 interaction model of heterochromatin is found to be capable of explaining features of Hi-C data and aid in the interpretation of ChIP-seq data. This interaction is used to provide a density-based physical explanation for the prevalence of peripheral heterochromatin

Keywords: Physics.
Advisor: Doniach S.
Spakowitz Andrew James 
Qin Jian (Professor of Chemical Engineering)
Coercing machine learning to output physically accurate results

Many machine/deep learning artificial neural networks are trained to simply be interpolation functions that map input variables to output values interpolated from the training data in a linear/nonlinear fashion. Even when the input/output pairs of the training data are physically accurate (e.g. the results of an experiment or numerical simulation), interpolated quantities can deviate quite far from being physically accurate. Although one could project the output of a network into a physically feasible region, such a postprocess is not captured by the energy function minimized when training the network; thus, the final projected result could incorrectly deviate quite far from the training data. We propose folding any such projection or postprocess directly into the network so that the final result is correctly compared to the training data by the energy function. Although we propose a general approach, we illustrate its efficacy on a specific convolutional neural network that takes in human pose parameters (joint rotations) and outputs a prediction of vertex positions representing a triangulated cloth mesh. While the original network outputs vertex positions with erroneously high stretching and compression energies, the new network trained with our physics "prior" remedies these issues producing highly improved results. We apply our physical postprocess in several applications to further demonstrate its effectiveness and robustness

Advisor: Fedkiw Ronald P.
Liu Cheng-Yun Karen
Wu Jiajun (Computer scientist)
Cognitive Development and Stimulating Parenting Practices: A Comparative Study of Infants and Toddlers in Rural China

Recent studies have found that children living in rural communities in China display substantial rates of cognitive delay. Low rates of stimulating parenting practices are one of the major contributors toward cognitive delay in children. This article compiles datasets from three Chinese provinces to explore the ways stimulating parenting practices relate to cognitive development, and the roles that family socioeconomic status (as measured by household assets), primary caregiver type, caregiver educational attainment, and geographic location play in cognitive delay. The results of our multivariate regression analyses reveal that there is a significant negative relationship between household assets and cognitive scores, as well as between stimulating parenting practices and cognitive scores. Additionally, our findings indicate that children of better educated caregivers and children born and raised in one particular province (Hebei) are exposed to significantly more frequent stimulating parenting practices and demonstrate higher cognitive abilities. Variables such as child age and sex are not statistically significant in our study.

Keywords: Infant and toddler cognitive development
infant and toddler stimulation
stimulating parenting
rural China
Stanford Graduate School of Education International Comparative Education and International Education Policy Analysis
Thesis
Coherent synchrotron radiation effect mitigation studies for high brightness beams/accelerators

Many particle accelerator applications today call for high-brightness electron beams. In this dissertation, research is described on the mechanisms driving self-radiating forces arising from high-brightness beam environments and the mitigation of such forces. In the first half of the dissertation, we will focus on quantifying the Coherent Synchrotron Radiation (CSR) Effect for various accelerator scenarios. New expressions for the CSR RMS energy spread are developed for transient regime with and without bunch compression and for the subsequent drift section after a bend. The expressions are rigorously tested with the particle tracking codes ELEGANT and CSRTrack [14, 15]. In the latter half, we will take a particular focus on the final-stage bunch compression systems of linacs for X-ray free-electron lasers (XFEL), where these self-forces are prevalent, and introduce a new bunch compressor chicane design: the 5-Bend chicane. The new chicane's performance is compared versus a standard 4-Bend chicane in terms of emittance preservation and XFEL performance in a comprehensive simulation study. Though the work presented here pertains to the XFEL linac environment, the physics and techniques can be applied to any sort of accelerator involving high-peak current, low-emittance charged particle beams

Keywords: Applied Physics.
Advisor: Raubenheimer Tor O.
Reis David A.
Huang Zhirong
Collaborative perception and learning between robots and the cloud

Augmenting robotic intelligence with cloud connectivity is considered one of the most promising solutions to cope with growing volumes of rich robotic sensory data and increasingly complex perception and decision-making tasks. While the benefits of cloud robotics have been envisioned long before, we have historically lacked flexible methods to trade-off the benefits of cloud computing with end-to-end systems costs of network delay, cloud storage, human annotation time, and cloud-computing time. To address this need, this thesis introduces decision-theoretic algorithms that allow robots to significantly transcend their on-board perception capabilities by using cloud computing, but in a low-cost, fault-tolerant manner. The utility of these algorithms is demonstrated on months of field data and experiments on state-of-the-art embedded deep learning hardware. Specifically, for compute-and-power-limited robots, this thesis presents a lightweight model selection algorithm that learns when a robot should exploit low-latency on-board computation, or, when highly uncertain, query a more accurate cloud model. Then, I present a collaborative learning algorithm that allows a diversity of robots to mine their real-time sensory streams for valuable training examples to send to the cloud for model improvement. This thesis concludes by surveying a number of future research directions on the systems and theoretical aspects of networked system control, some of which extend beyond cloud robotics

Advisor: Katti Sachin
Pavone Marco
Brunskill Emma
Sadigh Dorsa
Colloidal design of active, selective, and stable catalysts for methane utilization

Due to recent advances in locating and extracting natural gas resources, scientists in academia and industry are looking for new processes to take advantage of methane as a chemical precursor and fuel. However, there remain significant challenges in methane utilization; these are related to the strength of methane's carbon-hydrogen bonds, which makes this molecule difficult to activate and utilize. Without a catalyst, methane activation necessitates very high temperatures (~1000 oC), which lead to high energy costs, advanced infrastructure, toxic by-products, and poor product selectivity. Our work focuses on developing catalysts with well-defined structural properties to understand what makes materials active, selective, and stable for methane transformations. To understand which specific nanostructures are best for methane activation, size- and composition- controlled Pt/Pd nanocrystals were designed and studied to reveal the effect of catalyst structure on methane activation. Here, we discuss the effect of these unexplored parameters on methane activation rates, resistance to common catalytic poisons, and changes in oxidation state -- each of which has an important role in contributing to low-temperature activity. Perhaps the greatest challenge in methane activation is selective product formation. In this area, we studied how tuning catalyst support can help selectively produce valuable products (synthesis gas) rather than typical combustion products (carbon dioxide and water). Additionally, we started looking at even more unique nanostructures, involving both organic and inorganic components, for selective methane transformations. The high temperatures needed to activate methane require stable catalysts. By taking advantage of modular colloidal catalyst assembly, we demonstrated synthetic approaches to tune, and measure, the spatial properties of nanocrystal active sites. We found that in many conditions, the spatial properties of active sites determined catalyst stability. In Pd/Al2O3 materials we observed that closer nanocrystals are more stable, in a distant-dependent degradation process. However, in Pd/SiO2 materials we found the opposite - that stability properties are largely distant-independent. Overall, by developing colloidal approaches to catalyst synthesis, we created well-defined catalysts with precisely-controlled sizes, compositions, and spatial properties, which have helped us uncover important design rules for active, selective, and stable methane transformations

Keywords: Chemical Engineering.
Advisor: Cargnello Matteo
Abild-Pedersen Frank
Bent Stacey
Combating tumor heterogeneity with early cancer detection and immunotherapy

Tumor heterogeneity refers to the existence of clonal subpopulations of cells within and between neoplastic lesions and is an important driver of tumor progression, resistance to therapy, and relapse. Precision medicine offers one strategy of combating tumor heterogeneity by using genetic information about these subpopulations to inform personalized therapy. Despite notable successes, the degree of heterogeneity in tumors is often too great for this "drug cocktail" approach to be effective, and patients frequently develop refractory or relapsed disease. Here we present an alternative approach to precision medicine, arguing that tumor heterogeneity can best be addressed by either (1) avoiding its emergence with early cancer detection or (2) combating it with equally heterogeneous therapies that leverage the naturally diverse repertoire of the adaptive immune system. To this end, we describe four technologies that address key limitations in the fields of early cancer detection and cancer immunotherapy. Beginning with early cancer detection, we first address the problem of biomarker scarcity by engineering an intravascular system of magnetic enrichment capable of collecting circulating cancer biomarkers from the entire blood volume. We continue by asking how we can overcome low signal-to-noise ratios in cancer liquid biopsies, and we present a method of enriching rare mutant alleles in cell-free DNA to enable more sensitive mutation detection. Acknowledging that there are inherent limitations to early detection using endogenous biomarkers, we also present an engineered immune cell sensor for ultrasensitive tumor detection in living subjects. Finally, we consider how we can leverage the body's natural immune diversity to theoretically cover all neoplastic clonal subpopulations. Using chimeric antigen receptor T-cell therapy and epitope spreading as an example, we present a method of delivering ectopic surface antigens to malignant cells to generate a universal approach to adoptive cell therapy in solid tumors. The approaches detailed in this thesis provide a conceptual framework for how to strategically approach translational cancer research and can inform continued efforts towards reducing the global burden of cancer

Keywords: Bioengineering.
Advisor: Gambhir Sanjiv Sam
Diehn Maximilian
Swartz James R.
Comment est-ce que Maurice Barrès essaie de convaincre ses lecteurs de l’importance du nationalisme dans son livre Les Déracinés ? Une analyse littéraire, psycholinguistique et philosophique

Dans ce mémoire, j’explore les manières par lesquelles Maurice Barrès convainc ses lecteurs de la validité de sa vision du nationalisme. La thèse est que Maurice Barrès utilise le contraste entre le désir des sept lycéens lorrains d’aller à la capitale de la France et leurs malheurs à Paris pour discréditer l’éducation républicaine et démontrer l’importance de garder nos racines régionales, celle-ci est son idée du nationalisme français. Ce mémoire emploie les analyses littéraire, psycholinguistique et philosophique.

Keywords:  Department of Symbolic Systems
nationalism
Thesis
Advisor: Professor Cécile Alduy
Professor Marisa Galvez
Competition, pricing, and product entry in markets with costly search

In the first chapter of this paper, I study how much consumers benefit from new products in markets with information frictions. I analyze new products in the U.S. hard drive market, a market with ample product innovation. Using unique browsing data, I measure the magnitude of two frictions, limited consideration and costly search, and show that both play a crucial role in shaping consumer demand. Omitting these frictions from analysis makes the researcher underestimate consumer surplus from new hard drives, as it appears that consumers do not like the attributes these hard drives offer. Partly eliminating frictions substantially increases consumers' ability to benefit from new hard drives. In the second chapter, I study the estimation of preference heterogeneity in markets where consumers engage in costly search to learn product characteristics. Costly search amplifies the way consumer preferences translate into purchase probabilities, generating a seemingly large degree of preference heterogeneity. We develop a search model that allows for flexible heterogeneity in preferences and estimate its parameters using a unique panel dataset on the search and purchase behavior of consumers. The estimation results reveal that when search costs are ignored, the model overestimates standard deviations of product intercepts by 68%. We show that the bias in heterogeneity estimates leads to incorrect inference about price elasticities and seller markups and has important consequences for targeted marketing

Keywords: Business.
Advisor: Bronnenberg Bart J.
Seiler Stephan (Assistant professor of marketing)
Narayanan Sridhar
Reiss Peter C. (Peter Clemens)
The complexity of optimization beyond convexity

Gradient descent variants are the workhorse of modern machine learning and large-scale optimization more broadly, where objective functions are often non-convex. Could there be better general-purpose optimization methods than gradient descent, or is it in some sense unimprovable? This thesis addresses this question from the perspective of the worst-case oracle complexity of finding near-stationary points (i.e., points with small gradient norm) of smooth and possibly non-convex functions. On the negative side, we prove a lower bound showing that gradient descent is unimprovable for a natural class of problems. We further prove the worst-case optimality of stochastic gradient descent, recursive variance reduction, cubic regularization of Newton's method and high-order tensor methods, in each case under the set of assumptions for which the method was designed. To prove our lower bounds we extend theory of information-based oracle complexity to the realm of non-convex optimization. On the positive side, we use classical techniques from optimization (namely Nesterov momentum and Krylov subspace methods) to accelerate gradient descent in a large subclass of non-convex problems with higher-order smoothness. Furthermore, we show how recently proposed variance reduction techniques can further improve stochastic gradient descent when stochastic Hessian-vector products available

Keywords: Electrical Engineering.
Advisor: Duchi John
Boyd Stephen P. 
Candès Emmanuel J. (Emmanuel Jean)
Sidford Aaron