VictorJuiz/thesis huggingface dataset:
Neural Mechanisms of Juvenile Aggression

Aggression in juveniles is an evolutionarily adaptive behavior documented in many species, but the neural mechanisms behind these displays are poorly understood. In humans, the importance of knowing what is happening in the brain during maladaptive aggression is especially key in instances of psychiatric disorders that display as aggressive tendencies in childhood, such as autism spectrum disorders (ASD), schizophrenia, and borderline personality disorder. Mimetic poison frog (Ranitomeya imitator) tadpoles live in small pools with limited resources and have evolved offensive aggressive tendencies towards conspecific tadpoles in defense of these resources. Tadpoles will attack, kill, and cannibalize other tadpoles as a primary resource defense mechanism. I examined the neural basis of neonate aggression in these tadpoles by comparing individuals that were placed in aggressive encounters and individuals placed in an environment with a non-threatening stimulus. I first did a longitudinal study to determine that the tadpoles were most aggressive when they were around five or six weeks old. I then compared patterns of generalized neural activity using immunohistochemical detection of phosphorylated ribosomes and a candidate neuropeptide, arginine vasotocin, which has been implicated in aggression in other vertebrates. Vasotocin cells in the preoptic area of the hypothalamus were not more active in aggressive tadpoles. I then examined neural activation across several brain regions suspected to be involved in aggression, and found that the anterior amygdala had the most neural activation in aggressive tadpoles compared to controls and the other brain regions. Through molecularly profiling active neurons using phosphoTRAP, I identified proopiomelanocortin (POMC), a precursor polypeptide with multiple post-translational peptides, as enriched in active neurons during aggression. Through pharmacological manipulations with the peptides of POMC (aMSH, ACTH, and B-endorphin), I found that a B-endorphin antagonist significantly increased aggressive behavior in tadpoles. Overall, this project is advancing understanding of how aggression is regulated in the juvenile brain.

Keywords: Ludington, Sarah
violence, development, neuroethology, POMC, B-endorphins
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Thesis
Advisor: O'Connell Lauren
Luo Liqun
Neural network approaches to the study of word learning

I present three approaches to using neural network models for studying word learning in children. In the process, I show what we can conclude from these different approaches about how words are learnt by humans as well as by neural networks. The first approach takes neural networks to be cognitive models, allowing for stronger conclusion about the connection between model and human word learning outcomes. I show that the predictability of words in the linguistic contexts children are exposed to is a good predictor of the age at which children will begin to produce these words themselves. Additionally, I demonstrate that language models share similarities with children when it comes to the order in which they learn new words, but only if these words are predicates or function words. The second approach considers neural networks to be independent learners from humans and uses them as a "proof of concept" tool. These model learners can exemplify what behaviors are in practice learnable rather than inherent. Here, I show that the meanings of logical operators like "and" and "or" are in fact learnable by models that are exposed to visually grounded language, and therefore, could also be learnt without prior biases by human learners, supporting existing usage-based theories of their acquisition. I also find that these visually-grounded models can learn the meanings of other function words, favoring gradient semantic representations over threshold based semantics for comparative quantifiers and spatial prepositions. The third and final approach, like the second, considers models as independent learners, but instead of using them to differentiate between existing proposals, it considers them as a tool to generate novel hypotheses about what drives language learning in both models and children. We can establish novel causal relationships about what allows models, and by extension possibly humans, to learn a given outcome. I show that neural agents exposed to social interaction in addition to visual and linguistic input can acquire a shape bias when learning new lexical categories, and furthermore, that this bias follows from efficient communication strategies. I propose that the shape bias seen in humans could also follow from these same communication strategies

Keywords: Linguistics.
Advisor: Frank Michael C. (Professor of human biology)
Jurafsky Dan
Degen Judith
Laroche Romain
Neural network models of visual learning

Humans show remarkable ability in not only recognizing the complicated visual environment surrounding them but also efficiently learning from this environment. The ventral visual stream underlies this critical ability and is currently best modeled by deep neural networks both quantitatively and qualitatively. However, such networks have remained implausible as a model of the development of the ventral stream, in part because they are trained with supervised methods requiring many more labels than are accessible to infants during development. In this dissertation, we first propose strong learning algorithms that learn from totally unlabelled or only partially labelled data. Then, we show that these algorithms together have largely closed this gap. We find that neural network models learned with deep unsupervised contrastive embedding methods achieve neural prediction accuracy in multiple ventral visual cortical areas that equals or exceeds that of models derived using today's best supervised methods, even when trained solely with real human child developmental data collected from head-mounted cameras, despite the fact that these datasets are noisy and limited. The proposed semi-supervised method has also proven to leverage small numbers of labelled examples to produce representations with substantially improved error-pattern consistency to human behavior. Furthermore, we propose two learning benchmarks measuring how well unsupervised models are able to predict human visual learning effects in both real-time and life-long timescales. Testing multiple high-performing unsupervised learning algorithms at both time-scales, we show how specific algorithm designs are helping matching the human learning results. Taken together, these results illustrate one of the first uses of unsupervised learning to provide a quantitative model of a multi-area cortical brain system, and present a strong candidate for a biologically-plausible computational theory of primate sensory learning. In addition to this, we also present models of other functions or species, serving as pre-steps of extending the models of visual learning to these domains

Keywords: Psychology.
Advisor: Yamins Daniel
Finn Chelsea
Goodman Noah (Noah D.)
Grill-Spector Kalanit
Neural systems for informative conversations

Humans, through deep and expressive conversations, have perfected the art of exchanging information about the world around them seamlessly. But even with the latest NLP methods, chatbots struggle in being informative. In this dissertation, I describe my work on building neural systems for informative conversations. First, I describe Chirpy Cardinal, our Alexa Prize 2020 Socialbot, that was deployed to tens of thousands of users across the US, and served as a test-bed for an initial system for informative conversations. While we used state-of-the-art models that improved over prior work, they fell short of expectations when deployed in the real-world setting. In particular, our system had two components: a retriever to find conversationally relevant passages from a large corpus (like Wikipedia) and a language generator to weave it into the dialogue with conversational-sounding utterances, and these two components were unable to cohesively work together. Second, inspired by linguistics literature on conversations, I analyze human-human informative conversations and identify various strategies for acknowledgement, presentation, transition and detail-selection. I also present a case study, where I improve acknowledgements by using conditional mutual information to select better chatbot utterances. Third, I explore the possibility of learning these strategies from data by jointly training a neural retriever and a neural generator such that they work together cohesively. To train them, we need to know which passages are relevant to the conversation, but the abundant conversational data available for training is not annotated for relevant passages! Our method, HINDSIGHT, uses a posterior retriever to find relevant passages during training. The posterior retriever is jointly trained alongside the original retriever and the generator using the evidence lower bound (ELBo). We find that HINDSIGHT has better inductive biases than existing methods - at inference, the retriever finds more relevant passages and the generator is more grounded in the retrieved passages, resulting in better end-to-end performance. Together, these projects provide a strong practical motivation, rich linguistic guidance and an effective training method for our aim of building neural systems to have deep and topically broad conversations

Advisor: Manning Christopher D.
Hashimoto Tatsunori
Jurafsky Dan
Neutrino driven outflows in supernovae : from hydrodynamics to nucleosynthesis

Core-collapse supernovae (CCSNe) have shaped our galaxy, played an essential role in the formation of the solar system, and created many elements found on Earth. Some of the outstanding questions in the field of core collapse supernovae are intimately tied to neutrinos. Neutrinos are produced in copious numbers in a CCSNe explosion and are messengers of the physical processes occurring in and around the hot and dense supernova core where matter reaches nuclear densities. In the supernova explosion detected in 1987, known as SN1987A, only 25 neutrino events were detected, yet this already provided vital clues to the explosion mechanism. With the next-generation neutrino detectors such as DUNE and Hyper-Kamiokande, which will have the capacity to detect many thousands of neutrino events from the next Galactic supernova explosion, qualitatively new understanding of the supernova phenomenon will be achieved. The question is then what features one may expect to see in these detectors and how can one reconstruct the explosion dynamics from the neutrino observations. In this thesis, we will focus on the physics that takes place several seconds after the explosion is launched. We will focus on a region known as the `hot bubble' of supernova explosion, which hosts a plethora of interesting phenomena, ranging from non-trivial neutrino oscillations to nucleosynthesis of heavy elements. We will show that the hydrodynamics of this hot bubble can crucially impact neutrino oscillations and that the imprints of the resulting density features on the neutrino spectrum can be detected in DUNE. We will also show that the hot bubble can be a site of a certain nucleosynthesis process, known as the $\nu p-$ process, which helps resolve a 30 year old puzzle regarding the origin of certain proton-rich isotopes observed in the Solar System, such as $^{92,94}$Molybdenum and $^{96,98}$Ruthenium

Keywords: Physics.
Advisor: Friedland Alexander 
Hewett JoAnne L.
Blandford Roger D.
Dixon Lance Jenkins
Peskin Michael Edward
Neutron and X-ray scattering studies of disorder in quantum materials

Quantum materials generally refer to condensed matter systems whose properties are dictated by strongly interacting quantum mechanical effects. This includes many classes of materials at the forefront of condensed matter research, for example high temperature superconductors, quantum spin liquids, and many others. In addition to hosting their eponymous phases, frequently these materials also exhibit competing ground states, where minute parameter changes lead to qualitatively different phases. The existence of atomic scale disorder is often sufficient to alter the ground state of many such systems. Experimentally, disorder is an inherent component of real (and in particular, bulk) materials, commonly due to imperfections in synthesis and intentional introduction of substitution/dopant atoms. It is therefore important to characterize and model the disorder in such materials to fully understand their physics. The nature and time/length scales of disorder varies significantly between different materials, requiring specifically tailored techniques. This thesis details innovative neutron and x-ray scattering studies on several quantum materials of current interest with distinct types of disorder relevant to high temperature superconductivity and quantum spin liquids

Keywords: Applied Physics.
Advisor: Lee Young Sang
Fisher Ian R. (Ian Randal)
Kivelson Steven
New combinatorial bounds for error correcting codes

This thesis is about coding theory. Coding theory studies (error-correcting) codes, sets of strings that protect information from noise. To define a code, we typically imagine a communication setup: a sender, conventionally named Alice, wants to send a message to a receiver, conventionally named Bob, through a noisy channel, so she sends Bob an encoded message called a codeword with enough redundancy that Bob can decode the message, even in the presence of noise. The success of this protocol largely boils down to the mathematical properties of the code, the set of possible codewords Alice could send. The central challenge in coding theory is finding codes that are both ``less redundant'' (meaning Alice's encoded message is not too long) and ``more robust'' (meaning Alice and Bob's protocol can tolerate more noise). This thesis studies this central challenge in two basic contexts: deletion codes and list-decoding. In deletion codes, the noisy channel transmits a subsequence of Alice's encoded message. This setup is motivated by applications such as DNA storage, magnetic recording, and internet transmission. Though deletion codes is an old topic, our understanding was poor compared to other errors like substitutions and erasures, and many basic questions remained open until recently. We contribute to this recent progress, answering one extremely basic question: can positive rate binary codes correct a worst-case deletion fraction approaching the natural limit of 1/2? In list decoding, Bob only needs to output a small list of messages containing the correct message. This relaxation allows Alice and Bob to tolerate more noise (approximately twice as much). For this reason (and others), list-decoding finds various applications such as group testing, compressed sensing, algorithm design, pseudorandomness, complexity, and cryptography. Most applications require explicit list-decodable codes, but our best list-decodable codes are often nonexplicit random codes. Towards finding optimal explicit list-decodable codes, we show stronger list decoding results for more-structured ensembles of codes, such as random linear codes and random Reed Solomon codes

Advisor: Fox Jacob
Wootters Mary
Tan Li-Yang
New constructions in Ramsey theory

Ramsey theory is the study of the patterns and structures that must arise in any sufficiently large system. Many important results in Ramsey theory rely on certain constructions of large systems with special properties, such as Erdős's random construction of a graph with no large clique or independent set. In this thesis, we use probabilistic and combinatorial tools to find new constructions, and thus make progress on multiple Ramsey-theoretic problems

Keywords: Mathematics.
Advisor: Fox Jacob
Chatterjee Sourav
Vondrak Ján (Mathematician)
New metrics for evolving risks to human and wildlife health

Humans are altering the global environment at a rapid pace, which impacts ecosystems and creates opportunities for the spread of infectious diseases to new species. Healthy ecosystems are often dependent on healthy human populations and vice versa. However, resolving risks to human and wildlife populations requires insights provided by a scale of data that is not often available. New modeling approaches can be used to maximize the use of available data to achieve critical insights. In this dissertation, my collaborators and I develop new analytical and statistical approaches to understand the magnitude of risks to the health of human or wildlife populations and provide practical knowledge to mitigate those risks. We first examine the seasonal risk of spillover of a pathogenic acanthocephalan parasite to southern sea otters, which we find is driven by intermediate host population dynamics and sea otter foraging behavior. Then, we investigate the relationship between the genome sizes of viruses and their potential to spillover into new species by comparing the genomes of zoonotic and non-zoonotic viruses. We find that larger genomes increase spillover risk. The risk of death from viruses is difficult to discern due to incomplete sampling of affected individuals. We develop a statistical method to calculate the true risk of death for COVID-19 across countries and find this risk in the early stages of the pandemic was approximately 1\%. Lastly, we investigate through a diverse set of population genetic and epidemiological modeling how emerging gene drive technology can mitigate human risk to schistosomiasis. We find that schistosomiasis can be reduced under select conditions, such as low selfing rate in snail intermediate hosts. Together these chapters demonstrate how to leverage new quantitative approaches to guide conservation and disease management decisions, each important aspects of human and wildlife health

Keywords: Biology.
Advisor: De Leo Giulio A.
Lowe Christopher (Associate professor of biology)
Miller Melissa (Melissa Ann) 
Mordecai Erin
Pringle John R.
New primitives for convex optimization and graph algorithms

Iterative methods, especially those from convex optimization, form the basis of much of the modern algorithmic landscape. The success of such methods relies on their generality: methods such as gradient descent and Newton's method typically converge to high-quality minimizers with only minimal assumptions placed on the objective. However, in many real-world settings the theoretical guarantees obtained by such algorithms are often insufficient to see use in practice. This thesis addresses this issue by developing methods for convex optimization and graph algorithms by exploiting problem-specific structure. Part I gives a state-of-the-art algorithm for solving Laplacian linear systems, as well as a faster algorithm for minimum-cost flow. Our results are achieved through novel combinations of classical iterative methods from convex optimization with graph-based data structures and preconditioners. Part II gives new algorithms for several generic classes of structured convex optimization problems. We give near-optimal methods for minimizing convex functions admitting a ball-optimization oracle and minimizing the maximum of N convex functions, as well as new algorithms for projection-efficient and composite convex minimization. Our results are achieved through a more refined understanding of classical accelerated gradient methods, and give new algorithms for a variety of important machine learning tasks such as logistic regression and hard margin SVMs. Part III discusses advancements in algorithms for the discrete optimal transport problem, a task which has seen enormous interest in recent years due to new applications in deep learning. We give simple and parallel algorithms for approximating discrete optimal transport, and additionally demonstrate that these algorithms can be implemented in space-bounded and streaming settings. By leveraging our machinery further, we also give improved pass-complexity bounds for graph optimization problems (such as bipartite matching and transshipment) in the semi-streaming model

Advisor: Gerritsen Margot (Margot G.)
Iaccarino Gianluca
Sidford Aaron
Next generation cell therapies : from genome to receptor to vector engineering

The next generation of cell therapies will be leveraged to target solid tumors, clear viral infections, repair wounds, overcome autoimmunity, reverse aging, and beyond. To achieve this goal, diverse immune cell types need to respond to clinician- or disease-derived inputs and generate novel therapeutic responses. However, multiple challenges exist that inhibit the development of these next generation cell therapies including 1) primary immune cells silencing delivered transgenes, 2) engineered receptors lacking response to soluble inputs or activation of therapeutic pathways, and 3) clinically approved viral vectors that are incapable of engineering diverse immune cell types. To overcome these challenges, we expanded the cell therapy toolbox by developing a suite of technologies for genome, receptor, and vector engineering. Leveraging genome engineering, we develop a CRISPR-based virus-like particle (VLP) method to knockin very large genetic payloads into essential genomic loci. This methodology stabilizes expression of large and difficult to express transgenes in primary human T cells over a long period. With receptor engineering, we generate a chimeric transmembrane protein that oligomerizes in response to a small molecule drug or protein antigen to activate Toll-like Receptor (TLR) signaling. The receptor controllably increases the cytotoxicity of natural killer (NK) cell to enhance their anti-cancer capacity. With vector engineering, we reprogram lentiviruses to target specific cell types quickly and easily. This is leveraged to engineer cells that normally cannot be engineered by lentiviruses like NK cells or selectively engineer cell subtypes in a mixture. Together, these technologies create a new foundation for generating cell therapies that move beyond T cells targeting blood cancers and maximize the therapeutic benefit of this new drug class

Keywords: Bioengineering.
Advisor: Qi Lei (Professor of Bioengineering)
Covert Markus
Weinacht Katja
Next-generation earthquake monitoring

Earthquake monitoring programs such as the one operated by the United States Geological Survey provide near-real-time information of earthquakes occurring around the globe. However, the provided information is often incomplete or not detailed enough, so that it is often necessary to reprocess the recorded continuous waveform data when analyzing seismicity in specific regions. This can delay progress in understanding ongoing seismic sequences or making decisions that are time sensitive. In this dissertation, I introduce a concept of next-generation earthquake monitoring - an earthquake monitoring task that aims at averting data reprocessing needs and delivering near-real-time information in a way that users can better utilize and interpret. I develop and explain in detail various approaches that can boost the standard workflow of building earthquake catalogs to the next level where high-quality earthquake catalogs can be produced almost fully automatically. I also demonstrate the value of next-generation earthquake monitoring through various case studies of induced earthquakes in the mid-continent oil and gas province of the United States

Keywords: Geophysics.
Advisor: Beroza Gregory C. (Gregory Christian)
Baker Jack W. 
Ellsworth William L.