VictorJuiz/thesis huggingface dataset:
Modeling creative and social uses of language [electronic resource]

This thesis examines the computational basis of how people understand and appreciate creative uses of language. In the first half of the thesis, I describe a computational model that interprets diverse types of creative and figurative language use, such as hyperbole, irony, and metaphor. In the second half, I focus on modeling the social consequences of using language in creative ways, such as strengthening social bonds and evoking humor. Across the chapters, I describe computational models that formalize theories of language comprehension and show that these formalizations predict people's interpretations of a wide range of creative language use. Part of the goal of this thesis is to show that the interpretation of creative and social uses of language arises through many of the same rational principles that govern standard language understanding. In Chapter 2, I formalize basic principles of communication by extending the Rational Speech-Acts (RSA) framework (Frank & Goodman, 2012) with an explicit representation of speakers' communicative goals. I show that by incorporating inferences regarding which dimensions of the world the speaker wishes to address, the extended model produces appropriate interpretations for three types of figurative uses: hyperbole, verbal irony, and metaphor, suggesting that diverse figurative meanings may arise from the same principles of communication. In Chapter 3, I focus specifically on nonliteral interpretations of number words such as hyperbole and pragmatic halo. I provide a detailed account of how utterance cost, informativity, background knowledge, and communicative goals interact to produce hyperbolic and loose interpretations as well as associated affective subtexts. In Chapter 4, I explore the social consequences of figurative language use by formalizing the idea that listeners reason about common ground during communication. Through a series of simulations, I show that the extended RSA model infers a higher probability of common ground given figurative utterances than given literal ones, suggesting that figurative language may uniquely license inferences that strengthen social bonds. Finally, in Chapter 5, I use a simple model of sentence processing to derive quantitative measures of humor that are motivated by both humor theory and general theories of sentence comprehension. Taken together, this work sheds light on creative and social uses of language and advances formal approaches to language understanding, such that computational models can explain a broader range of phenomena that enrich our linguistic and social lives.

Keywords: Psychology.
Advisor: Goodman Noah (Noah D.) primary advisor. Thesis advisor
Clark Herbert H. advisor. Thesis advisor
Frank Michael C. (Professor of human biology) advisor. Thesis advisor
Potts Christopher
Modeling information flow in networks [electronic resource] : competition, evolution, and external influence

In online social networks such as Twitter and Facebook, users are constantly sharing information with people they are connected to, as well as re-sharing information posted by others. Through this process, a single piece of information called a contagion can spread from user to user over the connections until it has reached a large portion of the network. In this thesis, we develop a series of probabilistic methods for modeling the spread of contagions in social networks in order to better understand the factors that affect the process. Our work examines several different phenomena that affect information flows through social networks. One such phenomenon is unobserved sources of information influencing members of the network. We present a model that not only quantifies these hidden information sources but also provides a more accurate view of information spread. We find that as much as 29% of all information spreading through social networks like Twitter originates from sources outside the network. Next, we examine how different contagions spreading through a network can interact with each other. We observe and model competition (when one contagion can decrease the spread of another contagion) and cooperation (when one contagion increases the spread of another). We find that contagion interaction can increase or decrease the probability of contagion spread by more than 70% on average. We also explore the dynamic nature of social network structure, and how these dynamics are affected by the spread of information. As social network users are exposed to new contagions, they are constantly forming new connections with other users as well as deleting connections. We find that the spread of contagions can cause sudden "bursts" in both the creation of new connections and the deletion of old connections. We also find that contagions can change the network structure on a global scale by moving like-minded members closer to each other as well as pushing less similar users farther away. Additionally, we consider the problem of inferring the structure of a hidden network when only patterns of information spread are known. Given only the timing of when each user adopted each piece of information, our method accurately predicts which users are connected to which other users by converting the maximum likelihood estimation of the network into a series of independent convex optimization problems that can be solved efficiently. Taken together, the results presented in this thesis make contributions to understanding how information flows through networks, and they provide insights into the nature of how people exchange information.

Advisor: Leskovec Jurij primary advisor. Thesis advisor
Hastie Trevor advisor. Thesis advisor
Johari Ramesh
Modeling natural language semantics in learned representations [electronic resource]

The last few years have seen many striking successes from artificial neural network models on hard natural language processing tasks. These models replace complex hand-engineered systems for extracting and representing the meanings of sentences with learned functions that construct and use their own internal vector-based representations. Though these learned representations are effective in many domains, they aren't interpretable in familiar terms and their ability to capture the full range of meanings expressible in language is not yet well understood. In this dissertation, I argue that neural network models are capable of learning to represent and reason with the meanings of sentences to a substantial extent. First, I use entailment experiments over artificial languages to show that existing models can learn to reason logically over clean language-like data. I then present a large new corpus of entailments in English and use experiments on that corpus to show that these abilities extend to natural language as well. Finally, I introduce a new model that uses the semantic principle of compositionality to more efficiently and more effectively learn language from large volumes of data.

Keywords: Linguistics.
Advisor: Manning Christopher D. primary advisor. Thesis advisor
Potts Christopher
Icard Thomas advisor. Thesis advisor
Liang Percy advisor. Thesis advisor
Modeling, optimization, and decision support for integrated urban and infrastructure planning [electronic resource]

Reducing the energy consumption and associated greenhouse gas emissions of urban areas is paramount in research and practice. As the urban population grows to 6.2 billion people in 2050, advancing the practice of urban and energy infrastructure planning to enable livable, low-carbon growth is essential. Despite this imperative, prior approaches to urban and infrastructure planning have failed to connect analytical models of energy performance with planning and infrastructure decisions on building use, form, and energy supply. This dissertation introduces a new approach to integrated urban and energy infrastructure planning that is based on desired municipal outcomes, and uses a novel optimization and analysis model for early-stage urban and infrastructure planning decisions. The model developed to facilitate this planning approach is capable of simultaneously evaluating and optimizing energy supply technologies, individual building mix and form, and site layout. Case studies for neighborhoods in Oakland, CA, and San Francisco, CA, are used to show how this model-driven, outcome-based planning approach can be used by policymakers to evaluate policy options. It is shown that the model has value as a decision support tool by enabling identification of statistically significant differences between potential outcomes of municipal planning and investment decisions. Furthermore, the model is shown to have value in identifying patterns of development that lead to lower carbon emissions and improved municipal economic outcomes.

Keywords: Civil and Environmental Engineering.
Advisor: Lepech Michael primary advisor. Thesis advisor
Brandt Adam (Adam R.) advisor. Thesis advisor
Fischer Martin
Modeling organism-wide immunity in health and cancer [electronic resource]

Immune cells function in an interacting hierarchy that coordinates activities of various cell types according to genetic and environmental contexts. We developed graphical approaches to construct an extensible immune reference map from mass cytometry data of cells from different organs, incorporating landmark cell populations as flags on the map to compare cells from distinct samples. The maps recapitulated canonical cellular phenotypes and revealed reproducible, tissue-specific deviations. The approach revealed influences of genetic variation and circadian rhythms on immune system structure, enabled direct comparisons of murine and human blood cell phenotypes, and even enabled archival fluorescence-based flow cytometry data to be mapped onto the reference framework. This foundational reference map provides a working definition of systemic immune organization to which new data can be integrated to reveal deviations driven by genetics, environment, or pathology. We have further extended this concept to initiate a framework for modeling human immunity, using whole peripheral blood samples as an inaugural dataset. Moreover, we demonstrate that this method is capable of modeling the effects of immunotherapeutic interventions on immune responses across the system. We have recently developed novel strategies for inducing anti-tumor immune responses that leverage the stimulatory capacity of tumor-binding antibodies. When combined with adjuvants to activated dendritic cells, this combination therapy is capable of inducing sterilizing immunity in a number of translatable and autochthonous animal models as well as in primary cells from cancer patients in culture. Using these therapies as a model of an effective anti-tumor immune response, our system-wide modeling approach revealed the systemic events resulting in the eradication of spontaneous breast tumors.

Keywords: Immunology.
Advisor: Engleman Edgar G. primary advisor. Thesis advisor
Nolan Garry P. primary advisor. Thesis advisor
Davis Mark advisor. Thesis advisor
Khavari Paul A. advisor. Thesis advisor
Levy Ronald
Modeling the distribution of dark matter and its connection to galaxies [electronic resource]

Despite the mysterious nature of dark matter and dark energy, the Lambda-Cold Dark Matter (LCDM) model provides a reasonably accurate description of the evolution of the cosmos and the distribution of galaxies. Today, we are set to tackle more specific and quantitative questions about the galaxy formation physics, the nature of dark matter, and the connection between the dark and the visible components. The answers to these questions are however elusive, because dark matter is not directly observable, and various unknowns lie between what we can observe and what we can calculate. Hence, mathematical models that bridge the observable and the calculable are essential for the study of modern cosmology. The aim of my thesis work is to improve existing models and also to construct new models for various aspects of the dark matter distribution, as dark matter structures the cosmic web and forms the nests of visible galaxies. Utilizing a series of cosmological dark matter simulations which span a wide dynamical range and a statistical sample of zoom-in simulations which focus on individual dark matter halos, we develop models for the spatial and velocity distribution of dark matter particles, the abundance of dark substructures, and the empirical connection between dark matter and galaxies. As more precise observational results become available, more accurate models are then required to test the consistency between these results and the LCDM predictions. For all the models we investigate, we find that the formation history of dark matter halos always plays a crucial role. Neglecting the halo formation history would result in systematic biases when we interpret various observational results, including dark matter direct detection experiments, the detection of dark substructures with strong-lensed systems, the large-scale spatial clustering of galaxies, and the abundance of dwarf galaxies. Rectifying this, our work will enable us to fully utilize the complementary power of diverse observational datasets to test the LCDM model and to seek new physics.

Keywords: Physics.
Advisor: Wechsler Risa H. (Risa Heyrman) primary advisor. Thesis advisor
Abel Tom G.
Allen Steven
Modeling the Labor Market Effects of the Affordable Care Act

This paper develops a simulation model to predict the labor market effects of the 2010 Affordable Care Act (ACA). Two provisions of the ACA—the expansion of state Medicaid programs and the creation of public health insurance exchanges—are likely to have significant effects on individual labor supply. This paper incorporates these new provisions into the budget constraint of the individual labor-leisure choice model in order to predict optimal work hours in response to the ACA. Using Central Population Survey (CPS) and premiums data to empirically estimate model parameters, I predict an -11.8% decrease in work hours after the ACA, and bunching before Medicaid and exchange subsidy eligibility thresholds. I also predict heterogeneous results for different demographic groups, with retirement age workers predicted to exhibit the largest decrease in work hours, and low-income workers previously eligible for Medicaid predicted to increase work hours. The model serves as a base for future policy simulation and complements a growing empirical literature base on the Affordable Care Act.

Keywords:  Department of Economics
Affordable Care Act
Medicaid notch
simulation model
labor-leisure tradeoff
exchange subsidies
Stanford Department of Economics
Thesis
Advisor: Bhattacharya Jayanta
Models and large-scale coordination algorithms for autonomous mobility-on-demand [electronic resource]

Urban mobility in the 21st century faces significant challenges, as the unsustainable trends of urban population growth, congestion, pollution, and low vehicle utilization worsen in large cities around the world. As autonomous vehicle technology draws closer to realization, a solution is beginning to emerge in the form of autonomous mobility-on-demand (AMoD), whereby fleets of self-driving vehicles transport customers within an urban environment. This dissertation introduces a systematic approach to the design, control, and evaluation of these systems. In the first part of the dissertation, a stochastic queueing-theoretical model of AMoD is developed, which allows both the analysis of quality-of-service metrics as well as the synthesis of control policies. This model is then extended to one-way car sharing systems, or human-driven mobility-on-demand (MoD) systems. Based on these models, closed-loop control algorithms are designed to efficiently route empty (rebalancing) vehicles in very large systems with thousands of vehicles. The performance of the algorithms and the potential societal benefits of AMoD and MoD are evaluated through case studies of New York City and Singapore using real-world data. In the second part of the dissertation, additional structural and operational constraints are considered for AMoD systems. First, the impact of AMoD on traffic congestion with respect to the underlying structural properties of the road network is analyzed using a network flow model. In particular, it is shown that empty rebalancing vehicles in AMoD systems will not increase congestion, in stark contrast to popular belief. Finally, the control of AMoD systems with additional operational constraints is studied under a model predictive control framework, with a focus on range and charging constraints of electric vehicles. The technical approach developed in this dissertation allows us to evaluate the societal benefits of AMoD systems as well as lays the foundation for the design and control of future urban transportation networks.

Keywords: Aeronautics and Astronautics.
Advisor: Pavone Marco
Rock Stephen M. advisor. Thesis advisor
Schwager Mac
The molecular architecture of peptidoglycan and the rate of peptidoglycan biosynthesis relative to cytoplasmic expansion play essential roles in determining bacterial size and shape [electronic resource]

Cell size and shape are cell-scale properties many orders of magnitude larger than individual proteins, and it is largely unknown how cells coordinate the growth of such reproducible, uniform, large-scale structures. In this thesis I present several lines of inquiry to address what determines cell size and shape in several bacterial species. First, I investigate a point mutation in the morphogenetic protein MreB and find that the resulting variable-width phenotype reveals a role for surface area to volume ratio (SA/V) maintenance in determining cell shape and size. Next, I present a general mechanism for SA/V homeostasis in bacteria and demonstrate that this mechanism underlies size determination in bacteria, resolving many long-standing questions about the link between growth rate and cell size. Finally, I develop a novel method for detecting the underlying architecture of peptidoglycan that can be widely implemented to understand how genetic and chemical perturbations acting locally give rise to cell-scale morphological defects.

Advisor: Theriot Julie primary advisor. Thesis advisor
Fisher Daniel S. advisor. Thesis advisor
Huang Kerwyn Casey
Shapiro Lucy advisor. Thesis advisor
Molecular mechanisms governing integrin-mediated force transmission in living cells [electronic resource]

Living cells are exquisitely tuned to the mechanical properties of their surroundings. Indeed, physical forces such as substrate rigidity, fluid shear, and stretch guide cellular processes such as stem cell differentiation, wound healing, and development. Additionally, disruption in the ability of cells to sense the mechanical properties of their surroundings represents a hallmark of many diseases, including muscular dystrophy, arteriosclerosis, cardiomyopathies, and cancer. Although cells have numerous mechanisms for detecting mechanical inputs, one of the most prominent is through integrins, heterodimeric transmembrane proteins that cluster into micrometer-sized assemblies termed focal adhesions (FAs). FAs link the extracellular matrix (ECM) to the cell cytoskeleton and are composed of hundreds of different proteins, many of which are proposed to sense force. How the many proteins within FAs are organized into force sensing and transmitting structures is poorly understood. Additionally, the mechanisms describing how FAs regulate force transduction is not well established, due primarily to a lack of tools to visualize cell-generated forces at molecular resolution. Here, we describe Förster resonance energy transfer (FRET)-based molecular tension sensors that allow us to directly visualize cell-generated forces with single-molecule sensitivity. We apply these sensors to measure forces transmitted by integrins in living cells and find that relatively modest tensions at the molecular level are sufficient to drive robust cellular adhesion. We observe strikingly complex distributions of tension within individual FAs, and characterize the organization of various adhesion components with respect to tension generation. We combine fluorescent molecular tension sensors with super-resolution light microscopy to visualize traction forces within FAs at < 100 nm spatial resolution. Finally, using these sensors we establish that metastatic cancer cells generate modest increases in integrin-mediated forces and we use proteomics to identify potential molecular associations that contribute to this increase in tension and thus the progression of cancer.

Keywords: Chemical Engineering.
Advisor: Dunn Alexander Robert primary advisor. Thesis advisor
Khosla Chaitan
Nelson William advisor. Thesis advisor
Molecular tuning of synaptic vesicle fusion mode probed using novel pHdot optical nanosensor [electronic resource]

Regulated exo-endocytosis is essential for the function of many intercellular signaling networks. Using a new optical nanosensor termed pHdot, I explore the molecular and cellular signals that tune the balance between two modes of vesicle fusion and recycling: kiss-and-run (KR) and full collapse fusion (FF) in hippocampal synapses in primary culture. Stimulation after a period of rest generates a frank initial rise in pHdot fluorescence, an unmistakeable signature of early phase KR. The prevalence of KR was dependent on the available pool of readily releasable vesicles and modulated by internal Ca2+ dynamics. Intriguingly, pharmacological inhibition of the Ca2+-dependent phosphatase calcineurin and stimulation of synapses with phorbol esters had dramatic yet opposing effects on fusion mode balance; providing a means of turning KR "on" or "off" respectively. Emerging data further suggests calcineurin facilitates a molecular switch from KR to FF that is dependent on actin dynamics and the F-BAR domain-containing protein syndapin I. Altogether, this body of work provides mechanistic insight into what appears to be a rather complex molecular choreography underlying kiss-and-run fusion at small, central synapses. The rich set of modulatory effects described could conceivably account for disparate reports on the prevalence of kiss-and-run at nerve terminals, a source of longstanding controversy. More exciting however is the likelihood that these findings reflect the diversity of signals and proteins that participate in shaping the energetics of fusion pore formation thus providing a glimpse into the intricate biochemical milieu that a vesicle navigates to direct its dynamic release properties.

Keywords: Molecular and Cellular Physiology.
Advisor: Lewis Richard (Richard Sheridan) primary advisor. Thesis advisor
Tsien R. W. (Richard W.) primary advisor. Thesis advisor
Nachury Maxence
Reimer Richard J. advisor. Thesis advisor