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The Dish of Theseus
No full textIn this paper, I use the semiotic, practical, and material movements of cuisine over time to address a well-worn paradox in philosophy. Incorporating perspectives on intangible heritage, I ground the paper in linguistic approaches to foodways. By taking this standpoint, we can prod at long-standing issues of typology and temporality, as well as the iterative process of foodways generally and recipes specifically. The ancient Ship of Theseus puzzle is rooted in the physicality of objects, and can invoke subsistence as substance. But food occupies a special place given that it is equal parts material and immaterial; a meal is comprised of both tangible and intangible ingredients. If recipes "move" over time into new forms and meanings, when is the dish of Theseus no longer the dish of Theseus
Of Forts and Fairies
No full textThis article is an exploration of a particular place in two time periods: the period of my doctoral fieldwork in 2012; and, indirectly, the present of early 2022, a full decade after my initial (and so far only) encounters with the place in question. It is an attempt to think about place both in the immediate, embodied way enabled by conventional in-person ethnographic fieldwork, and in the more remote way demanded by physical and temporal separation. (The latter process, the critical work of relating to a place after the fact, is present mostly in the revisions to the original text, as well as in this introductory section and in the brief postscript.) The location in question, Fairy Fort Farm in Tipperary, Ireland, is a personal chronotope, an index of my time in a place and the subsequent years spent writing and thinking about Ireland without being there. It forms a portion of my own local cosmology, my own understanding of how I relate (and have related) to the world and other people via embodied, emplaced experiences. An important question raised by this exploration of place is the ontological status of a very particular set of “ruins”: the eponymous fairy fort located on the farm. The question of their status as ruins, or possibly an imitation of ruins, unsettled my own understanding of fairy places in Ireland’s supernatural landscape. Although I attempt to center my friend Michael’s interpretation, the question of the fairy fort’s nature remains a compelling one for what it reveals about the central role of experience in the construction of local cosmologies
Learning for Efficient, Scalable, and Constrained Bayesian Optimization in Real-World Applications
No full textBayesian Optimization (BO) has emerged as a powerful framework for optimizing black-box functions where evaluations are expensive. However, deploying BO in complex real-world scenarios presents significant challenges, including high-dimensional search spaces, the presence of unknown constraints, the need to balance multiple objectives, and the demand for efficient end-to-end modeling and decision-making. This proposal outlines a research agenda focused on developing novel BO methodologies that are efficient, scalable, and capable of handling constraints to address these challenges. The proposed work is structured around three main thrusts: (1) Efficient Bayesian Optimization via Regions of Interest (ROI): We explore methods to learn ROIs to make BO more efficient, particularly in high-dimensional or heterogeneous applications. This involves adaptive level-set estimation to identify promising sub-regions of the search space. (2) Efficient End-to-End Modeling and Decision Making (DRO): We investigate an end-to-end learning framework that moves beyond hand-crafted acquisition functions and myopic decision-making. This involves leveraging Decision Transformers for direct regret optimization, trained with a combination of simulated and real-world data. (3) Bayesian Optimization with Unknown Constraints: We develop principled approaches for BO problems where constraints are unknown and must be learned concurrently with the objective function. This includes COBAR for single-objective constrained BO, focusing on the principled treatment of feasibility, and CMOBO for constrained multi-objective BO, enabling a principled tradeoff among multiple objectives under learned constraints. These research thrusts aim to significantly advance the capabilities of BO, enabling its application to a wider range of challenging real-world problems such as protein design, material discovery, and drug development
Solidarity, Agonism and <i>Entre-soi</i> in the Village Meals of the Causse du Quercy
No full textIn the area of South West France known as Quercy, summer is epitomized by a succession of feasts in many of the villages: they last about 3 days and always culminate in a communal meal on the Monday night. Social actors and commentators claim that these feasts and the communal meals are festive affairs : indeed they are, but underlying tensions are particularly obvious during the preparation and consumption of the communal meals where features associated with community building coexist with mild rivalries, agonism and a desire for entre-soi (French noun: literally, a situation where one keeps company with people who are socially and culturally similar to oneself). Keeping this in mind, and using data gathered over the last 30 years in the Quercy, I am discussing the central role played by the communal meal in community building, but also in the reinforcement of agonism between villagers and villages. I am paying special attention to the semiotics of the food eaten during these meals, sustained as it is by metagastronomic discourses that complement the meaning of these meals
Hub-Plucking, Hub-Contestability, and Hub Power: Harnessing Network Science to Rethink Antitrust’s Analysis of Platform Competition
No full textThis JSD dissertation develops a framework for understanding competition within platform ecosystems by uncovering the structural and strategic role of hubs—highly connected nodes within these ecosystems. Drawing on contemporary network science, a computational discipline devoted to studying complex systems, it argues that hubs are not merely participants but critical loci of value creation and control. Their behavior can determine the competitive trajectory of the ecosystem and shape inter-platform rivalry. Network science shows that social, technological, biological, and economic systems follow common organizing principles, which the dissertation leverages to examine the emergence, strategic behavior, and contestability of hubs, thereby reconfiguring how market power should be understood in digital ecosystems. The first article, Uncovering the Role of Hubs: A Network Science Perspective on Platform Competition, challenges the entrenched dichotomy that views network effects as either inevitably leading to winner-takes-all outcomes or as inherently self-correcting. Drawing on concepts such as preferential attachment and node fitness, it introduces hub-plucking—the rivalry between platforms over highly connected nodes—and shows that this overlooked dynamic lies at the heart of digital competition. The second article, Hub-Contestability: A New Antitrust Paradigm for Platform Competition, advances the concept of hub-contestability to counter the threat posed by network effects entrenching dominant platforms. It builds on the insight that the seemingly insurmountable moat created by network effects contains a critical vulnerability to hub-plucking competition, yet cautions that dominant platforms may neutralize this threat through tactics aimed at locking, crushing, or suppressing hubs. By fusing network science with contestability theory, hub-contestability seeks to recalibrate antitrust doctrines—from merger control and abuse of dominance to remedies—so that hubs remain sufficiently contestable to sustain dynamic inter-platform competition. The final article, Hub Power and Hub(uses): Power Dynamics in Platform Ecosystems, examines the reverse phenomenon: the rise of hub power within platforms. It identifies four determinants—hub attractiveness, platform dependence, switching feasibility, and countervailing power—and demonstrates how dominant hubs can distort value creation and competition both within and across platforms. Through case studies spanning e-books, social media, streaming, and air travel, it shows how hub power reshapes market outcomes and complicates antitrust intervention. Together, these studies develop a unified framework for understanding competition in the networked economy. By revealing how hub dynamics both constrain and enable platform power, the dissertation bridges network science and antitrust law—laying the groundwork for a new generation of competition analysis attuned to the complex architecture of digital ecosystems
Explainability-driven AI: Improving Model Robustness and Supporting Scientific Discovery
No full textThe development of explainable artificial intelligence (AI) has become a crucial step in enhancing model transparency, robustness, and usability. This thesis explores the explainability in the context of natural language processing, proposing novel approaches to improve model performance and support scientific discovery. We investigate whether explainable AI techniques can be leveraged to improve out-of-distribution generalization and model decision-making. By incorporating natural language explanations and rationale-based models, we aim to address challenges in model interpretability and resilience, particularly in the face of adversarial attacks and misleading inputs. Additionally, we propose algorithms that leverage large language models to generate novel and robust scientific hypotheses in the social science domain. We further propose using mechanistic interpretability to understand what models have learned, particularly in scenarios where they exhibit superhuman performance, thereby providing insights into the internal workings of these models and aiding the generation of novel scientific hypotheses. This research contributes to advancing both the theoretical understanding of AI systems and their practical application in fields requiring high levels of reliability and transparency, such as scientific research and critical decision-making
A contextual fear conditioning paradigm in head-fixed mice exploring virtual reality
Get PDFContextual fear conditioning (CFC) is a classical laboratory task that tests associative memory formation and recall. Techniques such as multi-photon microscopy and holographic stimulation offer tremendous opportunities to understand the neural underpinnings of these memories. However, these techniques generally require animals to be head-fixed. Few paradigms examine contextual fear in head-fixed mice, and none use freezing—the most common measure of fear in freely moving animals—as the behavioral readout. To address this gap, we developed a CFC paradigm for head-fixed mice using virtual reality (VR). We designed an apparatus to deliver tail shocks while mice navigated a VR environment. We tested three versions of this paradigm and, in all of them, observed increased freezing, particularly on the first trial, in the shock-paired VR compared to a neutral one. These results demonstrate that head-fixed mice can be fear-conditioned in VR and exhibit context-specific freezing behavior. Additionally, using two-photon calcium imaging, we tracked large populations of hippocampal CA1 neurons before, during, and following CFC. As in freely moving mice, CA1 place cells remapped and developed narrower fields following fear conditioning. Thus, our approach enables new opportunities to study the neural mechanisms underlying the formation, recall, and extinction of contextual fear memories
Nitrogen-fixing microbes gain genes in diverse types of living environments
No full textBiological nitrogen fixation (BNF), which is catalyzed by a large nitrogenase enzyme complex, has evolved in both bacteria and archaea. Indeed, nitrogen-fixing species are found in diverse living environments, and BNF has evolved even in aerobic bacteria, although the function of nitrogenase is inhibited by oxygen. BNF is, however, highly energy-costing, requiring 16 ATPs in a single nitrogen fixation reaction. To explain this paradox, we hypothesized that nitrogen-fixing species gain not only nitrogen-fixing (nif) genes but also non-nif genes to facilitate nitrogen fixation. We examined over 3500 nitrogen-fixing genomes and found that they have gained genes directly or indirectly related to BNF in diverse types of living environments, so that nitrogen-fixing species tend to have larger genomes than their non-nitrogen-fixing relatives. Interestingly, the non-nif genes gained tend to be located near nif-gene clusters, probably to achieve proximity effects such as coordinated gene regulation. For example, the most frequent among the genes gained are ABC transporter genes, which facilitate the absorption and physiological metabolism of carbon (e.g., sugars), nitrogen (e.g., amino acids), and trace elements (e.g., molybdenum), and many ABC transporter genes lie close to nif-gene clusters. From our findings, we propose the following scenario: BNF evolved in many archaea and bacteria because BNF is advantageous to its hosts, although it incurs a high energy cost. Then, gaining genes to facilitate BNF compensates the cost of BNF, facilitating the spread of nitrogen fixers to all living habitats. This expansion benefits the biosphere, as nitrogen is essential for all organisms
ResearchBox 1992, 'Nudging Hypotheticals (2024)', https://ResearchBox.org/1992
No full textHypothetical scenarios provide an extremely useful alternative to field experiments for scholars interested in nudging behavior change, comprising a substantial proportion of the literature. Yet the extent to which hypotheticals accurately estimate real-world treatment effects is not well understood. To investigate, we identified five recent field studies of real-world nudges in distinct domains and designed four styles of hypothetical scenarios to approximate each one. This setup allows for clear comparison of old field data with new hypothetical data. Across our 20 experiments (N=16,114), hypothetical scenarios nearly always estimated the correct direction of treatment effects. However, they varied widely in estimating magnitudes, making them unreliable inputs to real-world policy applications such as cost-benefit analyses. Our findings underscore the promising value of hypotheticals, but also the need for greater investigation into strategies to calibrate their estimates
Locomotion on a lubricating fluid with spatial viscosity variations
No full textWe study locomotion of a model crawler corresponding to a deforming upper boundary of finite length above a thin Newtonian fluid film whose viscosity varies spatially. We first derive a general locomotion velocity formula with fluid viscosity variations via the lubrication theory. For further analysis, the surface of the crawler is described by a combination of transverse and longitudinal traveling waves and we find that under a uniform viscosity a transverse wave results in a retrograde crawler, while a longitudinal wave leads to a direct crawler. We then analyze the time-averaged locomotion behaviors under two scenarios: (i) a sharp viscosity interface and (ii) a linear viscosity gradient. Using the asymptotic expansions of small surface deformations and the method of multiple timescale analysis, we derive an explicit form of the average velocity that captures nonlinear, accumulative interactions between the crawler and the spatially varying environment. (i) In the case of a viscosity interface, the time-averaged speed of the crawler is always slower than that in the uniform viscosity, for both the transverse and longitudinal wave cases. Notably, the speed reduction is most significant when the crawler's front enters a more viscous layer and the crawler's rear exits from the same layer. (ii) In the case of a viscosity gradient, the crawler's speed becomes slower for the transverse wave, while for the longitudinal wave, the locomotion speed does not change significantly. Our analysis illustrates the fundamental importance of interactions between a locomotor and its environment, and separating the timescale behind the locomotion.</p