Alliances against rebellion

This paper investigates the domestic implications of alliances by combining insights from deterrence theory, bargaining theory, and the literature on the causes of civil war. I argue that alliances can reduce the probability of civil wars caused by commitment problems by deterring rebel groups from launching preventive war. To support this claim I first discuss the Concert of Europe and the Wars of the First Coalition, and illustrate the strategic dynamic between governments, allies, and rebel groups. I then propose and solve a model of deterrence to prove the formal conditions under which an ally will intervene in a potential civil war and how this affects rebel decisions to launch preventive war. To test the empirical implications derived from the model, I use data on civil war, defensive alliances, state power, and oil discoveries. The results of this paper demonstrate that alliances can stabilize the effects of anticipated shifts in government power, reducing the probability of civil war.Governmen

Multimodal understanding and generation : bridging vision and language

Over the last decade, deep learning and neural networks have fundamentally transformed visual understanding and generation. However, traditional vision models are single-modal, failing to generalize to real-world scenarios. In contrast, humans naturally leverage language to both comprehend and create visual content. This dissertation presents several approaches for multimodal understanding and generation with both vision and natural language input. First, we introduce an open-vocabulary semantic segmentation model, OVSeg, which can segment any categories through arbitrary text prompts. Next, we propose FlowVid, a conditional video diffusion model that enhances temporal consistency in text-guided video-to-video generation by utilizing imperfect temporal optical flow. Third, we present StreamV2V, a real-time text-guided video-to-video generation model designed to handle streaming inputs on a single GPU. Lastly, we introduce Movie Weaver, a multimodal video generation model that takes both multi-concept reference images and text prompts to produce personalized videos. We conclude by discussing future directions in multimodal research, aiming to further bridge vision and language for more generalizable and capable models.Electrical and Computer Engineerin

Behavioral and brain associations with academic achievement and skill change

Early success in school is predictive of future academic success, health and well-being. One factor contributing to academic success is executive function (EF) abilities. Summer learning loss, or the idea that students lose math and reading skills over the summer break from school, is another important component of academic success. Current research on EF deficits or summer learning loss tend to find negative impacts at the group level. However, learning differences or loss for any given individual is not inevitable. This dissertation examined behavioral and neural risk factors for academic underachievement and summer academic skill change across late childhood and adolescence. Chapter 1 examined how age, puberty, and attention-deficit/hyperactivity disorder (ADHD) burden related to EF abilities longitudinally across late childhood and adolescence. We found that earlier EF abilities predicted later burden, suggesting that early EF abilities could be a marker of later burden or worse trajectories. Chapter 2 examined the interactions between ADHD burden, academic achievement and summer academic skill change over the summer break from school. We found those with higher ADHD burden pre-summer had lower academic performance pre-summer, and interesting patterns of summer skill change that interacted with pre-summer academic abilities. In Chapter 3 we utilized task-based and resting-state functional connectivity to examine whether a set of neural communication measures measured before summer break could explain individual differences in academic achievement pre-summer and summer academic skill change. We found that measures of whole-brain functional integration and segregation differed across academic and resting-state tasks, and functional patterns of the control and attention networks also differed both within and across tasks. An individual’s rank in integration and segregation remained fairly consistent across tasks, suggesting differences in organization between individuals might be a trait-like feature, rather than fully dependent on task dynamics. The only relations to summer change in reading and math were found during rest and interacted with initial academic abilities. By testing multiple cognitive and neural associations of achievement and summer academic skill change, this dissertation informs neurocognitive theory of academic skill development and maintenance, and informs the development of more targeted academic interventions.Psycholog

From diversity to adaptivity : effective multitask learning and continual learning neural architectures

Despite remarkable successes from recent advances in deep learning, there remain many challenges. This dissertation focuses on two specific challenges: effectively optimizing a linear combination of multiple loss functions and enabling continual learning for deep neural networks. The first part of the thesis addresses the challenge of optimizing loss functions composed of heavily conflicting components. In deep learning, it is common practice to optimize a combination of several loss functions to satisfy multiple desiderata. However, standard optimization techniques can lead to poor local minima for these problems, as different loss functions often conflict with one another. A few loss functions with dominating gradients may tend to be disproportionately optimized, thereby dictating the entire optimization trajectory. To mitigate this issue, we propose a method to quantify the local conflict and design algorithms that minimize the overall loss following trajectories that achieve a more balanced descent of the individual sub-losses. Empirical results demonstrate that these methods produce better local minima. The second focus of the dissertation is on continual learning, enabling deep networks to autonomously adapt. Traditional deep learning models become static after training. We present two methods to overcome this limitation: dynamically expanding the network’s architecture in response to new data, and designing architectures that inherently support online learning. These innovations allow networks to autonomously update and adapt over time, reducing the need for frequent retraining. Together, these efforts take steps to enable deep learning models to learn from diverse loss functions and adapt continually to new data and problems.Computer Scienc

FactPICO : factuality evaluation for plain language summarization of medical evidence

Plain language summarization with LLMs can be useful for improving textual accessibility of technical content. But how factual are these summaries in a high-stakes domain like medicine? This paper presents FactPICO, a factuality benchmark for plain language summarization of medical texts describing randomized controlled trials (RCTs), which are the basis of evidence-based medicine and can directly inform patient treatment. FactPICO consists of 345 plain language summaries of RCT abstracts generated from three LLMs (i.e., GPT-4, Llama-2, and Alpaca), with fine-grained evaluation and natural language rationales from experts. We assess the factuality of critical elements of RCTs in those summaries: Populations, Interventions, Comparators, Outcomes (PICO), as well as the reported findings concerning these. We also evaluate the correctness of the extra information (e.g., explanations) added by LLMs. Using FactPICO, we benchmark a range of existing factuality metrics, including the newly devised ones based on LLMs. We find that plain language summarization of medical evidence is still challenging, especially when balancing between simplicity and factuality, and that existing metrics correlate poorly with expert judgments on the instance level.Computer Scienc

Synthesis by analysis : understanding and generating the 3D open world

In recent years, deep generative models have achieved remarkable success in text, image, and video generation. However, comparable breakthroughs have not yet been achieved in 3D generation. This dissertation focuses on three fundamental challenges in 3D generation. The first challenge concerns 3D representation. In contrast to the 2D domain, where data is structured on regular pixel grids, 3D generation requires modeling multiple aspects, including geometry, structure, and appearance. The second challenge is generalization to the open world. With recent progress in collecting large-scale 3D datasets, there is a growing need to extend 3D generative models to open-vocabulary settings, enabling them to capture a broader range of categories and scenarios. The third challenge is understanding. A 3D generative model should accurately capture geometric properties, structural relationships, and physical constraints to ensure the realism and coherence of the generated 3D content. To address the understanding challenge, this dissertation introduces a novel framework, Synthesis by Analysis. Unlike conventional generative approaches that rely solely on distribution alignment, this framework incorporates an analysis module to evaluate generated results, allowing the integration of prior knowledge to improve quality and consistency. Based on this framework, we first introduce GenCorres and GeoLatent, which focus on modeling the geometry of deformable shapes. Through as-rigid-as-possible (ARAP) analysis, we design geometric regularization losses to preserve deformation priors, ensuring that generated shapes maintain local rigidity. Extending this concept to scene-level generation, Sync2Gen addresses indoor scene structure modeling by incorporating scene prior analysis, which enforces structural consistency and physical constraints. To tackle the generalization challenge, the final work, Atlas Gaussians, focuses on appearance modeling in an open-world setting. This study pioneers the application of the VAE + latent diffusion model (LDM) paradigm to 3D Gaussians generation, leveraging our newly proposed Atlas Gaussians representation to improve the fidelity and scalability of 3D generation. Together, these four papers address the three key aspects of 3D representation by modeling geometry, structure, and appearance, while also tackling the challenges of generalization and understanding in 3D generation.Computer Scienc

Tricuspid Valve Transcatheter Edge-to-Edge Repair Simulations Predict Kinematic Alterations

We hypothesize that clips induce a force, and that clip placement influences the magnitude and direction of this force, which in turn leads to changes in annular size. We use three subject-specific finite element models of the human tricuspid valves, the Texas TriValves, to test our hypothesis in silico. Specifically, we quantify the change in force induced by TEER and explore how it varies with clip size and location. In total, 36 repair cases were simulated across three valves using Abaqus/Explicit on the TACC’s Lonestar6 cluster. We find that the total annular forces increase following TEER, and that larger clips produce significantly greater annular forces than smaller clips. Furthermore, we compare the angle of the clip to the angle of the maximum annular force vector. We find a strong correlation between the two orientations, and statistically significant dependence (p<0.001). In summary, our results support our hypothesis that annular force magnitude and direction depend on clip size and location. These findings address a current clinical question, and can help inform clinical and procedural decisions.Texas Advanced Computing Center (TACC

Surrogate Modeling of Cell Dynamics in Agent-Based Models Using Transformers and PINNs

Agent-based models (ABMs) capture individual behaviors and interactions, enabling the study of complex dynamics at the cellular scale. However, as the number of agents increases, computational cost rises sharply, limiting their feasibility for large scale studies in computational oncology. To address this, we compare transformers and physics-informed neural networks (PINNs) as surrogate models to replace the cell-movement component of our ABM. Since our ABM can generate unlimited training data, we evaluate each method's ability to generalize by testing predictions on unseen inputs. PINNs incorporate the underlying physics involved in the movement of cells, including the balance of forces acting on each cell. Transformers leverage attention mechanisms to identify spatial correlations and determine which neighboring cells influence a given cell’s motion. For both methods, we adjust key hyperparameters, including learning rate, number of layers, epochs, attention depth (for transformers), and loss functions (for PINNs). As we evaluate two modeling approaches across numerous hyperparameter combinations, the training process becomes computationally intensive and impractical to run on a personal computer. Access to TACC’s Lonestar6 system allowed us to perform these experiments efficiently using GPU nodes equipped with NVIDIA A100s, significantly accelerating our progress. Preliminary results on an unseen validation dataset demonstrate that the transformer model accurately identifies the neighborhood influencing each cell’s movement. On average, the predicted cell position deviates by only 13.2 ± 2.2 % of the cell radius (1.32 ± 0.22 µm from the ground truth). Similarly, the PINN-based approach effectively captures both the balance of forces and maximum cell velocity, achieving an average deviation of 25.4 ± 2.9 % of the cell radius (2.52 ± 0.29 µm) on the same validation set. These results support the feasibility of both modeling strategies. Ongoing work explores generalizability to higher cell counts than used in training. These methods provide a scalable framework for accelerating ABMs in oncology and other domains involving Newtonian motion.Texas Advanced Computing Center (TACC

Plestiodon laticeps (Broad-headed Skink). Combat Behavior.

This video demonstrates behavior. Most videos in this collection have no audible language and for those that do, the language isn't necessary to understand the behavior. For that reason, transcripts are not provided.Integrative Biolog

Examination of CO₂ reactivity and orexin activity as predictors of extinction memory for fear, food, and alcohol cues in rats

Trauma, anxiety, and substance use disorders are highly prevalent but only half of individuals achieve remission with the best available behavioral treatment, exposure therapy. The ability to identify likely responders to exposure therapy and provide an alternative treatment to likely non-responders would allow a greater number of individuals to achieve remission. To address this, I turned to the laboratory rat to model the associative learning processes that underlie these disorders and exposure therapy to examine behavioral and neural predictors of treatment response. Rats underwent Pavlovian conditioning, extinction training, and long-term memory testing of cues associated with fear (Chapters 3 & 4), food (Chapters 2 & 4), and alcohol (Chapter 5) as well as a CO₂ challenge prior to euthanasia to examine orexin activity in the lateral hypothalamus. Behavioral CO₂ reactivity predicted extinction memory for fear, food, and alcohol cues. CO₂ reactivity also predicted fear and alcohol memories after a different treatment, retrieval-extinction, although to a lesser degree. Orexin activity did not predict extinction memory for fear, food, nor alcohol cues. My studies show that CO₂ reactivity, but not CO₂-induced orexin activity, can be used to predict extinction memory for fear and reward cues. My work provides support for CO₂ reactivity to be examined as a predictor of exposure therapy response in individuals with trauma, anxiety, and substance use disorders.Neuroscienc