Learning Reasoning Paths over Semantic Graphs for Video-grounded Dialogues

Compared to traditional visual question answering, video-grounded dialogues require additional reasoning over dialogue context to answer questions in a multi-turn setting. Previous approaches to video-grounded dialogues mostly use dialogue context as a simple text input without modelling the inherent information flows at the turn level. In this paper, we propose a novel framework of Reasoning Paths in Dialogue Context (PDC). PDC model discovers information flows among dialogue turns through a semantic graph constructed based on lexical components in each question and answer. PDC model then learns to predict reasoning paths over this semantic graph. Our path prediction model predicts a path from the current turn through past dialogue turns that contain additional visual cues to answer the current question. Our reasoning model sequentially processes both visual and textual information through this reasoning path and the propagated features are used to generate the answer. Our experimental results demonstrate the effectiveness of our method and provide additional insights on how models use semantic dependencies in a dialogue context to retrieve visual cues.Comment: Accepted at ICLR (International Conference on Learning Representations) 202

NASA advanced design program: Analysis, design, and construction of a solar powered aircraft

Increase in energy demands coupled with rapid depletion of natural energy resources have deemed solar energy as the most logical alternative source of power. The major objective of this project was to build a solar powered remotely controlled aircraft to demonstrate the feasibility of solar energy as an effective, alternate source of power. The final design was optimized for minimum weight and maximum strength of the structure. These design constraints necessitated a carbon fiber composite structure. Surya is a lightweight, durable aircraft capable of achieving level flight powered entirely by solar cells

Multimodal Transformer Networks for End-to-End Video-Grounded Dialogue Systems

Developing Video-Grounded Dialogue Systems (VGDS), where a dialogue is conducted based on visual and audio aspects of a given video, is significantly more challenging than traditional image or text-grounded dialogue systems because (1) feature space of videos span across multiple picture frames, making it difficult to obtain semantic information; and (2) a dialogue agent must perceive and process information from different modalities (audio, video, caption, etc.) to obtain a comprehensive understanding. Most existing work is based on RNNs and sequence-to-sequence architectures, which are not very effective for capturing complex long-term dependencies (like in videos). To overcome this, we propose Multimodal Transformer Networks (MTN) to encode videos and incorporate information from different modalities. We also propose query-aware attention through an auto-encoder to extract query-aware features from non-text modalities. We develop a training procedure to simulate token-level decoding to improve the quality of generated responses during inference. We get state of the art performance on Dialogue System Technology Challenge 7 (DSTC7). Our model also generalizes to another multimodal visual-grounded dialogue task, and obtains promising performance. We implemented our models using PyTorch and the code is released at https://github.com/henryhungle/MTN.Comment: Accepted at ACL 2019 (Long Paper

RasGRP1 Transduces Low-Grade TCR Signals which Are Critical for T Cell Development, Homeostasis, and Differentiation

AbstractTwo important Ras-guanyl nucleotide exchange factors, Sos and RasGRP1, control Ras activation in thymocytes. However, the relative contribution of these two exchange factors to Ras/ERK activation and their resulting impact on positive and negative selection is unclear. We have produced two lines of RasGRP1−/− TCR transgenic mice to determine the effect of RasGRP1 in T cell development under conditions of defined TCR signaling. Our results demonstrate that RasGRP1 is crucial for thymocytes expressing weakly selecting TCRs whereas those that express stronger selecting TCRs are more effective at utilizing RasGRP1-independent mechanisms for ERK activation and positive selection. Analysis of RasGRP1−/− peripheral T cells also revealed hitherto unidentified functions of RasGRP1 in regulating T cell homeostasis and sustaining antigen-induced developmental programming

IL-33 ameliorates Alzheimer’s disease-like pathology and cognitive decline

Alzheimer’s disease (AD) is a devastating condition with no known effective treatment. AD is characterized by memory loss as well as impaired locomotor ability, reasoning, and judgment. Emerging evidence suggests that the innate immune response plays a major role in the pathogenesis of AD. In AD, the accumulation of β-amyloid (Aβ) in the brain perturbs physiological functions of the brain, including synaptic and neuronal dysfunction, microglial activation, and neuronal loss. Serum levels of soluble ST2 (sST2), a decoy receptor for interleukin (IL)-33, increase in patients with mild cognitive impairment, suggesting that impaired IL-33/ST2 signaling may contribute to the pathogenesis of AD. Therefore, we investigated the potential therapeutic role of IL-33 in AD, using transgenic mouse models. Here we report that IL-33 administration reverses synaptic plasticity impairment and memory deficits in APP/PS1 mice. IL-33 administration reduces soluble Aβ levels and amyloid plaque deposition by promoting the recruitment and Aβ phagocytic activity of microglia; this is mediated by ST2/p38 signaling activation. Furthermore, IL-33 injection modulates the innate immune response by polarizing microglia/macrophages toward an antiinflammatory phenotype and reducing the expression of proinflammatory genes, including IL-1β, IL-6, and NLRP3, in the cortices of APP/PS1 mice. Collectively, our results demonstrate a potential therapeutic role for IL-33 in AD

"Cultural additivity" and how the values and norms of Confucianism, Buddhism, and Taoism co-exist, interact, and influence Vietnamese society: A Bayesian analysis of long-standing folktales, using R and Stan

Every year, the Vietnamese people reportedly burned about 50,000 tons of joss papers, which took the form of not only bank notes, but iPhones, cars, clothes, even housekeepers, in hope of pleasing the dead. The practice was mistakenly attributed to traditional Buddhist teachings but originated in fact from China, which most Vietnamese were not aware of. In other aspects of life, there were many similar examples of Vietnamese so ready and comfortable with adding new norms, values, and beliefs, even contradictory ones, to their culture. This phenomenon, dubbed "cultural additivity", prompted us to study the co-existence, interaction, and influences among core values and norms of the Three Teachings--Confucianism, Buddhism, and Taoism--as shown through Vietnamese folktales. By applying Bayesian logistic regression, we evaluated the possibility of whether the key message of a story was dominated by a religion (dependent variables), as affected by the appearance of values and anti-values pertaining to the Three Teachings in the story (independent variables).Comment: 8 figures, 35 page

Senior Recital

List of performers and performances