Investigation of the Effect of Age on Regenerative Outcomes Following Treatment of Volumetric Muscle Loss Injuries

Volumetric muscle loss (VML) is a traumatic injury in skeletal muscle resulting in the bulk loss of more than 20% of the muscle’s volume. Included in the bulk loss of muscle is the skeletal muscle niche comprised of nerve bundles, vasculature, local progenitor cells, basal lamina, and muscle fibers, overwhelming innate repair mechanisms. The hallmark of VML injury is the excessive accumulation of non-contractile, fibrotic tissue and permanent functional deficits. Though predominant in the younger demographic, the elderly population is also captured within VML injuries. There are many factors that change with aging in skeletal muscle that may further hinder recovery and regeneration following VML. In an attempt to further our understanding on how age affects VML treatments, comparisons between young and aged animals following VML injury and repair were made. Presented in this dissertation is a summary of the current state of the tissue engineering field in skeletal muscle and explores strategies for repairing not only VML but also understanding what age-associated changes in skeletal muscle preclude effective tissue repair. The future directions and potential approaches to further the field’s understanding of VML repair in the aging microenvironment along with the remaining challenges in skeletal muscle tissue engineering are presented within

Realization of Integrated Coherent LiDAR

LiDAR (Light Detection and Ranging) captures high-definition real-time 3D images of the surrounding environment through active sensing with infrared lasers. It has unique advantages that can compensate the fundamental limitations in camera-based 3D imaging via vision algorithms or RADARs, which makes it an important sensing modality to guarantee robust autonomy in self-driving cars. However, high price tag of existing commercial LiDAR modules based on mechanical beam scanners and intensity-based detection scheme makes them unusable in the context of mass produced consumer products.The focus of thesis is on the integrated coherent LiDAR with optical phased array-based solid-state beam steering, which has great potential to dramatically bring down the cost of a LiDAR module. It begins with an overview of LiDAR implementation options and system requirements in the context of autonomous vehicles, which leads us to conclude that beam-steering coherent FMCW LiDAR in optical C-band is indeed the best implementation strategy to realize low-cost automotive LiDARs. Motivated by this observation, a quantitative framework for evaluating FMCW LiDAR performance is also introduced to predict the design that satisfies car-grade performance requirements. Then the thesis presents the silicon implementation results from our single-chip optical phased array and integrated coherent LiDAR prototype. Our implementations leverage the 3D heterogeneous integration platform, where custom silicon photonics and nanoscale CMOS fabricated at a 300 mm wafer facility are combined at the wafer-scale to minimize the unit cost without I/O density issues. After discussing remaining challenges and possible ways to enhance the operating range and system reliability, this thesis finally addresses the problem of fundamental trade-off between phase noise and wavelength tuning in FMCW laser source, and present circuit- and algorithm-level techniques to enable FMCW measurements beyond inherent laser coherence range limit

Generating Realistic Images from In-the-wild Sounds

Representing wild sounds as images is an important but challenging task due to the lack of paired datasets between sound and images and the significant differences in the characteristics of these two modalities. Previous studies have focused on generating images from sound in limited categories or music. In this paper, we propose a novel approach to generate images from in-the-wild sounds. First, we convert sound into text using audio captioning. Second, we propose audio attention and sentence attention to represent the rich characteristics of sound and visualize the sound. Lastly, we propose a direct sound optimization with CLIPscore and AudioCLIP and generate images with a diffusion-based model. In experiments, it shows that our model is able to generate high quality images from wild sounds and outperforms baselines in both quantitative and qualitative evaluations on wild audio datasets.Comment: Accepted to ICCV 202

Digital transformation as a demographic and economic integrated policy for Southeast Asian developing countries

The age of Southeast Asian developing countries’ populations is still younger than that of other regions around the world. However, recent statistics show that the tide is now turning in this regard, with many of these populations beginning to age at rates much faster than many other countries. Such developments require immediate policy action in order to create a sustainable path towards economic growth before demographic changes become less benign in the medium term. In this study, we discuss the economic consequences of population aging, increases in the economic support ratio, and a declining potential growth rate. We argue that it is essential for Southeast Asian developing countries to raise total factor productivity (TFP) growth rates so as to achieve more sustainable economic outcomes. By conducting panel regressions using data from 82 countries across the 1996–2019 study period, our study shows that increasing research and development (R&D) spending and the facilitation of structural changes that transform the digital economy landscape are key policy options that promote TFP growt

Three essays on retail price competition

Dr. Emek Basker, Dissertation Supervisor.Includes vita.Field of study: Economics."July 2018."This dissertation consists of three chapters. The first chapter examines price dispersion in retail gasoline and focuses on differentiation along the service dimension: full service versus self service. Consistent with more intensive search by self-service customers, I find that price dispersion always decreases with the number of nearby self-service stations, but does not decrease with the number of nearby full-service stations. When I segment the market by brand, I observe that the estimates are sensitive to how brands are separated into different types. These findings show that the market is more clearly segmented by service level than by brand type and also highlight the importance of product differentiation when modeling price dispersion. In the second chapter, I examine product positioning and pricing strategies of sellers in a market undergoing a significant restructuring using data from the introduction of self-service technology in the Korean gasoline market in the 2000s. I show that the decision of full-service sellers to exit or switch to self service is positively correlated with the intensity of competition they face. The pricing strategies of sellers differ by product position: self-service sellers compete for price-sensitive consumers, whereas full-service sellers differentiate their product by offering a variety of bundled products and services, such as coffee, carwash or even a nail salon, to compete for less-price-sensitive consumers. Taken together, these patterns have led to an increase in the full-service premium during the market transition. In the third chapter, I study the effect of a government contract on price. Since 2013, Korean government officials have been required to refuel at contracted gasoline stations, at about 5% discounts relative to the posted price. The initial contract terminated in November 2015 and a new group of sellers took over the contract. In this paper, I use this natural experiment to examine the impact of the government contract on gasoline prices, using a difference-in-difference analysis and price data on all gasoline stations in Seoul. I find that, all else equal, posted prices of contracted gasoline stations are about 2% higher than those of non-contracted stations. This finding is consistent with the prediction of models of price discrimination that prices decrease when the elasticity of demand falls. The effect on prices is not uniform across all stations, however. The contract leads to larger increases in full-service stations? posted prices than in self-service stations? prices, and larger increases at stations with fewer nearby competitors. The contract also decreases prices of non-contracted stations very close to contracted stations.Includes bibliographical references (pages 82-85)

Selective Token Generation for Few-shot Natural Language Generation

Natural language modeling with limited training data is a challenging problem, and many algorithms make use of large-scale pretrained language models (PLMs) for this due to its great generalization ability. Among them, additive learning that incorporates a task-specific adapter on top of the fixed large-scale PLM has been popularly used in the few-shot setting. However, this added adapter is still easy to disregard the knowledge of the PLM especially for few-shot natural language generation (NLG) since an entire sequence is usually generated by only the newly trained adapter. Therefore, in this work, we develop a novel additive learning algorithm based on reinforcement learning (RL) that selectively outputs language tokens between the task-general PLM and the task-specific adapter during both training and inference. This output token selection over the two generators allows the adapter to take into account solely the task-relevant parts in sequence generation, and therefore makes it more robust to overfitting as well as more stable in RL training. In addition, to obtain the complementary adapter from the PLM for each few-shot task, we exploit a separate selecting module that is also simultaneously trained using RL. Experimental results on various few-shot NLG tasks including question answering, data-to-text generation and text summarization demonstrate that the proposed selective token generation significantly outperforms the previous additive learning algorithms based on the PLMs.Comment: COLING 202

Sound of Story: Multi-modal Storytelling with Audio

Storytelling is multi-modal in the real world. When one tells a story, one may use all of the visualizations and sounds along with the story itself. However, prior studies on storytelling datasets and tasks have paid little attention to sound even though sound also conveys meaningful semantics of the story. Therefore, we propose to extend story understanding and telling areas by establishing a new component called "background sound" which is story context-based audio without any linguistic information. For this purpose, we introduce a new dataset, called "Sound of Story (SoS)", which has paired image and text sequences with corresponding sound or background music for a story. To the best of our knowledge, this is the largest well-curated dataset for storytelling with sound. Our SoS dataset consists of 27,354 stories with 19.6 images per story and 984 hours of speech-decoupled audio such as background music and other sounds. As benchmark tasks for storytelling with sound and the dataset, we propose retrieval tasks between modalities, and audio generation tasks from image-text sequences, introducing strong baselines for them. We believe the proposed dataset and tasks may shed light on the multi-modal understanding of storytelling in terms of sound. Downloading the dataset and baseline codes for each task will be released in the link: https://github.com/Sosdatasets/SoS_Dataset.Comment: Findings of EMNLP 2023, project: https://github.com/Sosdatasets/SoS_Dataset

Fine-Grained Retrieval of Sports Plays using Tree-Based Alignment of Trajectories

We propose a novel method for effective retrieval of multi-agent spatiotemporal tracking data. Retrieval of spatiotemporal tracking data offers several unique challenges compared to conventional text-based retrieval settings. Most notably, the data is fine-grained meaning that the specific location of agents is important in describing behavior. Additionally, the data often contains tracks of multiple agents (e.g., multiple players in a sports game), which generally leads to a permutational alignment problem when performing relevance estimation. Due to the frequent position swap of agents, it is difficult to maintain the correspondence of agents, and such issues make the pairwise comparison problematic for multi-agent spatiotemporal data. To address this issue, we propose a tree-based method to estimate the relevance between multi-agent spatiotemporal tracks. It uses a hierarchical structure to perform multi-agent data alignment and partitioning in a coarse-to-fine fashion. We validate our approach via user studies with domain experts. Our results show that our method boosts performance in retrieving similar sports plays -- especially in interactive situations where the user selects a subset of trajectories compared to current state-of-the-art methods

Hexa: Self-Improving for Knowledge-Grounded Dialogue System

A common practice in knowledge-grounded dialogue generation is to explicitly utilize intermediate steps (e.g., web-search, memory retrieval) with modular approaches. However, data for such steps are often inaccessible compared to those of dialogue responses as they are unobservable in an ordinary dialogue. To fill in the absence of these data, we develop a self-improving method to improve the generative performances of intermediate steps without the ground truth data. In particular, we propose a novel bootstrapping scheme with a guided prompt and a modified loss function to enhance the diversity of appropriate self-generated responses. Through experiments on various benchmark datasets, we empirically demonstrate that our method successfully leverages a self-improving mechanism in generating intermediate and final responses and improves the performances on the task of knowledge-grounded dialogue generation

Compact Implementations of LEA Block Cipher for Low-End Microprocessors

In WISA\u2713, a novel lightweight block cipher named LEA was released. This algorithm has certain useful features for hardware and software implementations, i.e., simple ARX operations, non-S-box architecture, and 32-bit word size. These features are realized in several platforms for practical usage with high performance and low overheads. In this paper, we further improve 128-, 192- and 256-bit LEA encryption for low-end embedded processors. Firstly we present speed optimization methods. The methods split a 32-bit word operation into four byte-wise operations and avoid several rotation operations by taking advantages of efficient byte-wise rotations. Secondly we reduce the code size to ensure minimum code size.We nd the minimum inner loops and optimize them in an instruction set level. After then we construct the whole algorithm in a partly unrolled fashion with reasonable speed. Finally, we achieved the fastest LEA implementations, which improves performance by 10.9% than previous best known results. For size optimization, our implemen- tation only occupies the 280B to conduct LEA encryption. After scaling, our implementation achieved the smallest ARX implementations so far, compared with other state-of-art ARX block ciphers such as SPECK and SIMON