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Title: Exhibition announcement for Mary Henry's show Recent Paintings and Drawings at the University Gallery, Pacific Lutheran University, Tacoma, Washington, 1987-1988, recto
Creator: Pacific Lutheran University
Date: 1987-1988
Description: Exhibition annoucement postcard for Mary Henry's show Recent Paintings and Drawings at the University Gallery, Pacific Lutheran University,...
Show moreExhibition annoucement postcard for Mary Henry's show Recent Paintings and Drawings at the University Gallery, Pacific Lutheran University, Tacoma, Washington, 1988.
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Collection: Mary Dill Henry Papers, 1913-2021

Title: Exhibition announcement for Mary Henry's show Recent Paintings and Drawings at the University Gallery, Pacific Lutheran University, Tacoma, Washington, 1987-1988, verso
Creator: Pacific Lutheran University
Date: 1987-1988
Description: Exhibition annoucement postcard for Mary Henry's show Recent Paintings and Drawings at the University Gallery, Pacific Lutheran University,...
Show moreExhibition annoucement postcard for Mary Henry's show Recent Paintings and Drawings at the University Gallery, Pacific Lutheran University, Tacoma, Washington, 1988.
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Collection: Mary Dill Henry Papers, 1913-2021

Title: Exhibition announcement for an art exhibition featuring Mary Henry and Mike Sweeney at Childers/Proctor Gallery, Langley, Washington, 1988, recto
Creator: Henry, Mary Dill, 1913-2009, Sweeney, Mike, Childer/Proctor Gallery
Date: 1988
Description: Exhibition annoucement postcard for an art exhibition pairing the paintings of Mary Henry with the sculptures of Mike Sweeney at Childers...
Show moreExhibition annoucement postcard for an art exhibition pairing the paintings of Mary Henry with the sculptures of Mike Sweeney at Childers/Proctor Gallery, Langley, Washington, 1988. The recto contains a detail of one of Mary Henry's paintings and an image of one of Mike Sweeney's sculptures.
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Collection: Mary Dill Henry Papers, 1913-2021

Title: Exhibition announcement for an art exhibition featuring Mary Henry and Mike Sweeney at Childers/Proctor Gallery, Langley, Washington, 1988, verso
Creator: Henry, Mary Dill, 1913-2009, Sweeney, Mike, Childer/Proctor Gallery
Date: 1988
Description: Exhibition annoucement postcard for an art exhibition pairing the paintings of Mary Henry with the sculptures of Mike Sweeney at Childers...
Show moreExhibition annoucement postcard for an art exhibition pairing the paintings of Mary Henry with the sculptures of Mike Sweeney at Childers/Proctor Gallery, Langley, Washington, 1988. The recto contains a detail of one of Mary Henry's paintings and an image of one of Mike Sweeney's sculptures.
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Collection: Mary Dill Henry Papers, 1913-2021

Title: Exhibition announcement for Mary Henry's show Selected Paintings 1965-1995 at The Linda Cannon Gallery, Seattle, Washington, 1995
Creator: Henry, Mary Dill, 1913-2009, Linda Cannon Gallery
Date: 1995
Description: Exhibition annoucement postcard for Mary Henry's show Selected Paintings 1965-1995 at The Linda Cannon Gallery, Seattle, Washington, October...
Show moreExhibition annoucement postcard for Mary Henry's show Selected Paintings 1965-1995 at The Linda Cannon Gallery, Seattle, Washington, October 18-November 15, 1995. The recto contains a reproduction of Henry's painting Visions of Johanna (acrylic on canvas, 1993). A separate exhibition introducing the work of Jeffrey Simmons is advertised in the gallery's H Room[?] on the card's verso. Text on the verso of the card is cut off, it is not known if this was due to a printing/cutting error or alteration after the fact.
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Collection: Mary Dill Henry Papers, 1913-2021

Title: Exhibition announcement for Mary Henry's show Recent Paintings and Drawings at the University Gallery, Pacific Lutheran University, Tacoma, Washington, 1987-1988
Creator: Pacific Lutheran University
Date: 1987-1988
Description: Exhibition annoucement postcard for Mary Henry's show Recent Paintings and Drawings at the University Gallery, Pacific Lutheran University,...
Show moreExhibition annoucement postcard for Mary Henry's show Recent Paintings and Drawings at the University Gallery, Pacific Lutheran University, Tacoma, Washington, 1988.
Show less
Collection: Mary Dill Henry Papers, 1913-2021

Title: Exhibition announcement for an art exhibition featuring Mary Henry and Mike Sweeney at Childers/Proctor Gallery, Langley, Washington, 1988
Creator: Henry, Mary Dill, 1913-2009, Sweeney, Mike, Childer/Proctor Gallery
Date: 1988
Description: Exhibition annoucement postcard for an art exhibition pairing the paintings of Mary Henry with the sculptures of Mike Sweeney at Childers...
Show moreExhibition annoucement postcard for an art exhibition pairing the paintings of Mary Henry with the sculptures of Mike Sweeney at Childers/Proctor Gallery, Langley, Washington, 1988. The recto contains a detail of one of Mary Henry's paintings and an image of one of Mike Sweeney's sculptures.
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Collection: Mary Dill Henry Papers, 1913-2021

Title: Defense-in-Depth for Cyber-Secure Network Architectures of Industrial Control Systems
Creator: Arnold, David James
Date: 2024
Description: Digitization and modernization efforts have yielded greater efficiency, safety, and cost-savings for Industrial Control Systems (ICS). To...
Show moreDigitization and modernization efforts have yielded greater efficiency, safety, and cost-savings for Industrial Control Systems (ICS). To achieve these gains, the Internet of Things (IoT) has become an integral component of network infrastructures. However, integrating embedded devices expands the network footprint and softens cyberattack resilience. Additionally, legacy devices and improper security configurations are weak points for ICS networks. As a result, ICSs are a valuable target for hackers searching for monetary gains or planning to cause destruction and chaos. Furthermore, recent attacks demonstrate a heightened understanding of ICS network configurations within hacking communities. A Defense-in-Depth strategy is the solution to these threats, applying multiple security layers to detect, interrupt, and prevent cyber threats before they cause damage. Our solution detects threats by deploying an Enhanced Data Historian for Detecting Cyberattacks. By introducing Machine Learning (ML), we enhance cyberattack detection by fusing network traffic and sensor data. Two computing models are examined: 1) a distributed computing model and 2) a localized computing model. The distributed computing model is powered by Apache Spark, introducing redundancy for detecting cyberattacks. In contrast, the localized computing model relies on a network traffic visualization methodology for efficiently detecting cyberattacks with a Convolutional Neural Network. These applications are effective in detecting cyberattacks with nearly 100% accuracy. Next, we prevent eavesdropping by applying Homomorphic Encryption for Secure Computing. HE cryptosystems are a unique family of public key algorithms that permit operations on encrypted data without revealing the underlying information. Through the Microsoft SEAL implementation of the CKKS algorithm, we explored the challenges of introducing Homomorphic Encryption to real-world applications. Despite these challenges, we implemented two ML models: 1) a Neural Network and 2) Principal Component Analysis. Finally, we hinder attackers by integrating a Cyberattack Lockdown Network with Secure Ultrasonic Communication. When a cyberattack is detected, communication for safety-critical elements is redirected through an ultrasonic communication channel, establishing physical network segmentation with compromised devices. We present proof-of-concept work in transmitting video via ultrasonic communication over an Aluminum Rectangular Bar. Within industrial environments, existing piping infrastructure presents an optimal solution for cost-effectively preventing eavesdropping. The effectiveness of these solutions is discussed within the scope of the nuclear industry.
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Title: Investigation in the Uncertainty of Chassis Dynamometer Testing for the Energy Characterization of Conventional, Electric and Automated Vehicles
Creator: Di Russo, Miriam
Date: 2023
Description: For conventional and electric vehicles tested in a standard chassis dynamometer environment precise regulations on the evaluation of their...
Show moreFor conventional and electric vehicles tested in a standard chassis dynamometer environment precise regulations on the evaluation of their energy performance exist. However, the regulations do not include requirements on the confidence value to associate with the results. As vehicles become more and more efficient to meet the stricter regulations mandates on emissions, fuel and energy consumption, traditional testing methods may become insufficient to validate these improvements, and may need revision. Without information about the accuracy associated with the results of those procedures however, adjustments and improvements are not possible, since no frame of reference exists. For connected and automated vehicles, there are no standard testing procedures, and researchers are still in the process of determining if current evaluation methods can be extended to test intelligent technologies and which metrics best represent their performance. For these vehicles is even more important to determine the uncertainty associated with these experimental methods and how they propagate to the final results. The work presented in this dissertation focuses on the development of a systematic framework for the evaluation of the uncertainty associated with the energy performance of conventional, electric and automated vehicles. The framework is based on a known statistical method, to determine the uncertainty associated with the different stages and processes involved in the experimental testing, and to evaluate how the accuracy of each parameter involved impacts the final results. The results demonstrate that the framework can be successfully applied to existing testing methods and provides a trustworthy value of accuracy to associate with the energy performance results, and can be easily extended to connected-automated vehicle testing to evaluate how novel experimental methods impact the accuracy and the confidence of the outputs. The framework can be easily be implemented into an existing laboratory environment to incorporate the uncertainty evaluation among the current results analyzed at the end of each test, and provide a reference for researchers to evaluate the actual benefits of new algorithms and optimization methods and understand margins for improvements, and by regulators to assess which parameters to enforce to ensure compliance and ensure projected benefits.
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Title: The Double-edged Sword of Executive Pay: How the CEO-TMT Pay Gap Influences Firm Performance
Creator: Haddadian Nekah, Pouya
Date: 2024
Description: This study examines the relationship between the chief executive officer (CEO) and top management team (TMT) pay gap and consequent firm...
Show moreThis study examines the relationship between the chief executive officer (CEO) and top management team (TMT) pay gap and consequent firm performance. Drawing on tournament theory and equity theory, I argue that the effect of the CEO-TMT pay gap on consequent firm performance is non-monotonic. Using data from 1995 to 2022 from S&P 1500 US firms, I explicate an inverted U-shaped relationship, such that an increase in the pay gap leads to an increase in firm performance up to a certain point, after which it declines. Additionally, multilevel analyses reveal that this curvilinear relationship is moderated by attributes of the TMT, and the industry in which the firm competes. My findings show that firms with higher TMT gender diversity suffer lower performance loss due to wider pay gaps. Furthermore, when firm executives are paid more compared to the industry norms, or when the firm has a long-tenured CEO, firm performance becomes less sensitive to larger CEO-TMT pay gaps. Lastly, when the firm competes in a masculine industry, firm performance is more negatively affected by larger CEO-TMT pay gaps. Contrary to my expectations, firm gender-diversity friendly policies failed to influence the CEO-TMT pay gap-firm performance relationship.
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Title: Using Niobium surface encapsulation and Rhenium to enhance the coherence of superconducting devices
Creator: Crisa, Francesco
Date: 2024
Description: In recent decades, the scientific community has grappled with escalating complexity, necessitating a more advanced tool capable of tackling...
Show moreIn recent decades, the scientific community has grappled with escalating complexity, necessitating a more advanced tool capable of tackling increasingly intricate simulations beyond the capabilities of classical computers. This tool, known as a quantum computer, features processors composed of individual units termed qubits. While various methods exist for constructing qubits, superconducting circuits have emerged as a leading approach, owing to their parallels with semiconductor technology.In recent years, significant strides have been made in optimizing the geometry and design of qubits. However, the current bottleneck in the performance of superconducting qubits lies in the presence of defects and impurities within the materials used. Niobium, owing to its desirable properties, such as high critical temperature and low kinetic inductance, stands out as the most prevalent superconducting material. Nonetheless, it is encumbered by a relatively thick oxide layer (approximately 5 nm) exhibiting three distinct oxidation states: NbO, NbO$_2$, and Nb$_2$O$_5$. The primary challenge with niobium lies in the multitude of defects localized within the highly disordered Nb$_2$O$_5$ layer and at the interfaces between the different oxides. In this study, I present an encapsulation strategy aimed at restraining surface oxide growth by depositing a thin layer (5 to 10 nm) of another material in vacuum atop the Nb thin film. This approach exploits the superconducting proximity effect, and it was successfully employed in the development of Josephson junction devices on Nb during the 1980s.In the past two years, tantalum and titanium nitride have emerged as promising alternative materials, with breakthrough qubit publications showcasing coherence times five to ten times superior to those achieved in Nb. The focus will be on the fabrication and RF testing of Nb-based qubits with Ta and Au capping layers. With Ta capping, we have achieved the best T1 (not average) decay time of nearly 600 us, which is more than a factor of 10 improvements over the bare Nb. This establishes the unique capping layer approach as a significant new direction for the development of superconducting qubits.Concurrently with the exploration of materials for encapsulation strategies, identifying materials conducive to enhancing the performance of superconducting qubits is imperative. Ideal candidates should exhibit a thin, low-loss surface oxide and establish a clean interface with the substrate, thereby minimizing defects and potential sources of losses. Rhenium, characterized by an extremely thin surface oxide (less than 1 nm) and nearly perfect crystal structure alignment with commonly used substrates such as sapphire, emerges as a promising material platform poised to elevate the performance of superconducting qubits.
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Title: Improving Localization Safety for Landmark-Based LiDAR Localization System
Creator: Chen, Yihe
Date: 2024
Description: Autonomous ground robots have gained traction in various commercial applications, with established safety protocols covering subsystem...
Show moreAutonomous ground robots have gained traction in various commercial applications, with established safety protocols covering subsystem reliability, control algorithm stability, path planning, and localization. This thesis specifically delves into the localizer, a critical component responsible for determining the vehicle’s state (e.g., position and orientation), assessing compliance with localization safety requirements, and proposing methods for enhancing localization safety.Within the robotics domain, diverse localizers are utilized, such as scan-matching techniques like normal distribution transformations (NDT), the iterative closest point (ICP) algorithm,probabilistic maps method, and semantic map-based localization.Notably, NDT stands out as a widely adopted standalone laser localization method, prevalent in autonomous driving software such as Autoware and Apollo platforms.In addition to the mentioned localizers, common state estimators include variants of Kalman Filter, particle filter-based, and factor graph-based estimators. The evaluation of localization performance typically involves quantifying the estimated state variance for these state estimators.While various localizer options exist, this study focuses on those utilizing extended Kalman filters and factor graph methods. Unlike methods like NDT and ICP algorithms, extended Kalman filters and factor graph based approaches guarantee bounding of estimated state uncertainty and have been extensively researched for integrity monitoring.Common variance analysis, employed for sensor readings and state estimators, has limitations, primarily focusing on non-faulted scenarios under nominal conditions. This approach proves impractical for real-world scenarios and falls short for safety-critical applications like autonomous vehicles (AVs).To overcome these limitations, this thesis utilizes a dedicated safety metric: integrity risk. Integrity risk assesses the reliability of a robot’s sensory readings and localization algorithm performance under both faulted and non-faulted conditions. With a proven track record in aviation, integrity risk has recently been applied to robotics applications, particularly for evaluating the safety of lidar localization.Despite the significance of improving localization integrity risk through laser landmark manipulation, this remains an under explored territory. Existing research on robot integrity risk primarily focuses on the vehicles themselves. To comprehensively understand the integrity risk of a lidar-based localization system, as addressed in this thesis, an exploration of lidar measurement faults’ modes is essential, a topic covered in this thesis.The primary contributions of this thesis include: A realistic error estimation method for state estimators in autonomous vehicles navigating using pole-shape lidar landmark maps, along with a compensatory method; A method for quantifying the risk associated with unmapped associations in urban environments, enhancing the realism of values provided by the integrity risk estimator; a novel approach to improve the localization integrity of autonomous vehicles equipped with lidar feature extractors in urban environments through minimal environmental modifications, mitigating the impact of unmapped association faults. Simulation results and experimental results are presented and discussed to illustrate the impact of each method, providing further insights into their contributions to localization safety.
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Title: Utilizing Concurrent Data Accesses for Data-Driven and AI Applications
Creator: Lu, Xiaoyang
Date: 2024
Description: In the evolving landscape of data-driven and AI applications, the imperative for reducing data access delay has never been more critical,...
Show moreIn the evolving landscape of data-driven and AI applications, the imperative for reducing data access delay has never been more critical, especially as these applications increasingly underpin modern daily life. Traditionally, architectural optimizations in computing systems have concentrated on data locality, utilizing temporal and spatial locality to enhance data access performance by maximizing data and data block reuse. However, as poor locality is a common characteristic of data-driven and AI applications, utilizing data access concurrency emerges as a promising avenue to optimize the performance of evolving data-driven and AI application workloads.This dissertation advocates utilizing concurrent data accesses to enhance performance in data-driven and AI applications, addressing a significant research gap in the integration of data concurrency for performance improvement. It introduces a suite of innovative case studies, including a prefetching framework that dynamically adjusts aggressiveness based on data concurrency, a cache partitioning framework that balances application demands with concurrency, a concurrency-aware cache management framework to reduce costly cache misses, a holistic cache management framework that considers both data locality and concurrency to fine-tune decisions, and an accelerator design for sparse matrix multiplication that optimizes adaptive execution flow and incorporates concurrency-aware cache optimizations.Our comprehensive evaluations demonstrate that the implemented concurrency-aware frameworks significantly enhance the performance of data-driven and AI applications by leveraging data access concurrency.Specifically, our prefetch framework boosts performance by 17.3%, our cache partitioning framework surpasses locality-based approaches by 15.5%, and our cache management framework achieves a 10.3% performance increase over prior works. Furthermore, our holistic cache management framework enhances performance further, achieving a 13.7% speedup. Additionally, our sparse matrix multiplication accelerator outperforms existing accelerators by a factor of 2.1.As optimizing data locality in data-driven and AI applications becomes increasingly challenging, this dissertation demonstrates that utilizing concurrency can still yield significant performance enhancements, offering new insights and actionable examples for the field. This dissertation not only bridges the identified research gap but also establishes a foundation for further exploration of the full potential of concurrency in data-driven and AI applications and architectures, aiming at fulfilling the evolving performance demands of modern and future computing systems.
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Title: Health and Well-Being Benefits of Different Types of Urban Green Spaces (UGS): A Cross-Sectional Study of Communities in Chicago, U.S.
Creator: Kang, Liwen
Date: 2023
Description: There are three main interrelated areas of focus in this doctoral research related tourban green spaces (UGS): general well-being, mental and...
Show moreThere are three main interrelated areas of focus in this doctoral research related tourban green spaces (UGS): general well-being, mental and physical health. In this study, these three different health aspects were analyzed separately. The data of these three health outcomes were collected from the Healthy Chicago Survey (HCS), an annual telephone survey that interviewed adults in Chicago, U.S., based on the randomly selected addresses.Urban green spaces have been associated with better health and well-being. Theyprovide sites for physical activity, buffer air and noise pollution, and alleviate thermal discomfort. Urban green spaces also promote social interaction and increase social cohesion. However, research is limited on the health benefits of different types of UGS exposure. This research aimed to reveal the associations between the provision of different UGS types and urban residents’ general, mental, and physical health in Chicago, the third-largest city in the U.S.Urban green spaces data were collected from the National Land Cover Database(NLCD), the Meter-Scale Urban Land Cover (MULC), and the Chicago Park District (CPD). Different types of UGS were obtained, namely 1) the percent tree canopy cover (TCC) from the first database; 2) the percentage of trees and the percentage of grass from the second database; and 3) the number of parks, park areas, percentage of park areas from the third database. Using hierarchical and logistic regression models that controlled for a range of confounding factors (age, gender, race, education level, employment status, and poverty level), this study assessed which type of UGS affects general well-being, mental health, and physical health, respectively. The results indicated that increased park area was significantly associated with better perceived general health; higher percent of TCC was significantly associated with a lower level of psychological distress (PD); and increased percentage of park areas and increased number of parks were associated with lower odds of being obese. Two micro-scaled on-site observations were conducted in the Avalon Park community and the Loop community to analyze some other UGS characteristics besides quantity and availability. Other characteristics of UGS, such as quality of facilities, attractiveness, and maintenance, are suggested to be taken into consideration for future studies. The study highlights that different UGS types have various impacts on general, mental, and physical health of urban residents. By providing scientific evidence, this study may help policymakers, urban planners, landscape architects, and other related professionals to make informed decisions on maximizing the health benefits of UGS and to achieve social equity. The findings of this study may be applied to other metropolitan cities.
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Title: Resolvent analysis of turbulent flows: Extensions, improvements and applications
Creator: Lopez-Doriga Costales, Barbara
Date: 2024
Description: This thesis presents several advances in both physics-based and data-driven modeling of turbulent fluid flows. In particular, the present...
Show moreThis thesis presents several advances in both physics-based and data-driven modeling of turbulent fluid flows. In particular, the present thesis focuses on resolvent analysis, a physics-based framework that identifies the coherent structures that are most amplified by the Navier-Stokes equations when they are linearized about a known turbulent mean flow via a singular value decomposition (SVD) of a discretized operator. This method has proven to effectively capture energetically-relevant features observed in various flows. However, it has some shortcomings that the present work intends to alleviate. First, the original formulation of resolvent analysis is restricted to statistically-stationary or time-periodic mean flows. To expand the applicability of this framework, this thesis presents a spatiotemporal variant of resolvent analysis that is able to account for time-varying systems. Moreover, sparsity (which manifests in localization) is also incorporated to the analysis through the addition of an l1-norm penalization term to the optimization associated with the SVD. This allows for the identification of energetically-relevant coherent structures that correspond to spatio-temporally localized amplification mechanisms, for flows with either a time-varying or stationary mean. The high computational cost associated with the discretization and analysis of a large discretized of the mean-linearized Navier-Stokes operator represents the second drawback of resolvent analysis. As a second contribution, this thesis provides an analytic form of resolvent analysis for planar flows based on wavepacket pseudomode theory, avoiding the numerical computations required in the original framework. The third contribution focuses on the characterization of the energetically-dominant coherent structures that arise in turbulent flow traveling through straight ducts with square and rectangular cross-sections. First, resolvent analysis is applied to predict the coherent structures that arise in this flow, and to study the sensitivity of this methodology to the secondary mean flow components that display a distinct pattern near the duct corners. Next, a data-driven causality analysis is performed to understand the physical mechanisms involved in the evolution of coherent structures near the duct corners. To do this, a nonlinear Granger causality analysis method is developed and applied to proper orthogonal decomposition coefficients of direct numerical simulation data, revealing that the structures associated with the secondary velocity components are behind the formation and translation of the near-wall and near-corner streamwise structures. A general discussion and future prospects are discussed at the end of this thesis.
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Title: Financialization in the Structured Products Market
Creator: Zhu, Lizi
Date: 2023
Description: This dissertation aims to study financialization in the structured products market. The structured products market has been undergoing a major...
Show moreThis dissertation aims to study financialization in the structured products market. The structured products market has been undergoing a major transformation in recent years. The market used to mainly serve institutional investors. However, as a few trading platforms powered by fintech companies emerged on the horizon, more and more banks are starting to compete in this market. The average trade size has also been declining significantly, thereby making the market increasingly accessible to retail investors. What are the factors that facilitate the development of this market? What are the economic incentives of issuers and investors? How do issuers compete? What does the future hold for this market? The main finding of this dissertation is that structured products provide utility to retail investors; As the level of risk aversion increases, an investor increasingly prefers structured products to other traditional asset classes; issuers develop three sources of competitive advantage to be a satisficer; the rise of fintech and improvement of financial education are the key to opening this market to retail investors.
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Title: Multimodal Learning and Generation Toward a Multisensory and Creative AI System
Creator: Zhu, Ye
Date: 2023
Description: We are perceiving and communicating with the world in a multisensory manner, where different information sources are sophisticatedly processed...
Show moreWe are perceiving and communicating with the world in a multisensory manner, where different information sources are sophisticatedly processed and interpreted by separate parts of the human brain to constitute a complex, yet harmonious and unified intelligent system. To endow the machines with true intelligence, multimodal machine learning that incorporates data from various modalities including vision, audio, and text, has become an increasingly popular research area with emerging technical advances in recent years. Under the context of multimodal learning, the creativity to generate and synthesize novel and meaningful data is a critical criterion to assess machine intelligence.As a step towards a multisensory and creative AI system, we study the problem of multimodal generation in this thesis by exploring the field from multiple perspectives. Firstly, we analyze different data modalities in a comprehensive manner by comparing the data natures, the semantics, and their corresponding mainstream technical designs. We then propose to investigate three multimodal generation application scenarios, namely text generation from visual data, audio generation from visual data, and visual generation from textual data, with diverse approaches to give an overview of the field. For the direction of text generation from visual data, we study a novel multimodal task in which the model is expected to summarize a given video with textual descriptions, under a challenging condition where the video can only be partially seen. We propose to supplement the missing visual information via a dialogue interaction and introduce QA-Cooperative network with a dynamic dialogue history update learning mechanism to tackle the challenge. For the direction of audio generation from visual data, we present a new multimodal task that aims to generate music for a given silent dance video clip. Unlike most existing conditional music generation works that generate specific types of mono-instrumental sounds using symbolic audio representations (e.g., MIDI), and that heavily rely on pre-defined musical synthesizers, we generate dance music in complex styles (e.g., pop, breaking, etc.) by employing a Vector-Quantized (VQ) audio representation via our proposed Dance2Music-GAN (D2M-GAN) framework. For the direction of visual generation from textual data, we tackle a key desideratum in conditional synthesis, which is to achieve high correspondence between the conditioning input and generated output using the state-of-the-art generative model -- Diffusion Probabilistic Model. While most existing methods learn such relationships implicitly, by incorporating the prior into the variational lower bound in model training. In this work, we take a different route by explicitly enhancing input-output connections by maximizing their mutual information, which is achieved by our proposed Conditional Discrete Contrastive Diffusion (CDCD) framework. For each direction, we conduct extensive experiments on multiple multimodal datasets and demonstrate that all of our proposed frameworks are able to effectively and substantially improve task performance in their corresponding contexts.
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Title: Parking Demand Forecasting Using Asymmetric Discrete Choice Models with Applications
Creator: Zhang, Ji
Date: 2023
Description: Using discrete choice models to forecast travelers parking location choice has been a branch of parking demand research for many years. The...
Show moreUsing discrete choice models to forecast travelers parking location choice has been a branch of parking demand research for many years. The most used discrete choice models have fairly simple mathematical expressions, such as the probit and logit models. The application of simple models helps release the computational burdens brought by parameter estimation tasks in practice, but the cost is the unwanted properties of classic models such as the “symmetry property” that we argue is often undesirable in many fields. To some extent, the symmetry property of related models limits the shape of curves that makes the model fitting less flexible technically. This study addresses the following question: “Can discrete choice models with asymmetry property outperform classic models with symmetry property in forecasting travelers’ parking location choices?” The contributions of this study include: (1) providing a new perspective of using asymmetric discrete choice models to explain and forecast individual’s parking location choice; and (2) completing the travel demand forecasting process from choices of the destination zone centroid to the parking location, enabling parking choice forecasting. This provides a generalized framework to calibrate and validate asymmetric discrete choice models with the field observed parking facility-specific arrival profile data integrated into a large-scale, high-fidelity regional travel demand model. Further, an experimental study is conducted to compare the performance of the proposed asymmetric discrete choice models in the parking demand forecasting framework. The results suggest that asymmetric discrete choice models for individual’s parking choice modeling outperform the symmetric discrete choice models such as the logit models owing largely to their flexibility of parameter fitting and training using the available dataset.
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Title: Empowering Visually Impaired Individuals With Holistic Assistance Using Real-Time Spatial Awareness System
Creator: Yu, Xinrui
Date: 2024
Description: The integration of artificial intelligence (AI) into daily life opens unprecedented avenues for enhancing the experiences of visually impaired...
Show moreThe integration of artificial intelligence (AI) into daily life opens unprecedented avenues for enhancing the experiences of visually impaired individuals, offering them greater autonomy and quality of life. This thesis introduces a Visually Impaired Spatial Awareness (VISA) system designed to assist visually impaired individuals holistically through a structured approach. At the foundational level, the VISA system incorporates several key technologies to interpret the surroundings and assist in basic navigation tasks. It utilizes Augmented Reality (AR) markers to facilitate recognition of places and aid in navigation, employs neural network models for advanced object detection and tracking, and leverages depth information for accurate object localization. Progressing to the intermediate level, the VISA system integrates the data obtained from object detection and depth sensing to assist in more complex navigational tasks such as obstacle avoidance and pathfinding toward a desired destination. At the advanced level, the VISA system synthesizes the capabilities developed at the foundational and intermediate levels to enhance the spatial awareness of visually impaired users, allowing them to undertake complex tasks, such as navigating complex environments and locating specific items. The VISA system also emphasizes efficient human-machine interaction, incorporating text-to-speech and speech-to-text technologies to facilitate natural and intuitive communication between the user and the system. The VISA system's performance was evaluated in different environments simulating real-world scenarios. The experimental results show that the user can interact with our system intuitively with minimal effort, and affirm that the VISA system can effectively assist the visually impaired user in locating and reaching for objects, navigating indoors, identifying merchandise, and recognizing both handwritten and printed texts.
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Title: Gwendolyn Brooks receiving an honorary degree at from Illinois Institute of Technology, Chicago, Illinois, 1968
Creator: Illinois Institute of Technology
Date: 1968
Description: Photograph of Gwendolyn Brooks, then Chicago's Poet Laureate, receiving an honorary degree from Illinois Institute of Technology. Photographer...
Show morePhotograph of Gwendolyn Brooks, then Chicago's Poet Laureate, receiving an honorary degree from Illinois Institute of Technology. Photographer unknown.
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Collection: Office of Communications and Marketing photographs, 1905-1999

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