University of Illinois at Chicago
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The Other Boomers: Chicago Youth and the Politics of Poverty in the Long 1960s
No full textIn March 1963, Chicago’s Hull House, under intense economic and political pressure, sold its iconic Near West Side social settlement property and reconstituted itself as the Hull House Association. Jane Addams and her partner Ellen Gates Starr had established Hull House in 1889, and it soon provided a model for similar efforts to ameliorate poverty across the United States. However, the settlement’s future came into question after World War II, when city officials began to focus their attention on “blighted areas” like the Near West Side. Urban renewal meant that Hull House and other residents would be displaced.
This dissertation is about what happened when Hull House embraced its displacement, with an emphasis on the agency of young people who were eligible for social services. The newly formed Hull House Association established two new neighborhood centers, in Uptown and Lakeview, and affiliated with two existing centers, in Woodlawn and at the LeClaire Courts public housing project. These four communities were distinct. Who lived in each, the way they functioned and were perceived, was the result of long and complicated processes of development. Into the 1960s, those differential processes of development included young people who had distinct relations with social services and a prevailing youth culture of resistance.
This dissertation consists of four community studies, in thick description, that contextualize youth agency as the result of long processes of community development, resulting in localized social and political specificity. Beyond thick description, these community studies are grounded in what feminist geographer Doreen Massey calls a progressive sense of place, an understanding of place as process. This allows us to see how young people navigated the complex specificity of each community, and its power-geometry, including the role of machine politics and social provisions in determining their future mobility, their ability to escape poverty
Human-Centric Physiological Approaches to Improve User Interaction and Adaptation in Human-Robot Teaming
No full textHuman-robot interaction (HRI) is a rapidly evolving field with a diverse range of applications, including manufacturing, aviation, surgery, rehabilitation, education, rescue, and military operations. As humans are key stakeholders or end users in these scenarios, there is a major need for research on human interaction aspects and user experience in the design and development of robots. Robots are expected to be aware of human preferences and internal states (via physiological signals) and adapt their behavior accordingly, so that they develop a symbiotic relationship with the human and maximize the beneficial outcomes of HRI (Gervasi et al., 2022, 2024). This dissertation comprises six projects which are focussed on two types of human-robot teaming tasks: (i) shared workspace tasks, where the human is an operator and (ii) physical augmentation tasks, where the human is a user. The research focus is on understanding human internal states (such as adaptation, fluency, effort, comfort), representation of internal states using psychophysiological measures and closing the human-robot loop to enhance user experience.
Shared workspace tasks (Human as operator): Fluency, referred to as a well-synchronized meshing of actions between human and robot, is an important factor that affects team performance and long-term sustained collaboration at the workplace. Chapter 2 investigates the potential for Electrocardiogram (ECG) metrics to indicate the fluency of human-robot teaming in shared space collaboration. The results showed that fluency in HRC tasks correlated with specific HRV features, enabling objective fluency prediction using machine learning models with useful accuracy. In manufacturing scenarios, automated robotic systems require varying degrees of supervisory control by a human. However, there is limited research on individual differences in worker preferences regarding the level of automation and decision authority, showing the need for more human-centric studies in human-robot collaboration tasks (Weiss et al., 2021). More specifically, physiological responses representative of changes in mental effort during different levels of automation in collaborative manufacturing have not been studied. Chapter 3 bridges this research gap. Findings reveal that individual differences exist in preferences for robot automation levels, with many favoring a medium automation level that includes human decision authority. Nonlinear ECG-derived heart rate variability (HRV) metrics reveal an increase in mental effort with lower automation, suggesting that it has applications in human-aware robotic interfaces. In addition to worker preferences and teaming fluency, adequate operator support is of crucial importance to enable the successful adoption of robots in the workplace. While Augmented Reality (AR) user interfaces have been found to improve engagement and user experience in educational settings, their effectiveness for human-robot interaction needs to be evaluated. Chapters 4 and 5 investigate this research direction. For robot control and operation, physical touch-based user interfaces such as the electronic tablet were perceived as more usable compared to AR based interfaces, indicating ergonomic improvements for future adoption.
Physical augmentation tasks (Human as user): In wearable robotics, particularly for lower limb exoskeletons that assist with movements such as walking and squatting, the human-in-the-loop (HIL) optimization method has been proven to be successful in reducing physical effort by delivering personalised assistance to each user (Ding et al., 2018; Kantharaju et al., 2022, 2023; J. Zhang et al., 2017). However, the current method of optimization is based on an energy cost function estimated using a respiratory system, which is uncomfortable, non-portable, produces noisy measurements and requires a long estimation time. These issues limit the application of human-in-the-loop exoskeleton optimization method in the real world. In Chapter 6, I address this gap by finding an alternate cost function based on users’ foot center of pressure that is faster, comfortable, portable, and reduces injury risk in exoskeleton-assisted squatting. Another limitation of the current state-of-the-art HIL optimization method is that it optimizes for only user effort and does not explicitly incorporate the human’s movement adaptation to the assistance from the device, which is important as adaptation due to training has been found to enhance the device’s performance and user outcomes (M. Kim et al., 2022; Poggensee & Collins, 2021). In Chapter 7, I show how heart rate complexity, measured using a wearable ECG sensor, can be used to assess a user’s adaptation status to assistive devices, based on its changes before and after training users to walk with a hip exosuit. Chapter 8 deals with the implementation and evaluation of a dual-objective human-in-the-loop optimization scheme that optimizes the assistance from a lower limb exoskeleton based on both energy expenditure as well as the user’s movement adaptation (gait symmetry) to robotic assistance. The results of this study show that the incorporation of an implicit coaching reward within a dual objective optimization method yields optimal assistance that is both energy efficient as well as user-preferred, showcasing its potential for enabling natural human-exoskeleton interaction and supporting exoskeleton adoption in the real world
Risk Factor Disclosures and Discount Rates
No full textThis dissertation investigates whether and how risk factor disclosures (RFDs) affect discount rates. Using a difference-in-difference (DID) test based on firms' staggered compliance with the 2005 SEC mandate on RFDs, I show that the mandate leads to higher discount rate news (e.g., a larger increase in discount rates), even after controlling firm betas and cash flow volatility. Larger increases in RFDs are positively associated with higher discount rate news post mandate, consistent with a dominant risk reflection impact of RFDs over the information transparency improvement effect. The association is stronger for firms with poorer performance, higher stock market risk, higher operation uncertainty, and higher bankruptcy risks. More RFDs pre-mandate are associated with lower discount rates, consistent with a dominant improvement effect of information asymmetry over the risk reflection effect. Additional tests suggest that the effect of more RFDs on discount rate news outweighs their effect on cash flow news. Analysts decrease target price forecasts and downgrade recommendations for firms with higher discount rate news adjusted by RFDs. Future firm stock performance and firm value increase for firms with higher RFDs after the mandate. Overall, the results suggest that RFDs convey important information for dynamic firm-specific discount rates useful for investors
Exploring Spatio-Temporal Networks: Analyzing Brain Traffic and Construction of Geographic Networks
No full textThis thesis integrates the analysis of traffic in brain networks and the construction of geographic networks, offering novel insights into spatio-temporal behavior across both biological and geographic systems.
The first part of the thesis examines Traffic Analysis in Human Brain Networks, exploring the pathways through which brain signals flow by combining both structural and dynamic perspectives. A primary question in this study is whether brain signal flow aligns more with a globally optimized System Optimum (SO) model or a decentralized User Equilibrium (UE) model. Findings indicate a closer alignment with the SO model, highlighting an inherent efficiency in signal routing within the brain. Building on this, the research investigates how brain coordination is impacted by declining levels of consciousness, providing insights into neural activity under unconscious states, such as during sleep or anesthesia. Additionally, an alternative model is introduced in which brain signals do not have predefined destinations but instead search for specific resources within the network. By employing Markov Decision Process (MDP)-based algorithms, simulations show that this model more accurately mimics brain signal propagation, challenging the assumption that brain signals always have clear, fixed destinations.
The second part of the thesis focuses on the extraction of structured geographic data from unstructured text using conversational large language models (LLMs). The study applies these models to convert unstructured sources into structured data, facilitating the construction of a geographic network. This network provides insights into travel patterns, logistical demands, and key locations, with the potential for future analysis of congestion and destination dynamics.
Through these investigations, this thesis contributes to a deeper understanding of complex network behavior in both the biological and geographic domains, offering new methods for analyzing traffic patterns, resource-seeking behaviors, and destination-based analyses across diverse systems
Measurements of DNA Dynamics and Flexibility and Their Impact on Protein-DNA Interactions
No full textDNA-protein interactions where DNA gets kinked, bent or otherwise deformed are vital for many biological processes. The flexibility and dynamics in these complexes remain poorly characterized because of the lack of adequate time resolution of many approaches. In this study, we use µs-resolved laser temperature-jump (T-jump) and fluorescence lifetime spectroscopies to measure DNA and protein-DNA conformational dynamics with high temporal resolution and sensitivity. We reveal novel findings on DNA dynamics in two main contexts: (1) bound to architectural proteins that facilitate chromatin packaging; and (2) when the DNA contains mismatches that are recognized by mismatch repair proteins.
Architectural proteins from the HMGB family induce transient DNA kinks and enhance DNA flexibility to facilitate DNA packaging. How these proteins accomplish this DNA “softening” is not well understood. For example, it was not known whether the proteins bind to unbent DNA and then deform it, or if bent DNA conformations are “captured” by protein binding. Using an array of thermodynamic probes, we unveiled for the first time unbent DNA while bound to yNhp6A (yeast ortholog of HMGB), thus supporting the “bind-then-bend” mechanism. With T-jump, we measured DNA bending/unbending kinetics in the yNhp6A-DNA complex on timescales of ~0.5-1 ms, providing the first observation of DNA bending dynamics in complex with a nonspecific DNA-binding protein.
DNA repair protein MutS is tasked with locating single mismatches (errors) introduced during replication and to initiate repair. These proteins must rapidly scan billions of base pairs of genomic DNA yet slow down to recognize mismatches—a daunting and puzzling task. What role DNA plays in facilitating mismatch recognition remains underexplored. We examined the conformations and dynamics of a series of mismatches repaired with different efficiencies. Weakly recognized substrates, e.g., T.T and T.C (in our sequence-context) were indistinguishable from the matched counterpart in their conformations/dynamics. In contrast, efficiently recognized mismatches induced severe disruptions in base stacking, exhibiting rapid (<10 µs) kinking fluctuations (e.g., +T) and/or shape distortions (e.g., G.T). These distortions/dynamics likely help stall a diffusing protein to facilitate recognition. How mismatch-induced DNA conformational distortions/dynamics, further modulated by sequence context, correlate with mismatch repair efficiencies remains to be explored
Success of Vital Pulp Therapy in Immature Permanent First Molars
No full textIntroduction: The objective of this study is to determine the factors that led to successful vital pulpotomy treatment of first permanent molars (FPMs). We hypothesize that a) immature FPM teeth have a higher success rate of Vital pulp therapy than mature teeth and b) final definitive restoration delivery within 6 months of pulp improves the success rate of pulp therapy on FPMs. Methods: A retrospective chart audit of patients aged 5-16 years who had at least one FPM that had vital pulpotomy treatment at the University of Illinois College of Dentistry (UIC COD) between January 1, 2016 and March 1st, 2023. Data was collected on patient demographics (ie. age, gender, race), the tooth treated (treatment type, root status, restoration) and follow-up visits. Successful treatment was determined by absence of symptoms or pathology at follow-up visits and no need for retreatment. Results: A total of 254 patients were treated that met the inclusion criteria: [Age-mean(SD) = 8.64(1.64)]. Fifty-four percent had immature root development at time of treatment. Pulp therapy performed included Cvek (2%), partial pulpotomy (17%), and full pulpotomy (81%). Most treated teeth (77%) were restored with Glass Ionomer (GI) on the day of treatment. Around 50% of the treated molars received SSC as final restoration with 1/3 delivered on the day of treatment. Within the subset sample population demographic (n=154), successful treatment was observed in 67% of teeth treated. No correlation was detected between the success rate of pulp therapy and root development or definitive restoration within 6 months period (p>0.05). Conclusions: The results suggest that success of pulpotomy treatment is independent of root development and type of restoration post treatment, but suggest same-day definitive restoration may be beneficial for clinics with high loss to follow up rates
Early Childhood Teacher Preparation, Lesson Planning, and Emotion-Focused Teaching Practices
No full textThis study employed an explanatory sequential mixed-methods design to examine early childhood teacher education, lesson planning, and emotion-focused teaching practices within an early childhood education Alternative Licensure Program (ALP) at the University of Illinois Chicago. Teachers’ (n=20) emotion-focused teaching (EFT) practices during implementation of their lesson plans were quantitatively evaluated using the EMOtion Teaching Rating Scale (EMOTERS). Their EMOTERS scores were analyzed and separated into High EFT, Moderate EFT, and Low EFT groups. Their emotion-focused lesson plans were qualitatively analyzed to determine characteristics and detect themes and patterns in the data. The qualitative and quantitative data were integrated and the findings revealed that teachers in the High EFT group had three lesson plan characteristics in common. These characteristics include focusing on emotional rather than behavioral challenges, aligning lesson plan objectives and strategies with the challenges teachers identified, and creating responsive strategies to help children learn something new about the emotion-focused topic. The implications this has for effective preservice SEL education are explored
The Role of Amyloid Precursor Protein in Deficits in Corticogenesis in Down Syndrome
No full textDown Syndrome (DS) due to trisomy 21 results in significant developmental cortical malformation and invariably accumulation of Alzheimer’s Disease (AD) pathology with aging. Approximately fifty percent of people with DS will develop clinical AD. It is theorized that deficits in corticogenesis lead to abnormal neuronal networks vulnerable to the early, pervasive AD pathology observed in DS. Amyloid Precursor Protein (APP) is located on human chromosome 21 (HSA21) and overexpressed in DS tissues. Overexpression and increased metabolism of APP is thought to underlie these deficits given its well established role in molecular mechanisms of cortical development and AD. However, given the hundreds of genes located on HSA21, it remains to be established if normalization of APP gene copy number would be sufficient to rescue DS phenotypes. To that end, CRISPR-Cas9 technology was utilized to eliminate one copy of APP from human DS patient derived induced pluripotent stem cells (DS APP +/+/-) to address the hypothesis that increased APP gene dosage disrupts corticogenesis and promotes neuronal network vulnerability and AD pathology accumulation in DS cortical organoids. Isogenic control, DS, mock CRISPR-Cas9 DS, and DS APP +/+/- induced pluripotent stem cell lines were differentiated into dorsal forebrain organoids for characterization of corticogenesis and AD pathology. APP gene copy number contributed to decreased expression of neurogenic proteins, deficits in neuronal differentiation, and increased secretion of Aβ in DS cortical organoids. Taken together, this suggests that increased APP gene copy number dysregulates corticogenesis and promotes AD pathology in DS
Speaker Orientation Estimation from Spatial and Spectral Features Using a Deep Neural Network
No full textAccurate estimation of speaker orientation significantly enhances applications such as hearing aids, teleconferencing systems, and voice-controlled interfaces. Speaker orientation information can help a hearing aid to decide whether to enhance that talker. It also enhances camera tracking and audio quality in teleconferencing systems. And help to decide whether a voice interface respond.
This thesis introduces a deep neural network method using combined spatial and spectral audio features for speaker orientation estimation. Spatial features are derived using a weighted Generalized Cross-Correlation with Phase Transform (GCC-PHAT) technique applied to three microphones placed around the speaker. Spectral features capture the directivity patterns of human speech from Mel spectrogram. The proposed method achieves significantly reduced estimation errors compared to approaches using single feature type. Experimental results show better accuracy, validating the effectiveness of the combined feature to be suitable for real world implementations
Hardware Aware Study of Near-Term Quantum Algorithms
No full textProblems predicted to have a clear quantum advantage often involve using quantum algorithms with a black box, or oracle, that encapsulates the solution. The engineering of quantum processors has accelerated to push beyond the near-term intermediate scale. As such, there is growing interest in studies of fundamental black box algorithms tailored to specific hardware designs to verify a quantum advantage experimentally. This thesis explores the Deutsch-Jozsa and Quantum Permutation algorithms as foundational problems in the context of various photonic and cloud-accessible transmon-based platforms that are expected to have a computational advantage over classical implementations.
First, we examined the Deutsch-Jozsa (DJ) algorithm implemented on a miniaturized metastructure-based quantum algorithm emulator (QAE) with an inverse-designed graded-index lens for operation in the THz frequency range. In the DJ problem, an oracle assesses a binary function f with n-bit values as input and outputs 0 or 1. The proposed QAE consists of two main components: the oracle subblock, which modulates the phase of the transmitted THz wave, and the Fourier subblock, composed of a GRIN lens to display the desired output signal. We evaluated the structure using numerical simulations and employed inverse design machine learning to validate and optimize parameters such as thickness and hole radii to enhance performance. As a result of this process, the initial design of the GRIN lens was improved for the full-width half-maximum and amplitude of the output THz wave, yielding the expected quantum advantage in the THz regime.
Next, we performed a hardware-aware study to implement the Quantum Permutation Algorithm (QPA) on cloud-accessible quantum processors. The QPA aims to determine the parity of an unknown permutation in a single measurement. Previous investigations of this algorithm were restricted to a single 5-qubit processor, which achieved an average success rate of 86% for 2- and 3-qubit circuits. In this thesis, we show the implementation of higher qubit processors up to 127 qubits and assess the impact of current hardware characteristics such as available gate sets, qubit topology, error, etc. We analyze the resource requirements for 2-, 3-, 4- and 5-qubit circuits, including qubit connectivity, circuit depth, and gate fidelity, to identify bottlenecks in execution. Our results demonstrate the feasibility of QPA for small-scale (n=5 and 7) and intermediate-scale (n=127) qubit quantum devices while highlighting the limitations posed by noise and limited coherence times for larger circuits. Through hardware-aware optimizations, including efficient decomposition of multi-qubit Toffoli gates and error mitigation strategies such as dynamical decoupling, we explore pathways for improving QPA performance. This study provides insights into the adaptability of quantum algorithms for near-term devices. It underscores the need for continued quantum software and hardware advancements to unlock quantum computing's full potential
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