Error Category Recognition in Procedural Videos With Vision-language Models

This study evaluates vision-language models (VLMs) for error category recognition in procedural videos. We use the CaptainCook4D (Peddi et al., 2023), an egocentric 4D dataset, to enhance AI systems’ understanding of procedural learning and error recognition in instructional videos. The comprehensive dataset, consisting of 384 long-form egocentric videos recorded in multiple real-world kitchen settings by various participants, is a testament to our meticulous data collection process. Unique in its inclusion of both error-free and error- prone videos, the dataset provides a robust resource for evaluating models on zero-shot error activity recognition. The data was collected using HoloLens2 and GoPro Hero 11 devices, capturing various sensory inputs, including depth and RGB video, hand and head tracking, and IMU data. Each recipe is represented as a task graph, detailing step-wise instructions and dependencies, aiding in the evaluation of AI models’ comprehension capabilities. Two Vision-Language Models (VLMs), Video-LLaVA (Lin et al., 2023) and TimeChat (Ren et al., 2024), were employed to assess the dataset’s utility in zero-shot error recognition tasks (Khattak et al., 2024). Video-LLaVA (Lin et al., 2023), an extension of the LLaVA model, integrates video processing for enhanced inference, while TimeChat (Ren et al., 2024) focuses on long video understanding through a time-sensitive multi-modal framework. Experiments involved prompt engineering using task graphs and a prompt-and-predict paradigm for error recognition, showcasing the models’ abilities to detect procedural mistakes for zero-shot evaluation. Evaluation metrics included precision, recall, and F1 scores, highlighting the models’ performance in classifying errors accurately. The study underscores the models’ potential to detect complex errors in procedural activities in egocentric videos

Changing the Default of a Long, Violent History: Inclusive Language and Relational Coordination Theory

In this dissertation, I examine how organizations espouse intentions to value diversity and how this intent often fails to translate into practices from these initiatives. As such, this research examines the gap between normative beliefs (ones that are adopted across organizations) and positive actions (empirically verified practices) which have resulted in conflicting discourse regarding the benefits of diversity. Previous research tells us that, in the short term, the inclusion of diverse backgrounds in the workforce is likely to hinder relational coordination as ingroup- outgroup differences are perpetuated. Thus, actions to enlighten individuals about their relational partner’s differences must be taken. I look toward identity disclosure as a mechanism which allows individuals to inform others about their differences. Conceptually, I rely on language as a mode of communication which opens the door to relational coordination. Empirically, I look examine to relational coordination—a mutually reinforcing process of communicating and relating for the purpose of task integration—as identity disclosure is inherently part of this process. identity disclosure creates shared knowledge prior to system thinking by informing individuals about each other’s identities. This information is critical to eventual communication around tasks. Relational coordination theory is critical to my dissertation because it begins with organizational structures that bring together diverse workers and explains how they work together and relate, eventually influencing work performance. In one qualitative study, a field study, and six experimental studies, I explore diversity initiatives and the identity disclosure process. Specifically, I examine the role of language in identity disclosure’s impact on relational outcomes, and eventual performance. Taken together, these studies contribute to the relational coordination literature by showing the effects of language and disclosure on organizational relationships. These findings are critical in terms of building relationships across differences as organizations try to leverage diversity within the workplace. I begin by conceptualizing the influence of default changing vernacular and identity disclosure within the organizational context and, I propose that the adoption of inclusive language in a heterogeneous organizational environment can influence identity management. In turn, identity disclosure will lead to greater societal change as these linguistic adoptions lead to changes in default conceptualizations of stigmatized groups. I draw on and develop two theoretical perspectives, relational coordination theory and stigma identity disclosure theory, to propose and later test relational outcomes through language defaults. The qualitative data were collected from 97 LGBTQIA+ individuals and the experimental data was collected online through Prolific. Next, I attempted to gain deeper insight into identity disclosure. Study 1 is a lab experiment manipulating a supervisor’s use of inclusive language. Using a sample of 160 Lesbian and Gay workers, I find that using the term significant other (an example of inclusive language) as opposed to misgendering the subordinate’s spouse (e.g., referencing a gay man’s spouse as wife instead of husband) leads to higher interpersonal awkwardness, lower trust, and increased relational conflict. Study 2 extends these findings (n=368) by manipulating a 2 (Identity Disclosure) x 2 (Inclusive Language) showing that interpersonal awkwardness and relational conflict are highest when identities are not disclosed, and inclusive language are not used. Study 3 uses a Lesbian and Gay population sample (n=398) to test a 2 (Supervisor Identity Receptiveness) x 2 (Inclusive Language) manipulation. I find similar results in terms of interpersonal awkwardness and relational conflict but extend the prior findings to include strategies for managing concealable stigmas. This experiment shows that subordinates are most likely to assimilate—project the characteristics of a more socially valued group—when inclusive language is used, and their supervisor is not receptive to their identity. This study highlights the detriment of organizational adoption of normative diversity practices without positive actions to change beliefs about outgroup individuals. Study 4 looks to a population sample of women (n=283). This study uses a 2 (Supervisor Inclusive Language) x 2 (Supervisor Gratitude) manipulation to examine supervisor-subordinate relationships based on the supervisor extending gratitude for pregnancy disclosure. In this instance, I find relational conflict to be highest in the no inclusive language/no gratitude condition and significant differences when collapsing across gratitude. Study 5 looks to African American/Black employees (n=275). This study uses a 2 (Supervisor Inclusive Language) x 2 (Supervisor Receptiveness) manipulation to examine supervisor-subordinate relationships based on the supervisor’s reaction to a parental leave policy. Here I find that fear of disclosing the intent for parental leave is highest in the inclusive language/no support condition. Finally, Study 6 looks to a population sample of gender non- conforming employees (n=288). This study uses a 2 (Supervisor Identity Receptiveness) x 2 (Supervisor Learning) manipulation to examine supervisor-subordinate relationships based on the supervisor’s attempt to learn about outgroup differences. As such, I find that relational conflict is lowest in the receptive/learning condition. These findings show that positive actions to learn about one’s identity who is different can impact a relationship even if one was not originally accepting of the other’s identity. In the final chapter, I examine the full model using a 25 week field study. This study uses Discontinuous Growth Modeling (DGM) to measure identity disclosure and the variables manipulated in the previous experiments across time. I found that several of the experimental results held true. The implications of these findings and future research directions are also discussed

Building Robust AI Systems: Addressing Uncertainty, Data Noise and Scarcity in Modern Machine Learning

The increasing complexity of real-world machine learning applications, such as autonomous systems, medical diagnostics, and natural language processing, demands models that can operate reliably in environments characterized by uncertainty, data scarcity, and noisy or ambiguous inputs. Traditional machine learning approaches, which rely on large, clean, well-labeled datasets, often fail when faced with ambiguous inputs, limited labeled data, or abundant but unlabeled data, leading to unreliable predictions and poor generalization. This thesis addresses these critical challenges by developing robust learning frameworks that enhance the uncertainty handling, adaptability, reliability, and performance of models in such challenging environments. First, the Hyper-Evidential Neural Network (HENN) is introduced to model vagueness uncertainty in classification tasks with composite class labels. By leveraging Subjective Logic and Dirichlet distributions, HENN quantifies uncertainty and improves decision-making in ambiguous data scenarios, such as medical diagnostics. Second, NestedMAML, a nested bi-level optimization framework, is proposed to improve robustness in corrupted few-shot learning with noisy and out-of-distribution tasks or instances. By weighting tasks and instances during meta-training, NestedMAML reduces the influence of noisy or irrelevant tasks and instances, improving robustness to distributional shifts and label noise during meta-training, ensuring better generalization. Third, a semi-supervised meta- learning framework, Platinum, is presented to leverage Submodular Mutual Information (SMI) functions to select the most informative unlabeled data during meta-training in inner and outer loops, ensuring that the model can leverage large amounts of unlabeled data while minimizing the impact of noise, leading to better generalization in diverse tasks, even when only a few labeled examples are available. These contributions provide a comprehensive approach to tackle vagueness uncertainty, data scarcity, and noisy inputs in machine learning, advancing methods for robust and adaptive learning in real-world applications

Search Costs, Working Capital Management and Asset Pricing

How might the cost of searching for information influence asset prices? This paper develops a production-based asset pricing model with imperfect information, in which firms can wait to buy inputs and sell inventories while searching for low cost suppliers and high value customers. Investors, meanwhile, can wait to buy assets while searching for cheap investment opportunities. The model predicts that when search costs rise, profits fall and the marginal utility of consumption spikes, leading investors to discount firms that are especially exposed to search cost risk ex ante. I identify observable firm characteristics related to search costs, and find that some of these variables do predict stock returns. Results suggest that excess inventory can be a hedge against the risk of higher search costs accompanying supply chain fragility or information suppression

The Effect of Private, Foreign and US Majority Ownership on Sporting and Financial Performance of English Soccer Clubs

European soccer clubs have been experiencing changes in their ownership structures in the last two decades. This study analyzes the impact of different ownership structures on sporting and financial performance with a scope on English Premier League for the seasons from 2007-2008 to 2021-2022. This study contributes to the literature, first by constructing a novel dataset of ownership structures of Premier League Soccer Clubs for the seasons from 2007-2008 to 2021-2022. Second, the time scope of the previous literature is expanded, with the inclusion of post-covid league seasons. The panel regression results show a negative effect of foreign private majority ownership on sporting and financial performance. When US majority ownership and all foreign majority ownership are compared, we see that US majority ownership does not perform better in the league, but it does so financially. We also suggest evidence for the positive effect of the seasons 2013-2014 and 2016-2017 on financial performance, pointing out respectively the introduction of Premier League profit regulations, and the new TV broadcasting deal. The negative effect of Covid shock in 2019-2020 is also present. In addition, the results support prior literature by showing the positive and significant effect of payroll costs on sporting performance. Lastly, the transfer expenses do not seem to improve sporting performance when controlled for payroll costs

The Enchantment of Embodied Living: Aesthetic Engagement and Modernity in Contemporary British Fiction

This dissertation analyzes a set of contemporary British novels that challenge a pervasive trend in Western philosophy, namely Cartesian dualistic thinking. Cartesian philosophy gives supreme importance to the human mind because of its association with reason and rationality and denigrates the human body. The analysis in the dissertation builds on recent work by literary scholars such as Elizabeth S. Anker, who maintains that the phenomenological understanding of the human body can restore faith in corporeality and correct certain shortcomings of instrumental reason. She adds that literature, because of its corporeal and embodied nature, has the potential to envision a different conception of being human. However, there is no scholarship in contemporary British fiction that examines how embodiment or corporeality works as an antidote to instrumental reason. The dissertation argues that the selected novels create an alternative imaginary of modernity exemplified by embodied living. The select group of novels in this dissertation are Home Fire (2017), Exit West (2017), Happiness (2018), and Spring (2019). This dissertation shows that these works of contemporary British fiction rehabilitate aspects of liberal modernity and rationality that they represent as redeemable. These novels link a rational understanding of political reality to embodied experience. For example, Home Fire (2017) and Exit West (2017) develop both in their aesthetic forms and character development a more expansive and liberating notion of autonomy, progress, and freedom by reorienting the idea of agency and personhood around embodied and corporeal experience. Happiness (2018) articulates its vision of an intertwined existence by challenging the anthropocentric worldview and competitive market economy. Spring (2019) advances its aesthetic theory of embodied art to counteract the onslaught of neoliberal commodity art

Investigation of Source Extension Methods, the Discrepancy Algorithm, and Noise Estimation to Overcome Cycle Skipping in Full Waveform Inversion

Full waveform inversion (FWI) is a geophysical technique used to create highly detailed mod- els of the Earth’s subsurface which can be used to explore for hydrocarbons and to predict natural hazards such as earthquakes. Seismic waves are generated from controlled sources such as vibroseis trucks on land and airguns in marine environments. These waves propagate through the Earth’s subsurface materials, reflecting off of interfaces underground. The for- ward problem in FWI predicts the data based on a given model of the subsurface, while the inverse problem estimates subsurface parameters, such as sound velocity, by minimizing the difference between recorded data and data we predict from solving our mathematical model (the wave equation). Solving this minimization problem is computationally prohibitive, so we rely on local gradient-based optimization methods. The success of such methods depends on the accuracy of the initial guess. Otherwise, FWI tends to get stuck at a suboptimal solution, a problem known as cycle-skipping. This dissertation explores several source extension methods to overcome the cycle-skipping problem in FWI for transmitted data. These methods add additional degrees of freedom to the objective function, expanding the solution space to include models which may or may not be physical. This updated objective function is convex with appropriate penalty parameters, allowing local gradient-based optimization methods to find the correct geological model from a wider range of initial models. When close to the correct model, the physical constraints are reimposed in the problem by the penalty term. For a simple homogeneous medium experiment with single trace acoustic data, we illustrate how extended source inversion (ESI) avoids cycle skipping by relaxing the requirement that the source must be compactly supported and by adding a soft penalty to control the extent of the source. The discrepancy algorithm dynamically adjusts the penalty weight to maintain data error within a specified range, ensuring accurate model estimates. The update of the penalty parameter relies on having an accurate estimate of the noise level in the data which is generally unknown a priori. Numerical examples show that the extended method successfully overcomes cycle-skipping without the need for a good initial model, that the algorithm can dynamically update the noise level in the data (and hence the penalty parameter), leading to a reliable and accurate solution to the inverse problem. Additionally, this dissertation investigates the matched source waveform inversion (MSWI) method which extends the solution space by assuming that each data trace is a function of both receiver and source location. For single arrival data, MSWI is closely related to travel- time inversion. The MSWI objective function includes a data misfit term and a penalty term to keep an adaptive filter close to the Dirac delta function. MSWI is equivalent to the source extension method described above when the penalty parameter in MSWI approaches zero. Experiments demonstrate that for more complex heterogeneous media experiments MSWI successfully reduces cycle-skipping in single-arrival transmission data (even when moderate amounts of noise are present in the data), while FWI often fails due to cycle skipping. The inverted model resulting from MSWI can, therefore, provide a good starting model for FWI. However, the results also demonstrate that MSWI applied to multi arrival transmission data fails

3D Printing of Piezoresistive Flex Sensors for Soft Robotic Grippers and Strain Sensing

Flexible piezoresistive sensors also referred to as strain gauges or flex sensors are extensively used in soft robotics to detect strain due to their inherent flexibility. They change their resistance in response to strain. This change in resistance can be used to detect bending in soft robots. 3D printing based on filament extrusion (or Fused Filament Fabrication/ Fused Deposition Modeling) of conductive polymers proffers the ability to fabricate inexpensive flex sensors quickly. There seems to be a dearth of literature that characterizes FFF printed flex sensors by cyclic bending. The effect of fabrication parameters, substrate materials, and sensing element geometry on the cyclic bending response of the sensor needs to be assessed. In this thesis, a voltage divider circuit is used for data acquisition and characterization experiments are conducted to determine parameters for fabricating flex sensors with high repeatability (with a coefficient of variation less than 2% which is lower than any other literature reviewed), good sensitivity (with a gauge factor of over 6.5, more than three times higher than conventional metal strain gauges), and longevity greater than a hundred thousand cycles. The longevity of the flex sensor is experimentally demonstrated. The flex sensors manufactured are compared with additively manufactured strain sensors in literature. To demonstrate their applications the flex sensors are incorporated in a soft silicone robotic gripper and TPU finger in which they successfully detect the bending angle. The temperature sensitivity of the sensor is also demonstrated. These sensors may have a potential for sensing in several other soft structures that need flexibility as a prime factor

Investigating the Impact of Organizational Resilience and Turnover in U.S. Local Governments: the Mediating Effect of Strategic Human Resources Management Practices

Research on resilience has mainly focused on crisis, or disaster management. However, more recently the concept of resilience has emerged into management research and organization studies, with scholars examining how organizations can prepare for the daily challenges that come with an ever changing and complex environment, thrive and capitalize on change, and uncertainty. Against the challenges that come with change and a complex environment, organizations are looking for means to maintain staff performance, ensure high performance and employee wellbeing. Resilient organizations manage the everyday stressors, and help employees learn, adapt and bounce back from setbacks. Organizations should therefore proactively prepare for future challenges and change, and continuously review policies and practices to create positive and efficient work environments. What is largely missing from studies is the examination of organizational level resilience, and particularly the role that human resource management can play in attaining organizational outcomes. This research investigates organizational resilience and its impact on turnover in U.S. local governments, and the mediating effect of Strategic Human Resources Management (SHRM) practices. A survey questionnaire was distributed to HR Directors in U.S. cities’ to determine organizational resilience, application of strategic human resources management practices, and employee turnover rate in 2018 in selected cities. The research adopted the Resilience Benchmark Survey developed by Resilient Organizations (2012) which is a tool that measures organizational resilience by the leadership and culture, networks and relationships, and change readiness. From the structural equation (SEM) results, leadership and change readiness were negatively associated with turnover in U.S. cities, and the mediating effect of the three SHRM practices showed both positive and negative relationship with turnover. The findings from this research will help advance the existing literature and improve our understanding of organizational resilience and the role of strategic human resources practices on employee turnover. The research findings have significant implications for HRM practitioners and researchers, for example, HR practitioners that attend to factors that contribute to organizational resilience create an organization’s adaptive capacity for a range of day-to-day challenges in an ever- changing environment

Near-unity Biexciton Quantum Yield and Generation of Correlated Photon Pairs From CDS/CDSE/CDS Quantum Shells

This dissertation presents colloidal CdS-CdSe-CdS nanocrystals, referred to as quantum shells (QSs), which exhibit enhanced biexciton quantum yield (BX QY) due to their unique geometry. A quasi-2D confinement layer is wrapped into a spherical surface, increasing the effective volume available for charge carriers and reducing electron-hole overlap. This structure effectively suppresses Auger recombination while maintaining strong confinement, making quantum shells promising candidates for optoelectronic applications that benefit from efficient multiexciton generation. Single-particle spectroscopy techniques were employed to investigate photoluminescence blinking, fluorescence lifetimes, and antibunching, demonstrating size- dependent improvements in BX QY, with some QSs achieving near-unity yields. A non- statistical scaling model is used to describe the evolution of radiative and non-radiative processes, including estimations of BX lifetimes and Auger rates. Additionally, the high BX QY enables the observation of stable and spectrally distinct photon pairs, a critical property for generating single photons, correlated photon pairs, and entangled photon states which are at the forefront of emerging quantum optics technologies. A rate equation model incorporating non-ideal spectral filters is introduced to describe the behavior of the observed photon correlations, and highlights the significance of unresolved emission lines and background contributions. To facilitate further exploration into the potential of quantum shells as sources of entangled photons, a theoretical description for the measurement of the polarization state of the photon pair is provided