Electrical and Computer Engineering Commons^™

Causal Event Graph-Guided Language-Based Spatiotemporal Question Answering, Kaushik Roy, Alessandro Oltramari, Yuxin Zi, Chathurangi Shyalika, Vignesh Narayanan, Amit Sheth

Publications

Large Language Models have excelled at encoding and leveraging language patterns in large text-based corpora for various tasks, including spatiotemporal event-based question answering (QA). However, due to encoding a text-based projection of the world, they have also been shown to lack a fullbodied understanding of such events, e.g., a sense of intuitive physics, and cause-and-effect relationships among events. In this work, we propose using causal event graphs (CEGs) to enhance language understanding of spatiotemporal events in language models, using a novel approach that also provides proofs for the model’s capture of the CEGs. A CEG consists of events denoted by …

Exploring Alternative Approaches To Language Modeling For Learning From Data And Knowledge, Yuxin Zi, Kaushik Roy, Vignesh Narayanan, Amit Sheth

Publications

Despite their wide applications to language understanding tasks, large language models (LLMs) still face challenges such as hallucinations - the occasional fabrication of information, and alignment issues - the lack of associations with human-curated world models (e.g., intuitive physics or common-sense knowledge). Additionally, the black-box nature of LLMs makes it highly challenging to train them meaningfully in order to achieve a desired behavior. Specifically, the attempt to adjust LLMs’ concept embedding spaces can be highly intractable, which involves analyzing the implicit impact on LLMs’ numerous parameters and the resulting inductive biases. This paper proposes a novel architecture that wraps powerful …

Proknow: Process Knowledge For Safety Constrained And Explainable Question Generation For Mental Health Diagnostic Assistance In The Age Of Large Language Models, Kaushik Roy, Manas Gaur, Misagh Soltani, Vipula Rawte, Ashwin Allen, Amit P. Sheth

Publications

Current Virtual Mental Health Assistants (VMHAs) primarily offer counseling and suggestive care but do not assist with patient diagnosis due to their lack of training in safety-constrained and specialized clinical process knowledge, referred to as ProKnow. In this work, we define ProKnow as an ordered set of information aligned with evidence-based guidelines or categories of conceptual understanding used by domain experts. We also introduce a new dataset of diagnostic conversations guided by safety constraints and Pro- Know, known as ProKnow-data. We develop a method for natural language question generation (NLG) designed to interactively gather diagnostic information from patients, termed ProKnow-algo. …

Towards Pragmatic Temporal Alignment In Stateful Generative Ai Systems: A Configurable Approach, Kaushik Roy, Yuxn Zi, Amit Sheth

Publications

Temporal alignment in stateful generative artificial intelligence (AI) systems remains an underexplored area, particularly beyond goal-driven approaches in planning. Stateful refers to maintaining a persistent memory or “state” across runs or sessions. This helps with referencing past information to make system outputs more contextual and relevant. This position paper proposes a framework for temporal alignment with several configurable toggles. We present four alignment mechanisms: knowledge graph path-based, neural score-based, vector similarity-based, and sequential process-guided alignment. By offering these interchangeable approaches, we aim to provide a flexible solution adaptable to complex and real-world applications. This paper discusses the potential benefits and …

An Ontology Design Pattern For Representing Causality, Utkarshani Jaimini, Cory Henson, Amit Sheth

Publications

The causal pattern is a proposed ontology design pattern for representing the structure of causal relations in a knowledge graph. This pattern is grounded in the concepts defined and used by the CausalAI community i.e., Causal Bayesian Networks and do-calculus. Specifically, the pattern models three primary concepts: (1) causal relations, (2) causal event roles, and (3) causal effect weights. Two use cases involving a sprinkler system and asthma patients are provided along with their relevant competency questions.

Design And Fabrication Of High-Resolution Epitaxial 4h-Sic Metal Insulator Semiconductor Detectors, Omerfaruk Karadavut

Theses and Dissertations

In the last two decades, significant strides have been made in the epitaxial film growth of 4H-silicon carbide (4H-SiC), establishing it as a premier wide bandgap material for radiation detection application under harsh environments. This is primarily attributed to its unique combination of physical properties such as high thermal conductivity, wide bandgap, robust breakdown field, and radiation hardness. Metal/4H-SiC epitaxial layer Schottky barrier diodes (SBDs) have emerged as reliable radiation detectors for harsh environments. However, the utilization of thicker epitaxial layer devices encounters challenges due to the minimum achievable doping concentration in 4H-SiC epitaxial layers, necessitating higher bias voltages for …

Robustness Of Convolutional Neural Networks: Analysis And Applications, Xin Zhang

Theses and Dissertations

Convolutional neural networks (CNNs) have gained increasing popularity and versatility in recent decades, finding applications in diverse domains including image recognition, natural language processing, recommendation systems, as well as safety-critical areas like autonomous driving, medical diagnostics, and military fields. However, the widespread use of CNNs in safety-critical applications has also given rise to concerns regarding their robustness. The low robustness of CNNs may lead to erroneous predictions and even significant losses of human life and property, especially when dealing with corner cases or boundary cases in real-world problem-solving. Most existing methods for evaluating CNNs still heavily rely on their accuracy …

Circularly-Shifted Chirps For Triple Functionality: Communications, Radar, And Computation, Safi Shams Muhtasimul Hoque

Theses and Dissertations

This dissertation presents circularly-shifted chirps (CSCs), synthesized within orthogonal frequency domain multiplexing (OFDM) framework, as a novel solution for integrating radar, communication, and computation functionalities to a wireless network. Firstly, {index modulation (IM)} with circularly-shifted chirps (CSCs) (CSC-IM) for dual-function radar and communication (DFRC) system is discussed. The proposed scheme encodes the information bits with the CSC indices and the phase-shift keying (PSK) symbols. It allows the receiver to exploit the frequency selectivity naturally in fading channels by combining IM and wideband CSCs. It also leverages the fact that a CSC is a constant-envelope signal to achieve a controllable peak-to-mean …

Robust Deep Learning Models For Multi-Label Image Classification In Limited And Free Annotation Scenarios, Rabab Ezzeldin Rabie Abdelfattah

Theses and Dissertations

This proposal introduces various methods for tackling the challenge of multi-label image classification in different scenarios, including partial labels and free-annotation settings. Multi-label image classification is typically formulated as a partial-label learning problem because it can be expensive to annotate all labels in every training image. Existing partial-label learning approaches typically focus on annotating each training image with only a subset of its labels, including a special case of annotating only one positive label per image. To further alleviate the annotation burden and improve classifier performance, this proposal introduces a new partial-label setting where only a subset of training images …

Probabilistic Cable Aging Diagnosis And Prognosis With Reflectometry And Capacitance Methods, Xuan Wang

Theses and Dissertations

Safe and reliable operation of power plants and power transmission are critical to economy and society. Cables in power generation and transmission are subject to various thermal, chemical, and mechanical stresses, which generally lead to aging and degradation of cable insulation. It is reported that some cables with a projected lifetime of 40 years need to be replaced in 10-15 years. Poorly maintained aged cables can adversely affect power delivery and lead to catastrophic events, such as blackout, fires, and loss of lives. The current research on cable is mainly focused on the detection and localization of hard faults, which …

Novel Approach To In-Situ Mocvd Oxide/Dielectric Deposition For Iii-Nitride-Based Heterojunction Field Effect Transistors, Samiul Hasan

Theses and Dissertations

III-Nitride-based compound semiconductors have unique properties such as high bandgap and high breakdown field, which make them attractive for a variety of applications, including high-power and high-frequency electronics and optoelectronics. The most common types of III-Nitride-based field effect transistors (FETs) are aluminum gallium nitride (AlGaN)/gallium nitride (GaN) based, which suffer from some inherent problems such as virtual gate effect, current collapse, gate leakage, etc. The solution to this problem can be the inclusion of a dielectric passivation layer under the gate. However, the addition of the dielectric layer impacts one of the most critical device-controlling parameters, “threshold voltage”, which suffers …

Simulation-Based Optimization Of A Dc Microgrid: With Machine-Learning-Based Models And Hybrid Meta-Heuristic Algorithms, Tyler Van Deese

Theses and Dissertations

The field of economic dispatch (ED) focuses on optimizing power flow in a power system to minimize costs. It has the potential to significantly enhance system effectiveness, and efficiency, and reduce operating costs. Various techniques have been employed to tackle this problem, each with its own strengths and weaknesses. One promising approach is simulation-based optimization (SBO), which allows for accurate modeling of system interactions and improved representation of expected results. However, SBO requires running numerous simulations to identify an optimal solution, and there is a possibility of not achieving the global optimum. This work aims to address these challenges using …

Reducing Brain Kynurenic Acid Synthesis Precludes Kynurenine-Induced Sleep Disturbances, Katherine M. Rentschler, Snezana Milosavljevic, Annalisa M. Baratta, Courtney J. Wright, Maria V. Piroli, Zachary Tentor, Homayoun Valafar, Christian O'Reilly, Ana Pocivavsek

Publications

Patients with neurocognitive disorders often battle sleep disturbances. Kynurenic acid is a tryptophan metabolite of the kynurenine pathway implicated in the pathology of these illnesses. Modest increases in kynurenic acid, an antagonist at glutamatergic and cholinergic receptors, result in cognitive impairments and sleep dysfunction. We explored the hypothesis that inhibition of the kynurenic acid synthesising enzyme, kynurenine aminotransferase II, may alleviate sleep disturbances. At the start of the light phase, adult male and female Wistar rats received systemic injections of either: (i) vehicle; (ii) kynurenine (100 mg kg−1; i.p.); (iii) the kynurenine aminotransferase II inhibitor, PF-04859989 (30 mg kg−1; s.c.); …

Ki-Cook: Clustering Multimodal Cooking Representations Through Knowledge-Infused Learning, Revathy Venkataramanan, Swati Padhee, Saini Rohan Rao, Ronak Kaoshik, Anirudh Sundara Rajan, Amit Sheth

Publications

Cross-modal recipe retrieval has gained prominence due to its ability to retrieve a text representation given an image representation and vice versa. Clustering these recipe representations based on similarity is essential to retrieve relevant information about unknown food images. Existing studies cluster similar recipe representations in the latent space based on class names. Due to inter-class similarity and intraclass variation, associating a recipe with a class name does not provide sufficient knowledge about recipes to determine similarity. However, recipe title, ingredients, and cooking actions provide detailed knowledge about recipes and are a better determinant of similar recipes. In this study, …

Ecg Recordings As Predictors Of Very Early Autism Likelihood: A Machine Learning Approach, Deepa Tilwani, Jessica Bradshaw, Amit Sheth, Christian O'Reilly

Publications

In recent years, there has been a rise in the prevalence of autism spectrum disorder (ASD). The diagnosis of ASD requires behavioral observation and standardized testing completed by highly trained experts. Early intervention for ASD can begin as early as 1–2 years of age, but ASD diagnoses are not typically made until ages 2–5 years, thus delaying the start of intervention. There is an urgent need for non-invasive biomarkers to detect ASD in infancy. While previous research using physiological recordings has focused on brain-based biomarkers of ASD, this study investigated the potential of electrocardiogram (ECG) recordings as an ASD biomarker …

Analysis, Measurement, And Modeling Of Millimeter Wave Channels For Aviation Applications, Zeenat Afroze

Theses and Dissertations

Millimeter wave (mmWave) communication systems can employ a large amount of spectrum, and can consequently offer large data rates, e.g., multi-Gigabits-per-second. This technology can be used in many sectors: aviation, vehicles, public transportation, robotics, autonomous factories, etc. Yet mmWave communication systems suffer from some propagation challenges, including large free space path loss (PL), large penetration loss, and large diffraction loss. Hence, it is vital to quantify these and other channel effects to ensure link reliability. Most mmWave systems will employ directional antennas to enable acceptable link distances. In many settings this will require directional receiver antennas to rotate in azimuth …

Optimization Of Ultrawide Bandgap Semiconductor Materials For Heterostructure Field Effect Transistors (Hfets), Mohi Uddin Jewel

Theses and Dissertations

Ultra-wide bandgap (UWBG) gallium oxide (Ga2O3), and aluminum gallium oxide (Al_xGa_1-x)₂O₃ materials with bandgap E_G ≥ 4.8 eV are promising for heterostructure field effect transistors (HFETs) with high breakdown voltage and operating at high temperatures. Despite some initial breakthroughs in good quality β-Ga₂O₃ thin films growth on (001), (100), (010), and (201) β-Ga₂O₃ substrates, and promising results on β- (Al_xGa_1-x)₂O₃/β-Ga₂O₃ HFETs fabricated on (010) β-Ga₂O₃ substrates, heat dissipation in Ga2O3 materials and …

Magnetic Softness Tuned Superparamagnetic Nanoparticles For Highly Efficient Cancer Theranostics, Jie Wang

Theses and Dissertations

Magnetic resonance imaging (MRI)-guided magnetic nanofluid hyperthermia (MNFH) using iron oxide based superparamagnetic nanoparticles (SPNPs) has recently attracted considerable attention as a treatment modality for cancer theranostics, because MRI-guided MNFH can allow for diagnosis, therapeutics, and prognosis simultaneously using the same administrated magnetic nanofluid agent. However, several primary limiting factors: (1) insufficient AC magnetic heating induction (specific loss power/intrinsic loss power, SLP/ILP) at the biologically safe and physically tolerable range of AC magnetic field (H_AC,safe: f_appl × H_appl < 3.0 ~ 5.0×10⁹ A·m^-1·s^-1), (2) low _r2- relaxivity directly related to the low resolution of …

Model-Driven Analysis Of Ecg Using Reinforcement Learning, Christian O'Reilly, Sai Durga Rithvik Oruganti, Deepa Tilwani, Jessica Bradshaw

Publications

Modeling is essential to better understand the generative mechanisms responsible for experimental observations gathered from complex systems. In this work, we are using such an approach to analyze the electrocardiogram (ECG). We present a systematic framework to decompose ECG signals into sums of overlapping lognormal components. We use reinforcement learning to train a deep neural network to estimate the modeling parameters from an ECG recorded in babies from 1 to 24 months of age. We demonstrate this model-driven approach by showing how the extracted parameters vary with age. From the 751,510 PQRST complexes modeled, 82.7% provided a signal-to-noise ratio that …

Quantized State Simulation Of Electrical Power Systems, Joseph Micah Hood

Theses and Dissertations

An alternative is proposed to the current state-of-the-art simulation methods for the transient simulation of electrical power systems. The proposed method combines the Latency Insertion Method (LIM), the Quantized Discrete Event Specification (QDEVS), and the Quantized State System (QSS) method of integration. Using LIM, the power system state equations are decoupled in a way that allows the formulation of the system into a QDEVS-compliant model, which can then be directly solved with various QSS integration techniques. This combination of methods is called the Quantized DEVS-LIM method, or simply, QDL. A key feature of QDL is the asynchronous updates of all …

High-Performance Wide Bandgap Semiconductor Power Modules Enabled By Advanced Two-Phase Mini-Channel Cooling, Bo Tian

Theses and Dissertations

There is a widespread need for high performance wide bandgap power modules in both commercial and military applications. However, given the rapid advancements of wide bandgap power module technology, conventional cooling solutions have not kept up and do not provide the thermal management performance needed for high power density. Based on the two-phase cooling approach, two-phase microchannels operating at low fluid flow rates with low pressure drops have huge potential in enabling higher power density applications. Several studies have illustrated the potential great advantages of two-phase cooling compared to single-phase cooling in terms of maximum device temperature, spatial thermal distribution …

Deep Learning Based Fault Diagnosis And Prognosis For Bearing, Guangxing Niu

Theses and Dissertations

Rolling element bearings are critical components in industrial rotating machines. Faults and failures of bearings can cause degradation of machine performance or even a catastrophe. Therefore, it is significant work to perform bearing fault diagnosis and prognosis (FDP) reliably and effectively. Diagnosis aims to detect and estimate the fault state of bearing in real-time. Prognosis aims to conduct a long-term prediction to predict the fault evolution and estimate the remaining useful life (RUL) of the bearing.

Deep learning (DL) based FDP methods have become an important branch of bearing FDP methods due to their powerful capabilities in feature automatic learning, …

Physics-Based And Behavioral Models For Fuel Cells, Charles Chima Anyim

Theses and Dissertations

In recent times there has been renewed interest in fuel cells for electricity production, especially for transportation applications. Fuel cells are attractive because the electricity production process from chemical energy does not cause pollution, and no carbon emissions occur. In this work, we will focus on Proton Exchange Membrane (PEM) fuel cells that use hydrogen and oxygen to generate electricity, producing only electricity, heat and water. If hydrogen can be produced in a green, non-polluting way, fuel cells can truly be an environmentally benign energy source.

In this work, we will look at the modeling of a fuel cell system. …

Novel Structures And Thin Film Techniques For Reconfigurable Rf Technologies With Improved Signal Integrity, Jinqun Ge

Theses and Dissertations

Over the past several decades, wireless communications have been tremendously developed with multiple frequency bands, standards, and functions to provide more convenient communication services. Nowadays, commercial wireless devices, especially customer premises equipment, are required for smaller size, higher integration, and higher data rates to meet the ever-increasing demands of communications. The transceiver front ends inside these devices need to provide high-speed and high-quality communication services without a substantial increase in cost and size. Hence, the primary objective of this research is to develop a few miniaturized, multifunctional, and reconfigurable RF technologies and to provide several signal integrity and heat dissipation …

Knowledge Graph Empowered Machine Learning Pipelines For Improved Efficiency, Reusability, And Explainability, Revathy Venkataramanan, Aalap Tripathy, Martin Foltin, Hong Yung Yip, Annmary Justine, Amit Sheth

Publications

Artificial intelligence (AI) pipelines are complex, heavily parameterized, and expensive to execute in terms of time and computational resources. Consequently, it is onerous to run experiments with all possible parameter combinations to achieve an optimal solution. However, these AI experiments can be optimized by recommending relevant parameters to commence the experiments, reducing search space significantly, which can be fine tuned further. The relevant parameters can be identified by observing the metadata of pipelines executed in the past, and the relevant pipeline with relevant parameters can be recommended to the user. Currently, there are various metadata frameworks that automatically record the …

Demo Alleviate: Demonstrating Artificial Intelligence Enabled Virtual Assistance For Telehealth: The Mental Health Case, Kaushik Roy, Vedant Khandelwal, Raxit Goswami, Nathan Dolbir, Jinendra Malekar, Amit Sheth

Publications

After the pandemic, artificial intelligence (AI) powered support for mental health care has become increasingly important. The breadth and complexity of significant challenges required to provide adequate care involve: (a) Personalized patient understanding, (b) Safety-constrained and medically validated chatbot patient interactions, and (c) Support for continued feedback-based refinements in design using chatbot-patient interactions. We propose Alleviate, a chatbot designed to assist patients suffering from mental health challenges with personalized care and assist clinicians with understanding their patients better. Alleviate draws from an array of publicly available clinically valid mental-health texts and databases, allowing Alleviate to make medically sound and informed …

Cook-Gen: Robust Generative Modeling Of Cooking Actions From Recipes, Revathy Venkataramanan, Kaushik Roy, Kanak Ray, Renjith Prasad, Yuxin Zi, Vignesh Narayanan, Amit Sheth

Publications

As people become more aware of their food choices, food computation models have become increasingly popular in assisting people in maintaining healthy eating habits. For example, food recommendation systems analyze recipe instructions to assess nutritional contents and provide recipe recommendations. The recent and remarkable successes of generative AI methods, such as auto-regressive large language models, can lead to robust methods for a more comprehensive understanding of recipes for healthy food recommendations beyond surface-level nutrition content assessments. In this study, we explore the use of generative AI methods to extend current food computation models, primarily involving the analysis of nutrition and …

Proknow: Process Knowledge For Safety Constrained And Explainable Question Generation For Mental Health Diagnostic Assistance, Kaushik Roy, Manas Gaur, Misagh Soltani, Vipula Rawte, Ashwin Kalyan, Amit Sheth

Publications

Current Virtual Mental Health Assistants (VMHAs) provide counseling and suggestive care. They refrain from patient diagnostic assistance because of a lack of training on safety-constrained and specialized clinical process knowledge (Pro-Know). In this work, we define ProKnow as an ordered set of information that maps to evidence-based guidelines or categories of conceptual understanding to experts in a domain. We also introduce a new dataset of diagnostic conversations guided by safety constraints and ProKnow that healthcare professionals use (ProKnow-data). We develop a method for natural language question generation (NLG) that collects diagnostic information from the patient interactively (ProKnow-algo). We demonstrate the …

Moment-Based Reinforcement Learning For Ensemble Control, Yao-Chi Yu, Vignesh Narayanan, Jr-Shin Li

Publications

Problems involving controlling the collective behavior of a population of structurally similar dynamical systems, the so-called ensemble control, arise in diverse emerging applications and pose a grand challenge in systems science and control engineering. Owing to the severely under-actuated nature and the difficulty of placing large-scale sensor networks, ensemble systems are limited to being actuated and monitored at the population level. Moreover, mathematical models describing the dynamics of ensemble systems are often elusive. Therefore, it is essential to design broadcast controls that excite the entire population in such a way that the heterogeneity in system dynamics are robustly compensated. In …

Tutorial - Shodhguru Labs: Knowledge-Infused Artificial Intelligence For Mental Healthcare, Kaushik Roy

Publications

Artificial Intelligence (AI) systems for mental healthcare (MHCare) have been ever-growing after realizing the importance of early interventions for patients with chronic mental health (MH) conditions. Social media (SocMedia) emerged as the go-to platform for supporting patients seeking MHCare. The creation of peer-support groups without social stigma has resulted in patients transitioning from clinical settings to SocMedia supported interactions for quick help. Researchers started exploring SocMedia content in search of cues that showcase correlation or causation between different MH conditions to design better interventional strategies. User-level Classification-based AI systems were designed to leverage diverse SocMedia data from various MH conditions, …