Introduction: During the coronavirus disease 2019 (COVID-19) pandemic, emergency providers are not only seeing an increasing number of patients with COVID-19 infections, but also associated complications and sequelae of this viral illness.

Case Report: We present the case of a 28-year-old female patient who presented after a confirmed COVID-19 infection with lower back pain, bilateral symmetric upper and lower extremity numbness, and urinary retention. The patient was diagnosed with acute transverse myelitis. She required intravenous corticosteroids and plasma exchange with significant improvement in symptoms and minimal residual effects.

Conclusion: This case illustrates the importance of prompt recognition and treatment of sequelae of COVID-19 infections.

This thesis presents an efficient and scalable sense-making framework using machine learning techniques for Internet-of-Things (IoTs) in order to understand users, contexts, and their environments to make meaningful decisions. The proposed sense-making IoT framework, called Essence, employs combination of participatory mobile crowdsensing along with infrastructure sensing to perform sense-making using machine learning techniques. While collaborative mobile crowdsensing enables information to be gathered and shared by users who are directly involved (participatory sensing) or integrated seamlessly as needed (opportunistic sensing) through user mobile platforms, the infrastructure sensing fabric of the Essence framework provides sense-making support for scenarios where mobile sensing platforms are inadequate. To address the scalability needs of the Essence framework, we employ dimensionality reduction techniques such as principal component analysis (PCA) based heterogeneous compressive sensing techniques for approximate gathering and processing of diverse sensor data. This requires new mechanisms for sensor data collection, tunable approximate processing and machine learning based decision making using a hierarchical networking architecture to create a compressive collaborative sense-making framework for IoTs. Essence uses our previous SenseDroid mobile sensing framework with machine learning enabled decision and actuation components for IoT paradigms. The Essence framework is build using a multi-tired hierarchical architecture for sensing spatial variations of a parameter of interest, perceive spatio-temporal fields, and enable energy efficient local mobile or infrastructure sensing with a small number of measurements. This approximate, yet tunable approach combines different sensing approaches opportunistically while trading scalability (and coverage) for data accuracy (and energy efficiency). We demonstrate the application of Essence sense-making framework in predicting the presence of West Nile Virus (WNV) in a region using both compressed sensing and Neural Networks (NN) as a case study. The thesis also discuss several challenges associated with sense-making approaches for emerging IoT applications.

This thesis presents an efficient and scalable sense-making framework using machine learning techniques for Internet-of-Things (IoTs) in order to understand users, contexts, and their environments to make meaningful decisions. The proposed sense-making IoT framework, called Essence, employs combination of participatory mobile crowdsensing along with infrastructure sensing to perform sense-making using machine learning techniques. While collaborative mobile crowdsensing enables information to be gathered and shared by users who are directly involved (participatory sensing) or integrated seamlessly as needed (opportunistic sensing) through user mobile platforms, the infrastructure sensing fabric of the Essence framework provides sense-making support for scenarios where mobile sensing platforms are inadequate. To address the scalability needs of the Essence framework, we employ dimensionality reduction techniques such as principal component analysis (PCA) based heterogeneous compressive sensing techniques for approximate gathering and processing of diverse sensor data. This requires new mechanisms for sensor data collection, tunable approximate processing and machine learning based decision making using a hierarchical networking architecture to create a compressive collaborative sense-making framework for IoTs. Essence uses our previous SenseDroid mobile sensing framework with machine learning enabled decision and actuation components for IoT paradigms. The Essence framework is build using a multi-tired hierarchical architecture for sensing spatial variations of a parameter of interest, perceive spatio-temporal fields, and enable energy efficient local mobile or infrastructure sensing with a small number of measurements. This approximate, yet tunable approach combines different sensing approaches opportunistically while trading scalability (and coverage) for data accuracy (and energy efficiency). We demonstrate the application of Essence sense-making framework in predicting the presence of West Nile Virus (WNV) in a region using both compressed sensing and Neural Networks (NN) as a case study. The thesis also discuss several challenges associated with sense-making approaches for emerging IoT applications.

Open AI's Universe, intended to foster the development of general AI agents that could use a computer like humans do, fell short in a number of ways. As a result, attention shifted to smaller sets of environments such as Gym Retro or the Arcade Learning Environment. My thesis shows a way to overcome several of the limitations in the design of Universe by abstracting the execution of an interactive application in a full-system virtual machine. By using virtual machines, we regain the broad scope of the original Universe project, as well as the ability to pause, save, and restore at arbitrary moments of interaction, allowing the usage of several sophisticated exploration algorithms. I describe an API that implements the virtual machine strategy using Oracle's Virtual Box and ways to extend it with a framework for sensors and input actions that allow a broad range of manipulations of the virtual machines. I then walk through experiments designed to characterize the interaction latency and jitter associated with my implementation, as well as analyze the space costs associated with these large VMs and their associated save points. Finally, I demonstrate the utility of this implementation by showing automated play of two games.

Embedded systems are increasingly seeing the need for self-awareness to operate autonomously in the face of uncertainty and unpredictability in the environment, the applications they execute, and in the manufactured hardware. The notion of self-awareness enables a system to monitor its own state and behavior such that it is capable of making judicious decisions and adapt intelligently. However, emerging Multiprocessor Systems-on-chip (MPSoCs), used by these embedded systems and devices, still treat the elements of intelligence, specifically self-awareness, as a second-class design requirement, supporting them with ad hoc and poorly-developed awareness mechanisms, architectural supports, and system software. This dissertation overcomes these limitations by providing the foundation for a new class of self-aware adaptive MPSoCs called a Cyber-Physical-System-on-Chip (CPSoC). Unlike traditional MPSoCs, CPSoCs are distinguished by an intelligent co-design of the control, communication, and computing (C3) infrastructure while considering both the cyber and physical aspects together so as to adaptively achieve desired objectives and goals. CPSoC's sensor-actuator rich scalable architecture intrinsically couples on-chip and cross-layer sensing and actuation to enable self-awareness in a principled way. The thesis corroborates, through experiments and FPGA prototypes, the key idea that giving the SoC the freedom to opportunistically adapt the software and the hardware stack by infusing self-awareness mechanisms and steerable knobs across the stack can open up new and otherwise untapped opportunities in energy efficiency, performance, and thermal resilience.