Agent-Based Markov Modeling for Improved COVID-19 Mitigation Policies

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Agent-Based Markov Modeling for Improved COVID-19 Mitigation Policies

The year 2020 saw the covid-19 virus lead to one of the worst global pandemics in history. As a result, governments around the world have been faced with the challenge of protecting public health while keeping the economy running to the greatest extent possible. Epidemiological models provide insight into the spread of these types of diseases and predict the e_ects of possible intervention policies. However, to date, even the most data-driven intervention policies rely on heuristics. In this paper, we study how reinforcement learning (RL) and Bayesian inference can be used to optimize mitigation policies that minimize economic impact without overwhelming hospital capacity. Our main contributions are (1) a novel agent-based pandemic simulator which, unlike traditional models, is able to model _ne-grained interactions among people at speci_c locations in a community; (2) an RL- based methodology for optimizing _ne-grained mitigation policies within this simulator; and (3) a Hidden Markov Model for predicting infected individuals based on partial observations regarding test results, presence of symptoms, and past physical contacts

Enhancing Electromagnetic Side-Channel Analysis in an Operational Environment

Side-channel attacks exploit the unintentional emissions from cryptographic devices to determine the secret encryption key. This research identifies methods to make attacks demonstrated in an academic environment more operationally relevant. Algebraic cryptanalysis is used to reconcile redundant information extracted from side-channel attacks on the AES key schedule. A novel thresholding technique is used to select key byte guesses for a satisfiability solver resulting in a 97.5% success rate despite failing for 100% of attacks using standard methods. Two techniques are developed to compensate for differences in emissions from training and test devices dramatically improving the effectiveness of cross device template attacks. Mean and variance normalization improves same part number attack success rates from 65.1% to 100%, and increases the number of locations an attack can be performed by 226%. When normalization is combined with a novel technique to identify and filter signals in collected traces not related to the encryption operation, the number of traces required to perform a successful attack is reduced by 85.8% on average. Finally, software-defined radios are shown to be an effective low-cost method for collecting side-channel emissions in real-time, eliminating the need to modify or profile the target encryption device to gain precise timing information

SEAM: An Integrated Activation-Coupled Model of Sentence Processing and Eye Movements in Reading

Models of eye-movement control during reading, developed largely within psychology, usually focus on visual, attentional, lexical, and motor processes but neglect post-lexical language processing; by contrast, models of sentence comprehension processes, developed largely within psycholinguistics, generally focus only on post-lexical language processes. We present a model that combines these two research threads, by integrating eye-movement control and sentence processing. Developing such an integrated model is extremely challenging and computationally demanding, but such an integration is an important step toward complete mathematical models of natural language comprehension in reading. We combine the SWIFT model of eye-movement control (Seelig et al., 2020, doi:10.1016/j.jmp.2019.102313) with key components of the Lewis and Vasishth sentence processing model (Lewis & Vasishth, 2005, doi:10.1207/s15516709cog0000_25). This integration becomes possible, for the first time, due in part to recent advances in successful parameter identification in dynamical models, which allows us to investigate profile log-likelihoods for individual model parameters. We present a fully implemented proof-of-concept model demonstrating how such an integrated model can be achieved; our approach includes Bayesian model inference with Markov Chain Monte Carlo (MCMC) sampling as a key computational tool. The integrated model, SEAM, can successfully reproduce eye movement patterns that arise due to similarity-based interference in reading. To our knowledge, this is the first-ever integration of a complete process model of eye-movement control with linguistic dependency completion processes in sentence comprehension. In future work, this proof of concept model will need to be evaluated using a comprehensive set of benchmark data

Sensor failure detection system

Advanced concepts for detecting, isolating, and accommodating sensor failures were studied to determine their applicability to the gas turbine control problem. Five concepts were formulated based upon such techniques as Kalman filters and a screening process led to the selection of one advanced concept for further evaluation. The selected advanced concept uses a Kalman filter to generate residuals, a weighted sum square residuals technique to detect soft failures, likelihood ratio testing of a bank of Kalman filters for isolation, and reconfiguring of the normal mode Kalman filter by eliminating the failed input to accommodate the failure. The advanced concept was compared to a baseline parameter synthesis technique. The advanced concept was shown to be a viable concept for detecting, isolating, and accommodating sensor failures for the gas turbine applications

Scheduling for Space Tracking and Heterogeneous Sensor Environments

This dissertation draws on the fields of heuristic and meta-heuristic algorithm development, resource allocation problems, and scheduling to address key Air Force problems. The world runs on many schedules. People depend upon them and expect these schedules to be accurate. A process is needed where schedules can be dynamically adjusted to allow tasks to be completed efficiently. For example, the Space Surveillance Network relies on a schedule to track objects in space. The schedule must use sensor resources to track as many high-priority satellites as possible to obtain orbit paths and to warn of collision paths. Any collisions that occurred between satellites and other orbiting material could be catastrophic. To address this critical problem domain, this dissertation introduces both a single objective evolutionary tasker algorithm and a multi-objective evolutionary algorithm approach. The aim of both methods is to produce space object tracking schedules to ensure that higher priority objects are appropriately assessed for potential problems. Simulations show that these evolutionary algorithm techniques effectively create schedules to assure that higher priority space objects are tracked. These algorithms have application to a range of dynamic scheduling domains including space object tracking, disaster search and rescue, and heterogeneous sensor scheduling

Exploring the Effects of Yard Management and Neighborhood Influence on Carbon Storage in Residential Subdivisions

The dramatic land-use shift from forest and agricultural to exurban residential land uses creates an excellent opportunity for ecosystem restoration and carbon sequestration through yard design and management. Yard management in a residential subdivision is rarely an autonomous endeavor. Cultural and local norms play an important role in how residents design and maintain their yards. Studies show that residents are influenced by the behavior of their neighbors. Yet, social influence has rarely been incorporated into carbon sequestration studies in residential landscapes. Agent-based modeling offers an ideal framework for exploring how social complexities among humans could affect their environment. An agent-based model called ELMST (Exploratory Land Management and Carbon Storage), was developed to explore how management of individual yards and neighborhood influence could affect carbon storage at the scale of a residential subdivision. The model was run under four scenarios: (tier-0) no management, (tier-1) individual management without influence (tier-2) individual management with opportunity to adapt based on neighbor behaviors, and (tier-3) adaptive management, as in tier-2, but several residents were given an incentive to innovate their yard to a native prairie design upon model start-up. The model was parameterized with interview and fieldwork data from exurban homes Southeast Michigan. Total carbon within the subdivision was compared among scenarios for year 30. Tier-1 showed a significantly higher quantity of carbon than all others, including tier-0 (no management). Results from tier-2 and tier-3 showed a greater variability of carbon storage at the subdivision level, suggesting that a wide range of outcomes can emerge as a result of neighborhood influence and divergent local norms. Considering model sensitivity of individual management behaviors, the model showed that turfgrass fertilization and mowing the lawn while allowing grass clippings to decompose on-site dramatically increased carbon stored at the parcel level, when compared with the no management scenario. Comparatively, removing grass clippings dramatically decreased carbon stored at the parcel level, when compared with the no management scenario. The native prairie innovation was able to propagate through the subdivision in tier-3 in the ELMST model. Prairie-based parcels were shown to store less carbon overall than the conventional lawn-based parcels that were fertilized or mown while allowing grass clippings to remain on-site, but stored more carbon than if grass clippings were removed all together. Model results imply that trade-off between carbon storage and other ecosystem services may need to be considered when developing policies for environmentally-friendly residential landscapes. The ELMST model was developed to be expanded and re-used for a variety of locales, cultures and climates. Results from this study may be used to formulate better research questions and hypothesis, inform data collection, expand intuition of policy makers, and advance the development of agent-based models with regards to coupled human and natural systems.Master of ScienceNatural Resources and EnvironmentUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/78211/1/Hutchins-Thesis-Final-20101013.pd