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  • Cyber-physical systems (CPS) involve the cyber components of computing and communication interacting with and controlling elements in the physical world. Emerging CPS are increasingly distributed and perform coordinated sensing and actuation over large geographical areas. Examples include local-scale industrial robots, city-scale traffic management, and regional/continental-scale smart grids. Hence, a hierarchy of resource constrained embedded sensing/actuation nodes, edge cloudlets and the cloud will be key to enable scalable coordination, while simultaneously hosting the intelligence behind these systems. To meet the low-latency real-time requirements of CPS, these platforms typically harness a variety of computing resources ranging from multi-core processors to hardware accelerators such as general-purpose Graphics-Processing Units (GP-GPUs). In conjunction with low latency, a shared and precise notion of time is key to enabling coordinated action in distributed CPS. Hence, in this dissertation, we introduce abstractions, system-design methodologies and frameworks that enable time-based coordination in geo-distributed cyber-physical systems. While a shared notion of time enables coordination at the distributed scope, to coordinate effectively it is also necessary to simultaneously schedule multiple application components at the scope of each node, such that all deadlines are met, while ensuring that the resource/physical constraints of the system are satisfied. Therefore, this dissertation also introduces energy-, thermaland resource-efficient analyzable real-time scheduling techniques for applications deployed on platforms utilizing both multi-core processors and hardware accelerators. Our proposed solutions are readily applicable to commodity embedded, edge and cloud platforms, and together can enable time-aware and energy-efficient CPS.
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  • This dissertation consists of ?five papers whose subjects are mostly disjoint. Below are their abstracts and citation information.On a fractional version of Haemers' bound. In this note, we present a fractional version of Haemers' bound on the Shannon capacity of a graph, which is originally due to Blasiak. This bound is a common strengthening of both Haemers' bound and the fractional chromatic numberof a graph. We show that this fractional version outperforms any bound on the Shannon capacity that could be attained through Haemers' bound. We show also that this bound is multiplicative, unlike Haemers' bound.With Boris Bukh.In IEEE Transactions on Information Theory, vol. 65, no. 6, pp. 3340{3348, Jun. 2019. Inverting the Turan problem. Classical questions in extremal graph theory concern the asymptotics of ex(G;H) where H is a ?fixed family of graphs and G = Gn is taken from a "standard" increasing sequence of host graphs (G1;G2; : : : ) most often Kn or Kn;n. Inverting the question, we can instead ask how large e(G) can be with respect to ex(G;H). We show that the standard sequences indeed maximize e(G) for some choices of H, but not for others. Many interesting questions and previous results arise very naturally in this context, which also, perhaps unusually, gives rise to sensible extremal questions concerning multigraphs and non-uniform hypergraphs.With Joe Briggs.In Discrete Mathematics, vol. 342, no. 7, pp. 1865{1884, Jul. 2019.
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  • Zines created by Jill Chisnell, Integrated Media & Design Librarian (2019-2020)Date Due: the Library ZineA Library Curiosity GuideBROWSE
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  • This thesis centers on the topic of how to automatically combine multiple heuristics. For most computationally challenging problems, there exist multiple heuristics, and it is generally the case that any such heuristic exploits only alimited number of aspects among all the possible problem characteristics that we can think of, and by definition, is not infallible. Thus, if the situation encountered does not align well with the nature of the employed heuristic, thealgorithm can progress very slowly or get trapped in a bad local optimum. In order to compensate for this, researchers have been investigating and experimentingwith the idea of combining multiple heuristics. The development ofthis idea is also motivated by the fact that we have progressed to a point at which we started to consider more complex problems that have subproblems or facets similar to simpler and more-studied problems. In this case, it is verynatural to attempt to reuse the heuristics that we developed for those more studied problem domains. In this study, we intend to build on this approach of combining multiple heuristics. At the broadest level, we would like to explorepossible ways to synthesize effective search processes for solving combinatorial optimization problems. The specific strategies we consider will be based on using existing heuristics as algorithmic components and combining them in an automatic fashion. Furthermore, this research will have an emphasis on creating and utilizing collaborations among heuristics as an underlying means. This leads to several research questions such as how to set up an environment so that collaborations among heuristics can emerge, how do we reuse the collaborated efforts, and how do we adjust the search process so that the benefit of collaboration can be amplified. In this thesis, we develop two types of integration architectures, each of which is specific to a broad class of heuristics. The first part of this thesis focuses on studying possible ways of combining neighborhood-based heuristics,which operate based on the idea of iteratively searching for improvements in the neighborhood of the current solutions. We will first present a basic architecture that we use as a foundation for enabling cooperation among multipleneighborhood-based heuristics. The fundamental idea of this architecture is to chain multiple heuristics in a pipelined fashion so that we can utilize the interaction between heuristics. Based on that, we will proceed to examine somesimple learning mechanisms that adjust the behavior of the search algorithm based on the collected data. Finally, we will explore how to learn more explicit collaboration patterns among the neighborhood-based heuristics, and wewill evaluate the benefit of using these learned patterns in a more rigorous cross-validation assessment.The second part of this thesis looks at how to combine multiple sampling- based heuristics, which compose a solution by sampling a probabilistic model that encodes the structures of potentially good candidate solutions. We willpropose a method that uses a linear interpolation to combine multiple sampling-based heuristics. The weights associated with the participating heuristics areestimated automatically based on observed data and dynamically changed from iteration to iteration. Finally, by analyzing this approach, we will further distill a generalized framework for combining sampling-based heuristics.
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  • Technologies such as mobile apps, web browsers, social networking sites, and IoT devices provide sophisticated services to users. At the same time, they are also increasingly collecting privacy-sensitive data about them. In some domains, such as mobile apps, this trend has resulted in an increase in the breadth of privacy settings made available to users. These settings are necessary because not all users feel comfortable having their data collected by some of these technologies. On mobile phones alone, the sheer number of apps users download is staggering. The variety of sensitive data and functionality requested by these apps has led to a demand for much more specific privacy settings. The same is true in other domains as well, such as social networks, browsers, and various IoT technologies. The result of this situation is that users feel overwhelmed by all of the settings available to them, and are thus unable to take advantage of them effectively. This dissertation examines whether machine learning techniques can be utilized to help users manage an increasingly large number of privacy settings. It specifically focuses on mobile app permissions. The research presented herein aims to simplify people’s tasks in regard to managing their large number of app privacy settings. We present the methods we used for developing models of users’ privacy preferences, and describe the interactive assistant we designed based on these models to help users configure their settings using personalized recommendations. The objective of this work is to alleviate the burden placed on users while increasing alignment between a their preferences and the privacy settings on their phones. This dissertation details three different studies. Specifically, in the first study, we used a dataset of mobile app permission settings obtained from over 200K Android users, explored different machine learning models, and analyzed different combinations of features to predict users’ mobile app permission settings. The study includes the development and evaluation of profile-based models as well as individual prediction models. It also includes simulation studies, wherein we explored the viability of different interactive configuration scenarios by testing different ways of combining dialogue inputs from users with recommendations based on machine learning models. The results of these simulations suggest that by selectively prompting users to indicate how they would like to configure a relatively small percentage of their permission settings, it is possible to accurately predict many of their remaining permission settings. Another significant finding of this first study is that a relatively small number of privacy profiles derived from clusters of like-minded users can help predict many of the permission settings that users in a given cluster prefer. The second study was designed to validate these findings in a field study with actual users. We designed an enhanced version of Android’s permission manager and collected rich information on users’ actual app permission settings. While results from this study involve a much smaller number of users, they were obtained using privacy nudges designed to increase user awareness of data being collected about them and as a result also their engagement with their permission settings. Using data collected as part of this study, we were able to generate and analyze privacy profiles built for groups of like-minded users who exhibited similar privacy preferences. Results of this study confirm that a relatively small number of profiles (or clusters of users) can capture s large percentahe of users’ diverse privacy preferences and help predict many of their desired privacy settings. They also indicate that privacy nudges can be very effective in motivating users to engage with their permission settings and in deriving privacy profiles with strong predictive power. In the third study, we evaluated our profile-based preference models by developing a privacy assistant that helps users configure their app permission settings based on the developed profiles from our second study. We report on the results of a pilot study (N=72) conducted with actual Android users who used our privacy assistant on their smartphones while performing their regular daily activities. The results indicate that participants accepted 78.7% of the recommendations made by the privacy assistant and kept 94.9% of these settings on their phones over the following six days, all while receiving daily nudges designed to motivate them to further review their settings. The dissertation also discusses the privacy profiles designed for this research and identifies essential attributes that separate people associated with different profiles (or clusters). A refined version of the Personalized Privacy Assistant was released to the Google Play store and used to collect some additional data. In summary, through a series of three studies, this dissertation shows that using a small number of privacy decisions made by a given smartphone user, it is often possible to predict a large fraction of the mobile app permission settings this user would want to have. The dissertation further shows how we have been able to effectively operationalize this finding in the form of personalized privacy assistants that can help users configure mobile app permission settings on their smartphones.
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  • The main scope of this thesis is to analyze the electrochemical processes in polymer electrolyte fuel cells (PEFC), focusing on the 3D geometry of electrode materials for performance and durability improvement. Specifically, the thesis includes new electrochemical modeling methods on real material-based structures and idealized representative geometry. To obtain the 3D structures of platinum group metal-free (PGM-free) cathode materials, the nano-scale X-ray computed tomography (nano-CT) technology is applied, and the 3D structures from the imaging are directly applied in a new transport-reaction physics modeling framework. In addition to the nano-CT scale modeling, this thesis also includes a work on performance and durability modeling at the PEFC catalyst particle scale. The effects of the heterogeneity of electrodes on the performance and durability are analyzed by using an idealized geometry and geometry generated from images obtained by high-resolution scanning transmission electron microscopy-computed tomography (STEM-CT). For most of the model methods presented in this thesis, we made an effort to take advantage of open-source, free of charge software packages for modeling flexibility and scalability. The core part of the modeling frameworks has been built on top of open-source, high-performance finite element simulation framework to accelerate the modeling process considering the inevitable large problem size in the 3D image-based modeling approach. This approach also should contribute to the availability of the models to other researchers in the research field. By conducting simulations on different types of materials and different conditions, it has been possible to infer the relationships between the electrode nanostructures and fuel cell performances. Remarkably, the PGM-free cathode model suggests that a uniformly distributed ionomer structure may enhance the total current density at a high electrode potential region by reducing the ohmic losses within the catalyst aggregates. Modeling of platinum (Pt) dissolution and re-deposition at the carbon support scale has provided us insight as to the mechanisms for Pt to migrate from the interior of the support to the exterior. The STEM-CT model of the carbon supported Pt catalyst aggregates suggests that larger catalyst particles inside a carbon support may have larger current density by increasing the proton concentration on the surface. In addition, we find that the particle spatial distribution inside carbon support has little impact on the performance due to thin electric double layers on the catalyst surfaces and short transport length scales. These results are particularly useful to understand the underlying electrochemical mechanisms in the electrodes and evaluate the performance in various fuel cell operating conditions. The insights obtained from this work should be beneficial for high-performance electrode design and more broad applications of PEFC.
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  • preprint of the paper. abstract:Recent findings suggest that both dorsal and ventral visual pathways process shape information. Nevertheless, a lesion to the ventral pathway alone can result in visual agnosia, an impairment in shape perception. Here, we explored the neural basis of shape processing in a patient with visual agnosia following a circumscribed right hemisphere ventral lesion and evaluated longitudinal changes in the neural profile of shape representations. The results revealed a reduction of shape sensitivity slopes along the patient’s right ventral pathway and a similar reduction in the contralesional left ventral pathway. Remarkably, posterior parts of the dorsal pathway bilaterally also evinced a reduction in shape sensitivity. These findings were similar over a two-year interval, revealing that a focal cortical lesion can lead to persistent large-scale alterations of the two visual pathways. These alterations are consistent with the view that a distributed network of regions contributes to shape perception.
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  • We aim to transform scientific discovery and innovation in education through a scalable data infrastructure that bridges across the many disciplines now contributing to learning science (e.g., cognitive, social, and motivational psychology), discipline-based education research (e.g., Physics, Chemistry, Computer Science), and educational technology (e.g., intelligent tutoring, dialogue systems, MOOCs). The data infrastructure building blocks (DIBBs) we are developing and integrating are available online at learnsphere.org.
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