京都大学 / 新制・課程博士 / 博士(情報学) / 甲第24743号 / 情博第831号 / 新制||情||139(附属図書館) / 京都大学大学院情報学研究科システム科学専攻 / (主査)教授 大塚 敏之, 教授 加納 学, 教授 東 俊一 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

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This thesis supports the Master of Fine Arts exhibition at the Reece Museum at East Tennessee State University from April 28-June 25, 2009. The exhibition is comprised of 19 monotype prints on paper. The exhibition presents the artist's investigation using natural materials combined with traditional printmaking techniques. Subjects discussed include ideas, methods, influences, and process of integrating natural materials that evoke a sense of place, earth, and memory.

// F - E- G // // F - E- G // is the title of my independent degree project, for the Master program New Performative Practices at Stockholm University of the Arts. The project was intended as a means for  presenting and understanding my artistic practice and its various aspects. It was a public presentation in the university’s theatre hall. The format chosen was a 50 minute solo performance with a 10 minute participatory section at the beginning of the show. I was the performer, as well as the music and recordings' editor, text writer, and project coordinator in collaboration with SKH's producer and staff. My aim with the project was to fit together the various themes and practices that I had been exploring during the two years of the course: a somatic sci-fi based practice, clown technique, the use of voice, the relationship of the performer to the audience, finding honesty and repeatability in performance, the use of chance and randomness in creation and performing, the use of language in guiding others toward physical and personal insights and experiences, what kind of social impact performance art can have, Nonviolent Communication (NVC) as a choice of life and, finally,  reconnecting to my ageing dancing body. I call the method chosen to explore these various interests and curiosities a personal-experiential method: the ideas were tried out physically and concretely, over a span of time that allowed for reflection and introspection. The outcome of the project was rich in personal learnings and challenges, as well as a milestone in my development as an artist and a person. I hope it served as a moment of poetry and enjoyment to those who visited and participated in the performances: that they might recognise themselves in similar situations and feelings and that they might be either relieved or comforted or somehow get the chance to witness their experience through the dramaturgy of the performance. For more on the project please find an brief expansion on this abstract in the uploaded pdf file. For a more comprehensive view of my artistic practice during the two years at SKH please visit my Research Catalogue site at https://www.researchcatalogue.net/shared/82db91825a5ca2dcf19ae6b16f28d33a (to navigate the site once exposition is open: hover cursor on the top left of the page for a menu).

Thesis advisor: Andrés Castro Samayoa / The involvement of colleges and universities in the provision of higher education opportunities in prison has reemerged after a long pause following the 1994 Omnibus Crime Bill, which effectively ended the majority of postsecondary prison education programs. The 2016 Second Chance Pell Program has been instrumental in the development and expansion of higher education opportunities in prison. Support for justice reform measures has led to the likely full restoration of Pell Grant availability in prisons, taking effect as early as 2023, with funding for the initiative included in the most recent congressional stimulus bill. Both Second Chance Pell and one of the most progressive state-level prison education policies, New York’s Right Priorities initiative, rely almost exclusively on positioning higher education in prison as a tool for meeting the market needs of the state: reduced recidivism equating to taxpayer savings. This dissertation extends prior research examining the pitfalls of justifications overly reliant on narratives of recidivism. Using a three-article approach, it explores justifications capable of articulating the full moral vigor necessary to sustain long-term commitments to such policies and programs, ones that prioritize humanized responses to incarceration. The first article amplifies justifications articulated by those who have been the beneficiaries of such educational opportunities, investigating formerly incarcerated student perspectives on the value, meaning, and purpose of such programs. The second article, by focusing on policy developments within the state of New York, examines how the rhetoric of recidivism emerges in media coverage of both federal and state level support for college-level prison education. And, finally, the third article considers the pedagogical implications of adjusting the lens through which programs are defended, exploring the use of andragogical teaching methods—those associated with the tenets of adult education—in the context of prison classrooms. Taken together, each study contributes to literatures examining justifications for higher education in prison, and develops deeper understandings of the need for the provision of such opportunities. / Thesis (PhD) — Boston College, 2022. / Submitted to: Boston College. Lynch School of Education. / Discipline: Educational Leadership and Higher Education.

Energy management strategies for microgrids, containing energy storage, renewable energy sources (RES), and electric vehicles (EVs); which interact with the grid on an individual basis; are presented in Chapter 3. An optimization problem to reduce cost, formulated over a rolling time horizon, using predicted values of load demand, EV connection/disconnection times, and charge levels at time of connection, is described. The solution provides the on-site storage and EV charge/discharge powers. For the first time, both bidirectional and unidirectional charging are considered for EVs and a controller which accommodates uncertainties in EV energy levels and connection/disconnection times is presented. In Chapter 4, a stochastic chance constraints based optimization is described. It affords significant improvement in robustness, over the conventional controller, to uncertainties in system parameters. Simulation results demonstrate that the stochastic controller is at least twice as effective at meeting the desired EV charge level at specific times compared to the non-stochastic version, in the presence of uncertainties. In Chapter 5, a network of microgrids, containing RES and batteries, which trade energy among themselves and with the utility grid is considered. A novel distributed energy management system (EMS), based on a central EMS using a Multi-Objective (MO) Rolling Horizon (RH) scheme, is presented. It uses Alternating Direction Method of Multipliers (ADMM) and Quadratic Programming (QP). It is inherently more data-secure and resilient to communication issues than the central EMS. It is shown that using an EMS in the network provides significant economic benefits over MGs connected directly to the grid. Simulations demonstrate that the distributed scheme produced solutions which are very close to those of the central EMS. Simulation results also reveal that the faster, less memory intensive distributed scheme is scalable to larger networks -- more than 1000 microgrids as opposed to a few hundreds for the central EMS. / Thesis / Doctor of Philosophy (PhD)

This thesis critically examines recent developments in Swedish tort law, regarding compensability of an elevated risk of future harm as a distinct injury in itself. The theme of the study is based upon a recent ruling by the Swedish Supreme Court on December 5, 2023, marking the first instance of Swedish legal scrutiny on the concept of elevated risks as compensable injuries. The case involved a compensation claim for personal injury arising from an exposure to PFAS substances and it is highlighting the challenges in both categorizing as well as compensating elevated risks of future harm in a Swedish legal context. The ruling clarified that the mere presence of an elevated risk cannot constitute a personal injury according to traditional Swedish tort law.  The primary objective of this study is to stimulate a broader legal discussion on how Swedish tort law should and could navigate the complex notion of compensating injuries in the form of elevated risks of future injury. The study unfolds in two interconnected parts. Initially, it scrutinizes the conventional concept of personal injury under Swedish law as well as the relationship between the elevated risk of future injury and the traditional personal injury concept. Subsequently, the focus shifts to discussing the need for incorporating the concept of elevated risks as a new category of compensable injury under Swedish tort law, with the legal approach adopted in the United States serving as backdrop for the discussion.  Using a legal dogmatic approach as a primary research method, the study involves an analysis of legal sources to comprehend the current legal landscape and to identify the need for potential reforms. The study also includes a comparative aspect, drawing upon American legal sources, to shed light on how injuries in the form of elevated risks are treated in various American jurisdictions. The American approach, as presented in this study, consists of a two-step process for qualifying elevated risks as compensable injuries. The discussion also touches upon the compensation issue for risk injuries, highlighting the challenge of compensating a potential injury before it manifests. The "loss of chance" doctrine, which is adopted in American law, is introduced to provide a predictable compensation model for lost opportunities as well as elevated risks.  The presentation of the American approach serves as a basis for a discussion of several considerations and issues that the Swedish legislator must address if compensation for risk-based injuries were to be established in Swedish law.  The discussions set forth in this thesis extend to the challenges arising when valuing an elevated risk as a form of personal injury and the complexities of fitting such risks into existing compensation frameworks. The discussions conclude that the concept of an elevated risk as an injury in itself should be classified as a distinct type of compensable injury under Swedish law. After this conclusion the focus of the discussion shifts towards the legislator’s challenging task of establishing a rational compensation model for this type of injury in Swe- dish tort law. The discussion includes some aspects that motivate the need for legislation, as well as reasons speaking against it, which highlights the complexity of balancing the legislator’s considerations in the context of Swedish legal traditions and systems.  In summary, this thesis serves the purpose of contributing to the legal discourse on compensating injuries arising from elevated risks within Swedish tort law. Using a legal dogmatic methodology, including comparative elements, it analyzes recent legal developments, explores challenges, draws comparisons with international practices, and reviews potential legal reforms. The study aims to inform and encourage Swedish legal practitioners, policymakers and scholars to initiate a discussion about the concept of compensating elevated risks, as well as advocate for a nuanced legislative approach to address these challenges effectively.

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While optimization problems are ubiquitous in all domains of engineering, they are of critical importance to power systems engineers. A safe and economical operation of the power systems entails solving many optimization problems such as security-constrained unit commitment, economic dispatch, optimal power flow, optimal planning, etc. Although traditional optimization solvers and software have been successful so far in solving these problems, there is a growing need to accelerate the solution process. This need arises on account of several aspects of grid modernization, such as distributed energy resources, renewable energy, smart inverters, batteries, etc, that increase the number of decision variables involved. Moreover, the technologies entail faster dynamics and unpredictability, further demanding a solution speedup. Yet another concern is the growing communication overhead that accompanies this large-scale, high-speed, decision-making process. This thesis explores three different directions to address such concerns. The first part of the thesis explores the learning-to-optimize paradigm whereby instead of solving the optimization problems, machine learning (ML) models such as deep neural networks (DNNs) are trained to predict the solution of the optimization problems. The second part of the thesis also employs deep learning, but in a different manner. DNNs are utilized to model the dynamics of IEEE 1547.8 standard-based local Volt/VAR control rules, and then leverage efficient deep learning libraries to solve the resulting optimization problem. The last part of the thesis dives into the evolving field of quantum computing and develops a general strategy for solving stochastic binary optimization problems using variational quantum eigensolvers (VQE). / Doctor of Philosophy / A reliable and economical operation of power systems entails solving large-scale decision-making mathematical problems, termed as optimization problems. Modern additions to power systems demand an acceleration of this decision-making process while managing the accompanying communication overheads efficiently. This thesis explores the application of two recent advancements in computer science -- machine learning (ML) and quantum computing (QC), to address the above needs. The research presented in this thesis can be divided into three parts. The first part proposes replacing conventional mathematical solvers for optimization problems, with ML models that can predict the solutions to these solvers. Colloquially referred to as learning-to-optimize, this paradigm learns from a historical dataset of good solutions and extrapolates them to make new decisions in a fast manner, while requiring potentially limited data. The second part of the thesis also uses ML models, but differently. ML models are used to represent the underlying physical dynamics, and convert an originally challenging optimization problem into a simpler one. The new problem can be solved efficiently using popular ML toolkits. The third and final part of the thesis aims at accelerating the process of finding optimal binary decisions under constraints, using QC.

Network optimization plays an important role in 5G/next-G networks, which requires knowledge of network parameters (e.g., channel state information). The majority of existing works assume that all network parameters are either given a prior or can be accurately estimated. However, in many practical scenarios, some parameters are uncertain at the time of allocating resources and can only be modeled by random variables. Further, we only have limited knowledge of those uncertain parameters. For instance, channel gains are not exactly known due to channel estimation errors, network delay, limited feedback, and a lack of cooperation (between networks). Therefore, a practical solution to network optimization must address such uncertainty inside wireless networks. There are three approaches to address such a network uncertainty: stochastic programming, worst-case optimization, and chance-constrained programming (CCP). Among the three, CCP has some unique benefits compared to the other two approaches. Stochastic programming explicitly requires full distribution knowledge, which is usually unavailable in practice. In comparison, CCP can work with various settings of available knowledge such as first and second order statistics, symmetric properties, or limited data samples. Therefore, CCP is more flexible to handle different network settings, which is important to address problems in 5G/next-G networks. Further, worst-case optimization assumes upper or lower bounds (i.e., worst cases) for the uncertain parameters and it is known to be conservative due to its focus on extreme cases. In contrast, CCP allows occasional and controllable violations for some constraints and thus offers much better performance in resource utilization compared to worst-case optimization. The only drawback of CCP is that it may lead to intractability due to its probabilistic formulation and limited knowledge of the underlying random variables. To date, CCP has not been well utilized in the wireless communication and networking community. The goal of this dissertation is to extend the state-of-the-art of CCP techniques and address a number of challenging network optimization problems. This dissertation is correspondingly organized into two parts. In the first part, we assume the uncertain parameters are only known by their mean and covariance (without distribution knowledge). We assume these statistics are rather stationary (i.e., time-invariant for a sufficiently long time) and thus can be accurately estimated. In this setting, we introduce a novel reformulation technique based on the mean and covariance to derive a solution. In the second part, we assume these statistics are time-varying and thus cannot be accurately estimated.In this setting, we employ limited data samples that are collected in a small time window and use them to derive a solution. For the first part, we investigate four research problems based on the mean and covariance of the uncertain parameters: - In the first problem, we study how to maximize spectrum efficiency in underlay coexistence.The interference from all secondary users to each primary user must be kept below a given threshold. However, there is much uncertainty about the channel gains between the primary users and the second users due to a lack of cooperation between them. We formulate probabilistic interference constraints using CCP for the primary users. For tractability, we introduce a novel and powerful reformulation technique called Exact Conic Reformulation (ECR). With limited knowledge of mean and covariance, ECR offers an equivalent reformulation for the intractable chance constraints with tractable deterministic constraints without relaxation errors. After reformulation, we employ linearization techniques to the mixed-integer non-linear problem to reduce the computation complexity. We show that our proposed approach can achieve near-optimal performance and stands as a performance benchmark for the underlay coexistence problem. - To find a solution for the same underlay coexistence problem that can be used in the real world, we need to find a solution in "real-time". The real-time requirement here refers to finding a solution in 125 us (the minimum time slot for small cells in 5G). Our proposed solution has three steps. First, it employs ECR to reformulate the original CCP into a deterministic optimization problem. Then it decomposes the problem and narrows down the search space into a smaller but promising one. By random sampling inside the promising search space and through local search, our proposed solution can meet the 125 us requirement in 5G while achieving 90% optimality on average. - We further apply CCP, predicated on the reformulation technique ECR, to two other problems. * We study the problem of power control in concurrent transmissions. Our objective is to maximize energy efficiency for all transmitter-receiver pairs with capacity requirements. This problem is challenging due to mutual interference among different transmitter-receiver pairs and the uncertain channel gain between any transmitter and receiver. We formulate a CCP and reformulate it into a deterministic problem using ECR. Then we employ Geometric Programming (GP) with a tight approximation to derive a near-optimal solution. * We study task offloading in Mobile Edge Computing (MEC) where the number of processing cycles of a task is unknown until completion. The goal is to minimize the energy consumption of the users while meeting probabilistic deadlines for the tasks. We formulate the probabilistic deadlines into chance constraints and then use ECR to reformulate them into deterministic constraints. We propose a solution that consists of periodic scheduling and schedule updates to choose the offloaded tasks and task-to-processor assignments at the base station. In the second part, we investigate two research problems based on limited data samples of the uncertain parameters: - We study MU-MIMO beamforming based on Channel State Information (CSI). The goal is to derive a beamforming solution---minimizing power consumption at the BS while meeting the probabilistic data rate requirements of the users---by using very limited CSI data samples. For our CCP formulation, we explore the idea of Wasserstein ambiguity set to quantify the distance between the true (but unknown) distribution and the empirical distribution based on the limited data samples. Our proposed solution---Data-Driven Beamforming (D^2BF)---reformulates the CCP into a non-convex deterministic optimization problem based on the properties of Wasserstein ambiguity set. Then D^2BF employs a novel convex approximation to the non-convex deterministic problem, which can be directly solved by commercial solvers. - For a solution to the MU-MIMO beamforming to be useful in the real world, it must meet the "real-time" requirement. Here, the real-time requirement refers to 1 ms, which is one transmission time interval (TTI) under 5G numerology 0. We present ReDBeam---a Real-time Data-driven Beamforming solution for the MU-MIMO beamforming problem (minimizing power consumption while offering probabilistic data rate guarantees to the users) with limited CSI data samples. RedBeam is a parallel algorithm and is purposefully designed to take advantage of the vast parallel processing capability offered by GPU. ReDBeam generates a large number of initial solutions from a promising search space and then refines each solution by a local search. We show that ReDBeam meets the 1 ms real-time requirement on a commercial GPU and is orders of magnitude faster than other state-of-the-art algorithms for the same problem. / Doctor of Philosophy / Network optimization plays an important role in 5G/next-G networks. In a wireless network optimization problem, we typically want to maximize or minimize an objective function under a set of performance or resource constraints. Knowledge of network parameters is typically required in these problems. The majority of existing works assume that all network parameters are either given a prior or can be accurately estimated. However, in many practical scenarios, some parameters are uncertain in nature and cannot be accurately estimated beforehand. This dissertation addresses uncertainty in wireless network optimizations using chance-constrained programming (CCP). CCP can work with limited knowledge of uncertain parameters such as statistics or data samples, instead of full distribution information. In a CCP formulation, violations of certain target performance or requirement thresholds are expressed as probabilistic constraints and the frequency of such violations is bounded through a risk parameter. By changing this risk level, CCP offers a unique trade-off between the guaranteed threshold violation probabilities and the achieved objective value. The only drawback of CCP is that it may lead to intractability due to its probabilistic formulation and limited knowledge of the underlying random variables. The goal of this dissertation is to extend the state-of-the-art of CCP techniques to address a number of challenging network optimization problems. This dissertation is organized into two parts. In the first part, the mean and covariance of the uncertain parameters are assumed to be stationary and thus can be accurately estimated. Our main contribution is a novel reformulation technique for CCP called Exact Conic Reformulation (ECR). Based on knowledge of mean and covariance, ECR is able to offer an equivalent reformulation for the intractable chance constraints with tractable deterministic constraints without relaxation errors. We apply CCP, predicated on ECR, to address three problems: (i) scheduling and power control in underlay coexistence; (ii) power control in concurrent transmissions, and (iii) task offloading in Mobile Edge Computing (MEC). For the first problem, we further address the "real-time" requirement in a solution and propose a solution that can meet the stringent timing requirement. In the second part, when the uncertain parameters are non-stationary and their statistics cannot be accurately estimated, we propose to employ limited data samples that are collected over a small window and use them to develop a solution. To demonstrate the efficacy of this approach, we investigate the MU-MIMO beamforming problem that minimizes the power consumption of the base station while providing probabilistic guarantees to users' data rates. We further address the timing requirement for such a solution in practice, and present a real-time data-driven beamforming solution for MU-MIMO.