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In this comprehensive survey of unconditional pseudorandom generators (PRGs), the authors present the reader with an intuitive introduction to some of the most important frameworks and techniques for constructing unconditional PRGs for restricted models of computation. The authors discuss four major paradigms for designing PRGs: several PRGs based on k-wise uniform generators, small-bias generators, and simple combinations thereof, several PRGs based on "recycling" random bits to take advantage of communication Bottlenecks, connections between PRGs and computational hardness, and PRG frameworks based on random restrictions. The authors explain how to use these paradigms to construct PRGs that work unconditionally, with no unproven mathematical assumptions. The PRG constructions use ingredients such as finite field arithmetic, expander graphs, and randomness extractors. The analyses use techniques such as Fourier analysis, sandwiching approximators, and simplification-under-restrictions lemmas. Paradigms for Unconditional Pseudorandom Generators offers the reader a grounding in an important topic widely used in theoretical computer science and cryptography.

Over the last decade, the long-running endeavour to automate high-level processes in machine learning (ML) has risen to mainstream prominence. Beyond this, an even loftier goal is the pursuit of autonomy, which describes the capability of the system to independently adjust an ML solution over a lifetime of changing contexts. This monograph provides an expansive perspective on what constitutes an automated/autonomous ML system. In doing so, the authors survey developments in hyperparameter optimisation, multicomponent models, neural architecture search, automated feature engineering, meta-learning, multi-level ensembling, dynamic adaptation, multi-objective evaluation, resource constraints, flexible user involvement, and the principles of generalisation. Furthermore, they develop a conceptual framework throughout to illustrate one possible way of fusing high-level mechanisms into an autonomous ML system. This monograph lays the groundwork for students and researchers to understand the factors limiting architectural integration, without which the field of automated ML risks stifling both its technical advantages and general uptake.

Non-Experimental Data, Hypothesis Testing, and the Likelihood Principle: A Social Science Perspective argues that frequentist hypothesis testing - the dominant statistical evaluation paradigm in empirical research - is fundamentally unsuited for analysis of the non-experimental data prevalent in economics and other social sciences. Frequentist tests comprise incompatible repeated sampling frameworks that do not obey the Likelihood Principle (LP). For probabilistic inference, methods that are guided by the LP, that do not rely on repeated sampling, and that focus on model comparison instead of testing (e.g., subjectivist Bayesian methods) are better suited for passively observed social science data and are better able to accommodate the huge model uncertainty and highly approximative nature of structural models in the social sciences. In addition to formal probabilistic inference, informal model evaluation along relevant substantive and practical dimensions should play a leading role. The authors sketch the ideas of an alternative paradigm containing these elements.

The study of uncertain systems has played a significant role throughout the history of control engineering due to unknown quantities often being present in the mathematical model of a plant. In this monograph the author provides a unified framework for the fundamental and challengingarea of robustness analysis of uncertain systems, where even the most basic problem of establishing robust stability may be still present. This framework uses linear matrix inequalities (LMIs) to exploit polynomials that can be expressed as sums of squares of polynomials (SOS). The author guides the reader through the motivations for using the framework including considering various types of uncertainties; providing guarantees for robust stability and robust performance; requiring the solution of convex optimization problems; allowing for trade-off between conservatism and complexity; and concluding with a number of special case methods. This monograph can be used by researchers and students to understand the issues and use the numerical examples to identify the use of the framework in modern controls systems.

In many contemporary and emerging applications of machine learning and statistical inference, the phenomena of interest are characterized by variables defined over large alphabets. This increasing size of both the data and the number of inferences, and the limited available training data means there is a need to understand which inference tasks can be most effectively carriedout, and, in turn, what features of the data are most relevant to them. In this monograph, the authors develop the idea of extracting "universally good" features, and establish that diverse notions of such universality lead to precisely the same features. The information-theoretic approach used results in a local information geometric analysis that facilitates their computation in a host of applications. The authors provide a comprehensive treatment that guides the reader through the basic principles to the advanced techniques including many new results. They emphasize a development from first-principles together with common, unifying terminology and notation, and pointers to the rich embodying literature, both historical and contemporary. Written for students and researchers, this monograph is a complete treatise on the information theoretic treatment of a recognized and current problem in machine learning and statistical inference.

The recent surge of interest in AI systems has raised concerns in moral quarters about their ethical use and whether they can demonstrate fair decision taking processes. Issues of unfairness and discrimination are pervasive when decisions are being made by humans, and are potentially amplified when decisions are made using machines with little transparency, accountability, and fairness. In this monograph, the authors introduce a framework for causal fairness analysis to understand, model, and possibly solve issues of fairness in AI decision-making settings. The authors link the quantification of the disparities present in the observed data with the underlying, often unobserved, collection of causal mechanisms that generate the disparity in the first place, a challenge they call the Fundamental Problem of Causal Fairness Analysis (FPCFA). In order to solve the FPCFA, they study the mapping variations and empirical measures of fairness to structural mechanisms and different units of the population, culminating in the Fairness Map.This monograph presents the first systematic attempt to organize and explain the relationship between various criteria in fairness and studies which causal assumptions are needed for performing causal fairness analysis. The resulting Fairness Cookbook allows anyone to assess the existence of disparate impact and disparate treatment. It is a timely and important introduction to developing future AI systems incorporating inherent fairness and as such will be of wide interest not only to AI system designers, but all who are interested in the wider impact AI will have on society.

Probably almost correct (PAC) bounds have been an intensive field of research over the last two decades. Hundreds of papers have been published and much progress has been made resulting in PAC-Bayes bounds becoming an important technique in machine learning. The proliferation of research has made the field for a newcomer somewhat daunting. In this tutorial, the author guides the reader through the topic's complexity and large body of publications. Covering both empirical and oracle PAC-bounds, this book serves as a primer for students and researchers who want to get to grips quickly with the subject. It provides a friendly introduction that illuminates the basic theory and points to the most important publications to gain deeper understanding of any particular aspect.

The Technological Emergence of AutoML presents a comprehensive snapshot of how AutoML has permeated into mainstream use within the early 2020s. This work surveys both their implementation and application in the context of industry. It also defines what a 'performant' AutoML system is - HCI support is valued highly here - and assesses how the current crop of available packages and services lives up to expectations. To do so in a systematic manner, this survey is structured as follows. Section 2 begins by elaborating on the notion of an ML workflow, conceptually framing AutoML in terms of the high-level operations required to develop, deploy and maintain an ML model. Section 3 uses this workflow to support the introduction of industry-related stakeholders and their interests/obligations. These requirements are unified into a comprehensive set of criteria, supported by methods of assessment, that determine whether an AutoML system can be considered performant. Section 4 launches the survey in earnest, assessing the nature and capabilities of existing AutoML technology beginning with an examination of open-source AutoML packages. The section additionally investigates AutoML systems that are designed for specific domains, as well as commercial products. Subsequently, Section 5 assesses where AutoML technology has been used and how it has fared. Academic work focusing on real-world applications is surveyed, as are vendor-based case studies. All key findings and assessments are then synthesized in Section 6, with commentary around how mature AutoML technology is, as well as whether there are obstacles and opportunities for future uptake. Finally, Section 7 provides a concluding overview on the technological emergence of AutoML.

The electrical distribution system has undergone significant transformations, which have had a profound impact on distribution system development and expansion. These changes have been primarily driven by changing load profiles, distributed generation sources, and increasingly extreme weather events. Advancements in sensor and communication technologies have played a pivotal role in addressing and adapting to these changes. These changes have also led to an increased focus on reliability and resilience in planning, with priority placed on ensuring robust grid connectivity and flexibility. Three decades ago, power distribution systems were primarily radial with unidirectional power flow. Today's electrical distribution systems have distributed energy resources, leading to bidirectional power flow. The utility's geographic information system network, advanced metering infrastructure, and other technologies are leveraged to allow feeders and distributed energy resources to be interconnected. This has facilitated the integration of the electric grid with networked microgrids, which has improved the overall resilience and efficiency of the distribution system. While there have been notable improvements in grid planning, the power grid remains vulnerable to high-impact, low-frequency events caused by climate change, such as hurricanes and tornadoes. This book outlines potential solutions for addressing future electric grid issues, including transformer overloading due to electric vehicles, optimization challenges, advanced feeder reconfiguration, and contingency planning for extreme events. The proposed approach focuses on the implementation and operation of new technologies, such as renewable energy sources, batteries, flexible loads, and advanced sensors, that have the potential to transform distribution network planning and operation. From traditional methods to innovative networked microgrids within existing infrastructure and non-wire alternative strategies, this book provides a comprehensive overview of state-of-the-art strategies for future problems.

Data-driven health informatics technologies such as mobile health apps and wearable and smart medical devices have become ubiquitous in people's daily lives. As these technologies advance and become more pervasive, the datafication of personal health research has grown substantially in recent years. The field is however primarily focused on adult users, leaving a limited understanding of children's data practices and technology for managing their health and well-being. In this work, the authors aim to delve deeper into children's health datafication practices, navigating the landscape of their technology use, caregiver involvement, and the distinct factors associated with their development and literacy. The authors' intention is to catalyze future innovations, improving the design and utility of health technologies tailored for children. The authors present an overview of the history of personal health datafication research, child development theories, and child-computer interaction studies. This work contributes to the literature by characterizing the trends in children's health datafication research, reflecting on key research themes to guide future health datafication research focused on children, and by providing recommendations for future research and design of data-driven technologies that support children's health and wellbeing.

Spin glass models were introduced by physicists in the 1970s to model the statistical properties of certain magnetic materials. Over the last half century, these models have motivated a blossoming line of mathematical work with applications to multiple fields, at first sight distant from physics. This tutorial is deliberately written in a somewhat non-standard style, from several viewpoints. Rather than developing the theory in the most general setting, the authors focus on two concrete problems that are motivated by questions in statistical estimation. Their treatment is far from exhaustive, but they do not hesitate to pursue detours that are interesting, but indirectly related to the original questions posed by the examples. The authors also present a mixture of non-rigorous and rigorous techniques. The authors clearly indicate when something is proven and explain non-rigorous techniques on examples for which rigorous alternatives are available. Written by two recognized experts and based on a course given at Stanford University, this tutorial is a unique introduction to a topic that has many avenues for furthering research in statistics, mathematics, and computer science. It provides an accessible tutorial to understand and use the theories being deployed in physics for over 50 years.

The recent development of cloud computing and edge computing shows great promise for the Connected and Automated Vehicle (CAV), by enabling CAVs to offload their massive on-board data and heavy computing tasks. Leveraging the Internet of Things (IoT) technology, different entities in the intelligent transportation system (e.g., vehicles, infrastructure, traffic management centers, etc.) get connected with each other, thus making the entire system smarter, faster, and more efficient. However, these advances also bring significant challenges to public authorities, industry, as well as scientific communities. In terms of system design and control, current cloud and edge architecture of CAVs need to be refined or even redesigned to better function under uncertainties in demand, and to better cooperate with existing conventional vehicles and infrastructure. From the performance assessment perspective, models and simulation tools based on artificial intelligence and big data have been widely developed for validation and evaluation of cloud computing and edge computing, but the validity of these models needs to be re-examined with field implementations. Finally, while the increasing connectivity among vehicles and infrastructures may help improve their perception of the environment and enable coordinated decision making, it also presents new challenges to ensure system safety and security, with inherent disturbances to wireless communication networks and also the inevitably larger attack surface that may be exploited by malicious attacks. In this tutorial, experts from academia and industry introduce the trends and challenges of applying cloud and edge computing for CAVs, highlight representative works in the literature and discuss their limitations, present new promising solutions, and outline future directions in research and engineering. Particular focus will be given to methodologies and tools for building digital twin frameworks with cloud and edge computing for CAVs, quantitative and formal analysis for ensuring CAV safety under disturbances and uncertainties, system-level CAV security threat landscape and defense solution space, and experiences from practical deployment of cloud and edge computing for CAVs.

Environmental monitoring is a crucial field encompassing diverse applications, including marine exploration, wildlife conservation, ecosystem assessment, and air quality monitoring. Collecting accurate and timely data from inaccessible locations and challenging environments is essential for understanding and addressing environmental issues. Robots offer a promising solution by enabling data collection at unprecedented spatio-temporal scales. However, relying solely on teleoperation is impractical and limits the efficiency and effectiveness of environmental monitoring efforts. Autonomy plays a pivotal role in unlocking the full potential of robots, allowing them to operate independently and intelligently in complex environments. This monograph focuses on high-level decision-making problems in autonomous environmental monitoring robots. Decision-making at the high level involves strategic planning and coordination to optimize data collection. Addressing these challenges allows robots to autonomously navigate, explore, and gather scientific data in a wide range of environmental monitoring applications. Included in the monograph are representations for different environments, as well as a discussion of using these presentations to solve tasks of interest, such as learning, localization, and monitoring. To efficiently implement the tasks, decision-theoretic optimization algorithms consider: (1) where to take measurements from, (2) which tasks to be assigned, (3) what samples to collect, (4) when to collect samples, (5) how to learn environment; and (6) who to communicate. Finally, this work concludes by presenting the challenges and opportunities in robotic environmental monitoring.

Smart Grid is a power grid system that uses digital communication technologies. By deploying intelligent devices throughout the power grid infrastructure, from power generation to consumption, and enabling communication among them, it revolutionizes the modern power grid industry with increased efficiency, reliability, and availability. However, reliance on information and communication technologies has also made the smart grids exposed to new vulnerabilities and complications that may negatively impact the availability and stability of electricity services, which are vital for people's daily lives. The purpose of this monograph is to provide an up-to-date and comprehensive survey and tutorial on the cybersecurity aspect of smart grids. The monograph focuses on the sources of the cybersecurity issues, the taxonomy of threats, and the survey of various approaches to overcome or mitigate such threats. It covers the state-of-the-art research results in recent years, along with remaining open challenges. This monograph can be used both as learning materials for beginners who are embarking on research in this area and as a useful reference for established researchers in this field.

An Operations Management Perspective on Design Thinking provides a map of what is known about mechanisms of design thinking when looking through an operations management lens and identifies areas where knowledge gaps still exist. In applying the operations management lens, the author constructs a simple framework for how to assess progress in design thinking activities. To provide improved design thinking progress measures, the author expands this framework by considering multiple dimensions of these measures in greater detail: the outcomes of an operation and its transformation function. Applying the reference set to these multiple dimensions of the expanded framework identifies contributions from other disciplines that can help explain the conditions under which design thinking operations can be managed successfully and pinpoints unexplained gaps that are worthy of future research. The monograph first prepares the methodological ground by putting the attempt to search for better design thinking process measures in the context of existing research approaches. The next section summarizes the origins and characteristics of design thinking and provides an overview of the progress measures that have been proposed for design thinking. The monograph then introduces an operations management perspective for design thinking as an innovation production process. The next section expands this perspective by introducing multiple dimensions and finer grained measures and apply this extended framework to the data set from earlier sections to pull together the current understanding of design thinking and to identify future research opportunities. The monograph concludes with some broader reflections.

Currency Risk Premiums: A Multi-Horizon Perspective reviews the literature on multi-horizon currency risk premiums. It shows how the multi-horizon implications arise from the classic present-value relationship. The authors further show how these implications manifest themselves in the interaction between bond and currency risk premiums. This link is strengthened by explicitly accounting for stochastic discount factors. Information about currency risk premiums at different horizons presents a wealth of new evidence and challenges for existing models.

A Simple Method for Predicting Covariance Matrices of Financial Returns makes three contributions. First, it proposes a new method for predicting the time-varying covariance matrix of a vector of financial returns, building on a specific covariance estimator suggested by Engle in 2002. The second contribution proposes a new method for evaluating a covariance predictor, by considering the regret of the log-likelihood over some time period such as a quarter. The third contribution is an extensive empirical study of covariance predictors. The authors compare their method to other popular predictors, including rolling window, exponentially weighted moving average (EWMA) and generalized autoregressive conditional heteroscedastic (GARCH) type methods. After an introduction, Section 2 describes some common predictors, including the one that this method builds on. Section 3 introduces the proposed covariance predictor. Section 4 discusses methods for validating covariance predictors that measure both overall performance and reactivity to market changes. Section 5 describes the data used in the authors' first empirical studies and the results are provided in Section 6. The authors then discuss some extensions of and variations on the method, including realized covariance prediction (Section 7), handling large universes via factor models (Section 8), obtaining smooth covariance estimates (Section 9), and using the authors' covariance model to generate simulated returns (Section 10).

A seminal case in corporate law (Dodge v. Ford Motor Co), set the cardinal principle that corporations must serve the interests of shareholders rather than the interests of employees, customers, or the community. This principle, referred to as "shareholder primacy," has been considered a tenet of the fiduciary duty owed by corporate directors. The shareholder primacy norm has influenced corporate behavior and encouraged short-term profit-seeking behavior with significant social ramifications. Corporations have been criticized for undermining the interests of employees, customers, and the community in the name of profit maximization. Shareholder Primacy as an Untenable Corporate Norm argues that corporate interests and broader social interests, such as benefits to consumers and employees, are not mutually exclusive and can be reconciled by allowing corporate managers and majority shareholders to define corporate interests more broadly, beyond the narrow confines of shareholder primacy. This article examines the flaws of shareholder primacy as the principle for corporate governance and discuss an alternative approach (the stakeholder approach). It also discusses the necessity of a statutory adjustment and propose legal reform to clarify the current ambiguity about the legal status of shareholder primacy.

The primary purpose of Entrepreneurs' Search for Sources of Knowledge is to explore the search process for knowledge used by entrepreneurs and entrepreneurial firms in pursuit of new opportunities, new product innovation opportunities in particular. The second purpose of this monograph is to present empirical evidence about the sources of knowledge that entrepreneurs and entrepreneurial firms actually use (and actually do not use) in an effort to allow observed behavior to inform future economics and management theory about the search for and use of knowledge sources. And, the third purpose of this monograph is to generate new and more complete empirical efforts to construct databases and to conduct analyses-empirical analyses and case studies-related not only to entrepreneur's and entrepreneurial firm's search for and use of sources of knowledge but also to measure the trends in the impacts of their use.

Aesthetic design is pervasive in the marketplace, where it influences consumer behavior, endows products with value, and differentiates between brands. In fact, research suggests that aesthetic appeal drives sales across most product categories. The time is ripe for taking stock of the state of research in this domain. Aesthetics in Marketing begins with a characterization of this domain of research and then organizes extant literature in two ways: First, it provides an overview of aesthetics principles, outcomes stemming from these principles, and contexts in which these principles operate. Second, it zooms in on the principle of ambiguity in specific to provide a detailed discussion of ambiguous versus accessible aesthetic elements. The author also provides directions for future research.

Financial Machine Learning surveys the nascent literature on machine learning in the study of financial markets. The authors highlight the best examples of what this line of research has to offer and recommend promising directions for future research. This survey is designed for both financial economists interested in grasping machine learning tools, as well as for statisticians and machine learners seeking interesting financial contexts where advanced methods may be deployed.This survey is organized as follows. Section 2 analyzes the theoretical benefits of highly parameterized machine learning models in financial economics. Section 3 surveys the variety of machine learning methods employed in the empirical analysis of asset return predictability. Section 4 focuses on machine learning analyses of factor pricing models and the resulting empirical conclusions for risk-return tradeoffs. Section 5 presents the role of machine learning in identifying optimal portfolios and stochastic discount factors. Section 6 offers brief conclusions and directions for future work.

Supply Chain Transparency and Sustainability examines the academic literature that investigates both the visibility and disclosure dimensions of supply chain transparency within the context of social and environmental responsibility. In order to present a clear picture of the research landscape for the operations management community, the discussions are focused on research from the behavioral and analytical modeling literature. The primary goal is to discuss the most representative and emerging works in this space so as to highlight future research directions and inspire more research on supply chain transparency. While supply chain transparency is a topic of relevance for many management contexts, this monograph focuses on its role in the context of sustainability. The monograph is organized as follows. First, there is a brief background on the topic of supply chain transparency. The authors then review the behavioral literature on supply chain transparency. This is then followed by a review of the analytical modeling literature that examines transparency-related contexts. Finally, the monograph concludes by discussing potential future research directions.

Ultra-Reliable Low-Latency Communications (URLLC) was introduced into 5G networks to facilitate machine to machine communication for such applications as the Internet of Things. But designing URLLC systems, with disjointed treatment of the topic in the literature, has proven challenging. In this work, the authors present a comprehensive coverage of the URLLC including the motivation, theory, practical enablers and future evolution. The unified level of details provides a balanced coverage between its fundamental communication- and information theoretic background and its practical enablers, including 5G system design aspects. The authors conclude by offering an outlook on URLLC evolution in the sixth-generation (6G) era towards dependable and resilient wireless communications. This is the first book to give the reader a complete, yet concise, introduction to the theoretical and application oriented aspects of a topic at the core of both 5G and 6G wireless communication systems. As such, it is essential reading for designers and students of such systems.

This book provides a detailed overview of possible applications of distributed optimization in power systems. Centralized algorithms are widely used for optimization and control in power system applications. These algorithms require all the measurements and data to be accumulated at a central location and hence suffer from single-point-of-failure. Additionally, these algorithms lack scalability in the number of sensors and actuators, especially with the increasing integration of distributed energy resources (DERs). As the power system becomes a confluence of a diverse set of decision-making entities with a multitude of objectives, the preservation of privacy and operation of the system with limited information has been a growing concern. Distributed optimization techniques solve these challenges while also ensuring resilient computational solutions for the power system operation in the presence of both natural and man-made adversaries. There are numerous commonly-used distributed optimization approaches, and a comprehensive classification of these is discussed and detailed in this work. All of these algorithms have displayed efficient identification of global optimum solutions for convex continuous distributed optimization problems. The algorithms discussed in the literature thus far are predominantly used to manage continuous state variables, however, the inclusion of integer variables in the decision support is needed for specific power system problems. The mixed integer programming (MIP) problem arises in a power system operation and control due to tap changing transformers, capacitors and switches. There are numerous global optimization techniques for MIPs. Whilst most are able to solve NP-hard convexified MIP problems centrally, they are time consuming and do not scale well for large scale distributed problems. Decomposition and a solution approach of distributed coordination can help to resolve the scalability issue. Despite the fact that a large body of work on the centralized solution methods for convexified MIP problems already exists, the literature on distributed MIPs is relatively limited. The distributed optimization algorithms applied in power networks to solve MIPs are included in this book. Machine Learning (ML) based solutions can help to get faster convergence for distributed optimization or can replace optimization techniques depending on the problem. Finally, a summary and path forward are provided, and the advancement needed in distributed optimization for the power grid is also presented.

Blockchains are meant to provide an append-only sequence (ledger) of transactions. Security commonly relies on a consensus protocol in which forks in the sequence are either prevented completely or are exponentially unlikely to last more than a few blocks. This monograph proposes the design of algorithms and a system to achieve high performance (a few seconds from the time of initiation for transactions to enter the blockchain), the absence of forks, and a very low energy cost (a per transaction cost that is a factor of a billion or more less than bitcoin). The foundational component of this setup is a group of satellites whose blockchain protocol code can be verified and burned into read-only memory. Because such satellites can perhaps be destroyed but cannot be captured (unlike even fortified terrestrial servers), a reasonable assumption is that the blockchain protocol code in the satellites may fail to make progress either permanently or intermittently but will not be traitorous. A second component of this setup is a group of terrestrial sites whose job is to broadcast information about blocks and to summarize the blockchain ledger. These can be individuals who are eager to get a fee for service. Even if many of these behave traitorously (against their interests as fee-collectors), a small number of honest ones is sufficient to ensure safety and liveness. The third component of this setup is a Mission Control entity which will act very occasionally to assign roles to terrestrial sites and time slots to satellites. These assignments will be multi-signed using the digital signatures of a widely distributed group of human governors. Given these components and these reasonable assumptions, the protocol described in this monograph, called Bounce, will achieve ledger functionality for arbitrarily sized blocks at under five seconds per block and at negligible energy cost. This monograph will discuss the overall architecture and algorithms of such a system, the assumptions it makes, and the guarantees it gives.

A Complete Framework for Model-Free Difference-in-Differences Estimation proposes a complete framework for data-driven difference-in-differences analysis with covariates, in particular nonparametric estimation and testing. The authors start with simultaneously choosing confounders and a scale of the outcome along identification conditions. They estimate first heterogeneous treatment effects stratified along the covariates, then the average effect(s) for the treated. This provides the asymptotic and finite sample behavior of our estimators and tests, bootstrap procedures for their standard errors and p-values, and an automatic bandwidth choice. The pertinence of these methods is shown with a study of the impact of the Deferred Action for Childhood Arrivals program on educational outcomes for non-citizen immigrants in the US.

Dynamic information-flow control (IFC) is a principled approach to protecting the confidentiality and integrity of data in software systems. This tutorial provides a complete and homogeneous account of the latest advances in fine- and coarse-grained dynamic information-flow control security. Written for students, practitioners and researchers, the authors first introduce both fine- and coarse-grained IFC in a gentle and accessible way, laying the groundwork for subsequent chapters. They proceed to show that, contrary to common belief, the granularity of the tracking system is not a fundamental feature of IFC systems and hence does not restrict how precise or permissive dynamic IFC systems can be. To achieve this, the authors demonstrate practical examples of both Fine to Coarse-Grained and Coarse- to Fine-Grained Program Translation. This tutorial will give readers the insights required to understand, develop and implement dynamic information-flow control to improve the security of a wide variety of software systems.

In the past decade of marketing scholarship, researchers have begun to examine the promise of AI technology to address practical problems through a consumer lens. Artificial Intelligence in Marketing and Consumer Behavior Research reviews the state of the art of behavioral consumer research involving AI-human interactions and divides the literature into two primary areas based on whether the reported effects are instantiations of consumers displaying a positive or negative response to encounters with AI. This monograph aims to contribute to the literature by integrating the growing body of AI research in marketing and consumer psychology. In doing so, the authors focus on the burgeoning yet less examined behavioral studies conducted in marketing and consumer behavior. They also identify the theories and process mechanisms that explain the reported effects. Artificial Intelligence in Marketing and Consumer Behavior Research proceeds as follows. Section 1 examines the history of AI research in marketing. Section 2 reviews and categorizes the decision contexts explored to date in this literature, while identifying the key theoretical constructs explored in these contexts. Section 3 provides an overview of moderators that have been demonstrated to alter the effects of AI-related consumption. Section 4 examines psychological processes that underlie consumer responses to and decisions involving AI. Section 5 provides the stimuli and manipulations employed in this research to date, while also suggesting a taxonomy of AI agents to guide future research designs. Section 6 offers future research directions for behavioral AI research in marketing.

The modern era is witnessing a revolution in the ability to scale computations to massively large data sets. A key breakthrough in scalability was the introduction of fast and easy-to-use distributed programming models such as the Massively Parallel Model of Computation (MPC) framework (also known as MapReduce). The framework describes algorithmic tools that have been developed to leverage the unique features of the MPC framework. These tools were chosen for their broad applicability, as they can serve as building blocks to design new algorithms. In this monograph the authors describe in detail certain tools available in the framework that are generally applicable and can be used as building blocks to design algorithms in the area. These include Partitioning and Coresets, sample and prune, dynamic programming, round compression, and lower bounds. This monograph provides the reader with an accessible introduction to the most important tools of a framework used for the design of new algorithms deployed in systems using massively parallel computation.