Pinterest is a social media platform that allows users to assemble images or other media into customized lists, then share those lists with others. Pinterest calls these lists “pinboards” and the items added to each board “pins,” analogous to real-world physical bulletin boards. Like other social media systems, Pinterest wants to recommend new content to its users to keep them engaged with the service. In 2018, Pinterest introduced a system called Pixie as a component of their overall recommendation infrastructure (Eksombatchai et al., 2018). It uses a graph model to represent the connections among items, then explores that graph in a randomized way to generate recommendations. In this chapter, we’ll build our own system based on the graph algorithms used by Pixie.

We live in a networked world. Professional networks, social networks, neural networks – we’re all familiar with the idea that connections matter. This chapter introduces graphs, our last major topic. Graphs are the primary tool for modeling connections or relationships among a set of items; binary trees, for example, are a special type of graph. Graph models illustrate the power of abstraction: They capture the underlying structure of a network, independent of what the elements actually represent. Therefore, graph algorithms are flexible – they’re not tied to one particular application or problem domain.

Subgrid-scale processes refer to the processes that are vital for describing atmospheric motion but cannot be explicitly resolved due to insufficient model resolution. Although these processes occur at small scales, they depend on and, in turn, affect the larger-scale fields and processes that are explicitly resolved by a numerical model. Due to this two-way interaction, neglecting those subgrid-scale processes will degrade the quality of the weather forecast. To reproduce this two-way interaction, the subgrid-scale processes are “parameterized” by formulating their effects in terms of the resolved fields. Using the prognostic equation for water vapor as an example, we illustrate the general principle of parameterization. We then outline the crucial processes parameterized in today’s numerical weather prediction models. To facilitate the understanding about how parameterizations are implemented in a weather model, a simplified general circulation model with simple parameterizations, SPEEDY, is introduced to the readers.

So far, we’ve considered four data structures: arrays, lists, stacks, and queues. All four could be described as linear, in that they maintain their items as ordered sequences: arrays and lists are indexed by position, stacks are LIFO, and queues are FIFO. In this chapter, we’ll consider the new problem of building a lookup structure, like a table, that can take an input called the key and return its associated value. For example, we might fetch a record of information about a museum artifact given its ID number as the key. None of our previous data structures are a good fit for this problem.

The applied psychology of religion takes information from the knowledge base developed by psychologists of religion and uses this information for some social, psychological, or spiritual/religious purpose. When we seek to apply research and theory in this field we must first answer questions about our objectives, and it is unlikely that we will arrive at much agreement on ultimate goals. Still, it is possible that some consensual objectives and applications can emerge among researchers and those who seek to apply what researchers have learned. This chapter lists and discusses a broad range of potential applications. One major domain of application concerns clinical psychology, counseling psychology, psychiatry, social work, and related fields. We see that in recent decades, there have been many proposals – partly driven by the findings of empirical research – to integrate religious and spiritual approaches into mainstream psychotherapy. As with nearly everything else in the psychology of religion, these proposals can be controversial. In addition, the chapter discusses proposed spiritual and religious competencies for psychologists.

Survey data, despite limitations, offer the clearest window on the current state of global religiosity, showing the sharply divergent ways religious impulses and their absence have manifested in different nations and regions. After a discussion of religious literacy, we explore what cross-cultural survey research teaches about the global distribution of religious belief. Research suggests that atheism is rare, especially outside of Europe and a few industrialized countries. Beyond this, studies confirm that countries differ greatly in the prevalence of various religious beliefs, including belief in a personal God who intervenes in human affairs. Some careful projections also suggest that significant changes are coming over the next few decades in the relative sizes of different religious groups around the world. In the United States, survey data suggest that – despite some recent changes -- people continue to be relatively religious when compared with other highly industrialized and economically developed nations. The second half of the chapter looks at the empirical relationships between religiosity and education, intellect, thinking styles, gender, age, and personality.

This chapter describes the events that led to the birth of clinical psychology as a science and a profession. It outlines three traditions that shaped the field and continue to influence it: (a) the use of scientific research methods – the empirical tradition; (b) the measurement of individual differences – the psychometric tradition; and (c) the classification and treatment of psychological disorders – the clinical tradition. It shows how the field grew slowly but steadily during the first half of the twentieth century, then saw explosive growth both in size and in the diversity of its major theoretical approaches, including the psychodynamic, humanistic, behavioral, cognitive, cognitive behavioral, social systems, and biological. It also tells the story of how these approaches developed and presents examples of how various approaches might be applied in clinical cases. The chapter concludes with a summary of the latest developments in clinical psychology that will surely shape its future.

In practice, channels often cause linear dispersive signal distortions (e.g., due to low-pass properties of cables or multipath propagation in wireless communications). Consequently, in this chapter we study PAM transmission over time-invariant linear dispersive channels, where so-called intersymbol interference (ISI) occurs. First, receiver-side equalization strategies for linear dispersive channels are introduced and analyzed. Besides the optimum procedure, which follows immediately from the general signal space concept, we assess low-complexity receivers, specifically linear equalization and decision-feedback equalization. In each case, we are interested in the achievable error performance; the loss caused by ISI is quantified. In addition, transmitter-side techniques for pre-equalization are addressed. The duality between receiver-side and transmitter-side schemes is highlighted. A unified theoretic framework for filter design and the calculation of the error performance of the various strategies for digital transmission over linear dispersive channels is presented.

This chapter describes how clinical psychologists work with medical professionals to treat disorders, help patients to cope with the stress of medical conditions, and to adhere to medical treatment recommendations. It also describes factors that contribute to disease, focusing on relationships between psychosocial factors (such as stress and unhelpful patterns of thinking) and physical factors (such as nervous system activity, circulation, and immune system functioning). The chapter also includes a description of behavioral and psychological risk factors that enhance the likelihood of illnesses such as cardiovascular disease, chronic pain, and cancer. Also discussed are programs for preventing or minimizing the impact of those risk factors.