This chapter covers:

1. • The complexities and challenges of human verbal interaction;

2. • The components of speech in human and human–robot interaction (HRI);

3. • The basic principles of speech recognition and its application to HRI;

4. • Dialogue management systems in HRI;

5. • Natural-language interaction in HRI, including the use of chatbots.

This chapter covers:

1. • The importance of the spatial placement of agents in social interaction;

2. • Basic human proxemics: how people manage space in relation to others;

3. • How a robot manages the space around it, including interactions such as approaching, initiating interaction, maintaining distance, and navigating around people;

4. • How the properties of spatial interaction can be used as cues for robots.

This chapter gives a proof of generic interpretability for (pre)hod pairs, studies derived models of hod mice, and proves branch condensation holds on a tail for anomalous hod pairs of type II and III.

This chapter presents a construction of the minimal model of LSA from a hypothesis implied by strong forcing axioms such as PFA and by large cardinal hypotheses such as the existence of a strongly compact cardinal. Consequently, LSA is consistent relative to PFA and LSA is consistent relative to the existence of a strongly compact cardinal. This chapter is an application of the theory developed in the previous chapters and the core model induction technique, which is a general method for calibrating consistency strength of strong theories.

This chapter covers:

1. • Methodological considerations and various decisions you need to make in setting up and performing a human–robot interaction (HRI) study;

2. • The strengths and weaknesses of different research methods and how to identify them for understanding and evaluating HRI;

3. • How the choice of robot, environment, and context matter for study results;

4. • The importance of looking at new ways of reporting data and insights befitting HRI, even though there is a tradition of reporting experimental work.

This chapter covers:

1. • The influence of the media on human–robot interaction (HRI) research;

2. • Stereotypes of robots in the media;

3. • Positive and negative visions of HRI;

4. • Ethical considerations when designing an HRI study;

5. • Ethical issues of robots that fulfill a user’s emotional needs;

6. • The dilemmas associated with behavior toward robots (e.g., robots’ right to be treated in a moral way);

7. • The issue of job losses as a result of the increasing number of robots in the workforce.

This chapter covers:

1. • What different social science theories say about how people form perceptions about others;

2. • How we understand anthropomorphism of robots based on prior social science literature;

3. • How anthropomorphism makes us see robots as uncanny, trustworthy, or likable.

This chapter introduces recursive difference equations where the initial conditions are nonzero. The output of such a system is studied in detail. One application is in the design of digital waveform generators such as oscillators, and this is explained in considerable detail. The coupled-form oscillator, which simultaneously generates synchronized sine and cosine waveforms at a given frequency, is presented. The chapter also introduces another application of recursive difference equations, namely the computation of mortgages. It is shown that the monthly payment on a loan can be computed using a first-order recursive difference equation. The equation also allows one to calculate the interest and principal parts of the payment every month, as shown. Poles play a crucial role in the behavior of recursive difference equations with zero or nonzero initial conditions. Many different manifestations of the effect of a pole are also summarized, including some time-domain dynamical meanings of poles.

Networks can get big. Really big. Examples include web crawls, online social networks, and knowledge graphs. Networks from these domains can have billions of nodes and hundreds of billions of edges. Systems biology is yet another area where networks will continue to grow. As sequencing methods continue to advance, more networks and larger, denser networks will need to be analyzed. This chapter discusses some of the challenges you face and solutions you can try when scaling up to massive networks. These range from implementation details to new algorithms and strategies to reduce the burden of such big data. Various tools, such as graph databases, probabilistic data structures, and local algorithms, are at our disposal, especially if we can accept sampling effects and uncertainty.

Every network has a corresponding matrix representation. This is powerful. We can leverage tools from linear algebra within network science, and doing so brings great insights. The branch of graph theory concerned with such connections is called spectral graph theory. This chapter will introduce some of its central principles as we explore tools and techniques that use matrices and spectral analysis to work with network data. Many matrices appear in different cases when studying networks, including the modularity matrix, nonbacktracking matrix, and the precision matrix. But one matrix stands out—the graph Laplacian. Not only does it capture dynamical processes unfolding over a networks structure, its spectral properties have deep connections to that structure. We show many relationships between the Laplacians eigendecomposition and network problems, such as graph bisection and optimal partitioning tasks. Combining the dynamical information and the connections with partitioning also motivates spectral clustering, a powerful and successful way to find groups of data in general. This kind of technique is now at the heart of machine learning, which well explore soon.

This chapter introduces structures and structural interconnections for LTI systems and then considers several examples of digital filters. Examples include moving average filters, difference operators, and ideal lowpass filters. It is then shown how to convert lowpass filters into other types, such as highpass, bandpass, and so on, by use of simple transformations. Phase distortion is explained, and linear-phase digital filters are introduced, which do not create phase distortion. The use of digital filters in noise removal (denoising) is also demonstrated for 1D signals and 2D images. The filtering of an image into low and high-frequency subbands is demonstrated, and the motivation for subband decomposition in audio and image compression is explained. Finally, it is shown that the convolution operation can be represented as a matrix vector multiplication, where the matrix has Toeplitz structure. The matrix representation also shows us how to undo a filtering operation through a process called deconvolution.

This chapter introduces different types of signals, and studies the properties of many kinds of systems that are encountered in signal processing. Signals discussed include the exponential signal, the unit step, single-frequency signals, rectangular pulses, Dirac delta signals, and periodic signals. Two-dimensional signals, especially 2D frequencies and sinusoids, are also demonstrated. Many types of systems are discussed, such as homogeneous systems, additive systems, linear systems, stable systems, time-invariant systems, and causal systems. Both continuous and discrete-time cases are discussed. Examples are presented throughout, such as music signals, ECG signals, and so on, to demonstrate the concepts. Subtle differences between discrete-time and continuous-time signals and systems are also pointed out.

This chapter introduces bandlimited signals, sampling theory, and the method of reconstruction from samples. Uniform sampling with a Dirac delta train is considered, and the Fourier transform of the sampled signal is derived. The reconstruction from samples is based on the use of a linear filter called an interpolator. When the sampling rate is not sufficiently large, the sampling process leads to a phenomenon called aliasing. This is discussed in detail and several real-world manifestations of aliasing are also discussed. In practice, the sampled signal is typically processed by a digital signal processing device, before it is converted back into a continuous-time signal. The building blocks in such a digital signal processing system are discussed. Extensions of the lowpass sampling theorem to the bandpass case are also presented. Also proved is the pulse sampling theorem, where the sampling pulse is spread out over a short duration, unlike the Dirac delta train. Bandlimited channels are discussed and it is explained how the data rate that can be transmitted over a channel is limited by channel bandwidth.

The fundamental practices and principles of network data are presented in this book, and the preface serves as an important starting point for readers to understand the goals and objectives of this text. The preface explains how the practical and fundamental aspects of network data are intertwined, and how they can be used to solve real-world problems. It also gives advice on how to use the book, including the boxes that will be featured throughout the book to highlight key concepts and provide practical examples of working with network data. Readers will find this preface to be a valuable resource as they begin their journey into the world of network science.

This chapter introduces the continuous-time Fourier transform (CTFT) and its properties. Many examples are presented to illustrate the properties. The inverse CTFT is derived. As one example of its application, the impulse response of the ideal lowpass filter is obtained. The derivative properties of the CTFT are used to derive many Fourier transform pairs. One result is that the normalized Gaussian signal is its own Fourier transform, and constitutes an eigenfunction of the Fourier transform operator. Many such eigenfunctions are presented. The relation between the smoothness of a signal in the time domain and its decay rate in the frequency domain is studied. Smooth signals have rapidly decaying Fourier transforms. Spline signals are introduced, which have provable smoothness properties in the time domain. For causal signals it is proved that the real and imaginary parts of the CTFT are related to each other. This is called the Hilbert transform, Poisson’’s transform, or the Kramers–Kronig transform. It is also shown that Mother Nature “computes” a Fourier transform when a plane wave is propagating across an aperture and impinging on a distant screen – a well-known result in optics, crystallography, and quantum physics.

This chapter presents the Laplace transform, which is as fundamental to continuous-time systems as the z-transform is to discrete-time systems. Several properties and examples are presented. Similar to the z-transform, the Laplace transform can be regarded as a generalization of the appropriate Fourier transform. In continuous time, the Laplace transform is very useful in the study of systems represented by linear constant-coefficient differential equations (i.e., rational LTI systems). Frequency responses, resonances, and oscillations in electric circuits (and in mechanical systems) can be studied using the Laplace transform. The application in electrical circuit analysis is demonstrated with the help of an LCR circuit. The inverse Laplace transformation is also discussed, and it is shown that the inverse is unique only when the region of convergence (ROC) of the Laplace transform is specified. Depending on the ROC, the inverse of a given Laplace transform expression may be causal, noncausal, two-sided, bounded, or unbounded. This is very similar to the theory of inverse z-transformation. Because of these similarities, the discussion of the Laplace transform in this chapter is brief.