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Adoption of Innovations reviews the Bass model and its new generation extensions as well as the alternative approach - the threshold model, which analyzes an individual's decision whether, and to what extent, to use a new technology given economic conditions. The authors show that the threshold model has a notable advantage in incorporating marketing tools that are targeted to and/or aim at reducing risk. This monograph describes the two models. It shows how they relate to each other, and then discusses how marketing tools, and in particular marketing tools that are designed to reduce risk, can be incorporated into each of the two models.

Common information measures the amount of matching variables in two or more information sources. It is ubiquitous in information theory and related areas such as theoretical computer science and discrete probability. However, because there are multiple notions of common information, a unified understanding of the deep interconnections between them is lacking. In this monograph the authors fill this gap by leveraging a small set of mathematical techniques that are applicable across seemingly disparate problems. The reader is introduced in Part I to the operational tasks and properties associated with the two main measures of common information, namely Wyner's and Gács-Körner-Witsenhausen's (GKW). In the subsequent two Parts, the authors take a deeper look at each of these. In Part II they discuss extensions to Wyner's common information from the perspective of distributed source simulation, including the Rényi common information. In Part III, GKW common information comes under the spotlight. Having laid the groundwork, the authors seamlessly transition to discussing their connections to various conjectures in information theory and discrete probability. This monograph provides students and researchers in Information Theory with a comprehensive resource for understanding common information and points the way forward to creating a unified set of techniques applicable over a wide range of problems.

In this monograph, an overview of recent developments and the state-of-the-art in image/video restoration and super-resolution (SR) using deep learning is presented. Deep learning has made a significant impact, not only on computer vision and natural language processing but also on classical signal processing problems such as image/video restoration/SR and compression. Recent advances in neural architectures led to significant improvements in the performance of learned image/video restoration and SR. An important benefit of data-driven deep learning approaches is that neural models can be optimized for any differentiable loss function, including visual perceptual loss functions, leading to perceptual video restoration and SR, which cannot be easily handled by traditional model-based approaches. The publication starts with a problem statement and a short discussion on traditional vs. data-driven solutions. Thereafter, recent advances in neural architectures are considered, and the loss functions and evaluation criteria for image/video restoration and SR are discussed. Also considered are the learned image restoration and SR, as learning either a mapping from the space of degraded images to ideal images based on the universal approximation theorem, or a generative model that captures the probability distribution of ideal images. Practical problems in applying supervised training to real-life restoration and SR are also included, as well as the solution models. In the section on learned video SR, approaches to exploit temporal correlations in learned video processing are covered, and then the perceptual optimization of the network parameters to obtain natural texture and motion is discussed. A comparative discussion of various approaches concludes the publication.

Methods for image recovery and reconstruction aim to estimate a good-quality image from noisy, incomplete, or indirect measurements. Such methods are also known as computational imaging. New methods for image reconstruction attempt to lower complexity, decrease data requirements, or improve image quality for a given input data quality. Image reconstruction typically involves optimizing a cost function to recover a vector of unknown variables that agrees with collected measurements and prior assumptions. State-of-the-art image reconstruction methods learn these prior assumptions from training data using various machine learning techniques, such as bilevel methods. This review discusses methods for learning parameters for image reconstruction problems using bilevel formulations, and it lies at the intersection of a specific machine learning method, bilevel, and a specific application, filter learning for image reconstruction. The review discusses multiple perspectives to motivate the use of bilevel methods and to make them more easily accessible to different audiences. Various ways to optimize the bilevel problem are covered, providing pros and cons of the variety of proposed approaches. Finally, an overview of bilevel applications in image reconstruction is provided.

Rank-metric codes date back to the 1970s and today play a vital role in many areas of coding theory and cryptography. In this survey the authors provide a comprehensive overview of the known properties of rank-metric codes and their applications. The authors begin with an accessible and complete introduction to rank-metric codes, their properties and their decoding. They then discuss at length rank-metric code-based quantum resistant encryption and authentication schemes. The application of rank-metric codes to distributed data storage is also outlined. Finally, the constructions of network codes based on MRD codes, constructions of subspace codes by lifting rank-metric codes, bounds on the cardinality, and the list decoding capability of subspace codes is covered in depth. Rank-Metric Codes and Their Applications provides the reader with a concise, yet complete, general introduction to rank-metric codes, explains their most important applications, and highlights their relevance to these areas of research.

This book provides students, researchers and practitioners with a deep understanding of the theory of online auctions and gives practical examples of how to implement in modern-day internet systems.