This yearbook on acne has collection of original abstract of path breaking research published in high impact factor journals that were published in the previous year. It presents breakthrough developments articles which were reviewed thoroughly and commented on the value of the article. This book brings to you a collection of meticulously chosen articles focusing on the recent advances in the management of acne. Articles selected from various peer-reviewed journals that were published in the last 12 month period. In-depth evidence based discussion on clinical management of acne with a variety of treatment options such as hormonal, pharmacological and nonpharmacological therapy provides useful insights. Each article has been reviewed by subject reviewers who have written comments on value of the article, previous knowledge on the subject, what the article add to the prevailing knowledge and practices, strong points and weaknesses of the article and other similar observations that have come in the last 12-month period. Each article is accompanied by key messages, summarizing the article as well as highlighting the clinical experience of the reviewers. A must read book for dermatologists, physicians and postgraduate students.

Recent advancements in the field of telecommunications, medical imaging and signal processing deal with signals that are inherently time varying, nonlinear and complex-valued. The time varying, nonlinear characteristics of these signals can be effectively analyzed using artificial neural networks. Furthermore, to efficiently preserve the physical characteristics of these complex-valued signals, it is important to develop complex-valued neural networks and derive their learning algorithms to represent these signals at every step of the learning process. This monograph comprises a collection of new supervised learning algorithms along with novel architectures for complex-valued neural networks. The concepts of meta-cognition equipped with a self-regulated learning have been known to be the best human learning strategy. In this monograph, the principles of meta-cognition have been introduced for complex-valued neural networks in both the batch and sequential learning modes. For applications where the computation time of the training process is critical, a fast learning complex-valued neural network called as a fully complex-valued relaxation network along with its learning algorithm has been presented. The presence of orthogonal decision boundaries helps complex-valued neural networks to outperform real-valued networks in performing classification tasks. This aspect has been highlighted. The performances of various complex-valued neural networks are evaluated on a set of benchmark and real-world function approximation and real-valued classification problems.

Recent advancements in the field of telecommunications, medical imaging and signal processing deal with signals that are inherently time varying, nonlinear and complex-valued. The time varying, nonlinear characteristics of these signals can be effectively analyzed using artificial neural networks. Furthermore, to efficiently preserve the physical characteristics of these complex-valued signals, it is important to develop complex-valued neural networks and derive their learning algorithms to represent these signals at every step of the learning process. This monograph comprises a collection of new supervised learning algorithms along with novel architectures for complex-valued neural networks. The concepts of meta-cognition equipped with a self-regulated learning have been known to be the best human learning strategy. In this monograph, the principles of meta-cognition have been introduced for complex-valued neural networks in both the batch and sequential learning modes. For applications where the computation time of the training process is critical, a fast learning complex-valued neural network called as a fully complex-valued relaxation network along with its learning algorithm has been presented. The presence of orthogonal decision boundaries helps complex-valued neural networks to outperform real-valued networks in performing classification tasks. This aspect has been highlighted. The performances of various complex-valued neural networks are evaluated on a set of benchmark and real-world function approximation and real-valued classification problems.

This book is based on the workshop "Information Retrieval Techniques for Speech Applications", held as part of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in New Orleans, USA, in September 2001.The book presents 10 papers based on workshop presentations. The topics range from traditional information retrieval techniques over adaptations of these techniques to spoken documents and multimedia collections finally to new applications.