In this book a new model for data classification was developed. This new model is based on the competitive neural network Learning Vector Quantization (LVQ) and type-2 fuzzy logic. This computational model consists of the hybridization of the aforementioned techniques, using a fuzzy logic system within the competitive layer of the LVQ network to determine the shortest distance between a centroid and an input vector. This new model is based on a modular LVQ architecture to further improve its performance on complex classification problems. It also implements a data-similarity process for preprocessing the datasets, in order to build dynamic architectures, having the classes with the highest degree of similarity in different modules. Some architectures were developed in order to work mainly with two datasets, an arrhythmia dataset (using ECG signals) for classifying 15 different types of arrhythmias, and a satellite images segments dataset used for classifying six different types of soil. Both datasets show interesting features that makes them interesting for testing new classification methods.
The theatrum mundi metaphor was well-known in the Golden Age, and was often employed, notably by Calderón in his religious theatre. However, little account has been given of the everyday exploitation of the idea of the world as stage in the mainstream drama of the Golden Age. This study examines how and why playwrights of the period time and again created characters who dramatize themselves, who re-invent themselves by performing new roles and inventing new plots within the larger frame of the play. The prevalence of metatheatrical techniques among Golden Age dramatists, including Lope de Vega, Tirso de Molina, Calderón de la Barca and Guillén de Castro, reveals a fascination with role-playing and its implications. Thacker argues that in comedy, these playwrights saw role-playing as a means by which they could comment on and criticize the society in which they lived, and he reveals a drama far less supportive of the social status quo in Golden Age Spain than has been traditionally thought to be the case.
Metacommunity ecology links smaller-scale processes that have been the provenance of population and community ecology—such as birth-death processes, species interactions, selection, and stochasticity—with larger-scale issues such as dispersal and habitat heterogeneity. Until now, the field has focused on evaluating the relative importance of distinct processes, with niche-based environmental sorting on one side and neutral-based ecological drift and dispersal limitation on the other. This book moves beyond these artificial categorizations, showing how environmental sorting, dispersal, ecological drift, and other processes influence metacommunity structure simultaneously. Mathew Leibold and Jonathan Chase argue that the relative importance of these processes depends on the characteristics of the organisms, the strengths and types of their interactions, the degree of habitat heterogeneity, the rates of dispersal, and the scale at which the system is observed. Using this synthetic perspective, they explore metacommunity patterns in time and space, including patterns of coexistence, distribution, and diversity. Leibold and Chase demonstrate how these processes and patterns are altered by micro- and macroevolution, traits and phylogenetic relationships, and food web interactions. They then use this scale-explicit perspective to illustrate how metacommunity processes are essential for understanding macroecological and biogeographical patterns as well as ecosystem-level processes. Moving seamlessly across scales and subdisciplines, Metacommunity Ecology is an invaluable reference, one that offers a more integrated approach to ecological patterns and processes.
In Dreams in Double Time Jonathan Leal examines how the musical revolution of bebop opened up new futures for racialized and minoritized communities. Blending lyrical nonfiction with transdisciplinary critique and moving beyond standard Black/white binary narratives of jazz history, Leal focuses on the stories and experiences of three musicians and writers of color: James Araki, a Nisei multi-instrumentalist, soldier-translator, and literature and folklore scholar; Raúl Salinas, a Chicano poet, jazz critic, and longtime activist who endured the US carceral system for over a decade; and Harold Wing, an Afro-Chinese American drummer, pianist, and songwriter who performed with bebop pioneers before working as a public servant. Leal foregrounds that for these men and their collaborators, bebop was an affectively and intellectually powerful force that helped them build community and dream new social possibilities. Bebop’s complexity and radicality, Leal contends, made it possible for those like Araki, Salinas, and Wing who grappled daily with state-sanctioned violence to challenge a racially supremacist, imperial nation, all while hearing and making the world anew.
Organized Crime, Drug Trafficking, and Violence in Mexico: The Transition from Felipe Calderón to Enrique Peña Nieto examines the major trends in organized crime and drug trafficking in Mexico. The book provides an exhaustive analysis of drug-related violence in the country. This work highlights the transition from the Felipe Calderón administration to the Enrique Peña Nieto government, focusing on differences and continuities in counternarcotics policies as well as other trends such as violence and drug trafficking.
In this book a new model for data classification was developed. This new model is based on the competitive neural network Learning Vector Quantization (LVQ) and type-2 fuzzy logic. This computational model consists of the hybridization of the aforementioned techniques, using a fuzzy logic system within the competitive layer of the LVQ network to determine the shortest distance between a centroid and an input vector. This new model is based on a modular LVQ architecture to further improve its performance on complex classification problems. It also implements a data-similarity process for preprocessing the datasets, in order to build dynamic architectures, having the classes with the highest degree of similarity in different modules. Some architectures were developed in order to work mainly with two datasets, an arrhythmia dataset (using ECG signals) for classifying 15 different types of arrhythmias, and a satellite images segments dataset used for classifying six different types of soil. Both datasets show interesting features that makes them interesting for testing new classification methods.
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