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Reservoir Computing
This book is the first comprehensive book about reservoir computing (RC). RC is a powerful and broadly applicable computational framework based on recurrent neural networks. Its advantages lie in small training data set requirements, fast training, inherent memory and high flexibility for various hardware implementations. It originated from computational neuroscience and machine learning but has, in recent years, spread dramatically, and has been introduced into a wide variety of fields, including complex systems science, physics, material science, biological science, quantum machine learning, optical communication systems, and robotics. Reviewing the current state of the art and providing a...
The Science of Soft Robots
The goal of this textbook is to equip readers with as structured knowledge of soft robotics as possible. Seeking to overcome the limitations of conventional robots by making them more flexible, gentle and adaptable, soft robotics has become one of the most active fields over the last decade. Soft robotics is also highly interdisciplinary, bringing together robotics, computer science, material science, biology, etc. After the introduction, the content is divided into three parts: Design of Soft Robots; Soft Materials; and Autonomous Soft Robots. Part I addresses soft mechanisms, biological mechanisms, and soft manipulation & locomotion. In Part II, the basics of polymer, biological materials,...
High Performance Computing on Vector Systems 2008
This book covers the results obtained in the Tera op Workbench project during a four years period from 2004 to 2008. The Tera op Workbench project is a colla- ration betweenthe High PerformanceComputingCenter Stuttgart (HLRS) and NEC Deutschland GmbH (NEC-HPCE) to support users to achieve their research goals using high performance computing. The Tera op Workbench supports users of the HLRS systems to enable and - cilitate leading edge scienti c research. This is achieved by optimizing their codes and improving the process work ow which results from the integration of diff- ent modules into a “hybrid vector system”. The assessment and demonstration of industrial relevance is another goal...
High Performance Computing on Vector Systems 2010
This book covers the results of the 11th and 12th Tera?op Workshop and continued a series initiated by NEC and the HLRS in 2004. As part of the Tera?op Workbench, it has become a meeting platform for scientists, application developers, international experts and hardware designers to discuss the current state and future directions of supercomputing with the aim of achieving the highest sustained application perf- mance. The Tera?op Workbench Project is a collaboration between the High Perf- mance Computing Center Stuttgart (HLRS) and NEC Deutschland GmbH (NEC HPCE) to support users to achieve their research goals using High Performance Computing. The ?rst stage of the Tera?op Workbench project (2004–2008) c- centrated on user’s applications and their optimization for the 72-node NEC SX-8 installation at HLRS. During this stage, numerous individual codes, developed and maintained by researchers or commercial organizations, have been analyzed and - timized. Several of the codes have shown the ability to outreach the TFlop/s thre- old of sustained performance. This created the possibility for new science and a deeper understanding of the underlying physics.
Transformations of Sensibility
First published in Japan in 1983, this book is now a classic in modern Japanese literary studies. Covering an astonishing range of texts from the Meiji period (1868–1912), it presents sophisticated analyses of the ways that experiments in literary language produced multiple new—and sometimes revolutionary—forms of sensibility and subjectivity. Along the way, Kamei Hideo carries on an extended debate with Western theorists such as Saussure, Bakhtin, and Lotman, as well as with such contemporary Japanese critics as Karatani Kojin and Noguchi Takehiko. Transformations of Sensibility deliberately challenges conventional wisdom about the rise of modern literature in Japan and offers highly ...
Modularity in Motor Control: From Muscle Synergies to Cognitive Action Representation
Mastering a rich repertoire of motor behaviors, as humans and other animals do, is a surprising and still poorly understood outcome of evolution, development, and learning. Many degrees-of-freedom, non-linear dynamics, and sensory delays provide formidable challenges for controlling even simple actions. Modularity as a functional element, both structural and computational, of a control architecture might be the key organizational principle that the central nervous system employs for achieving versatility and adaptability in motor control. Recent investigations of muscle synergies, motor primitives, compositionality, basic action concepts, and related work in machine learning have contributed...
Bio-Inspired Self-Organizing Robotic Systems
Self-organizing approaches inspired from biological systems, such as social insects, genetic, molecular and cellular systems under morphogenesis, and human mental development, has enjoyed great success in advanced robotic systems that need to work in dynamic and changing environments. Compared with classical control methods for robotic systems, the major advantages of bio-inspired self-organizing robotic systems include robustness, self-repair and self-healing in the presence of system failures and/or malfunctions, high adaptability to environmental changes, and autonomous self-organization and self-reconfiguration without a centralized control. “Bio-inspired Self-organizing Robotic Systems” provides a valuable reference for scientists, practitioners and research students working on developing control algorithms for self-organizing engineered collective systems, such as swarm robotic systems, self-reconfigurable modular robots, smart material based robotic devices, unmanned aerial vehicles, and satellite constellations.
Artificial Neural Networks and Machine Learning – ICANN 2019: Deep Learning
The proceedings set LNCS 11727, 11728, 11729, 11730, and 11731 constitute the proceedings of the 28th International Conference on Artificial Neural Networks, ICANN 2019, held in Munich, Germany, in September 2019. The total of 277 full papers and 43 short papers presented in these proceedings was carefully reviewed and selected from 494 submissions. They were organized in 5 volumes focusing on theoretical neural computation; deep learning; image processing; text and time series; and workshop and special sessions.
Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series
The proceedings set LNCS 11727, 11728, 11729, 11730, and 11731 constitute the proceedings of the 28th International Conference on Artificial Neural Networks, ICANN 2019, held in Munich, Germany, in September 2019. The total of 277 full papers and 43 short papers presented in these proceedings was carefully reviewed and selected from 494 submissions. They were organized in 5 volumes focusing on theoretical neural computation; deep learning; image processing; text and time series; and workshop and special sessions.
