I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

物件導向軟體之架構(Object-Oriented Software Construction)探討

I. the origin of species by means of natural selection

In the Hamadryas baboon, males are substantially larger than females. A troop of baboons is subdivided into a number of ‘one-male groups’, consisting of one adult male and one or more females with their young. The male prevents any of ‘his’ females from moving too far from him. Kummer (1971) performed the following experiment. Two males, A and B, previously unknown to each other, were placed in a large enclosure. Male A was free to move about the enclosure, but male B was shut in a small cage, from which he could observe A but not interfere. A female, unknown to both males, was then placed in the enclosure. Within 20 minutes male A had persuaded the female to accept his ownership. Male B was then released into the open enclosure. Instead of challenging male A , B avoided any contact, accepting A’s ownership.

Evolution and the Theory of Games

https://diuf.unifr.ch/main/pai/sites/diuf.unifr.ch.main.pai/files/publications/2011_Huang_Bessis_Norrington_Kuonen_Hirsbrunner_Exploring_decentralized.pdf

Exploring decentralized dynamic scheduling for grids and clouds using the community-aware scheduling algorithm

Scientific researchers from computer science, communication and as well from sociology and epidemiology reveal a strong interest in the study of networks. One important feature studied in complex network is the community structure. A new evolutionary technique for community detection in complex networks is proposed in this paper. The new algorithm is based on an information sharing mechanism between the individuals of a population. A real-world network is considered for numerical experiments.

Community detection in complex networks using collaborative evolutionary algorithms

Services can be defined as software components, or building blocks that are provided in order to be assembled, and reused in a distributed Internet-based environment. The development of new services through the integration of existing ones (referred to as service composition) is generating considerable interest in recent years in several computer science communities. Existing service composition systems miss out on the opportunity of enormous benefits by exploiting useful contextual information relevant to service discovery and composition. This paper presents the CB-SeC framework (CB-SeC stands from Context Based Service Composition). The approach adopted in this framework is the combination of context-aware computing and agent technology, in order to provide better user-tailored services in pervasive environments. Keywords—Context-aware computing, mobile services, multi-agent systems, pervasive computing, service composition.

Towards a Context-Based Service Composition Framework.

In principle, with Web services application creation is simply a matter of discovering, selecting the right services and composing them into a solution (1). Existing approaches for service discovery and composition, typically facilitate or- chestration only, while neglecting information about the context of users and services as well. However while computing today is becoming increasingly pervasive there is an unaddressed need of more location and context-aware support. In this paper we present a generic framework that combines service-oriented and context-aware computing in order to provide users with more tailored composite services.

Using Context Information for Service Discovery and Composition.

This paper examines the interest of a developmental approach applied to the design of autonomous robots and the understanding of adaptive behaviors, such as imitation. The proposed model is a neural network architecture that learns and uses associations between vision and arm movements, even if the problem is ill posed (in the case of mapping problems between the visual space and the joints space of the arm). The central part of the model is a visuo-motor map able to represent the arm end point’s position in an ego-centered space (constrained by the vision) according to motor information (the proprioception). Sensorimotor behaviors such as tracking, pointing, spontaneous imitating, and sequences learning can then be obtained as the consequence of different internal dynamics computed on neural fields triggered by the visuo-motor map. The readout mechanism also explains how an apparently complex behavior can be generated and controlled from one simple internal dynamics and how at the same time the learning ...

Béat Hirsbrunner

Papers

Exploring decentralized dynamic scheduling for grids and clouds using the community-aware scheduling algorithm

Community detection in complex networks using collaborative evolutionary algorithms

Towards a Context-Based Service Composition Framework.

Using Context Information for Service Discovery and Composition.

Learning Invariant Sensorimotor Behaviors: A Developmental Approach to Imitation Mechanisms