A hierarchical model for structure learning based on the physiological characteristics of neurons

Almost all applications of Artificial Neural Networks (ANNs) depend mainly on their memory ability. The characteristics of typical ANN models are fixed connections, with evolved weights, globalized representations, and globalized optimizations, all based on a mathematical approach. This makes those models to be deficient in robustness, efficiency of learning, capacity, anti-jamming between training sets, and correlativity of samples, etc. In this paper, we attempt to address these problems by adopting the characteristics of biological neurons in morphology and signal processing. A hierarchical neural network was designed and realized to implement structure learning and representations based on connected structures. The basic characteristics of this model are localized and random connections, field limitations of neuron fan-in and fan-out, dynamic behavior of neurons, and samples represented through different sub-circuits of neurons specialized into different response patterns. At the end of this paper, some important aspects of error correction, capacity, learning efficiency, and soundness of structural representation are analyzed theoretically. This paper has demonstrated the feasibility and advantages of structure learning and representation. This model can serve as a fundamental element of cognitive systems such as perception and associative memory.     

复杂条件下发挥锚杆支护作用的相关技术

介绍了在特殊地质和开采条件下,发挥锚杆支护作用的几种相关技术。     

Tracing success: graphical methods for analysing successful collaborative problem solving

The aim of this paper is to evaluate the use of trace diagrams for analysing collaborative problem solving. The paper describes a study where trace diagrams were used to analyse joint navigation in a virtual environment. Ten pairs of undergraduates worked together on a distributed virtual task to collect five flowers using two bees with each participant controlling one of the bees. This task is used extensively in research on multi-robot systems. The joint navigation of the pairs was analysed using trace diagrams. They showed that more successful pairs divided the task up, showed very little overlap and very little backtracking. Whereas, the less successful pairs, showed no task division, there was significant overlap and extensive backtracking. From this analysis we developed numerical measures of task division, overlap and backtracking. Task division was significantly and negatively related with task performance. Backtracking and overlap were significantly and positively correlated.     

Global Level Path Planning for Mobile Robots in Dynamic Environments

This paper presents a self-adapting approach to global level path planning in dynamic environments. The aim of this work is to minimize risk and delays in possible applications of mobile robots (e.g., in industrial processes). We introduce a hybrid system that uses case-based reasoning as well as grid-based maps for decision-making. Maps are used to suggest several alternative paths between specific start and goal point. The casebase stores these solutions and remembers their characteristics. Environment representation and casebase design are discussed. To solve the problem of exploration vs. exploitation, a decision-making strategy is proposed that is based on the irreversibility of decisions. Forgetting strategies are discussed and evaluated in the context of case-based maintenance. The adaptability of the system is evaluated in a domain based on real sensor data with simulated occupancy probabilities. Forgetting strategies and decision-making strategies are evaluated in simulated environments. Experiments show that a robot is able to adapt in dynamic environments and can learn to use paths that are less risky to follow.     

Review of The achievement test desk reference: A guide to learning disability identification, second edition.

Reviews the book, The achievement test desk reference: A guide to learning disability identification, second edition by Dawn P. Flanagan, Samuel O. Ortiz, Vincent C. Alfonso, and Jennifer T. Mascolo (2006). The Second Edition of the Achievement test desk reference comes only four years after its first edition. Its content echoes the first edition, with the chapters now re-organized and updated. However, this edition is also timely; its significant contributions are its theoretical and practical model for defining and assessing learning disabilities (LD) and its update of practical information for interpreting many of the new standardized achievement tests. The goal for this edition was to provide an integrated and systematic framework for achievement testing within the framework of the Cattell-Horn-Carroll (CHC) theory of human cognitive abilities and the Response to Intervention (RTI) movement. Part 1 of provides an update of the theories, definitions, assessment, interpretation, and identification of LD. Part 2 describes and reviews the psychometric, theoretical, and qualitative features of achievement tests published between 1996 and 2006 and normed on people from the United States. Part 3 provides a comprehensive, step-by-step process for assessing learning disabilities under the Individuals with Disabilities Education Improvement Act (IDEA 2004). This volume guides practitioners to choose the best achievement tests to use. It shows how to get the most efficient and effective evaluations of students' academic skills that could account for their difficulties and possible learning disabilities. This book will be essential reference material for effective LD assessments. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Localization of Function Within the Dorsal Hippocampus: The Role of the CA3 Subregion in Paired-Associate Learning.

Computational models and electrophysiological data suggest that the CA3 subregion of the hippocampus supports the formation of arbitrary associations; however, no behavioral studies have been conducted to test this hypothesis. Rats with neurotoxin-induced lesions of dorsal dentate gyrus (DG), CA3, or CA1 were tested on object-place and odor-place paired-associate tasks to test whether the mechanism that supports paired-associate learning is localized to the CA3 subregion of the dorsal hippocampus or whether all hippocampal subregions contribute to paired-associate learning. The data indicate that rats with DG or CA1 lesions learned the tasks as well as controls; however, CA3-lesioned rats were impaired in learning the tasks. Thus, the CA3 subregion of the dorsal hippocampus contains a mechanism to support paired-associate learning. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Three perspectives of data mining

This paper reviews three recent books on data mining written from three different perspectives, i.e., databases, machine learning, and statistics. Although the exploration in this paper is suggestive instead of conclusive, it reveals that besides some common properties, different perspectives lay strong emphases on different aspects of data mining. The emphasis of the database perspective is on efficiency because this perspective strongly concerns the whole discovery process and huge data volume. The emphasis of the machine learning perspective is on effectiveness because this perspective is heavily attracted by substantive heuristics working well in data analysis although they may not always be useful. As for the statistics perspective, its emphasis is on validity because this perspective cares much for mathematical soundness behind mining methods.     

The time course of spoken word learning and recognition: Studies with artificial lexicons.

The time course of spoken word recognition depends largely on the frequencies of a word and its competitors, or neighbors (similar-sounding words). However, variability in natural lexicons makes systematic analysis of frequency and neighbor similarity difficult. Artificial lexicons were used to achieve precise control over word frequency and phonological similarity. Eye tracking provided time course measures of lexical activation and competition (during spoken instructions to perform visually guided tasks) both during and after word learning, as a function of word frequency, neighbor type, and neighbor frequency. Apparent shifts from holistic to incremental competitor effects were observed in adults and neural network simulations, suggesting such shifts reflect general properties of learning rather than changes in the nature of lexical representations. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

网络电视的发展及其对远程教育的影响

计算机网络技术的发展给电视节目的播出方式带来了巨大的变化，网络电视这一传播媒体应运而生。从而改变了目前广播电视媒体信息单向传播的缺陷。介绍了网络电视的发展，就网络电视对远程教育的影响进行了探讨。