Age-related differences in rapid muscle activation after rate of force development training of the elbow flexors

In young adults, improvements in the rate of force development as a result of resistance training are accompanied by increases in neural drive in the very initial phase of muscle activation. The purpose of this experiment was to determine if older adults also exhibit similar adaptations in response to rate of force development (RFD) training. Eight young (21-35 years) and eight older (60-79 years) adults were assessed during the production of maximum rapid contractions, before and after four weeks of progressive resistance training for the elbow flexors. Young and older adults exhibited significant increases (P< 0.01) in peak RFD, of 25.6% and 28.6% respectively. For both groups the increase in RFD was accompanied by an increase in the root mean square (RMS) amplitude and in the rate of rise (RER) in the electromyogram (EMG) throughout the initial 100 ms of activation. For older adults, however, this training response was only apparent in the brachialis and brachioradialis muscles. This response was not observed in surface EMG recorded from the biceps brachii muscle during either RFD testing or throughout training, nor was it observed in the pronator teres muscle. The minimal adaptations observed for older adults in the bifunctional muscles biceps brachii and pronator teres are considered to indicate a compromise of the neural adaptations older adults might experience in response to resistance training.

Description and training of neural network dynamics

Attractor properties of a popular discrete-time neural network model are illustrated through numerical simulations. The most complex dynamics is found to occur within particular ranges of parameters controlling the symmetry and magnitude of the weight matrix. A small network model is observed to produce fixed points, limit cycles, mode-locking, the Ruelle-Takens route to chaos, and the period-doubling route to chaos. Training algorithms for tuning this dynamical behaviour are discussed. Training can be an easy or difficult task, depending whether the problem requires the use of temporal information distributed over long time intervals. Such problems require training algorithms which can handle hidden nodes. The most prominent of these algorithms, back propagation through time, solves the temporal credit assignment problem in a way which can work only if the relevant information is distributed locally in time. The Moving Targets algorithm works for the more general case, but is computationally intensive, and prone to local minima.

Efficient training of RBF networks for classification.

Radial Basis Function networks with linear outputs are often used in regression problems because they can be substantially faster to train than Multi-layer Perceptrons. For classification problems, the use of linear outputs is less appropriate as the outputs are not guaranteed to represent probabilities. We show how RBFs with logistic and softmax outputs can be trained efficiently using the Fisher scoring algorithm. This approach can be used with any model which consists of a generalised linear output function applied to a model which is linear in its parameters. We compare this approach with standard non-linear optimisation algorithms on a number of datasets.

Bayesian training of mixture density networks

Mixture Density Networks (MDNs) are a well-established method for modelling the conditional probability density which is useful for complex multi-valued functions where regression methods (such as MLPs) fail. In this paper we extend earlier research of a regularisation method for a special case of MDNs to the general case using evidence based regularisation and we show how the Hessian of the MDN error function can be evaluated using R-propagation. The method is tested on two data sets and compared with early stopping.

Efficient training of RBF networks for classification

Radial Basis Function networks with linear outputs are often used in regression problems because they can be substantially faster to train than Multi-layer Perceptrons. For classification problems, the use of linear outputs is less appropriate as the outputs are not guaranteed to represent probabilities. In this paper we show how RBFs with logistic and softmax outputs can be trained efficiently using algorithms derived from Generalised Linear Models. This approach is compared with standard non-linear optimisation algorithms on a number of datasets.

Anger and perceived legitimacy of aggression in male Hong Kong Chinese athletes: Effects of type of sport and level of competition

Problem: The vast majority of research examining the interplay between aggressive emotions, beliefs, behaviors, cognitions, and situational contingencies in competitive athletes has focused on Western populations and only select sports (e.g., ice hockey). Research involving Eastern, particularly Chinese, athletes is surprisingly sparse given the sheer size of these populations. Thus, this study examines the aggressive emotions, beliefs, behaviors, and cognitions, of competitive Chinese athletes. Method: Several measures related to aggression were distributed to a large sample (N ¼ 471) of male athletes, representing four sports (basketball, rugby union, association football/soccer, and squash). Results: Higher levels of anger and aggression tended to be associated with higher levels of play for rugby and low levels of play for contact (e.g., football, basketball) and individual sports (e.g., squash). Conclusions: The results suggest that the experience of angry emotions and aggressive behaviors of Chinese athletes are similar to Western populations, but that sport psychology practitioners should be aware of some potentially important differences, such as the general tendency of Chinese athletes to disapprove of aggressive behavior.

Discriminative training of the hidden vector state model for semantic parsing

In this paper, we discuss how discriminative training can be applied to the hidden vector state (HVS) model in different task domains. The HVS model is a discrete hidden Markov model (HMM) in which each HMM state represents the state of a push-down automaton with a finite stack size. In previous applications, maximum-likelihood estimation (MLE) is used to derive the parameters of the HVS model. However, MLE makes a number of assumptions and unfortunately some of these assumptions do not hold. Discriminative training, without making such assumptions, can improve the performance of the HVS model by discriminating the correct hypothesis from the competing hypotheses. Experiments have been conducted in two domains: the travel domain for the semantic parsing task using the DARPA Communicator data and the Air Travel Information Services (ATIS) data and the bioinformatics domain for the information extraction task using the GENIA corpus. The results demonstrate modest improvements of the performance of the HVS model using discriminative training. In the travel domain, discriminative training of the HVS model gives a relative error reduction rate of 31 percent in F-measure when compared with MLE on the DARPA Communicator data and 9 percent on the ATIS data. In the bioinformatics domain, a relative error reduction rate of 4 percent in F-measure is achieved on the GENIA corpus.

The use of open learning in the training of neurotechnologists:a regional case study

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The education and training of technicians in the construction industry

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The effectiveness of an organisations' equal opportunities policy for the role and training of women, particularly in the fire service

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Special Features of Musical Education in Distant Learning (Training of Intonation Thinking)

The problem of using modern technologies in distant learning of intonation thinking is described in this article. An importance of intonation learning for musician students and the possibilities, provided by World Wide Web and multimedia technologies are the main point of this article.

Some Aspects of the Theme "Information Platform" in the Training of Students in Economic Majors

Information Platform has a major impact on the core activities and development of businesses. In this connection it is necessary for students of economics (the future leaders of such entities) to submit any problems related to the Information Platform, the risks they may pose, and exemplary approach to solve part or all of the problems and minimizing risks. The current issue examines the adaptation of the above problems when presenting them to students in economic majors. To the students are presented generalizations based on long observation of the occurrence and development of Information Platforms to the businesses in Bulgaria in a growing market economy.

A New Approach Model in Post Graduate Training of Local Government Officers

In 2013-15 there was a new type of post graduate training elaborated and piloted in Hungary at the Institute of Executive Training and Continuing Education (VTKI) within the National University of Public Service (NKE). Although the pilot financed by the State Administration Reform Operative Program (ÁROP) had not lacked the previously established attempts to include interactivity in the training, it was the first to observe and apply the actual principles of the European Union 2020 expressed in the threefold criteria of economic growth: smartness, sustainability and inclusiveness. All of them are represented by a pillar of the program like e-learning, class training and field training with the inclusion of local society. According to the objectives of the program there were at least 10 thousand attendees from the civil service sphere set as project indicators, so it has been a large scale training program that took place in 2014 in Hungary. The following article shows the innovations included in this new approach model of post graduate training civil servants.