WorldWideScience

Sample records for network training set

  1. Artificial neural network classification using a minimal training set - Comparison to conventional supervised classification

    Science.gov (United States)

    Hepner, George F.; Logan, Thomas; Ritter, Niles; Bryant, Nevin

    1990-01-01

    Recent research has shown an artificial neural network (ANN) to be capable of pattern recognition and the classification of image data. This paper examines the potential for the application of neural network computing to satellite image processing. A second objective is to provide a preliminary comparison and ANN classification. An artificial neural network can be trained to do land-cover classification of satellite imagery using selected sites representative of each class in a manner similar to conventional supervised classification. One of the major problems associated with recognition and classifications of pattern from remotely sensed data is the time and cost of developing a set of training sites. This reseach compares the use of an ANN back propagation classification procedure with a conventional supervised maximum likelihood classification procedure using a minimal training set. When using a minimal training set, the neural network is able to provide a land-cover classification superior to the classification derived from the conventional classification procedure. This research is the foundation for developing application parameters for further prototyping of software and hardware implementations for artificial neural networks in satellite image and geographic information processing.

  2. Influence of the Training Set Value on the Quality of the Neural Network to Identify Selected Moulding Sand Properties

    Directory of Open Access Journals (Sweden)

    Jakubski J.

    2013-06-01

    Full Text Available Artificial neural networks are one of the modern methods of the production optimisation. An attempt to apply neural networks for controlling the quality of bentonite moulding sands is presented in this paper. This is the assessment method of sands suitability by means of detecting correlations between their individual parameters. This paper presents the next part of the study on usefulness of artificial neural networks to support rebonding of green moulding sand, using chosen properties of moulding sands, which can be determined fast. The effect of changes in the training set quantity on the quality of the network is presented in this article. It has been shown that a small change in the data set would change the quality of the network, and may also make it necessary to change the type of network in order to obtain good results.

  3. Setting up virtual private network

    International Nuclear Information System (INIS)

    Huang Hongmei; Zhang Chengjun

    2003-01-01

    Setting up virtual private network for business enterprise provides a low cost network foundation, increases enterprise's network function and enlarges its private scope. The text introduces virtual private network's principal, privileges and protocols that use in virtual private network. At last, this paper introduces several setting up virtual private network's technologies which based on LAN

  4. Setting up virtual private network

    International Nuclear Information System (INIS)

    Huang Hongmei; Zhang Chengjun

    2003-01-01

    Setting up virtual private network for business enterprise provides a low cost network foundation, increases enterprise network function and enlarges its private scope. This text introduces virtual private network principal, privileges and protocols applied in virtual private network. At last, this paper introduces several setting up virtual private network technologies which is based on LAN

  5. Training Recurrent Networks

    DEFF Research Database (Denmark)

    Pedersen, Morten With

    1997-01-01

    Training recurrent networks is generally believed to be a difficult task. Excessive training times and lack of convergence to an acceptable solution are frequently reported. In this paper we seek to explain the reason for this from a numerical point of view and show how to avoid problems when...... training. In particular we investigate ill-conditioning, the need for and effect of regularization and illustrate the superiority of second-order methods for training...

  6. Controllability of Train Service Network

    Directory of Open Access Journals (Sweden)

    Xuelei Meng

    2015-01-01

    Full Text Available Train service network is a network form of train service plan. The controllability of the train service plan determines the recovery possibility of the train service plan in emergencies. We first build the small-world model for train service network and analyze the scale-free character of it. Then based on the linear network controllability theory, we discuss the LB model adaptability in train service network controllability analysis. The LB model is improved and we construct the train service network and define the connotation of the driver nodes based on the immune propagation and cascading failure in the train service network. An algorithm to search for the driver nodes, turning the train service network into a bipartite graph, is proposed and applied in the train service network. We analyze the controllability of the train service network of China with the method and the results of the computing case prove the feasibility of it.

  7. Hematopoietic cell transplantation: Training challenges and potential opportunities through networking and integration of modern technologies to the practice setting.

    Science.gov (United States)

    Kharfan-Dabaja, Mohamed A; Aljurf, Mahmoud

    2017-12-01

    Hematopoietic cell transplantation (HCT), particularly allogeneic HCT, is a complex and a high-risk procedure requiring expertise to manage potential treatment complications. Published data supports the value of quality management systems in improving post-transplant outcomes; however, there are no universally established, or agreed upon, criteria to assess adequacy of training of physicians, transplant or nontransplant, and supporting staff, among others. It is of paramount importance for transplant centers to identify the needed area(s) of expertise in order to seek appropriate training for their staff. Moreover, transplant physicians need to keep up-to-date with the rapidly occurring advances in the field. Outcomes of patients undergoing HCT are affected by various factors related to patient, disease, procedure, preventative, and supportive strategies, among others. Accordingly, availability of databases is necessary to collect information on these variables and use to benchmark future prospective clinical trials aiming at further improving clinical outcomes. Twinning with leading centers worldwide is helping to not only bridge the survival gap of patients diagnosed with cancer in the developing vis-à-vis the developed world, but eventually closing it. The advent of the World Wide Web and revolution in telecommunication has made access to information more readily available to various sectors including healthcare. Telemedicine is enabling healthcare delivery to remote and underserved geographic areas. In the setting of HCT, ensuring compliance to prescribed therapies and post-transplant surveillance are some areas where implementing telemedicine programs could fulfill an unmet need. Copyright © 2017 King Faisal Specialist Hospital & Research Centre. Published by Elsevier B.V. All rights reserved.

  8. Training a whole-book LSTM-based recognizer with an optimal training set

    Science.gov (United States)

    Soheili, Mohammad Reza; Yousefi, Mohammad Reza; Kabir, Ehsanollah; Stricker, Didier

    2018-04-01

    Despite the recent progress in OCR technologies, whole-book recognition, is still a challenging task, in particular in case of old and historical books, that the unknown font faces or low quality of paper and print contributes to the challenge. Therefore, pre-trained recognizers and generic methods do not usually perform up to required standards, and usually the performance degrades for larger scale recognition tasks, such as of a book. Such reportedly low error-rate methods turn out to require a great deal of manual correction. Generally, such methodologies do not make effective use of concepts such redundancy in whole-book recognition. In this work, we propose to train Long Short Term Memory (LSTM) networks on a minimal training set obtained from the book to be recognized. We show that clustering all the sub-words in the book, and using the sub-word cluster centers as the training set for the LSTM network, we can train models that outperform any identical network that is trained with randomly selected pages of the book. In our experiments, we also show that although the sub-word cluster centers are equivalent to about 8 pages of text for a 101- page book, a LSTM network trained on such a set performs competitively compared to an identical network that is trained on a set of 60 randomly selected pages of the book.

  9. Data Programming: Creating Large Training Sets, Quickly

    Science.gov (United States)

    Ratner, Alexander; De Sa, Christopher; Wu, Sen; Selsam, Daniel; Ré, Christopher

    2018-01-01

    Large labeled training sets are the critical building blocks of supervised learning methods and are key enablers of deep learning techniques. For some applications, creating labeled training sets is the most time-consuming and expensive part of applying machine learning. We therefore propose a paradigm for the programmatic creation of training sets called data programming in which users express weak supervision strategies or domain heuristics as labeling functions, which are programs that label subsets of the data, but that are noisy and may conflict. We show that by explicitly representing this training set labeling process as a generative model, we can “denoise” the generated training set, and establish theoretically that we can recover the parameters of these generative models in a handful of settings. We then show how to modify a discriminative loss function to make it noise-aware, and demonstrate our method over a range of discriminative models including logistic regression and LSTMs. Experimentally, on the 2014 TAC-KBP Slot Filling challenge, we show that data programming would have led to a new winning score, and also show that applying data programming to an LSTM model leads to a TAC-KBP score almost 6 F1 points over a state-of-the-art LSTM baseline (and into second place in the competition). Additionally, in initial user studies we observed that data programming may be an easier way for non-experts to create machine learning models when training data is limited or unavailable. PMID:29872252

  10. Settings in Social Networks : a Measurement Model

    NARCIS (Netherlands)

    Schweinberger, Michael; Snijders, Tom A.B.

    2003-01-01

    A class of statistical models is proposed that aims to recover latent settings structures in social networks. Settings may be regarded as clusters of vertices. The measurement model is based on two assumptions. (1) The observed network is generated by hierarchically nested latent transitive

  11. Applications of neural networks in training science.

    Science.gov (United States)

    Pfeiffer, Mark; Hohmann, Andreas

    2012-04-01

    Training science views itself as an integrated and applied science, developing practical measures founded on scientific method. Therefore, it demands consideration of a wide spectrum of approaches and methods. Especially in the field of competitive sports, research questions are usually located in complex environments, so that mainly field studies are drawn upon to obtain broad external validity. Here, the interrelations between different variables or variable sets are mostly of a nonlinear character. In these cases, methods like neural networks, e.g., the pattern recognizing methods of Self-Organizing Kohonen Feature Maps or similar instruments to identify interactions might be successfully applied to analyze data. Following on from a classification of data analysis methods in training-science research, the aim of the contribution is to give examples of varied sports in which network approaches can be effectually used in training science. First, two examples are given in which neural networks are employed for pattern recognition. While one investigation deals with the detection of sporting talent in swimming, the other is located in game sports research, identifying tactical patterns in team handball. The third and last example shows how an artificial neural network can be used to predict competitive performance in swimming. Copyright © 2011 Elsevier B.V. All rights reserved.

  12. An accelerated training method for back propagation networks

    Science.gov (United States)

    Shelton, Robert O. (Inventor)

    1993-01-01

    The principal objective is to provide a training procedure for a feed forward, back propagation neural network which greatly accelerates the training process. A set of orthogonal singular vectors are determined from the input matrix such that the standard deviations of the projections of the input vectors along these singular vectors, as a set, are substantially maximized, thus providing an optimal means of presenting the input data. Novelty exists in the method of extracting from the set of input data, a set of features which can serve to represent the input data in a simplified manner, thus greatly reducing the time/expense to training the system.

  13. Partially ordered sets in complex networks

    International Nuclear Information System (INIS)

    Xuan Qi; Du Fang; Wu Tiejun

    2010-01-01

    In this paper, a partial-order relation is defined among vertices of a network to describe which vertex is more important than another on its contribution to the connectivity of the network. A maximum linearly ordered subset of vertices is defined as a chain and the chains sharing the same end-vertex are grouped as a family. Through combining the same vertices appearing in different chains, a directed chain graph is obtained. Based on these definitions, a series of new network measurements, such as chain length distribution, family diversity distribution, as well as the centrality of families, are proposed. By studying the partially ordered sets in three kinds of real-world networks, many interesting results are revealed. For instance, the similar approximately power-law chain length distribution may be attributed to a chain-based positive feedback mechanism, i.e. new vertices prefer to participate in longer chains, which can be inferred by combining the notable preferential attachment rule with a well-ordered recommendation manner. Moreover, the relatively large average incoming degree of the chain graphs may indicate an efficient substitution mechanism in these networks. Most of the partially ordered set-based properties cannot be explained by the current well-known scale-free network models; therefore, we are required to propose more appropriate network models in the future.

  14. Autocatalytic sets in a partitioned biochemical network.

    Science.gov (United States)

    Smith, Joshua I; Steel, Mike; Hordijk, Wim

    2014-01-01

    In previous work, RAF theory has been developed as a tool for making theoretical progress on the origin of life question, providing insight into the structure and occurrence of self-sustaining and collectively autocatalytic sets within catalytic polymer networks. We present here an extension in which there are two "independent" polymer sets, where catalysis occurs within and between the sets, but there are no reactions combining polymers from both sets. Such an extension reflects the interaction between nucleic acids and peptides observed in modern cells and proposed forms of early life. We present theoretical work and simulations which suggest that the occurrence of autocatalytic sets is robust to the partitioned structure of the network. We also show that autocatalytic sets remain likely even when the molecules in the system are not polymers, and a low level of inhibition is present. Finally, we present a kinetic extension which assigns a rate to each reaction in the system, and show that identifying autocatalytic sets within such a system is an NP-complete problem. Recent experimental work has challenged the necessity of an RNA world by suggesting that peptide-nucleic acid interactions occurred early in chemical evolution. The present work indicates that such a peptide-RNA world could support the spontaneous development of autocatalytic sets and is thus a feasible alternative worthy of investigation.

  15. Training trajectories by continuous recurrent multilayer networks.

    Science.gov (United States)

    Leistritz, L; Galicki, M; Witte, H; Kochs, E

    2002-01-01

    This paper addresses the problem of training trajectories by means of continuous recurrent neural networks whose feedforward parts are multilayer perceptrons. Such networks can approximate a general nonlinear dynamic system with arbitrary accuracy. The learning process is transformed into an optimal control framework where the weights are the controls to be determined. A training algorithm based upon a variational formulation of Pontryagin's maximum principle is proposed for such networks. Computer examples demonstrating the efficiency of the given approach are also presented.

  16. Supervised learning with restricted training sets: a generating functional analysis

    Energy Technology Data Exchange (ETDEWEB)

    Heimel, J.A.F.; Coolen, A.C.C. [Department of Mathematics, King' s College London, Strand, London (United Kingdom)

    2001-10-26

    We study the dynamics of supervised on-line learning of realizable tasks in feed-forward neural networks. We focus on the regime where the number of examples used for training is proportional to the number of input channels N. Using generating functional techniques from spin glass theory, we are able to average over the composition of the training set and transform the problem for N{yields}{infinity} to an effective single pattern system described completely by the student autocovariance, the student-teacher overlap and the student response function with exact closed equations. Our method applies to arbitrary learning rules, i.e., not necessarily of a gradient-descent type. The resulting exact macroscopic dynamical equations can be integrated without finite-size effects up to any degree of accuracy, but their main value is in providing an exact and simple starting point for analytical approximation schemes. Finally, we show how, in the region of absent anomalous response and using the hypothesis that (as in detailed balance systems) the short-time part of the various operators can be transformed away, one can describe the stationary state of the network successfully by a set of coupled equations involving only four scalar order parameters. (author)

  17. Local Dynamics in Trained Recurrent Neural Networks.

    Science.gov (United States)

    Rivkind, Alexander; Barak, Omri

    2017-06-23

    Learning a task induces connectivity changes in neural circuits, thereby changing their dynamics. To elucidate task-related neural dynamics, we study trained recurrent neural networks. We develop a mean field theory for reservoir computing networks trained to have multiple fixed point attractors. Our main result is that the dynamics of the network's output in the vicinity of attractors is governed by a low-order linear ordinary differential equation. The stability of the resulting equation can be assessed, predicting training success or failure. As a consequence, networks of rectified linear units and of sigmoidal nonlinearities are shown to have diametrically different properties when it comes to learning attractors. Furthermore, a characteristic time constant, which remains finite at the edge of chaos, offers an explanation of the network's output robustness in the presence of variability of the internal neural dynamics. Finally, the proposed theory predicts state-dependent frequency selectivity in the network response.

  18. Local Dynamics in Trained Recurrent Neural Networks

    Science.gov (United States)

    Rivkind, Alexander; Barak, Omri

    2017-06-01

    Learning a task induces connectivity changes in neural circuits, thereby changing their dynamics. To elucidate task-related neural dynamics, we study trained recurrent neural networks. We develop a mean field theory for reservoir computing networks trained to have multiple fixed point attractors. Our main result is that the dynamics of the network's output in the vicinity of attractors is governed by a low-order linear ordinary differential equation. The stability of the resulting equation can be assessed, predicting training success or failure. As a consequence, networks of rectified linear units and of sigmoidal nonlinearities are shown to have diametrically different properties when it comes to learning attractors. Furthermore, a characteristic time constant, which remains finite at the edge of chaos, offers an explanation of the network's output robustness in the presence of variability of the internal neural dynamics. Finally, the proposed theory predicts state-dependent frequency selectivity in the network response.

  19. Training Results and Information Network

    Data.gov (United States)

    US Agency for International Development — TraiNet is USAID's official training data management system that is accessed from a web browser and the entry point for data about training programs and participants...

  20. Limited Effects of Set Shifting Training in Healthy Older Adults

    Directory of Open Access Journals (Sweden)

    Petra Grönholm-Nyman

    2017-03-01

    Full Text Available Our ability to flexibly shift between tasks or task sets declines in older age. As this decline may have adverse effects on everyday life of elderly people, it is of interest to study whether set shifting ability can be trained, and if training effects generalize to other cognitive tasks. Here, we report a randomized controlled trial where healthy older adults trained set shifting with three different set shifting tasks. The training group (n = 17 performed adaptive set shifting training for 5 weeks with three training sessions a week (45 min/session, while the active control group (n = 16 played three different computer games for the same period. Both groups underwent extensive pre- and post-testing and a 1-year follow-up. Compared to the controls, the training group showed significant improvements on the trained tasks. Evidence for near transfer in the training group was very limited, as it was seen only on overall accuracy on an untrained computerized set shifting task. No far transfer to other cognitive functions was observed. One year later, the training group was still better on the trained tasks but the single near transfer effect had vanished. The results suggest that computerized set shifting training in the elderly shows long-lasting effects on the trained tasks but very little benefit in terms of generalization.

  1. Global Optimization for Transport Network Expansion and Signal Setting

    OpenAIRE

    Liu, Haoxiang; Wang, David Z. W.; Yue, Hao

    2015-01-01

    This paper proposes a model to address an urban transport planning problem involving combined network design and signal setting in a saturated network. Conventional transport planning models usually deal with the network design problem and signal setting problem separately. However, the fact that network capacity design and capacity allocation determined by network signal setting combine to govern the transport network performance requires the optimal transport planning to consider the two pr...

  2. Method Accelerates Training Of Some Neural Networks

    Science.gov (United States)

    Shelton, Robert O.

    1992-01-01

    Three-layer networks trained faster provided two conditions are satisfied: numbers of neurons in layers are such that majority of work done in synaptic connections between input and hidden layers, and number of neurons in input layer at least as great as number of training pairs of input and output vectors. Based on modified version of back-propagation method.

  3. Nuclear safety education and training network

    International Nuclear Information System (INIS)

    Bastos, J.; Ulfkjaer, L.

    2004-01-01

    In March 2001, the Secretariat convened an Advisory Group on Education and Training in nuclear safety. The Advisory Group considered structure, scope and means related to the implementation of an IAEA Programme on Education and Training . A strategic plan was agreed and the following outputs were envisaged: 1. A Training Support Programme in nuclear safety, including a standardized and harmonized approach for training developed by the IAEA and in use by Member States. 2. National and regional training centres, established to support sustainable national nuclear safety infrastructures. 3. Training material for use by lecturers and students developed by the IAEA in English and translated to other languages. The implementation of the plan was initiated in 2002 emphasizing the preparation of training materials. In 2003 a pilot project for a network on Education and Training in Asia was initiated

  4. Learning Data Set Influence on Identification Accuracy of Gas Turbine Neural Network Model

    Science.gov (United States)

    Kuznetsov, A. V.; Makaryants, G. M.

    2018-01-01

    There are many gas turbine engine identification researches via dynamic neural network models. It should minimize errors between model and real object during identification process. Questions about training data set processing of neural networks are usually missed. This article presents a study about influence of data set type on gas turbine neural network model accuracy. The identification object is thermodynamic model of micro gas turbine engine. The thermodynamic model input signal is the fuel consumption and output signal is the engine rotor rotation frequency. Four types input signals was used for creating training and testing data sets of dynamic neural network models - step, fast, slow and mixed. Four dynamic neural networks were created based on these types of training data sets. Each neural network was tested via four types test data sets. In the result 16 transition processes from four neural networks and four test data sets from analogous solving results of thermodynamic model were compared. The errors comparison was made between all neural network errors in each test data set. In the comparison result it was shown error value ranges of each test data set. It is shown that error values ranges is small therefore the influence of data set types on identification accuracy is low.

  5. Dynamic training algorithm for dynamic neural networks

    International Nuclear Information System (INIS)

    Tan, Y.; Van Cauwenberghe, A.; Liu, Z.

    1996-01-01

    The widely used backpropagation algorithm for training neural networks based on the gradient descent has a significant drawback of slow convergence. A Gauss-Newton method based recursive least squares (RLS) type algorithm with dynamic error backpropagation is presented to speed-up the learning procedure of neural networks with local recurrent terms. Finally, simulation examples concerning the applications of the RLS type algorithm to identification of nonlinear processes using a local recurrent neural network are also included in this paper

  6. NATO Education and Training Network

    Science.gov (United States)

    2012-02-01

    Federated Battle Laboratories Network (CFBLNet) .............................................. 15  5.1  History ...CFBLNet countries, NATO nations and Partners perspective (January 2009) 5.1 History In April 1999, the US made a proposal to the NATO C3 Board to...permanent subscription provides standard access to the: • CFBLNet Blackbone ( IPv4 (IPv6) transport network) • CFBLNet CUE (Unclassified Enclave all

  7. Deliberate Practice of Creativity Training Set Series - A Creativity Training Material for Education (work in progress)

    DEFF Research Database (Denmark)

    Byrge, Christian

    2018-01-01

    Five training sets including 450 unique thinking direction cards and 120 exercise cards. Designed for Educational Purposes.......Five training sets including 450 unique thinking direction cards and 120 exercise cards. Designed for Educational Purposes....

  8. Training Deep Spiking Neural Networks Using Backpropagation.

    Science.gov (United States)

    Lee, Jun Haeng; Delbruck, Tobi; Pfeiffer, Michael

    2016-01-01

    Deep spiking neural networks (SNNs) hold the potential for improving the latency and energy efficiency of deep neural networks through data-driven event-based computation. However, training such networks is difficult due to the non-differentiable nature of spike events. In this paper, we introduce a novel technique, which treats the membrane potentials of spiking neurons as differentiable signals, where discontinuities at spike times are considered as noise. This enables an error backpropagation mechanism for deep SNNs that follows the same principles as in conventional deep networks, but works directly on spike signals and membrane potentials. Compared with previous methods relying on indirect training and conversion, our technique has the potential to capture the statistics of spikes more precisely. We evaluate the proposed framework on artificially generated events from the original MNIST handwritten digit benchmark, and also on the N-MNIST benchmark recorded with an event-based dynamic vision sensor, in which the proposed method reduces the error rate by a factor of more than three compared to the best previous SNN, and also achieves a higher accuracy than a conventional convolutional neural network (CNN) trained and tested on the same data. We demonstrate in the context of the MNIST task that thanks to their event-driven operation, deep SNNs (both fully connected and convolutional) trained with our method achieve accuracy equivalent with conventional neural networks. In the N-MNIST example, equivalent accuracy is achieved with about five times fewer computational operations.

  9. Solar Training Network and Solar Ready Vets

    Energy Technology Data Exchange (ETDEWEB)

    Dalstrom, Tenley Ann

    2016-09-14

    In 2016, the White House announced the Solar Ready Vets program, funded under DOE's SunShot initiative would be administered by The Solar Foundation to connect transitioning military personnel to solar training and employment as they separate from service. This presentation is geared to informing and recruiting employer partners for the Solar Ready Vets program, and the Solar Training Network. It describes the programs, and the benefits to employers that choose to connect to the programs.

  10. Challenges in Food Scientist Training in a global setting

    Directory of Open Access Journals (Sweden)

    Andreas Höhl

    2012-10-01

    Full Text Available Normal 0 21 false false false EN-GB X-NONE X-NONE Education and training were an integral part of the MoniQA Network of Excellence. Embedded in the "Spreading of excellence programme", Work Package 9 (Joint education programmes and training tools was responsible for establishing a joint training programme for food safety and quality within and beyond the network. So-called `MoniQA Food Scientist Training' (MoniQA FST was offered to provide technical knowledge on different levels and research management skills as well. Training needs for different regions as well as for different target groups (scientists, industry personnel, authorities had to be considered as well as developing strong collaboration links between network partners and related projects. Beside face-to-face workshops e-learning modules have been developed and web seminars were organized. In order to achieve high quality training, a quality assurance concept has been implemented. It turned out that these types of training are of high value in terms of bringing together scientists from different regions and cultures of the globe, involving highly qualified trainers as basis for a sustainable network in the future.

  11. Diarrhea Management Training in Early Childhood Settings.

    Science.gov (United States)

    Winnail, Scott D.; Artz, Lynn M.; Geiger, Brian F.; Petri, Cynthia J.; Bailey, Rebecca; Mason, J.W.

    2001-01-01

    Addresses the health of young children and how to safely and effectively care for children with diarrhea in the home and in early child care settings. Discusses specific intervention and program activities, including specially designed materials for mixing homemade oral rehydration usage. (Author/SD)

  12. Negative Interpersonal Interactions in Student Training Settings

    Science.gov (United States)

    Ferris, Patricia A.; Kline, Theresa J. B.

    2009-01-01

    Studies demonstrate that negative interpersonal interaction(s) (NII) such as bullying are frequent and harmful to individuals in workplace and higher education student settings. Nevertheless, it is unclear whether the degree of perceived severity of NII varies by the status of the actor. The present study explored the moderating effect of actor…

  13. Modelling electric trains energy consumption using Neural Networks

    Energy Technology Data Exchange (ETDEWEB)

    Martinez Fernandez, P.; Garcia Roman, C.; Insa Franco, R.

    2016-07-01

    Nowadays there is an evident concern regarding the efficiency and sustainability of the transport sector due to both the threat of climate change and the current financial crisis. This concern explains the growth of railways over the last years as they present an inherent efficiency compared to other transport means. However, in order to further expand their role, it is necessary to optimise their energy consumption so as to increase their competitiveness. Improving railways energy efficiency requires both reliable data and modelling tools that will allow the study of different variables and alternatives. With this need in mind, this paper presents the development of consumption models based on neural networks that calculate the energy consumption of electric trains. These networks have been trained based on an extensive set of consumption data measured in line 1 of the Valencia Metro Network. Once trained, the neural networks provide a reliable estimation of the vehicles consumption along a specific route when fed with input data such as train speed, acceleration or track longitudinal slope. These networks represent a useful modelling tool that may allow a deeper study of railway lines in terms of energy expenditure with the objective of reducing the costs and environmental impact associated to railways. (Author)

  14. THE EXPERIENCE OF NETWORKING POSTGRADUATE TRAINING PROGRAMMES

    Directory of Open Access Journals (Sweden)

    E. A. Teplyashina

    2017-01-01

    Full Text Available Introduction. Present scientific and innovative education programmes focus on the development of applied research in priority areas of industry, cross-industry and regional development. Implementation of such programs is most effective along with the network organization of the process of training. In accordance with the Federal Law on Education in the Russian Federation, this model of networking as «educational institution – educational organization» is a very convenient form of academic mobility realisation.The aim of the present paper is to analyse the model of interaction of the networking postgraduate training programmes at Krasnoyarsk State Medical University named after Prof. V. F. Voino-Yasenetsky and Medical School of Niigata University (Japan.Methodology and research methods involve theoretical analysis of the scientific outcomes of implementing a networking postgraduate training programme, comparative-teaching method, generalization, and pedagogical modeling.Results. The mechanisms of developing the partnership between universities of different countries are detailed. The experience of network international education in a postgraduate study is presented. The presented experience allowed the authors to develop an integrated strategy of cooperation with foreign colleagues in this direction. The advantages and problems of use of a network form of training of academic and teaching staff in a postgraduate school are revealed. The proposals and recommendations on optimization and harmonization of the purposes, tasks and programs of network interaction of the educational organizations are formulated.Practical significance. The proposed materials of the publication can form the base for creation and designing of an effective system of postgraduate education and competitiveness growth of the Russian universities. 

  15. Revisiting Social Network Utilization by Physicians-in-Training.

    Science.gov (United States)

    Black, Erik W; Thompson, Lindsay A; Duff, W Patrick; Dawson, Kara; Saliba, Heidi; Black, Nicole M Paradise

    2010-06-01

    To measure and compare the frequency and content of online social networking among 2 cohorts of medical students and residents (2007 and 2009). Using the online social networking application Facebook, we evaluated social networking profiles for 2 cohorts of medical students (n  =  528) and residents (n  =  712) at the University of Florida in Gainesville. Objective measures included existence of a profile, whether it was made private, and whether any personally identifiable information was included. Subjective outcomes included photographic content, affiliated social groups, and personal information not generally disclosed in a doctor-patient encounter. We compared our results to our previously published and reported data from 2007. Social networking continues to be common amongst physicians-in-training, with 39.8% of residents and 69.5% of medical students maintaining Facebook accounts. Residents' participation significantly increased (P privacy settings (P privacy and the expansive and impersonal networks of online "friends" who may view profiles.

  16. Psychotherapeutic training in an institutional setting.

    Science.gov (United States)

    Bettschart, W

    1990-01-01

    Many child and adolescent psychotherapists are asked to work in specialized institutions (where the children are either interns or externs), with children presenting behaviour problems, learning difficulties, mental handicap or important psychosocial problems. They learn through diverse treatment forms, or parent accompaniment during the child's treatment (bifocal or conjoint treatment more or less regular therapeutic sessions with both parents and children, etc.). The treatment of children within an institution makes the treatment modalities and technique more complicated. The psychotherapist must have a perfect knowledge of the specific environment of the child, and keep in mind the desires and requests of the direction and the people who work directly with the child (teachers, specialized teachers, etc.). How can the problems brought up by the rivalry between the institution and the psychotherapist be canalized: length of therapy, merits (how did the pedagogical intervention help, recognition of a specific action or of work done in conjunction with the educative action)? If these facts are not recognized, the treatment will often be interrupted and the psychotherapist may be excluded from the institution. This will be avoided by ensuring further training of the therapist.

  17. Securing Mobile Networks in an Operational Setting

    Science.gov (United States)

    Ivancic, William D.; Stewart, David H.; Bell, Terry L.; Paulsen, Phillip E.; Shell, Dan

    2004-01-01

    This paper describes a network demonstration and three month field trial of mobile networking using mobile-IPv4. The network was implemented as part of the US Coast Guard operational network which is a ".mil" network and requires stringent levels of security. The initial demonstrations took place in November 2002 and a three month field trial took place from July through September of 2003. The mobile network utilized encryptors capable of NSA-approved Type 1 algorithms, mobile router from Cisco Systems and 802.11 and satellite wireless links. This paper also describes a conceptual architecture for wide-scale deployment of secure mobile networking in operational environments where both private and public infrastructure is used. Additional issues presented include link costs, placement of encryptors and running routing protocols over layer-3 encryption devices.

  18. NGFATOS : national guidelines for first aid training in occupational settings

    Science.gov (United States)

    2002-05-01

    NGFATOS is a course development guideline containing the essential elements of what can be considered safe, helpful and effective first aid training in occupational settings. This guide is intended for use by first aid program developers, institution...

  19. Adaptive training of feedforward neural networks by Kalman filtering

    International Nuclear Information System (INIS)

    Ciftcioglu, Oe.

    1995-02-01

    Adaptive training of feedforward neural networks by Kalman filtering is described. Adaptive training is particularly important in estimation by neural network in real-time environmental where the trained network is used for system estimation while the network is further trained by means of the information provided by the experienced/exercised ongoing operation. As result of this, neural network adapts itself to a changing environment to perform its mission without recourse to re-training. The performance of the training method is demonstrated by means of actual process signals from a nuclear power plant. (orig.)

  20. Character Recognition Using Genetically Trained Neural Networks

    Energy Technology Data Exchange (ETDEWEB)

    Diniz, C.; Stantz, K.M.; Trahan, M.W.; Wagner, J.S.

    1998-10-01

    Computationally intelligent recognition of characters and symbols addresses a wide range of applications including foreign language translation and chemical formula identification. The combination of intelligent learning and optimization algorithms with layered neural structures offers powerful techniques for character recognition. These techniques were originally developed by Sandia National Laboratories for pattern and spectral analysis; however, their ability to optimize vast amounts of data make them ideal for character recognition. An adaptation of the Neural Network Designer soflsvare allows the user to create a neural network (NN_) trained by a genetic algorithm (GA) that correctly identifies multiple distinct characters. The initial successfid recognition of standard capital letters can be expanded to include chemical and mathematical symbols and alphabets of foreign languages, especially Arabic and Chinese. The FIN model constructed for this project uses a three layer feed-forward architecture. To facilitate the input of characters and symbols, a graphic user interface (GUI) has been developed to convert the traditional representation of each character or symbol to a bitmap. The 8 x 8 bitmap representations used for these tests are mapped onto the input nodes of the feed-forward neural network (FFNN) in a one-to-one correspondence. The input nodes feed forward into a hidden layer, and the hidden layer feeds into five output nodes correlated to possible character outcomes. During the training period the GA optimizes the weights of the NN until it can successfully recognize distinct characters. Systematic deviations from the base design test the network's range of applicability. Increasing capacity, the number of letters to be recognized, requires a nonlinear increase in the number of hidden layer neurodes. Optimal character recognition performance necessitates a minimum threshold for the number of cases when genetically training the net. And, the

  1. Distributed computing methodology for training neural networks in an image-guided diagnostic application.

    Science.gov (United States)

    Plagianakos, V P; Magoulas, G D; Vrahatis, M N

    2006-03-01

    Distributed computing is a process through which a set of computers connected by a network is used collectively to solve a single problem. In this paper, we propose a distributed computing methodology for training neural networks for the detection of lesions in colonoscopy. Our approach is based on partitioning the training set across multiple processors using a parallel virtual machine. In this way, interconnected computers of varied architectures can be used for the distributed evaluation of the error function and gradient values, and, thus, training neural networks utilizing various learning methods. The proposed methodology has large granularity and low synchronization, and has been implemented and tested. Our results indicate that the parallel virtual machine implementation of the training algorithms developed leads to considerable speedup, especially when large network architectures and training sets are used.

  2. Permitted and forbidden sets in symmetric threshold-linear networks.

    Science.gov (United States)

    Hahnloser, Richard H R; Seung, H Sebastian; Slotine, Jean-Jacques

    2003-03-01

    The richness and complexity of recurrent cortical circuits is an inexhaustible source of inspiration for thinking about high-level biological computation. In past theoretical studies, constraints on the synaptic connection patterns of threshold-linear networks were found that guaranteed bounded network dynamics, convergence to attractive fixed points, and multistability, all fundamental aspects of cortical information processing. However, these conditions were only sufficient, and it remained unclear which were the minimal (necessary) conditions for convergence and multistability. We show that symmetric threshold-linear networks converge to a set of attractive fixed points if and only if the network matrix is copositive. Furthermore, the set of attractive fixed points is nonconnected (the network is multiattractive) if and only if the network matrix is not positive semidefinite. There are permitted sets of neurons that can be coactive at a stable steady state and forbidden sets that cannot. Permitted sets are clustered in the sense that subsets of permitted sets are permitted and supersets of forbidden sets are forbidden. By viewing permitted sets as memories stored in the synaptic connections, we provide a formulation of long-term memory that is more general than the traditional perspective of fixed-point attractor networks. There is a close correspondence between threshold-linear networks and networks defined by the generalized Lotka-Volterra equations.

  3. EHV network operation, maintenance, organization and training

    Energy Technology Data Exchange (ETDEWEB)

    Gravier, J P [Electricite de France (EDF), 75 - Paris (France)

    1994-12-31

    The service interruptions of electricity have an ever increasing social and industrial impact, it is thus fundamental to operate the network to its best level of performances. To face these changing conditions, Electricite de France has consequently adapted its strategy to improve its organization for maintenance and operation, clarify the operation procedures and give further training to the staff. This work presents the above mentioned issues. (author) 2 figs.

  4. Consistently Trained Artificial Neural Network for Automatic Ship Berthing Control

    Directory of Open Access Journals (Sweden)

    Y.A. Ahmed

    2015-09-01

    Full Text Available In this paper, consistently trained Artificial Neural Network controller for automatic ship berthing is discussed. Minimum time course changing manoeuvre is utilised to ensure such consistency and a new concept named ‘virtual window’ is introduced. Such consistent teaching data are then used to train two separate multi-layered feed forward neural networks for command rudder and propeller revolution output. After proper training, several known and unknown conditions are tested to judge the effectiveness of the proposed controller using Monte Carlo simulations. After getting acceptable percentages of success, the trained networks are implemented for the free running experiment system to judge the network’s real time response for Esso Osaka 3-m model ship. The network’s behaviour during such experiments is also investigated for possible effect of initial conditions as well as wind disturbances. Moreover, since the final goal point of the proposed controller is set at some distance from the actual pier to ensure safety, therefore a study on automatic tug assistance is also discussed for the final alignment of the ship with actual pier.

  5. Novel maximum-margin training algorithms for supervised neural networks.

    Science.gov (United States)

    Ludwig, Oswaldo; Nunes, Urbano

    2010-06-01

    This paper proposes three novel training methods, two of them based on the backpropagation approach and a third one based on information theory for multilayer perceptron (MLP) binary classifiers. Both backpropagation methods are based on the maximal-margin (MM) principle. The first one, based on the gradient descent with adaptive learning rate algorithm (GDX) and named maximum-margin GDX (MMGDX), directly increases the margin of the MLP output-layer hyperplane. The proposed method jointly optimizes both MLP layers in a single process, backpropagating the gradient of an MM-based objective function, through the output and hidden layers, in order to create a hidden-layer space that enables a higher margin for the output-layer hyperplane, avoiding the testing of many arbitrary kernels, as occurs in case of support vector machine (SVM) training. The proposed MM-based objective function aims to stretch out the margin to its limit. An objective function based on Lp-norm is also proposed in order to take into account the idea of support vectors, however, overcoming the complexity involved in solving a constrained optimization problem, usually in SVM training. In fact, all the training methods proposed in this paper have time and space complexities O(N) while usual SVM training methods have time complexity O(N (3)) and space complexity O(N (2)) , where N is the training-data-set size. The second approach, named minimization of interclass interference (MICI), has an objective function inspired on the Fisher discriminant analysis. Such algorithm aims to create an MLP hidden output where the patterns have a desirable statistical distribution. In both training methods, the maximum area under ROC curve (AUC) is applied as stop criterion. The third approach offers a robust training framework able to take the best of each proposed training method. The main idea is to compose a neural model by using neurons extracted from three other neural networks, each one previously trained by

  6. Edge union of networks on the same vertex set

    International Nuclear Information System (INIS)

    Loe, Chuan Wen; Jensen, Henrik Jeldtoft

    2013-01-01

    Random network generators such as Erdős–Rényi, Watts–Strogatz and Barabási–Albert models are used as models to study real-world networks. Let G 1 (V, E 1 ) and G 2 (V, E 2 ) be two such networks on the same vertex set V. This paper studies the degree distribution and clustering coefficient of the resultant networks, G(V, E 1 ∪E 2 ). (paper)

  7. Edge union of networks on the same vertex set

    Science.gov (United States)

    Loe, Chuan Wen; Jeldtoft Jensen, Henrik

    2013-06-01

    Random network generators such as Erdős-Rényi, Watts-Strogatz and Barabási-Albert models are used as models to study real-world networks. Let G1(V, E1) and G2(V, E2) be two such networks on the same vertex set V. This paper studies the degree distribution and clustering coefficient of the resultant networks, G(V, E1∪E2).

  8. Exploring empowerment in settings: mapping distributions of network power.

    Science.gov (United States)

    Neal, Jennifer Watling

    2014-06-01

    This paper brings together two trends in the empowerment literature-understanding empowerment in settings and understanding empowerment as relational-by examining what makes settings empowering from a social network perspective. Specifically, extending Neal and Neal's (Am J Community Psychol 48(3/4):157-167, 2011) conception of network power, an empowering setting is defined as one in which (1) actors have existing relationships that allow for the exchange of resources and (2) the distribution of network power among actors in the setting is roughly equal. The paper includes a description of how researchers can examine distributions of network power in settings. Next, this process is illustrated in both an abstract example and using empirical data on early adolescents' peer relationships in urban classrooms. Finally, implications for theory, methods, and intervention related to understanding empowering settings are explored.

  9. A comparison of Landsat point and rectangular field training sets for land-use classification

    Science.gov (United States)

    Tom, C. H.; Miller, L. D.

    1984-01-01

    Rectangular training fields of homogeneous spectroreflectance are commonly used in supervised pattern recognition efforts. Trial image classification with manually selected training sets gives irregular and misleading results due to statistical bias. A self-verifying, grid-sampled training point approach is proposed as a more statistically valid feature extraction technique. A systematic pixel sampling network of every ninth row and ninth column efficiently replaced the full image scene with smaller statistical vectors which preserved the necessary characteristics for classification. The composite second- and third-order average classification accuracy of 50.1 percent for 331,776 pixels in the full image substantially agreed with the 51 percent value predicted by the grid-sampled, 4,100-point training set.

  10. On Training Bi-directional Neural Network Language Model with Noise Contrastive Estimation

    OpenAIRE

    He, Tianxing; Zhang, Yu; Droppo, Jasha; Yu, Kai

    2016-01-01

    We propose to train bi-directional neural network language model(NNLM) with noise contrastive estimation(NCE). Experiments are conducted on a rescore task on the PTB data set. It is shown that NCE-trained bi-directional NNLM outperformed the one trained by conventional maximum likelihood training. But still(regretfully), it did not out-perform the baseline uni-directional NNLM.

  11. Utilizing Maximal Independent Sets as Dominating Sets in Scale-Free Networks

    Science.gov (United States)

    Derzsy, N.; Molnar, F., Jr.; Szymanski, B. K.; Korniss, G.

    Dominating sets provide key solution to various critical problems in networked systems, such as detecting, monitoring, or controlling the behavior of nodes. Motivated by graph theory literature [Erdos, Israel J. Math. 4, 233 (1966)], we studied maximal independent sets (MIS) as dominating sets in scale-free networks. We investigated the scaling behavior of the size of MIS in artificial scale-free networks with respect to multiple topological properties (size, average degree, power-law exponent, assortativity), evaluated its resilience to network damage resulting from random failure or targeted attack [Molnar et al., Sci. Rep. 5, 8321 (2015)], and compared its efficiency to previously proposed dominating set selection strategies. We showed that, despite its small set size, MIS provides very high resilience against network damage. Using extensive numerical analysis on both synthetic and real-world (social, biological, technological) network samples, we demonstrate that our method effectively satisfies four essential requirements of dominating sets for their practical applicability on large-scale real-world systems: 1.) small set size, 2.) minimal network information required for their construction scheme, 3.) fast and easy computational implementation, and 4.) resiliency to network damage. Supported by DARPA, DTRA, and NSF.

  12. Peer-Assisted Learning in the Athletic Training Clinical Setting

    Science.gov (United States)

    Henning, Jolene M; Weidner, Thomas G; Jones, James

    2006-01-01

    Context: Athletic training educators often anecdotally suggest that athletic training students enhance their learning by teaching their peers. However, peer-assisted learning (PAL) has not been examined within athletic training education in order to provide evidence for its current use or as a pedagogic tool. Objective: To describe the prevalence of PAL in athletic training clinical education and to identify students' perceptions of PAL. Design: Descriptive. Setting: “The Athletic Training Student Seminar” at the National Athletic Trainers' Association 2002 Annual Meeting and Clinical Symposia. Patients or Other Participants: A convenience sample of 138 entry-level male and female athletic training students. Main Outcome Measure(s): Students' perceptions regarding the prevalence and benefits of and preferences for PAL were measured using the Athletic Training Peer-Assisted Learning Assessment Survey. The Survey is a self-report tool with 4 items regarding the prevalence of PAL and 7 items regarding perceived benefits and preferences. Results: A total of 66% of participants practiced a moderate to large amount of their clinical skills with other athletic training students. Sixty percent of students reported feeling less anxious when performing clinical skills on patients in front of other athletic training students than in front of their clinical instructors. Chi-square analysis revealed that 91% of students enrolled in Commission on Accreditation of Allied Health Education Programs–accredited athletic training education programs learned a minimal to small amount of clinical skills from their peers compared with 65% of students in Joint Review Committee on Educational Programs in Athletic Training–candidacy schools (χ2 3 = 14.57, P < .01). Multiple analysis of variance revealed significant interactions between sex and academic level on several items regarding benefits and preferences. Conclusions: According to athletic training students, PAL is occurring in

  13. An Improved Walk Model for Train Movement on Railway Network

    International Nuclear Information System (INIS)

    Li Keping; Mao Bohua; Gao Ziyou

    2009-01-01

    In this paper, we propose an improved walk model for simulating the train movement on railway network. In the proposed method, walkers represent trains. The improved walk model is a kind of the network-based simulation analysis model. Using some management rules for walker movement, walker can dynamically determine its departure and arrival times at stations. In order to test the proposed method, we simulate the train movement on a part of railway network. The numerical simulation and analytical results demonstrate that the improved model is an effective tool for simulating the train movement on railway network. Moreover, it can well capture the characteristic behaviors of train scheduling in railway traffic. (general)

  14. Routing Trains Through Railway Junctions: A New Set Packing Approach

    DEFF Research Database (Denmark)

    Lusby, Richard; Larsen, Jesper; Ryan, David

    how the problem can be formulated as a set packing model. To exploit the structure of the problem we present a solution procedure which entails solving the dual of this formulation through the dynamic addition of violated cuts (primal variables). A discussion of the variable (train path) generation...

  15. Identifying Learning Preferences in Vocational Education and Training Classroom Settings

    Science.gov (United States)

    Smith, Peter J.

    2006-01-01

    This research was designed to assess whether teachers and trainers of vocational learners noted and valued differences in individual learning preferences and, if so, how those differences were observed in natural classroom, workshop or other formal learning settings. Data were collected from six vocational education and training (VET) learning…

  16. Picture this: Managed change and resistance in business network settings

    DEFF Research Database (Denmark)

    Kragh, Hanne; Andersen, Poul Houman

    2009-01-01

    This paper discusses change management in networks. The literature on business networks tends to downplay the role of managerial initiative in network change. The change management literature addresses such initiative, but with its single-firm perspective it overlooks the interdependence of network...... actors. In exploring the void between these two streams of literature, we deploy the concept of network pictures to discuss managed change in network settings. We analyze a change project from the furniture industry and address the consequences of attempting to manage change activities in a network...... context characterized by limited managerial authority over these activities. Our analysis suggests that change efforts unfold as a negotiated process during which the change project is re-negotiated to fit the multiple actor constituencies. The degree of overlap in the co-existing network pictures...

  17. Shakeout: A New Approach to Regularized Deep Neural Network Training.

    Science.gov (United States)

    Kang, Guoliang; Li, Jun; Tao, Dacheng

    2018-05-01

    Recent years have witnessed the success of deep neural networks in dealing with a plenty of practical problems. Dropout has played an essential role in many successful deep neural networks, by inducing regularization in the model training. In this paper, we present a new regularized training approach: Shakeout. Instead of randomly discarding units as Dropout does at the training stage, Shakeout randomly chooses to enhance or reverse each unit's contribution to the next layer. This minor modification of Dropout has the statistical trait: the regularizer induced by Shakeout adaptively combines , and regularization terms. Our classification experiments with representative deep architectures on image datasets MNIST, CIFAR-10 and ImageNet show that Shakeout deals with over-fitting effectively and outperforms Dropout. We empirically demonstrate that Shakeout leads to sparser weights under both unsupervised and supervised settings. Shakeout also leads to the grouping effect of the input units in a layer. Considering the weights in reflecting the importance of connections, Shakeout is superior to Dropout, which is valuable for the deep model compression. Moreover, we demonstrate that Shakeout can effectively reduce the instability of the training process of the deep architecture.

  18. Artificial Neural Network with Hardware Training and Hardware Refresh

    Science.gov (United States)

    Duong, Tuan A. (Inventor)

    2003-01-01

    A neural network circuit is provided having a plurality of circuits capable of charge storage. Also provided is a plurality of circuits each coupled to at least one of the plurality of charge storage circuits and constructed to generate an output in accordance with a neuron transfer function. Each of a plurality of circuits is coupled to one of the plurality of neuron transfer function circuits and constructed to generate a derivative of the output. A weight update circuit updates the charge storage circuits based upon output from the plurality of transfer function circuits and output from the plurality of derivative circuits. In preferred embodiments, separate training and validation networks share the same set of charge storage circuits and may operate concurrently. The validation network has a separate transfer function circuits each being coupled to the charge storage circuits so as to replicate the training network s coupling of the plurality of charge storage to the plurality of transfer function circuits. The plurality of transfer function circuits may be constructed each having a transconductance amplifier providing differential currents combined to provide an output in accordance with a transfer function. The derivative circuits may have a circuit constructed to generate a biased differential currents combined so as to provide the derivative of the transfer function.

  19. Physicists set new record for network data transfer

    CERN Multimedia

    2007-01-01

    "An international team of physicists, computer scientists, and network engineers joined forces to set new records for sustained data transfer between storage systems durint the SuperComputing 2006 (SC06) Bandwidth Challenge (BWC). (3 pages)

  20. Connected Dominating Set Based Topology Control in Wireless Sensor Networks

    Science.gov (United States)

    He, Jing

    2012-01-01

    Wireless Sensor Networks (WSNs) are now widely used for monitoring and controlling of systems where human intervention is not desirable or possible. Connected Dominating Sets (CDSs) based topology control in WSNs is one kind of hierarchical method to ensure sufficient coverage while reducing redundant connections in a relatively crowded network.…

  1. Introduction to the EC's Marie Curie Initial Training Network (MC-ITN) project: Particle Training Network for European Radiotherapy (PARTNER).

    Science.gov (United States)

    Dosanjh, Manjit; Magrin, Giulio

    2013-07-01

    PARTNER (Particle Training Network for European Radiotherapy) is a project funded by the European Commission's Marie Curie-ITN funding scheme through the ENLIGHT Platform for 5.6 million Euro. PARTNER has brought together academic institutes, research centres and leading European companies, focusing in particular on a specialized radiotherapy (RT) called hadron therapy (HT), interchangeably referred to as particle therapy (PT). The ultimate goal of HT is to deliver more effective treatment to cancer patients leading to major improvement in the health of citizens. In Europe, several hundred million Euro have been invested, since the beginning of this century, in PT. In this decade, the use of HT is rapidly growing across Europe, and there is an urgent need for qualified researchers from a range of disciplines to work on its translational research. In response to this need, the European community of HT, and in particular 10 leading academic institutes, research centres, companies and small and medium-sized enterprises, joined together to form the PARTNER consortium. All partners have international reputations in the diverse but complementary fields associated with PT: clinical, radiobiological and technological. Thus the network incorporates a unique set of competencies, expertise, infrastructures and training possibilities. This paper describes the status and needs of PT research in Europe, the importance of and challenges associated with the creation of a training network, the objectives, the initial results, and the expected long-term benefits of the PARTNER initiative.

  2. Introduction to the EC's marie curie initial training network (MC-ITN) project. Particle training network for European radiotherapy (PARTNER)

    International Nuclear Information System (INIS)

    Dosanjh, Manjit; Magrin, Giulio

    2013-01-01

    PARTNER (Particle Training Network for European Radiotherapy) is a project funded by the European Commission's Marie Curie-ITN funding scheme through the ENLIGHT Platform for 5.6 million Euro. PARTNER has brought together academic institutes, research centres and leading European companies, focusing in particular on a specialized radiotherapy (RT) called hadron therapy (HT), interchangeably referred to as particle therapy (PT). The ultimate goal of HT is to deliver more effective treatment to cancer patients leading to major improvement in the health of citizens. In Europe, several hundred million Euro have been invested, since the beginning of this century, in PT. In this decade, the use of HT is rapidly growing across Europe, and there is an urgent need for qualified researchers from a range of disciplines to work on its translational research. In response to this need, the European community of HT, and in particular 10 leading academic institutes, research centres, companies and small and medium-sized enterprises, joined together to form the PARTNER consortium. All partners have international reputations in the diverse but complementary fields associated with PT: clinical, radiobiological and technological. Thus the network incorporates a unique set of competencies, expertise, infrastructures and training possibilities. This paper describes the status and needs of PT research in Europe, the importance of and challenges associated with the creation of a training network, the objectives, the initial results, and the expected long-term benefits of the PARTNER initiative. (author)

  3. Training and validation of the ATLAS pixel clustering neural networks

    CERN Document Server

    The ATLAS collaboration

    2018-01-01

    The high centre-of-mass energy of the LHC gives rise to dense environments, such as the core of high-pT jets, in which the charge clusters left by ionising particles in the silicon sensors of the pixel detector can merge, compromising the tracking and vertexing efficiency. To recover optimal performance, a neural network-based approach is used to separate clusters originating from single and multiple particles and to estimate all hit positions within clusters. This note presents the training strategy employed and a set of benchmark performance measurements on a Monte Carlo sample of high-pT dijet events.

  4. Training set optimization under population structure in genomic selection.

    Science.gov (United States)

    Isidro, Julio; Jannink, Jean-Luc; Akdemir, Deniz; Poland, Jesse; Heslot, Nicolas; Sorrells, Mark E

    2015-01-01

    Population structure must be evaluated before optimization of the training set population. Maximizing the phenotypic variance captured by the training set is important for optimal performance. The optimization of the training set (TRS) in genomic selection has received much interest in both animal and plant breeding, because it is critical to the accuracy of the prediction models. In this study, five different TRS sampling algorithms, stratified sampling, mean of the coefficient of determination (CDmean), mean of predictor error variance (PEVmean), stratified CDmean (StratCDmean) and random sampling, were evaluated for prediction accuracy in the presence of different levels of population structure. In the presence of population structure, the most phenotypic variation captured by a sampling method in the TRS is desirable. The wheat dataset showed mild population structure, and CDmean and stratified CDmean methods showed the highest accuracies for all the traits except for test weight and heading date. The rice dataset had strong population structure and the approach based on stratified sampling showed the highest accuracies for all traits. In general, CDmean minimized the relationship between genotypes in the TRS, maximizing the relationship between TRS and the test set. This makes it suitable as an optimization criterion for long-term selection. Our results indicated that the best selection criterion used to optimize the TRS seems to depend on the interaction of trait architecture and population structure.

  5. Parallelization of Neural Network Training for NLP with Hogwild!

    Directory of Open Access Journals (Sweden)

    Deyringer Valentin

    2017-10-01

    Full Text Available Neural Networks are prevalent in todays NLP research. Despite their success for different tasks, training time is relatively long. We use Hogwild! to counteract this phenomenon and show that it is a suitable method to speed up training Neural Networks of different architectures and complexity. For POS tagging and translation we report considerable speedups of training, especially for the latter. We show that Hogwild! can be an important tool for training complex NLP architectures.

  6. Designing Serious Games for getting transferable skills in training settings

    Directory of Open Access Journals (Sweden)

    Félix Buendía-García

    2014-02-01

    Full Text Available Nowadays, serious games are present in almost every educational context. The current work deals with the design of serious games oriented towards getting transferable skills in different kinds of training settings. These games can be a valuable way of engaging citizens and workers in the learning process by means of metaphors or similar mechanisms close to their user experience. They also contain an encouragement factor to uptake generic job competencies. An approach is proposed to develop this type of game by mixing traditional design steps with an instructional strategy to provide structured learning bites in training settings. Several game prototypes have been developed to test this approach in the context of courses for public employees. The obtained outcomes reveal the wider possibilities of serious games as educational resources, as well as the use of game achievements to evaluate the acquisition of transferable skills.

  7. Designing a Pattern Recognition Neural Network with a Reject Output and Many Sets of Weights and Biases

    OpenAIRE

    Dung, Le; Mizukawa, Makoto

    2008-01-01

    Adding the reject output to the pattern recognition neural network is an approach to help the neural network can classify almost all patterns of a training data set by using many sets of weights and biases, even if the neural network is small. With a smaller number of neurons, we can implement the neural network on a hardware-based platform more easily and also reduce the response time of it. With the reject output the neural network can produce not only right or wrong results but also reject...

  8. Towards dropout training for convolutional neural networks.

    Science.gov (United States)

    Wu, Haibing; Gu, Xiaodong

    2015-11-01

    Recently, dropout has seen increasing use in deep learning. For deep convolutional neural networks, dropout is known to work well in fully-connected layers. However, its effect in convolutional and pooling layers is still not clear. This paper demonstrates that max-pooling dropout is equivalent to randomly picking activation based on a multinomial distribution at training time. In light of this insight, we advocate employing our proposed probabilistic weighted pooling, instead of commonly used max-pooling, to act as model averaging at test time. Empirical evidence validates the superiority of probabilistic weighted pooling. We also empirically show that the effect of convolutional dropout is not trivial, despite the dramatically reduced possibility of over-fitting due to the convolutional architecture. Elaborately designing dropout training simultaneously in max-pooling and fully-connected layers, we achieve state-of-the-art performance on MNIST, and very competitive results on CIFAR-10 and CIFAR-100, relative to other approaches without data augmentation. Finally, we compare max-pooling dropout and stochastic pooling, both of which introduce stochasticity based on multinomial distributions at pooling stage. Copyright © 2015 Elsevier Ltd. All rights reserved.

  9. Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?

    OpenAIRE

    Tajbakhsh, Nima; Shin, Jae Y.; Gurudu, Suryakanth R.; Hurst, R. Todd; Kendall, Christopher B.; Gotway, Michael B.; Liang, Jianming

    2017-01-01

    Training a deep convolutional neural network (CNN) from scratch is difficult because it requires a large amount of labeled training data and a great deal of expertise to ensure proper convergence. A promising alternative is to fine-tune a CNN that has been pre-trained using, for instance, a large set of labeled natural images. However, the substantial differences between natural and medical images may advise against such knowledge transfer. In this paper, we seek to answer the following centr...

  10. Superimposed Training-Based Channel Estimation for MIMO Relay Networks

    Directory of Open Access Journals (Sweden)

    Xiaoyan Xu

    2012-01-01

    Full Text Available We introduce the superimposed training strategy into the multiple-input multiple-output (MIMO amplify-and-forward (AF one-way relay network (OWRN to perform the individual channel estimation at the destination. Through the superposition of a group of additional training vectors at the relay subject to power allocation, the separated estimates of the source-relay and relay-destination channels can be obtained directly at the destination, and the accordance with the two-hop AF strategy can be guaranteed at the same time. The closed-form Bayesian Cramér-Rao lower bound (CRLB is derived for the estimation of two sets of flat-fading MIMO channel under random channel parameters and further exploited to design the optimal training vectors. A specific suboptimal channel estimation algorithm is applied in the MIMO AF OWRN using the optimal training sequences, and the normalized mean square error performance for the estimation is provided to verify the Bayesian CRLB results.

  11. Transfer Learning for Video Recognition with Scarce Training Data for Deep Convolutional Neural Network

    OpenAIRE

    Su, Yu-Chuan; Chiu, Tzu-Hsuan; Yeh, Chun-Yen; Huang, Hsin-Fu; Hsu, Winston H.

    2014-01-01

    Unconstrained video recognition and Deep Convolution Network (DCN) are two active topics in computer vision recently. In this work, we apply DCNs as frame-based recognizers for video recognition. Our preliminary studies, however, show that video corpora with complete ground truth are usually not large and diverse enough to learn a robust model. The networks trained directly on the video data set suffer from significant overfitting and have poor recognition rate on the test set. The same lack-...

  12. Enumeration of minimal stoichiometric precursor sets in metabolic networks.

    Science.gov (United States)

    Andrade, Ricardo; Wannagat, Martin; Klein, Cecilia C; Acuña, Vicente; Marchetti-Spaccamela, Alberto; Milreu, Paulo V; Stougie, Leen; Sagot, Marie-France

    2016-01-01

    What an organism needs at least from its environment to produce a set of metabolites, e.g. target(s) of interest and/or biomass, has been called a minimal precursor set. Early approaches to enumerate all minimal precursor sets took into account only the topology of the metabolic network (topological precursor sets). Due to cycles and the stoichiometric values of the reactions, it is often not possible to produce the target(s) from a topological precursor set in the sense that there is no feasible flux. Although considering the stoichiometry makes the problem harder, it enables to obtain biologically reasonable precursor sets that we call stoichiometric. Recently a method to enumerate all minimal stoichiometric precursor sets was proposed in the literature. The relationship between topological and stoichiometric precursor sets had however not yet been studied. Such relationship between topological and stoichiometric precursor sets is highlighted. We also present two algorithms that enumerate all minimal stoichiometric precursor sets. The first one is of theoretical interest only and is based on the above mentioned relationship. The second approach solves a series of mixed integer linear programming problems. We compared the computed minimal precursor sets to experimentally obtained growth media of several Escherichia coli strains using genome-scale metabolic networks. The results show that the second approach efficiently enumerates minimal precursor sets taking stoichiometry into account, and allows for broad in silico studies of strains or species interactions that may help to understand e.g. pathotype and niche-specific metabolic capabilities. sasita is written in Java, uses cplex as LP solver and can be downloaded together with all networks and input files used in this paper at http://www.sasita.gforge.inria.fr.

  13. Statistical and optimization methods to expedite neural network training for transient identification

    International Nuclear Information System (INIS)

    Reifman, J.; Vitela, E.J.; Lee, J.C.

    1993-01-01

    Two complementary methods, statistical feature selection and nonlinear optimization through conjugate gradients, are used to expedite feedforward neural network training. Statistical feature selection techniques in the form of linear correlation coefficients and information-theoretic entropy are used to eliminate redundant and non-informative plant parameters to reduce the size of the network. The method of conjugate gradients is used to accelerate the network training convergence and to systematically calculate the Teaming and momentum constants at each iteration. The proposed techniques are compared with the backpropagation algorithm using the entire set of plant parameters in the training of neural networks to identify transients simulated with the Midland Nuclear Power Plant Unit 2 simulator. By using 25% of the plant parameters and the conjugate gradients, a 30-fold reduction in CPU time was obtained without degrading the diagnostic ability of the network

  14. Supervised learning in spiking neural networks with FORCE training.

    Science.gov (United States)

    Nicola, Wilten; Clopath, Claudia

    2017-12-20

    Populations of neurons display an extraordinary diversity in the behaviors they affect and display. Machine learning techniques have recently emerged that allow us to create networks of model neurons that display behaviors of similar complexity. Here we demonstrate the direct applicability of one such technique, the FORCE method, to spiking neural networks. We train these networks to mimic dynamical systems, classify inputs, and store discrete sequences that correspond to the notes of a song. Finally, we use FORCE training to create two biologically motivated model circuits. One is inspired by the zebra finch and successfully reproduces songbird singing. The second network is motivated by the hippocampus and is trained to store and replay a movie scene. FORCE trained networks reproduce behaviors comparable in complexity to their inspired circuits and yield information not easily obtainable with other techniques, such as behavioral responses to pharmacological manipulations and spike timing statistics.

  15. Neural network training by Kalman filtering in process system monitoring

    International Nuclear Information System (INIS)

    Ciftcioglu, Oe.

    1996-03-01

    Kalman filtering approach for neural network training is described. Its extended form is used as an adaptive filter in a nonlinear environment of the form a feedforward neural network. Kalman filtering approach generally provides fast training as well as avoiding excessive learning which results in enhanced generalization capability. The network is used in a process monitoring application where the inputs are measurement signals. Since the measurement errors are also modelled in Kalman filter the approach yields accurate training with the implication of accurate neural network model representing the input and output relationships in the application. As the process of concern is a dynamic system, the input source of information to neural network is time dependent so that the training algorithm presents an adaptive form for real-time operation for the monitoring task. (orig.)

  16. Behaviour in O of the Neural Networks Training Cost

    DEFF Research Database (Denmark)

    Goutte, Cyril

    1998-01-01

    We study the behaviour in zero of the derivatives of the cost function used when training non-linear neural networks. It is shown that a fair number offirst, second and higher order derivatives vanish in zero, validating the belief that 0 is a peculiar and potentially harmful location. These calc......We study the behaviour in zero of the derivatives of the cost function used when training non-linear neural networks. It is shown that a fair number offirst, second and higher order derivatives vanish in zero, validating the belief that 0 is a peculiar and potentially harmful location....... These calculations arerelated to practical and theoretical aspects of neural networks training....

  17. Computing autocatalytic sets to unravel inconsistencies in metabolic network reconstructions

    DEFF Research Database (Denmark)

    Schmidt, R.; Waschina, S.; Boettger-Schmidt, D.

    2015-01-01

    , the method we report represents a powerful tool to identify inconsistencies in large-scale metabolic networks. AVAILABILITY AND IMPLEMENTATION: The method is available as source code on http://users.minet.uni-jena.de/ approximately m3kach/ASBIG/ASBIG.zip. CONTACT: christoph.kaleta@uni-jena.de SUPPLEMENTARY...... by inherent inconsistencies and gaps. RESULTS: Here we present a novel method to validate metabolic network reconstructions based on the concept of autocatalytic sets. Autocatalytic sets correspond to collections of metabolites that, besides enzymes and a growth medium, are required to produce all biomass...... components in a metabolic model. These autocatalytic sets are well-conserved across all domains of life, and their identification in specific genome-scale reconstructions allows us to draw conclusions about potential inconsistencies in these models. The method is capable of detecting inconsistencies, which...

  18. Radar Training Facility Local Area Network -

    Data.gov (United States)

    Department of Transportation — The RTF LAN system provides a progressive training environment for initial and refresher radar training qualification for new and re-hired FAA employees. Its purpose...

  19. Training of reverse propagation neural networks applied to neutron dosimetry

    International Nuclear Information System (INIS)

    Hernandez P, C. F.; Martinez B, M. R.; Leon P, A. A.; Espinoza G, J. G.; Castaneda M, V. H.; Solis S, L. O.; Castaneda M, R.; Ortiz R, M.; Vega C, H. R.; Mendez V, R.; Gallego, E.; De Sousa L, M. A.

    2016-10-01

    Neutron dosimetry is of great importance in radiation protection as aims to provide dosimetric quantities to assess the magnitude of detrimental health effects due to exposure of neutron radiation. To quantify detriment to health is necessary to evaluate the dose received by the occupationally exposed personnel using different detection systems called dosimeters, which have very dependent responses to the energy distribution of neutrons. The neutron detection is a much more complex problem than the detection of charged particles, since it does not carry an electric charge, does not cause direct ionization and has a greater penetration power giving the possibility of interacting with matter in a different way. Because of this, various neutron detection systems have been developed, among which the Bonner spheres spectrometric system stands out due to the advantages that possesses, such as a wide range of energy, high sensitivity and easy operation. However, once obtained the counting rates, the problem lies in the neutron spectrum deconvolution, necessary for the calculation of the doses, using different mathematical methods such as Monte Carlo, maximum entropy, iterative methods among others, which present various difficulties that have motivated the development of new technologies. Nowadays, methods based on artificial intelligence technologies are being used to perform neutron dosimetry, mainly using the theory of artificial neural networks. In these new methods the need for spectrum reconstruction can be eliminated for the calculation of the doses. In this work an artificial neural network or reverse propagation was trained for the calculation of 15 equivalent doses from the counting rates of the Bonner spheres spectrometric system using a set of 7 spheres, one of 2 spheres and two of a single sphere of different sizes, testing different error values until finding the most appropriate. The optimum network topology was obtained through the robust design

  20. AUTOMATIC RETINA EXUDATES SEGMENTATION WITHOUT A MANUALLY LABELLED TRAINING SET

    Energy Technology Data Exchange (ETDEWEB)

    Giancardo, Luca [ORNL; Meriaudeau, Fabrice [ORNL; Karnowski, Thomas Paul [ORNL; Li, Yaquin [University of Tennessee, Knoxville (UTK); Tobin Jr, Kenneth William [ORNL; Chaum, Edward [University of Tennessee, Knoxville (UTK)

    2011-01-01

    Diabetic macular edema (DME) is a common vision threatening complication of diabetic retinopathy which can be assessed by detecting exudates (a type of bright lesion) in fundus images. In this work, two new methods for the detection of exudates are presented which do not use a supervised learning step and therefore do not require ground-truthed lesion training sets which are time consuming to create, difficult to obtain, and prone to human error. We introduce a new dataset of fundus images from various ethnic groups and levels of DME which we have made publicly available. We evaluate our algorithm with this dataset and compare our results with two recent exudate segmentation algorithms. In all of our tests, our algorithms perform better or comparable with an order of magnitude reduction in computational time.

  1. Training strategy for convolutional neural networks in pedestrian gender classification

    Science.gov (United States)

    Ng, Choon-Boon; Tay, Yong-Haur; Goi, Bok-Min

    2017-06-01

    In this work, we studied a strategy for training a convolutional neural network in pedestrian gender classification with limited amount of labeled training data. Unsupervised learning by k-means clustering on pedestrian images was used to learn the filters to initialize the first layer of the network. As a form of pre-training, supervised learning for the related task of pedestrian classification was performed. Finally, the network was fine-tuned for gender classification. We found that this strategy improved the network's generalization ability in gender classification, achieving better test results when compared to random weights initialization and slightly more beneficial than merely initializing the first layer filters by unsupervised learning. This shows that unsupervised learning followed by pre-training with pedestrian images is an effective strategy to learn useful features for pedestrian gender classification.

  2. Digital intelligent booster for DCC miniature train networks

    Science.gov (United States)

    Ursu, M. P.; Condruz, D. A.

    2017-08-01

    Modern miniature trains are now driven by means of the DCC (Digital Command and Control) system, which allows the human operator or a personal computer to launch commands to each individual train or even to control different features of the same train. The digital command station encodes these commands and sends them to the trains by means of electrical pulses via the rails of the railway network. Due to the development of the miniature railway network, it may happen that the power requirement of the increasing number of digital locomotives, carriages and accessories exceeds the nominal output power of the digital command station. This digital intelligent booster relieves the digital command station from powering the entire railway network all by itself, and it automatically handles the multiple powered sections of the network. This electronic device is also able to detect and process short-circuits and overload conditions, without the intervention of the digital command station.

  3. SET UP OF THE NEW AUTOMATIC HYDROMETEOROLOGICAL NETWORK IN HUNGARY

    Directory of Open Access Journals (Sweden)

    J. NAGy

    2013-03-01

    Full Text Available The Hungarian Meteorological Service (OMSZ and General Directorate of Water Management (OVF in Hungary run conventional precipitation measurement networks consisting of at least 1000 stations. OMSZ automated its synoptic and climatological network in 90’s and now more than 100 automatic stations give data every 1-10 minutes via GPRS channel. In 2007 the experts from both institutions determined the requirements of a common network. The predecessor in title of OVF is general Directorate for Water and Environment gave a project proposal in 2008 for establishment of a new hydrometeorological network based on common aims for meteorology and hydrology. The new hydrometeorological network was set up in 2012 financed by KEOP project. This network has got 141 weighing precipitation gauges, 118 temperature - humidity sensors and 25 soil moisture and soil temperature instruments. Near by Tisza-Lake two wind sensors have been installed. The network is operated by OMSZ and OVF together. OVF and its institutions maintain the stations itself and support the electricity. OMSZ operates data collection and transmission, maintaines and calibrates the sensors. Using precipitation data of enhanced network the radar precipitation field quality may be more precise, which are input of run-off model. Thereby the time allowance may be increased in flood-control events. Based on soil moisture and temperature water balance in soil may be modelled and forecast can be produced in different conditions. It is very important task in drought and inland water conditions. Considering OMSZ investment project in which new Doppler dual polarisation radar and 14 disdrometers will be installed, the precipitation estimation may be improved since 2015.

  4. EXPERIENCE NETWORKING UNIVERSITY OF EDUCATION TRAINING MASTERS SAFETY OF LIFE

    OpenAIRE

    Elvira Mikhailovna Rebko

    2016-01-01

    The article discloses experience networking of universities (Herzen State Pedagogical University and Sakhalin State University) in the development and implementation of joint training programs for master’s education in the field of life safety «Social security in the urban environment». The novelty of the work is to create a schematic design of basic educational training program for master’s education in the mode of networking, and to identify effective instructional techniques and conditions...

  5. Transfer of Training: Adding Insight through Social Network Analysis

    Science.gov (United States)

    Van den Bossche, Piet; Segers, Mien

    2013-01-01

    This article reviews studies which apply a social network perspective to examine transfer of training. The theory behind social networks focuses on the interpersonal mechanisms and social structures that exist among interacting units such as people within an organization. A premise of this perspective is that individual's behaviors and outcomes…

  6. African Network Operators Group (AfNOG) Training Workshops and ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    The African Network Operators Group (AfNOG) is a forum for technical cooperation and coordination between African network operators and engineers from the region's universities, research institutions and industry. This year, AfNOG's training workshops and meetings will be held in Rabat, Morocco, between 24 May and 6 ...

  7. Increasing the effectiveness of instrumentation and control training programs using integrated training settings and a systematic approach to training

    International Nuclear Information System (INIS)

    McMahon, J.F.; Rakos, N.

    1992-01-01

    The performance of plant maintenance-related tasks assigned to instrumentation and control (I ampersand C) technicians can be broken down into physical skills required to do the task; resident knowledge of how to do the task; effect of maintenance on plant operating conditions; interactions with other plant organizations such as operations, radiation protection, and quality control; and knowledge of consequences of miss-action. A technician who has learned about the task in formal classroom presentations has not had the advantage of integrating that knowledge with the requisite physical and communication skills; hence, the first time these distinct and vital parts of the task equation are put together is on the job, during initial task performance. On-the-job training provides for the integration of skills and knowledge; however, this form of training is limited by plant conditions, availability of supporting players, and training experience levels of the personnel conducting the exercise. For licensed operations personnel, most nuclear utilities use formal classroom and a full-scope control room simulator to achieve the integration of skills and knowledge in a controlled training environment. TU Electric has taken that same approach into maintenance areas by including identical plant equipment in a laboratory setting for the large portion of training received by maintenance personnel at its Comanche Peak steam electric station. The policy of determining training needs and defining the scope of training by using the systematic approach to training has been highly effective and provided training at a reasonable cost (approximately $18.00/student contact hour)

  8. Improving the Robustness of Deep Neural Networks via Stability Training

    OpenAIRE

    Zheng, Stephan; Song, Yang; Leung, Thomas; Goodfellow, Ian

    2016-01-01

    In this paper we address the issue of output instability of deep neural networks: small perturbations in the visual input can significantly distort the feature embeddings and output of a neural network. Such instability affects many deep architectures with state-of-the-art performance on a wide range of computer vision tasks. We present a general stability training method to stabilize deep networks against small input distortions that result from various types of common image processing, such...

  9. A Comprehensive Training Data Set for the Development of Satellite-Based Volcanic Ash Detection Algorithms

    Science.gov (United States)

    Schmidl, Marius

    2017-04-01

    We present a comprehensive training data set covering a large range of atmospheric conditions, including disperse volcanic ash and desert dust layers. These data sets contain all information required for the development of volcanic ash detection algorithms based on artificial neural networks, urgently needed since volcanic ash in the airspace is a major concern of aviation safety authorities. Selected parts of the data are used to train the volcanic ash detection algorithm VADUGS. They contain atmospheric and surface-related quantities as well as the corresponding simulated satellite data for the channels in the infrared spectral range of the SEVIRI instrument on board MSG-2. To get realistic results, ECMWF, IASI-based, and GEOS-Chem data are used to calculate all parameters describing the environment, whereas the software package libRadtran is used to perform radiative transfer simulations returning the brightness temperatures for each atmospheric state. As optical properties are a prerequisite for radiative simulations accounting for aerosol layers, the development also included the computation of optical properties for a set of different aerosol types from different sources. A description of the developed software and the used methods is given, besides an overview of the resulting data sets.

  10. Fault Diagnosis Method of Polymerization Kettle Equipment Based on Rough Sets and BP Neural Network

    Directory of Open Access Journals (Sweden)

    Shu-zhi Gao

    2013-01-01

    Full Text Available Polyvinyl chloride (PVC polymerizing production process is a typical complex controlled object, with complexity features, such as nonlinear, multivariable, strong coupling, and large time-delay. Aiming at the real-time fault diagnosis and optimized monitoring requirements of the large-scale key polymerization equipment of PVC production process, a real-time fault diagnosis strategy is proposed based on rough sets theory with the improved discernibility matrix and BP neural networks. The improved discernibility matrix is adopted to reduct the attributes of rough sets in order to decrease the input dimensionality of fault characteristics effectively. Levenberg-Marquardt BP neural network is trained to diagnose the polymerize faults according to the reducted decision table, which realizes the nonlinear mapping from fault symptom set to polymerize fault set. Simulation experiments are carried out combining with the industry history datum to show the effectiveness of the proposed rough set neural networks fault diagnosis method. The proposed strategy greatly increased the accuracy rate and efficiency of the polymerization fault diagnosis system.

  11. Accelerating deep neural network training with inconsistent stochastic gradient descent.

    Science.gov (United States)

    Wang, Linnan; Yang, Yi; Min, Renqiang; Chakradhar, Srimat

    2017-09-01

    Stochastic Gradient Descent (SGD) updates Convolutional Neural Network (CNN) with a noisy gradient computed from a random batch, and each batch evenly updates the network once in an epoch. This model applies the same training effort to each batch, but it overlooks the fact that the gradient variance, induced by Sampling Bias and Intrinsic Image Difference, renders different training dynamics on batches. In this paper, we develop a new training strategy for SGD, referred to as Inconsistent Stochastic Gradient Descent (ISGD) to address this problem. The core concept of ISGD is the inconsistent training, which dynamically adjusts the training effort w.r.t the loss. ISGD models the training as a stochastic process that gradually reduces down the mean of batch's loss, and it utilizes a dynamic upper control limit to identify a large loss batch on the fly. ISGD stays on the identified batch to accelerate the training with additional gradient updates, and it also has a constraint to penalize drastic parameter changes. ISGD is straightforward, computationally efficient and without requiring auxiliary memories. A series of empirical evaluations on real world datasets and networks demonstrate the promising performance of inconsistent training. Copyright © 2017 Elsevier Ltd. All rights reserved.

  12. Identifying a set of influential spreaders in complex networks

    Science.gov (United States)

    Zhang, Jian-Xiong; Chen, Duan-Bing; Dong, Qiang; Zhao, Zhi-Dan

    2016-06-01

    Identifying a set of influential spreaders in complex networks plays a crucial role in effective information spreading. A simple strategy is to choose top-r ranked nodes as spreaders according to influence ranking method such as PageRank, ClusterRank and k-shell decomposition. Besides, some heuristic methods such as hill-climbing, SPIN, degree discount and independent set based are also proposed. However, these approaches suffer from a possibility that some spreaders are so close together that they overlap sphere of influence or time consuming. In this report, we present a simply yet effectively iterative method named VoteRank to identify a set of decentralized spreaders with the best spreading ability. In this approach, all nodes vote in a spreader in each turn, and the voting ability of neighbors of elected spreader will be decreased in subsequent turn. Experimental results on four real networks show that under Susceptible-Infected-Recovered (SIR) and Susceptible-Infected (SI) models, VoteRank outperforms the traditional benchmark methods on both spreading rate and final affected scale. What’s more, VoteRank has superior computational efficiency.

  13. Foreign Language Optical Character Recognition, Phase II: Arabic and Persian Training and Test Data Sets

    National Research Council Canada - National Science Library

    Davidson, Robert

    1997-01-01

    .... Each data set is divided into a training set, which is made available to developers, and a carefully matched equal-sized set of closely analogous samples, which is reserved for testing of the developers' products...

  14. Physicists set new record for network data transfer

    CERN Multimedia

    2006-01-01

    "An internatinal team of physicists, computer scientists, and network engineers led by the California Institute of Technology, CERN and the University of Michigan and partners at the University of Florida and Vanderbilt, as well as participants from Brazil (Rio de Janeiro State University, UERJ, and the State Universities of Sao Paulo, USP and UNESP) and Korea (Kyungpook National University, KISTI) joined forces to set new records for sustained data transfer between storage systems during the SuperComputing 2006 (SC06) Bandwidth Challenge (BWC)." (2 pages)

  15. Virtual setting for training in interpreting mammography images

    Science.gov (United States)

    Pezzuol, J. L.; Abreu, F. D. L.; Silva, S. M.; Tendolini, A.; Bissaco, M. A. Se; Rodrigues, S. C. M.

    2017-03-01

    This work presents a web system for the training of students or residents (users) interested in the detection of breast density in mammography images. The system consists of a breast imaging database with breast density types classified and demarcated by the specialist (tutor) or online database. The planning was based on ISO / IEC 12207. Through the browser (desktop or notebook), the user will visualize the breast images and in them will realize the markings of the density region and even classify them per the BI-RADS protocol. After marking, this will be compared to the gold standard already existing in the image base, and then the system will inform if the area demarcation has been set or not. The shape of this marking is similar to the paint brush. The evaluation was based on ISO / IEC 1926 or 25010: 2011 by 3 software development specialists and 3 in mammary radiology, evaluating usability, configuration, performance and System interface through the Likert scale-based questionnaire. Where they have totally agreed on usability, configuration, performance and partially on the interface. And as a good thing: the system is able to be accessed anywhere and at any time, the hit or error response is in real time, it can be used in the educational area, the limit of the amount of images will depend on the size of the computer memory, At the end the system sends the results achieved by e-mail to the user, reproduction of the system on any type of screen, complementation of the system with other types of breast structures. Negative points are the need for internet.

  16. EXPERIENCE NETWORKING UNIVERSITY OF EDUCATION TRAINING MASTERS SAFETY OF LIFE

    Directory of Open Access Journals (Sweden)

    Elvira Mikhailovna Rebko

    2016-02-01

    Full Text Available The article discloses experience networking of universities (Herzen State Pedagogical University and Sakhalin State University in the development and implementation of joint training programs for master’s education in the field of life safety «Social security in the urban environment». The novelty of the work is to create a schematic design of basic educational training program for master’s education in the mode of networking, and to identify effective instructional techniques and conditions of networking.Purpose – present the results of the joint development of a network of the basic educational program (BEP, to identify the stages of networking, to design a generalized scheme of development and implementation of a network of educational training program for master’s education in the field of life safety.Results generalized model of networking partner institutions to develop and implement the basic educational program master.Practical implications: the education process for Master of Education in the field of health and safety in Herzen State Pedagogical University and Sakhalin State University.

  17. The Applicant Based Training Model Setting Conditions for Recruiting Success

    Science.gov (United States)

    2002-07-01

    the RS XO is another critical 32. function that falls into the scope of their responsibly and requires specific training in marketing and advertising . During...Phase I require a solid working knowledge of marketing and advertising . OpsO: Phase II actions require the OpsO receive advanced training in data

  18. Role of physical and mental training in brain network configuration.

    Science.gov (United States)

    Foster, Philip P

    2015-01-01

    It is hypothesized that the topology of brain networks is constructed by connecting nodes which may be continuously remodeled by appropriate training. Efficiency of physical and/or mental training on the brain relies on the flexibility of networks' architecture molded by local remodeling of proteins and synapses of excitatory neurons producing transformations in network topology. Continuous remodeling of proteins of excitatory neurons is fine-tuning the scaling and strength of excitatory synapses up or down via regulation of intra-cellular metabolic and regulatory networks of the genome-transcriptome-proteome interface. Alzheimer's disease is a model of "energy cost-driven small-world network disorder" with dysfunction of high-energy cost wiring as the network global efficiency is impaired by the deposition of an informed agent, the amyloid-β, selectively targeting high-degree nodes. In schizophrenia, the interconnectivity and density of rich-club networks are significantly reduced. Training-induced homeostatic synaptogenesis-enhancement, presumably via reconfiguration of brain networks into greater small-worldness, appears essential in learning, memory, and executive functions. A macroscopic cartography of creation-removal of synaptic connections in a macro-network, and at the intra-cellular scale, micro-networks regulate the physiological mechanisms for the preferential attachment of synapses. The strongest molecular relationship of exercise and functional connectivity was identified for brain-derived neurotrophic factor (BDNF). The allele variant, rs7294919, also shows a powerful relationship with the hippocampal volume. How the brain achieves this unique quest of reconfiguration remains a puzzle. What are the underlying mechanisms of synaptogenesis promoting communications brain ↔ muscle and brain ↔ brain in such trainings? What is the respective role of independent mental, physical, or combined-mental-physical trainings? Physical practice seems to be

  19. Role of physical and mental training in brain network configuration

    Directory of Open Access Journals (Sweden)

    Philip P. Foster

    2015-06-01

    Full Text Available Continuous remodeling of proteins of excitatory neurons is fine-tuning the scaling and strength of excitatory synapses up or down via regulation of intra-cellular metabolic and regulatory networks of the genome-transcriptome-proteome interface. Alzheimer's disease is a model of energy cost-driven small-world network disorder as the network global efficiency is impaired by the deposition of an informed agent, the amyloid-β, selectively targeting high-degree nodes. In schizophrenia, the interconnectivity and density of rich-club networks are significantly reduced. Training-induced homeostatic synaptogenesis-enhancement produces a reconfiguration of brain networks into greater small-worldness. Creation of synaptic connections in a macro-network, and, at the intra-cellular scale, micro-networks regulate the physiological mechanisms for the preferential attachment of synapses. The strongest molecular relationship of exercise and functional connectivity was identified for brain-derived neurotrophic factor (BDNF. The allele variant, rs7294919, also shows a powerful relationship with the hippocampal volume. How the brain achieves this unique quest of reconfiguration remains a puzzle. What are the underlying mechanisms of synaptogenesis promoting communications brain ↔ muscle and brain ↔ brain in such trainings? What is the respective role of independent mental, physical or combined-mental-physical trainings? Physical practice seems to be playing an instrumental role in the cognitive enhancement (brain ↔ muscle com.. However, mental training, meditation or virtual reality (films, games require only minimal motor activity and cardio-respiratory stimulation. Therefore, other potential paths (brain ↔ brain com. molding brain networks are nonetheless essential. Patients with motor neuron disease/injury (e.g. amyotrophic lateral sclerosis, traumatism also achieve successful cognitive enhancement albeit they may only elicit mental practice

  20. The Effect of Training Data Set Composition on the Performance of a Neural Image Caption Generator

    Science.gov (United States)

    2017-09-01

    REPORT TYPE Technical Report 3. DATES COVERED (From - To) 4. TITLE AND SUBTITLE The Effect of Training Data Set Composition on the Performance of a...ARL-TR-8124 ● SEP 2017 US Army Research Laboratory The Effect of Training Data Set Composition on the Performance of a Neural...Laboratory The Effect of Training Data Set Composition on the Performance of a Neural Image Caption Generator by Abigail Wilson Montgomery Blair

  1. Mining big data sets of plankton images: a zero-shot learning approach to retrieve labels without training data

    Science.gov (United States)

    Orenstein, E. C.; Morgado, P. M.; Peacock, E.; Sosik, H. M.; Jaffe, J. S.

    2016-02-01

    Technological advances in instrumentation and computing have allowed oceanographers to develop imaging systems capable of collecting extremely large data sets. With the advent of in situ plankton imaging systems, scientists must now commonly deal with "big data" sets containing tens of millions of samples spanning hundreds of classes, making manual classification untenable. Automated annotation methods are now considered to be the bottleneck between collection and interpretation. Typically, such classifiers learn to approximate a function that predicts a predefined set of classes for which a considerable amount of labeled training data is available. The requirement that the training data span all the classes of concern is problematic for plankton imaging systems since they sample such diverse, rapidly changing populations. These data sets may contain relatively rare, sparsely distributed, taxa that will not have associated training data; a classifier trained on a limited set of classes will miss these samples. The computer vision community, leveraging advances in Convolutional Neural Networks (CNNs), has recently attempted to tackle such problems using "zero-shot" object categorization methods. Under a zero-shot framework, a classifier is trained to map samples onto a set of attributes rather than a class label. These attributes can include visual and non-visual information such as what an organism is made out of, where it is distributed globally, or how it reproduces. A second stage classifier is then used to extrapolate a class. In this work, we demonstrate a zero-shot classifier, implemented with a CNN, to retrieve out-of-training-set labels from images. This method is applied to data from two continuously imaging, moored instruments: the Scripps Plankton Camera System (SPCS) and the Imaging FlowCytobot (IFCB). Results from simulated deployment scenarios indicate zero-shot classifiers could be successful at recovering samples of rare taxa in image sets. This

  2. Ebola 2014: Setting up a port health screening programme at an international train station.

    Science.gov (United States)

    Cleary, Vivien; Wynne-Evans, Edward; Freed, James; Fleet, Katie; Thorn, Simone; Turbitt, Deborah

    2017-12-01

    An outbreak of Ebola virus disease (EVD) began in Guinea in December 2013 and was declared a Public Health Emergency of International Concern by the World Health Organization in August 2014. In October, the UK government tasked Public Health England (PHE) to set up EVD screening at key ports. The key aim of port-of-entry screening was to identify passengers coming from areas with high risk of EVD, and give them advice to raise their awareness of symptoms and what actions to take. Direct flights from Sierra Leone, Guinea or Liberia had all been cancelled, so intelligence on passenger numbers and routes was used to identify the most commonly used routes from the affected countries into the UK. One of these was St Pancras International train station. Screening had never previously been implemented at a UK train station so had to be set up from scratch. Key to the success of this was excellent multi-agency working between PHE, the UK Border Force, Eurostar, Network Rail and the Cabinet Office. This paper gives an overview of the activation of EVD screening at St Pancras International and the subsequent decommissioning.

  3. Bayesian model ensembling using meta-trained recurrent neural networks

    NARCIS (Netherlands)

    Ambrogioni, L.; Berezutskaya, Y.; Gü ç lü , U.; Borne, E.W.P. van den; Gü ç lü tü rk, Y.; Gerven, M.A.J. van; Maris, E.G.G.

    2017-01-01

    In this paper we demonstrate that a recurrent neural network meta-trained on an ensemble of arbitrary classification tasks can be used as an approximation of the Bayes optimal classifier. This result is obtained by relying on the framework of e-free approximate Bayesian inference, where the Bayesian

  4. Bioinformatics Training Network (BTN): a community resource for bioinformatics trainers

    DEFF Research Database (Denmark)

    Schneider, Maria V.; Walter, Peter; Blatter, Marie-Claude

    2012-01-01

    and clearly tagged in relation to target audiences, learning objectives, etc. Ideally, they would also be peer reviewed, and easily and efficiently accessible for downloading. Here, we present the Bioinformatics Training Network (BTN), a new enterprise that has been initiated to address these needs and review...

  5. Effects of cluster vs. traditional plyometric training sets on maximal-intensity exercise performance

    Directory of Open Access Journals (Sweden)

    Abbas Asadi

    2016-01-01

    Conclusions: Although both plyometric training methods improved lower body maximal-intensity exercise performance, the traditional sets methods resulted in greater adaptations in sprint performance, while the cluster sets method resulted in greater jump and agility adaptations.

  6. MO-DE-BRA-04: The CREATE Medical Physics Research Training Network: Training of New Generation Innovators

    Energy Technology Data Exchange (ETDEWEB)

    Seuntjens, J; Collins, L; Devic, S; El Naqa, I; Nadeau, J; Reader, A [McGill University, Montreal, QC (Canada); Beaulieu, L; Despres, P [Centre Hospitalier Univ de Quebec, Quebec, QC (Canada); Pike, B [University of Calgary, Calgary, Alberta (Canada)

    2015-06-15

    Purpose: Over the past century, physicists have played a major role in transforming scientific discovery into everyday clinical applications. However, with the increasingly stringent requirements to regulate medical physics as a health profession, the role of physicists as scientists and innovators has become at serious risk of erosion. These challenges trigger the need for a new, revolutionized training program at the graduate level that respects scientific rigor, attention for medical physics-relevant developments in basic sciences, innovation and entrepreneurship. Methods: A grant proposal was funded by the Collaborative REsearch and Training Experience program (CREATE) of the Natural Sciences and Engineering Research Council (NSERC) of Canada. This enabled the creation of the Medical Physics Research Training Network (MPRTN) around two CAMPEP-accredited medical physics programs. Members of the network consist of medical device companies, government (research and regulatory) and academia. The MPRTN/CREATE program proposes a curriculum with three main themes: (1) radiation physics, (2) imaging & image processing and (3) radiation response, outcomes and modeling. Results: The MPRTN was created mid 2013 (mprtn.com) and features (1) four new basic Ph.D. courses; (2) industry participation in research projects; (3) formal job-readiness training with involvement of guest faculty from academia, government and industry. MPRTN activities since 2013 include 22 conferences; 7 workshops and 4 exchange travels. Three patents were filed or issued, nine awards/best papers were won. Fifteen journal publications were accepted/published, 102 conference abstracts. There are now 13 industry partners. Conclusion: A medical physics research training network has been set up with the goal to harness graduate student’s job-readiness for industry, government and academia in addition to the conventional clinical role. Two years after inception, significant successes have been booked

  7. MO-DE-BRA-04: The CREATE Medical Physics Research Training Network: Training of New Generation Innovators

    International Nuclear Information System (INIS)

    Seuntjens, J; Collins, L; Devic, S; El Naqa, I; Nadeau, J; Reader, A; Beaulieu, L; Despres, P; Pike, B

    2015-01-01

    Purpose: Over the past century, physicists have played a major role in transforming scientific discovery into everyday clinical applications. However, with the increasingly stringent requirements to regulate medical physics as a health profession, the role of physicists as scientists and innovators has become at serious risk of erosion. These challenges trigger the need for a new, revolutionized training program at the graduate level that respects scientific rigor, attention for medical physics-relevant developments in basic sciences, innovation and entrepreneurship. Methods: A grant proposal was funded by the Collaborative REsearch and Training Experience program (CREATE) of the Natural Sciences and Engineering Research Council (NSERC) of Canada. This enabled the creation of the Medical Physics Research Training Network (MPRTN) around two CAMPEP-accredited medical physics programs. Members of the network consist of medical device companies, government (research and regulatory) and academia. The MPRTN/CREATE program proposes a curriculum with three main themes: (1) radiation physics, (2) imaging & image processing and (3) radiation response, outcomes and modeling. Results: The MPRTN was created mid 2013 (mprtn.com) and features (1) four new basic Ph.D. courses; (2) industry participation in research projects; (3) formal job-readiness training with involvement of guest faculty from academia, government and industry. MPRTN activities since 2013 include 22 conferences; 7 workshops and 4 exchange travels. Three patents were filed or issued, nine awards/best papers were won. Fifteen journal publications were accepted/published, 102 conference abstracts. There are now 13 industry partners. Conclusion: A medical physics research training network has been set up with the goal to harness graduate student’s job-readiness for industry, government and academia in addition to the conventional clinical role. Two years after inception, significant successes have been booked

  8. The Analysis of User Behaviour of a Network Management Training Tool using a Neural Network

    Directory of Open Access Journals (Sweden)

    Helen Donelan

    2005-10-01

    Full Text Available A novel method for the analysis and interpretation of data that describes the interaction between trainee network managers and a network management training tool is presented. A simulation based approach is currently being used to train network managers, through the use of a simulated network. The motivation is to provide a tool for exposing trainees to a life like situation without disrupting a live network. The data logged by this system describes the detailed interaction between trainee network manager and simulated network. The work presented here provides an analysis of this interaction data that enables an assessment of the capabilities of the trainee network manager as well as an understanding of how the network management tasks are being approached. A neural network architecture is implemented in order to perform an exploratory data analysis of the interaction data. The neural network employs a novel form of continuous self-organisation to discover key features in the data and thus provide new insights into the learning and teaching strategies employed.

  9. Parallel Evolutionary Optimization for Neuromorphic Network Training

    Energy Technology Data Exchange (ETDEWEB)

    Schuman, Catherine D [ORNL; Disney, Adam [University of Tennessee (UT); Singh, Susheela [North Carolina State University (NCSU), Raleigh; Bruer, Grant [University of Tennessee (UT); Mitchell, John Parker [University of Tennessee (UT); Klibisz, Aleksander [University of Tennessee (UT); Plank, James [University of Tennessee (UT)

    2016-01-01

    One of the key impediments to the success of current neuromorphic computing architectures is the issue of how best to program them. Evolutionary optimization (EO) is one promising programming technique; in particular, its wide applicability makes it especially attractive for neuromorphic architectures, which can have many different characteristics. In this paper, we explore different facets of EO on a spiking neuromorphic computing model called DANNA. We focus on the performance of EO in the design of our DANNA simulator, and on how to structure EO on both multicore and massively parallel computing systems. We evaluate how our parallel methods impact the performance of EO on Titan, the U.S.'s largest open science supercomputer, and BOB, a Beowulf-style cluster of Raspberry Pi's. We also focus on how to improve the EO by evaluating commonality in higher performing neural networks, and present the result of a study that evaluates the EO performed by Titan.

  10. European training network on full-parallax imaging (Conference Presentation)

    Science.gov (United States)

    Martínez-Corral, Manuel; Saavedra, Genaro

    2017-05-01

    Current displays are far from truly recreating visual reality. This requires a full-parallax display that can reproduce radiance field emanated from the real scenes. The develop-ment of such technology will require a new generation of researchers trained both in the physics, and in the biology of human vision. The European Training Network on Full-Parallax Imaging (ETN-FPI) aims at developing this new generation. Under H2020 funding ETN-FPI brings together 8 beneficiaries and 8 partner organizations from five EU countries with the aim of training 15 talented pre-doctoral students to become future research leaders in this area. In this contribution we will explain the main objectives of the network, and specifically the advances obtained at the University of Valencia.

  11. Training set extension for SVM ensemble in P300-speller with familiar face paradigm.

    Science.gov (United States)

    Li, Qi; Shi, Kaiyang; Gao, Ning; Li, Jian; Bai, Ou

    2018-03-27

    P300-spellers are brain-computer interface (BCI)-based character input systems. Support vector machine (SVM) ensembles are trained with large-scale training sets and used as classifiers in these systems. However, the required large-scale training data necessitate a prolonged collection time for each subject, which results in data collected toward the end of the period being contaminated by the subject's fatigue. This study aimed to develop a method for acquiring more training data based on a collected small training set. A new method was developed in which two corresponding training datasets in two sequences are superposed and averaged to extend the training set. The proposed method was tested offline on a P300-speller with the familiar face paradigm. The SVM ensemble with extended training set achieved 85% classification accuracy for the averaged results of four sequences, and 100% for 11 sequences in the P300-speller. In contrast, the conventional SVM ensemble with non-extended training set achieved only 65% accuracy for four sequences, and 92% for 11 sequences. The SVM ensemble with extended training set achieves higher classification accuracies than the conventional SVM ensemble, which verifies that the proposed method effectively improves the classification performance of BCI P300-spellers, thus enhancing their practicality.

  12. Convolutional neural networks based on augmented training samples for synthetic aperture radar target recognition

    Science.gov (United States)

    Yan, Yue

    2018-03-01

    A synthetic aperture radar (SAR) automatic target recognition (ATR) method based on the convolutional neural networks (CNN) trained by augmented training samples is proposed. To enhance the robustness of CNN to various extended operating conditions (EOCs), the original training images are used to generate the noisy samples at different signal-to-noise ratios (SNRs), multiresolution representations, and partially occluded images. Then, the generated images together with the original ones are used to train a designed CNN for target recognition. The augmented training samples can contrapuntally improve the robustness of the trained CNN to the covered EOCs, i.e., the noise corruption, resolution variance, and partial occlusion. Moreover, the significantly larger training set effectively enhances the representation capability for other conditions, e.g., the standard operating condition (SOC), as well as the stability of the network. Therefore, better performance can be achieved by the proposed method for SAR ATR. For experimental evaluation, extensive experiments are conducted on the Moving and Stationary Target Acquisition and Recognition dataset under SOC and several typical EOCs.

  13. Training Platoon Leader Adaptive Thinking Skills in a Classroom Setting

    Science.gov (United States)

    2011-06-01

    procedural aspects of the mission planning module, the costs involved in implementing this approach far exceed the benefits . Considerations for not using...areas covered in this class will clearly benefit 11 me). Coefficient alpha for this scale was .91. A three item scale delivered following...2006). Videogame -based training success: The impact of trainee characteristics - Year 2 (Technical Report 1188). Arlington, VA: U. S

  14. Training Feedforward Neural Networks Using Symbiotic Organisms Search Algorithm

    Directory of Open Access Journals (Sweden)

    Haizhou Wu

    2016-01-01

    Full Text Available Symbiotic organisms search (SOS is a new robust and powerful metaheuristic algorithm, which stimulates the symbiotic interaction strategies adopted by organisms to survive and propagate in the ecosystem. In the supervised learning area, it is a challenging task to present a satisfactory and efficient training algorithm for feedforward neural networks (FNNs. In this paper, SOS is employed as a new method for training FNNs. To investigate the performance of the aforementioned method, eight different datasets selected from the UCI machine learning repository are employed for experiment and the results are compared among seven metaheuristic algorithms. The results show that SOS performs better than other algorithms for training FNNs in terms of converging speed. It is also proven that an FNN trained by the method of SOS has better accuracy than most algorithms compared.

  15. Feature Set Evaluation for Offline Handwriting Recognition Systems: Application to the Recurrent Neural Network Model.

    Science.gov (United States)

    Chherawala, Youssouf; Roy, Partha Pratim; Cheriet, Mohamed

    2016-12-01

    The performance of handwriting recognition systems is dependent on the features extracted from the word image. A large body of features exists in the literature, but no method has yet been proposed to identify the most promising of these, other than a straightforward comparison based on the recognition rate. In this paper, we propose a framework for feature set evaluation based on a collaborative setting. We use a weighted vote combination of recurrent neural network (RNN) classifiers, each trained with a particular feature set. This combination is modeled in a probabilistic framework as a mixture model and two methods for weight estimation are described. The main contribution of this paper is to quantify the importance of feature sets through the combination weights, which reflect their strength and complementarity. We chose the RNN classifier because of its state-of-the-art performance. Also, we provide the first feature set benchmark for this classifier. We evaluated several feature sets on the IFN/ENIT and RIMES databases of Arabic and Latin script, respectively. The resulting combination model is competitive with state-of-the-art systems.

  16. Electronic collaboration in dermatology resident training through social networking.

    Science.gov (United States)

    Meeks, Natalie M; McGuire, April L; Carroll, Bryan T

    2017-04-01

    The use of online educational resources and professional social networking sites is increasing. The field of dermatology is currently under-utilizing online social networking as a means of professional collaboration and sharing of training materials. In this study, we sought to assess the current structure of and satisfaction with dermatology resident education and gauge interest for a professional social networking site for educational collaboration. Two surveys-one for residents and one for faculty-were electronically distributed via the American Society for Dermatologic Surgery and Association of Professors of Dermatology (APD) listserves. The surveys confirmed that there is interest among dermatology residents and faculty in a dermatology professional networking site with the goal to enhance educational collaboration.

  17. Does rational selection of training and test sets improve the outcome of QSAR modeling?

    Science.gov (United States)

    Martin, Todd M; Harten, Paul; Young, Douglas M; Muratov, Eugene N; Golbraikh, Alexander; Zhu, Hao; Tropsha, Alexander

    2012-10-22

    Prior to using a quantitative structure activity relationship (QSAR) model for external predictions, its predictive power should be established and validated. In the absence of a true external data set, the best way to validate the predictive ability of a model is to perform its statistical external validation. In statistical external validation, the overall data set is divided into training and test sets. Commonly, this splitting is performed using random division. Rational splitting methods can divide data sets into training and test sets in an intelligent fashion. The purpose of this study was to determine whether rational division methods lead to more predictive models compared to random division. A special data splitting procedure was used to facilitate the comparison between random and rational division methods. For each toxicity end point, the overall data set was divided into a modeling set (80% of the overall set) and an external evaluation set (20% of the overall set) using random division. The modeling set was then subdivided into a training set (80% of the modeling set) and a test set (20% of the modeling set) using rational division methods and by using random division. The Kennard-Stone, minimal test set dissimilarity, and sphere exclusion algorithms were used as the rational division methods. The hierarchical clustering, random forest, and k-nearest neighbor (kNN) methods were used to develop QSAR models based on the training sets. For kNN QSAR, multiple training and test sets were generated, and multiple QSAR models were built. The results of this study indicate that models based on rational division methods generate better statistical results for the test sets than models based on random division, but the predictive power of both types of models are comparable.

  18. Deep Convolutional Neural Networks: Structure, Feature Extraction and Training

    Directory of Open Access Journals (Sweden)

    Namatēvs Ivars

    2017-12-01

    Full Text Available Deep convolutional neural networks (CNNs are aimed at processing data that have a known network like topology. They are widely used to recognise objects in images and diagnose patterns in time series data as well as in sensor data classification. The aim of the paper is to present theoretical and practical aspects of deep CNNs in terms of convolution operation, typical layers and basic methods to be used for training and learning. Some practical applications are included for signal and image classification. Finally, the present paper describes the proposed block structure of CNN for classifying crucial features from 3D sensor data.

  19. Jointly Optimize Data Augmentation and Network Training: Adversarial Data Augmentation in Human Pose Estimation

    OpenAIRE

    Peng, Xi; Tang, Zhiqiang; Yang, Fei; Feris, Rogerio; Metaxas, Dimitris

    2018-01-01

    Random data augmentation is a critical technique to avoid overfitting in training deep neural network models. However, data augmentation and network training are usually treated as two isolated processes, limiting the effectiveness of network training. Why not jointly optimize the two? We propose adversarial data augmentation to address this limitation. The main idea is to design an augmentation network (generator) that competes against a target network (discriminator) by generating `hard' au...

  20. Permanent Set of Cross-Linking Networks: Comparison of Theory with Molecular Dynamics Simulations

    DEFF Research Database (Denmark)

    Rottach, Dana R.; Curro, John G.; Budzien, Joanne

    2006-01-01

    The permanent set of cross-linking networks is studied by molecular dynamics. The uniaxial stress for a bead-spring polymer network is investigated as a function of strain and cross-link density history, where cross-links are introduced in unstrained and strained networks. The permanent set...

  1. Implementation and Outcomes of a Collaborative Multi-Center Network Aimed at Web-Based Cognitive Training - COGWEB Network.

    Science.gov (United States)

    Tedim Cruz, Vítor; Pais, Joana; Ruano, Luis; Mateus, Cátia; Colunas, Márcio; Alves, Ivânia; Barreto, Rui; Conde, Eduardo; Sousa, Andreia; Araújo, Isabel; Bento, Virgílio; Coutinho, Paula; Rocha, Nelson

    2014-01-01

    Cognitive care for the most prevalent neurologic and psychiatric conditions will only improve through the implementation of new sustainable approaches. Innovative cognitive training methodologies and collaborative professional networks are necessary evolutions in the mental health sector. The objective of the study was to describe the implementation process and early outcomes of a nationwide multi-organizational network supported on a Web-based cognitive training system (COGWEB). The setting for network implementation was the Portuguese mental health system and the hospital-, academic-, community-based institutions and professionals providing cognitive training. The network started in August 2012, with 16 centers, and was monitored until September 2013 (inclusions were open). After onsite training, all were allowed to use COGWEB in their clinical or research activities. For supervision and maintenance were implemented newsletters, questionnaires, visits and webinars. The following outcomes were prospectively measured: (1) number, (2) type, (3) time to start, and (4) activity state of centers; age, gender, level of education, and medical diagnosis of patients enrolled. The network included 68 professionals from 41 centers, (33/41) 80% clinical, (8/41) 19% nonclinical. A total of 298 patients received cognitive training; 45.3% (n=135) female, mean age 54.4 years (SD 18.7), mean educational level 9.8 years (SD 4.8). The number enrolled each month increased significantly (r=0.6; P=.031). At 12 months, 205 remained on treatment. The major causes of cognitive impairment were: (1) neurodegenerative (115/298, 38.6%), (2) structural brain lesions (63/298, 21.1%), (3) autoimmune (40/298, 13.4%), (4) schizophrenia (30/298, 10.1%), and (5) others (50/298, 16.8%). The comparison of the patient profiles, promoter versus all other clinical centers, showed significant increases in the diversity of causes and spectrums of ages and education. Over its first year, there was a major

  2. Training Excitatory-Inhibitory Recurrent Neural Networks for Cognitive Tasks: A Simple and Flexible Framework.

    Directory of Open Access Journals (Sweden)

    H Francis Song

    2016-02-01

    Full Text Available The ability to simultaneously record from large numbers of neurons in behaving animals has ushered in a new era for the study of the neural circuit mechanisms underlying cognitive functions. One promising approach to uncovering the dynamical and computational principles governing population responses is to analyze model recurrent neural networks (RNNs that have been optimized to perform the same tasks as behaving animals. Because the optimization of network parameters specifies the desired output but not the manner in which to achieve this output, "trained" networks serve as a source of mechanistic hypotheses and a testing ground for data analyses that link neural computation to behavior. Complete access to the activity and connectivity of the circuit, and the ability to manipulate them arbitrarily, make trained networks a convenient proxy for biological circuits and a valuable platform for theoretical investigation. However, existing RNNs lack basic biological features such as the distinction between excitatory and inhibitory units (Dale's principle, which are essential if RNNs are to provide insights into the operation of biological circuits. Moreover, trained networks can achieve the same behavioral performance but differ substantially in their structure and dynamics, highlighting the need for a simple and flexible framework for the exploratory training of RNNs. Here, we describe a framework for gradient descent-based training of excitatory-inhibitory RNNs that can incorporate a variety of biological knowledge. We provide an implementation based on the machine learning library Theano, whose automatic differentiation capabilities facilitate modifications and extensions. We validate this framework by applying it to well-known experimental paradigms such as perceptual decision-making, context-dependent integration, multisensory integration, parametric working memory, and motor sequence generation. Our results demonstrate the wide range of neural

  3. Setting Up a Public Use Local Area Network.

    Science.gov (United States)

    Flower, Eric; Thulstrup, Lisa

    1988-01-01

    Describes a public use microcomputer cluster at the University of Maine, Orono. Various network topologies, hardware and software options, installation problems, system management, and performance are discussed. (MES)

  4. Monitoring training response in young Friesian dressage horses using two different standardised exercise tests (SETs)

    NARCIS (Netherlands)

    de Bruijn, Cornelis Marinus; Houterman, Willem; Ploeg, Margreet; Ducro, Bart; Boshuizen, Berit; Goethals, Klaartje; Verdegaal, Elisabeth-Lidwien; Delesalle, Catherine

    2017-01-01

    BACKGROUND: Most Friesian horses reach their anaerobic threshold during a standardized exercise test (SET) which requires lower intensity exercise than daily routine training. AIM: to study strengths and weaknesses of an alternative SET-protocol. Two different SETs (SETA and SETB) were applied

  5. Monitoring training response in young Friesian dressage horses using two different standardised exercise tests (SETs)

    NARCIS (Netherlands)

    Bruijn, de Cornelis Marinus; Houterman, Willem; Ploeg, Margreet; Ducro, Bart; Boshuizen, Berit; Goethals, Klaartje; Verdegaal, Elisabeth Lidwien; Delesalle, Catherine

    2017-01-01

    Background: Most Friesian horses reach their anaerobic threshold during a standardized exercise test (SET) which requires lower intensity exercise than daily routine training. Aim: to study strengths and weaknesses of an alternative SET-protocol. Two different SETs (SETA and SETB) were applied

  6. Reflection in the training of nurses in clinical practice settings

    DEFF Research Database (Denmark)

    Schumann Scheel, Linda; Peters, Micah D J; Meinertz Møbjerg, Anna Christine

    2017-01-01

    REVIEW QUESTION/OBJECTIVE: This scoping review will seek to find answers for the following questions which will focus on the use of reflection in the education of nurses in clinical settings:The review will also extract and map data regarding: i) what outcomes have been found in relation to the use...... (e.g. first or second year undergraduate nursing students etc.); and v) barriers/challenges to the use of reflection approaches/tools. Additional details may also be extracted and mapped during the process of the scoping review and this will be explained in the final scoping review report....

  7. STACCATO: a novel solution to supernova photometric classification with biased training sets

    Science.gov (United States)

    Revsbech, E. A.; Trotta, R.; van Dyk, D. A.

    2018-01-01

    We present a new solution to the problem of classifying Type Ia supernovae from their light curves alone given a spectroscopically confirmed but biased training set, circumventing the need to obtain an observationally expensive unbiased training set. We use Gaussian processes (GPs) to model the supernovae's (SN's) light curves, and demonstrate that the choice of covariance function has only a small influence on the GPs ability to accurately classify SNe. We extend and improve the approach of Richards et al. - a diffusion map combined with a random forest classifier - to deal specifically with the case of biased training sets. We propose a novel method called Synthetically Augmented Light Curve Classification (STACCATO) that synthetically augments a biased training set by generating additional training data from the fitted GPs. Key to the success of the method is the partitioning of the observations into subgroups based on their propensity score of being included in the training set. Using simulated light curve data, we show that STACCATO increases performance, as measured by the area under the Receiver Operating Characteristic curve (AUC), from 0.93 to 0.96, close to the AUC of 0.977 obtained using the 'gold standard' of an unbiased training set and significantly improving on the previous best result of 0.88. STACCATO also increases the true positive rate for SNIa classification by up to a factor of 50 for high-redshift/low-brightness SNe.

  8. Maximizing lipocalin prediction through balanced and diversified training set and decision fusion.

    Science.gov (United States)

    Nath, Abhigyan; Subbiah, Karthikeyan

    2015-12-01

    Lipocalins are short in sequence length and perform several important biological functions. These proteins are having less than 20% sequence similarity among paralogs. Experimentally identifying them is an expensive and time consuming process. The computational methods based on the sequence similarity for allocating putative members to this family are also far elusive due to the low sequence similarity existing among the members of this family. Consequently, the machine learning methods become a viable alternative for their prediction by using the underlying sequence/structurally derived features as the input. Ideally, any machine learning based prediction method must be trained with all possible variations in the input feature vector (all the sub-class input patterns) to achieve perfect learning. A near perfect learning can be achieved by training the model with diverse types of input instances belonging to the different regions of the entire input space. Furthermore, the prediction performance can be improved through balancing the training set as the imbalanced data sets will tend to produce the prediction bias towards majority class and its sub-classes. This paper is aimed to achieve (i) the high generalization ability without any classification bias through the diversified and balanced training sets as well as (ii) enhanced the prediction accuracy by combining the results of individual classifiers with an appropriate fusion scheme. Instead of creating the training set randomly, we have first used the unsupervised Kmeans clustering algorithm to create diversified clusters of input patterns and created the diversified and balanced training set by selecting an equal number of patterns from each of these clusters. Finally, probability based classifier fusion scheme was applied on boosted random forest algorithm (which produced greater sensitivity) and K nearest neighbour algorithm (which produced greater specificity) to achieve the enhanced predictive performance

  9. Communication skills training in dementia care: a systematic review of effectiveness, training content, and didactic methods in different care settings.

    Science.gov (United States)

    Eggenberger, Eva; Heimerl, Katharina; Bennett, Michael I

    2013-03-01

    Caring for and caring about people with dementia require specific communication skills. Healthcare professionals and family caregivers usually receive little training to enable them to meet the communicative needs of people with dementia. This review identifies existent interventions to enhance communication in dementia care in various care settings. We searched MEDLINE, AMED, EMBASE, PsychINFO, CINAHL, The Cochrane Library, Gerolit, and Web of Science for scientific articles reporting interventions in both English and German. An intervention was defined as communication skills training by means of face-to-face interaction with the aim of improving basic communicative skills. Both professional and family caregivers were included. The effectiveness of such training was analyzed. Different types of training were defined. Didactic methods, training content, and additional organizational features were qualitatively examined. This review included 12 trials totaling 831 persons with dementia, 519 professional caregivers, and 162 family caregivers. Most studies were carried out in the USA, the UK, and Germany. Eight studies took place in nursing homes; four studies were located in a home-care setting. No studies could be found in an acute-care setting. We provide a list of basic communicative principles for good communication in dementia care. Didactic methods included lectures, hands-on training, group discussions, and role-play. This review shows that communication skills training in dementia care significantly improves the quality of life and wellbeing of people with dementia and increases positive interactions in various care settings. Communication skills training shows significant impact on professional and family caregivers' communication skills, competencies, and knowledge. Additional organizational features improve the sustainability of communication interventions.

  10. An Efficient Supervised Training Algorithm for Multilayer Spiking Neural Networks.

    Science.gov (United States)

    Xie, Xiurui; Qu, Hong; Liu, Guisong; Zhang, Malu; Kurths, Jürgen

    2016-01-01

    The spiking neural networks (SNNs) are the third generation of neural networks and perform remarkably well in cognitive tasks such as pattern recognition. The spike emitting and information processing mechanisms found in biological cognitive systems motivate the application of the hierarchical structure and temporal encoding mechanism in spiking neural networks, which have exhibited strong computational capability. However, the hierarchical structure and temporal encoding approach require neurons to process information serially in space and time respectively, which reduce the training efficiency significantly. For training the hierarchical SNNs, most existing methods are based on the traditional back-propagation algorithm, inheriting its drawbacks of the gradient diffusion and the sensitivity on parameters. To keep the powerful computation capability of the hierarchical structure and temporal encoding mechanism, but to overcome the low efficiency of the existing algorithms, a new training algorithm, the Normalized Spiking Error Back Propagation (NSEBP) is proposed in this paper. In the feedforward calculation, the output spike times are calculated by solving the quadratic function in the spike response model instead of detecting postsynaptic voltage states at all time points in traditional algorithms. Besides, in the feedback weight modification, the computational error is propagated to previous layers by the presynaptic spike jitter instead of the gradient decent rule, which realizes the layer-wised training. Furthermore, our algorithm investigates the mathematical relation between the weight variation and voltage error change, which makes the normalization in the weight modification applicable. Adopting these strategies, our algorithm outperforms the traditional SNN multi-layer algorithms in terms of learning efficiency and parameter sensitivity, that are also demonstrated by the comprehensive experimental results in this paper.

  11. Combining similarity in time and space for training set formation under concept drift

    NARCIS (Netherlands)

    Zliobaite, I.

    2011-01-01

    Concept drift is a challenge in supervised learning for sequential data. It describes a phenomenon when the data distributions change over time. In such a case accuracy of a classifier benefits from the selective sampling for training. We develop a method for training set selection, particularly

  12. Performance of a visuomotor walking task in an augmented reality training setting

    NARCIS (Netherlands)

    Haarman, Juliet A.M.; Choi, Julia T.; Buurke, Jaap H.; Rietman, Johan S.; Reenalda, Jasper

    2017-01-01

    Visual cues can be used to train walking patterns. Here, we studied the performance and learning capacities of healthy subjects executing a high-precision visuomotor walking task, in an augmented reality training set-up. A beamer was used to project visual stepping targets on the walking surface of

  13. Residents' perceived needs in communication skills training across in- and outpatient clinical settings.

    Science.gov (United States)

    Junod Perron, Noelle; Sommer, Johanna; Hudelson, Patricia; Demaurex, Florence; Luthy, Christophe; Louis-Simonet, Martine; Nendaz, Mathieu; De Grave, Willem; Dolmans, Diana; Van der Vleuten, Cees

    2009-05-01

    Residents' perceived needs in communication skills training are important to identify before designing context-specific training programmes, since learrners' perceived needs can influence the effectiveness of training. To explore residents' perceptions of their training needs and training experiences around communication skills, and whether these differ between residents training in inpatient and outpatient clinical settings. Four focus groups (FG) and a self-administered questionnaire were conducted with residents working in in- and outpatient medical service settings at a Swiss University Hospital. Focus groups explored residents' perceptions of their communication needs, their past training experiences and suggestions for future training programmes in communication skills. Transcripts were analysed in a thematic way using qualitative analytic approaches. All residents from both settings were asked to complete a questionnaire that queried their sociodemographics and amount of prior training in communication skills. In focus groups, outpatient residents felt that communication skills were especially useful in addressing chronic diseases and social issues. In contrast, inpatient residents emphasized the importance of good communication skills for dealing with family conflicts and end-of-life issues. Felt needs reflected residents' differing service priorities: outpatient residents saw the need for skills to structure the consultation and explore patients' perspectives in order to build therapeutic alliances, whereas inpatient residents wanted techniques to help them break bad news, provide information and increase their own well-being. The survey's overall response rate was 56%. Its data showed that outpatient residents received more training in communication skills and more of them than inpatient residents considered communication skills training to be useful (100% vs 74%). Outpatient residents' perceived needs in communication skills were more patient

  14. Food hygiene training in small to medium-sized care settings.

    Science.gov (United States)

    Seaman, Phillip; Eves, Anita

    2008-10-01

    Adoption of safe food handling practices is essential to effectively manage food safety. This study explores the impact of basic or foundation level food hygiene training on the attitudes and intentions of food handlers in care settings, using questionnaires based on the Theory of Planned Behaviour. Interviews were also conducted with food handlers and their managers to ascertain beliefs about the efficacy of, perceived barriers to, and relevance of food hygiene training. Most food handlers had undertaken formal food hygiene training; however, many who had not yet received training were preparing food, including high risk foods. Appropriate pre-training support and on-going supervision appeared to be lacking, thus limiting the effectiveness of training. Findings showed Subjective Norm to be the most significant influence on food handlers' intention to perform safe food handling practices, irrespective of training status, emphasising the role of important others in determining desirable behaviours.

  15. NeuroRecovery Network provides standardization of locomotor training for persons with incomplete spinal cord injury.

    Science.gov (United States)

    Morrison, Sarah A; Forrest, Gail F; VanHiel, Leslie R; Davé, Michele; D'Urso, Denise

    2012-09-01

    To illustrate the continuity of care afforded by a standardized locomotor training program across a multisite network setting within the Christopher and Dana Reeve Foundation NeuroRecovery Network (NRN). Single patient case study. Two geographically different hospital-based outpatient facilities. This case highlights a 25-year-old man diagnosed with C4 motor incomplete spinal cord injury with American Spinal Injury Association Impairment Scale grade D. Standardized locomotor training program 5 sessions per week for 1.5 hours per session, for a total of 100 treatment sessions, with 40 sessions at 1 center and 60 at another. Ten-meter walk test and 6-minute walk test were assessed at admission and discharge across both facilities. For each of the 100 treatment sessions percent body weight support, average, and maximum treadmill speed were evaluated. Locomotor endurance, as measured by the 6-minute walk test, and overground gait speed showed consistent improvement from admission to discharge. Throughout training, the patient decreased the need for body weight support and was able to tolerate faster treadmill speeds. Data indicate that the patient continued to improve on both treatment parameters and walking function. Standardization across the NRN centers provided a mechanism for delivering consistent and reproducible locomotor training programs across 2 facilities without disrupting training or recovery progression. Copyright © 2012 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

  16. On attracting sets in artificial networks: cross activation

    Directory of Open Access Journals (Sweden)

    Sadyrbaev Felix

    2018-01-01

    Full Text Available Mathematical models of artificial networks can be formulated in terms of dynamical systems describing the behaviour of a network over time. The interrelation between nodes (elements of a network is encoded in the regulatory matrix. We consider a system of ordinary differential equations that describes in particular also genomic regulatory networks (GRN and contains a sigmoidal function. The results are presented on attractors of such systems for a particular case of cross activation. The regulatory matrix is then of particular form consisting of unit entries everywhere except the main diagonal. We show that such a system can have not more than three critical points. At least n–1 eigenvalues corresponding to any of the critical points are negative. An example for a particular choice of sigmoidal function is considered.

  17. Train Stop Scheduling in a High-Speed Rail Network by Utilizing a Two-Stage Approach

    Directory of Open Access Journals (Sweden)

    Huiling Fu

    2012-01-01

    Full Text Available Among the most commonly used methods of scheduling train stops are practical experience and various “one-step” optimal models. These methods face problems of direct transferability and computational complexity when considering a large-scale high-speed rail (HSR network such as the one in China. This paper introduces a two-stage approach for train stop scheduling with a goal of efficiently organizing passenger traffic into a rational train stop pattern combination while retaining features of regularity, connectivity, and rapidity (RCR. Based on a three-level station classification definition, a mixed integer programming model and a train operating tactics descriptive model along with the computing algorithm are developed and presented for the two stages. A real-world numerical example is presented using the Chinese HSR network as the setting. The performance of the train stop schedule and the applicability of the proposed approach are evaluated from the perspective of maintaining RCR.

  18. A Web Based Educational Programming Logic Controller Training Set Based on Vocational High School Students' Demands

    Directory of Open Access Journals (Sweden)

    Abdullah Alper Efe

    2018-01-01

    Full Text Available The purpose of this study was to design and develop aProgramming Logic Controller Training Set according to vocational high school students’ educational needs. In this regard, by using the properties of distance education the proposed system supported “hands-on” PLC programming laboratory exercises in industrial automation area. The system allowed students to access and control the PLC training set remotely. For this purpose, researcher designed a web site to facilitate students’ interactivity and support PLC programming. In the training set, Induction Motor, Frequency Converter and Encoder tripart controlled by Siemens Simatic S7-200 PLC controller by the help of SIMATIC Step 7 Programming Software were used to make the system more effective and efficient. Moreover, training set included an IP camera system allowing to monitor devices and pilot application. By working with this novel remote accessible training set, students and researchers recieved a chance to inhere self paced learning experiences. Also, The PLC training set offered an effective learning enviroenment for distance education, which is based on presenting the content on the web and opening it to the online users and provided a safe and economical solution for multiple users in a workplace to enhance the quality of education with less overall cost.

  19. Assessment of Random Assignment in Training and Test Sets using Generalized Cluster Analysis Technique

    Directory of Open Access Journals (Sweden)

    Sorana D. BOLBOACĂ

    2011-06-01

    Full Text Available Aim: The properness of random assignment of compounds in training and validation sets was assessed using the generalized cluster technique. Material and Method: A quantitative Structure-Activity Relationship model using Molecular Descriptors Family on Vertices was evaluated in terms of assignment of carboquinone derivatives in training and test sets during the leave-many-out analysis. Assignment of compounds was investigated using five variables: observed anticancer activity and four structure descriptors. Generalized cluster analysis with K-means algorithm was applied in order to investigate if the assignment of compounds was or not proper. The Euclidian distance and maximization of the initial distance using a cross-validation with a v-fold of 10 was applied. Results: All five variables included in analysis proved to have statistically significant contribution in identification of clusters. Three clusters were identified, each of them containing both carboquinone derivatives belonging to training as well as to test sets. The observed activity of carboquinone derivatives proved to be normal distributed on every. The presence of training and test sets in all clusters identified using generalized cluster analysis with K-means algorithm and the distribution of observed activity within clusters sustain a proper assignment of compounds in training and test set. Conclusion: Generalized cluster analysis using the K-means algorithm proved to be a valid method in assessment of random assignment of carboquinone derivatives in training and test sets.

  20. An Improved Neural Network Training Algorithm for Wi-Fi Fingerprinting Positioning

    Directory of Open Access Journals (Sweden)

    Esmond Mok

    2013-09-01

    Full Text Available Ubiquitous positioning provides continuous positional information in both indoor and outdoor environments for a wide spectrum of location based service (LBS applications. With the rapid development of the low-cost and high speed data communication, Wi-Fi networks in many metropolitan cities, strength of signals propagated from the Wi-Fi access points (APs namely received signal strength (RSS have been cleverly adopted for indoor positioning. In this paper, a Wi-Fi positioning algorithm based on neural network modeling of Wi-Fi signal patterns is proposed. This algorithm is based on the correlation between the initial parameter setting for neural network training and output of the mean square error to obtain better modeling of the nonlinear highly complex Wi-Fi signal power propagation surface. The test results show that this neural network based data processing algorithm can significantly improve the neural network training surface to achieve the highest possible accuracy of the Wi-Fi fingerprinting positioning method.

  1. Training feed-forward neural networks with gain constraints

    Science.gov (United States)

    Hartman

    2000-04-01

    Inaccurate input-output gains (partial derivatives of outputs with respect to inputs) are common in neural network models when input variables are correlated or when data are incomplete or inaccurate. Accurate gains are essential for optimization, control, and other purposes. We develop and explore a method for training feedforward neural networks subject to inequality or equality-bound constraints on the gains of the learned mapping. Gain constraints are implemented as penalty terms added to the objective function, and training is done using gradient descent. Adaptive and robust procedures are devised for balancing the relative strengths of the various terms in the objective function, which is essential when the constraints are inconsistent with the data. The approach has the virtue that the model domain of validity can be extended via extrapolation training, which can dramatically improve generalization. The algorithm is demonstrated here on artificial and real-world problems with very good results and has been advantageously applied to dozens of models currently in commercial use.

  2. SETS, Boolean Manipulation for Network Analysis and Fault Tree Analysis

    International Nuclear Information System (INIS)

    Worrell, R.B.

    1985-01-01

    Description of problem or function - SETS is used for symbolic manipulation of set (or Boolean) equations, particularly the reduction of set equations by the application of set identities. It is a flexible and efficient tool for performing probabilistic risk analysis (PRA), vital area analysis, and common cause analysis. The equation manipulation capabilities of SETS can also be used to analyze non-coherent fault trees and determine prime implicants of Boolean functions, to verify circuit design implementation, to determine minimum cost fire protection requirements for nuclear reactor plants, to obtain solutions to combinatorial optimization problems with Boolean constraints, and to determine the susceptibility of a facility to unauthorized access through nullification of sensors in its protection system. 4. Method of solution - The SETS program is used to read, interpret, and execute the statements of a SETS user program which is an algorithm that specifies the particular manipulations to be performed and the order in which they are to occur. 5. Restrictions on the complexity of the problem - Any properly formed set equation involving the set operations of union, intersection, and complement is acceptable for processing by the SETS program. Restrictions on the size of a set equation that can be processed are not absolute but rather are related to the number of terms in the disjunctive normal form of the equation, the number of literals in the equation, etc. Nevertheless, set equations involving thousands and even hundreds of thousands of terms can be processed successfully

  3. Training Excitatory-Inhibitory Recurrent Neural Networks for Cognitive Tasks: A Simple and Flexible Framework

    Science.gov (United States)

    Wang, Xiao-Jing

    2016-01-01

    The ability to simultaneously record from large numbers of neurons in behaving animals has ushered in a new era for the study of the neural circuit mechanisms underlying cognitive functions. One promising approach to uncovering the dynamical and computational principles governing population responses is to analyze model recurrent neural networks (RNNs) that have been optimized to perform the same tasks as behaving animals. Because the optimization of network parameters specifies the desired output but not the manner in which to achieve this output, “trained” networks serve as a source of mechanistic hypotheses and a testing ground for data analyses that link neural computation to behavior. Complete access to the activity and connectivity of the circuit, and the ability to manipulate them arbitrarily, make trained networks a convenient proxy for biological circuits and a valuable platform for theoretical investigation. However, existing RNNs lack basic biological features such as the distinction between excitatory and inhibitory units (Dale’s principle), which are essential if RNNs are to provide insights into the operation of biological circuits. Moreover, trained networks can achieve the same behavioral performance but differ substantially in their structure and dynamics, highlighting the need for a simple and flexible framework for the exploratory training of RNNs. Here, we describe a framework for gradient descent-based training of excitatory-inhibitory RNNs that can incorporate a variety of biological knowledge. We provide an implementation based on the machine learning library Theano, whose automatic differentiation capabilities facilitate modifications and extensions. We validate this framework by applying it to well-known experimental paradigms such as perceptual decision-making, context-dependent integration, multisensory integration, parametric working memory, and motor sequence generation. Our results demonstrate the wide range of neural activity

  4. Reconstruction of sparse connectivity in neural networks from spike train covariances

    International Nuclear Information System (INIS)

    Pernice, Volker; Rotter, Stefan

    2013-01-01

    The inference of causation from correlation is in general highly problematic. Correspondingly, it is difficult to infer the existence of physical synaptic connections between neurons from correlations in their activity. Covariances in neural spike trains and their relation to network structure have been the subject of intense research, both experimentally and theoretically. The influence of recurrent connections on covariances can be characterized directly in linear models, where connectivity in the network is described by a matrix of linear coupling kernels. However, as indirect connections also give rise to covariances, the inverse problem of inferring network structure from covariances can generally not be solved unambiguously. Here we study to what degree this ambiguity can be resolved if the sparseness of neural networks is taken into account. To reconstruct a sparse network, we determine the minimal set of linear couplings consistent with the measured covariances by minimizing the L 1 norm of the coupling matrix under appropriate constraints. Contrary to intuition, after stochastic optimization of the coupling matrix, the resulting estimate of the underlying network is directed, despite the fact that a symmetric matrix of count covariances is used for inference. The performance of the new method is best if connections are neither exceedingly sparse, nor too dense, and it is easily applicable for networks of a few hundred nodes. Full coupling kernels can be obtained from the matrix of full covariance functions. We apply our method to networks of leaky integrate-and-fire neurons in an asynchronous–irregular state, where spike train covariances are well described by a linear model. (paper)

  5. Improving probe set selection for microbial community analysis by leveraging taxonomic information of training sequences

    Directory of Open Access Journals (Sweden)

    Jiang Tao

    2011-10-01

    Full Text Available Abstract Background Population levels of microbial phylotypes can be examined using a hybridization-based method that utilizes a small set of computationally-designed DNA probes targeted to a gene common to all. Our previous algorithm attempts to select a set of probes such that each training sequence manifests a unique theoretical hybridization pattern (a binary fingerprint to a probe set. It does so without taking into account similarity between training gene sequences or their putative taxonomic classifications, however. We present an improved algorithm for probe set selection that utilizes the available taxonomic information of training gene sequences and attempts to choose probes such that the resultant binary fingerprints cluster into real taxonomic groups. Results Gene sequences manifesting identical fingerprints with probes chosen by the new algorithm are more likely to be from the same taxonomic group than probes chosen by the previous algorithm. In cases where they are from different taxonomic groups, underlying DNA sequences of identical fingerprints are more similar to each other in probe sets made with the new versus the previous algorithm. Complete removal of large taxonomic groups from training data does not greatly decrease the ability of probe sets to distinguish those groups. Conclusions Probe sets made from the new algorithm create fingerprints that more reliably cluster into biologically meaningful groups. The method can readily distinguish microbial phylotypes that were excluded from the training sequences, suggesting novel microbes can also be detected.

  6. Improving probe set selection for microbial community analysis by leveraging taxonomic information of training sequences.

    Science.gov (United States)

    Ruegger, Paul M; Della Vedova, Gianluca; Jiang, Tao; Borneman, James

    2011-10-10

    Population levels of microbial phylotypes can be examined using a hybridization-based method that utilizes a small set of computationally-designed DNA probes targeted to a gene common to all. Our previous algorithm attempts to select a set of probes such that each training sequence manifests a unique theoretical hybridization pattern (a binary fingerprint) to a probe set. It does so without taking into account similarity between training gene sequences or their putative taxonomic classifications, however. We present an improved algorithm for probe set selection that utilizes the available taxonomic information of training gene sequences and attempts to choose probes such that the resultant binary fingerprints cluster into real taxonomic groups. Gene sequences manifesting identical fingerprints with probes chosen by the new algorithm are more likely to be from the same taxonomic group than probes chosen by the previous algorithm. In cases where they are from different taxonomic groups, underlying DNA sequences of identical fingerprints are more similar to each other in probe sets made with the new versus the previous algorithm. Complete removal of large taxonomic groups from training data does not greatly decrease the ability of probe sets to distinguish those groups. Probe sets made from the new algorithm create fingerprints that more reliably cluster into biologically meaningful groups. The method can readily distinguish microbial phylotypes that were excluded from the training sequences, suggesting novel microbes can also be detected.

  7. How to Train Safe Drivers: Setting Up and Evaluating a Fatigue Training Program

    Directory of Open Access Journals (Sweden)

    Adamos Giannis

    2015-02-01

    Full Text Available Fatigue is considered as a serious risk driving behavior, causing road accidents, which in many cases involve fatalities and severe injuries. According to CARE database statistics, professional drivers are indicated as a high-risk group to be involved in a fatigue-related accident. Acknowledging these statistics, a training program on driving fatigue was organized, aiming at raising awareness of professional drivers of a leading company in building materials, in Greece. Selected experimental methods were used for collecting data before and after the training program, which allowed monitoring and assessing the potential behavioural changes. A questionnaire survey was conducted before the program implementation to 162 drivers of the company, while two months after the program, the same drivers replied to a second questionnaire. Impact assessment of the program relied on statistical analysis of the responses. Results showed the degree of penetration of the training program in the professional drivers' behavior towards safe driving.

  8. Integrating Soft Set Theory and Fuzzy Linguistic Model to Evaluate the Performance of Training Simulation Systems.

    Science.gov (United States)

    Chang, Kuei-Hu; Chang, Yung-Chia; Chain, Kai; Chung, Hsiang-Yu

    2016-01-01

    The advancement of high technologies and the arrival of the information age have caused changes to the modern warfare. The military forces of many countries have replaced partially real training drills with training simulation systems to achieve combat readiness. However, considerable types of training simulation systems are used in military settings. In addition, differences in system set up time, functions, the environment, and the competency of system operators, as well as incomplete information have made it difficult to evaluate the performance of training simulation systems. To address the aforementioned problems, this study integrated analytic hierarchy process, soft set theory, and the fuzzy linguistic representation model to evaluate the performance of various training simulation systems. Furthermore, importance-performance analysis was adopted to examine the influence of saving costs and training safety of training simulation systems. The findings of this study are expected to facilitate applying military training simulation systems, avoiding wasting of resources (e.g., low utility and idle time), and providing data for subsequent applications and analysis. To verify the method proposed in this study, the numerical examples of the performance evaluation of training simulation systems were adopted and compared with the numerical results of an AHP and a novel AHP-based ranking technique. The results verified that not only could expert-provided questionnaire information be fully considered to lower the repetition rate of performance ranking, but a two-dimensional graph could also be used to help administrators allocate limited resources, thereby enhancing the investment benefits and training effectiveness of a training simulation system.

  9. Effect of creatine supplementation and drop-set resistance training in untrained aging adults.

    Science.gov (United States)

    Johannsmeyer, Sarah; Candow, Darren G; Brahms, C Markus; Michel, Deborah; Zello, Gordon A

    2016-10-01

    To investigate the effects of creatine supplementation and drop-set resistance training in untrained aging adults. Participants were randomized to one of two groups: Creatine (CR: n=14, 7 females, 7 males; 58.0±3.0yrs, 0.1g/kg/day of creatine+0.1g/kg/day of maltodextrin) or Placebo (PLA: n=17, 7 females, 10 males; age: 57.6±5.0yrs, 0.2g/kg/day of maltodextrin) during 12weeks of drop-set resistance training (3days/week; 2 sets of leg press, chest press, hack squat and lat pull-down exercises performed to muscle fatigue at 80% baseline 1-repetition maximum [1-RM] immediately followed by repetitions to muscle fatigue at 30% baseline 1-RM). Prior to and following training and supplementation, assessments were made for body composition, muscle strength, muscle endurance, tasks of functionality, muscle protein catabolism and diet. Drop-set resistance training improved muscle mass, muscle strength, muscle endurance and tasks of functionality (pcreatine to drop-set resistance training significantly increased body mass (p=0.002) and muscle mass (p=0.007) compared to placebo. Males on creatine increased muscle strength (lat pull-down only) to a greater extent than females on creatine (p=0.005). Creatine enabled males to resistance train at a greater capacity over time compared to males on placebo (p=0.049) and females on creatine (p=0.012). Males on creatine (p=0.019) and females on placebo (p=0.014) decreased 3-MH compared to females on creatine. The addition of creatine to drop-set resistance training augments the gains in muscle mass from resistance training alone. Creatine is more effective in untrained aging males compared to untrained aging females. Copyright © 2016 Elsevier Inc. All rights reserved.

  10. [Training of institutional research networks as a strategy of improvement].

    Science.gov (United States)

    Galván-Plata, María Eugenia; Almeida-Gutiérrez, Eduardo; Salamanca-Gómez, Fabio Abdel

    2017-01-01

    The Instituto Mexicano del Seguro Social (IMSS) through the Coordinación de Investigación en Salud (Health Research Council) has promoted a strong link between the generation of scientific knowledge and the clinical care through the program Redes Institucionales de Investigación (Institutional Research Network Program), whose main aim is to promote and generate collaborative research between clinical, basic, epidemiologic, educational, economic and health services researchers, seeking direct benefits for patients, as well as to generate a positive impact on institutional processes. All of these research lines have focused on high-priority health issues in Mexico. The IMSS internal structure, as well as the sufficient health services coverage, allows the integration of researchers at the three levels of health care into these networks. A few years after their creation, these networks have already generated significant results, and these are currently applied in the institutional regulations in diseases that represent a high burden to health care. Two examples are the National Health Care Program for Patients with Acute Myocardial Infarction "Código Infarto", and the Early Detection Program on Chronic Kidney Disease; another result is the generation of multiple scientific publications, and the promotion of training of human resources in research from the same members of our Research Networks. There is no doubt that the Coordinación de Investigación en Salud advances steadily implementing the translational research, which will keep being fruitful to the benefit of our patients, and of our own institution.

  11. Routing trains through railway junctions: A new set-packing approach

    DEFF Research Database (Denmark)

    Lusby, Richard Martin; Larsen, Jesper; Ryan, David

    2011-01-01

    The problem of routing trains through railway junctions is an integral part of railway operations. Large junctions are highly interconnected networks of track where multiple railway lines merge, intersect, and split. The number of possible routings makes this a very complicated problem. We show h...

  12. An Analysis of Training Focused on Improving SMART Goal Setting for Specific Employee Groups

    Science.gov (United States)

    Worden, Jeannie M.

    2014-01-01

    This quantitative study examined the proficiency of employee SMART goal setting following the intervention of employee SMART goal setting training. Current challenges in higher education substantiate the need for employees to align their performance with the mission, vision, and strategic directions of the organization. A performance management…

  13. Network Training for a Boy with Learning Disabilities and Behaviours That Challenge

    Science.gov (United States)

    Cooper, Kate; McElwee, Jennifer

    2016-01-01

    Background: Network Training is an intervention that draws upon systemic ideas and behavioural principles to promote positive change in networks of support for people defined as having a learning disability. To date, there are no published case studies looking at the outcomes of Network Training. Materials and Methods: This study aimed to…

  14. Neuromuscular and blood lactate responses to squat power training with different rest intervals between sets.

    Science.gov (United States)

    Martorelli, André; Bottaro, Martim; Vieira, Amilton; Rocha-Júnior, Valdinar; Cadore, Eduardo; Prestes, Jonato; Wagner, Dale; Martorelli, Saulo

    2015-06-01

    Studies investigating the effect of rest interval length (RI) between sets on neuromuscular performance and metabolic response during power training are scarce. Therefore, the purpose of this study was to compare maximal power output, muscular activity and blood lactate concentration following 1, 2 or 3 minutes RI between sets during a squat power training protocol. Twelve resistance-trained men (22.7 ± 3.2 years; 1.79 ± 0.08 cm; 81.8 ± 11.3 kg) performed 6 sets of 6 repetitions of squat exercise at 60% of their 1 repetition maximum. Peak and average power were obtained for each repetition and set using a linear position transducer. Muscular activity and blood lactate were measured pre and post-exercise session. There was no significant difference between RI on peak power and average power. However, peak power decreased 5.6%, 1.9%, and 5.9% after 6 sets using 1, 2 and 3 minutes of RI, respectively. Average power also decreased 10.5% (1 min), 2.6% (2 min), and 4.3% (3 min) after 6 sets. Blood lactate increased similarly during the three training sessions (1-min: 5.5 mMol, 2-min: 4.3 mMol, and 3-min: 4.0 mMol) and no significant changes were observed in the muscle activity after multiple sets, independent of RI length (pooled ES for 1-min: 0.47, 2-min: 0.65, and 3-min: 1.39). From a practical point of view, the results suggest that 1 to 2 minute of RI between sets during squat exercise may be sufficient to recover power output in a designed power training protocol. However, if training duration is malleable, we recommend 2 min of RI for optimal recovery and power output maintenance during the subsequent exercise sets. Key pointsThis study demonstrates that 1 minute of RI between sets is sufficient to maintain maximal power output during multiple sets of a power-based exercise when it is composed of few repetitions and the sets are not performed until failure. Therefore, a short RI should be considered when designing training programs for the development of

  15. Cut set-based risk and reliability analysis for arbitrarily interconnected networks

    Science.gov (United States)

    Wyss, Gregory D.

    2000-01-01

    Method for computing all-terminal reliability for arbitrarily interconnected networks such as the United States public switched telephone network. The method includes an efficient search algorithm to generate minimal cut sets for nonhierarchical networks directly from the network connectivity diagram. Efficiency of the search algorithm stems in part from its basis on only link failures. The method also includes a novel quantification scheme that likewise reduces computational effort associated with assessing network reliability based on traditional risk importance measures. Vast reductions in computational effort are realized since combinatorial expansion and subsequent Boolean reduction steps are eliminated through analysis of network segmentations using a technique of assuming node failures to occur on only one side of a break in the network, and repeating the technique for all minimal cut sets generated with the search algorithm. The method functions equally well for planar and non-planar networks.

  16. A simulation training evaluation method for distribution network fault based on radar chart

    Directory of Open Access Journals (Sweden)

    Yuhang Xu

    2018-01-01

    Full Text Available In order to solve the problem of automatic evaluation of dispatcher fault simulation training in distribution network, a simulation training evaluation method based on radar chart for distribution network fault is proposed. The fault handling information matrix is established to record the dispatcher fault handling operation sequence and operation information. The four situations of the dispatcher fault isolation operation are analyzed. The fault handling anti-misoperation rule set is established to describe the rules prohibiting dispatcher operation. Based on the idea of artificial intelligence reasoning, the feasibility of dispatcher fault handling is described by the feasibility index. The relevant factors and evaluation methods are discussed from the three aspects of the fault handling result feasibility, the anti-misoperation correctness and the operation process conciseness. The detailed calculation formula is given. Combining the independence and correlation between the three evaluation angles, a comprehensive evaluation method of distribution network fault simulation training based on radar chart is proposed. The method can comprehensively reflect the fault handling process of dispatchers, and comprehensively evaluate the fault handling process from various angles, which has good practical value.

  17. The diagnosis of autism in community pediatric settings: does advanced training facilitate practice change?

    Science.gov (United States)

    Swanson, Amy R; Warren, Zachary E; Stone, Wendy L; Vehorn, Alison C; Dohrmann, Elizabeth; Humberd, Quentin

    2014-07-01

    The increased prevalence of autism spectrum disorder and documented benefits of early intensive intervention have created a need for flexible systems for determining eligibility for autism-specific services. This study evaluated the effectiveness of a training program designed to enhance autism spectrum disorder identification and assessment within community pediatric settings across the state. Twenty-seven pediatric providers participated in regional trainings across a 3.5-year period. Trainings provided clinicians with strategies for conducting relatively brief within-practice interactive assessments following positive autism spectrum disorder screenings. Program evaluation was measured approximately 1.5 years following training through (a) clinician self-reports of practice change and (b) blind diagnostic verification of a subset of children assessed. Pediatric providers participating in the training reported significant changes in screening and consultation practices following training, with a reported 85% increase in diagnostic identification of children with autism spectrum disorder within their own practice setting. In addition, substantial agreement (86%-93%) was found between pediatrician diagnostic judgments and independent, comprehensive blinded diagnostic evaluations. Collaborative training methods that allow autism spectrum disorder identification within broader community pediatric settings may help translate enhanced screening initiatives into more effective and efficient diagnosis and treatment. © The Author(s) 2013.

  18. Combined Ozone Retrieval From METOP Sensors Using META-Training Of Deep Neural Networks

    Science.gov (United States)

    Felder, Martin; Sehnke, Frank; Kaifel, Anton

    2013-12-01

    The newest installment of our well-proven Neural Net- work Ozone Retrieval System (NNORSY) combines the METOP sensors GOME-2 and IASI with cloud information from AVHRR. Through the use of advanced meta- learning techniques like automatic feature selection and automatic architecture search applied to a set of deep neural networks, having at least two or three hidden layers, we have been able to avoid many technical issues normally encountered during the construction of such a joint retrieval system. This has been made possible by harnessing the processing power of modern consumer graphics cards with high performance graphic processors (GPU), which decreases training times by about two orders of magnitude. The system was trained on data from 2009 and 2010, including target ozone profiles from ozone sondes, ACE- FTS and MLS-AURA. To make maximum use of tropospheric information in the spectra, the data were partitioned into several sets of different cloud fraction ranges with the GOME-2 FOV, on which specialized retrieval networks are being trained. For the final ozone retrieval processing the different specialized networks are combined. The resulting retrieval system is very stable and does not show any systematic dependence on solar zenith angle, scan angle or sensor degradation. We present several sensitivity studies with regard to cloud fraction and target sensor type, as well as the performance in several latitude bands and with respect to independent validation stations. A visual cross-comparison against high-resolution ozone profiles from the KNMI EUMETSAT Ozone SAF product has also been performed and shows some distinctive features which we will briefly discuss. Overall, we demonstrate that a complex retrieval system can now be constructed with a minimum of ma- chine learning knowledge, using automated algorithms for many design decisions previously requiring expert knowledge. Provided sufficient training data and computation power of GPUs is available, the

  19. Multi-Objective Evaluation of Target Sets for Logistics Networks

    National Research Council Canada - National Science Library

    Emslie, Paul

    2000-01-01

    .... In the presence of many objectives--such as reducing maximum flow, lengthening routes, avoiding collateral damage, all at minimal risk to our pilots--the problem of determining the best target set is complex...

  20. Effect of exercise order on the number of repeats and training volume in the tri-set training method

    Directory of Open Access Journals (Sweden)

    Waynne Ferreira de Faria

    2016-05-01

    Full Text Available DOI: http://dx.doi.org/10.5007/1980-0037.2016v18n2p187   Although the tri-set system is widely adopted by athletes and experienced weight training practitioners aimed at optimizing the metabolic overload, there are still few works in literature on the effect of exercise order manipulation on this training system. Therefore, this work was aimed at investigating the effect of exercise order manipulation on the number of repeats and training volume using the tri-set system for lower limbs. This is a randomized cross-over design study. The experimental group consisted of 14 healthy men (23.53 ± 5.40 years; 24.51 ± 2.96 kg/m2. Subjects were submitted to two experimental sessions at different exercise order for lower limbs: Sequence A: squat on guided bar, leg press 45° and bilateral leg extension; sequence B: bilateral leg extension, leg press 45° and squat on guided bar. Three sets to volitional fatigue in all exercises were performed, with intensity of 75% 1RM. Superiority for sequence B in the total number of repeats (70.14 ± 13 vs 60.93 ± 7.94 repeats, p = 0.004 and total training volume (9129.64 ± 2830.05 vs 8238.29 ± 2354.20 kg, p = 0.014 was observed. Based on the above, the performance of single-joint exercises before multi-joint exercises in the tri-set system adopted for lower limbs induced higher number of repeats and total training volume.

  1. Microsurgery simulation training system and set up: An essential system to complement every training programme.

    Science.gov (United States)

    Masud, Dhalia; Haram, Nadine; Moustaki, Margarita; Chow, Whitney; Saour, Samer; Mohanna, Pari Naz

    2017-07-01

    Microsurgical techniques are essential in plastic surgery; however, inconsistent training practices, acquiring these skills can be difficult. To address this, we designed a standardised laboratory-based microsurgical training programme, which allows trainees to develop their dexterity, visuospatial ability, operative flow and judgement as separate components. Thirty trainees completed an initial microsurgical anastomosis on a chicken femoral artery, assessed using the structured assessment of microsurgical skills (SAMS) method. The study group (n = 18) then completed a 3-month training programme, while the control group (n = 19) did not. A final anastomosis was completed by all trainees (n = 30). The study group had a significant improvement in the microsurgical technique, assessed using the SAMS score, when the initial and final scores were compared (Mean: 24 SAMS initial versus 49 SAMS final) (p group had a significantly lower rate of improvement (Mean: 23 SAMS initial versus 25 SAMS final). There was a significant difference between the final SAMS score of the study group and that of senior surgeons (Mean: 49 study final SAMS versus 58 senior SAMS). This validated programme is a safe, cost-effective and flexible method of allowing trainees to develop microsurgical skills in a non-pressurized environment. In addition, the objectified skills allow trainers to assess the trainees' level of proficiency before operating on patients. Copyright © 2017 British Association of Plastic, Reconstructive and Aesthetic Surgeons. Published by Elsevier Ltd. All rights reserved.

  2. Illusory Motion Reproduced by Deep Neural Networks Trained for Prediction.

    Science.gov (United States)

    Watanabe, Eiji; Kitaoka, Akiyoshi; Sakamoto, Kiwako; Yasugi, Masaki; Tanaka, Kenta

    2018-01-01

    The cerebral cortex predicts visual motion to adapt human behavior to surrounding objects moving in real time. Although the underlying mechanisms are still unknown, predictive coding is one of the leading theories. Predictive coding assumes that the brain's internal models (which are acquired through learning) predict the visual world at all times and that errors between the prediction and the actual sensory input further refine the internal models. In the past year, deep neural networks based on predictive coding were reported for a video prediction machine called PredNet. If the theory substantially reproduces the visual information processing of the cerebral cortex, then PredNet can be expected to represent the human visual perception of motion. In this study, PredNet was trained with natural scene videos of the self-motion of the viewer, and the motion prediction ability of the obtained computer model was verified using unlearned videos. We found that the computer model accurately predicted the magnitude and direction of motion of a rotating propeller in unlearned videos. Surprisingly, it also represented the rotational motion for illusion images that were not moving physically, much like human visual perception. While the trained network accurately reproduced the direction of illusory rotation, it did not detect motion components in negative control pictures wherein people do not perceive illusory motion. This research supports the exciting idea that the mechanism assumed by the predictive coding theory is one of basis of motion illusion generation. Using sensory illusions as indicators of human perception, deep neural networks are expected to contribute significantly to the development of brain research.

  3. Illusory Motion Reproduced by Deep Neural Networks Trained for Prediction

    Directory of Open Access Journals (Sweden)

    Eiji Watanabe

    2018-03-01

    Full Text Available The cerebral cortex predicts visual motion to adapt human behavior to surrounding objects moving in real time. Although the underlying mechanisms are still unknown, predictive coding is one of the leading theories. Predictive coding assumes that the brain's internal models (which are acquired through learning predict the visual world at all times and that errors between the prediction and the actual sensory input further refine the internal models. In the past year, deep neural networks based on predictive coding were reported for a video prediction machine called PredNet. If the theory substantially reproduces the visual information processing of the cerebral cortex, then PredNet can be expected to represent the human visual perception of motion. In this study, PredNet was trained with natural scene videos of the self-motion of the viewer, and the motion prediction ability of the obtained computer model was verified using unlearned videos. We found that the computer model accurately predicted the magnitude and direction of motion of a rotating propeller in unlearned videos. Surprisingly, it also represented the rotational motion for illusion images that were not moving physically, much like human visual perception. While the trained network accurately reproduced the direction of illusory rotation, it did not detect motion components in negative control pictures wherein people do not perceive illusory motion. This research supports the exciting idea that the mechanism assumed by the predictive coding theory is one of basis of motion illusion generation. Using sensory illusions as indicators of human perception, deep neural networks are expected to contribute significantly to the development of brain research.

  4. Case Study: Does training of private networks of Family Planning clinicians in urban Pakistan affect service utilization?

    Science.gov (United States)

    2010-01-01

    Background To determine whether training of providers participating in franchise clinic networks is associated with increased Family Planning service use among low-income urban families in Pakistan. Methods The study uses 2001 survey data consisting of interviews with 1113 clinical and non-clinical providers working in public and private hospitals/clinics. Data analysis excludes non-clinical providers reducing sample size to 822. Variables for the analysis are divided into client volume, and training in family planning. Regression models are used to compute the association between training and service use in franchise versus private non-franchise clinics. Results In franchise clinic networks, staff are 6.5 times more likely to receive family planning training (P = 0.00) relative to private non-franchises. Service use was significantly associated with training (P = 0.00), franchise affiliation (P = 0.01), providers' years of family planning experience (P = 0.02) and the number of trained staff working at government owned clinics (P = 0.00). In this setting, nurses are significantly less likely to receive training compared to doctors (P = 0.00). Conclusions These findings suggest that franchises recruit and train various cadres of health workers and training maybe associated with increased service use through improvement in quality of services. PMID:21062460

  5. Case Study: Does training of private networks of Family Planning clinicians in urban Pakistan affect service utilization?

    Directory of Open Access Journals (Sweden)

    Qureshi Asma M

    2010-11-01

    Full Text Available Abstract Background To determine whether training of providers participating in franchise clinic networks is associated with increased Family Planning service use among low-income urban families in Pakistan. Methods The study uses 2001 survey data consisting of interviews with 1113 clinical and non-clinical providers working in public and private hospitals/clinics. Data analysis excludes non-clinical providers reducing sample size to 822. Variables for the analysis are divided into client volume, and training in family planning. Regression models are used to compute the association between training and service use in franchise versus private non-franchise clinics. Results In franchise clinic networks, staff are 6.5 times more likely to receive family planning training (P = 0.00 relative to private non-franchises. Service use was significantly associated with training (P = 0.00, franchise affiliation (P = 0.01, providers' years of family planning experience (P = 0.02 and the number of trained staff working at government owned clinics (P = 0.00. In this setting, nurses are significantly less likely to receive training compared to doctors (P = 0.00. Conclusions These findings suggest that franchises recruit and train various cadres of health workers and training maybe associated with increased service use through improvement in quality of services.

  6. Case Study: Does training of private networks of Family Planning clinicians in urban Pakistan affect service utilization?

    Science.gov (United States)

    Qureshi, Asma M

    2010-11-09

    To determine whether training of providers participating in franchise clinic networks is associated with increased Family Planning service use among low-income urban families in Pakistan. The study uses 2001 survey data consisting of interviews with 1113 clinical and non-clinical providers working in public and private hospitals/clinics. Data analysis excludes non-clinical providers reducing sample size to 822. Variables for the analysis are divided into client volume, and training in family planning. Regression models are used to compute the association between training and service use in franchise versus private non-franchise clinics. In franchise clinic networks, staff are 6.5 times more likely to receive family planning training (P = 0.00) relative to private non-franchises. Service use was significantly associated with training (P = 0.00), franchise affiliation (P = 0.01), providers' years of family planning experience (P = 0.02) and the number of trained staff working at government owned clinics (P = 0.00). In this setting, nurses are significantly less likely to receive training compared to doctors (P = 0.00). These findings suggest that franchises recruit and train various cadres of health workers and training maybe associated with increased service use through improvement in quality of services.

  7. Deconstructing myths, building alliances: a networking model to enhance tobacco control in hospital mental health settings.

    Science.gov (United States)

    Ballbè, Montse; Gual, Antoni; Nieva, Gemma; Saltó, Esteve; Fernández, Esteve

    2016-01-01

    Life expectancy for people with severe mental disorders is up to 25 years less in comparison to the general population, mainly due to diseases caused or worsened by smoking. However, smoking is usually a neglected issue in mental healthcare settings. The aim of this article is to describe a strategy to improve tobacco control in the hospital mental healthcare services of Catalonia (Spain). To bridge this gap, the Catalan Network of Smoke-free Hospitals launched a nationwide bottom-up strategy in Catalonia in 2007. The strategy relied on the creation of a working group of key professionals from various hospitals -the early adopters- based on Rogers' theory of the Diffusion of Innovations. In 2016, the working group is composed of professionals from 17 hospitals (70.8% of all hospitals in the region with mental health inpatient units). Since 2007, tobacco control has improved in different areas such as increasing mental health professionals' awareness of smoking, training professionals on smoking cessation interventions and achieving good compliance with the national smoking ban. The working group has produced and disseminated various materials, including clinical practice and best practice guidelines, implemented smoking cessation programmes and organised seminars and training sessions on smoking cessation measures in patients with mental illnesses. The next challenge is to ensure effective follow-up for smoking cessation after discharge. While some areas of tobacco control within these services still require significant improvement, the aforementioned initiative promotes successful tobacco control in these settings. Copyright © 2016 SESPAS. Publicado por Elsevier España, S.L.U. All rights reserved.

  8. Exhaustively characterizing feasible logic models of a signaling network using Answer Set Programming.

    Science.gov (United States)

    Guziolowski, Carito; Videla, Santiago; Eduati, Federica; Thiele, Sven; Cokelaer, Thomas; Siegel, Anne; Saez-Rodriguez, Julio

    2013-09-15

    Logic modeling is a useful tool to study signal transduction across multiple pathways. Logic models can be generated by training a network containing the prior knowledge to phospho-proteomics data. The training can be performed using stochastic optimization procedures, but these are unable to guarantee a global optima or to report the complete family of feasible models. This, however, is essential to provide precise insight in the mechanisms underlaying signal transduction and generate reliable predictions. We propose the use of Answer Set Programming to explore exhaustively the space of feasible logic models. Toward this end, we have developed caspo, an open-source Python package that provides a powerful platform to learn and characterize logic models by leveraging the rich modeling language and solving technologies of Answer Set Programming. We illustrate the usefulness of caspo by revisiting a model of pro-growth and inflammatory pathways in liver cells. We show that, if experimental error is taken into account, there are thousands (11 700) of models compatible with the data. Despite the large number, we can extract structural features from the models, such as links that are always (or never) present or modules that appear in a mutual exclusive fashion. To further characterize this family of models, we investigate the input-output behavior of the models. We find 91 behaviors across the 11 700 models and we suggest new experiments to discriminate among them. Our results underscore the importance of characterizing in a global and exhaustive manner the family of feasible models, with important implications for experimental design. caspo is freely available for download (license GPLv3) and as a web service at http://caspo.genouest.org/. Supplementary materials are available at Bioinformatics online. santiago.videla@irisa.fr.

  9. Improvement of training set structure in fusion data cleaning using Time-Domain Global Similarity method

    International Nuclear Information System (INIS)

    Liu, J.; Lan, T.; Qin, H.

    2017-01-01

    Traditional data cleaning identifies dirty data by classifying original data sequences, which is a class-imbalanced problem since the proportion of incorrect data is much less than the proportion of correct ones for most diagnostic systems in Magnetic Confinement Fusion (MCF) devices. When using machine learning algorithms to classify diagnostic data based on class-imbalanced training set, most classifiers are biased towards the major class and show very poor classification rates on the minor class. By transforming the direct classification problem about original data sequences into a classification problem about the physical similarity between data sequences, the class-balanced effect of Time-Domain Global Similarity (TDGS) method on training set structure is investigated in this paper. Meanwhile, the impact of improved training set structure on data cleaning performance of TDGS method is demonstrated with an application example in EAST POlarimetry-INTerferometry (POINT) system.

  10. Clustering and training set selection methods for improving the accuracy of quantitative laser induced breakdown spectroscopy

    International Nuclear Information System (INIS)

    Anderson, Ryan B.; Bell, James F.; Wiens, Roger C.; Morris, Richard V.; Clegg, Samuel M.

    2012-01-01

    We investigated five clustering and training set selection methods to improve the accuracy of quantitative chemical analysis of geologic samples by laser induced breakdown spectroscopy (LIBS) using partial least squares (PLS) regression. The LIBS spectra were previously acquired for 195 rock slabs and 31 pressed powder geostandards under 7 Torr CO 2 at a stand-off distance of 7 m at 17 mJ per pulse to simulate the operational conditions of the ChemCam LIBS instrument on the Mars Science Laboratory Curiosity rover. The clustering and training set selection methods, which do not require prior knowledge of the chemical composition of the test-set samples, are based on grouping similar spectra and selecting appropriate training spectra for the partial least squares (PLS2) model. These methods were: (1) hierarchical clustering of the full set of training spectra and selection of a subset for use in training; (2) k-means clustering of all spectra and generation of PLS2 models based on the training samples within each cluster; (3) iterative use of PLS2 to predict sample composition and k-means clustering of the predicted compositions to subdivide the groups of spectra; (4) soft independent modeling of class analogy (SIMCA) classification of spectra, and generation of PLS2 models based on the training samples within each class; (5) use of Bayesian information criteria (BIC) to determine an optimal number of clusters and generation of PLS2 models based on the training samples within each cluster. The iterative method and the k-means method using 5 clusters showed the best performance, improving the absolute quadrature root mean squared error (RMSE) by ∼ 3 wt.%. The statistical significance of these improvements was ∼ 85%. Our results show that although clustering methods can modestly improve results, a large and diverse training set is the most reliable way to improve the accuracy of quantitative LIBS. In particular, additional sulfate standards and specifically

  11. How large a training set is needed to develop a classifier for microarray data?

    Science.gov (United States)

    Dobbin, Kevin K; Zhao, Yingdong; Simon, Richard M

    2008-01-01

    A common goal of gene expression microarray studies is the development of a classifier that can be used to divide patients into groups with different prognoses, or with different expected responses to a therapy. These types of classifiers are developed on a training set, which is the set of samples used to train a classifier. The question of how many samples are needed in the training set to produce a good classifier from high-dimensional microarray data is challenging. We present a model-based approach to determining the sample size required to adequately train a classifier. It is shown that sample size can be determined from three quantities: standardized fold change, class prevalence, and number of genes or features on the arrays. Numerous examples and important experimental design issues are discussed. The method is adapted to address ex post facto determination of whether the size of a training set used to develop a classifier was adequate. An interactive web site for performing the sample size calculations is provided. We showed that sample size calculations for classifier development from high-dimensional microarray data are feasible, discussed numerous important considerations, and presented examples.

  12. Gradual DropIn of Layers to Train Very Deep Neural Networks

    OpenAIRE

    Smith, Leslie N.; Hand, Emily M.; Doster, Timothy

    2015-01-01

    We introduce the concept of dynamically growing a neural network during training. In particular, an untrainable deep network starts as a trainable shallow network and newly added layers are slowly, organically added during training, thereby increasing the network's depth. This is accomplished by a new layer, which we call DropIn. The DropIn layer starts by passing the output from a previous layer (effectively skipping over the newly added layers), then increasingly including units from the ne...

  13. Effectiveness of behavioral skills training on staff performance in a job training setting for high-functioning adolescents with autism spectrum disorders

    NARCIS (Netherlands)

    Palmen, A.M.J.W.; Didden, H.C.M.; Korzilius, H.P.L.M.

    2010-01-01

    Few studies have focused on improving staff performance in naturalistic training settings for high-functioning adolescents with autism spectrum disorders. Behavioral skills training, consisting of group instruction and supervisory feedback, was used to improve staff performance on (a) providing

  14. Effects of cluster vs. traditional plyometric training sets on maximal-intensity exercise performance.

    Science.gov (United States)

    Asadi, Abbas; Ramírez-Campillo, Rodrigo

    2016-01-01

    The aim of this study was to compare the effects of 6-week cluster versus traditional plyometric training sets on jumping ability, sprint and agility performance. Thirteen college students were assigned to a cluster sets group (N=6) or traditional sets group (N=7). Both training groups completed the same training program. The traditional group completed five sets of 20 repetitions with 2min of rest between sets each session, while the cluster group completed five sets of 20 [2×10] repetitions with 30/90-s rest each session. Subjects were evaluated for countermovement jump (CMJ), standing long jump (SLJ), t test, 20-m and 40-m sprint test performance before and after the intervention. Both groups had similar improvements (Psets methods resulted in greater adaptations in sprint performance, while the cluster sets method resulted in greater jump and agility adaptations. Copyright © 2016 The Lithuanian University of Health Sciences. Production and hosting by Elsevier Urban & Partner Sp. z o.o. All rights reserved.

  15. Higher surgical training opportunities in the general hospital setting; getting the balance right.

    Science.gov (United States)

    Robertson, I; Traynor, O; Khan, W; Waldron, R; Barry, K

    2013-12-01

    The general hospital can play an important role in training of higher surgical trainees (HSTs) in Ireland and abroad. Training opportunities in such a setting have not been closely analysed to date. The aim of this study was to quantify operative exposure for HSTs over a 5-year period in a single institution. Analysis of electronic training logbooks (over a 5-year period, 2007-2012) was performed for general surgery trainees on the higher surgical training programme in Ireland. The most commonly performed adult and paediatric procedures per trainee, per year were analysed. Standard general surgery operations such as herniae (average 58, range 32-86) and cholecystectomy (average 60, range 49-72) ranked highly in each logbook. The most frequently performed emergency operations were appendicectomy (average 45, range 33-53) and laparotomy for acute abdomen (average 48, range 10-79). Paediatric surgical experience included appendicectomy, circumcision, orchidopexy and hernia/hydrocoele repair. Overall, the procedure most commonly performed in the adult setting was endoscopy, with each trainee recording an average of 116 (range 98-132) oesophagogastroduodenoscopies and 284 (range 227-354) colonoscopies. General hospitals continue to play a major role in the training of higher surgical trainees. Analysis of the electronic logbooks over a 5-year period reveals the high volume of procedures available to trainees in a non-specialist centre. Such training opportunities are invaluable in the context of changing work practices and limited resources.

  16. Networked simulation for team training of Space Station astronauts, ground controllers, and scientists - A training and development environment

    Science.gov (United States)

    Hajare, Ankur R.; Wick, Daniel T.; Bovenzi, James J.

    1991-01-01

    The purpose of this paper is to describe plans for the Space Station Training Facility (SSTF) which has been designed to meet the envisioned training needs for Space Station Freedom. To meet these needs, the SSTF will integrate networked simulators with real-world systems in five training modes: Stand-Alone, Combined, Joint-Combined, Integrated, and Joint-Integrated. This paper describes the five training modes within the context of three training scenaries. In addition, this paper describes an authoring system which will support the rapid integration of new real-world system changes in the Space Station Freedom Program.

  17. Early Wheel Train Damage Detection Using Wireless Sensor Network Antenna

    Science.gov (United States)

    Fazilah, A. F. M.; Azemi, S. N.; Azremi, A. A. H.; Soh, P. J.; Kamarudin, L. M.

    2018-03-01

    Antenna for a wireless sensor network for early wheel trains damage detection has successfully developed and fabricated with the aim to minimize the risk and increase the safety guaranty for train. Current antenna design is suffered in gain and big in size. For the sensor, current existing sensor only detect when the wheel malfunction. Thus, a compact microstrip patch antenna with operating frequency at 2.45GHz is design with high gain of 4.95dB will attach to the wireless sensor device. Simulation result shows that the antenna is working at frequency 2.45GHz and the return loss at -34.46dB are in a good agreement. The result also shows the good radiation pattern and almost ideal VSWR which is 1.04. The Arduino Nano, LM35DZ and ESP8266-07 Wi-Fi module is applied to the core system with capability to sense the temperature and send the data wirelessly to the cloud. An android application has been created to monitor the temperature reading based on the real time basis. The mainly focuses for the future improvement is by minimize the size of the antenna in order to make in more compact. In addition, upgrade an android application that can collect the raw data from cloud and make an alarm system to alert the loco pilot.

  18. An Issue of Boundary Value for Velocity and Training Overhead Using Cooperative MIMO Technique in Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    M. R. Islam

    2011-06-01

    Full Text Available A boundary value of velocity of data gathering node (DGN and a critical value for training overhead beyond which the cooperative communication in wireless sensor network will not be feasible is proposed in this paper. Multiple Input Multiple Outputs (MIMO cooperative communication is taken as an application. The performance in terms of energy efficiency and delay for a combination of two transmitting and two receiving antennas is analyzed. The results show that a set of critical value of velocity and training overhead pair is present for the long haul communication from the sensors to the data gathering node. Later a graphical relation between boundary value of training overhead and velocity is simulated. A mathematical relation between velocity and training overhead is also developed. The effects of several parameters on training overhead and velocity are analyzed.

  19. The Train Driver Recovery Problem - a Set Partitioning Based Model and Solution Method

    DEFF Research Database (Denmark)

    Rezanova, Natalia Jurjevna; Ryan, David

    2010-01-01

    The need to recover a train driver schedule occurs during major disruptions in the daily railway operations. Based on data from the Danish passenger railway operator DSB S-tog A/S, a solution method to the train driver recovery problem (TDRP) is developed. The TDRP is formulated as a set...... branching strategy using the depth-first search of the Branch & Bound tree. The LP relaxation of the TDRP possesses strong integer properties. We present test scenarios generated from the historical real-life operations data of DSB S-tog A/S. The numerical results show that all but one tested instances...... partitioning problem. We define a disruption neighbourhood by identifying a small set of drivers and train tasks directly affected by the disruption. Based on the disruption neighbourhood, the TDRP model is formed and solved. If the TDRP solution provides a feasible recovery for the drivers within...

  20. Impact of Play Therapy on Parent-Child Relationship Stress at a Mental Health Training Setting

    Science.gov (United States)

    Ray, Dee C.

    2008-01-01

    This study investigated the impact of Child-Centred Play Therapy (CCPT)/Non-Directive Play Therapy on parent-child relationship stress using archival data from 202 child clients divided into clinical behavioural groups over 3-74 sessions in a mental health training setting. Results demonstrated significant differences between pre and post testing…

  1. Evaluating Question, Persuade, Refer (QPR) Suicide Prevention Training in a College Setting

    Science.gov (United States)

    Mitchell, Sharon L.; Kader, Mahrin; Darrow, Sherri A.; Haggerty, Melinda Z.; Keating, Niki L.

    2013-01-01

    This study assesses short-term and long-term learning outcomes of Question, Persuade, Refer (QPR) suicide prevention training in a college setting. Two hundred seventy-three participants completed pretest, posttest, and follow-up surveys regarding suicide prevention knowledge, attitudes, and skills. Results indicated: (a) increases in suicide…

  2. City and County Solar PV Training Program, Module 1: Goal Setting and Clarification

    Energy Technology Data Exchange (ETDEWEB)

    McLaren, Joyce A. [National Renewable Energy Lab. (NREL), Golden, CO (United States)

    2018-02-12

    This module will help attendees understand nuances between different types of renewable energy goals, the importance of terminology when setting and announcing goals, the value of formally clarifying priorities, and how priorities may impact procurement options. It is the first training in a series intended to help municipal staff procure solar PV for their land and buildings.

  3. Multidisciplinary team training in a simulation setting for acute abstetric emergencies : a systematic review

    NARCIS (Netherlands)

    Merién, A.E.R.; Ven, van de J.; Mol, B.W.J.; Houterman, S.; Oei, S.G.

    2010-01-01

    OBJECTIVE: To perform a systematic review of the literature on the effectiveness of multidisciplinary teamwork training in a simulation setting for the reduction of medical adverse outcomes in obstetric emergency situations. DATA SOURCES: We searched Medline, Embase, and the Cochrane Library from

  4. Effects of Crew Resource Management Training on Medical Errors in a Simulated Prehospital Setting

    Science.gov (United States)

    Carhart, Elliot D.

    2012-01-01

    This applied dissertation investigated the effect of crew resource management (CRM) training on medical errors in a simulated prehospital setting. Specific areas addressed by this program included situational awareness, decision making, task management, teamwork, and communication. This study is believed to be the first investigation of CRM…

  5. Multidisciplinary Team Training in a Simulation Setting for Acute Obstetric Emergencies A Systematic Review

    NARCIS (Netherlands)

    Merién, A. E. R.; van de Ven, J.; Mol, B. W.; Houterman, S.; Oei, S. G.

    2010-01-01

    OBJECTIVE: To perform a systematic review of the literature on the effectiveness of multidisciplinary teamwork training in a simulation setting for the reduction of medical adverse outcomes in obstetric emergency situations. DATA SOURCES: We searched Medline, Embase, and the Cochrane Library from

  6. Degradation analysis in the estimation of photometric redshifts from non-representative training sets

    Science.gov (United States)

    Rivera, J. D.; Moraes, B.; Merson, A. I.; Jouvel, S.; Abdalla, F. B.; Abdalla, M. C. B.

    2018-04-01

    We perform an analysis of photometric redshifts estimated by using a non-representative training sets in magnitude space. We use the ANNz2 and GPz algorithms to estimate the photometric redshift both in simulations as well as in real data from the Sloan Digital Sky Survey (DR12). We show that for the representative case, the results obtained by using both algorithms have the same quality, either using magnitudes or colours as input. In order to reduce the errors when estimating the redshifts with a non-representative training set, we perform the training in colour space. We estimate the quality of our results by using a mock catalogue which is split samples cuts in the r-band between 19.4 manage to define a photometric estimator which fits well the spectroscopic distribution of galaxies in the mock testing set, but with a larger scatter. To complete this work, we perform an analysis of the impact on the detection of clusters via density of galaxies in a field by using the photometric redshifts obtained with a non-representative training set.

  7. Application of Multiple-Population Genetic Algorithm in Optimizing the Train-Set Circulation Plan Problem

    Directory of Open Access Journals (Sweden)

    Yu Zhou

    2017-01-01

    Full Text Available The train-set circulation plan problem (TCPP belongs to the rolling stock scheduling (RSS problem and is similar to the aircraft routing problem (ARP in airline operations and the vehicle routing problem (VRP in the logistics field. However, TCPP involves additional complexity due to the maintenance constraint of train-sets: train-sets must conduct maintenance tasks after running for a certain time and distance. The TCPP is nondeterministic polynomial hard (NP-hard. There is no available algorithm that can obtain the optimal global solution, and many factors such as the utilization mode and the maintenance mode impact the solution of the TCPP. This paper proposes a train-set circulation optimization model to minimize the total connection time and maintenance costs and describes the design of an efficient multiple-population genetic algorithm (MPGA to solve this model. A realistic high-speed railway (HSR case is selected to verify our model and algorithm, and, then, a comparison of different algorithms is carried out. Furthermore, a new maintenance mode is proposed, and related implementation requirements are discussed.

  8. Evaluation of farmed cod products by a trained sensory panel and consumers in different test settings

    NARCIS (Netherlands)

    Sveinsdottir, K.; Martinsdottir, E.; Thorsdottir, F.; Schelvis-Smit, A.A.M.; Kole, A.; Thorsdottir, I.

    2010-01-01

    Sensory characteristics of farmed cod exposed to low or conventional stress levels prior to slaughter were evaluated by a trained sensory panel. Consumers in two different settings, central location test (CLT) and home-use test (HUT), also tasted the products and rated them according to overall

  9. Training set optimization and classifier performance in a top-down diabetic retinopathy screening system

    Science.gov (United States)

    Wigdahl, J.; Agurto, C.; Murray, V.; Barriga, S.; Soliz, P.

    2013-03-01

    Diabetic retinopathy (DR) affects more than 4.4 million Americans age 40 and over. Automatic screening for DR has shown to be an efficient and cost-effective way to lower the burden on the healthcare system, by triaging diabetic patients and ensuring timely care for those presenting with DR. Several supervised algorithms have been developed to detect pathologies related to DR, but little work has been done in determining the size of the training set that optimizes an algorithm's performance. In this paper we analyze the effect of the training sample size on the performance of a top-down DR screening algorithm for different types of statistical classifiers. Results are based on partial least squares (PLS), support vector machines (SVM), k-nearest neighbor (kNN), and Naïve Bayes classifiers. Our dataset consisted of digital retinal images collected from a total of 745 cases (595 controls, 150 with DR). We varied the number of normal controls in the training set, while keeping the number of DR samples constant, and repeated the procedure 10 times using randomized training sets to avoid bias. Results show increasing performance in terms of area under the ROC curve (AUC) when the number of DR subjects in the training set increased, with similar trends for each of the classifiers. Of these, PLS and k-NN had the highest average AUC. Lower standard deviation and a flattening of the AUC curve gives evidence that there is a limit to the learning ability of the classifiers and an optimal number of cases to train on.

  10. Performance of a visuomotor walking task in an augmented reality training setting.

    Science.gov (United States)

    Haarman, Juliet A M; Choi, Julia T; Buurke, Jaap H; Rietman, Johan S; Reenalda, Jasper

    2017-12-01

    Visual cues can be used to train walking patterns. Here, we studied the performance and learning capacities of healthy subjects executing a high-precision visuomotor walking task, in an augmented reality training set-up. A beamer was used to project visual stepping targets on the walking surface of an instrumented treadmill. Two speeds were used to manipulate task difficulty. All participants (n = 20) had to change their step length to hit visual stepping targets with a specific part of their foot, while walking on a treadmill over seven consecutive training blocks, each block composed of 100 stepping targets. Distance between stepping targets was varied between short, medium and long steps. Training blocks could either be composed of random stepping targets (no fixed sequence was present in the distance between the stepping targets) or sequenced stepping targets (repeating fixed sequence was present). Random training blocks were used to measure non-specific learning and sequenced training blocks were used to measure sequence-specific learning. Primary outcome measures were performance (% of correct hits), and learning effects (increase in performance over the training blocks: both sequence-specific and non-specific). Secondary outcome measures were the performance and stepping-error in relation to the step length (distance between stepping target). Subjects were able to score 76% and 54% at first try for lower speed (2.3 km/h) and higher speed (3.3 km/h) trials, respectively. Performance scores did not increase over the course of the trials, nor did the subjects show the ability to learn a sequenced walking task. Subjects were better able to hit targets while increasing their step length, compared to shortening it. In conclusion, augmented reality training by use of the current set-up was intuitive for the user. Suboptimal feedback presentation might have limited the learning effects of the subjects. Copyright © 2017 Elsevier B.V. All rights reserved.

  11. Introduction to the EC's Marie Curie Initial Training Network (MC-ITN) project: Particle Training Network for European Radiotherapy (PARTNER)

    CERN Document Server

    Dosanjh, Manjit

    2013-01-01

    PARTNER (Particle Training Network for European Radiotherapy) is a project funded by the European Commission’s Marie Curie-ITN funding scheme through the ENLIGHT Platform for 5.6 million Euro. PARTNER has brought together academic institutes, research centres and leading European companies, focusing in particular on a specialized radiotherapy (RT) called hadron therapy (HT), interchangeably referred to as particle therapy (PT). The ultimate goal of HT is to deliver more effective treatment to cancer patients leading to major improvement in the health of citizens. In Europe, several hundred million Euro have been invested, since the beginning of this century, in PT. In this decade, the use of HT is rapidly growing across Europe, and there is an urgent need for qualified researchers from a range of disciplines to work on its translational research. In response to this need, the European community of HT, and in particular 10 leading academic institutes, research centres, companies and small and medium-sized en...

  12. Impedance-Based Harmonic Instability Assessment in Multiple Electric Trains and Traction Network Interaction System

    DEFF Research Database (Denmark)

    Tao, Haidong; Hu, Haitao; Wang, Xiongfei

    2018-01-01

    This paper presents an impedance-based method to systematically investigate the interaction between multi-train and traction networks, focusing on evaluating the harmonic instability problems. Firstly, the interaction mechanism of multi-train and the traction network is represented as a feedback ...

  13. Social Networking in School Psychology Training Programs: A Survey of Faculty and Graduate Students

    Science.gov (United States)

    Pham, Andy V.; Goforth, Anisa N.; Segool, Natasha; Burt, Isaac

    2014-01-01

    The increasing use of social networking sites has become an emerging focus in school psychology training, policy, and research. The purpose of the current study is to present data from a survey on social networking among faculty and graduate students in school psychology training programs. A total of 110 faculty and 112 graduate students in school…

  14. The lateralization of intrinsic networks in the aging brain implicates the effects of cognitive training

    Directory of Open Access Journals (Sweden)

    Cheng eLuo

    2016-03-01

    Full Text Available Lateralization of function is an important organization of human brain. The distribution of intrinsic networks in the resting brain is strongly related to the cognitive function, gender and age. In this study, the longitudinal design with one year duration was used to evaluate the cognitive training effects on the lateralization of intrinsic networks among healthy older adults. The subjects were divided into two groups randomly: one with multi-domain cognitive training in three month, the other as a wait-list control group. Resting state fMRI data were acquired before training and one year after training. We analyzed the functional lateralization in ten common resting state fMRI networks. We observed statically significant training effects on the lateralization of two important RSNs related to high-level cognition: right- and left- frontoparietal networks. Especially, the lateralization of left-frontoparietal network were retained well in training group, but decreased in control group. The increased lateralization with aging was observed on the cerebellum network, in which the lateralization was significantly increased in control group although the same change tendency was observed in training group. These findings indicate that the lateralization of the high-level cognitive intrinsic networks is sensitive to the multi-domain cognitive training. This study provides a neuroimaging evidence to support that the cognitive training should have advantages to the cognitive decline in healthy older adults.

  15. International Nuclear Security Education Network (INSEN) and the Nuclear Security Training and Support Centre (NSSC) Network

    International Nuclear Information System (INIS)

    Nikonov, Dmitriy

    2013-01-01

    International Nuclear Security Education Network established in 2010: A partnership between the IAEA and universities, research institutions and other stakeholders - •Promotion of nuclear security education; • Development of educational materials; • Professional development for faculty members; • Collaborative research and resource sharing. Currently over 90 members from 38 member states. Mission: to enhance global nuclear security by developing, sharing and promoting excellence in nuclear security education. Nuclear Security Support Centre: Primary objectives are: • Develop human resources through the implementation of a tailored training programme; • Develop a network of experts; • Provide technical support for lifecycle equipment management and scientific support for the detection of and the response to nuclear security events

  16. Method of Parallel-Hierarchical Network Self-Training and its Application for Pattern Classification and Recognition

    Directory of Open Access Journals (Sweden)

    TIMCHENKO, L.

    2012-11-01

    Full Text Available Propositions necessary for development of parallel-hierarchical (PH network training methods are discussed in this article. Unlike already known structures of the artificial neural network, where non-normalized (absolute similarity criteria are used for comparison, the suggested structure uses a normalized criterion. Based on the analysis of training rules, a conclusion is made that application of two training methods with a teacher is optimal for PH network training: error correction-based training and memory-based training. Mathematical models of training and a combined method of PH network training for recognition of static and dynamic patterns are developed.

  17. The influence of negative training set size on machine learning-based virtual screening.

    Science.gov (United States)

    Kurczab, Rafał; Smusz, Sabina; Bojarski, Andrzej J

    2014-01-01

    The paper presents a thorough analysis of the influence of the number of negative training examples on the performance of machine learning methods. The impact of this rather neglected aspect of machine learning methods application was examined for sets containing a fixed number of positive and a varying number of negative examples randomly selected from the ZINC database. An increase in the ratio of positive to negative training instances was found to greatly influence most of the investigated evaluating parameters of ML methods in simulated virtual screening experiments. In a majority of cases, substantial increases in precision and MCC were observed in conjunction with some decreases in hit recall. The analysis of dynamics of those variations let us recommend an optimal composition of training data. The study was performed on several protein targets, 5 machine learning algorithms (SMO, Naïve Bayes, Ibk, J48 and Random Forest) and 2 types of molecular fingerprints (MACCS and CDK FP). The most effective classification was provided by the combination of CDK FP with SMO or Random Forest algorithms. The Naïve Bayes models appeared to be hardly sensitive to changes in the number of negative instances in the training set. In conclusion, the ratio of positive to negative training instances should be taken into account during the preparation of machine learning experiments, as it might significantly influence the performance of particular classifier. What is more, the optimization of negative training set size can be applied as a boosting-like approach in machine learning-based virtual screening.

  18. Upper-Lower Bounds Candidate Sets Searching Algorithm for Bayesian Network Structure Learning

    Directory of Open Access Journals (Sweden)

    Guangyi Liu

    2014-01-01

    Full Text Available Bayesian network is an important theoretical model in artificial intelligence field and also a powerful tool for processing uncertainty issues. Considering the slow convergence speed of current Bayesian network structure learning algorithms, a fast hybrid learning method is proposed in this paper. We start with further analysis of information provided by low-order conditional independence testing, and then two methods are given for constructing graph model of network, which is theoretically proved to be upper and lower bounds of the structure space of target network, so that candidate sets are given as a result; after that a search and scoring algorithm is operated based on the candidate sets to find the final structure of the network. Simulation results show that the algorithm proposed in this paper is more efficient than similar algorithms with the same learning precision.

  19. C-RNN-GAN: Continuous recurrent neural networks with adversarial training

    OpenAIRE

    Mogren, Olof

    2016-01-01

    Generative adversarial networks have been proposed as a way of efficiently training deep generative neural networks. We propose a generative adversarial model that works on continuous sequential data, and apply it by training it on a collection of classical music. We conclude that it generates music that sounds better and better as the model is trained, report statistics on generated music, and let the reader judge the quality by downloading the generated songs.

  20. Why a Train Set Helps Participants Co-Construct Meaning in Business Model Innovation

    DEFF Research Database (Denmark)

    Beuthel, Maria Rosa; Buur, Jacob

    In this position paper we show how participants in an innovation workshop employ tangible material – a toy train set – to co-construct understandings of a new business model. In multidisciplinary teams the process of developing new terms and concepts together is crucial for work to progress. Every...... to understand how they construct a concept. We observe that the final result of the workshop is indeed innovative and is co-constructed by all group members. We discuss why the toy train works: It keeps both hands and mind busy, it allows silent participation, and it expands the vocabulary of the discussion....

  1. Application of autogenic training for anxiety disorders: a clinical study in a psychiatric setting.

    Science.gov (United States)

    Sakai, M

    1997-03-01

    The effects of autogenic training for anxiety disorders were investigated in a psychiatric setting of a medical school hospital and the predictors of this treatment outcome were identified. Fifty-five patients who meet the DSM-III-R criteria for anxiety disorders were treated individually with autogenic training by the author from October 1981 to October 1995. The medical records of the patients were investigated retrospectively. The results showed that the autogenic training was successful. Twenty-eight patients (51%) were cured, fourteen (25%) much improved, eight (15%) improved and five (9%) unchanged at the end of the treatment. Forty-two patients (76%) were assessed as having had successful treatment. Pretreatment variables, such as patient's clinical characteristics, did not provide a useful guide to the outcome. Four treatment variables did have a bearing on outcome. First, practicing the second standard autogenic training exercise was a satisfactory predictor of a better outcome. Second, practicing generalization training also was a useful predictor. Third, the application of other behavioral treatment techniques was found to be positively associated with outcome. Fourth, longer treatment periods were associated with a better outcome. These findings suggested that autogenic training could be of significant benefit for the treatment of anxiety disorders.

  2. Non-Linear State Estimation Using Pre-Trained Neural Networks

    DEFF Research Database (Denmark)

    Bayramoglu, Enis; Andersen, Nils Axel; Ravn, Ole

    2010-01-01

    effecting the transformation. This function is approximated by a neural network using offline training. The training is based on monte carlo sampling. A way to obtain parametric distributions of flexible shape to be used easily with these networks is also presented. The method can also be used to improve...... other parametric methods around regions with strong non-linearities by including them inside the network....

  3. Monitoring of Students' Interaction in Online Learning Settings by Structural Network Analysis and Indicators.

    Science.gov (United States)

    Ammenwerth, Elske; Hackl, Werner O

    2017-01-01

    Learning as a constructive process works best in interaction with other learners. Support of social interaction processes is a particular challenge within online learning settings due to the spatial and temporal distribution of participants. It should thus be carefully monitored. We present structural network analysis and related indicators to analyse and visualize interaction patterns of participants in online learning settings. We validate this approach in two online courses and show how the visualization helps to monitor interaction and to identify activity profiles of learners. Structural network analysis is a feasible approach for an analysis of the intensity and direction of interaction in online learning settings.

  4. Internal measuring models in trained neural networks for parameter estimation from images

    NARCIS (Netherlands)

    Feng, Tian-Jin; Feng, T.J.; Houkes, Z.; Korsten, Maarten J.; Spreeuwers, Lieuwe Jan

    1992-01-01

    The internal representations of 'learned' knowledge in neural networks are still poorly understood, even for backpropagation networks. The paper discusses a possible interpretation of learned knowledge of a network trained for parameter estimation from images. The outputs of the hidden layer are the

  5. Internal-state analysis in layered artificial neural network trained to categorize lung sounds

    NARCIS (Netherlands)

    Oud, M

    2002-01-01

    In regular use of artificial neural networks, only input and output states of the network are known to the user. Weight and bias values can be extracted but are difficult to interpret. We analyzed internal states of networks trained to map asthmatic lung sound spectra onto lung function parameters.

  6. Annotating gene sets by mining large literature collections with protein networks.

    Science.gov (United States)

    Wang, Sheng; Ma, Jianzhu; Yu, Michael Ku; Zheng, Fan; Huang, Edward W; Han, Jiawei; Peng, Jian; Ideker, Trey

    2018-01-01

    Analysis of patient genomes and transcriptomes routinely recognizes new gene sets associated with human disease. Here we present an integrative natural language processing system which infers common functions for a gene set through automatic mining of the scientific literature with biological networks. This system links genes with associated literature phrases and combines these links with protein interactions in a single heterogeneous network. Multiscale functional annotations are inferred based on network distances between phrases and genes and then visualized as an ontology of biological concepts. To evaluate this system, we predict functions for gene sets representing known pathways and find that our approach achieves substantial improvement over the conventional text-mining baseline method. Moreover, our system discovers novel annotations for gene sets or pathways without previously known functions. Two case studies demonstrate how the system is used in discovery of new cancer-related pathways with ontological annotations.

  7. Clustering and training set selection methods for improving the accuracy of quantitative laser induced breakdown spectroscopy

    Energy Technology Data Exchange (ETDEWEB)

    Anderson, Ryan B., E-mail: randerson@astro.cornell.edu [Cornell University Department of Astronomy, 406 Space Sciences Building, Ithaca, NY 14853 (United States); Bell, James F., E-mail: Jim.Bell@asu.edu [Arizona State University School of Earth and Space Exploration, Bldg.: INTDS-A, Room: 115B, Box 871404, Tempe, AZ 85287 (United States); Wiens, Roger C., E-mail: rwiens@lanl.gov [Los Alamos National Laboratory, P.O. Box 1663 MS J565, Los Alamos, NM 87545 (United States); Morris, Richard V., E-mail: richard.v.morris@nasa.gov [NASA Johnson Space Center, 2101 NASA Parkway, Houston, TX 77058 (United States); Clegg, Samuel M., E-mail: sclegg@lanl.gov [Los Alamos National Laboratory, P.O. Box 1663 MS J565, Los Alamos, NM 87545 (United States)

    2012-04-15

    We investigated five clustering and training set selection methods to improve the accuracy of quantitative chemical analysis of geologic samples by laser induced breakdown spectroscopy (LIBS) using partial least squares (PLS) regression. The LIBS spectra were previously acquired for 195 rock slabs and 31 pressed powder geostandards under 7 Torr CO{sub 2} at a stand-off distance of 7 m at 17 mJ per pulse to simulate the operational conditions of the ChemCam LIBS instrument on the Mars Science Laboratory Curiosity rover. The clustering and training set selection methods, which do not require prior knowledge of the chemical composition of the test-set samples, are based on grouping similar spectra and selecting appropriate training spectra for the partial least squares (PLS2) model. These methods were: (1) hierarchical clustering of the full set of training spectra and selection of a subset for use in training; (2) k-means clustering of all spectra and generation of PLS2 models based on the training samples within each cluster; (3) iterative use of PLS2 to predict sample composition and k-means clustering of the predicted compositions to subdivide the groups of spectra; (4) soft independent modeling of class analogy (SIMCA) classification of spectra, and generation of PLS2 models based on the training samples within each class; (5) use of Bayesian information criteria (BIC) to determine an optimal number of clusters and generation of PLS2 models based on the training samples within each cluster. The iterative method and the k-means method using 5 clusters showed the best performance, improving the absolute quadrature root mean squared error (RMSE) by {approx} 3 wt.%. The statistical significance of these improvements was {approx} 85%. Our results show that although clustering methods can modestly improve results, a large and diverse training set is the most reliable way to improve the accuracy of quantitative LIBS. In particular, additional sulfate standards and

  8. Creation of a Unified Set of Core-Collapse Supernovae for Training of Photometric Classifiers

    Science.gov (United States)

    D'Arcy Kenworthy, William; Scolnic, Daniel; Kessler, Richard

    2017-01-01

    One of the key tasks for future supernova cosmology analyses is to photometrically distinguish type Ia supernovae (SNe) from their core collapse (CC) counterparts. In order to train programs for this purpose, it is necessary to train on a large number of core-collapse SNe. However, there are only a handful used for current programs. We plan to use the large amount of CC lightcurves available on the Open Supernova Catalog (OSC). Since this data is scraped from many different surveys, it is given in a number of photometric systems with different calibration and filters. We therefore created a program to fit smooth lightcurves (as a function of time) to photometric observations of arbitrary SNe. The Supercal method is then used to translate the smoothed lightcurves to a single photometric system. We can thus compile a training set of 782 supernovae, of which 127 are not type Ia. These smoothed lightcurves are also being contributed upstream to the OSC as derived data.

  9. GIONET (GMES Initial Operations Network for Earth Observation Research Training)

    Science.gov (United States)

    Nicolas, V.; Balzter, H.

    2013-12-01

    GMES Initial Operations - Network for Earth Observation Research Training (GIONET) is a Marie Curie funded project that aims to establish the first of a kind European Centre of Excellence for Earth Observation Research Training. Copernicus (previously known as GMES (Global Monitoring for Environment and Security) is a joint undertaking of the European Space Agency and the European Commission. It develops fully operational Earth Observation monitoring services for a community of end users from the public and private sector. The first services that are considered fully operational are the land monitoring and emergency monitoring core services. In GIONET, 14 early stage researchers are being trained at PhD level in understanding the complex physical processes that determine how electromagnetic radiation interacts with the atmosphere and the land surface ultimately form the signal received by a satellite. In order to achieve this, the researchers are based in industry and universities across Europe, as well as receiving the best technical training and scientific education. The training programme through supervised research focuses on 14 research topics. Each topic is carried out by an Early Stage Researcher based in one of the partner organisations and is expected to lead to a PhD degree. The 14 topics are grouped in 5 research themes: Forest monitoring Land cover and change Coastal zone and freshwater monitoring Geohazards and emergency response Climate adaptation and emergency response The methods developed and used in GIONET are as diverse as its research topics. GIONET has already held two summer schools; one at Friedrich Schiller University in Jena (Germany), on 'New operational radar satellite applications: Introduction to SAR, Interferometry and Polarimetry for Land Surface Mapping'. The 2nd summer school took place last September at the University of Leicester (UK )on 'Remote sensing of land cover and forest in GMES'. The next Summer School in September 2013

  10. GMES Initial Operations - Network for Earth Observation Research Training (GIONET)

    Science.gov (United States)

    Nicolas-Perea, V.; Balzter, H.

    2012-12-01

    GMES Initial Operations - Network for Earth Observation Research Training (GIONET) is a Marie Curie funded project that aims to establish the first of a kind European Centre of Excellence for Earth Observation Research Training. GIONET is a partnership of leading Universities, research institutes and private companies from across Europe aiming to cultivate a community of early stage researchers in the areas of optical and radar remote sensing skilled for the emerging GMES land monitoring services during the GMES Initial Operations period (2011-2013) and beyond. GIONET is expected to satisfy the demand for highly skilled researchers and provide personnel for operational phase of the GMES and monitoring and emergency services. It will achieve this by: -Providing postgraduate training in Earth Observation Science that exposes students to different research disciplines and complementary skills, providing work experiences in the private and academic sectors, and leading to a recognized qualification (Doctorate). -Enabling access to first class training in both fundamental and applied research skills to early-stage researchers at world-class academic centers and market leaders in the private sector. -Building on the experience from previous GMES research and development projects in the land monitoring and emergency information services. The training program through supervised research focuses on 14 research topics (each carried out by an Early Stage Researchers based in one of the partner organization) divided in 5 main areas: Forest monitoring: Global biomass information systems Forest Monitoring of the Congo Basin using Synthetic Aperture radar (SAR) Multi-concept Earth Observation Capabilities for Biomass Mapping and Change Detection: Synergy of Multi-temporal and Multi-frequency Interferometric Radar and Optical Satellite Data Land cover and change: Multi-scale Remote Sensing Synergy for Land Process Studies: from field Spectrometry to Airborne Hyperspectral and

  11. Practical guidelines for setting up neurosurgery skills training cadaver laboratory in India.

    Science.gov (United States)

    Suri, Ashish; Roy, Tara Sankar; Lalwani, Sanjeev; Deo, Rama Chandra; Tripathi, Manjul; Dhingra, Renu; Bhardwaj, Daya Nand; Sharma, Bhawani Shankar

    2014-01-01

    Though the necessity of cadaver dissection is felt by the medical fraternity, and described as early as 600 BC, in India, there are no practical guidelines available in the world literature for setting up a basic cadaver dissection laboratory for neurosurgery skills training. Hands-on dissection practice on microscopic and endoscopic procedures is essential in technologically demanding modern neurosurgery training where ethical issues, cost constraints, medico-legal pitfalls, and resident duty time restrictions have resulted in lesser opportunities to learn. Collaboration of anatomy, forensic medicine, and neurosurgery is essential for development of a workflow of cadaver procurement, preservation, storage, dissection, and disposal along with setting up the guidelines for ethical and legal concerns.

  12. A Telescopic Binary Learning Machine for Training Neural Networks.

    Science.gov (United States)

    Brunato, Mauro; Battiti, Roberto

    2017-03-01

    This paper proposes a new algorithm based on multiscale stochastic local search with binary representation for training neural networks [binary learning machine (BLM)]. We study the effects of neighborhood evaluation strategies, the effect of the number of bits per weight and that of the maximum weight range used for mapping binary strings to real values. Following this preliminary investigation, we propose a telescopic multiscale version of local search, where the number of bits is increased in an adaptive manner, leading to a faster search and to local minima of better quality. An analysis related to adapting the number of bits in a dynamic way is presented. The control on the number of bits, which happens in a natural manner in the proposed method, is effective to increase the generalization performance. The learning dynamics are discussed and validated on a highly nonlinear artificial problem and on real-world tasks in many application domains; BLM is finally applied to a problem requiring either feedforward or recurrent architectures for feedback control.

  13. Metadynamics for training neural network model chemistries: A competitive assessment

    Science.gov (United States)

    Herr, John E.; Yao, Kun; McIntyre, Ryker; Toth, David W.; Parkhill, John

    2018-06-01

    Neural network model chemistries (NNMCs) promise to facilitate the accurate exploration of chemical space and simulation of large reactive systems. One important path to improving these models is to add layers of physical detail, especially long-range forces. At short range, however, these models are data driven and data limited. Little is systematically known about how data should be sampled, and "test data" chosen randomly from some sampling techniques can provide poor information about generality. If the sampling method is narrow, "test error" can appear encouragingly tiny while the model fails catastrophically elsewhere. In this manuscript, we competitively evaluate two common sampling methods: molecular dynamics (MD), normal-mode sampling, and one uncommon alternative, Metadynamics (MetaMD), for preparing training geometries. We show that MD is an inefficient sampling method in the sense that additional samples do not improve generality. We also show that MetaMD is easily implemented in any NNMC software package with cost that scales linearly with the number of atoms in a sample molecule. MetaMD is a black-box way to ensure samples always reach out to new regions of chemical space, while remaining relevant to chemistry near kbT. It is a cheap tool to address the issue of generalization.

  14. Measuring dynamic process of working memory training with functional brain networks

    Directory of Open Access Journals (Sweden)

    Hong Wang

    2015-12-01

    Full Text Available In this paper, we proposed the functional brain networks and graphic theory method to measure the effect of working memory training on the neural activities. 12 subjects were recruited in this study, and they did the same working memory task before they had been trained and after training. We architected functional brain networks based on EEG coherence and calculated properties of brain networks to measure the neural co-activities and the working memory level of subjects. As the result, the internal connections in frontal region decreased after working memory training, but the connection between frontal region and top region increased. And the more small-world feature was observed after training. The features observed above were in alpha (8-13 Hz and beta (13-30 Hz bands. The functional brain networks based on EEG coherence proposed in this paper can be used as the indicator of working memory level.

  15. Evaluation of mental health first aid training in a diverse community setting.

    Science.gov (United States)

    Morawska, Alina; Fletcher, Renee; Pope, Susan; Heathwood, Ellen; Anderson, Emily; McAuliffe, Christine

    2013-02-01

    Mental health first aid (MHFA) training has been disseminated in the community and has yielded positive outcomes in terms of increasing help-seeking behaviour and mental health literacy. However, there has been limited research investigating the effectiveness of this programme in multicultural communities. Given the increasing levels of multiculturalism in many countries, as well as the large number of barriers presented to these groups when trying to seek help for mental illnesses, the present study aimed to investigate the effectiveness of MHFA in these settings. A total of 458 participants, who were recruited from multicultural organizations, participated in a series of MHFA training courses. Participants completed questionnaires pre and post the training course, and 6-month follow-up interviews were conducted with a subsample of participants. Findings suggested that MHFA training increased participant recognition of mental illnesses, concordance with primary care physicians about treatments, confidence in providing first aid, actual help provided to others, and a reduction in stigmatizing attitudes. A 6-month follow up also yielded positive long-term effects of MHFA. The results have implications for further dissemination and the use of MHFA in diverse communities. In addition, the results highlight the need for mental health training in health-care service providers. © 2012 The Authors. International Journal of Mental Health Nursing © 2012 Australian College of Mental Health Nurses Inc.

  16. Dementia training programmes for staff working in general hospital settings - a systematic review of the literature.

    Science.gov (United States)

    Scerri, Anthony; Innes, Anthea; Scerri, Charles

    2017-08-01

    Although literature describing and evaluating training programmes in hospital settings increased in recent years, there are no reviews that summarise these programmes. This review sought to address this, by collecting the current evidence on dementia training programmes directed to staff working in general hospitals. Literature from five databases were searched, based on a number of inclusion criteria. The selected studies were summarised and data was extracted and compared using narrative synthesis based on a set of pre-defined categories. Methodological quality was assessed. Fourteen peer-reviewed studies were identified with the majority being pre-test post-test investigations. No randomised controlled trials were found. Methodological quality was variable with selection bias being the major limitation. There was a great variability in the development and mode of delivery although, interdisciplinary ward based, tailor-made, short sessions using experiential and active learning were the most utilised. The majority of the studies mainly evaluated learning, with few studies evaluating changes in staff behaviour/practices and patients' outcomes. This review indicates that high quality studies are needed that especially evaluate staff behaviours and patient outcomes and their sustainability over time. It also highlights measures that could be used to develop and deliver training programmes in hospital settings.

  17. Timetable-based simulation method for choice set generation in large-scale public transport networks

    DEFF Research Database (Denmark)

    Rasmussen, Thomas Kjær; Anderson, Marie Karen; Nielsen, Otto Anker

    2016-01-01

    The composition and size of the choice sets are a key for the correct estimation of and prediction by route choice models. While existing literature has posed a great deal of attention towards the generation of path choice sets for private transport problems, the same does not apply to public...... transport problems. This study proposes a timetable-based simulation method for generating path choice sets in a multimodal public transport network. Moreover, this study illustrates the feasibility of its implementation by applying the method to reproduce 5131 real-life trips in the Greater Copenhagen Area...... and to assess the choice set quality in a complex multimodal transport network. Results illustrate the applicability of the algorithm and the relevance of the utility specification chosen for the reproduction of real-life path choices. Moreover, results show that the level of stochasticity used in choice set...

  18. Standardized network order sets in rural Ontario: a follow-up report on successes and sustainability.

    Science.gov (United States)

    Rawn, Andrea; Wilson, Katrina

    2011-01-01

    Unifying, implementing and sustaining a large order set project requires strategic placement of key organizational professionals to provide ongoing user education, communication and support. This article will outline the successful strategies implemented by the Grey Bruce Health Network, Evidence-Based Care Program to reduce length of stay, increase patient satisfaction and increase the use of best practices resulting in quality outcomes, safer practice and better allocation of resources by using standardized Order Sets within a network of 11 hospital sites. Audits conducted in 2007 and again in 2008 revealed a reduced length of stay of 0.96 in-patient days when order sets were used on admission and readmission for the same or a related diagnosis within one month decreased from 5.5% without order sets to 3.5% with order sets.

  19. The effects of working memory training on functional brain network efficiency.

    Science.gov (United States)

    Langer, Nicolas; von Bastian, Claudia C; Wirz, Helen; Oberauer, Klaus; Jäncke, Lutz

    2013-10-01

    The human brain is a highly interconnected network. Recent studies have shown that the functional and anatomical features of this network are organized in an efficient small-world manner that confers high efficiency of information processing at relatively low connection cost. However, it has been unclear how the architecture of functional brain networks is related to performance in working memory (WM) tasks and if these networks can be modified by WM training. Therefore, we conducted a double-blind training study enrolling 66 young adults. Half of the subjects practiced three WM tasks and were compared to an active control group practicing three tasks with low WM demand. High-density resting-state electroencephalography (EEG) was recorded before and after training to analyze graph-theoretical functional network characteristics at an intracortical level. WM performance was uniquely correlated with power in the theta frequency, and theta power was increased by WM training. Moreover, the better a person's WM performance, the more their network exhibited small-world topology. WM training shifted network characteristics in the direction of high performers, showing increased small-worldness within a distributed fronto-parietal network. Taken together, this is the first longitudinal study that provides evidence for the plasticity of the functional brain network underlying WM. Copyright © 2013 Elsevier Ltd. All rights reserved.

  20. Korean efforts for education and training network in nuclear technology

    International Nuclear Information System (INIS)

    Han, Kyong-Won; Lee, Eui-Jin

    2007-01-01

    education programs along with a career in the nuclear fields at home and abroad should raise young generation's interests. Global network will serve as a vehicle that drives nuclear education and training forward. NTC of KAERI has developed the ANENT temporary web site (www.anent-temp.org) for the IAEA Consultancy Meting on Establishment of ANENT held in June 2003 at KAERI. According to the results from the discussion of the meeting, KAERI has requested to continue to work toward establishment of a web site for all activities related to ANENT. The followings are KAERI's efforts made for the ANENT: Installation of a portable cyber education system (Edu-V producer) and cyber studio for the effective production of VOD materials; Production of VOD type learning materials: 3 IAEA courses containing 52 lectures. For the progress of the establishment of ANENT, it is believed that exchange of informational and materials on education and training should be considered in advance among the member states. The followings are our suggestions for the exchange of information and materials to be discussed among member states: Formulation of a working group; Identification of the scope of activities; Establishment of cooperative mechanism; Design of ANENT web, and loading of existing information and materials on the web; Production and loading of new materials including cyber education and training materials; Sustainable operation of ANENT web site

  1. SuperNeurons: Dynamic GPU Memory Management for Training Deep Neural Networks

    OpenAIRE

    Wang, Linnan; Ye, Jinmian; Zhao, Yiyang; Wu, Wei; Li, Ang; Song, Shuaiwen Leon; Xu, Zenglin; Kraska, Tim

    2018-01-01

    Going deeper and wider in neural architectures improves the accuracy, while the limited GPU DRAM places an undesired restriction on the network design domain. Deep Learning (DL) practitioners either need change to less desired network architectures, or nontrivially dissect a network across multiGPUs. These distract DL practitioners from concentrating on their original machine learning tasks. We present SuperNeurons: a dynamic GPU memory scheduling runtime to enable the network training far be...

  2. Reward-based training of recurrent neural networks for cognitive and value-based tasks.

    Science.gov (United States)

    Song, H Francis; Yang, Guangyu R; Wang, Xiao-Jing

    2017-01-13

    Trained neural network models, which exhibit features of neural activity recorded from behaving animals, may provide insights into the circuit mechanisms of cognitive functions through systematic analysis of network activity and connectivity. However, in contrast to the graded error signals commonly used to train networks through supervised learning, animals learn from reward feedback on definite actions through reinforcement learning. Reward maximization is particularly relevant when optimal behavior depends on an animal's internal judgment of confidence or subjective preferences. Here, we implement reward-based training of recurrent neural networks in which a value network guides learning by using the activity of the decision network to predict future reward. We show that such models capture behavioral and electrophysiological findings from well-known experimental paradigms. Our work provides a unified framework for investigating diverse cognitive and value-based computations, and predicts a role for value representation that is essential for learning, but not executing, a task.

  3. Automated contour detection in cardiac MRI using active appearance models: the effect of the composition of the training set

    NARCIS (Netherlands)

    Angelié, Emmanuelle; Oost, Elco R.; Hendriksen, Dennis; Lelieveldt, Boudewijn P. F.; van der Geest, Rob J.; Reiber, Johan H. C.

    2007-01-01

    Definition of the optimal training set for the automated segmentation of short-axis left ventricular magnetic resonance (MR) imaging studies in clinical practice based on active appearance model (AAM). We investigated the segmentation accuracy by varying the size and composition of the training set

  4. Influence of the Training Methods in the Diagnosis of Multiple Sclerosis Using Radial Basis Functions Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Ángel Gutiérrez

    2015-04-01

    Full Text Available The data available in the average clinical study of a disease is very often small. This is one of the main obstacles in the application of neural networks to the classification of biological signals used for diagnosing diseases. A rule of thumb states that the number of parameters (weights that can be used for training a neural network should be around 15% of the available data, to avoid overlearning. This condition puts a limit on the dimension of the input space. Different authors have used different approaches to solve this problem, like eliminating redundancy in the data, preprocessing the data to find centers for the radial basis functions, or extracting a small number of features that were used as inputs. It is clear that the classification would be better the more features we could feed into the network. The approach utilized in this paper is incrementing the number of training elements with randomly expanding training sets. This way the number of original signals does not constraint the dimension of the input set in the radial basis network. Then we train the network using the method that minimizes the error function using the gradient descent algorithm and the method that uses the particle swarm optimization technique. A comparison between the two methods showed that for the same number of iterations on both methods, the particle swarm optimization was faster, it was learning to recognize only the sick people. On the other hand, the gradient method was not as good in general better at identifying those people.

  5. Efficient Gatherings in Wireless Sensor Networks Using Distributed Computation of Connected Dominating Sets

    Directory of Open Access Journals (Sweden)

    Vincent BOUDET

    2012-03-01

    Full Text Available In this paper, we are interested in enhancing lifetime of wireless sensor networks trying to collect data from all the nodes to a “sink”-node for non-safety critical applications. Connected Dominating Sets are used as a basis for routing messages to the sink. We present a simple distributed algorithm, which computes several CDS trying to distribute the consumption of energy over all the nodes of the network. The simulations show a significant improvement of the network lifetime.

  6. A managed clinical network for cardiac services: set-up, operation and impact on patient care

    Directory of Open Access Journals (Sweden)

    Karen E. Hamilton

    2005-09-01

    Full Text Available Purpose: To investigate the set up and operation of a Managed Clinical Network for cardiac services and assess its impact on patient care. Methods: This single case study used process evaluation with observational before and after comparison of indicators of quality of care and costs. The study was conducted in Dumfries and Galloway, Scotland and used a three-level framework. Process evaluation of the network set-up and operation through a documentary review of minutes; guidelines and protocols; transcripts of fourteen semi-structured interviews with health service personnel including senior managers, general practitioners, nurses, cardiologists and members of the public. Outcome evaluation of the impact of the network through interrupted time series analysis of clinical data of 202 patients aged less than 76 years admitted to hospital with a confirmed myocardial infarction one-year pre and one-year post, the establishment of the network. The main outcome measures were differences between indicators of quality of care targeted by network protocols. Economic evaluation of the transaction costs of the set-up and operation of the network and the resource costs of the clinical care of the 202 myocardial infarction patients from the time of hospital admission to 6 months post discharge through interrupted time series analysis. The outcome measure was different in National Health Service resource use. Results: Despite early difficulties, the network was successful in bringing together clinicians, patients and managers to redesign services, exhibiting most features of good network management. The role of the energetic lead clinician was crucial, but the network took time to develop and ‘bed down’. Its primary “modus operand” was the development of a myocardial infarction pathway and associated protocols. Of sixteen clinical care indicators, two improved significantly following the launch of the network and nine showed improvements, which were

  7. A managed clinical network for cardiac services: set-up, operation and impact on patient care.

    Science.gov (United States)

    Stc Hamilton, Karen E; Sullivan, Frank M; Donnan, Peter T; Taylor, Rex; Ikenwilo, Divine; Scott, Anthony; Baker, Chris; Wyke, Sally

    2005-01-01

    To investigate the set up and operation of a Managed Clinical Network for cardiac services and assess its impact on patient care. This single case study used process evaluation with observational before and after comparison of indicators of quality of care and costs. The study was conducted in Dumfries and Galloway, Scotland and used a three-level framework. Process evaluation of the network set-up and operation through a documentary review of minutes; guidelines and protocols; transcripts of fourteen semi-structured interviews with health service personnel including senior managers, general practitioners, nurses, cardiologists and members of the public. Outcome evaluation of the impact of the network through interrupted time series analysis of clinical data of 202 patients aged less than 76 years admitted to hospital with a confirmed myocardial infarction one-year pre and one-year post, the establishment of the network. The main outcome measures were differences between indicators of quality of care targeted by network protocols. Economic evaluation of the transaction costs of the set-up and operation of the network and the resource costs of the clinical care of the 202 myocardial infarction patients from the time of hospital admission to 6 months post discharge through interrupted time series analysis. The outcome measure was different in National Health Service resource use. Despite early difficulties, the network was successful in bringing together clinicians, patients and managers to redesign services, exhibiting most features of good network management. The role of the energetic lead clinician was crucial, but the network took time to develop and 'bed down'. Its primary "modus operand" was the development of a myocardial infarction pathway and associated protocols. Of sixteen clinical care indicators, two improved significantly following the launch of the network and nine showed improvements, which were not statistically significant. There was no difference

  8. A neural network driving curve generation method for the heavy-haul train

    Directory of Open Access Journals (Sweden)

    Youneng Huang

    2016-05-01

    Full Text Available The heavy-haul train has a series of characteristics, such as the locomotive traction properties, the longer length of train, and the nonlinear train pipe pressure during train braking. When the train is running on a continuous long and steep downgrade railway line, the safety of the train is ensured by cycle braking, which puts high demands on the driving skills of the driver. In this article, a driving curve generation method for the heavy-haul train based on a neural network is proposed. First, in order to describe the nonlinear characteristics of train braking, the neural network model is constructed and trained by practical driving data. In the neural network model, various nonlinear neurons are interconnected to work for information processing and transmission. The target value of train braking pressure reduction and release time is achieved by modeling the braking process. The equation of train motion is computed to obtain the driving curve. Finally, in four typical operation scenarios, comparing the curve data generated by the method with corresponding practical data of the Shuohuang heavy-haul railway line, the results show that the method is effective.

  9. An ILP based memetic algorithm for finding minimum positive influence dominating sets in social networks

    Science.gov (United States)

    Lin, Geng; Guan, Jian; Feng, Huibin

    2018-06-01

    The positive influence dominating set problem is a variant of the minimum dominating set problem, and has lots of applications in social networks. It is NP-hard, and receives more and more attention. Various methods have been proposed to solve the positive influence dominating set problem. However, most of the existing work focused on greedy algorithms, and the solution quality needs to be improved. In this paper, we formulate the minimum positive influence dominating set problem as an integer linear programming (ILP), and propose an ILP based memetic algorithm (ILPMA) for solving the problem. The ILPMA integrates a greedy randomized adaptive construction procedure, a crossover operator, a repair operator, and a tabu search procedure. The performance of ILPMA is validated on nine real-world social networks with nodes up to 36,692. The results show that ILPMA significantly improves the solution quality, and is robust.

  10. Effects of dialectical behavior therapy skills training on outcomes for mental health staff in a child and adolescent residential setting.

    Science.gov (United States)

    Haynos, Ann F; Fruzzetti, Alan E; Anderson, Calli; Briggs, David; Walenta, Jason

    2016-04-01

    Training in Dialectical Behavior Therapy (DBT) skills coaching is desirable for staff in psychiatric settings, due to the efficacy of DBT in treating difficult patient populations. In such settings, training resources are typically limited, and staff turnover is high, necessitating brief training. This study evaluated the effects of a brief training in DBT skills coaching for nursing staff working in a child and adolescent psychiatric residential program. Nursing staff ( n = 22) completed assessments of DBT skill knowledge, burnout, and stigma towards patients with borderline personality disorder (BPD) before and after a six-week DBT skills coaching training. Repeated measure ANOVAs were conducted to examine changes on all measures from pre- to post- treatment and hierarchical linear regressions to examine relationships between pre- training DBT knowledge, burnout, and BPD stigma and these same measures post-training. The brief DBT skill coaching training significantly increased DBT knowledge ( p = .007) and decreased staff personal ( p = .02) and work ( p = .03) burnout and stigma towards BPD patients ( p = .02). Burnout indices and BPD stigma were highly correlated at both time points ( p training BPD stigma significantly predicted post-training client burnout ( p = .04), pre-training burnout did not predict post-training BPD stigma. These findings suggest that brief training of psychiatric nursing staff in DBT skills and coaching techniques can result in significant benefits, including reduced staff burnout and stigma toward patients with BPD-related problems, and that reducing BPD stigma may particularly promote lower burnout.

  11. Estimating the similarity of alternative Affymetrix probe sets using transcriptional networks

    Science.gov (United States)

    2013-01-01

    Background The usefulness of the data from Affymetrix microarray analysis depends largely on the reliability of the files describing the correspondence between probe sets, genes and transcripts. Particularly, when a gene is targeted by several probe sets, these files should give information about the similarity of each alternative probe set pair. Transcriptional networks integrate the multiple correlations that exist between all probe sets and supply much more information than a simple correlation coefficient calculated for two series of signals. In this study, we used the PSAWN (Probe Set Assignment With Networks) programme we developed to investigate whether similarity of alternative probe sets resulted in some specific properties. Findings PSAWNpy delivered a full textual description of each probe set and information on the number and properties of secondary targets. PSAWNml calculated the similarity of each alternative probe set pair and allowed finding relationships between similarity and localisation of probes in common transcripts or exons. Similar alternative probe sets had very low negative correlation, high positive correlation and similar neighbourhood overlap. Using these properties, we devised a test that allowed grouping similar probe sets in a given network. By considering several networks, additional information concerning the similarity reproducibility was obtained, which allowed defining the actual similarity of alternative probe set pairs. In particular, we calculated the common localisation of probes in exons and in known transcripts and we showed that similarity was correctly correlated with them. The information collected on all pairs of alternative probe sets in the most popular 3’ IVT Affymetrix chips is available in tabular form at http://bns.crbm.cnrs.fr/download.html. Conclusions These processed data can be used to obtain a finer interpretation when comparing microarray data between biological conditions. They are particularly well

  12. Estimating the similarity of alternative Affymetrix probe sets using transcriptional networks.

    Science.gov (United States)

    Bellis, Michel

    2013-03-21

    The usefulness of the data from Affymetrix microarray analysis depends largely on the reliability of the files describing the correspondence between probe sets, genes and transcripts. Particularly, when a gene is targeted by several probe sets, these files should give information about the similarity of each alternative probe set pair. Transcriptional networks integrate the multiple correlations that exist between all probe sets and supply much more information than a simple correlation coefficient calculated for two series of signals. In this study, we used the PSAWN (Probe Set Assignment With Networks) programme we developed to investigate whether similarity of alternative probe sets resulted in some specific properties. PSAWNpy delivered a full textual description of each probe set and information on the number and properties of secondary targets. PSAWNml calculated the similarity of each alternative probe set pair and allowed finding relationships between similarity and localisation of probes in common transcripts or exons. Similar alternative probe sets had very low negative correlation, high positive correlation and similar neighbourhood overlap. Using these properties, we devised a test that allowed grouping similar probe sets in a given network. By considering several networks, additional information concerning the similarity reproducibility was obtained, which allowed defining the actual similarity of alternative probe set pairs. In particular, we calculated the common localisation of probes in exons and in known transcripts and we showed that similarity was correctly correlated with them. The information collected on all pairs of alternative probe sets in the most popular 3' IVT Affymetrix chips is available in tabular form at http://bns.crbm.cnrs.fr/download.html. These processed data can be used to obtain a finer interpretation when comparing microarray data between biological conditions. They are particularly well adapted for searching 3' alternative

  13. A set packing inspired method for real-time junction train routing

    DEFF Research Database (Denmark)

    Lusby, Richard Martin; Larsen, Jesper; Ehrgott, Matthias

    2013-01-01

    Efficiently coordinating the often large number of interdependent, timetabled train movements on a railway junction, while satisfying a number of operational requirements, is one of the most important problems faced by a railway company. The most critical variant of the problem arises on a daily...... basis at major railway junctions where disruptions to rail traffic make the planned schedule/routing infeasible and rolling stock planners are forced to re-schedule/re-route trains in order to recover feasibility. The dynamic nature of the problem means that good solutions must be obtained quickly....... In this paper we describe a set packing inspired formulation of this problem and develop a branch-and-price based solution approach. A real life test instance arising in Germany and supplied by the major German railway company, Deutsche Bahn, indicates the efficiency of the proposed approach by confirming...

  14. A Set Packing Inspired Method for Real-Time Junction Train Routing

    DEFF Research Database (Denmark)

    Lusby, Richard Martin; Larsen, Jesper; Ehrgott, Matthias

    Efficiently coordinating the often large number of interdependent, timetabled train movements on a railway junction, while satisfying a number of operational requirements, is one of the most important problems faced by a railway company. The most critical variant of the problem arises on a daily...... basis at major railway junctions where disruptions to rail traffi c make the planned schedule/routing infeasible and rolling stock planners are forced to reschedule/re-route trains in order to recover feasibility. The dynamic nature of the problem means that good solutions must be obtained quickly....... In this paper we describe a set packing inspired formulation of this problem and develop a branch-and-price based solution approach. A real life test instance arising in Germany and supplied by the major German railway company, Deutsche Bahn, indicates the efficiency of the proposed approach by confirming...

  15. Contemporary social network sites: Relevance in anesthesiology teaching, training, and research.

    Science.gov (United States)

    Haldar, Rudrashish; Kaushal, Ashutosh; Samanta, Sukhen; Ambesh, Paurush; Srivastava, Shashi; Singh, Prabhat K

    2016-01-01

    The phenomenal popularity of social networking sites has been used globally by medical professionals to boost professional associations and scientific developments. They have tremendous potential to forge professional liaisons, generate employment,upgrading skills and publicizing scientific achievements. We highlight the role of social networking mediums in influencing teaching, training and research in anaesthesiology. The growth of social networking sites have been prompted by the limitations of previous facilities in terms of ease of data and interface sharing and the amalgamation of audio visual aids on common platforms in the newer facilities. Contemporary social networking sites like Facebook, Twitter, Tumblr,Linkedn etc and their respective features based on anaesthesiology training or practice have been discussed. A host of advantages which these sites confer are also discussed. Likewise the potential pitfalls and drawbacks of these facilities have also been addressed. Social networking sites have immense potential for development of training and research in Anaesthesiology. However responsible and cautious utilization is advocated.

  16. Sex and Employment-Setting Differences in Work-Family Conflict in Athletic Training.

    Science.gov (United States)

    Mazerolle, Stephanie M; Eason, Christianne M; Pitney, William A; Mueller, Megan N

    2015-09-01

    Work-family conflict (WFC) has received much attention in athletic training, yet several factors related to this phenomenon have not been examined, specifically a practitioner's sex, occupational setting, willingness to leave the profession, and willingness to use work-leave benefits. To examine how sex and occupational differences in athletic training affect WFC and to examine willingness to leave the profession and use work-leave benefits. Cross-sectional study. Multiple occupational settings, including clinic/outreach, education, collegiate, industrial, professional sports, secondary school, and sales. A total of 246 athletic trainers (ATs) (men = 110, women = 136) participated. Of these, 61.4% (n = 151) were between 20 and 39 years old. Participants responded to a previously validated and reliable WFC instrument. We created and validated a 3-item instrument that assessed willingness to use work-leave benefits, which demonstrated good internal consistency (Cronbach α = 0.88), as well as a single question about willingness to leave the profession. The mean (± SD) WFC score was 16.88 ± 4.4 (range = 5 [least amount of conflict] to 25 [highest amount of conflict]). Men scored 17.01 ± 4.5, and women scored 16.76 ± 4.36, indicating above-average WFC. We observed no difference between men and women based on conflict scores (t244 = 0.492, P = .95) or their willingness to leave the profession (t244 = -1.27, P = .21). We noted differences among ATs in different practice settings (F8,245 = 5.015, P work-leave benefits (2-tailed r = -0.533, P work-leave benefits was different among practice settings (F8,245 = 3.01, P = .003). The ATs employed in traditional practice settings reported higher levels of WFC. Male and female ATs had comparable experiences of WFC and willingness to leave the profession.

  17. Working memory training mostly engages general-purpose large-scale networks for learning.

    Science.gov (United States)

    Salmi, Juha; Nyberg, Lars; Laine, Matti

    2018-03-21

    The present meta-analytic study examined brain activation changes following working memory (WM) training, a form of cognitive training that has attracted considerable interest. Comparisons with perceptual-motor (PM) learning revealed that WM training engages domain-general large-scale networks for learning encompassing the dorsal attention and salience networks, sensory areas, and striatum. Also the dynamics of the training-induced brain activation changes within these networks showed a high overlap between WM and PM training. The distinguishing feature for WM training was the consistent modulation of the dorso- and ventrolateral prefrontal cortex (DLPFC/VLPFC) activity. The strongest candidate for mediating transfer to similar untrained WM tasks was the frontostriatal system, showing higher striatal and VLPFC activations, and lower DLPFC activations after training. Modulation of transfer-related areas occurred mostly with longer training periods. Overall, our findings place WM training effects into a general perception-action cycle, where some modulations may depend on the specific cognitive demands of a training task. Copyright © 2018 Elsevier Ltd. All rights reserved.

  18. How Do Social Networks Influence Learning Outcomes? A Case Study in an Industrial Setting

    Science.gov (United States)

    Maglajlic, Seid; Helic, Denis

    2012-01-01

    and Purpose: The purpose of this research is to shed light on the impact of implicit social networks to the learning outcome of e-learning participants in an industrial setting. Design/methodology/approach: The paper presents a theoretical framework that allows the authors to measure correlation coefficients between the different affiliations that…

  19. Collecting sustainability data in different organisational settings of the European Farm Accountancy Data Network

    NARCIS (Netherlands)

    Vrolijk, H.C.J.; Poppe, K.J.; Keszthelyi, Szilard

    2016-01-01

    The European Farm Accountancy Data Network (FADN) collects detailed financial economic information on a sample of farms in Europe. These data are used intensively for the evaluation of the European Union’s Common Agricultural Policy. Owing to changes in policies, there is a need for a broader set of

  20. A Game Theoretic Approach for Modeling Privacy Settings of an Online Social Network

    Directory of Open Access Journals (Sweden)

    Jundong Chen

    2014-05-01

    Full Text Available Users of online social networks often adjust their privacy settings to control how much information on their profiles is accessible to other users of the networks. While a variety of factors have been shown to affect the privacy strategies of these users, very little work has been done in analyzing how these factors influence each other and collectively contribute towards the users’ privacy strategies. In this paper, we analyze the influence of attribute importance, benefit, risk and network topology on the users’ attribute disclosure behavior by introducing a weighted evolutionary game model. Results show that: irrespective of risk, users aremore likely to reveal theirmost important attributes than their least important attributes; when the users’ range of influence is increased, the risk factor plays a smaller role in attribute disclosure; the network topology exhibits a considerable effect on the privacy in an environment with risk.

  1. Get SET: aligning anatomy demonstrator programmes with Surgical Education and Training selection criteria.

    Science.gov (United States)

    Rhodes, Danielle; Fogg, Quentin A; Lazarus, Michelle D

    2018-05-01

    Prevocational doctors aspiring to surgical careers are commonly recruited as anatomy demonstrators for undergraduate and graduate medical programmes. Entry into Surgical Education and Training (SET) is highly competitive and a unique opportunity exists to align anatomy demonstrator programmes with the selection criteria and core competencies of SET programmes. This study used a qualitative approach to (i) determine what criteria applicants for SET are assessed on and (ii) identify criteria that could be aligned with and enhanced by an anatomy demonstrator programme. The selection guidelines of all nine surgical specialties for the 2017 intake of SET trainees were analysed using qualitative content analysis methodology. The Royal Australasian College of Surgeons adopted a holistic approach to trainee selection that assessed both discipline-specific and discipline-independent skills. Qualitative content analysis identified eight categories of key selection criteria: medical expertise, scholarly activity, professional identity, interpersonal skills, integrity, self-management, insight and self-awareness and community involvement. The structured curriculum vitae was heavily weighted towards discipline-specific skills, such as medical expertise and scholarly activity. Insufficient information was available to determine the weighting of selection criteria assessed by the structured referee reports or interviews. Anatomy demonstrator programmes provide prevocational doctors with unique opportunities to develop surgical skills and competencies in a non-clinical setting. Constructively aligned anatomy demonstrator programmes may be particularly beneficial for prevocational doctors seeking to improve their anatomical knowledge, teaching skills or scholarly activity. © 2017 Royal Australasian College of Surgeons.

  2. Restorative justice training in intercultural settings in Serbia, and the contribution of the arts

    Directory of Open Access Journals (Sweden)

    Liebmann Marian

    2016-01-01

    Full Text Available This paper describes restorative justice training courses the author delivered in Serbia and Montenegro in the period 2003-2006, set in the context of the post-conflict situation, and reflects on the intercultural elements added to this course. The author also makes reference to recent work on hate crime and restorative justice in the UK as an extreme example of intercultural conflict. The final two sections discuss the potential of the arts in providing an extra (non-verbal tool in this work, using as examples two courses the author ran in Serbia.

  3. Optimizing Intermodal Train Schedules with a Design Balanced Network Design Model

    DEFF Research Database (Denmark)

    Pedersen, Michael Berliner; Crainic, Teodor Gabriel

    We present a modeling approach for optimizing intermodal trains schedules based on an infrastructure divided into time-dependent train paths. The formulation can be generalized to a capacitated multi commodity network design model with additional design balance constraints. We present a Tabu Search...

  4. Adaptive training of neural networks for control of autonomous mobile robots

    NARCIS (Netherlands)

    Steur, E.; Vromen, T.; Nijmeijer, H.; Fossen, T.I.; Nijmeijer, H.; Pettersen, K.Y.

    2017-01-01

    We present an adaptive training procedure for a spiking neural network, which is used for control of a mobile robot. Because of manufacturing tolerances, any hardware implementation of a spiking neural network has non-identical nodes, which limit the performance of the controller. The adaptive

  5. Navigating Social Networking and Social Media in School Psychology: Ethical and Professional Considerations in Training Programs

    Science.gov (United States)

    Pham, Andy V.

    2014-01-01

    Social networking and social media have undoubtedly proliferated within the past decade, allowing widespread communication and dissemination of user-generated content and information. Some psychology graduate programs, including school psychology, have started to embrace social networking and media for instructional and training purposes; however,…

  6. Assessing CPR training: The willingness of teaching credential candidates to provide CPR in a school setting.

    Science.gov (United States)

    Winkelman, Jack L; Fischbach, Ronald; Spinello, Elio F

    2009-12-01

    The study explores the anticipated willingness of teacher credential candidates at one California public university in the U.S. to perform cardiopulmonary resuscitation (CPR) or foreign body airway obstruction (FBAO) skills in a school setting. Objectives included (1) identifying reasons that credential candidates would elect or decline to perform CPR, (2) assisting schools to remediate cardiac/respiratory emergency preparedness, and (3) assessing CPR training courses to determine how they may influence teachers' willingness to perform CPR. Participants included 582 teacher credential candidates, who were 95.2% of those surveyed after completion of a health science course and CPR certification. Participants described their attitudes regarding the importance of CPR, the CPR training course, and their willingness to perform CPR in a school environment. Based upon chi-square analysis, an association was found between the willingness to perform CPR and the presence of any one concern regarding training, with 68.6% of those expressing concerns willing to perform CPR compared to 81.9% of those expressing no concerns (pteachers (76.9% vs. 43.5%, pteachers' willingness to perform CPR. Recommendations based on these findings include pedagogical changes to CPR curricula, focusing on the importance of CPR as a teacher skill and additional time for hands-on practice. Future research should include U.S. and international participants from a broader geographic area and assessment of both learning and affective outcomes.

  7. Chemical name extraction based on automatic training data generation and rich feature set.

    Science.gov (United States)

    Yan, Su; Spangler, W Scott; Chen, Ying

    2013-01-01

    The automation of extracting chemical names from text has significant value to biomedical and life science research. A major barrier in this task is the difficulty of getting a sizable and good quality data to train a reliable entity extraction model. Another difficulty is the selection of informative features of chemical names, since comprehensive domain knowledge on chemistry nomenclature is required. Leveraging random text generation techniques, we explore the idea of automatically creating training sets for the task of chemical name extraction. Assuming the availability of an incomplete list of chemical names, called a dictionary, we are able to generate well-controlled, random, yet realistic chemical-like training documents. We statistically analyze the construction of chemical names based on the incomplete dictionary, and propose a series of new features, without relying on any domain knowledge. Compared to state-of-the-art models learned from manually labeled data and domain knowledge, our solution shows better or comparable results in annotating real-world data with less human effort. Moreover, we report an interesting observation about the language for chemical names. That is, both the structural and semantic components of chemical names follow a Zipfian distribution, which resembles many natural languages.

  8. Mapping, Awareness, and Virtualization Network Administrator Training Tool (MAVNATT) Architecture and Framework

    Science.gov (United States)

    2015-06-01

    unit may setup and teardown the entire tactical infrastructure multiple times per day. This tactical network administrator training is a critical...language and runs on Linux and Unix based systems. All provisioning is based around the Nagios Core application, a powerful backend solution for network...start up a large number of virtual machines quickly. CORE supports the simulation of fixed and mobile networks. CORE is open-source, written in Python

  9. Contemporary social network sites: Relevance in anesthesiology teaching, training, and research

    OpenAIRE

    Rudrashish Haldar; Ashutosh Kaushal; Sukhen Samanta; Paurush Ambesh; Shashi Srivastava; Prabhat K Singh

    2016-01-01

    Objective: The phenomenal popularity of social networking sites has been used globally by medical professionals to boost professional associations and scientific developments. They have tremendous potential to forge professional liaisons, generate employment,upgrading skills and publicizing scientific achievements. We highlight the role of social networking mediums in influencing teaching, training and research in anaesthesiology. Background: The growth of social networking sites have been pr...

  10. Climate change education in informal settings: Using boundary objects to frame network dissemination

    Science.gov (United States)

    Steiner, Mary Ann

    This study of climate change education dissemination takes place in the context of a larger project where institutions in four cities worked together to develop a linked set of informal learning experiences about climate change. Each city developed an organizational network to explore new ways to connect urban audiences with climate change education. The four city-specific networks shared tools, resources, and knowledge with each other. The networks were related in mission and goals, but were structured and functioned differently depending on the city context. This study illustrates how the tools, resources, and knowledge developed in one network were shared with networks in two additional cities. Boundary crossing theory frames the study to describe the role of objects and processes in sharing between networks. Findings suggest that the goals, capacity and composition of networks resulted in a different emphasis in dissemination efforts, in one case to push the approach out to partners for their own work and in the other to pull partners into a more collaborative stance. Learning experiences developed in each city as a result of the dissemination reflected these differences in the city-specific emphasis with the push city diving into messy examples of the approach to make their own examples, and the pull city offering polished experiences to partners in order to build confidence in the climate change messaging. The networks themselves underwent different kinds of growth and change as a result of dissemination. The emphasis on push and use of messy examples resulted in active use of the principles of the approach and the pull emphasis with polished examples resulted in the cultivation of partnerships with the hub and the potential to engage in the educational approach. These findings have implications for boundary object theory as a useful grounding for dissemination designs in the context of networks of informal learning organizations to support a shift in

  11. Education and Training, and Knowledge Networks for Capacity-Building in Nuclear Security

    International Nuclear Information System (INIS)

    Mrabit, Khammar

    2014-01-01

    Conclusions: • Capacity Building (CB) is critical for States to establish and maintain effective and sustainable nuclear security regime. • IAEA is a worldwide platform promoting international cooperation for CB in nuclear security involving more than 160 countries and over 20 Organizations and Initiatives. • IAEA Division of Nuclear Security is ready to continue supporting States in developing their CB through: – Comprehensive Training Programme: more than 80 training events annually – International Nuclear Security Training and Support Centre Network (NSSC) – Comprehensive Education Programme – International Nuclear Security Network (INSEN)

  12. Effect of the manipulation of exercise order in the tri-set training system

    Directory of Open Access Journals (Sweden)

    Alex Silva Ribeiro

    2013-07-01

    Full Text Available The aim of this study was to analyze the effect of the manipulation of two different exercise orders using the tn-set system on the motor performance in exercises for the chest. Ten male (25.6 ± 5.7 years, 77.0 ± 5.8 kg, 172.9 ± 5.0 cm, 25.7 ± 1.4 kg/m2 with experience in resistance training underwent two experimental sessions, in which the subjects performed two sequences of exercises for the chest: SEQA (bench press, incline bench press, and peck deck and SEQB (peck deck, incline bench press, and bench press. The load used allowed 8 to 12 repetitions (80% of 1RM in each exercise. A higher number of repetitions (29 ± 2 reps vs. 26 ± 3 reps, P < 0.001 and a greater total overload (resistance used x repetitions performed = 1,942 ± 172 kg vs. 1,728 ± 234 kg, P < 0.001 were observed in SEQB. The results suggest that in the tn-set system the higher number of repetitions and a greater training volume occur when the single-joint exercise is included before multiple-joint exercises.

  13. Network-Based Coordination of Civil-Service Training: Lessons from the Case of Estonia

    Directory of Open Access Journals (Sweden)

    Metsma Merilin

    2017-06-01

    Full Text Available The focus of this article is on the coordination of civil-service training in a decentralized civil-service system. The Estonian case is studied. The article investigates network-based coordination, analyzes the power sources of the central coordinator and discusses the opportunities and limitations of creating coherence through network-type cooperation. The article concludes that the key power sources for the central coordinator are financial, human and technical resources paired with knowledge, leadership and commitment. The case study shows that, in a decentralized civil service system, a common understanding on training and development can be fostered by intense collaboration through networks.

  14. Computerized cognitive training to improve mood in senior living settings: design of a randomized controlled trial

    Directory of Open Access Journals (Sweden)

    Smith M

    2018-04-01

    Full Text Available Marianne Smith,1 Michael P Jones,2 Megan M Dotson,1 Fredric D Wolinsky3 1College of Nursing, The University of Iowa, Iowa City, IA, USA; 2Department of Biostatistics, College of Public Health, The University of Iowa, Iowa City, IA, USA; 3Department of Health, Management and Policy, College of Public Health, the University of Iowa, Iowa City, IA, USA Purpose: This two-arm, randomized controlled trial was designed to evaluate a computerized cognitive speed of processing (SOP training known as Road Tour in the generally older group of adults residing in assisted living (AL and related senior housing. Study aims focused on depression-related outcomes that were observed in earlier SOP studies using Road Tour with younger, home-dwelling seniors. Study design and baseline outcomes are discussed. Participants and methods: A community-based design engaged AL and related senior living settings as partners in research. Selected staff served as on-site research assistants who were trained to recruit, consent, and train a target of 300 participants from AL and independent living (IL programs to use the intervention and attention control computerized training. Ten hours of initial computerized training was followed by two booster sessions at 5 and 11 months. Outcome measures included Useful Field of View, 9-item Patient Health Questionnaire, 12-item Centers for Epidemiological Studies Depression scale, 7-item Generalized Anxiety Disorders, Brief Pain Inventory, and SF-36 Health Survey. Assessments occurred before randomization (pretraining and posttraining, 26 and 52 weeks. Results: A total of 351 participants were randomized to the intervention (n = 173 and attention control (n = 178 groups. There were no significant differences between groups in demographic characteristics, with the exception of education and reported osteoporosis. There were no significant differences in study outcomes between groups at baseline. Participants in AL had significantly lower

  15. Errorless learning for training individuals with schizophrenia at a community mental health setting providing work experience.

    Science.gov (United States)

    Kern, Robert S; Liberman, Robert P; Becker, Deborah R; Drake, Robert E; Sugar, Catherine A; Green, Michael F

    2009-07-01

    The effects of errorless learning (EL) on work performance, tenure, and personal well-being were compared with conventional job training in a community mental health fellowship club offering 12-week time-limited work experience. Participants were 40 clinically stable schizophrenia and schizoaffective disorder outpatients randomly assigned to EL vs conventional instruction (CI) at a thrift-type clothing store. EL participants received training on how to perform their assigned job tasks based on principles of EL, such as error reduction and automation of task performance. CI participants received training common to other community-based entry-level jobs that included verbal instruction, a visual demonstration, independent practice, and corrective feedback. Participants were scheduled to work 2 hours per week for 12 weeks. For both groups, job training occurred during the first 2 weeks at the worksite. Work performance (assessed using the Work Behavior Inventory, WBI) and personal well-being (self-esteem, job satisfaction, and work stress) were assessed at weeks 2, 4, and 12. Job tenure was defined as the number of weeks on the job or total number of hours worked prior to quitting or study end. The EL group performed better than the CI group on the Work Quality Scale from the WBI, and the group differences were relatively consistent over time. Results from the survival analyses of job tenure revealed a non-significant trend favoring EL. There were no group differences on self-esteem, job satisfaction, or work stress. The findings provide modest support for the extensions of EL to community settings for enhancing work performance.

  16. Pinning Synchronization of Linear Complex Coupling Synchronous Generators Network of Hydroelectric Generating Set

    Directory of Open Access Journals (Sweden)

    Xuefei Wu

    2014-01-01

    Full Text Available A novel linear complex system for hydroturbine-generator sets in multimachine power systems is suggested in this paper and synchronization of the power-grid networks is studied. The advanced graph theory and stability theory are combined to solve the problem. Here we derive a sufficient condition under which the synchronous state of power-grid networks is stable in disturbance attenuation. Finally, numerical simulations are provided to illustrate the effectiveness of the results by the IEEE 39 bus system.

  17. Training the next generation of psychotraumatologists: COllaborative Network for Training and EXcellence in psychoTraumatology (CONTEXT)

    Science.gov (United States)

    Vallières, Frédérique; Hyland, Philip; Murphy, Jamie; Hansen, Maj; Shevlin, Mark; Elklit, Ask; Ceannt, Ruth; Armour, Cherie; Wiedemann, Nana; Munk, Mette; Dinesen, Cecilie; O’Hare, Geraldine; Cunningham, Twylla; Askerod, Ditte; Spitz, Pernille; Blackwell, Noeline; McCarthy, Angela; O’Dowd, Leonie; Scott, Shirley; Reid, Tracey; Mokake, Andreas; Halpin, Rory; Perera, Camila; Gleeson, Christina; Frost, Rachel; Flanagan, Natalie; Aldamman, Kinan; Tamrakar, Trina; Louison Vang, Maria; Sherwood, Larissa; Travers, Áine; Haahr-Pedersen, Ida; Walshe, Catherine; McDonagh, Tracey; Bramsen, Rikke Holm

    2018-01-01

    ABSTRACT In this paper we present a description of the Horizon2020, Marie Skłodowska-Curie Action funded, research and training programme CONTEXT: COllaborative Network for Training and EXcellence in psychoTraumatology. The three objectives of the programme are put forward, each of which refers to a key component of the CONTEXT programme. First, we summarize the 12 individual research projects that will take place across three priority populations: (i) refugees and asylum seekers, (ii) first responders, and (iii) perpetrators and survivors of childhood and gender-based violence. Second, we detail the mentoring and training programme central to CONTEXT. Finally, we describe how the research, together with the training, will contribute towards better policy, guidelines, and practice within the field of psychotraumatology. PMID:29372015

  18. Training the next generation of psychotraumatologists: COllaborative Network for Training and EXcellence in psychoTraumatology (CONTEXT).

    Science.gov (United States)

    Vallières, Frédérique; Hyland, Philip; Murphy, Jamie; Hansen, Maj; Shevlin, Mark; Elklit, Ask; Ceannt, Ruth; Armour, Cherie; Wiedemann, Nana; Munk, Mette; Dinesen, Cecilie; O'Hare, Geraldine; Cunningham, Twylla; Askerod, Ditte; Spitz, Pernille; Blackwell, Noeline; McCarthy, Angela; O'Dowd, Leonie; Scott, Shirley; Reid, Tracey; Mokake, Andreas; Halpin, Rory; Perera, Camila; Gleeson, Christina; Frost, Rachel; Flanagan, Natalie; Aldamman, Kinan; Tamrakar, Trina; Louison Vang, Maria; Sherwood, Larissa; Travers, Áine; Haahr-Pedersen, Ida; Walshe, Catherine; McDonagh, Tracey; Bramsen, Rikke Holm

    2018-01-01

    In this paper we present a description of the Horizon2020, Marie Skłodowska-Curie Action funded, research and training programme CONTEXT: COllaborative Network for Training and EXcellence in psychoTraumatology. The three objectives of the programme are put forward, each of which refers to a key component of the CONTEXT programme. First, we summarize the 12 individual research projects that will take place across three priority populations: (i) refugees and asylum seekers, (ii) first responders, and (iii) perpetrators and survivors of childhood and gender-based violence. Second, we detail the mentoring and training programme central to CONTEXT. Finally, we describe how the research, together with the training, will contribute towards better policy, guidelines, and practice within the field of psychotraumatology.

  19. A Dynamic Linear Hashing Method for Redundancy Management in Train Ethernet Consist Network

    Directory of Open Access Journals (Sweden)

    Xiaobo Nie

    2016-01-01

    Full Text Available Massive transportation systems like trains are considered critical systems because they use the communication network to control essential subsystems on board. Critical system requires zero recovery time when a failure occurs in a communication network. The newly published IEC62439-3 defines the high-availability seamless redundancy protocol, which fulfills this requirement and ensures no frame loss in the presence of an error. This paper adopts these for train Ethernet consist network. The challenge is management of the circulating frames, capable of dealing with real-time processing requirements, fast switching times, high throughout, and deterministic behavior. The main contribution of this paper is the in-depth analysis it makes of network parameters imposed by the application of the protocols to train control and monitoring system (TCMS and the redundant circulating frames discarding method based on a dynamic linear hashing, using the fastest method in order to resolve all the issues that are dealt with.

  20. Maximizing the Lifetime of Wireless Sensor Networks Using Multiple Sets of Rendezvous

    Directory of Open Access Journals (Sweden)

    Bo Li

    2015-01-01

    Full Text Available In wireless sensor networks (WSNs, there is a “crowded center effect” where the energy of nodes located near a data sink drains much faster than other nodes resulting in a short network lifetime. To mitigate the “crowded center effect,” rendezvous points (RPs are used to gather data from other nodes. In order to prolong the lifetime of WSN further, we propose using multiple sets of RPs in turn to average the energy consumption of the RPs. The problem is how to select the multiple sets of RPs and how long to use each set of RPs. An optimal algorithm and a heuristic algorithm are proposed to address this problem. The optimal algorithm is highly complex and only suitable for small scale WSN. The performance of the proposed algorithms is evaluated through simulations. The simulation results indicate that the heuristic algorithm approaches the optimal one and that using multiple RP sets can significantly prolong network lifetime.

  1. Aging and Network Properties: Stability Over Time and Links with Learning during Working Memory Training

    Directory of Open Access Journals (Sweden)

    Alexandru D. Iordan

    2018-01-01

    Full Text Available Growing evidence suggests that healthy aging affects the configuration of large-scale functional brain networks. This includes reducing network modularity and local efficiency. However, the stability of these effects over time and their potential role in learning remain poorly understood. The goal of the present study was to further clarify previously reported age effects on “resting-state” networks, to test their reliability over time, and to assess their relation to subsequent learning during training. Resting-state fMRI data from 23 young (YA and 20 older adults (OA were acquired in 2 sessions 2 weeks apart. Graph-theoretic analyses identified both consistencies in network structure and differences in module composition between YA and OA, suggesting topological changes and less stability of functional network configuration with aging. Brain-wide, OA showed lower modularity and local efficiency compared to YA, consistent with the idea of age-related functional dedifferentiation, and these effects were replicable over time. At the level of individual networks, OA consistently showed greater participation and lower local efficiency and within-network connectivity in the cingulo-opercular network, as well as lower intra-network connectivity in the default-mode network and greater participation of the somato-sensorimotor network, suggesting age-related differential effects at the level of specialized brain modules. Finally, brain-wide network properties showed associations, albeit limited, with learning rates, as assessed with 10 days of computerized working memory training administered after the resting-state sessions, suggesting that baseline network configuration may influence subsequent learning outcomes. Identification of neural mechanisms associated with learning-induced plasticity is important for further clarifying whether and how such changes predict the magnitude and maintenance of training gains, as well as the extent and limits of

  2. Associating Human-Centered Concepts with Social Networks Using Fuzzy Sets

    Science.gov (United States)

    Yager, Ronald R.

    that allows us to determine how true it is that a particular node is a leader. In this work we look at the use of fuzzy set methodologies [8-10] to provide a bridge between the human analyst and the formal model of the network.

  3. Let's Wiggle with 5-2-1-0: Curriculum Development for Training Childcare Providers to Promote Activity in Childcare Settings.

    Science.gov (United States)

    Vinci, Debra M; Whitt-Glover, Melicia C; Wirth, Christopher K; Kraus, Caroline; Venezia, Alexandra P

    2016-01-01

    Overweight and obesity are increasing in preschool children in the US. Policy, systems, and environmental change interventions in childcare settings can improve obesity-related behaviors. The aim of this study was to develop and pilot an intervention to train childcare providers to promote physical activity (PA) in childcare classrooms. An evidence scan, key informant (n = 34) and focus group (n = 20) interviews with childcare directors and staff, and environmental self-assessment of childcare facilities (n = 22) informed the design of the training curriculum. Feedback from the interviews indicated that childcare providers believed in the importance of teaching children about PA and were supportive of training teachers to incorporate PA into classroom settings. The Promoting Physical Activity in Childcare Setting Curriculum was developed and training was implemented with 16 teachers. Participants reported a positive experience with the hands-on training and reported acquiring new knowledge that they intended to implement in their childcare settings. Our findings highlight the feasibility of working with childcare staff to develop PA training and curriculum. Next steps include evaluating the curriculum in additional childcare settings and childcare staff implementation of the curriculum to understand the effectiveness of the training on PA levels of children.

  4. Provider Training to Screen and Initiate Evidence-Based Pediatric Obesity Treatment in Routine Practice Settings: A Randomized Pilot Trial.

    Science.gov (United States)

    Kolko, Rachel P; Kass, Andrea E; Hayes, Jacqueline F; Levine, Michele D; Garbutt, Jane M; Proctor, Enola K; Wilfley, Denise E

    This randomized pilot trial evaluated two training modalities for first-line, evidence-based pediatric obesity services (screening and goal setting) among nursing students. Participants (N = 63) were randomized to live interactive training or Web-facilitated self-study training. Pretraining, post-training, and 1-month follow-up assessments evaluated training feasibility, acceptability, and impact (knowledge and skill via simulation). Moderator (previous experience) and predictor (content engagement) analyses were conducted. Nearly all participants (98%) completed assessments. Both types of training were acceptable, with higher ratings for live training and participants with previous experience (ps pediatric obesity services. Copyright © 2016 National Association of Pediatric Nurse Practitioners. Published by Elsevier Inc. All rights reserved.

  5. A dynamic evolutionary clustering perspective: Community detection in signed networks by reconstructing neighbor sets

    Science.gov (United States)

    Chen, Jianrui; Wang, Hua; Wang, Lina; Liu, Weiwei

    2016-04-01

    Community detection in social networks has been intensively studied in recent years. In this paper, a novel similarity measurement is defined according to social balance theory for signed networks. Inter-community positive links are found and deleted due to their low similarity. The positive neighbor sets are reconstructed by this method. Then, differential equations are proposed to imitate the constantly changing states of nodes. Each node will update its state based on the difference between its state and average state of its positive neighbors. Nodes in the same community will evolve together with time and nodes in the different communities will evolve far away. Communities are detected ultimately when states of nodes are stable. Experiments on real world and synthetic networks are implemented to verify detection performance. The thorough comparisons demonstrate the presented method is more efficient than two acknowledged better algorithms.

  6. Construction of Pipelined Strategic Connected Dominating Set for Mobile Ad Hoc Networks

    Directory of Open Access Journals (Sweden)

    Ceronmani Sharmila

    2016-06-01

    Full Text Available Efficient routing between nodes is the most important challenge in a Mobile Ad Hoc Network (MANET. A Connected Dominating Set (CDS acts as a virtual backbone for routing in a MANET. Hence, the construction of CDS based on the need and its application plays a vital role in the applications of MANET. The PipeLined Strategic CDS (PLS-CDS is constructed based on strategy, dynamic diameter and transmission range. The strategy used for selecting the starting node is, any source node in the network, which has its entire destination within a virtual pipelined coverage, instead of the node with maximum connectivity. The other nodes are then selected based on density and velocity. The proposed CDS also utilizes the energy of the nodes in the network in an optimized manner. Simulation results showed that the proposed algorithm is better in terms of size of the CDS and average hop per path length.

  7. Reduced Functional Connectivity of Default Mode and Set-Maintenance Networks in Ornithine Transcarbamylase Deficiency.

    Directory of Open Access Journals (Sweden)

    Ileana Pacheco-Colón

    Full Text Available Ornithine transcarbamylase deficiency (OTCD is an X-chromosome linked urea cycle disorder (UCD that causes hyperammonemic episodes leading to white matter injury and impairments in executive functioning, working memory, and motor planning. This study aims to investigate differences in functional connectivity of two resting-state networks--default mode and set-maintenance--between OTCD patients and healthy controls.Sixteen patients with partial OTCD and twenty-two control participants underwent a resting-state scan using 3T fMRI. Combining independent component analysis (ICA and region-of-interest (ROI analyses, we identified the nodes that comprised each network in each group, and assessed internodal connectivity.Group comparisons revealed reduced functional connectivity in the default mode network (DMN of OTCD patients, particularly between the anterior cingulate cortex/medial prefrontal cortex (ACC/mPFC node and bilateral inferior parietal lobule (IPL, as well as between the ACC/mPFC node and the posterior cingulate cortex (PCC node. Patients also showed reduced connectivity in the set-maintenance network, especially between right anterior insula/frontal operculum (aI/fO node and bilateral superior frontal gyrus (SFG, as well as between the right aI/fO and ACC and between the ACC and right SFG.Internodal functional connectivity in the DMN and set-maintenance network is reduced in patients with partial OTCD compared to controls, most likely due to hyperammonemia-related white matter damage. Because several of the affected areas are involved in executive functioning, it is postulated that this reduced connectivity is an underlying cause of the deficits OTCD patients display in this cognitive domain.

  8. A jazz-based approach for optimal setting of pressure reducing valves in water distribution networks

    Science.gov (United States)

    De Paola, Francesco; Galdiero, Enzo; Giugni, Maurizio

    2016-05-01

    This study presents a model for valve setting in water distribution networks (WDNs), with the aim of reducing the level of leakage. The approach is based on the harmony search (HS) optimization algorithm. The HS mimics a jazz improvisation process able to find the best solutions, in this case corresponding to valve settings in a WDN. The model also interfaces with the improved version of a popular hydraulic simulator, EPANET 2.0, to check the hydraulic constraints and to evaluate the performances of the solutions. Penalties are introduced in the objective function in case of violation of the hydraulic constraints. The model is applied to two case studies, and the obtained results in terms of pressure reductions are comparable with those of competitive metaheuristic algorithms (e.g. genetic algorithms). The results demonstrate the suitability of the HS algorithm for water network management and optimization.

  9. Weighted complex network analysis of the Beijing subway system: Train and passenger flows

    Science.gov (United States)

    Feng, Jia; Li, Xiamiao; Mao, Baohua; Xu, Qi; Bai, Yun

    2017-05-01

    In recent years, complex network theory has become an important approach to the study of the structure and dynamics of traffic networks. However, because traffic data is difficult to collect, previous studies have usually focused on the physical topology of subway systems, whereas few studies have considered the characteristics of traffic flows through the network. Therefore, in this paper, we present a multi-layer model to analyze traffic flow patterns in subway networks, based on trip data and an operation timetable obtained from the Beijing Subway System. We characterize the patterns in terms of the spatiotemporal flow size distributions of both the train flow network and the passenger flow network. In addition, we describe the essential interactions between these two networks based on statistical analyses. The results of this study suggest that layered models of transportation systems can elucidate fundamental differences between the coexisting traffic flows and can also clarify the mechanism that causes these differences.

  10. Applying the Internal Referencing Strategy to the Evaluation of Transfer of Training in Field Settings

    National Research Council Canada - National Science Library

    Watola, Daniel

    1997-01-01

    ...), a quasi-experimental research design that infers training effectiveness when trainee pretest-posttest change on training-relevant test items is greater than pretest-posttest change on training-irrelevant test items...

  11. Acoustic Metadata Management and Transparent Access to Networked Oceanographic Data Sets

    Science.gov (United States)

    2015-09-30

    Transparent Access to Networked Oceanographic Data Sets Marie A. Roch Dept. of Computer Science San Diego State University 5500 Campanile Drive San...specific technologies for processing Excel spreadsheets and Access databases. The architecture (Figure 4) is based on a client-server model...Keesey, M. S., Lieske, J. H., Ostro, S. J., Standish, E. M., and Wimberly, R. N. (1996). "JPL’s On-Line Solar System Data Service," B. Am. Astron

  12. The ability to store energy in pea protein gels is set by network dimensions smaller than 50 nm

    NARCIS (Netherlands)

    Munialo, C.D.; Linden, van der E.; Jongh, de H.H.J.

    2014-01-01

    The objective of this study was to identify which length scales set the ability to elastically store energy in pea protein network structures. Various network structures were obtained frompea proteins by varying the pH and salt conditions during gel formation. The coarseness of the network structure

  13. German MedicalTeachingNetwork (MDN) implementing national standards for teacher training.

    Science.gov (United States)

    Lammerding-Koeppel, M; Ebert, T; Goerlitz, A; Karsten, G; Nounla, C; Schmidt, S; Stosch, C; Dieter, P

    2016-01-01

    An increasing demand for proof of professionalism in higher education strives for quality assurance (QA) and improvement in medical education. A wide range of teacher trainings is available to medical staff in Germany. Cross-institutional approval of individual certificates is usually a difficult and time consuming task for institutions. In case of non-acceptance it may hinder medical teachers in their professional mobility. The faculties of medicine aimed to develop a comprehensive national framework, to promote standards for formal faculty development programmes across institutions and to foster professionalization of medical teaching. Addressing the above challenges in a joint approach, the faculties set up the national MedicalTeacherNetwork (MDN). Great importance is attributed to work out nationally concerted standards for faculty development and an agreed-upon quality control process across Germany. Medical teachers benefit from these advantages due to portability of faculty development credentials from one faculty of medicine to another within the MDN system. The report outlines the process of setting up the MDN and the national faculty development programme in Germany. Success factors, strengths and limitations are discussed from an institutional, individual and general perspective. Faculties engaged in similar developments might be encouraged to transfer the MDN concept to their countries.

  14. Efficient probabilistic inference in generic neural networks trained with non-probabilistic feedback.

    Science.gov (United States)

    Orhan, A Emin; Ma, Wei Ji

    2017-07-26

    Animals perform near-optimal probabilistic inference in a wide range of psychophysical tasks. Probabilistic inference requires trial-to-trial representation of the uncertainties associated with task variables and subsequent use of this representation. Previous work has implemented such computations using neural networks with hand-crafted and task-dependent operations. We show that generic neural networks trained with a simple error-based learning rule perform near-optimal probabilistic inference in nine common psychophysical tasks. In a probabilistic categorization task, error-based learning in a generic network simultaneously explains a monkey's learning curve and the evolution of qualitative aspects of its choice behavior. In all tasks, the number of neurons required for a given level of performance grows sublinearly with the input population size, a substantial improvement on previous implementations of probabilistic inference. The trained networks develop a novel sparsity-based probabilistic population code. Our results suggest that probabilistic inference emerges naturally in generic neural networks trained with error-based learning rules.Behavioural tasks often require probability distributions to be inferred about task specific variables. Here, the authors demonstrate that generic neural networks can be trained using a simple error-based learning rule to perform such probabilistic computations efficiently without any need for task specific operations.

  15. Mobile learning for HIV/AIDS healthcare worker training in resource-limited settings

    Directory of Open Access Journals (Sweden)

    Zolfo Maria

    2010-09-01

    Full Text Available Abstract Background We present an innovative approach to healthcare worker (HCW training using mobile phones as a personal learning environment. Twenty physicians used individual Smartphones (Nokia N95 and iPhone, each equipped with a portable solar charger. Doctors worked in urban and peri-urban HIV/AIDS clinics in Peru, where almost 70% of the nation's HIV patients in need are on treatment. A set of 3D learning scenarios simulating interactive clinical cases was developed and adapted to the Smartphones for a continuing medical education program lasting 3 months. A mobile educational platform supporting learning events tracked participant learning progress. A discussion forum accessible via mobile connected participants to a group of HIV specialists available for back-up of the medical information. Learning outcomes were verified through mobile quizzes using multiple choice questions at the end of each module. Methods In December 2009, a mid-term evaluation was conducted, targeting both technical feasibility and user satisfaction. It also highlighted user perception of the program and the technical challenges encountered using mobile devices for lifelong learning. Results With a response rate of 90% (18/20 questionnaires returned, the overall satisfaction of using mobile tools was generally greater for the iPhone. Access to Skype and Facebook, screen/keyboard size, and image quality were cited as more troublesome for the Nokia N95 compared to the iPhone. Conclusions Training, supervision and clinical mentoring of health workers are the cornerstone of the scaling up process of HIV/AIDS care in resource-limited settings (RLSs. Educational modules on mobile phones can give flexibility to HCWs for accessing learning content anywhere. However lack of softwares interoperability and the high investment cost for the Smartphones' purchase could represent a limitation to the wide spread use of such kind mLearning programs in RLSs.

  16. ENETRAP: European network on education and training in radiological protection

    International Nuclear Information System (INIS)

    Michele Coeck; Cecile Etard; Siegurd Moebius; Annemarie Schmitt-Hannig; Andrea Luciani; Jan van der Steen; Marisa Marco; Joanne Stewart; Jacques Balosso; Rosemary Thompson

    2006-01-01

    Recent studies have shown that there is a wide variety of approaches to education and training of the Qualified Expert across the EU. As they stand, such differences are a barrier to the mutual recognition of the Qualified Expert status and, in part, are contributing to a perceived shortage in expertise in radiation protection and safety. The overall aim of ENETRAP is to determine mechanisms that in the longer term will facilitate better integration of education and training activities (with a view to mutual recognition across the EU) and to ensure the ongoing provision of the necessary competence and expertise at the level of the Qualified Expert. (authors)

  17. Effects of training strategies implemented in a complex videogame on functional connectivity of attentional networks.

    Science.gov (United States)

    Voss, Michelle W; Prakash, Ruchika Shaurya; Erickson, Kirk I; Boot, Walter R; Basak, Chandramallika; Neider, Mark B; Simons, Daniel J; Fabiani, Monica; Gratton, Gabriele; Kramer, Arthur F

    2012-01-02

    We used the Space Fortress videogame, originally developed by cognitive psychologists to study skill acquisition, as a platform to examine learning-induced plasticity of interacting brain networks. Novice videogame players learned Space Fortress using one of two training strategies: (a) focus on all aspects of the game during learning (fixed priority), or (b) focus on improving separate game components in the context of the whole game (variable priority). Participants were scanned during game play using functional magnetic resonance imaging (fMRI), both before and after 20 h of training. As expected, variable priority training enhanced learning, particularly for individuals who initially performed poorly. Functional connectivity analysis revealed changes in brain network interaction reflective of more flexible skill learning and retrieval with variable priority training, compared to procedural learning and skill implementation with fixed priority training. These results provide the first evidence for differences in the interaction of large-scale brain networks when learning with different training strategies. Our approach and findings also provide a foundation for exploring the brain plasticity involved in transfer of trained abilities to novel real-world tasks such as driving, sport, or neurorehabilitation. Copyright © 2011 Elsevier Inc. All rights reserved.

  18. Design of cognitive engine for cognitive radio based on the rough sets and radial basis function neural network

    Science.gov (United States)

    Yang, Yanchao; Jiang, Hong; Liu, Congbin; Lan, Zhongli

    2013-03-01

    Cognitive radio (CR) is an intelligent wireless communication system which can dynamically adjust the parameters to improve system performance depending on the environmental change and quality of service. The core technology for CR is the design of cognitive engine, which introduces reasoning and learning methods in the field of artificial intelligence, to achieve the perception, adaptation and learning capability. Considering the dynamical wireless environment and demands, this paper proposes a design of cognitive engine based on the rough sets (RS) and radial basis function neural network (RBF_NN). The method uses experienced knowledge and environment information processed by RS module to train the RBF_NN, and then the learning model is used to reconfigure communication parameters to allocate resources rationally and improve system performance. After training learning model, the performance is evaluated according to two benchmark functions. The simulation results demonstrate the effectiveness of the model and the proposed cognitive engine can effectively achieve the goal of learning and reconfiguration in cognitive radio.

  19. Asia-Pacific Research and Training Network on Trade (ARTNET ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    During Phase II, ARTNET will continue its training and capacity building efforts, focusing on trade facilitation, preferential trade agreements (PTAs) and other trade agreements. Given the complexity of the trade and investment environment in the region, ARTNET will explore the interaction between trade, investment, ...

  20. Effect of training algorithms on neural networks aided pavement ...

    African Journals Online (AJOL)

    Especially, the use of Finite Element (FE) based pavement modeling results for training the NN aided inverse analysis is considered to be accurate in realistically characterizing the non-linear stress-sensitive response of underlying pavement layers in real-time. Efficient NN learning algorithms have been developed and ...

  1. Helping mothers survive bleeding after birth: an educational of simulation-based training in a low resource setting

    NARCIS (Netherlands)

    Nelissen, E.J.T.; Ersdal, H.; Ostergaard, D.; Mduma, E.; Broerse, J.E.W.; Evjen-Olsen, B.; van Roosmalen, J.; Stekelenburg, J.

    2014-01-01

    Objective To evaluate "Helping Mothers Survive Bleeding After Birth" (HMS BAB) simulation-based training in a low-resource setting. Design Educational intervention study. Setting Rural referral hospital in Northern Tanzania. Population Clinicians, nurse-midwives, medical attendants, and ambulance

  2. Train-Network Interactions and Stability Evaluation in High-Speed Railways--Part I: Phenomena and Modeling

    DEFF Research Database (Denmark)

    Hu, Haitao; Tao, Haidong; Blaabjerg, Frede

    2018-01-01

    of the electric trains and traction network are equally modeled. In which, an impedance-based input behavior of the train is fully investigated with considering available controllers and their parameters in DQ-domain. While, the entire traction network, including traction transformer, catenary, supply lines......, is represented in a frequency-domain nodal matrix. Furthermore, the impedance-frequency responses of both electric train and traction network are measured and validated through frequency scan method. Finally, a generalized train-network simulation and experimental systems are proposed for verifying...

  3. Effects of Cognitive Training on Resting-State Functional Connectivity of Default Mode, Salience, and Central Executive Networks.

    Science.gov (United States)

    Cao, Weifang; Cao, Xinyi; Hou, Changyue; Li, Ting; Cheng, Yan; Jiang, Lijuan; Luo, Cheng; Li, Chunbo; Yao, Dezhong

    2016-01-01

    Neuroimaging studies have documented that aging can disrupt certain higher cognitive systems such as the default mode network (DMN), the salience network and the central executive network (CEN). The effect of cognitive training on higher cognitive systems remains unclear. This study used a 1-year longitudinal design to explore the cognitive training effect on three higher cognitive networks in healthy older adults. The community-living healthy older adults were divided into two groups: the multi-domain cognitive training group (24 sessions of cognitive training over a 3-months period) and the wait-list control group. All subjects underwent cognitive measurements and resting-state functional magnetic resonance imaging scanning at baseline and at 1 year after the training ended. We examined training-related changes in functional connectivity (FC) within and between three networks. Compared with the baseline, we observed maintained or increased FC within all three networks after training. The scans after training also showed maintained anti-correlation of FC between the DMN and CEN compared to the baseline. These findings demonstrated that cognitive training maintained or improved the functional integration within networks and the coupling between the DMN and CEN in older adults. Our findings suggested that multi-domain cognitive training can mitigate the aging-related dysfunction of higher cognitive networks.

  4. Establishing Network Interaction between Resource Training Centers for People with Disabilities and Partner Universities

    Directory of Open Access Journals (Sweden)

    Panyukova S.V.,

    2018-05-01

    Full Text Available The paper focuses on the problem of accessibility and quality of higher education for students with disabilities. We describe our experience in organising network interaction between the MSUPE Resource and Training Center for Disabled People established in 2016-2017 and partner universities in ‘fixed territories’. The need for cooperation and network interaction arises from the high demand for the cooperation of efforts of leading experts, researchers, methodologists and instructors necessary for improving the quality and accessibility of higher education for persons with disabilities. The Resource and Training Center offers counseling for the partner universities, arranges advanced training for those responsible for teaching of the disabled, and offers specialized equipment for temporary use. In this article, we emphasize the importance of organizing network interactions with universities and social partners in order to ensure accessibility of higher education for students with disabilities.

  5. Statistical generation of training sets for measuring NO3(-), NH4(+) and major ions in natural waters using an ion selective electrode array.

    Science.gov (United States)

    Mueller, Amy V; Hemond, Harold F

    2016-05-18

    Knowledge of ionic concentrations in natural waters is essential to understand watershed processes. Inorganic nitrogen, in the form of nitrate and ammonium ions, is a key nutrient as well as a participant in redox, acid-base, and photochemical processes of natural waters, leading to spatiotemporal patterns of ion concentrations at scales as small as meters or hours. Current options for measurement in situ are costly, relying primarily on instruments adapted from laboratory methods (e.g., colorimetric, UV absorption); free-standing and inexpensive ISE sensors for NO3(-) and NH4(+) could be attractive alternatives if interferences from other constituents were overcome. Multi-sensor arrays, coupled with appropriate non-linear signal processing, offer promise in this capacity but have not yet successfully achieved signal separation for NO3(-) and NH4(+)in situ at naturally occurring levels in unprocessed water samples. A novel signal processor, underpinned by an appropriate sensor array, is proposed that overcomes previous limitations by explicitly integrating basic chemical constraints (e.g., charge balance). This work further presents a rationalized process for the development of such in situ instrumentation for NO3(-) and NH4(+), including a statistical-modeling strategy for instrument design, training/calibration, and validation. Statistical analysis reveals that historical concentrations of major ionic constituents in natural waters across New England strongly covary and are multi-modal. This informs the design of a statistically appropriate training set, suggesting that the strong covariance of constituents across environmental samples can be exploited through appropriate signal processing mechanisms to further improve estimates of minor constituents. Two artificial neural network architectures, one expanded to incorporate knowledge of basic chemical constraints, were tested to process outputs of a multi-sensor array, trained using datasets of varying degrees of

  6. A Set of Functional Brain Networks for the Comprehensive Evaluation of Human Characteristics

    Directory of Open Access Journals (Sweden)

    Yul-Wan Sung

    2018-03-01

    Full Text Available Many human characteristics must be evaluated to comprehensively understand an individual, and measurements of the corresponding cognition/behavior are required. Brain imaging by functional MRI (fMRI has been widely used to examine brain function related to human cognition/behavior. However, few aspects of cognition/behavior of individuals or experimental groups can be examined through task-based fMRI. Recently, resting state fMRI (rs-fMRI signals have been shown to represent functional infrastructure in the brain that is highly involved in processing information related to cognition/behavior. Using rs-fMRI may allow diverse information about the brain through a single MRI scan to be obtained, as rs-fMRI does not require stimulus tasks. In this study, we attempted to identify a set of functional networks representing cognition/behavior that are related to a wide variety of human characteristics and to evaluate these characteristics using rs-fMRI data. If possible, these findings would support the potential of rs-fMRI to provide diverse information about the brain. We used resting-state fMRI and a set of 130 psychometric parameters that cover most human characteristics, including those related to intelligence and emotional quotients and social ability/skill. We identified 163 brain regions by VBM analysis using regression analysis with 130 psychometric parameters. Next, using a 163 × 163 correlation matrix, we identified functional networks related to 111 of the 130 psychometric parameters. Finally, we made an 8-class support vector machine classifiers corresponding to these 111 functional networks. Our results demonstrate that rs-fMRI signals contain intrinsic information about brain function related to cognition/behaviors and that this set of 111 networks/classifiers can be used to comprehensively evaluate human characteristics.

  7. Training Convolutional Neural Networks for Translational Invariance on SAR ATR

    DEFF Research Database (Denmark)

    Malmgren-Hansen, David; Engholm, Rasmus; Østergaard Pedersen, Morten

    2016-01-01

    In this paper we present a comparison of the robustness of Convolutional Neural Networks (CNN) to other classifiers in the presence of uncertainty of the objects localization in SAR image. We present a framework for simulating simple SAR images, translating the object of interest systematically...

  8. Asia-Pacific Research and Training Network on Trade (ARTNET ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    During the first phase of support (102568), the Network produced a number of high quality trade policy studies, disseminated the results to policymakers and increased the capacity of research institutions - notably those in the least developed countries - to conduct trade policy ... Agent(e) responsable du CRDI. Due, Evan ...

  9. Linking plant specialization to dependence in interactions for seed set in pollination networks.

    Science.gov (United States)

    Tur, Cristina; Castro-Urgal, Rocío; Traveset, Anna

    2013-01-01

    Studies on pollination networks have provided valuable information on the number, frequency, distribution and identity of interactions between plants and pollinators. However, little is still known on the functional effect of these interactions on plant reproductive success. Information on the extent to which plants depend on such interactions will help to make more realistic predictions of the potential impacts of disturbances on plant-pollinator networks. Plant functional dependence on pollinators (all interactions pooled) can be estimated by comparing seed set with and without pollinators (i.e. bagging flowers to exclude them). Our main goal in this study was thus to determine whether plant dependence on current insect interactions is related to plant specialization in a pollination network. We studied two networks from different communities, one in a coastal dune and one in a mountain. For ca. 30% of plant species in each community, we obtained the following specialization measures: (i) linkage level (number of interactions), (ii) diversity of interactions, and (iii) closeness centrality (a measure of how much a species is connected to other plants via shared pollinators). Phylogenetically controlled regression analyses revealed that, for the largest and most diverse coastal community, plants highly dependent on pollinators were the most generalists showing the highest number and diversity of interactions as well as occupying central positions in the network. The mountain community, by contrast, did not show such functional relationship, what might be attributable to their lower flower-resource heterogeneity and diversity of interactions. We conclude that plants with a wide array of pollinator interactions tend to be those that are more strongly dependent upon them for seed production and thus might be those more functionally vulnerable to the loss of network interaction, although these outcomes might be context-dependent.

  10. Linking plant specialization to dependence in interactions for seed set in pollination networks.

    Directory of Open Access Journals (Sweden)

    Cristina Tur

    Full Text Available Studies on pollination networks have provided valuable information on the number, frequency, distribution and identity of interactions between plants and pollinators. However, little is still known on the functional effect of these interactions on plant reproductive success. Information on the extent to which plants depend on such interactions will help to make more realistic predictions of the potential impacts of disturbances on plant-pollinator networks. Plant functional dependence on pollinators (all interactions pooled can be estimated by comparing seed set with and without pollinators (i.e. bagging flowers to exclude them. Our main goal in this study was thus to determine whether plant dependence on current insect interactions is related to plant specialization in a pollination network. We studied two networks from different communities, one in a coastal dune and one in a mountain. For ca. 30% of plant species in each community, we obtained the following specialization measures: (i linkage level (number of interactions, (ii diversity of interactions, and (iii closeness centrality (a measure of how much a species is connected to other plants via shared pollinators. Phylogenetically controlled regression analyses revealed that, for the largest and most diverse coastal community, plants highly dependent on pollinators were the most generalists showing the highest number and diversity of interactions as well as occupying central positions in the network. The mountain community, by contrast, did not show such functional relationship, what might be attributable to their lower flower-resource heterogeneity and diversity of interactions. We conclude that plants with a wide array of pollinator interactions tend to be those that are more strongly dependent upon them for seed production and thus might be those more functionally vulnerable to the loss of network interaction, although these outcomes might be context-dependent.

  11. Adaptations to the coping power program's structure, delivery settings, and clinician training.

    Science.gov (United States)

    Lochman, John E; Powell, Nicole; Boxmeyer, Caroline; Andrade, Brendan; Stromeyer, Sara L; Jimenez-Camargo, Luis Alberto

    2012-06-01

    This article describes the conceptual framework for the Coping Power program that has focused on proximal risk factors that can actively alter preadolescent children's aggressive behavior. The results of initial controlled efficacy trials are summarized. However, consistent with the theme of this special section, some clinicians and workshop participants have indicated barriers to the implementation of the Coping Power program in their service settings. In response to these types of concerns, three key areas of programmatic adaptation of the program that serve to address these concerns are then described in the article. First, existing and in-process studies of variations in how the program can be delivered are presented. Existing findings indicate how the child component fares when delivered by itself without the parent component, how simple monthly boosters affect intervention effects, and whether the program can be reduced by a third of its length and still be effective. Research planned or in progress on program variations examines whether group versus individual delivery of the program affects outcomes, whether the program can be adapted for early adolescents, whether the program can be delivered in an adaptive manner with the use of the Family Check Up, and whether a brief, efficient version of the program in conjunction with Internet programming can be developed and be effective. Second, the program has been and is being developed for use in different settings, other than the school-based delivery in the efficacy trials. Research has examined its use with aggressive deaf youth in a residential setting, with Oppositional Defiant Disorder and Conduct Disorder children in outpatient clinics, and in after-school programs. Third, the article reports how variations in training clinicians affect their ability to effectively use the program. PsycINFO Database Record (c) 2012 APA, all rights reserved.

  12. Holding-based network of nations based on listed energy companies: An empirical study on two-mode affiliation network of two sets of actors

    Science.gov (United States)

    Li, Huajiao; Fang, Wei; An, Haizhong; Gao, Xiangyun; Yan, Lili

    2016-05-01

    Economic networks in the real world are not homogeneous; therefore, it is important to study economic networks with heterogeneous nodes and edges to simulate a real network more precisely. In this paper, we present an empirical study of the one-mode derivative holding-based network constructed by the two-mode affiliation network of two sets of actors using the data of worldwide listed energy companies and their shareholders. First, we identify the primitive relationship in the two-mode affiliation network of the two sets of actors. Then, we present the method used to construct the derivative network based on the shareholding relationship between two sets of actors and the affiliation relationship between actors and events. After constructing the derivative network, we analyze different topological features on the node level, edge level and entire network level and explain the meanings of the different values of the topological features combining the empirical data. This study is helpful for expanding the usage of complex networks to heterogeneous economic networks. For empirical research on the worldwide listed energy stock market, this study is useful for discovering the inner relationships between the nations and regions from a new perspective.

  13. Pap-smear Classification Using Efficient Second Order Neural Network Training Algorithms

    DEFF Research Database (Denmark)

    Ampazis, Nikolaos; Dounias, George; Jantzen, Jan

    2004-01-01

    In this paper we make use of two highly efficient second order neural network training algorithms, namely the LMAM (Levenberg-Marquardt with Adaptive Momentum) and OLMAM (Optimized Levenberg-Marquardt with Adaptive Momentum), for the construction of an efficient pap-smear test classifier. The alg......In this paper we make use of two highly efficient second order neural network training algorithms, namely the LMAM (Levenberg-Marquardt with Adaptive Momentum) and OLMAM (Optimized Levenberg-Marquardt with Adaptive Momentum), for the construction of an efficient pap-smear test classifier...

  14. Optimal Path Choice in Railway Passenger Travel Network Based on Residual Train Capacity

    Directory of Open Access Journals (Sweden)

    Fei Dou

    2014-01-01

    Full Text Available Passenger’s optimal path choice is one of the prominent research topics in the field of railway passenger transport organization. More and more different train types are available, increasing path choices from departure to destination for travelers are unstoppable. However, travelers cannot avoid being confused when they hope to choose a perfect travel plan based on various travel time and cost constraints before departure. In this study, railway passenger travel network is constructed based on train timetable. Both the generalized cost function we developed and the residual train capacity are considered to be the foundation of path searching procedure. The railway passenger travel network topology is analyzed based on residual train capacity. Considering the total travel time, the total travel cost, and the total number of passengers, we propose an optimal path searching algorithm based on residual train capacity in railway passenger travel network. Finally, the rationale of the railway passenger travel network and the optimal path generation algorithm are verified positively by case study.

  15. The Effects of Long-term Abacus Training on Topological Properties of Brain Functional Networks.

    Science.gov (United States)

    Weng, Jian; Xie, Ye; Wang, Chunjie; Chen, Feiyan

    2017-08-18

    Previous studies in the field of abacus-based mental calculation (AMC) training have shown that this training has the potential to enhance a wide variety of cognitive abilities. It can also generate specific changes in brain structure and function. However, there is lack of studies investigating the impact of AMC training on the characteristics of brain networks. In this study, utilizing graph-based network analysis, we compared topological properties of brain functional networks between an AMC group and a matched control group. Relative to the control group, the AMC group exhibited higher nodal degrees in bilateral calcarine sulcus and increased local efficiency in bilateral superior occipital gyrus and right cuneus. The AMC group also showed higher nodal local efficiency in right fusiform gyrus, which was associated with better math ability. However, no relationship was significant in the control group. These findings provide evidence that long-term AMC training may improve information processing efficiency in visual-spatial related regions, which extend our understanding of training plasticity at the brain network level.

  16. Online Sequence Training of Recurrent Neural Networks with Connectionist Temporal Classification

    OpenAIRE

    Hwang, Kyuyeon; Sung, Wonyong

    2015-01-01

    Connectionist temporal classification (CTC) based supervised sequence training of recurrent neural networks (RNNs) has shown great success in many machine learning areas including end-to-end speech and handwritten character recognition. For the CTC training, however, it is required to unroll (or unfold) the RNN by the length of an input sequence. This unrolling requires a lot of memory and hinders a small footprint implementation of online learning or adaptation. Furthermore, the length of tr...

  17. Engine cylinder pressure reconstruction using crank kinematics and recurrently-trained neural networks

    Science.gov (United States)

    Bennett, C.; Dunne, J. F.; Trimby, S.; Richardson, D.

    2017-02-01

    A recurrent non-linear autoregressive with exogenous input (NARX) neural network is proposed, and a suitable fully-recurrent training methodology is adapted and tuned, for reconstructing cylinder pressure in multi-cylinder IC engines using measured crank kinematics. This type of indirect sensing is important for cost effective closed-loop combustion control and for On-Board Diagnostics. The challenge addressed is to accurately predict cylinder pressure traces within the cycle under generalisation conditions: i.e. using data not previously seen by the network during training. This involves direct construction and calibration of a suitable inverse crank dynamic model, which owing to singular behaviour at top-dead-centre (TDC), has proved difficult via physical model construction, calibration, and inversion. The NARX architecture is specialised and adapted to cylinder pressure reconstruction, using a fully-recurrent training methodology which is needed because the alternatives are too slow and unreliable for practical network training on production engines. The fully-recurrent Robust Adaptive Gradient Descent (RAGD) algorithm, is tuned initially using synthesised crank kinematics, and then tested on real engine data to assess the reconstruction capability. Real data is obtained from a 1.125 l, 3-cylinder, in-line, direct injection spark ignition (DISI) engine involving synchronised measurements of crank kinematics and cylinder pressure across a range of steady-state speed and load conditions. The paper shows that a RAGD-trained NARX network using both crank velocity and crank acceleration as input information, provides fast and robust training. By using the optimum epoch identified during RAGD training, acceptably accurate cylinder pressures, and especially accurate location-of-peak-pressure, can be reconstructed robustly under generalisation conditions, making it the most practical NARX configuration and recurrent training methodology for use on production engines.

  18. Multi-link faults localization and restoration based on fuzzy fault set for dynamic optical networks.

    Science.gov (United States)

    Zhao, Yongli; Li, Xin; Li, Huadong; Wang, Xinbo; Zhang, Jie; Huang, Shanguo

    2013-01-28

    Based on a distributed method of bit-error-rate (BER) monitoring, a novel multi-link faults restoration algorithm is proposed for dynamic optical networks. The concept of fuzzy fault set (FFS) is first introduced for multi-link faults localization, which includes all possible optical equipment or fiber links with a membership describing the possibility of faults. Such a set is characterized by a membership function which assigns each object a grade of membership ranging from zero to one. OSPF protocol extension is designed for the BER information flooding in the network. The BER information can be correlated to link faults through FFS. Based on the BER information and FFS, multi-link faults localization mechanism and restoration algorithm are implemented and experimentally demonstrated on a GMPLS enabled optical network testbed with 40 wavelengths in each fiber link. Experimental results show that the novel localization mechanism has better performance compared with the extended limited perimeter vector matching (LVM) protocol and the restoration algorithm can improve the restoration success rate under multi-link faults scenario.

  19. Online social networking sites-a novel setting for health promotion?

    Science.gov (United States)

    Loss, Julika; Lindacher, Verena; Curbach, Janina

    2014-03-01

    Among adolescents, online social networking sites (SNS) such as Facebook are popular platforms for social interaction and may therefore be considered as 'novel settings' that could be exploited for health promotion. In this article, we examine the relevant definitions in health promotion and literature in order to analyze whether key characteristics of 'settings for health promotion' and the socio-ecological settings approach can be transferred to SNS. As many of our daily activities have shifted to cyberspace, we argue that online social interaction may gain more importance than geographic closeness for defining a 'setting'. While exposition to positive references to risk behavior by peers may render the SNS environment detrimental to health, SNS may allow people to create their own content and therefore foster participation. However, those health promotion projects delivered on SNS up until today solely relied on health education directed at end users. It remains unclear how health promotion on SNS can meet other requirements of the settings approach (e.g. building partnerships, changing the environment). As yet, one should be cautious in terming SNS a 'setting'. Copyright © 2013 Elsevier Ltd. All rights reserved.

  20. Identification of Abnormal System Noise Temperature Patterns in Deep Space Network Antennas Using Neural Network Trained Fuzzy Logic

    Science.gov (United States)

    Lu, Thomas; Pham, Timothy; Liao, Jason

    2011-01-01

    This paper presents the development of a fuzzy logic function trained by an artificial neural network to classify the system noise temperature (SNT) of antennas in the NASA Deep Space Network (DSN). The SNT data were classified into normal, marginal, and abnormal classes. The irregular SNT pattern was further correlated with link margin and weather data. A reasonably good correlation is detected among high SNT, low link margin and the effect of bad weather; however we also saw some unexpected non-correlations which merit further study in the future.

  1. Training the Recurrent neural network by the Fuzzy Min-Max algorithm for fault prediction

    International Nuclear Information System (INIS)

    Zemouri, Ryad; Racoceanu, Daniel; Zerhouni, Noureddine; Minca, Eugenia; Filip, Florin

    2009-01-01

    In this paper, we present a training technique of a Recurrent Radial Basis Function neural network for fault prediction. We use the Fuzzy Min-Max technique to initialize the k-center of the RRBF neural network. The k-means algorithm is then applied to calculate the centers that minimize the mean square error of the prediction task. The performances of the k-means algorithm are then boosted by the Fuzzy Min-Max technique.

  2. A Control Simulation Method of High-Speed Trains on Railway Network with Irregular Influence

    International Nuclear Information System (INIS)

    Yang Lixing; Li Xiang; Li Keping

    2011-01-01

    Based on the discrete time method, an effective movement control model is designed for a group of highspeed trains on a rail network. The purpose of the model is to investigate the specific traffic characteristics of high-speed trains under the interruption of stochastic irregular events. In the model, the high-speed rail traffic system is supposed to be equipped with the moving-block signalling system to guarantee maximum traversing capacity of the railway. To keep the safety of trains' movements, some operational strategies are proposed to control the movements of trains in the model, including traction operation, braking operation, and entering-station operation. The numerical simulations show that the designed model can well describe the movements of high-speed trains on the rail network. The research results can provide the useful information not only for investigating the propagation features of relevant delays under the irregular disturbance but also for rerouting and rescheduling trains on the rail network. (general)

  3. Knowledge Mining from Clinical Datasets Using Rough Sets and Backpropagation Neural Network

    Directory of Open Access Journals (Sweden)

    Kindie Biredagn Nahato

    2015-01-01

    Full Text Available The availability of clinical datasets and knowledge mining methodologies encourages the researchers to pursue research in extracting knowledge from clinical datasets. Different data mining techniques have been used for mining rules, and mathematical models have been developed to assist the clinician in decision making. The objective of this research is to build a classifier that will predict the presence or absence of a disease by learning from the minimal set of attributes that has been extracted from the clinical dataset. In this work rough set indiscernibility relation method with backpropagation neural network (RS-BPNN is used. This work has two stages. The first stage is handling of missing values to obtain a smooth data set and selection of appropriate attributes from the clinical dataset by indiscernibility relation method. The second stage is classification using backpropagation neural network on the selected reducts of the dataset. The classifier has been tested with hepatitis, Wisconsin breast cancer, and Statlog heart disease datasets obtained from the University of California at Irvine (UCI machine learning repository. The accuracy obtained from the proposed method is 97.3%, 98.6%, and 90.4% for hepatitis, breast cancer, and heart disease, respectively. The proposed system provides an effective classification model for clinical datasets.

  4. Interaction of multiple networks modulated by the working memory training based on real-time fMRI

    Science.gov (United States)

    Shen, Jiahui; Zhang, Gaoyan; Zhu, Chaozhe; Yao, Li; Zhao, Xiaojie

    2015-03-01

    Neuroimaging studies of working memory training have identified the alteration of brain activity as well as the regional interactions within the functional networks such as central executive network (CEN) and default mode network (DMN). However, how the interaction within and between these multiple networks is modulated by the training remains unclear. In this paper, we examined the interaction of three training-induced brain networks during working memory training based on real-time functional magnetic resonance imaging (rtfMRI). Thirty subjects assigned to the experimental and control group respectively participated in two times training separated by seven days. Three networks including silence network (SN), CEN and DMN were identified by the training data with the calculated function connections within each network. Structural equation modeling (SEM) approach was used to construct the directional connectivity patterns. The results showed that the causal influences from the percent signal changes of target ROI to the SN were positively changed in both two groups, as well as the causal influence from the SN to CEN was positively changed in experimental group but negatively changed in control group from the SN to DMN. Further correlation analysis of the changes in each network with the behavioral improvements showed that the changes in SN were stronger positively correlated with the behavioral improvement of letter memory task. These findings indicated that the SN was not only a switch between the target ROI and the other networks in the feedback training but also an essential factor to the behavioral improvement.

  5. Twelve tips on how to set up postgraduate training via remote clinical supervision

    DEFF Research Database (Denmark)

    Wearne, Susan; Dornan, Tim; Teunissen, Pim W.

    2013-01-01

    Doctors-in-training can now be supervised remotely by specialist clinicians using information and communication technology. This provides an intermediate stage of professional development between on-site supervision and independent medical practice. Remote supervision could increase training capa...

  6. Low-Volume High-Intensity Interval Training in a Gym Setting Improves Cardio-Metabolic and Psychological Health

    OpenAIRE

    Shepherd, Sam O.; Wilson, Oliver J.; Taylor, Alexandra S.; Thøgersen-Ntoumani, Cecilie; Adlan, Ahmed M.; Wagenmakers, Anton J. M.; Shaw, Christopher S.

    2015-01-01

    Background\\ud Within a controlled laboratory environment, high-intensity interval training (HIT) elicits similar cardiovascular and metabolic benefits as traditional moderate-intensity continuous training (MICT). It is currently unclear how HIT can be applied effectively in a real-world environment.\\ud Purpose\\ud To investigate the hypothesis that 10 weeks of HIT, performed in an instructor-led, group-based gym setting, elicits improvements in aerobic capacity (VO2max), cardio-metabolic risk ...

  7. On the use of harmony search algorithm in the training of wavelet neural networks

    Science.gov (United States)

    Lai, Kee Huong; Zainuddin, Zarita; Ong, Pauline

    2015-10-01

    Wavelet neural networks (WNNs) are a class of feedforward neural networks that have been used in a wide range of industrial and engineering applications to model the complex relationships between the given inputs and outputs. The training of WNNs involves the configuration of the weight values between neurons. The backpropagation training algorithm, which is a gradient-descent method, can be used for this training purpose. Nonetheless, the solutions found by this algorithm often get trapped at local minima. In this paper, a harmony search-based algorithm is proposed for the training of WNNs. The training of WNNs, thus can be formulated as a continuous optimization problem, where the objective is to maximize the overall classification accuracy. Each candidate solution proposed by the harmony search algorithm represents a specific WNN architecture. In order to speed up the training process, the solution space is divided into disjoint partitions during the random initialization step of harmony search algorithm. The proposed training algorithm is tested onthree benchmark problems from the UCI machine learning repository, as well as one real life application, namely, the classification of electroencephalography signals in the task of epileptic seizure detection. The results obtained show that the proposed algorithm outperforms the traditional harmony search algorithm in terms of overall classification accuracy.

  8. A Fast C++ Implementation of Neural Network Backpropagation Training Algorithm: Application to Bayesian Optimal Image Demosaicing

    Directory of Open Access Journals (Sweden)

    Yi-Qing Wang

    2015-09-01

    Full Text Available Recent years have seen a surge of interest in multilayer neural networks fueled by their successful applications in numerous image processing and computer vision tasks. In this article, we describe a C++ implementation of the stochastic gradient descent to train a multilayer neural network, where a fast and accurate acceleration of tanh(· is achieved with linear interpolation. As an example of application, we present a neural network able to deliver state-of-the-art performance in image demosaicing.

  9. Establishment of Oversea HRD Network and Operation of International Nuclear Education/Training Program

    International Nuclear Information System (INIS)

    Lee, E. J.; Min, B. J.; Han, K. W.

    2008-02-01

    The project deals with establishment of international network for human resources and the development of international nuclear education and training programs. The primary result is the establishment of KAERI International Nuclear R and D Academy as a new activity on cooperation for human resource development and building network. For this purpose, KAERI concluded the MOU with Vietnamese Universities and selected 3 students to provide Master and Ph. D. Courses in 2008. KAERI also held the 3rd World Nuclear University Summer Institute, in which some 150 international nuclear professionals attended for 6 weeks. Also, as part of regional networking, the Asian Network for Education in Nuclear Technology (ANENT) was promoted through development of a cyber platform and accomplishment the first IAEA e-training course. There were 3 kind of development activities for the international cooperation of human resources development. Firstly, the project provided training courses on nuclear energy development for the Egyptian Nuclear personnel under the bilateral cooperation. Secondly, the project published the English textbook and its lecture materials on introduction to nuclear engineering and fundamentals on OPR 1000 system technology. Lastly, the project developed a new KOICA training course on research reactor and radioisotope application technology to expand the KOICA sponsorship from 2008. The international nuclear education/training program had offered 15 courses to 314 people from 52 countries. In parallel, the project developed 11 kinds of lecturer materials and also developed 29 kinds of cyber lecturer materials. The operation of the International Nuclear Training and Education Center (INTEC) has contributed remarkably not only to the effective implementation of education/training activities of this project, but also to the promotion of other domestic and international activities of KAERI and other organizations

  10. Establishment of Oversea HRD Network and Operation of International Nuclear Education/Training Program

    Energy Technology Data Exchange (ETDEWEB)

    Lee, E. J.; Min, B. J.; Han, K. W. (and others)

    2008-02-15

    The project deals with establishment of international network for human resources and the development of international nuclear education and training programs. The primary result is the establishment of KAERI International Nuclear R and D Academy as a new activity on cooperation for human resource development and building network. For this purpose, KAERI concluded the MOU with Vietnamese Universities and selected 3 students to provide Master and Ph. D. Courses in 2008. KAERI also held the 3rd World Nuclear University Summer Institute, in which some 150 international nuclear professionals attended for 6 weeks. Also, as part of regional networking, the Asian Network for Education in Nuclear Technology (ANENT) was promoted through development of a cyber platform and accomplishment the first IAEA e-training course. There were 3 kind of development activities for the international cooperation of human resources development. Firstly, the project provided training courses on nuclear energy development for the Egyptian Nuclear personnel under the bilateral cooperation. Secondly, the project published the English textbook and its lecture materials on introduction to nuclear engineering and fundamentals on OPR 1000 system technology. Lastly, the project developed a new KOICA training course on research reactor and radioisotope application technology to expand the KOICA sponsorship from 2008. The international nuclear education/training program had offered 15 courses to 314 people from 52 countries. In parallel, the project developed 11 kinds of lecturer materials and also developed 29 kinds of cyber lecturer materials. The operation of the International Nuclear Training and Education Center (INTEC) has contributed remarkably not only to the effective implementation of education/training activities of this project, but also to the promotion of other domestic and international activities of KAERI and other organizations.

  11. The Evaluation on Data Mining Methods of Horizontal Bar Training Based on BP Neural Network

    Directory of Open Access Journals (Sweden)

    Zhang Yanhui

    2015-01-01

    Full Text Available With the rapid development of science and technology, data analysis has become an indispensable part of people’s work and life. Horizontal bar training has multiple categories. It is an emphasis for the re-search of related workers that categories of the training and match should be reduced. The application of data mining methods is discussed based on the problem of reducing categories of horizontal bar training. The BP neural network is applied to the cluster analysis and the principal component analysis, which are used to evaluate horizontal bar training. Two kinds of data mining methods are analyzed from two aspects, namely the operational convenience of data mining and the rationality of results. It turns out that the principal component analysis is more suitable for data processing of horizontal bar training.

  12. VET in Schools: The Adoption of National Training Packages in a Secondary School Setting.

    Science.gov (United States)

    Dixon, Kathryn; Pelliccione, Lina

    2003-01-01

    Vocational education and training (VET) teachers in Australian secondary schools (n=11) identified the following influences on adoption of National Training Packages: ways in which teachers construct meaning for innovations, organizational culture, infrastructure, leadership, and policy. More time, training, and a coordinator helped embed and…

  13. Rough set soft computing cancer classification and network: one stone, two birds.

    Science.gov (United States)

    Zhang, Yue

    2010-07-15

    Gene expression profiling provides tremendous information to help unravel the complexity of cancer. The selection of the most informative genes from huge noise for cancer classification has taken centre stage, along with predicting the function of such identified genes and the construction of direct gene regulatory networks at different system levels with a tuneable parameter. A new study by Wang and Gotoh described a novel Variable Precision Rough Sets-rooted robust soft computing method to successfully address these problems and has yielded some new insights. The significance of this progress and its perspectives will be discussed in this article.

  14. Improving quantitative structure-activity relationship models using Artificial Neural Networks trained with dropout.

    Science.gov (United States)

    Mendenhall, Jeffrey; Meiler, Jens

    2016-02-01

    Dropout is an Artificial Neural Network (ANN) training technique that has been shown to improve ANN performance across canonical machine learning (ML) datasets. Quantitative Structure Activity Relationship (QSAR) datasets used to relate chemical structure to biological activity in Ligand-Based Computer-Aided Drug Discovery pose unique challenges for ML techniques, such as heavily biased dataset composition, and relatively large number of descriptors relative to the number of actives. To test the hypothesis that dropout also improves QSAR ANNs, we conduct a benchmark on nine large QSAR datasets. Use of dropout improved both enrichment false positive rate and log-scaled area under the receiver-operating characteristic curve (logAUC) by 22-46 % over conventional ANN implementations. Optimal dropout rates are found to be a function of the signal-to-noise ratio of the descriptor set, and relatively independent of the dataset. Dropout ANNs with 2D and 3D autocorrelation descriptors outperform conventional ANNs as well as optimized fingerprint similarity search methods.

  15. Exercise order affects the total training volume and the ratings of perceived exertion in response to a super-set resistance training session

    Directory of Open Access Journals (Sweden)

    Balsamo S

    2012-02-01

    Full Text Available Sandor Balsamo1–3, Ramires Alsamir Tibana1,2,4, Dahan da Cunha Nascimento1,2, Gleyverton Landim de Farias1,2, Zeno Petruccelli1,2, Frederico dos Santos de Santana1,2, Otávio Vanni Martins1,2, Fernando de Aguiar1,2, Guilherme Borges Pereira4, Jéssica Cardoso de Souza4, Jonato Prestes41Department of Physical Education, Centro Universitário UNIEURO, Brasília, 2GEPEEFS (Resistance training and Health Research Group, Brasília/DF, 3Graduate Program in Medical Sciences, School of Medicine, Universidade de Brasília (UnB, Brasília, 4Graduation Program in Physical Education, Catholic University of Brasilia (UCB, Brasília/DF, BrazilAbstract: The super-set is a widely used resistance training method consisting of exercises for agonist and antagonist muscles with limited or no rest interval between them – for example, bench press followed by bent-over rows. In this sense, the aim of the present study was to compare the effects of different super-set exercise sequences on the total training volume. A secondary aim was to evaluate the ratings of perceived exertion and fatigue index in response to different exercise order. On separate testing days, twelve resistance-trained men, aged 23.0 ± 4.3 years, height 174.8 ± 6.75 cm, body mass 77.8 ± 13.27 kg, body fat 12.0% ± 4.7%, were submitted to a super-set method by using two different exercise orders: quadriceps (leg extension + hamstrings (leg curl (QH or hamstrings (leg curl + quadriceps (leg extension (HQ. Sessions consisted of three sets with a ten-repetition maximum load with 90 seconds rest between sets. Results revealed that the total training volume was higher for the HQ exercise order (P = 0.02 with lower perceived exertion than the inverse order (P = 0.04. These results suggest that HQ exercise order involving lower limbs may benefit practitioners interested in reaching a higher total training volume with lower ratings of perceived exertion compared with the leg extension plus leg curl

  16. Social cognition and interaction training for patients with stable schizophrenia in Chinese community settings.

    Science.gov (United States)

    Wang, Yongguang; Roberts, David L; Xu, Baihua; Cao, Rifang; Yan, Min; Jiang, Qiongping

    2013-12-30

    Accumulated evidence suggests that Social Cognition and Interaction Training (SCIT) is associated with improved performance in social cognition and social skills in patients diagnosed with psychotic disorders. The current study examined the clinical utility of SCIT in patients with schizophrenia in Chinese community settings. Adults with stable schizophrenia were recruited from local community health institutions, and were randomly assigned to SCIT group (n = 22) or a waiting-list control group (n = 17). The SCIT group received the SCIT intervention plus treatment-as-usual, whereas the waiting-list group received only treatment-as-usual during the period of the study. All patients were administered the Chinese versions of the Personal and Social Performance Scale (PSP), Face Emotion Identification Task (FEIT), Eyes task, and Attributional Style Questionnaire (ASQ) at baseline of the SCIT treatment period and at follow-up, 6 months after completion of the 20-week treatment period. Patients in SCIT group showed a significant improvement in the domains of emotion perception, theory of mind, attributional style, and social functioning compared to those in waiting-list group. Findings indicate that SCIT is a feasible and promising method for improving social cognition and social functioning among Chinese outpatients with stable schizophrenia. © 2013 Elsevier Ireland Ltd. All rights reserved.

  17. Networked Intermedia Agenda Setting: The Geography of a Hyperlinked Scandinavian News Ecology

    DEFF Research Database (Denmark)

    Sjøvaag, Helle; Stavelin, Eirik; Karlsson, Michael

    How does agenda setting work within the hyperlinked Scandinavian news ecology? This paper investigates intermedia agenda setting within and between the local, regional, national and supra-national levels in Sweden, Denmark and Norway; analyses the center/periphery dimensions of hyperlink connecti......, social geography and hyperlinked news agendas in Scandinavia, adding to the research on the political implications of the Internet on national public spheres....... March 2016, amounting to approximately 2 million hyperlinks, each geotagged with publication origin. The visualisation of the hyperlink structure is one of the main results of the analysis, illuminating a) the relative disconnect between local and national hyperlinked agendas, b) the relative disconnect...... between news agendas in the three countries, and c) the connectedness enabled by size, resources and central location in the Scandinavian hyperlinked information structure. The network analysis provides new insights into the relationship between centralized political structures, media ownership dispersal...

  18. Innovation in European Vocational Education and Training: Network Learning in England, Finland and Germany

    Science.gov (United States)

    Heikkila, Eila

    2013-01-01

    This article presents a comparative study of innovation in vocational education and training (VET) in three innovative European countries: England, Finland and Germany. The focus is on innovation emerging from VET practitioners' (directors, teachers, project coordinators, etc.) participation in inter-organisational networks with local, regional,…

  19. Can surgical simulation be used to train detection and classification of neural networks?

    Science.gov (United States)

    Zisimopoulos, Odysseas; Flouty, Evangello; Stacey, Mark; Muscroft, Sam; Giataganas, Petros; Nehme, Jean; Chow, Andre; Stoyanov, Danail

    2017-10-01

    Computer-assisted interventions (CAI) aim to increase the effectiveness, precision and repeatability of procedures to improve surgical outcomes. The presence and motion of surgical tools is a key information input for CAI surgical phase recognition algorithms. Vision-based tool detection and recognition approaches are an attractive solution and can be designed to take advantage of the powerful deep learning paradigm that is rapidly advancing image recognition and classification. The challenge for such algorithms is the availability and quality of labelled data used for training. In this Letter, surgical simulation is used to train tool detection and segmentation based on deep convolutional neural networks and generative adversarial networks. The authors experiment with two network architectures for image segmentation in tool classes commonly encountered during cataract surgery. A commercially-available simulator is used to create a simulated cataract dataset for training models prior to performing transfer learning on real surgical data. To the best of authors' knowledge, this is the first attempt to train deep learning models for surgical instrument detection on simulated data while demonstrating promising results to generalise on real data. Results indicate that simulated data does have some potential for training advanced classification methods for CAI systems.

  20. Training and development through the IAEA's global research network

    International Nuclear Information System (INIS)

    Benson, T.

    1988-01-01

    The Agency's research contract programme stimulates and co-ordinates the undertaking of research, in selected nuclear fields of interest, by scientists in IAEA Member States. Benefits of the research contract programme can be direct or indirect. Direct benefits include increased scientific knowledge in a specific field and case-by-case application of this knowledge. Indirect benefits include the training effects - what participants in the programme learn via work carried out under the contract or at regularly held RCMs. The educational effect of CRPs is substantial as many institutes, guided by Agency scientific staff, learn how to conduct research without assistance. Unanticipated spin-off benefits can also result from a CRP through information exchanges at RCMs that stimulate ideas for other research programmes or methods of research

  1. Epidemiologic comparison of injured high school basketball athletes reporting to emergency departments and the athletic training setting.

    Science.gov (United States)

    Fletcher, Erica N; McKenzie, Lara B; Comstock, R Dawn

    2014-01-01

    Basketball is a popular US high school sport with more than 1 million participants annually. To compare patterns of athletes with basketball-related injuries presenting to US emergency departments from 2005 through 2010 and the high school athletic training setting from the 2005-2011 seasons. Descriptive epidemiology study. Data from the National Electronic Injury Surveillance System of the US Consumer Product Safety Commission and the High School Reporting Information Online database. Complex sample weights were used to calculate national estimates of basketball-related injuries for comparison. Adolescents from 13 to 19 years of age treated in US emergency departments for basketball-related injuries and athletes from 13 to 19 years of age from schools participating in High School Reporting Information Online who were injured while playing basketball. Nationally, an estimated 1,514,957 (95% confidence interval = 1,337,441, 1,692,474) athletes with basketball-related injuries reported to the emergency department and 1,064,551 (95% confidence interval = 1,055,482, 1,073,620) presented to the athletic training setting. Overall, the most frequent injuries seen in the emergency department were lacerations and fractures (injury proportion ratios [IPRs] = 3.45 and 1.72, respectively), whereas those seen in the athletic training setting were more commonly concussions and strains/sprains (IPRs = 2.23 and 1.19, respectively; all P values training setting (IPR = 1.18; all P values basketball players presenting for treatment in the emergency department and the athletic training setting. Understanding differences specific to clinical settings is crucial to grasping the full epidemiologic and clinical picture of sport-related injuries. Certified athletic trainers play an important role in identifying, assessing, and treating athletes with sport-related injuries who might otherwise present to clinical settings with higher costs, such as the emergency department.

  2. Do banks differently set their liquidity ratios based on their network characteristics? Do banks differently set their liquidity ratios based on their network characteristics?

    OpenAIRE

    Distinguin, Isabelle; Mahdavi-Ardekani, Aref; Tarazi, Amine

    2017-01-01

    This paper investigates the impact of interbank network topology on bank liquidity ratios. Whereas more emphasis has been put on liquidity requirements by regulators since the global financial crisis of 2007-2008, how differently shaped interbank networks impact individual bank liquidity behavior remains an open issue. We look at how bank interconnectedness within interbank loan and deposit networks affects their decision to hold more or less liquidity during normal times and distress times a...

  3. Pap-smear Classification Using Efficient Second Order Neural Network Training Algorithms

    DEFF Research Database (Denmark)

    Ampazis, Nikolaos; Dounias, George; Jantzen, Jan

    2004-01-01

    In this paper we make use of two highly efficient second order neural network training algorithms, namely the LMAM (Levenberg-Marquardt with Adaptive Momentum) and OLMAM (Optimized Levenberg-Marquardt with Adaptive Momentum), for the construction of an efficient pap-smear test classifier. The alg......In this paper we make use of two highly efficient second order neural network training algorithms, namely the LMAM (Levenberg-Marquardt with Adaptive Momentum) and OLMAM (Optimized Levenberg-Marquardt with Adaptive Momentum), for the construction of an efficient pap-smear test classifier....... The algorithms are methodologically similar, and are based on iterations of the form employed in the Levenberg-Marquardt (LM) method for non-linear least squares problems with the inclusion of an additional adaptive momentum term arising from the formulation of the training task as a constrained optimization...

  4. Nonlinear Robust Observer-Based Fault Detection for Networked Suspension Control System of Maglev Train

    Directory of Open Access Journals (Sweden)

    Yun Li

    2013-01-01

    Full Text Available A fault detection approach based on nonlinear robust observer is designed for the networked suspension control system of Maglev train with random induced time delay. First, considering random bounded time-delay and external disturbance, the nonlinear model of the networked suspension control system is established. Then, a nonlinear robust observer is designed using the input of the suspension gap. And the estimate error is proved to be bounded with arbitrary precision by adopting an appropriate parameter. When sensor faults happen, the residual between the real states and the observer outputs indicates which kind of sensor failures occurs. Finally, simulation results using the actual parameters of CMS-04 maglev train indicate that the proposed method is effective for maglev train.

  5. ENETRAP II: European network of education and training in radiation protection, data base training

    International Nuclear Information System (INIS)

    Marco Arboli, M.; Llorente, C.; Coeck, M.

    2012-01-01

    Development and implementation of a European standard for high quality initial training and professional development continued in the R adiation Protection Expert-RPE and Radiation Protection Officer-RPO, also of a methodology for the mutual recognition of these professionals by making use of the available instruments of the European Union (GE).

  6. Conditions for addressing environmental determinants of health behavior in intersectoral policy networks: A fuzzy set Qualitative Comparative Analysis.

    Science.gov (United States)

    Peters, D T J M; Verweij, S; Grêaux, K; Stronks, K; Harting, J

    2017-12-01

    Improving health requires changes in the social, physical, economic and political determinants of health behavior. For the realization of policies that address these environmental determinants, intersectoral policy networks are considered necessary for the pooling of resources to implement different policy instruments. However, such network diversity may increase network complexity and therefore hamper network performance. Network complexity may be reduced by network management and the provision of financial resources. This study examined whether network diversity - amidst the other conditions - is indeed needed to address environmental determinants of health behavior. We included 25 intersectoral policy networks in Dutch municipalities aimed at reducing overweight, smoking, and alcohol/drugs abuse. For our fuzzy set Qualitative Comparative Analysis we used data from three web-based surveys among (a) project leaders regarding network diversity and size (n = 38); (b) project leaders and project partners regarding management (n = 278); and (c) implementation professionals regarding types of environmental determinants addressed (n = 137). Data on budgets were retrieved from project application forms. Contrary to their intentions, most policy networks typically addressed personal determinants. If the environment was addressed too, it was mostly the social environment. To address environmental determinants of health behavior, network diversity (>50% of the actors are non-public health) was necessary in networks that were either small (policy networks in improving health behaviors by addressing a variety of environmental determinants. Copyright © 2017. Published by Elsevier Ltd.

  7. Relationships between music training, speech processing, and word learning: a network perspective.

    Science.gov (United States)

    Elmer, Stefan; Jäncke, Lutz

    2018-03-15

    Numerous studies have documented the behavioral advantages conferred on professional musicians and children undergoing music training in processing speech sounds varying in the spectral and temporal dimensions. These beneficial effects have previously often been associated with local functional and structural changes in the auditory cortex (AC). However, this perspective is oversimplified, in that it does not take into account the intrinsic organization of the human brain, namely, neural networks and oscillatory dynamics. Therefore, we propose a new framework for extending these previous findings to a network perspective by integrating multimodal imaging, electrophysiology, and neural oscillations. In particular, we provide concrete examples of how functional and structural connectivity can be used to model simple neural circuits exerting a modulatory influence on AC activity. In addition, we describe how such a network approach can be used for better comprehending the beneficial effects of music training on more complex speech functions, such as word learning. © 2018 New York Academy of Sciences.

  8. Diagnostics of Nuclear Reactor Accidents Based on Particle Swarm Optimization Trained Neural Networks

    International Nuclear Information System (INIS)

    Abdel-Aal, M.M.Z.

    2004-01-01

    Automation in large, complex systems such as chemical plants, electrical power generation, aerospace and nuclear plants has been steadily increasing in the recent past. automated diagnosis and control forms a necessary part of these systems,this contains thousands of alarms processing in every component, subsystem and system. so the accurate and speed of diagnosis of faults is an important factors in operation and maintaining their health and continued operation and in reducing of repair and recovery time. using of artificial intelligence facilitates the alarm classifications and faults diagnosis to control any abnormal events during the operation cycle of the plant. thesis work uses the artificial neural network as a powerful classification tool. the work basically is has two components, the first is to effectively train the neural network using particle swarm optimization, which non-derivative based technique. to achieve proper training of the neural network to fault classification problem and comparing this technique to already existing techniques

  9. Mindfulness Meditation Training and Executive Control Network Resting State Functional Connectivity: A Randomized Controlled Trial.

    Science.gov (United States)

    Taren, Adrienne A; Gianaros, Peter J; Greco, Carol M; Lindsay, Emily K; Fairgrieve, April; Brown, Kirk Warren; Rosen, Rhonda K; Ferris, Jennifer L; Julson, Erica; Marsland, Anna L; Creswell, J David

    Mindfulness meditation training has been previously shown to enhance behavioral measures of executive control (e.g., attention, working memory, cognitive control), but the neural mechanisms underlying these improvements are largely unknown. Here, we test whether mindfulness training interventions foster executive control by strengthening functional connections between dorsolateral prefrontal cortex (dlPFC)-a hub of the executive control network-and frontoparietal regions that coordinate executive function. Thirty-five adults with elevated levels of psychological distress participated in a 3-day randomized controlled trial of intensive mindfulness meditation or relaxation training. Participants completed a resting state functional magnetic resonance imaging scan before and after the intervention. We tested whether mindfulness meditation training increased resting state functional connectivity (rsFC) between dlPFC and frontoparietal control network regions. Left dlPFC showed increased connectivity to the right inferior frontal gyrus (T = 3.74), right middle frontal gyrus (MFG) (T = 3.98), right supplementary eye field (T = 4.29), right parietal cortex (T = 4.44), and left middle temporal gyrus (T = 3.97, all p < .05) after mindfulness training relative to the relaxation control. Right dlPFC showed increased connectivity to right MFG (T = 4.97, p < .05). We report that mindfulness training increases rsFC between dlPFC and dorsal network (superior parietal lobule, supplementary eye field, MFG) and ventral network (right IFG, middle temporal/angular gyrus) regions. These findings extend previous work showing increased functional connectivity among brain regions associated with executive function during active meditation by identifying specific neural circuits in which rsFC is enhanced by a mindfulness intervention in individuals with high levels of psychological distress. Clinicaltrials.gov,NCT01628809.

  10. The efficacy of staff training on improving internal customer satisfaction in a rural health setting.

    Science.gov (United States)

    Hartley, R; Turner, R

    1995-09-01

    The NSW Health Department is 3 years into its customer satisfaction initiative. North West Health Service, one of the largest rural health districts, was among the first centres to embrace the customer satisfaction philosophy starting with compulsory training of all staff. This paper reports on changes in staff morale (internal satisfaction) as a result of that training. The data suggest that training per se has had minimal effect and argues for management development, particularly regarding leadership, rather than fiscal skills.

  11. Computer-aided diagnosis of lung cancer: the effect of training data sets on classification accuracy of lung nodules

    Science.gov (United States)

    Gong, Jing; Liu, Ji-Yu; Sun, Xi-Wen; Zheng, Bin; Nie, Sheng-Dong

    2018-02-01

    This study aims to develop a computer-aided diagnosis (CADx) scheme for classification between malignant and benign lung nodules, and also assess whether CADx performance changes in detecting nodules associated with early and advanced stage lung cancer. The study involves 243 biopsy-confirmed pulmonary nodules. Among them, 76 are benign, 81 are stage I and 86 are stage III malignant nodules. The cases are separated into three data sets involving: (1) all nodules, (2) benign and stage I malignant nodules, and (3) benign and stage III malignant nodules. A CADx scheme is applied to segment lung nodules depicted on computed tomography images and we initially computed 66 3D image features. Then, three machine learning models namely, a support vector machine, naïve Bayes classifier and linear discriminant analysis, are separately trained and tested by using three data sets and a leave-one-case-out cross-validation method embedded with a Relief-F feature selection algorithm. When separately using three data sets to train and test three classifiers, the average areas under receiver operating characteristic curves (AUC) are 0.94, 0.90 and 0.99, respectively. When using the classifiers trained using data sets with all nodules, average AUC values are 0.88 and 0.99 for detecting early and advanced stage nodules, respectively. AUC values computed from three classifiers trained using the same data set are consistent without statistically significant difference (p  >  0.05). This study demonstrates (1) the feasibility of applying a CADx scheme to accurately distinguish between benign and malignant lung nodules, and (2) a positive trend between CADx performance and cancer progression stage. Thus, in order to increase CADx performance in detecting subtle and early cancer, training data sets should include more diverse early stage cancer cases.

  12. Cooperative VET in Training Networks: Analysing the Free-Rider Problem in a Sociology-of-Conventions Perspective

    Science.gov (United States)

    Leemann, Regula Julia; Imdorf, Christian

    2015-01-01

    In training networks, particularly small and medium-sized enterprises pool their resources to train apprentices within the framework of the dual VET system, while an intermediary organisation is tasked with managing operations. Over the course of their apprenticeship, the apprentices switch from one training company to another on a (half-) yearly…

  13. ON OPERATION OF 740 M LONG FREIGHT TRAINS ON CZECH TEN-T RAILWAY NETWORK

    Directory of Open Access Journals (Sweden)

    Michal Drábek

    2016-09-01

    Full Text Available Regulation (EU No 1315/2013 defines actual scope of core and comprehensive TEN-T network, including both networks for railway freight transport. For the core network, possibility to operate 740 m long freight trains is required. The aim of this paper is to analyse availability of appropriate overtaking tracks for 740 m long freight trains. Due to ETCS braking curves and odometry, such trains, after ETCS implementation, will require 780-800 m long overtaking tracks. For practical reasons (e.g. bypass lines, whole Czech railway TEN-T network is analysed. The overtaking track, whose occupation means influence on scheduled traffic or threat to boarding passengers, are excluded. The data was collected from station schemes from Collection of Official Requisites for 2015/16 Timetable, issued by SŽDC, Czech state Infrastructure Manager. Most of appropriate tracks are over 800 m long, but their density in the network and in particular directions varies considerably. For freight traffic, gradient of the line is important, so in the resulting figure, there are marked significant peaks for particular lines as well. Czech TEN-T lines are further segmented on the basis of number of tracks and their traffic character. Then, specific issues on overtaking or crossing of 740 m long freight trains are discussed. As a conclusion, for long-term development of Czech TEN-T lines, targeted investment is recommended not only for passenger railway, but also for freight railway. An attractive capacity offer for railway undertakings, which can stimulate freight traffic on European Rail freight corridors, can be represented by network-bound periodic freight train paths with suitable long overtaking tracks outside bottlenecks. After the overtaking by passenger trains, a freight train should run without stop through large node station or a bottleneck area. Before the sections with high gradients, coupling of additional locomotives should be connected with the overtaking

  14. Effects of drop sets with resistance training on increases in muscle CSA, strength, and endurance: a pilot study.

    Science.gov (United States)

    Ozaki, Hayao; Kubota, Atsushi; Natsume, Toshiharu; Loenneke, Jeremy P; Abe, Takashi; Machida, Shuichi; Naito, Hisashi

    2018-03-01

    To investigate the effects of a single high-load (80% of one repetition maximum [1RM]) set with additional drop sets descending to a low-load (30% 1RM) without recovery intervals on muscle strength, endurance, and size in untrained young men. Nine untrained young men performed dumbbell curls to concentric failure 2-3 days per week for 8 weeks. Each arm was randomly assigned to one of the following three conditions: 3 sets of high-load (HL, 80% 1RM) resistance exercise, 3 sets of low-load [LL, 30% 1RM] resistance exercise, and a single high-load (SDS) set with additional drop sets descending to a low-load. The mean training time per session, including recovery intervals, was lowest in the SDS condition. Elbow flexor muscle cross-sectional area (CSA) increased similarly in all three conditions. Maximum isometric and 1RM strength of the elbow flexors increased from pre to post only in the HL and SDS conditions. Muscular endurance measured by maximum repetitions at 30% 1RM increased only in the LL and SDS conditions. A SDS resistance training program can simultaneously increase muscle CSA, strength, and endurance in untrained young men, even with lower training time compared to typical resistance exercise protocols using only high- or low-loads.

  15. Access Selection Algorithm of Heterogeneous Wireless Networks for Smart Distribution Grid Based on Entropy-Weight and Rough Set

    Science.gov (United States)

    Xiang, Min; Qu, Qinqin; Chen, Cheng; Tian, Li; Zeng, Lingkang

    2017-11-01

    To improve the reliability of communication service in smart distribution grid (SDG), an access selection algorithm based on dynamic network status and different service types for heterogeneous wireless networks was proposed. The network performance index values were obtained in real time by multimode terminal and the variation trend of index values was analyzed by the growth matrix. The index weights were calculated by entropy-weight and then modified by rough set to get the final weights. Combining the grey relational analysis to sort the candidate networks, and the optimum communication network is selected. Simulation results show that the proposed algorithm can implement dynamically access selection in heterogeneous wireless networks of SDG effectively and reduce the network blocking probability.

  16. Training the CSR Sensitive Mind-Set: The Integration of CSR into the Training of Business Administration Professionals

    OpenAIRE

    Eglė Stonkutė; Jolita Vveinhardt; Włodzimierz Sroka

    2018-01-01

    Current corporations are subject to stringent requirements in terms of sustainable development; however, a relevant problem is highlighted on the basis of the studies conducted. On the one hand, corporations experience greater or lesser pressure, while on the other hand, it must be admitted that the problem of demand for professionals, which is presupposed by the insufficient quality of training in higher education institutions, is important. This is somewhat strange given that the issues of ...

  17. Training the CSR Sensitive Mind-Set: The Integration of CSR into the Training of Business Administration Professionals

    Directory of Open Access Journals (Sweden)

    Eglė Stonkutė

    2018-03-01

    Full Text Available Current corporations are subject to stringent requirements in terms of sustainable development; however, a relevant problem is highlighted on the basis of the studies conducted. On the one hand, corporations experience greater or lesser pressure, while on the other hand, it must be admitted that the problem of demand for professionals, which is presupposed by the insufficient quality of training in higher education institutions, is important. This is somewhat strange given that the issues of business ethics, corporate social responsibility, and sustainability have attracted increased attention in management education in recent years, and a five-fold increase in the number of stand-alone ethics courses has been noted since 1988. This interaction could contribute to the development of CSR, however a certain dissonance of cooperation between higher education and business exists, as there is a lack of leadership in this area in the study programs of business administration approved by the states, as well as in higher education institutions. Given these facts, the goal of the paper is to analyze the Master of Business Administration programs in North America, Europe, Asia, and Australia to offer direction to the challenge of integrating corporate social responsibility (CSR into management and training. The method of analysis of professional business and administration training program content in terms of the integration of CSR was used during the survey. Using panel data of 28 full-time MBA programs, our findings show that that the core parts of MBAs under analysis merely—and mostly indirectly—cover CSR issues through one core course on business ethics. However, the leading MBA programs are currently missing an opportunity by ignoring their responsibility to support the training of CSR-minded future business administration professionals. The results of our research may act as a guide to which areas should be modified and/or changed.

  18. Reorganization of functional brain networks mediates the improvement of cognitive performance following real-time neurofeedback training of working memory.

    Science.gov (United States)

    Zhang, Gaoyan; Yao, Li; Shen, Jiahui; Yang, Yihong; Zhao, Xiaojie

    2015-05-01

    Working memory (WM) is essential for individuals' cognitive functions. Neuroimaging studies indicated that WM fundamentally relied on a frontoparietal working memory network (WMN) and a cinguloparietal default mode network (DMN). Behavioral training studies demonstrated that the two networks can be modulated by WM training. Different from the behavioral training, our recent study used a real-time functional MRI (rtfMRI)-based neurofeedback method to conduct WM training, demonstrating that WM performance can be significantly improved after successfully upregulating the activity of the target region of interest (ROI) in the left dorsolateral prefrontal cortex (Zhang et al., [2013]: PloS One 8:e73735); however, the neural substrate of rtfMRI-based WM training remains unclear. In this work, we assessed the intranetwork and internetwork connectivity changes of WMN and DMN during the training, and their correlations with the change of brain activity in the target ROI as well as with the improvement of post-training behavior. Our analysis revealed an "ROI-network-behavior" correlation relationship underlying the rtfMRI training. Further mediation analysis indicated that the reorganization of functional brain networks mediated the effect of self-regulation of the target brain activity on the improvement of cognitive performance following the neurofeedback training. The results of this study enhance our understanding of the neural basis of real-time neurofeedback and suggest a new direction to improve WM performance by regulating the functional connectivity in the WM related networks. © 2014 Wiley Periodicals, Inc.

  19. Setting-up of remote reactor LAB and tapping into CARRN for distance education and training in nuclear field

    Energy Technology Data Exchange (ETDEWEB)

    Park, Eugene [The Nelson Mandeal African Institute of Science and Technology, Arusha (Tanzania, United Republic of)

    2013-07-01

    For a developing country embarking on a research reactor project, building adequate human resource capacity is one of the biggest challenges. Tanzania has been considering a research reactor for some time. The success of future research reactor project impinges on vigorous education and training of necessary personnel to operate and fully utilize the facility. In Africa, underutilization of research reactors is a chronic issue. It is not only misuse of valuable resources but also poses potential safety and security concerns. To mitigate such concerns and to promote education and training, Central African Research Reactor Network (CARRN) was formed in June of 2011. Borrowing from Jordan's success, this paper presents customised curricula to take advantage of CARRN for distance education and training in nuclear field.

  20. Setting-up of remote reactor LAB and tapping into CARRN for distance education and training in nuclear field

    International Nuclear Information System (INIS)

    Park, Eugene

    2013-01-01

    For a developing country embarking on a research reactor project, building adequate human resource capacity is one of the biggest challenges. Tanzania has been considering a research reactor for some time. The success of future research reactor project impinges on vigorous education and training of necessary personnel to operate and fully utilize the facility. In Africa, underutilization of research reactors is a chronic issue. It is not only misuse of valuable resources but also poses potential safety and security concerns. To mitigate such concerns and to promote education and training, Central African Research Reactor Network (CARRN) was formed in June of 2011. Borrowing from Jordan's success, this paper presents customised curricula to take advantage of CARRN for distance education and training in nuclear field

  1. The effects of traditional, superset, and tri-set resistance training structures on perceived intensity and physiological responses.

    Science.gov (United States)

    Weakley, Jonathon J S; Till, Kevin; Read, Dale B; Roe, Gregory A B; Darrall-Jones, Joshua; Phibbs, Padraic J; Jones, Ben

    2017-09-01

    Investigate the acute and short-term (i.e., 24 h) effects of traditional (TRAD), superset (SS), and tri-set (TRI) resistance training protocols on perceptions of intensity and physiological responses. Fourteen male participants completed a familiarisation session and three resistance training protocols (i.e., TRAD, SS, and TRI) in a randomised-crossover design. Rating of perceived exertion, lactate concentration ([Lac]), creatine kinase concentration ([CK]), countermovement jump (CMJ), testosterone, and cortisol concentrations was measured pre, immediately, and 24-h post the resistance training sessions with magnitude-based inferences assessing changes/differences within/between protocols. TRI reported possible to almost certainly greater efficiency and rate of perceived exertion, although session perceived load was very likely lower. SS and TRI had very likely to almost certainly greater lactate responses during the protocols, with changes in [CK] being very likely and likely increased at 24 h, respectively. At 24-h post-training, CMJ variables in the TRAD protocol had returned to baseline; however, SS and TRI were still possibly to likely reduced. Possible increases in testosterone immediately post SS and TRI protocols were reported, with SS showing possible increases at 24-h post-training. TRAD and SS showed almost certain and likely decreases in cortisol immediately post, respectively, with TRAD reporting likely decreases at 24-h post-training. SS and TRI can enhance training efficiency and reduce training time. However, acute and short-term physiological responses differ between protocols. Athletes can utilise SS and TRI resistance training, but may require additional recovery post-training to minimise effects of fatigue.

  2. A Program to Provide Vocational Training to Limited English Speaking Adults in a Correctional Setting. Final Report.

    Science.gov (United States)

    Murray, Lane

    The Windham School System implemented a pilot project designed to provide bilingual vocational training to limited English-speaking adults in a correctional setting. Inmate students enrolled in Windham bilingual academic classes on the Eastham Unit of the Texas Department of Corrections were interviewed, and procedures for student screening and…

  3. Coping with Challenging Behaviours of Children with Autism: Effectiveness of Brief Training Workshop for Frontline Staff in Special Education Settings

    Science.gov (United States)

    Ling, C. Y. M.; Mak, W. W. S.

    2012-01-01

    Background: The present study examined the effectiveness of three staff training elements: psychoeducation (PE) on autism, introduction of functional behavioural analysis (FBA) and emotional management (EM), on the reaction of challenging behaviours for frontline staff towards children with autism in Hong Kong special education settings. Methods:…

  4. Retrieving forest stand parameters from SAR backscatter data using a neural network trained by a canopy backscatter model

    International Nuclear Information System (INIS)

    Wang, Y.; Dong, D.

    1997-01-01

    It was possible to retrieve the stand mean dbh (tree trunk diameter at breast height) and stand density from the Jet Propulsion Laboratory (JPL) Airborne Synthetic Aperture Radar (AIRSAR) backscatter data by using threelayered perceptron neural networks (NNs). Two sets of NNs were trained by the Santa Barbara microwave canopy backscatter model. One set of the trained NNs was used to retrieve the stand mean dbh, and the other to retrieve the stand density. Each set of the NNs consisted of seven individual NNs for all possible combinations of one, two, and three radar wavelengths. Ground and multiple wavelength AIRSAR backscatter data from two ponderosa pine forest stands near Mt. Shasta, California (U.S.A.) were used to evaluate the accuracy of the retrievals. The r.m.s. and relative errors of the retrieval for stand mean dbh were 6.1 cm and 15.6 per cent for one stand (St2), and 3.1 cm and 6.7 per cent for the other stand (St11). The r.m.s. and relative errors of the retrieval for stand density were 71.2 treesha-1 and 23.0 per cent for St2, and 49.7 treesha-1 and 21.3 per cent for St11. (author)

  5. Agenda setting for maternal survival: the power of global health networks and norms.

    Science.gov (United States)

    Smith, Stephanie L; Rodriguez, Mariela A

    2016-04-01

    Nearly 300,000 women--almost all poor women in low-income countries--died from pregnancy-related complications in 2010. This represents a decline since the 1980s, when an estimated half million women died each year, but is still far higher than the aims set in the United Nations Millennium Development Goals (MDGs) at the turn of the century. The 1970s, 1980s and 1990 s witnessed a shift from near complete neglect of the issue to emergence of a network of individuals and organizations with a shared concern for reducing maternal deaths and growth in the number of organizations and governments with maternal health strategies and programmes. Maternal health experienced a marked change in agenda status in the 2000s, attracting significantly higher level attention (e.g. from world leaders) and greater resource commitments (e.g. as one issue addressed by US$40 billion in pledges to the 2010 Global Strategy for Women's and Children's Health) than ever before. Several differences between network and actor features, issue characteristics and the policy environment pre- and post-2000 help to explain the change in agenda status for global maternal mortality reduction. Significantly, a strong poverty reduction norm emerged at the turn of the century; represented by the United Nations MDGs framework, the norm set unusually strong expectations for international development actors to advance included issues. As the norm grew, it drew policy attention to the maternal health goal (MDG 5). Seeking to advance the goals agenda, world leaders launched initiatives addressing maternal and child health. New network governance and framing strategies that closely linked maternal, newborn and child health shaped the initiatives. Diverse network composition--expanding beyond a relatively narrowly focused and technically oriented group to encompass allies and leaders that brought additional resources to bear on the problem--was crucial to maternal health's rise on the agenda in the 2000s

  6. Tutor Training Packet. "Ready-Set-ABE" To Ease Students' Transition into ABE Level Studies.

    Science.gov (United States)

    Molek, Carol

    This training packet, consisting of a workshop guide, two instructional guides, and assorted pamphlets and brochures, is intended for use by volunteer tutors who are themselves learning how to work with adults enrolled in an adult literacy program. The following topics are covered in the training workshop guide: the objectives and workings of…

  7. End-to-End Delay Model for Train Messaging over Public Land Mobile Networks

    Directory of Open Access Journals (Sweden)

    Franco Mazzenga

    2017-11-01

    Full Text Available Modern train control systems rely on a dedicated radio network for train to ground communications. A number of possible alternatives have been analysed to adopt the European Rail Traffic Management System/European Train Control System (ERTMS/ETCS control system on local/regional lines to improve transport capacity. Among them, a communication system based on public networks (cellular&satellite provides an interesting, effective and alternative solution to proprietary and expensive radio networks. To analyse performance of this solution, it is necessary to model the end-to-end delay and message loss to fully characterize the message transfer process from train to ground and vice versa. Starting from the results of a railway test campaign over a 300 km railway line for a cumulative 12,000 traveled km in 21 days, in this paper, we derive a statistical model for the end-to-end delay required for delivering messages. In particular, we propose a two states model allowing for reproducing the main behavioral characteristics of the end-to-end delay as observed experimentally. Model formulation has been derived after deep analysis of the recorded experimental data. When it is applied to model a realistic scenario, it allows for explicitly accounting for radio coverage characteristics, the received power level, the handover points along the line and for the serving radio technology. As an example, the proposed model is used to generate the end-to-end delay profile in a realistic scenario.

  8. Outcomes from the GLEON fellowship program. Training graduate students in data driven network science.

    Science.gov (United States)

    Dugan, H.; Hanson, P. C.; Weathers, K. C.

    2016-12-01

    In the water sciences there is a massive need for graduate students who possess the analytical and technical skills to deal with large datasets and function in the new paradigm of open, collaborative -science. The Global Lake Ecological Observatory Network (GLEON) graduate fellowship program (GFP) was developed as an interdisciplinary training program to supplement the intensive disciplinary training of traditional graduate education. The primary goal of the GFP was to train a diverse cohort of graduate students in network science, open-web technologies, collaboration, and data analytics, and importantly to provide the opportunity to use these skills to conduct collaborative research resulting in publishable scientific products. The GFP is run as a series of three week-long workshops over two years that brings together a cohort of twelve students. In addition, fellows are expected to attend and contribute to at least one international GLEON all-hands' meeting. Here, we provide examples of training modules in the GFP (model building, data QA/QC, information management, bayesian modeling, open coding/version control, national data programs), as well as scientific outputs (manuscripts, software products, and new global datasets) produced by the fellows, as well as the process by which this team science was catalyzed. Data driven education that lets students apply learned skills to real research projects reinforces concepts, provides motivation, and can benefit their publication record. This program design is extendable to other institutions and networks.

  9. SAGRAD: A Program for Neural Network Training with Simulated Annealing and the Conjugate Gradient Method.

    Science.gov (United States)

    Bernal, Javier; Torres-Jimenez, Jose

    2015-01-01

    SAGRAD (Simulated Annealing GRADient), a Fortran 77 program for computing neural networks for classification using batch learning, is discussed. Neural network training in SAGRAD is based on a combination of simulated annealing and Møller's scaled conjugate gradient algorithm, the latter a variation of the traditional conjugate gradient method, better suited for the nonquadratic nature of neural networks. Different aspects of the implementation of the training process in SAGRAD are discussed, such as the efficient computation of gradients and multiplication of vectors by Hessian matrices that are required by Møller's algorithm; the (re)initialization of weights with simulated annealing required to (re)start Møller's algorithm the first time and each time thereafter that it shows insufficient progress in reaching a possibly local minimum; and the use of simulated annealing when Møller's algorithm, after possibly making considerable progress, becomes stuck at a local minimum or flat area of weight space. Outlines of the scaled conjugate gradient algorithm, the simulated annealing procedure and the training process used in SAGRAD are presented together with results from running SAGRAD on two examples of training data.

  10. Hip abduction strength training in the clinical setting: with or without external loading?

    DEFF Research Database (Denmark)

    Thorborg, Kristian; Bandholm, T; Petersen, Jesper

    2010-01-01

    only the weight of the leg as resistance, whereas training with external loading was performed with a relative load corresponding to 10 repetition maximum. Hip abduction strength was measured pre- and post-intervention. Isometric and eccentric hip abduction strength of the trained leg increased after......The side-lying hip abduction exercise is one of the most commonly used exercises in rehabilitation to increase hip abduction strength, and is often performed without external loading. The aim of this study was to compare the effect of 6 weeks of side-lying hip abduction training, with and without...... external loading, on hip abduction strength in healthy subjects. Thirty-one healthy, physically active men and women were included in a randomised controlled trial and allocated to side-lying hip abduction training, with or without external loading. Training without external loading was performed using...

  11. Training algorithms evaluation for artificial neural network to temporal prediction of photovoltaic generation

    International Nuclear Information System (INIS)

    Arantes Monteiro, Raul Vitor; Caixeta Guimarães, Geraldo; Rocio Castillo, Madeleine; Matheus Moura, Fabrício Augusto; Tamashiro, Márcio Augusto

    2016-01-01

    Current energy policies are encouraging the connection of power generation based on low-polluting technologies, mainly those using renewable sources, to distribution networks. Hence, it becomes increasingly important to understand technical challenges, facing high penetration of PV systems at the grid, especially considering the effects of intermittence of this source on the power quality, reliability and stability of the electric distribution system. This fact can affect the distribution networks on which they are attached causing overvoltage, undervoltage and frequency oscillations. In order to predict these disturbs, artificial neural networks are used. This article aims to analyze 3 training algorithms used in artificial neural networks for temporal prediction of the generated active power thru photovoltaic panels. As a result it was concluded that the algorithm with the best performance among the 3 analyzed was the Levenberg-Marquadrt.

  12. Training-Image Based Geostatistical Inversion Using a Spatial Generative Adversarial Neural Network

    Science.gov (United States)

    Laloy, Eric; Hérault, Romain; Jacques, Diederik; Linde, Niklas

    2018-01-01

    Probabilistic inversion within a multiple-point statistics framework is often computationally prohibitive for high-dimensional problems. To partly address this, we introduce and evaluate a new training-image based inversion approach for complex geologic media. Our approach relies on a deep neural network of the generative adversarial network (GAN) type. After training using a training image (TI), our proposed spatial GAN (SGAN) can quickly generate 2-D and 3-D unconditional realizations. A key characteristic of our SGAN is that it defines a (very) low-dimensional parameterization, thereby allowing for efficient probabilistic inversion using state-of-the-art Markov chain Monte Carlo (MCMC) methods. In addition, available direct conditioning data can be incorporated within the inversion. Several 2-D and 3-D categorical TIs are first used to analyze the performance of our SGAN for unconditional geostatistical simulation. Training our deep network can take several hours. After training, realizations containing a few millions of pixels/voxels can be produced in a matter of seconds. This makes it especially useful for simulating many thousands of realizations (e.g., for MCMC inversion) as the relative cost of the training per realization diminishes with the considered number of realizations. Synthetic inversion case studies involving 2-D steady state flow and 3-D transient hydraulic tomography with and without direct conditioning data are used to illustrate the effectiveness of our proposed SGAN-based inversion. For the 2-D case, the inversion rapidly explores the posterior model distribution. For the 3-D case, the inversion recovers model realizations that fit the data close to the target level and visually resemble the true model well.

  13. The efficacy of a multifactorial memory training in older adults living in residential care settings.

    Science.gov (United States)

    Vranić, Andrea; Španić, Ana Marija; Carretti, Barbara; Borella, Erika

    2013-11-01

    Several studies have shown an increase in memory performance after teaching mnemonic techniques to older participants. However, transfer effects to non-trained tasks are generally either very small, or not found. The present study investigates the efficacy of a multifactorial memory training program for older adults living in a residential care center. The program combines teaching of memory strategies with activities based on metacognitive (metamemory) and motivational aspects. Specific training-related gains in the Immediate list recall task (criterion task), as well as transfer effects on measures of short-term memory, long-term memory, working memory, motivational (need for cognition), and metacognitive aspects (subjective measure of one's memory) were examined. Maintenance of training benefits was assessed after seven months. Fifty-one older adults living in a residential care center, with no cognitive impairments, participated in the study. Participants were randomly assigned to two programs: the experimental group attended the training program, while the active control group was involved in a program in which different psychological issues were discussed. A benefit in the criterion task and substantial general transfer effects were found for the trained group, but not for the active control, and they were maintained at the seven months follow-up. Our results suggest that training procedures, which combine teaching of strategies with metacognitive-motivational aspects, can improve cognitive functioning and attitude toward cognitive activities in older adults.

  14. Clustering and artificial neural networks: classification of variable lengths of Helminth antigens in set of domains

    Directory of Open Access Journals (Sweden)

    Thiago de Souza Rodrigues

    2004-01-01

    Full Text Available A new scheme for representing proteins of different lengths in number of amino acids that can be presented to a fixed number of inputs Artificial Neural Networks (ANNs speel-out classification is described. K-Means's clustering of the new vectors with subsequent classification was then possible with the dimension reduction technique Principal Component Analysis applied previously. The new representation scheme was applied to a set of 112 antigens sequences from several parasitic helminths, selected in the National Center for Biotechnology Information and classified into fourth different groups. This bioinformatic tool permitted the establishment of a good correlation with domains that are already well characterized, regardless of the differences between the sequences that were confirmed by the PFAM database. Additionally, sequences were grouped according to their similarity, confirmed by hierarchical clustering using ClustalW.

  15. Implementing evidence-based policy in a network setting: road safety policy in the Netherlands.

    Science.gov (United States)

    Bax, Charlotte; de Jong, Martin; Koppenjan, Joop

    2010-01-01

    In the early 1990s, in order to improve road safety in The Netherlands, the Institute for Road Safety Research (SWOV) developed an evidence-based "Sustainable Safety" concept. Based on this concept, Dutch road safety policy, was seen as successful and as a best practice in Europe. In The Netherlands, the policy context has now changed from a sectoral policy setting towards a fragmented network in which safety is a facet of other transport-related policies. In this contribution, it is argued that the implementation strategy underlying Sustainable Safety should be aligned with the changed context. In order to explore the adjustments needed, two perspectives of policy implementation are discussed: (1) national evidence-based policies with sectoral implementation; and (2) decentralized negotiation on transport policy in which road safety is but one aspect. We argue that the latter approach matches the characteristics of the newly evolved policy context best, and conclude with recommendations for reformulating the implementation strategy.

  16. Hydro-Climatic Data Network (HCDN) Streamflow Data Set, 1874-1988

    Science.gov (United States)

    Slack, James Richard; Lumb, Alan M.; Landwehr, Jurate Maciunas

    1993-01-01

    The potential consequences of climate change to continental water resources are of great concern in the management of those resources. Critically important to society is what effect fluctuations in the prevailing climate may have on hydrologic conditions, such as the occurrence and magnitude of floods or droughts and the seasonal distribution of water supplies within a region. Records of streamflow that are unaffected by artificial diversions, storage, or other works of man in or on the natural stream channels or in the watershed can provide an account of hydrologic responses to fluctuations in climate. By examining such records given known past meteorologic conditions, we can better understand hydrologic responses to those conditions and anticipate the effects of postulated changes in current climate regimes. Furthermore, patterns in streamflow records can indicate when a change in the prevailing climate regime may have occurred in the past, even in the absence of concurrent meteorologic records. A streamflow data set, which is specifically suitable for the study of surface-water conditions throughout the United States under fluctuations in the prevailing climatic conditions, has been developed. This data set, called the Hydro-Climatic Data Network, or HCDN, consists of streamflow records for 1,659 sites throughout United States and its Territories. Records cumulatively span the period 1874 through 1988, inclusive, and represent a total of 73,231 water years of information. Development of the HCDN Data Set: Records for the HCDN were obtained through a comprehensive search of the extensive surface- water data holdings of the U.S. Geological Survey (USGS), which are contained in the USGS National Water Storage and Retrieval System (WATSTORE). All streamflow discharge records in WATSTORE through September 30, 1988, were examined for inclusion in the HCDN in accordance with strictly defined criteria of measurement accuracy and natural conditions. No reconstructed

  17. The Train Driver Recovery Problem - a Set Partitioning Based Model and Solution Method

    DEFF Research Database (Denmark)

    Rezanova, Natalia Jurjevna; Ryan, David

    The need to recover a train driver schedule occurs during major disruptions in the daily railway operations. Using data from the train driver schedule of the Danish passenger railway operator DSB S-tog A/S, a solution method to the Train Driver Recovery Problem (TDRP) is developed. The TDRP...... the depth-first search of the Branch & Bound tree. Preliminarily results are encouraging, showing that nearly all tested real-life instances produce integer solutions to the LP relaxation and solutions are found within a few seconds....

  18. Analysis of Roadway Traffic Accidents Based on Rough Sets and Bayesian Networks

    Directory of Open Access Journals (Sweden)

    Xiaoxia Xiong

    2018-02-01

    Full Text Available The paper integrates Rough Sets (RS and Bayesian Networks (BN for roadway traffic accident analysis. RS reduction of attributes is first employed to generate the key set of attributes affecting accident outcomes, which are then fed into a BN structure as nodes for BN construction and accident outcome classification. Such RS-based BN framework combines the advantages of RS in knowledge reduction capability and BN in describing interrelationships among different attributes. The framework is demonstrated using the 100-car naturalistic driving data from Virginia Tech Transportation Institute to predict accident type. Comparative evaluation with the baseline BNs shows the RS-based BNs generally have a higher prediction accuracy and lower network complexity while with comparable prediction coverage and receiver operating characteristic curve area, proving that the proposed RS-based BN overall outperforms the BNs with/without traditional feature selection approaches. The proposed RS-based BN indicates the most significant attributes that affect accident types include pre-crash manoeuvre, driver’s attention from forward roadway to centre mirror, number of secondary tasks undertaken, traffic density, and relation to junction, most of which feature pre-crash driver states and driver behaviours that have not been extensively researched in literature, and could give further insight into the nature of traffic accidents.

  19. Modeling of an ionic polymer metal composite actuator based on an extended Kalman filter trained neural network

    International Nuclear Information System (INIS)

    Truong, Dinh Quang; Ahn, Kyoung Kwan

    2014-01-01

    An ion polymer metal composite (IPMC) is an electroactive polymer that bends in response to a small applied electric field as a result of mobility of cations in the polymer network and vice versa. This paper presents an innovative and accurate nonlinear black-box model (NBBM) for estimating the bending behavior of IPMC actuators. The model is constructed via a general multilayer perceptron neural network (GMLPNN) integrated with a smart learning mechanism (SLM) that is based on an extended Kalman filter with self-decoupling ability (SDEKF). Here the GMLPNN is built with an ability to autoadjust its structure based on its characteristic vector. Furthermore, by using the SLM based on the SDEKF, the GMLPNN parameters are optimized with small computational effort, and the modeling accuracy is improved. An apparatus employing an IPMC actuator is first set up to investigate the IPMC characteristics and to generate the data for training and validating the model. The advanced NBBM model for the IPMC system is then created with the proper inputs to estimate IPMC tip displacement. Next, the model is optimized using the SLM mechanism with the training data. Finally, the optimized NBBM model is verified with the validating data. A comparison between this model and the previously developed model is also carried out to prove the effectiveness of the proposed modeling technique. (paper)

  20. Mercury Deposition Network Site Operator Training for the System Blank and Blind Audit Programs

    Science.gov (United States)

    Wetherbee, Gregory A.; Lehmann, Christopher M.B.

    2008-01-01

    The U.S. Geological Survey operates the external quality assurance project for the National Atmospheric Deposition Program/Mercury Deposition Network. The project includes the system blank and blind audit programs for assessment of total mercury concentration data quality for wet-deposition samples. This presentation was prepared to train new site operators and to refresh experienced site operators to successfully process and submit system blank and blind audit samples for chemical analysis. Analytical results are used to estimate chemical stability and contamination levels of National Atmospheric Deposition Program/Mercury Deposition Network samples and to evaluate laboratory variability and bias.

  1. Enlarge the training set based on inter-class relationship for face recognition from one image per person.

    Science.gov (United States)

    Li, Qin; Wang, Hua Jing; You, Jane; Li, Zhao Ming; Li, Jin Xue

    2013-01-01

    In some large-scale face recognition task, such as driver license identification and law enforcement, the training set only contains one image per person. This situation is referred to as one sample problem. Because many face recognition techniques implicitly assume that several (at least two) images per person are available for training, they cannot deal with the one sample problem. This paper investigates principal component analysis (PCA), Fisher linear discriminant analysis (LDA), and locality preserving projections (LPP) and shows why they cannot perform well in one sample problem. After that, this paper presents four reasons that make one sample problem itself difficult: the small sample size problem; the lack of representative samples; the underestimated intra-class variation; and the overestimated inter-class variation. Based on the analysis, this paper proposes to enlarge the training set based on the inter-class relationship. This paper also extends LDA and LPP to extract features from the enlarged training set. The experimental results show the effectiveness of the proposed method.

  2. Enlarge the training set based on inter-class relationship for face recognition from one image per person.

    Directory of Open Access Journals (Sweden)

    Qin Li

    Full Text Available In some large-scale face recognition task, such as driver license identification and law enforcement, the training set only contains one image per person. This situation is referred to as one sample problem. Because many face recognition techniques implicitly assume that several (at least two images per person are available for training, they cannot deal with the one sample problem. This paper investigates principal component analysis (PCA, Fisher linear discriminant analysis (LDA, and locality preserving projections (LPP and shows why they cannot perform well in one sample problem. After that, this paper presents four reasons that make one sample problem itself difficult: the small sample size problem; the lack of representative samples; the underestimated intra-class variation; and the overestimated inter-class variation. Based on the analysis, this paper proposes to enlarge the training set based on the inter-class relationship. This paper also extends LDA and LPP to extract features from the enlarged training set. The experimental results show the effectiveness of the proposed method.

  3. Training for cervical cancer prevention programs in low-resource settings: focus on visual inspection with acetic acid and cryotherapy.

    Science.gov (United States)

    Blumenthal, P D; Lauterbach, M; Sellors, J W; Sankaranarayanan, R

    2005-05-01

    The modern approach to cervical cancer prevention, characterized by use of cytology and multiple visits for diagnosis and treatment, has frequently proven challenging and unworkable in low-resource settings. Because of this, the Alliance for Cervical Cancer Prevention (ACCP) has made it a priority to investigate and assess alternative approaches, particularly the use of visual screening methods, such as visual inspection with acetic acid (VIA) and visual inspection with Lugol's iodine (VILI), for precancer and cancer detection and the use of cryotherapy as a precancer treatment method. As a result of ACCP experience in providing training to nurses and doctors in these techniques, it is now widely agreed that training should be competency based, combining both didactic and hands-on approaches, and should be done in a clinical setting that resembles the service-delivery conditions at the program site. This article reviews ACCP experiences and perceptions about the essentials of training in visual inspection and cryotherapy and presents some lessons learned with regard to training in these techniques in low-resource settings.

  4. Effect of ultrasound training of physicians working in the prehospital setting

    DEFF Research Database (Denmark)

    Krogh, Charlotte Loumann; Steinmetz, Jacob; Rudolph, Søren Steemann

    2016-01-01

    measure was US performance assessed by the total score in a modified version of the Objective Structured Assessment of Ultrasound Skills scale (mOSAUS). METHODS: Prehospital physicians participated in a four-hour US course consisting of both hands-on training and e-learning including a pre- and a post-learning...... test. Prior to the hands-on training a pre-training test was applied comprising of five videos in which the participants should identify pathology and a five-minute US examination of a healthy volunteer portraying to be a shocked patient after a blunt torso trauma. Following the pre-training test...... the study. A significant improvement was identified in e-learning performance and US performance, (37.5 (SD: 10.0)) vs. (51.3 (SD: 5.9) p = 

  5. Exploring training needs of nursing staff in rural Cretan primary care settings.

    Science.gov (United States)

    Markaki, Adelais; Alegakis, Athanasios; Antonakis, Nikos; Kalokerinou-Anagnostopoulou, Athena; Lionis, Christos

    2009-05-01

    The purpose of this exploratory study was to assess occupational profile, level of performance, and on-the-job training needs of nursing staff employed in all government primary health care centers in rural Crete, Greece. The translated, culturally adapted, and validated Greek version of the Training Needs Assessment questionnaire was used. There were no significant differences between 2-year degree graduates (LPNs) and 3- or 4-year degree graduates (RNs, midwives, and health visitors) in terms of importance for 28 of 30 assigned tasks, whereas level of performance did not differ in any tasks. Significant training needs were reported by all staff, mainly in research/audit and clinical skills. Systematic overview of skill deficits in relation to skill requirements should be implemented by regional health authorities to enhance delivery of on-the-job training targeting group-specific, local needs.

  6. Study on intelligence fault diagnosis method for nuclear power plant equipment based on rough set and fuzzy neural network

    International Nuclear Information System (INIS)

    Liu Yongkuo; Xia Hong; Xie Chunli; Chen Zhihui; Chen Hongxia

    2007-01-01

    Rough set theory and fuzzy neural network are combined, to take full advantages of the two of them. Based on the reduction technology to knowledge of Rough set method, and by drawing the simple rule from a large number of initial data, the fuzzy neural network was set up, which was with better topological structure, improved study speed, accurate judgment, strong fault-tolerant ability, and more practical. In order to test the validity of the method, the inverted U-tubes break accident of Steam Generator and etc are used as examples, and many simulation experiments are performed. The test result shows that it is feasible to incorporate the fault intelligence diagnosis method based on rough set and fuzzy neural network in the nuclear power plant equipment, and the method is simple and convenience, with small calculation amount and reliable result. (authors)

  7. The Bilevel Design Problem for Communication Networks on Trains: Model, Algorithm, and Verification

    Directory of Open Access Journals (Sweden)

    Yin Tian

    2014-01-01

    Full Text Available This paper proposes a novel method to solve the problem of train communication network design. Firstly, we put forward a general description of such problem. Then, taking advantage of the bilevel programming theory, we created the cost-reliability-delay model (CRD model that consisted of two parts: the physical topology part aimed at obtaining the networks with the maximum reliability under constrained cost, while the logical topology part focused on the communication paths yielding minimum delay based on the physical topology delivered from upper level. We also suggested a method to solve the CRD model, which combined the genetic algorithm and the Floyd-Warshall algorithm. Finally, we used a practical example to verify the accuracy and the effectiveness of the CRD model and further applied the novel method on a train with six carriages.

  8. LAI inversion from optical reflectance using a neural network trained with a multiple scattering model

    Science.gov (United States)

    Smith, James A.

    1992-01-01

    The inversion of the leaf area index (LAI) canopy parameter from optical spectral reflectance measurements is obtained using a backpropagation artificial neural network trained using input-output pairs generated by a multiple scattering reflectance model. The problem of LAI estimation over sparse canopies (LAI 1000 percent for low LAI. Minimization methods applied to merit functions constructed from differences between measured reflectances and predicted reflectances using multiple-scattering models are unacceptably sensitive to a good initial guess for the desired parameter. In contrast, the neural network reported generally yields absolute percentage errors of <30 percent when weighting coefficients trained on one soil type were applied to predicted canopy reflectance at a different soil background.

  9. A summarization approach for Affymetrix GeneChip data using a reference training set from a large, biologically diverse database

    Directory of Open Access Journals (Sweden)

    Tripputi Mark

    2006-10-01

    Full Text Available Abstract Background Many of the most popular pre-processing methods for Affymetrix expression arrays, such as RMA, gcRMA, and PLIER, simultaneously analyze data across a set of predetermined arrays to improve precision of the final measures of expression. One problem associated with these algorithms is that expression measurements for a particular sample are highly dependent on the set of samples used for normalization and results obtained by normalization with a different set may not be comparable. A related problem is that an organization producing and/or storing large amounts of data in a sequential fashion will need to either re-run the pre-processing algorithm every time an array is added or store them in batches that are pre-processed together. Furthermore, pre-processing of large numbers of arrays requires loading all the feature-level data into memory which is a difficult task even with modern computers. We utilize a scheme that produces all the information necessary for pre-processing using a very large training set that can be used for summarization of samples outside of the training set. All subsequent pre-processing tasks can be done on an individual array basis. We demonstrate the utility of this approach by defining a new version of the Robust Multi-chip Averaging (RMA algorithm which we refer to as refRMA. Results We assess performance based on multiple sets of samples processed over HG U133A Affymetrix GeneChip® arrays. We show that the refRMA workflow, when used in conjunction with a large, biologically diverse training set, results in the same general characteristics as that of RMA in its classic form when comparing overall data structure, sample-to-sample correlation, and variation. Further, we demonstrate that the refRMA workflow and reference set can be robustly applied to naïve organ types and to benchmark data where its performance indicates respectable results. Conclusion Our results indicate that a biologically diverse

  10. Planning Training Loads for the 400 M Hurdles in Three-Month Mesocycles using Artificial Neural Networks.

    Science.gov (United States)

    Przednowek, Krzysztof; Iskra, Janusz; Wiktorowicz, Krzysztof; Krzeszowski, Tomasz; Maszczyk, Adam

    2017-12-01

    This paper presents a novel approach to planning training loads in hurdling using artificial neural networks. The neural models performed the task of generating loads for athletes' training for the 400 meters hurdles. All the models were calculated based on the training data of 21 Polish National Team hurdlers, aged 22.25 ± 1.96, competing between 1989 and 2012. The analysis included 144 training plans that represented different stages in the annual training cycle. The main contribution of this paper is to develop neural models for planning training loads for the entire career of a typical hurdler. In the models, 29 variables were used, where four characterized the runner and 25 described the training process. Two artificial neural networks were used: a multi-layer perceptron and a network with radial basis functions. To assess the quality of the models, the leave-one-out cross-validation method was used in which the Normalized Root Mean Squared Error was calculated. The analysis shows that the method generating the smallest error was the radial basis function network with nine neurons in the hidden layer. Most of the calculated training loads demonstrated a non-linear relationship across the entire competitive period. The resulting model can be used as a tool to assist a coach in planning training loads during a selected training period.

  11. Planning Training Loads for The 400 M Hurdles in Three-Month Mesocycles Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Przednowek Krzysztof

    2017-12-01

    Full Text Available This paper presents a novel approach to planning training loads in hurdling using artificial neural networks. The neural models performed the task of generating loads for athletes’ training for the 400 meters hurdles. All the models were calculated based on the training data of 21 Polish National Team hurdlers, aged 22.25 ± 1.96, competing between 1989 and 2012. The analysis included 144 training plans that represented different stages in the annual training cycle. The main contribution of this paper is to develop neural models for planning training loads for the entire career of a typical hurdler. In the models, 29 variables were used, where four characterized the runner and 25 described the training process. Two artificial neural networks were used: a multi-layer perceptron and a network with radial basis functions. To assess the quality of the models, the leave-one-out cross-validation method was used in which the Normalized Root Mean Squared Error was calculated. The analysis shows that the method generating the smallest error was the radial basis function network with nine neurons in the hidden layer. Most of the calculated training loads demonstrated a non-linear relationship across the entire competitive period. The resulting model can be used as a tool to assist a coach in planning training loads during a selected training period.

  12. Optimal Parameter for the Training of Multilayer Perceptron Neural Networks by Using Hierarchical Genetic Algorithm

    International Nuclear Information System (INIS)

    Orozco-Monteagudo, Maykel; Taboada-Crispi, Alberto; Gutierrez-Hernandez, Liliana

    2008-01-01

    This paper deals with the controversial topic of the selection of the parameters of a genetic algorithm, in this case hierarchical, used for training of multilayer perceptron neural networks for the binary classification. The parameters to select are the crossover and mutation probabilities of the control and parametric genes and the permanency percent. The results can be considered as a guide for using this kind of algorithm.

  13. Postural stability and quality of life after guided and self-training among older adults residing in an institutional setting

    Directory of Open Access Journals (Sweden)

    Tuunainen E

    2013-09-01

    Full Text Available Eeva Tuunainen,1 Jyrki Rasku,1 Pirkko Jäntti,2 Päivi Moisio-Vilenius,3 Erja Mäkinen,3 Esko Toppila,4 Ilmari Pyykkö1 1Department of Otolaryngology, Section of Hearing and Balance Research Unit, University of Tampere and University Hospital of Tampere, Finland; 2Department of Geriatric Medicine, Hatanpää City Hospital, Tampere, Finland; 3Koukkuniemi Residential Home, Tampere, Finland; 4Finnish Institute of Occupational Health, Helsinki, Finland Purpose: To evaluate whether rehabilitation of muscle force or balance improves postural stability and quality of life (QoL, and whether self-administered training is comparable with guided training among older adults residing in an institutional setting. Patients and methods: A randomized, prospective intervention study was undertaken among 55 elderly patients. Three intervention groups were evaluated: a muscle force training group; a balance and muscle force training group; and a self-administered training group. Each group underwent 1-hour-long training sessions, twice a week, for 3 months. Postural stability was measured at onset, after 3 months, and after 6 months. Time-domain-dependent body sway variables were calculated. The fall rate was evaluated for 3 years. General health related quality of life (HRQoL was measured with a 15D instrument. Postural stability was used as a primary outcome, with QoL and falls used as secondary outcomes. Results: Muscle force trainees were able to undertake training, progressing towards more strenuous exercises. In posturography, the number of spiky oscillations was reduced after training, and stationary fields of torque moments of the ankle increased, providing better postural stability in all groups; in particular, the zero crossing rate of weight signal and the number of low variability episodes in the stabilogram were improved after training. While no difference was found between different training groups in posturography outcomes, a reduction of fall rate

  14. Individual Channel Estimation in a Diamond Relay Network Using Relay-Assisted Training

    Directory of Open Access Journals (Sweden)

    Xianwen He

    2017-01-01

    Full Text Available We consider the training design and channel estimation in the amplify-and-forward (AF diamond relay network. Our strategy is to transmit the source training in time-multiplexing (TM mode while each relay node superimposes its own relay training over the amplified received data signal without bandwidth expansion. The principal challenge is to obtain accurate channel state information (CSI of second-hop link due to the multiaccess interference (MAI and cooperative data interference (CDI. To maintain the orthogonality between data and training, a modified relay-assisted training scheme is proposed to migrate the CDI, where some of the cooperative data at the relay are discarded to accommodate relay training. Meanwhile, a couple of optimal zero-correlation zone (ZCZ relay-assisted sequences are designed to avoid MAI. At the destination node, the received signals from the two relay nodes are combined to achieve spatial diversity and enhanced data reliability. The simulation results are presented to validate the performance of the proposed schemes.

  15. Malaria training for community health workers in the setting of elimination: a qualitative study from China.

    Science.gov (United States)

    Lu, Guangyu; Liu, Yaobao; Wang, Jinsong; Li, Xiangming; Liu, Xing; Beiersmann, Claudia; Feng, Yu; Cao, Jun; Müller, Olaf

    2018-02-23

    Continuous training of health workers is a key intervention to maintain their good performance and keep their vigilance during malaria elimination programmes. However, countries progressing toward malaria elimination have a largely decreased malaria disease burden, less frequent exposure of health workers to malaria patients, and new challenges in the epidemiology of the remaining malaria cases. Moreover, competing health priorities and usually a decline in resources and in political commitment also pose challenges to the elimination programme. As a consequence, the acceptability, sustainability, and impact of malaria training and education programmes face challenges. However, little is known of the perceptions and expectations of malaria training and education programmes of health workers being engaged in countries with malaria elimination programmes. This qualitative study provides information on perceptions and expectations of health workers of malaria training programmes from China, which aims to malaria elimination by the year 2020. This study was embedded into a larger study on the challenges and lessons learned during the malaria surveillance strategy in China, involving 42 interviews with malaria experts, health staff, laboratory practitioners, and village doctors at the provincial, city, county, township, and village levels from Gansu province (northwestern China) and Jiangsu province (southeastern China). In the context of an increasing number of imported malaria cases in China, the majority of respondents emphasized the necessity and importance of such programmes and complained about a decreasing frequency of training courses. Moreover, they called for innovative strategies to improve the implementation and sustainability of the malaria training programmes until the elimination goal has been achieved. Perceptions and expectations of health workers from different health centres were quite different. Health workers from higher-level facilities were more

  16. Simulation training for medical emergencies in the dental setting using an inexpensive software application.

    Science.gov (United States)

    Kishimoto, N; Mukai, N; Honda, Y; Hirata, Y; Tanaka, M; Momota, Y

    2017-11-09

    Every dental provider needs to be educated about medical emergencies to provide safe dental care. Simulation training is available with simulators such as advanced life support manikins and robot patients. However, the purchase and development costs of these simulators are high. We have developed a simulation training course on medical emergencies using an inexpensive software application. The purpose of this study was to evaluate the educational effectiveness of this course. Fifty-one dental providers participated in this study from December 2014 to March 2015. Medical simulation software was used to simulate a patient's vital signs. We evaluated participants' ability to diagnose and treat vasovagal syncope or anaphylaxis with an evaluation sheet and conducted a questionnaire before and after the scenario-based simulation training. The median evaluation sheet score for vasovagal syncope increased significantly from 7/9 before to 9/9 after simulation training. The median score for anaphylaxis also increased significantly from 8/12 to 12/12 (P simulation training. This simulation course improved participants' ability to diagnose and treat medical emergencies and improved their confidence. This course can be offered inexpensively using a software application. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  17. Otoscopy simulation training in a classroom setting: a novel approach to teaching otoscopy to medical students.

    Science.gov (United States)

    Davies, Joel; Djelic, Lucas; Campisi, Paolo; Forte, Vito; Chiodo, Albino

    2014-11-01

    To determine the effectiveness of using of an otoscopy stimulator to teach medical students the primary principles of otoscopy in large group training sessions and improve their confidence in making otologic diagnoses. Cross-sectional survey design. In March 2013, the Department of Otolaryngology-Head and Neck Surgery held a large-scale otoscopy simulator teaching session at the MaRS Innovation Center for 92 first and second year University of Toronto medical students. Following the training session, students were provided with an optional electronic, nine-question survey related to their experience with learning otoscopy using the simulators alone, and in comparison to traditional methods of teaching. Thirty-four medical students completed the survey. Ninety-one percent of the respondents indicated that the overall quality of the event was either very good or excellent. A total of 71% of respondents either agreed, or strongly agreed, that the otoscopy simulator training session improved their confidence in diagnosing pathologies of the ear. The majority (70%) of students indicated that the training session had stimulated their interest in otolaryngology-head and neck surgery as a medical specialty. Organizing large-group otoscopy simulator training sessions is one method whereby students can become familiar with a wide variety of pathologies of the ear and improve both their diagnostic accuracy and their confidence in making otologic diagnoses. NA © 2014 The American Laryngological, Rhinological and Otological Society, Inc.

  18. Tracking Historical NASA EVA Training: Lifetime Surveillance of Astronaut Health (LSAH) Development of the EVA Suit Exposure Tracker (EVA SET)

    Science.gov (United States)

    Laughlin, Mitzi S.; Murray, Jocelyn D.; Lee, Lesley R.; Wear, Mary L.; Van Baalen, Mary

    2017-01-01

    astronauts. This activity places astronauts at risk for decompression sickness and barotrauma as well as various musculoskeletal disorders from working in the spacesuit. The medical, operational and research communities over the years have requested access to EVA training data to better understand the risks. As a result of these requests, epidemiologists within the Lifetime Surveillance of Astronaut Health (LSAH) team have compiled records from numerous EVA training venues to quantify the exposure to EVA training. The EVA Suit Exposure Tracker (EVA SET) dataset is a compilation of ground-based training activities using the extravehicular mobility unit (EMU) in neutrally buoyant pools to enhance EVA performance on orbit. These data can be used by the current ISS program and future exploration missions by informing physicians, researchers, and operational personnel on the risks of EVA training in order that future suit and mission designs incorporate greater safety. The purpose of this technical report is to document briefly the various facilities where NASA astronauts have performed EVA training while describing in detail the EVA training records used to generate the EVA SET dataset.

  19. Impact of real-time fMRI working memory feedback training on the interactions between three core brain networks.

    Science.gov (United States)

    Zhang, Qiushi; Zhang, Gaoyan; Yao, Li; Zhao, Xiaojie

    2015-01-01

    Working memory (WM) refers to the temporary holding and manipulation of information during the performance of a range of cognitive tasks, and WM training is a promising method for improving an individual's cognitive functions. Our previous work demonstrated that WM performance can be improved through self-regulation of dorsal lateral prefrontal cortex (PFC) activation using real-time functional magnetic resonance imaging (rtfMRI), which enables individuals to control local brain activities volitionally according to the neurofeedback. Furthermore, research concerning large-scale brain networks has demonstrated that WM training requires the engagement of several networks, including the central executive network (CEN), the default mode network (DMN) and the salience network (SN), and functional connectivity within the CEN and DMN can be changed by WM training. Although a switching role of the SN between the CEN and DMN has been demonstrated, it remains unclear whether WM training can affect the interactions between the three networks and whether a similar mechanism also exists during the training process. In this study, we investigated the dynamic functional connectivity between the three networks during the rtfMRI feedback training using independent component analysis (ICA) and correlation analysis. The results indicated that functional connectivity within and between the three networks were significantly enhanced by feedback training, and most of the changes were associated with the insula and correlated with behavioral improvements. These findings suggest that the insula plays a critical role in the reorganization of functional connectivity among the three networks induced by rtfMRI training and in WM performance, thus providing new insights into the mechanisms of high-level functions and the clinical treatment of related functional impairments.

  20. Wind power forecast using wavelet neural network trained by improved Clonal selection algorithm

    International Nuclear Information System (INIS)

    Chitsaz, Hamed; Amjady, Nima; Zareipour, Hamidreza

    2015-01-01

    Highlights: • Presenting a Morlet wavelet neural network for wind power forecasting. • Proposing improved Clonal selection algorithm for training the model. • Applying Maximum Correntropy Criterion to evaluate the training performance. • Extensive testing of the proposed wind power forecast method on real-world data. - Abstract: With the integration of wind farms into electric power grids, an accurate wind power prediction is becoming increasingly important for the operation of these power plants. In this paper, a new forecasting engine for wind power prediction is proposed. The proposed engine has the structure of Wavelet Neural Network (WNN) with the activation functions of the hidden neurons constructed based on multi-dimensional Morlet wavelets. This forecast engine is trained by a new improved Clonal selection algorithm, which optimizes the free parameters of the WNN for wind power prediction. Furthermore, Maximum Correntropy Criterion (MCC) has been utilized instead of Mean Squared Error as the error measure in training phase of the forecasting model. The proposed wind power forecaster is tested with real-world hourly data of system level wind power generation in Alberta, Canada. In order to demonstrate the efficiency of the proposed method, it is compared with several other wind power forecast techniques. The obtained results confirm the validity of the developed approach

  1. A pre-trained convolutional neural network based method for thyroid nodule diagnosis.

    Science.gov (United States)

    Ma, Jinlian; Wu, Fa; Zhu, Jiang; Xu, Dong; Kong, Dexing

    2017-01-01

    In ultrasound images, most thyroid nodules are in heterogeneous appearances with various internal components and also have vague boundaries, so it is difficult for physicians to discriminate malignant thyroid nodules from benign ones. In this study, we propose a hybrid method for thyroid nodule diagnosis, which is a fusion of two pre-trained convolutional neural networks (CNNs) with different convolutional layers and fully-connected layers. Firstly, the two networks pre-trained with ImageNet database are separately trained. Secondly, we fuse feature maps learned by trained convolutional filters, pooling and normalization operations of the two CNNs. Finally, with the fused feature maps, a softmax classifier is used to diagnose thyroid nodules. The proposed method is validated on 15,000 ultrasound images collected from two local hospitals. Experiment results show that the proposed CNN based methods can accurately and effectively diagnose thyroid nodules. In addition, the fusion of the two CNN based models lead to significant performance improvement, with an accuracy of 83.02%±0.72%. These demonstrate the potential clinical applications of this method. Copyright © 2016 Elsevier B.V. All rights reserved.

  2. Training Small Networks for Scene Classification of Remote Sensing Images via Knowledge Distillation

    Directory of Open Access Journals (Sweden)

    Guanzhou Chen

    2018-05-01

    Full Text Available Scene classification, aiming to identify the land-cover categories of remotely sensed image patches, is now a fundamental task in the remote sensing image analysis field. Deep-learning-model-based algorithms are widely applied in scene classification and achieve remarkable performance, but these high-level methods are computationally expensive and time-consuming. Consequently in this paper, we introduce a knowledge distillation framework, currently a mainstream model compression method, into remote sensing scene classification to improve the performance of smaller and shallower network models. Our knowledge distillation training method makes the high-temperature softmax output of a small and shallow student model match the large and deep teacher model. In our experiments, we evaluate knowledge distillation training method for remote sensing scene classification on four public datasets: AID dataset, UCMerced dataset, NWPU-RESISC dataset, and EuroSAT dataset. Results show that our proposed training method was effective and increased overall accuracy (3% in AID experiments, 5% in UCMerced experiments, 1% in NWPU-RESISC and EuroSAT experiments for small and shallow models. We further explored the performance of the student model on small and unbalanced datasets. Our findings indicate that knowledge distillation can improve the performance of small network models on datasets with lower spatial resolution images, numerous categories, as well as fewer training samples.

  3. PARTICLE SWARM OPTIMIZATION (PSO FOR TRAINING OPTIMIZATION ON CONVOLUTIONAL NEURAL NETWORK (CNN

    Directory of Open Access Journals (Sweden)

    Arie Rachmad Syulistyo

    2016-02-01

    Full Text Available Neural network attracts plenty of researchers lately. Substantial number of renowned universities have developed neural network for various both academically and industrially applications. Neural network shows considerable performance on various purposes. Nevertheless, for complex applications, neural network’s accuracy significantly deteriorates. To tackle the aforementioned drawback, lot of researches had been undertaken on the improvement of the standard neural network. One of the most promising modifications on standard neural network for complex applications is deep learning method. In this paper, we proposed the utilization of Particle Swarm Optimization (PSO in Convolutional Neural Networks (CNNs, which is one of the basic methods in deep learning. The use of PSO on the training process aims to optimize the results of the solution vectors on CNN in order to improve the recognition accuracy. The data used in this research is handwritten digit from MNIST. The experiments exhibited that the accuracy can be attained in 4 epoch is 95.08%. This result was better than the conventional CNN and DBN.  The execution time was also almost similar to the conventional CNN. Therefore, the proposed method was a promising method.

  4. Assessment training for practice in American Indian and Alaska Native settings.

    Science.gov (United States)

    Allen, James

    2002-10-01

    The collaborative assessment model is extended as a training model. The experience of psychological assessment in American Indian and Alaska Native communities is often negative due to culturally inappropriate services and test interpretation. It is productive to address this negative experience, using it as a catalyst for learning. Training in measurement and construct validation provides initial basis for critique of negative experience. Training in collaborative assessment procedures then focuses on culturally appropriate assessment service practices, cultural orientation's affect on test interpretation, and multicultural assessment ethics. Writing skills are emphasized, including procedures in report writing for description of local adaptations, norms, and interpretative rules, and integration of the Diagnostic and Statistical Manual for Mental Disorders (4th ed., text revision; American Psychiatric Association, 2000) cultural formulation. Development of local norms and emic tests are emphasized.

  5. Self-Consistent Scheme for Spike-Train Power Spectra in Heterogeneous Sparse Networks.

    Science.gov (United States)

    Pena, Rodrigo F O; Vellmer, Sebastian; Bernardi, Davide; Roque, Antonio C; Lindner, Benjamin

    2018-01-01

    Recurrent networks of spiking neurons can be in an asynchronous state characterized by low or absent cross-correlations and spike statistics which resemble those of cortical neurons. Although spatial correlations are negligible in this state, neurons can show pronounced temporal correlations in their spike trains that can be quantified by the autocorrelation function or the spike-train power spectrum. Depending on cellular and network parameters, correlations display diverse patterns (ranging from simple refractory-period effects and stochastic oscillations to slow fluctuations) and it is generally not well-understood how these dependencies come about. Previous work has explored how the single-cell correlations in a homogeneous network (excitatory and inhibitory integrate-and-fire neurons with nearly balanced mean recurrent input) can be determined numerically from an iterative single-neuron simulation. Such a scheme is based on the fact that every neuron is driven by the network noise (i.e., the input currents from all its presynaptic partners) but also contributes to the network noise, leading to a self-consistency condition for the input and output spectra. Here we first extend this scheme to homogeneous networks with strong recurrent inhibition and a synaptic filter, in which instabilities of the previous scheme are avoided by an averaging procedure. We then extend the scheme to heterogeneous networks in which (i) different neural subpopulations (e.g., excitatory and inhibitory neurons) have different cellular or connectivity parameters; (ii) the number and strength of the input connections are random (Erdős-Rényi topology) and thus different among neurons. In all heterogeneous cases, neurons are lumped in different classes each of which is represented by a single neuron in the iterative scheme; in addition, we make a Gaussian approximation of the input current to the neuron. These approximations seem to be justified over a broad range of parameters as

  6. Self-Consistent Scheme for Spike-Train Power Spectra in Heterogeneous Sparse Networks

    Directory of Open Access Journals (Sweden)

    Rodrigo F. O. Pena

    2018-03-01

    Full Text Available Recurrent networks of spiking neurons can be in an asynchronous state characterized by low or absent cross-correlations and spike statistics which resemble those of cortical neurons. Although spatial correlations are negligible in this state, neurons can show pronounced temporal correlations in their spike trains that can be quantified by the autocorrelation function or the spike-train power spectrum. Depending on cellular and network parameters, correlations display diverse patterns (ranging from simple refractory-period effects and stochastic oscillations to slow fluctuations and it is generally not well-understood how these dependencies come about. Previous work has explored how the single-cell correlations in a homogeneous network (excitatory and inhibitory integrate-and-fire neurons with nearly balanced mean recurrent input can be determined numerically from an iterative single-neuron simulation. Such a scheme is based on the fact that every neuron is driven by the network noise (i.e., the input currents from all its presynaptic partners but also contributes to the network noise, leading to a self-consistency condition for the input and output spectra. Here we first extend this scheme to homogeneous networks with strong recurrent inhibition and a synaptic filter, in which instabilities of the previous scheme are avoided by an averaging procedure. We then extend the scheme to heterogeneous networks in which (i different neural subpopulations (e.g., excitatory and inhibitory neurons have different cellular or connectivity parameters; (ii the number and strength of the input connections are random (Erdős-Rényi topology and thus different among neurons. In all heterogeneous cases, neurons are lumped in different classes each of which is represented by a single neuron in the iterative scheme; in addition, we make a Gaussian approximation of the input current to the neuron. These approximations seem to be justified over a broad range of

  7. Influence of number of sets on blood pressure and heart rate variability after a strength training session.

    Science.gov (United States)

    Figueiredo, Tiago; Rhea, Matthew R; Peterson, Mark; Miranda, Humberto; Bentes, Claudio M; dos Reis, Victor Machado de Ribeiro; Simão, Roberto

    2015-06-01

    The purpose of this study was to compare the acute effects of 1, 3, and 5 sets of strength training (ST), on heart rate variability (HRV) and blood pressure. Eleven male volunteers (age: 26.1 ± 3.6 years; body mass: 74.1 ± 8.1 kg; height: 172 ± 4 cm) with at least 6 months previous experience in ST participated in the study. After determining the 1 repetition maximum (1RM) load for the bench press (BP), lat pull down (LPD), shoulder press (SP), biceps curl (BC), triceps extension (TE), leg press (LP), leg extension (LE), and leg curl (LC), the participants performed 3 different exercise sequences in a random order and 72 hours apart. During the first sequence, subjects performed a single set of 8-10 repetitions, at 70% 1RM, and with 2-minute rest interval between exercises. Exercises were performed in the following order: BP, LPD, SP, BC, TE, LP, LE, and LC. During the second sequence, subjects performed the same exercise sequence, with the same intensity, 2-minute rest interval between sets and exercises, but with 3 consecutive sets of each exercise. During the third sequence, the same protocol was followed but with 5 sets of each exercise. Before and after the training sessions, blood pressure and HRV were measured. The statistical analysis demonstrated a greater duration of postexercise hypotension after the 5-set program vs. the 1 set or 3 sets (p ≤ 0.05). However, the 5-set program promoted a substantial cardiac stress, as demonstrated by HRV (p ≤ 0.05). These results indicate that 5 sets of 8-10 repetitions at 70% 1RM load may provide the ideal stimulus for a postexercise hypotensive response. Therefore, ST composed of upper- and lower-body exercises and performed with high volumes are capable of producing significant and extended postexercise hypotensive response. In conclusion, strength and conditioning professionals can prescribe 5 sets per exercises if the goal is to reduce blood pressure after training. In addition, these findings may have

  8. THE INTERIM RESULTS AND THE WAYS TO IMPLEMENT THE PROGRAMS TEACHER TRAINING IN NETWORK FORM

    Directory of Open Access Journals (Sweden)

    A. A. Tolsteneva

    2016-01-01

    Full Text Available The paper presents the results of approbation of new modules primary educational undergraduate specialties Group expanded education and pedagogy (training areas-economics, involving academic mobility of students of universities in terms of networking of Novosibirsk and Nizhny Novgorod pedagogical universities. The article describes the structure of established affiliate networks, conducted pedagogical and methodical analysis modules have passed testing, recommendations for improvement and suggested ways for the development of a modular approach to building educational programs in teacher education system. The implementation of educational modules require their integration into the curricula of the Nizhny Novgorod State Pedagogical University, with no loss of content, giving the existing curriculum structure saturation. Thus, it was achieved 100% consistency of curriculum, opening further opportunities for the implementation of educational programs in terms of networking.

  9. COMSKIL Communication Training in Oncology-Adaptation to German Cancer Care Settings.

    Science.gov (United States)

    Hartung, Tim J; Kissane, David; Mehnert, Anja

    2018-01-01

    Medical communication is a skill which can be learned and taught and which can substantially improve treatment outcomes, especially if patients' communication preferences are taken into account. Here, we give an overview of communication training research and outline the COMSKIL program as a state-of-the-art communication skills training in oncology. COMSKIL has a solid theoretical foundation and teaches core elements of medical communication in up to ten fully operationalized modules. These address typical situations ranging from breaking bad news to responding to difficult emotions, shared decision-making, and communicating via interpreters.

  10. German cooperation-network 'equity in health'-health promotion in settings.

    Science.gov (United States)

    Mielck, Andreas; Kilian, Holger; Lehmann, Frank; Richter-Kornweitz, Antje; Kaba-Schönstein, Lotte

    2018-04-01

    In 2003, the German Federal Centre for Health Education (BZgA) initiated the national Cooperation-Network (CN) 'Equity in Health'. The CN is constantly increasing in size and scope, supporting setting approaches aimed at reducing health inequalities. A detailed description of the CN has not yet been available in English. The CN comprises a total of 66 institutional cooperation partners. Information concerning the structure and activities can be found on a special website. Coordination Centres (CC) have been established in the 16 federal states, for the coordination of all state-specific activities. Funding for the CN and CC is provided by the BZgA, the German statutory sickness funds and by the state-specific ministries of health. These partners also support the continuous quality improvement, which is based on the good-practice criteria developed by the Advisory Committee of the CN. In 2011, the 'Municipal Partner Process (MPP)' has been launched, specifically supporting local partners and integrated life-course approaches focussing on children. In 2015, the focus has been widened to include all age-groups. In July 2015, a new national health law concerning health promotion and prevention has been ratified by the federal Parliament, with a focus on reducing health inequalities. Currently, the details of its implementation are discussed on a nationwide basis. The CN has long advocated for such a law, and today the CN is a well-accepted partner providing concepts, methods and a strong and long-standing network. The article closes with future challenges faced by the CN.

  11. Evaluation of a train-the-trainer program for stable coronary artery disease management in community settings: A pilot study.

    Science.gov (United States)

    Shen, Zhiyun; Jiang, Changying; Chen, Liqun

    2018-02-01

    To evaluate the feasibility and effectiveness of conducting a train-the-trainer (TTT) program for stable coronary artery disease (SCAD) management in community settings. The study involved two steps: (1) tutors trained community nurses as trainers and (2) the community nurses trained patients. 51 community nurses attended a 2-day TTT program and completed questionnaires assessing knowledge, self-efficacy, and satisfaction. By a feasibility and non-randomized control study, 120 SCAD patients were assigned either to intervention group (which received interventions from trained nurses) or control group (which received routine management). Pre- and post-intervention, patients' self-management behaviors and satisfaction were assessed to determine the program's overall impact. Community nurses' knowledge and self-efficacy improved (Pmanagement behaviors (Pmanagement in community settings in China was generally feasible and effective, but many obstacles remain including patients' noncompliance, nurses' busy work schedules, and lack of policy supports. Finding ways to enhance the motivation of community nurses and patients with SCAD are important in implementing community-based TTT programs for SCAD management; further multicenter and randomized control trials are needed. Copyright © 2017 Elsevier B.V. All rights reserved.

  12. A systematic review of Functional Communication Training (FCT) interventions involving augmentative and alternative communication in school settings.

    Science.gov (United States)

    Walker, Virginia L; Lyon, Kristin J; Loman, Sheldon L; Sennott, Samuel

    2018-06-01

    The purpose of this meta-analysis was to summarize single-case intervention studies in which Functional Communication Training (FCT) involving augmentative and alternative communication (AAC) was implemented in school settings. Overall, the findings suggest that FCT involving AAC was effective in reducing challenging behaviour and promoting aided or unaided AAC use among participants with disability. FCT was more effective for the participants who engaged in less severe forms of challenging behaviour prior to intervention. Additionally, FCT was more effective when informed by a descriptive functional behaviour assessment and delivered within inclusive school settings. Implications for practice and directions for future research related to FCT for students who use AAC are addressed.

  13. Evaluation of a pilot training program in alcohol screening, brief intervention, and referral to treatment for nurses in inpatient settings.

    Science.gov (United States)

    Broyles, Lauren M; Gordon, Adam J; Rodriguez, Keri L; Hanusa, Barbara H; Kengor, Caroline; Kraemer, Kevin L

    2013-01-01

    Alcohol screening, brief intervention, and referral to treatment (SBIRT) is a set of clinical strategies for reducing the burden of alcohol-related injury, disease, and disability. SBIRT is typically considered a physician responsibility but calls for interdisciplinary involvement requiring basic SBIRT knowledge and skills training for all healthcare disciplines. The purpose of this pilot study was to design, implement, and evaluate a theory-driven SBIRT training program for nurses in inpatient settings (RN-SBIRT) that was developed through an interdisciplinary collaboration of nursing, medical, and public health professionals and tailored for registered nurses in the inpatient setting. In this three-phase study, we evaluated (1) RN-SBIRT's effectiveness for changing nurses' alcohol-related knowledge, clinical practice, and attitudes and (2) the feasibility of implementing the inpatient curriculum. In a quasi-experimental design, two general medical units at our facility were randomized to receive RN-SBIRT or a self-directed Web site on alcohol-related care. We performed a formative evaluation of RN-SBIRT, guided by the RE-AIM implementation framework. After training, nurses in the experimental condition had significant increases in Role Adequacy for working with drinkers and reported increased performance and increased competence for a greater number of SBIRT care tasks. Despite some scheduling challenges for the nurses to attend RN-SBIRT, nurse stakeholders were highly satisfied with RN-SBIRT. Results suggest that with adequate training and ongoing role support, nurses in inpatient settings could play active roles in interdisciplinary initiatives to address unhealthy alcohol use among hospitalized patients.

  14. Acute Effects of Back Squats on Countermovement Jump Performance Across Multiple Sets of A Contrast Training Protocol in Resistance-Trained Males.

    Science.gov (United States)

    Bauer, Pascal; Sansone, Pierpaolo; Mitter, Benedikt; Makivic, Bojan; Seitz, Laurent B; Tschan, Harald

    2018-01-03

    The present study was designed to evaluate the voluntary post-activation potentiation (PAP) effects of moderate (MI) or high intensity (HI) back squat exercises on countermovement jump (CMJ) performance across multiple sets of a contrast training protocol. Sixty resistance-trained male subjects (age, 23.3 ± 3.3 y; body mass, 86.0 ± 13.9 kg; parallel back squat 1-repetition maximum [1-RM], 155.2 ± 30.0 kg) participated in a randomized, cross-over study. After familiarization, the subjects visited the laboratory on three separate occasions. They performed a contrast PAP protocol comprising three sets of either MI (6×60% of 1-RM) or HI back squats (4x90% of 1-RM) or 20 s of recovery (CTRL) alternated with seven CMJs that were performed at 15 s, and 1, 3, 5, 7, 9 and 11 min after the back squats or recovery. Jump height and relative peak power output recorded with a force platform during MI and HI conditions were compared to those recorded during control condition to calculate the voluntary PAP effect. CMJ performance was decreased immediately after the squats but increased across all three sets of MI and HI between 3 - 7 minutes post-recovery. However, voluntary PAP effects were small or trivial and no difference between the three sets could be found. These findings demonstrate that practitioners can use MI and HI back squats to potentiate CMJs across a contrast training protocol, but a minimum of 3 min of recovery after the squats is needed to benefit from voluntary PAP.

  15. Country-overlapping radiation protection education and training by the CHERNE network; Laenderuebergreifende Strahlenschutzausbildung im Rahmen des CHERNE-Netzwerks

    Energy Technology Data Exchange (ETDEWEB)

    Hoyler, Frieder [Fachhochschule Aachen, Juelich (Germany). Strahlenschutzkursstaette

    2013-09-01

    The CHERNE network is promoting the cooperation between colleges and research facilities at the training of students. The article describes particular study courses in the field of radiation protection. (orig.)

  16. Effects of acute caffeine ingestion on resistance training performance and perceptual responses during repeated sets to failure.

    Science.gov (United States)

    Da Silva, V L; Messias, F R; Zanchi, N E; Gerlinger-Romero, F; Duncan, M J; Guimarães-Ferreira, L

    2015-05-01

    The aim of the present study was to evaluate the effect of oral caffeine ingestion during repeated sets of resistance. Fourteen moderately resistance-trained men (20.9 ± 0.36 years and 77.62 ± 2.07 kg of body weight) ingested a dose of caffeine (5 mg.kg-1) or placebo prior to 3 sets of bench press and 3 sets of leg press exercises, respectively. The study used a double-blind, counterbalanced, crossover design. Repetitions completed and total weight lifted were recorded in each set. Readiness to invest in both physical (RTIPE) and mental (RTIME) effort were assessed prior each set, and rating of perceived exertion (RPE) was recorded after each set. Rest and peak heart rates were determined via telemetry. Caffeine ingestion result in increased number of repetitions to failure in bench press (F[1,13]=6.16, P=0.027) and leg press (F[1,13]=9.33, P=0.009) compared to placebo. The sum of repetitions performed in the 3 sets was 11.60% higher in bench press (26.86 ± 1.74; caffeine: 30.00 ± 1.87; P=0.027) and 19.10% in leg press (placebo: 40.0 ± 4.22; caffeine: 47.64 ± 4.69; P=0.009). Also, RTIME was increased in the caffeine condition both in bench press (F[1,13]=7.02, P=0.02) and in leg press (F[1,13]=5.41, P=0.03). There were no differences in RPE, RTIPE and HR (P>0.05) across conditions. Acute caffeine ingestion can improve performance in repeated sets to failure and increase RTIME in resistance-trained men.

  17. Nutrition in the Early Childhood Setting: Arizona HSST/CDA Competency Based Training Module #15.

    Science.gov (United States)

    Terrell, Ann

    The purpose of this Child Development Associate (CDA) training module is to provide the CDA intern with knowledge of how to use nutrition information with children and parents, as well as how to structure and carry out a nutrition program, including mealtime and food preparation activities. Objectives are presented along with suggested activities…

  18. MHC class I epitope binding prediction trained on small data sets

    DEFF Research Database (Denmark)

    Lundegaard, Claus; Nielsen, Morten; Lamberth, K.

    2004-01-01

    The identification of potential T-cell epitopes is important for development of new human or vetenary vaccines, both considering single protein/subunit vaccines, and for epitope/peptide vaccines as such. The highly diverse MHC class I alleles bind very different peptides, and accurate binding pre...... in situations where only very limited data are available for training....

  19. Developing a Marketing Mind-Set: Training and Mentoring for County Extension Employees

    Science.gov (United States)

    Sneed, Christopher T.; Elizer, Amy Hastings; Hastings, Shirley; Barry, Michael

    2016-01-01

    Marketing the county Extension program is a critical responsibility of the entire county staff. This article describes a unique peer-to-peer training and mentoring program developed to assist county Extension staff in improving marketing skills and successfully developing and implementing a county Extension marketing plan. Data demonstrating…

  20. The Diagnosis of Autism in Community Pediatric Settings: Does Advanced Training Facilitate Practice Change?

    Science.gov (United States)

    Swanson, Amy R.; Warren, Zachary E.; Stone, Wendy L.; Vehorn, Alison C.; Dohrmann, Elizabeth; Humberd, Quentin

    2014-01-01

    The increased prevalence of autism spectrum disorder and documented benefits of early intensive intervention have created a need for flexible systems for determining eligibility for autism-specific services. This study evaluated the effectiveness of a training program designed to enhance autism spectrum disorder identification and assessment…

  1. Trial-Based Functional Analysis and Functional Communication Training in an Early Childhood Setting

    Science.gov (United States)

    Lambert, Joseph M.; Bloom, Sarah E.; Irvin, Jennifer

    2012-01-01

    Problem behavior is common in early childhood special education classrooms. Functional communication training (FCT; Carr & Durand, 1985) may reduce problem behavior but requires identification of its function. The trial-based functional analysis (FA) is a method that can be used to identify problem behavior function in schools. We conducted…

  2. Mindful Parenting Training in Child Psychiatric Settings : Heightened Parental Mindfulness Reduces Parents' and Children's Psychopathology

    NARCIS (Netherlands)

    Meppelink, Renee; de Bruin, Esther I.; Wanders-Mulder, Femy H.; Vennik, Corinne J.; Bogels, Susan M.

    Mindful parenting training is an application of mindfulness-based interventions that allows parents to perceive their children with unbiased and open attention without prejudgment and become more attentive and less reactive in their parenting. This study examined the effectiveness of mindful

  3. The Long-Term Effects of Functional Communication Training Conducted in Young Children's Home Settings

    Science.gov (United States)

    Wacker, David P.; Schieltz, Kelly M.; Berg, Wendy K.; Harding, Jay W.; Padilla Dalmau, Yaniz C.; Lee, John F.

    2017-01-01

    This article describes the results of a series of studies that involved functional communication training (FCT) conducted in children's homes by their parents. The 103 children who participated were six years old or younger, had developmental delays, and engaged in destructive behaviors such as self-injury. The core procedures used in each study…

  4. Sport-specific Outdoor Rehabilitation in a Group Setting : Do the Intentions Match Actual Training Load?

    NARCIS (Netherlands)

    de Bruijn, Jeroen; van der Worp, Henk; Korte, Mark; de Vries, Astrid J; Nijland, Rick; Brink, Michel S

    2017-01-01

    CONTEXT: Previous research has shown a weak relationship between intended and actual training load in various sports. Due to variety in group and content, this relationship is expected to be even weaker during group rehabilitation. OBJECTIVE: The goal of our study was to examine the relationship

  5. Vocational Education and Training Manager Discursive Practices at the Frontline: Alternative Possibilities in a Victorian Setting

    Science.gov (United States)

    Foley, Annette

    2011-01-01

    This article looks at how the neoliberal reform process is affecting the professional identity of frontline managers in the Australian vocational education and training sector. The article examines how frontline managers are required to negotiate their working practices between their understandings and experiences as educators and the new…

  6. The effects of spaced retrieval training in improving hyperphagia of people living with dementia in residential settings.

    Science.gov (United States)

    Hsu, Chia-Ning; Lin, Li-Chan; Wu, Shiao-Chi

    2017-10-01

    To investigate the effectiveness of spaced retrieval for improving hyperphagia in patients with dementia in residential care settings. Although 10-30% of patients with dementia have hyperphagia, most studies have focused on eating difficulties. Only a few studies have focused on hyperphagia. Various memory problems cause hyperphagia in patients with dementia. Spaced retrieval, a cognitive technique for information learning, can be used as a training method to improve memory loss. Recent studies showed that patients who received the training successfully memorised information learned in the training and correctly applied it to their daily lives. Single-blind experiments were performed. The 97 subjects with dementia were recruited from seven institutions. All research participants were stratified into three groups according to cognitive impairment severity and Hyperphagic Behavior Scale scores and then randomly assigned to the experimental and control groups. The experimental group received a six-week one-by-one spaced retrieval training for hyperphagia behaviour. The control group received routine care. After the intervention, the frequency and severity of hyperphagia in the patients with dementia, and food intake were significantly lower in the experimental group than in the control group. However, body mass index did not significantly differ. Our results suggest that the spaced retrieval training could decrease the frequency and severity of hyperphagia in patients with dementia. The content of this training programme is consistent with the normal manner of eating in daily life and is easy for patients to understand and perform. Therefore, it can be applied in residents' daily lives. This study confirms the efficacy of the spaced retrieval training protocol for hyperphagia in patients with dementia. In future studies, the follow-up duration can be increased to determine the long-term effectiveness of the intervention. © 2016 John Wiley & Sons Ltd.

  7. Methodical approach to training of IT-professionals based on networking

    Directory of Open Access Journals (Sweden)

    Vyacheslav V. Zolotarev

    2017-12-01

    Full Text Available Increasing requirements to the content and form of higher education in conditions of digital economy set new tasks for professors: formation of applied competences, the involvement of students in project activities, provision of students’ online support, their individual and project work. The growing load on university professors complicates satisfaction of these requirements. The development of the professors’ network interaction makes it possible to redistribute the load for disciplines methodological provision. The article reveals possibilities of professors’ network interaction by using innovative teaching methods including gaming forms and online courses. The research scientific novelty is to implement the professors’ network interaction and experimental application of innovative teaching methods. Network interaction was carried out through the educational process of students’ preparation in following areas: information security, applied information technology, business informatics.

  8. A Quasi-Experimental Study of the Effects of Teacher Training on Attitudes towards Inclusion Settings

    Science.gov (United States)

    Woodward, Jillian

    2017-01-01

    Least restrictive environment (LRE) is defined as teaching children with disabilities alongside non-disabled children; these children have not been identified as students with disabilities. An inclusion setting is the integration of students with disabilities in the general education setting to provide the least restrictive environment. Inclusion…

  9. Improving the Dominating-Set Routing over Delay-Tolerant Mobile Ad-Hoc Networks via Estimating Node Intermeeting Times

    Directory of Open Access Journals (Sweden)

    Preiss Bruno

    2011-01-01

    Full Text Available With limited coverage of wireless networks and frequent roaming of mobile users, providing a seamless communication service poses a technical challenge. In our previous research, we presented a supernode system architecture that employs the delay-tolerant network (DTN concept to provide seamless communications for roaming users over interconnected heterogeneous wireless networks. Mobile ad hoc networks (MANETs are considered a key component of the supernode system for services over an area not covered by other wireless networks. Within the super node system, a dominating-set routing technique is proposed to improve message delivery over MANETs and to achieve better resource utilization. The performance of the dominating-set routing technique depends on estimation accuracy of the probability of a future contact between nodes. This paper studies how node mobility can be modeled and used to better estimate the probability of a contact. We derive a distribution for the node-to-node intermeeting time and present numerical results to demonstrate that the distribution can be used to improve the dominating-set routing technique performance. Moreover, we investigate how the distribution can be employed to relax the constraints of selecting the dominating-set members in order to improve the system resource utilization.

  10. Evaluation of tactical training in team handball by means of artificial neural networks.

    Science.gov (United States)

    Hassan, Amr; Schrapf, Norbert; Ramadan, Wael; Tilp, Markus

    2017-04-01

    While tactical performance in competition has been analysed extensively, the assessment of training processes of tactical behaviour has rather been neglected in the literature. Therefore, the purpose of this study is to provide a methodology to assess the acquisition and implementation of offensive tactical behaviour in team handball. The use of game analysis software combined with an artificial neural network (ANN) software enabled identifying tactical target patterns from high level junior players based on their positions during offensive actions. These patterns were then trained by an amateur junior handball team (n = 14, 17 (0.5) years)). Following 6 weeks of tactical training an exhibition game was performed where the players were advised to use the target patterns as often as possible. Subsequently, the position data of the game was analysed with an ANN. The test revealed that 58% of the played patterns could be related to the trained target patterns. The similarity between executed patterns and target patterns was assessed by calculating the mean distance between key positions of the players in the game and the target pattern which was 0.49 (0.20) m. In summary, the presented method appears to be a valid instrument to assess tactical training.

  11. QoS-Aware Resource Allocation for Network Virtualization in an Integrated Train Ground Communication System

    Directory of Open Access Journals (Sweden)

    Li Zhu

    2018-01-01

    Full Text Available Urban rail transit plays an increasingly important role in urbanization processes. Communications-Based Train Control (CBTC Systems, Passenger Information Systems (PIS, and Closed Circuit Television (CCTV are key applications of urban rail transit to ensure its normal operation. In existing urban rail transit systems, different applications are deployed with independent train ground communication systems. When the train ground communication systems are built repeatedly, limited wireless spectrum will be wasted, and the maintenance work will also become complicated. In this paper, we design a network virtualization based integrated train ground communication system, in which all the applications in urban rail transit can share the same physical infrastructure. In order to better satisfy the Quality of Service (QoS requirement of each application, this paper proposes a virtual resource allocation algorithm based on QoS guarantee, base station load balance, and application station fairness. Moreover, with the latest achievement of distributed convex optimization, we exploit a novel distributed optimization method based on alternating direction method of multipliers (ADMM to solve the virtual resource allocation problem. Extensive simulation results indicate that the QoS of the designed integrated train ground communication system can be improved significantly using the proposed algorithm.

  12. Training Working Memory in Childhood Enhances Coupling between Frontoparietal Control Network and Task-Related Regions.

    Science.gov (United States)

    Barnes, Jessica J; Nobre, Anna Christina; Woolrich, Mark W; Baker, Kate; Astle, Duncan E

    2016-08-24

    Working memory is a capacity upon which many everyday tasks depend and which constrains a child's educational progress. We show that a child's working memory can be significantly enhanced by intensive computer-based training, relative to a placebo control intervention, in terms of both standardized assessments of working memory and performance on a working memory task performed in a magnetoencephalography scanner. Neurophysiologically, we identified significantly increased cross-frequency phase amplitude coupling in children who completed training. Following training, the coupling between the upper alpha rhythm (at 16 Hz), recorded in superior frontal and parietal cortex, became significantly coupled with high gamma activity (at ∼90 Hz) in inferior temporal cortex. This altered neural network activity associated with cognitive skill enhancement is consistent with a framework in which slower cortical rhythms enable the dynamic regulation of higher-frequency oscillatory activity related to task-related cognitive processes. Whether we can enhance cognitive abilities through intensive training is one of the most controversial topics of cognitive psychology in recent years. This is particularly controversial in childhood, where aspects of cognition, such as working memory, are closely related to school success and are implicated in numerous developmental disorders. We provide the first neurophysiological account of how working memory training may enhance ability in childhood, using a brain recording technique called magnetoencephalography. We borrowed an analysis approach previously used with intracranial recordings in adults, or more typically in other animal models, called "phase amplitude coupling." Copyright © 2016 Barnes et al.

  13. Female Genital Mutilation/Cutting: Innovative Training Approach for Nurse-Midwives in High Prevalent Settings

    Directory of Open Access Journals (Sweden)

    Samuel Kimani

    2018-01-01

    Full Text Available Background. Female genital mutilation/cutting (FGM/C has no medical benefits and is associated with serious health complications. FGM/C including medicalization is illegal in Kenya. Capacity building for nurse-midwives to manage and prevent FGM/C is therefore critical. Objective. Determine the current FGM/C knowledge and effect of training among nurse-midwives using an electronic tool derived from a paper-based quiz on FGM/C among nurse-midwives. Methods. Nurse-midwives n=26 were assessed pre- and post-FGM/C training using a quiz comprising 12 questions. The quiz assessed the following factors: definition, classification, determining factors, epidemiology, medicalization, prevention, health consequences, and nurse-midwives’ roles in FGM/C prevention themes. The scores for individuals and all the questions were computed and compared using SPSS V22. Results. The mean scores for the quiz were 64.8%, improving to 96.2% p<0.05 after training. Before the training, the following proportions of participants correctly answered questions demonstrating their knowledge of types of cutting (84.6%, link with health problems (96.2%, FGM/C-related complications (96.2%, communities that practice FGM/C (61.5%, medicalization (43.6%, reinfibulation (46.2%, dissociation from religion (46.2%, and the law as it relates to FGM/C (46.2%. The participants demonstrated knowledge of FGM/C-related complications with the proportion of nurse-midwives correctly answering questions relating to physical impact (69.2%, psychological impact (69.2%, sexual impact (57.7%, and social impact (38.5%. Additionally, participant awareness of NM roles in managing FGM/C included the following: knowledge of the nurse-midwife as counselor (69.2%, advocate (80.8%, leader (26.9%, role model (42.3%, and caregiver (34.6%. These scores improved significantly after training. Conclusion. Substantial FGM/C-related knowledge was demonstrated by nurse-midwives. They, however, showed challenges in

  14. Adaptive Conflict-Free Optimization of Rule Sets for Network Security Packet Filtering Devices

    Directory of Open Access Journals (Sweden)

    Andrea Baiocchi

    2015-01-01

    Full Text Available Packet filtering and processing rules management in firewalls and security gateways has become commonplace in increasingly complex networks. On one side there is a need to maintain the logic of high level policies, which requires administrators to implement and update a large amount of filtering rules while keeping them conflict-free, that is, avoiding security inconsistencies. On the other side, traffic adaptive optimization of large rule lists is useful for general purpose computers used as filtering devices, without specific designed hardware, to face growing link speeds and to harden filtering devices against DoS and DDoS attacks. Our work joins the two issues in an innovative way and defines a traffic adaptive algorithm to find conflict-free optimized rule sets, by relying on information gathered with traffic logs. The proposed approach suits current technology architectures and exploits available features, like traffic log databases, to minimize the impact of ACO development on the packet filtering devices. We demonstrate the benefit entailed by the proposed algorithm through measurements on a test bed made up of real-life, commercial packet filtering devices.

  15. Set-base dynamical parameter estimation and model invalidation for biochemical reaction networks.

    Science.gov (United States)

    Rumschinski, Philipp; Borchers, Steffen; Bosio, Sandro; Weismantel, Robert; Findeisen, Rolf

    2010-05-25

    Mathematical modeling and analysis have become, for the study of biological and cellular processes, an important complement to experimental research. However, the structural and quantitative knowledge available for such processes is frequently limited, and measurements are often subject to inherent and possibly large uncertainties. This results in competing model hypotheses, whose kinetic parameters may not be experimentally determinable. Discriminating among these alternatives and estimating their kinetic parameters is crucial to improve the understanding of the considered process, and to benefit from the analytical tools at hand. In this work we present a set-based framework that allows to discriminate between competing model hypotheses and to provide guaranteed outer estimates on the model parameters that are consistent with the (possibly sparse and uncertain) experimental measurements. This is obtained by means of exact proofs of model invalidity that exploit the polynomial/rational structure of biochemical reaction networks, and by making use of an efficient strategy to balance solution accuracy and computational effort. The practicability of our approach is illustrated with two case studies. The first study shows that our approach allows to conclusively rule out wrong model hypotheses. The second study focuses on parameter estimation, and shows that the proposed method allows to evaluate the global influence of measurement sparsity, uncertainty, and prior knowledge on the parameter estimates. This can help in designing further experiments leading to improved parameter estimates.

  16. Automatic abdominal multi-organ segmentation using deep convolutional neural network and time-implicit level sets.

    Science.gov (United States)

    Hu, Peijun; Wu, Fa; Peng, Jialin; Bao, Yuanyuan; Chen, Feng; Kong, Dexing

    2017-03-01

    Multi-organ segmentation from CT images is an essential step for computer-aided diagnosis and surgery planning. However, manual delineation of the organs by radiologists is tedious, time-consuming and poorly reproducible. Therefore, we propose a fully automatic method for the segmentation of multiple organs from three-dimensional abdominal CT images. The proposed method employs deep fully convolutional neural networks (CNNs) for organ detection and segmentation, which is further refined by a time-implicit multi-phase evolution method. Firstly, a 3D CNN is trained to automatically localize and delineate the organs of interest with a probability prediction map. The learned probability map provides both subject-specific spatial priors and initialization for subsequent fine segmentation. Then, for the refinement of the multi-organ segmentation, image intensity models, probability priors as well as a disjoint region constraint are incorporated into an unified energy functional. Finally, a novel time-implicit multi-phase level-set algorithm is utilized to efficiently optimize the proposed energy functional model. Our method has been evaluated on 140 abdominal CT scans for the segmentation of four organs (liver, spleen and both kidneys). With respect to the ground truth, average Dice overlap ratios for the liver, spleen and both kidneys are 96.0, 94.2 and 95.4%, respectively, and average symmetric surface distance is less than 1.3 mm for all the segmented organs. The computation time for a CT volume is 125 s in average. The achieved accuracy compares well to state-of-the-art methods with much higher efficiency. A fully automatic method for multi-organ segmentation from abdominal CT images was developed and evaluated. The results demonstrated its potential in clinical usage with high effectiveness, robustness and efficiency.

  17. Training for an effective health and safety committee in a small business setting.

    Science.gov (United States)

    Crollard, Allison; Neitzel, Richard L; Dominguez, Carlos F; Seixas, Noah S

    2013-01-01

    Health and safety committees are often heralded as a key element of successful health and safety programs, and are thought to represent a means of engaging workers in health and safety efforts. While the understanding of the factors that make these committees effective is growing, there are few resources for how to assist committees in developing these characteristics. This paper describes one approach to creating and implementing a training intervention aimed at improving health and safety committee function at one multilingual worksite. Short-term impacts were evaluated via questionnaire and qualitative observations of committee function. Results indicated high satisfaction with the training as well as modest increases in participation, cooperation, role clarity, and comfort with health and safety skills among committee members. The committee also made considerable achievements in establishing new processes for effective function. Similar interventions may be useful in other workplaces to increase health and safety committee success.

  18. Effect of a short training on neonatal face-mask ventilation performance in a low resource setting.

    Directory of Open Access Journals (Sweden)

    Alessandro Mazza

    Full Text Available We assessed whether a short training, effective in a high resource country, was able to improve the quality of face-mask ventilation (FMV in a low resource setting.Local healthcare providers at the Centre Médico-Social, Kouvè, Togo were asked to ventilate a neonatal leak-free manikin before (time-t1 and after (t2 a two-minute training session. Immediately after this section, a further two-minute training with participants aware of the data monitor was offered. Finally, a third 1-minute FMV round (t3 was performed by each participant. Ventilatory parameters were recorded using a computerized system. Primary outcome was the percentage of breaths with relevant mask leak (>25%. Secondary outcomes were percentages of breaths with a low peak inspiratory pressure (PIP35 cm H2O.Twenty-six subjects participated in the study. The percentage of relevant mask leak significantly decreased (p35 cm H2O was 19.5% (SD 32.8% at t1 and 39.2% (SD 37.7% at t2 (padj = 0.27; β = +0.61, SE = 0.36 and significantly decreased (padj = 0.01; β = -1.61, SE = 0.55 to 6.0% (SD 15.4% at t3.A 2-minute training on FMV, effective in a high resource country, had a positive effect also in a low resource setting. FMV performance further improved after an extra 2-minute verbal recall plus real time feedback. Although the training was extended, it still does not cost much time and effort. Further studies are needed to establish if these basic skills are transferred in real patients and if they are maintained over time.

  19. Technical skill set training in natural orifice transluminal endoscopic surgery: how should we approach it?

    LENUS (Irish Health Repository)

    Nugent, Emmeline

    2011-03-01

    The boundaries in minimally invasive techniques are continually being pushed further. Recent years have brought new and exciting changes with the advent of natural orifice transluminal endoscopic surgery. With the evolution of this field of surgery come challenges in the development of new instruments and the actual steps of the procedure. Included in these challenges is the idea of developing a proficiency-based curriculum for training.

  20. The training and use of research assistants for a survey in a third world setting

    Directory of Open Access Journals (Sweden)

    E.F. Hildebrandt

    1991-09-01

    Full Text Available This article describes an approach to gathering data in a black township. It emphasizes the importance of using indigenous interviewers and offers suggestions for their training. Innovative techniques were used to help the field workers to understand and apply the concept of randomization to the streets and houses of their Township. It emphasizes the need to supervise and reinforce the research standards throughout the data collection process.

  1. Effect of passive concentration as instructional set for training enhancement of EEG alpha.

    Science.gov (United States)

    Knox, S S

    1980-12-01

    The technique of passive concentration, employed by autogenic training and Transcendental Meditation for achieving relaxation, was tested here as a technique for enhancing EEG alpha. Of 30 subjects displaying between 15% and 74% alpha in their resting EEGs recruited, 10 had to be eliminated. The remaining 20 constituted two groups. One was instructed only to attempt to maintain a tone indicating alpha but given no information about technique (control group). The other was given additional instructions in passive concentration (experimental group). Both were given four 5-min. trials a day for 4 consecutive days. Heart rate and skin conductance were measured to monitor autonomic arousal. The group receiving instructions in passive concentration had significantly less alpha than the control group, which did not increase amount of alpha above baseline. The reduction of alpha in the experimental group was interpreted as resulting from beginning long training periods (20 min. per day), a practice advocated by Transcendental Meditation but discouraged by autogenic training. It was concluded that the relevance of passive concentration for alpha enhancement is doubtful.

  2. Working memory training in congenitally blind individuals results in an integration of occipital cortex in functional networks.

    Science.gov (United States)

    Gudi-Mindermann, Helene; Rimmele, Johanna M; Nolte, Guido; Bruns, Patrick; Engel, Andreas K; Röder, Brigitte

    2018-08-01

    The functional relevance of crossmodal activation (e.g. auditory activation of occipital brain regions) in congenitally blind individuals is still not fully understood. The present study tested whether the occipital cortex of blind individuals is integrated into a challenged functional network. A working memory (WM) training over four sessions was implemented. Congenitally blind and matched sighted participants were adaptively trained with an n-back task employing either voices (auditory training) or tactile stimuli (tactile training). In addition, a minimally demanding 1-back task served as an active control condition. Power and functional connectivity of EEG activity evolving during the maintenance period of an auditory 2-back task were analyzed, run prior to and after the WM training. Modality-specific (following auditory training) and modality-independent WM training effects (following both auditory and tactile training) were assessed. Improvements in auditory WM were observed in all groups, and blind and sighted individuals did not differ in training gains. Auditory and tactile training of sighted participants led, relative to the active control group, to an increase in fronto-parietal theta-band power, suggesting a training-induced strengthening of the existing modality-independent WM network. No power effects were observed in the blind. Rather, after auditory training the blind showed a decrease in theta-band connectivity between central, parietal, and occipital electrodes compared to the blind tactile training and active control groups. Furthermore, in the blind auditory training increased beta-band connectivity between fronto-parietal, central and occipital electrodes. In the congenitally blind, these findings suggest a stronger integration of occipital areas into the auditory WM network. Copyright © 2018 Elsevier B.V. All rights reserved.

  3. Electricity price forecast using Combinatorial Neural Network trained by a new stochastic search method

    International Nuclear Information System (INIS)

    Abedinia, O.; Amjady, N.; Shafie-khah, M.; Catalão, J.P.S.

    2015-01-01

    Highlights: • Presenting a Combinatorial Neural Network. • Suggesting a new stochastic search method. • Adapting the suggested method as a training mechanism. • Proposing a new forecast strategy. • Testing the proposed strategy on real-world electricity markets. - Abstract: Electricity price forecast is key information for successful operation of electricity market participants. However, the time series of electricity price has nonlinear, non-stationary and volatile behaviour and so its forecast method should have high learning capability to extract the complex input/output mapping function of electricity price. In this paper, a Combinatorial Neural Network (CNN) based forecasting engine is proposed to predict the future values of price data. The CNN-based forecasting engine is equipped with a new training mechanism for optimizing the weights of the CNN. This training mechanism is based on an efficient stochastic search method, which is a modified version of chemical reaction optimization algorithm, giving high learning ability to the CNN. The proposed price forecast strategy is tested on the real-world electricity markets of Pennsylvania–New Jersey–Maryland (PJM) and mainland Spain and its obtained results are extensively compared with the results obtained from several other forecast methods. These comparisons illustrate effectiveness of the proposed strategy.

  4. Pre-trained convolutional neural networks as feature extractors for tuberculosis detection.

    Science.gov (United States)

    Lopes, U K; Valiati, J F

    2017-10-01

    It is estimated that in 2015, approximately 1.8 million people infected by tuberculosis died, most of them in developing countries. Many of those deaths could have been prevented if the disease had been detected at an earlier stage, but the most advanced diagnosis methods are still cost prohibitive for mass adoption. One of the most popular tuberculosis diagnosis methods is the analysis of frontal thoracic radiographs; however, the impact of this method is diminished by the need for individual analysis of each radiography by properly trained radiologists. Significant research can be found on automating diagnosis by applying computational techniques to medical images, thereby eliminating the need for individual image analysis and greatly diminishing overall costs. In addition, recent improvements on deep learning accomplished excellent results classifying images on diverse domains, but its application for tuberculosis diagnosis remains limited. Thus, the focus of this work is to produce an investigation that will advance the research in the area, presenting three proposals to the application of pre-trained convolutional neural networks as feature extractors to detect the disease. The proposals presented in this work are implemented and compared to the current literature. The obtained results are competitive with published works demonstrating the potential of pre-trained convolutional networks as medical image feature extractors. Copyright © 2017 Elsevier Ltd. All rights reserved.

  5. Deep learning quick reference useful hacks for training and optimizing deep neural networks with TensorFlow and Keras

    CERN Document Server

    Bernico, Michael

    2018-01-01

    This book is a practical guide to applying deep neural networks including MLPs, CNNs, LSTMs, and more in Keras and TensorFlow. Packed with useful hacks to solve real-world challenges along with the supported math and theory around each topic, this book will be a quick reference for training and optimize your deep neural networks.

  6. Manifold absolute pressure estimation using neural network with hybrid training algorithm.

    Directory of Open Access Journals (Sweden)

    Mohd Taufiq Muslim

    Full Text Available In a modern small gasoline engine fuel injection system, the load of the engine is estimated based on the measurement of the manifold absolute pressure (MAP sensor, which took place in the intake manifold. This paper present a more economical approach on estimating the MAP by using only the measurements of the throttle position and engine speed, resulting in lower implementation cost. The estimation was done via two-stage multilayer feed-forward neural network by combining Levenberg-Marquardt (LM algorithm, Bayesian Regularization (BR algorithm and Particle Swarm Optimization (PSO algorithm. Based on the results found in 20 runs, the second variant of the hybrid algorithm yields a better network performance than the first variant of hybrid algorithm, LM, LM with BR and PSO by estimating the MAP closely to the simulated MAP values. By using a valid experimental training data, the estimator network that trained with the second variant of the hybrid algorithm showed the best performance among other algorithms when used in an actual retrofit fuel injection system (RFIS. The performance of the estimator was also validated in steady-state and transient condition by showing a closer MAP estimation to the actual value.

  7. Training Deep Convolutional Neural Networks with Resistive Cross-Point Devices.

    Science.gov (United States)

    Gokmen, Tayfun; Onen, Murat; Haensch, Wilfried

    2017-01-01

    In a previous work we have detailed the requirements for obtaining maximal deep learning performance benefit by implementing fully connected deep neural networks (DNN) in the form of arrays of resistive devices. Here we extend the concept of Resistive Processing Unit (RPU) devices to convolutional neural networks (CNNs). We show how to map the convolutional layers to fully connected RPU arrays such that the parallelism of the hardware can be fully utilized in all three cycles of the backpropagation algorithm. We find that the noise and bound limitations imposed by the analog nature of the computations performed on the arrays significantly affect the training accuracy of the CNNs. Noise and bound management techniques are presented that mitigate these problems without introducing any additional complexity in the analog circuits and that can be addressed by the digital circuits. In addition, we discuss digitally programmable update management and device variability reduction techniques that can be used selectively for some of the layers in a CNN. We show that a combination of all those techniques enables a successful application of the RPU concept for training CNNs. The techniques discussed here are more general and can be applied beyond CNN architectures and therefore enables applicability of the RPU approach to a large class of neural network architectures.

  8. Training Deep Convolutional Neural Networks with Resistive Cross-Point Devices

    Directory of Open Access Journals (Sweden)

    Tayfun Gokmen

    2017-10-01

    Full Text Available In a previous work we have detailed the requirements for obtaining maximal deep learning performance benefit by implementing fully connected deep neural networks (DNN in the form of arrays of resistive devices. Here we extend the concept of Resistive Processing Unit (RPU devices to convolutional neural networks (CNNs. We show how to map the convolutional layers to fully connected RPU arrays such that the parallelism of the hardware can be fully utilized in all three cycles of the backpropagation algorithm. We find that the noise and bound limitations imposed by the analog nature of the computations performed on the arrays significantly affect the training accuracy of the CNNs. Noise and bound management techniques are presented that mitigate these problems without introducing any additional complexity in the analog circuits and that can be addressed by the digital circuits. In addition, we discuss digitally programmable update management and device variability reduction techniques that can be used selectively for some of the layers in a CNN. We show that a combination of all those techniques enables a successful application of the RPU concept for training CNNs. The techniques discussed here are more general and can be applied beyond CNN architectures and therefore enables applicability of the RPU approach to a large class of neural network architectures.

  9. EEG signal classification using PSO trained RBF neural network for epilepsy identification

    Directory of Open Access Journals (Sweden)

    Sandeep Kumar Satapathy

    Full Text Available The electroencephalogram (EEG is a low amplitude signal generated in the brain, as a result of information flow during the communication of several neurons. Hence, careful analysis of these signals could be useful in understanding many human brain disorder diseases. One such disease topic is epileptic seizure identification, which can be identified via a classification process of the EEG signal after preprocessing with the discrete wavelet transform (DWT. To classify the EEG signal, we used a radial basis function neural network (RBFNN. As shown herein, the network can be trained to optimize the mean square error (MSE by using a modified particle swarm optimization (PSO algorithm. The key idea behind the modification of PSO is to introduce a method to overcome the problem of slow searching in and around the global optimum solution. The effectiveness of this procedure was verified by an experimental analysis on a benchmark dataset which is publicly available. The result of our experimental analysis revealed that the improvement in the algorithm is significant with respect to RBF trained by gradient descent and canonical PSO. Here, two classes of EEG signals were considered: the first being an epileptic and the other being non-epileptic. The proposed method produced a maximum accuracy of 99% as compared to the other techniques. Keywords: Electroencephalography, Radial basis function neural network, Particle swarm optimization, Discrete wavelet transform, Machine learning

  10. Training Deep Convolutional Neural Networks with Resistive Cross-Point Devices

    Science.gov (United States)

    Gokmen, Tayfun; Onen, Murat; Haensch, Wilfried

    2017-01-01

    In a previous work we have detailed the requirements for obtaining maximal deep learning performance benefit by implementing fully connected deep neural networks (DNN) in the form of arrays of resistive devices. Here we extend the concept of Resistive Processing Unit (RPU) devices to convolutional neural networks (CNNs). We show how to map the convolutional layers to fully connected RPU arrays such that the parallelism of the hardware can be fully utilized in all three cycles of the backpropagation algorithm. We find that the noise and bound limitations imposed by the analog nature of the computations performed on the arrays significantly affect the training accuracy of the CNNs. Noise and bound management techniques are presented that mitigate these problems without introducing any additional complexity in the analog circuits and that can be addressed by the digital circuits. In addition, we discuss digitally programmable update management and device variability reduction techniques that can be used selectively for some of the layers in a CNN. We show that a combination of all those techniques enables a successful application of the RPU concept for training CNNs. The techniques discussed here are more general and can be applied beyond CNN architectures and therefore enables applicability of the RPU approach to a large class of neural network architectures. PMID:29066942

  11. Manifold absolute pressure estimation using neural network with hybrid training algorithm.

    Science.gov (United States)

    Muslim, Mohd Taufiq; Selamat, Hazlina; Alimin, Ahmad Jais; Haniff, Mohamad Fadzli

    2017-01-01

    In a modern small gasoline engine fuel injection system, the load of the engine is estimated based on the measurement of the manifold absolute pressure (MAP) sensor, which took place in the intake manifold. This paper present a more economical approach on estimating the MAP by using only the measurements of the throttle position and engine speed, resulting in lower implementation cost. The estimation was done via two-stage multilayer feed-forward neural network by combining Levenberg-Marquardt (LM) algorithm, Bayesian Regularization (BR) algorithm and Particle Swarm Optimization (PSO) algorithm. Based on the results found in 20 runs, the second variant of the hybrid algorithm yields a better network performance than the first variant of hybrid algorithm, LM, LM with BR and PSO by estimating the MAP closely to the simulated MAP values. By using a valid experimental training data, the estimator network that trained with the second variant of the hybrid algorithm showed the best performance among other algorithms when used in an actual retrofit fuel injection system (RFIS). The performance of the estimator was also validated in steady-state and transient condition by showing a closer MAP estimation to the actual value.

  12. Creating Online Training for Procedures in Global Health with PEARLS (Procedural Education for Adaptation to Resource-Limited Settings).

    Science.gov (United States)

    Bensman, Rachel S; Slusher, Tina M; Butteris, Sabrina M; Pitt, Michael B; On Behalf Of The Sugar Pearls Investigators; Becker, Amanda; Desai, Brinda; George, Alisha; Hagen, Scott; Kiragu, Andrew; Johannsen, Ron; Miller, Kathleen; Rule, Amy; Webber, Sarah

    2017-11-01

    The authors describe a multiinstitutional collaborative project to address a gap in global health training by creating a free online platform to share a curriculum for performing procedures in resource-limited settings. This curriculum called PEARLS (Procedural Education for Adaptation to Resource-Limited Settings) consists of peer-reviewed instructional and demonstration videos describing modifications for performing common pediatric procedures in resource-limited settings. Adaptations range from the creation of a low-cost spacer for inhaled medications to a suction chamber for continued evacuation of a chest tube. By describing the collaborative process, we provide a model for educators in other fields to collate and disseminate procedural modifications adapted for their own specialty and location, ideally expanding this crowd-sourced curriculum to reach a wide audience of trainees and providers in global health.

  13. Using Goal Achievement Training in juvenile justice settings to improve substance use services for youth on community supervision.

    Science.gov (United States)

    Fisher, Jacqueline Horan; Becan, Jennifer E; Harris, Philip W; Nager, Alexis; Baird-Thomas, Connie; Hogue, Aaron; Bartkowski, John P; Wiley, Tisha

    2018-04-30

    The link between substance use and involvement in the juvenile justice system has been well established. Justice-involved youth tend to have higher rates of drug use than their non-offending peers. At the same time, continued use can contribute to an elevated risk of recidivism, which leads to further, and oftentimes more serious, involvement with the juvenile justice system. Because of these high rates of use, the juvenile justice system is well positioned to help identify youth with substance use problems and connect them to treatment. However, research has found that only about 60% of juvenile probation agencies screen all youth for substance involvement, and even fewer provide comprehensive assessment or help youth enroll in substance use treatment. This paper describes an integrated training curriculum that was developed to help juvenile justice agencies improve their continuum of care for youth probationers with substance use problems. Goal Achievement Training (GAT) provides a platform for continuous quality improvement via two sessions delivered onsite to small groups of staff from juvenile justice and behavioral health agencies. In the first session, participants are taught to identify goals and goal steps for addressing identified areas of unmet need (i.e., screening, assessment, and linkage to treatment services). In the second session, participants learn principles and strategies of data-driven decision-making for achieving these goals. This paper highlights GAT as a model for the effective implementation of cost-efficient training strategies designed to increase self-directed quality improvement activities that can be applied to any performance domain within juvenile justice settings. Efforts to monitor implementation fidelity of GAT within the specific context of the juvenile justice settings are highlighted. Challenges to setting the stage for process improvement generally, as well as specific hurdles within juvenile justice settings are discussed

  14. Self-consistent determination of the spike-train power spectrum in a neural network with sparse connectivity

    Directory of Open Access Journals (Sweden)

    Benjamin eDummer

    2014-09-01

    Full Text Available A major source of random variability in cortical networks is the quasi-random arrival of presynaptic action potentials from many other cells. In network studies as well as in the study of the response properties of single cells embedded in a network, synaptic background input is often approximated by Poissonian spike trains. However, the output statistics of the cells is in most cases far from being Poisson. This is inconsistent with the assumption of similar spike-train statistics for pre- and postsynaptic cells in a recurrent network. Here we tackle this problem for the popular class of integrate-and-fire neurons and study a self-consistent statistics of input and output spectra of neural spike trains. Instead of actually using a large network, we use an iterative scheme, in which we simulate a single neuron over several generations. In each of these generations, the neuron is stimulated with surrogate stochastic input that has a similar statistics as the output of the previous generation. For the surrogate input, we employ two distinct approximations: (i a superposition of renewal spike trains with the same interspike interval density as observed in the previous generation and (ii a Gaussian current with a power spectrum proportional to that observed in the previous generation. For input parameters that correspond to balanced input in the network, both the renewal and the Gaussian iteration procedure converge quickly and yield comparable results for the self-consistent spike-train power spectrum. We compare our results to large-scale simulations of a random sparsely connected network of leaky integrate-and-fire neurons (Brunel, J. Comp. Neurosci. 2000 and show that in the asynchronous regime close to a state of balanced synaptic input from the network, our iterative schemes provide excellent approximations to the autocorrelation of spike trains in the recurrent network.

  15. The connection-set algebra--a novel formalism for the representation of connectivity structure in neuronal network models.

    Science.gov (United States)

    Djurfeldt, Mikael

    2012-07-01

    The connection-set algebra (CSA) is a novel and general formalism for the description of connectivity in neuronal network models, from small-scale to large-scale structure. The algebra provides operators to form more complex sets of connections from simpler ones and also provides parameterization of such sets. CSA is expressive enough to describe a wide range of connection patterns, including multiple types of random and/or geometrically dependent connectivity, and can serve as a concise notation for network structure in scientific writing. CSA implementations allow for scalable and efficient representation of connectivity in parallel neuronal network simulators and could even allow for avoiding explicit representation of connections in computer memory. The expressiveness of CSA makes prototyping of network structure easy. A C+ + version of the algebra has been implemented and used in a large-scale neuronal network simulation (Djurfeldt et al., IBM J Res Dev 52(1/2):31-42, 2008b) and an implementation in Python has been publicly released.

  16. Train-Network Interactions and Stability Evaluation in High-Speed Railways--Part II: Influential Factors and Verifications

    DEFF Research Database (Denmark)

    Hu, Haitao; Tao, Haidong; Wang, Xiongfei

    2018-01-01

    Low-frequency oscillation (LFO), harmonic resonance and resonance instability phenomena happened in high speed railways (HSRs) are resulted from the interactions between multiple electric trains and traction network. A train-network interaction system and a unified impedance-based model......, catenary lines and autotransformers (ATs); 3) different numbers and positions of trains and railway lines will also be considered and discussed. In order to validate the theoretical results, the time-domain simulation and experiment system have been conducted. Finally, the differences and the relations...

  17. Do banks differently set their liquidity ratios based on their network characteristics?

    OpenAIRE

    Distinguin, Isabelle; Mahdavi-Ardekani, Aref; Tarazi, Amine

    2016-01-01

    This paper investigates the impact of interbank network topology on bank liquidity ratios. Whereas more emphasis has been put on liquidity requirements by regulators since the global financial crisis of 2007-2008, how differently shaped interbank networks impact individual bank liquidity behavior remains an open issue. We look at how bank interconnectedness within interbank loan and deposit networks affects their decision to hold more or less liquidity during normal times and distress times a...

  18. Do banks differently set their liquidity ratios based on their network characteristics?

    OpenAIRE

    Distinguin , Isabelle; Mahdavi-Ardekani , Aref; Tarazi , Amine

    2017-01-01

    This paper investigates the impact of interbank network topology on bank liquidity ratios. Whereas more emphasis has been put on liquidity requirements by regulators since the global financial crisis of 2007-2008, how differently shaped interbank networks impact individual bank liquidity behavior remains an open issue. We look at how bank interconnectedness within interbank loan and deposit networks affects their decision to hold more or less liquidity during normal times and distress times a...

  19. Acceleration of Deep Neural Network Training with Resistive Cross-Point Devices: Design Considerations

    Directory of Open Access Journals (Sweden)

    Tayfun Gokmen

    2016-07-01

    Full Text Available In recent years, deep neural networks (DNN have demonstrated significant business impact in large scale analysis and classification tasks such as speech recognition, visual object detection, pattern extraction, etc. Training of large DNNs, however, is universally considered as time consuming and computationally intensive task that demands datacenter-scale computational resources recruited for many days. Here we propose a concept of resistive processing unit (RPU devices that can potentially accelerate DNN training by orders of magnitude while using much less power. The proposed RPU device can store and update the weight values locally thus minimizing data movement during training and allowing to fully exploit the locality and the parallelism of the training algorithm. We evaluate the effect of various RPU device features/non-idealities and system parameters on performance in order to derive the device and system level specifications for implementation of an accelerator chip for DNN training in a realistic CMOS-compatible technology. For large DNNs with about 1 billion weights this massively parallel RPU architecture can achieve acceleration factors of 30,000X compared to state-of-the-art microprocessors while providing power efficiency of 84,000 GigaOps/s/W. Problems that currently require days of training on a datacenter-size cluster with thousands of machines can be addressed within hours on a single RPU accelerator. A system consisting of a cluster of RPU accelerators will be able to tackle Big Data problems with trillions of parameters that is impossible to address today like, for example, natural speech recognition and translation between all world languages, real-time analytics on large streams of business and scientific data, integration and analysis of multimodal sensory data flows from a massive number of IoT (Internet of Things sensors.

  20. Acceleration of Deep Neural Network Training with Resistive Cross-Point Devices: Design Considerations.

    Science.gov (United States)

    Gokmen, Tayfun; Vlasov, Yurii

    2016-01-01

    In recent years, deep neural networks (DNN) have demonstrated significant business impact in large scale analysis and classification tasks such as speech recognition, visual object detection, pattern extraction, etc. Training of large DNNs, however, is universally considered as time consuming and computationally intensive task that demands datacenter-scale computational resources recruited for many days. Here we propose a concept of resistive processing unit (RPU) devices that can potentially accelerate DNN training by orders of magnitude while using much less power. The proposed RPU device can store and update the weight values locally thus minimizing data movement during training and allowing to fully exploit the locality and the parallelism of the training algorithm. We evaluate the effect of various RPU device features/non-idealities and system parameters on performance in order to derive the device and system level specifications for implementation of an accelerator chip for DNN training in a realistic CMOS-compatible technology. For large DNNs with about 1 billion weights this massively parallel RPU architecture can achieve acceleration factors of 30, 000 × compared to state-of-the-art microprocessors while providing power efficiency of 84, 000 GigaOps∕s∕W. Problems that currently require days of training on a datacenter-size cluster with thousands of machines can be addressed within hours on a single RPU accelerator. A system consisting of a cluster of RPU accelerators will be able to tackle Big Data problems with trillions of parameters that is impossible to address today like, for example, natural speech recognition and translation between all world languages, real-time analytics on large streams of business and scientific data, integration, and analysis of multimodal sensory data flows from a massive number of IoT (Internet of Things) sensors.

  1. SmartRoads: training Indonesian workers to become road safety ambassadors in industrial and community settings.

    Science.gov (United States)

    Montero, Kerry; Spencer, Graham; Ariens, Bernadette

    2012-06-01

    This paper reports on a programme to improve road safety awareness in an industrial community in the vicinity of Jakarta, in Indonesia. Adapting the model of a successful community and school-based programme in Victoria, in Australia, and using a peer education approach, 16 employees of a major manufacturing company were trained to implement road safety education programmes amongst their peers. Specific target groups for the educators were colleagues, schools and the local community. Over 2 days the employees, from areas as diverse as production, public relations, personnel services, administration and management, learned about road safety facts, causes of traffic casualties, prevention approaches and peer education strategies. They explored and developed strategies to use with their respective target groups and practised health education skills. The newly trained workers received certificates to acknowledge them as 'SmartRoads Ambassadors' and, with follow-up support and development, became road safety educators with a commitment and responsibility to deliver education to their respective work and local communities. This paper argues that the model has potential to provide an effective and locally relevant response to road safety issues in similar communities.

  2. Communication partner training for health care professionals in an inpatient rehabilitation setting: A parallel randomised trial.

    Science.gov (United States)

    Heard, Renee; O'Halloran, Robyn; McKinley, Kathryn

    2017-06-01

    The purpose of this study is to determine if the E-Learning Plus communication partner training (CPT) programme is as effective as the Supported Conversation for Adults with Aphasia (SCA TM ) CPT programme in improving healthcare professionals' confidence and knowledge communicating with patients with aphasia. Forty-eight healthcare professionals working in inpatient rehabilitation participated. Participants were randomised to one of the CPT programmes. The three outcome measures were self-rating of confidence, self-rating of knowledge and a test of knowledge of aphasia. Measures were taken pre-, immediately post- and 3-4 months post-training. Data were analysed using mixed between within ANOVAs. Homogeneity of variance was adequate for self-rating of confidence and test of knowledge of aphasia data to continue analysis. There was a statistically significant difference in self-rating of confidence and knowledge of aphasia for both interventions across time. No statistically significant difference was found between the two interventions. Both CPT interventions were associated with an increase in health care professionals' confidence and knowledge of aphasia, but neither programme was superior. As the E-Learning Plus CPT programme is more accessible and sustainable in the Australian healthcare context, further work will continue on this CPT programme.

  3. Assessment of neural networks training strategies for histomorphometric analysis of synchrotron radiation medical images

    Energy Technology Data Exchange (ETDEWEB)

    Alvarenga de Moura Meneses, Anderson, E-mail: ameneses@lmp.ufrj.b [Federal University of Rio de Janeiro, COPPE, Nuclear Engineering Program, CP 68509, CEP 21.941-972, Rio de Janeiro, RJ (Brazil); IDSIA (Dalle Molle Institute for Artificial Intelligence), University of Lugano (Switzerland); Gomes Pinheiro, Christiano Jorge [State University of Rio de Janeiro, RJ (Brazil); Rancoita, Paola [IDSIA (Dalle Molle Institute for Artificial Intelligence), University of Lugano (Switzerland); Mathematics Department, Universita degli Studi di Milano (Italy); Schaul, Tom; Gambardella, Luca Maria [IDSIA (Dalle Molle Institute for Artificial Intelligence), University of Lugano (Switzerland); Schirru, Roberto [Federal University of Rio de Janeiro, COPPE, Nuclear Engineering Program, CP 68509, CEP 21.941-972, Rio de Janeiro, RJ (Brazil); Barroso, Regina Cely; Oliveira, Luis Fernando de [State University of Rio de Janeiro, RJ (Brazil)

    2010-09-21

    Micro-computed tomography ({mu}CT) obtained by synchrotron radiation (SR) enables magnified images with a high space resolution that might be used as a non-invasive and non-destructive technique for the quantitative analysis of medical images, in particular the histomorphometry (HMM) of bony mass. In the preprocessing of such images, conventional operations such as binarization and morphological filtering are used before calculating the stereological parameters related, for example, to the trabecular bone microarchitecture. However, there is no standardization of methods for HMM based on {mu}CT images, especially the ones obtained with SR X-ray. Notwithstanding the several uses of artificial neural networks (ANNs) in medical imaging, their application to the HMM of SR-{mu}CT medical images is still incipient, despite the potential of both techniques. The contribution of this paper is the assessment and comparison of well-known training algorithms as well as the proposal of training strategies (combinations of training algorithms, sub-image kernel and symmetry information) for feed-forward ANNs in the task of bone pixels recognition in SR-{mu}CT medical images. For a quantitative comparison, the results of a cross validation and a statistical analysis of the results for 36 training strategies are presented. The ANNs demonstrated both very low mean square errors in the validation, and good quality segmentation of the image of interest for application to HMM in SR-{mu}CT medical images.

  4. Assessment of neural networks training strategies for histomorphometric analysis of synchrotron radiation medical images

    International Nuclear Information System (INIS)

    Alvarenga de Moura Meneses, Anderson; Gomes Pinheiro, Christiano Jorge; Rancoita, Paola; Schaul, Tom; Gambardella, Luca Maria; Schirru, Roberto; Barroso, Regina Cely; Oliveira, Luis Fernando de

    2010-01-01

    Micro-computed tomography (μCT) obtained by synchrotron radiation (SR) enables magnified images with a high space resolution that might be used as a non-invasive and non-destructive technique for the quantitative analysis of medical images, in particular the histomorphometry (HMM) of bony mass. In the preprocessing of such images, conventional operations such as binarization and morphological filtering are used before calculating the stereological parameters related, for example, to the trabecular bone microarchitecture. However, there is no standardization of methods for HMM based on μCT images, especially the ones obtained with SR X-ray. Notwithstanding the several uses of artificial neural networks (ANNs) in medical imaging, their application to the HMM of SR-μCT medical images is still incipient, despite the potential of both techniques. The contribution of this paper is the assessment and comparison of well-known training algorithms as well as the proposal of training strategies (combinations of training algorithms, sub-image kernel and symmetry information) for feed-forward ANNs in the task of bone pixels recognition in SR-μCT medical images. For a quantitative comparison, the results of a cross validation and a statistical analysis of the results for 36 training strategies are presented. The ANNs demonstrated both very low mean square errors in the validation, and good quality segmentation of the image of interest for application to HMM in SR-μCT medical images.

  5. A comparative study of breast cancer diagnosis based on neural network ensemble via improved training algorithms.

    Science.gov (United States)

    Azami, Hamed; Escudero, Javier

    2015-08-01

    Breast cancer is one of the most common types of cancer in women all over the world. Early diagnosis of this kind of cancer can significantly increase the chances of long-term survival. Since diagnosis of breast cancer is a complex problem, neural network (NN) approaches have been used as a promising solution. Considering the low speed of the back-propagation (BP) algorithm to train a feed-forward NN, we consider a number of improved NN trainings for the Wisconsin breast cancer dataset: BP with momentum, BP with adaptive learning rate, BP with adaptive learning rate and momentum, Polak-Ribikre conjugate gradient algorithm (CGA), Fletcher-Reeves CGA, Powell-Beale CGA, scaled CGA, resilient BP (RBP), one-step secant and quasi-Newton methods. An NN ensemble, which is a learning paradigm to combine a number of NN outputs, is used to improve the accuracy of the classification task. Results demonstrate that NN ensemble-based classification methods have better performance than NN-based algorithms. The highest overall average accuracy is 97.68% obtained by NN ensemble trained by RBP for 50%-50% training-test evaluation method.

  6. Pre-Trained Neural Networks used for Non-Linear State Estimation

    DEFF Research Database (Denmark)

    Bayramoglu, Enis; Andersen, Nils Axel; Ravn, Ole

    2011-01-01

    of the paramters in the distribution. This transformation is approximated by a neural network using offline training, which is based on monte carlo sampling. In the paper, there will also be presented a method to construct a flexible distributions well suited for covering the effect of the non-linearities......The paper focuses on nonlinear state estimation assuming non-Gaussian distributions of the states and the disturbances. The posterior distribution and the aposteriori distribution is described by a chosen family of paramtric distributions. The state transformation then results in a transformation...

  7. Training MA Psychologists for Work in Rural Settings: Issues and Models.

    Science.gov (United States)

    Keller, Peter A.

    Despite the assumptions some have naively made about various stresses and the quality of life associated with rural settings, most who have studied people residing in rural areas would acknowledge the strong need for mental health services. However psychologists, like most other health care professionals prefer the amenities of more metropolitan…

  8. GLANAM (Glaciated North Atlantic Margins): A Marie Curie Initial Training Network between Norway, the UK & Denmark

    Science.gov (United States)

    Petter Sejrup, Hans; Oline Hjelstuen, Berit

    2015-04-01

    GLANAM (Glaciated North Atlantic Margins) is an Initial Training Network (ITN) funded under the EU Marie Curie Programme. It comprises 10 research partners from Norway, UK and Denmark, including 7 University research teams, 1 industrial full partner and 2 industrial associate partners. The GLANAM network will employ and train 15 early career researchers (Fellows). The aim of GLANAM is to improve the career prospects and development of young researchers in both the public and private sector within the field of earth science, focusing on North Atlantic glaciated margins. The young scientists will perform multi-disciplinary research and receive training in geophysics, remote sensing, GIS, sedimentology, geomorphology, stratigraphy, geochemistry and numerical modeling through three interconnected work packages that collectively address knowledge gaps related to the large, glacial age, sedimentary depocentres on the North Atlantic margin. The 15 Fellows will work on projects that geographically extend from Ireland in the south to the High Arctic. Filling these gaps will not only result in major new insights regarding glacial age processes on continental margins in general, but will also provide paleoclimate information essential for understanding the role of marine-based ice sheets in the climate system and for the testing of climate models. GLANAM brings together leading European research groups working on glaciated margins in a coordinated and collaborative research and training project. Focusing on the North Atlantic margins, this coordinated approach will lead to a major advance in the understanding of glaciated margins more widely and will fundamentally strengthen European research and build capacity in this field.

  9. Change in power output across a high-repetition set of bench throws and jump squats in highly trained athletes.

    Science.gov (United States)

    Baker, Daniel G; Newton, Robert U

    2007-11-01

    Athletes experienced in maximal-power and power-endurance training performed 1 set of 2 common power training exercises in an effort to determine the effects of moderately high repetitions upon power output levels throughout the set. Twenty-four and 15 athletes, respectively, performed a set of 10 repetitions in both the bench throw (BT P60) and jump squat exercise (JS P60) with a resistance of 60 kg. For both exercises, power output was highest on either the second (JS P60) or the third repetition (BT P60) and was then maintained until the fifth repetition. Significant declines in power output occurred from the sixth repetition onwards until the 10th repetition (11.2% for BT P60 and 5% for JS P60 by the 10th repetition). These findings suggest that athletes attempting to increase maximal power limit their repetitions to 2 to 5 when using resistances of 35 to 45% 1RM in these exercises.

  10. Experience with low-cost telemedicine in three different settings. Recommendations based on a proposed framework for network performance evaluation

    Science.gov (United States)

    Wootton, Richard; Vladzymyrskyy, Anton; Zolfo, Maria; Bonnardot, Laurent

    2011-01-01

    Background Telemedicine has been used for many years to support doctors in the developing world. Several networks provide services in different settings and in different ways. However, to draw conclusions about which telemedicine networks are successful requires a method of evaluating them. No general consensus or validated framework exists for this purpose. Objective To define a basic method of performance measurement that can be used to improve and compare teleconsultation networks; to employ the proposed framework in an evaluation of three existing networks; to make recommendations about the future implementation and follow-up of such networks. Methods Analysis based on the experience of three telemedicine networks (in operation for 7–10 years) that provide services to doctors in low-resource settings and which employ the same basic design. Findings Although there are many possible indicators and metrics that might be relevant, five measures for each of the three user groups appear to be sufficient for the proposed framework. In addition, from the societal perspective, information about clinical- and cost-effectiveness is also required. The proposed performance measurement framework was applied to three mature telemedicine networks. Despite their differences in terms of activity, size and objectives, their performance in certain respects is very similar. For example, the time to first reply from an expert is about 24 hours for each network. Although all three networks had systems in place to collect data from the user perspective, none of them collected information about the coordinator's time required or about ease of system usage. They had only limited information about quality and cost. Conclusion Measuring the performance of a telemedicine network is essential in understanding whether the network is working as intended and what effect it is having. Based on long-term field experience, the suggested framework is a practical tool that will permit

  11. Experience with low-cost telemedicine in three different settings. Recommendations based on a proposed framework for network performance evaluation

    Directory of Open Access Journals (Sweden)

    Richard Wootton

    2011-12-01

    Full Text Available Telemedicine has been used for many years to support doctors in the developing world. Several networks provide services in different settings and in different ways. However, to draw conclusions about which telemedicine networks are successful requires a method of evaluating them. No general consensus or validated framework exists for this purpose.To define a basic method of performance measurement that can be used to improve and compare teleconsultation networks; to employ the proposed framework in an evaluation of three existing networks; to make recommendations about the future implementation and follow-up of such networks.Analysis based on the experience of three telemedicine networks (in operation for 7–10 years that provide services to doctors in low-resource settings and which employ the same basic design.Although there are many possible indicators and metrics that might be relevant, five measures for each of the three user groups appear to be sufficient for the proposed framework. In addition, from the societal perspective, information about clinical- and cost-effectiveness is also required. The proposed performance measurement framework was applied to three mature telemedicine networks. Despite their differences in terms of activity, size and objectives, their performance in certain respects is very similar. For example, the time to first reply from an expert is about 24 hours for each network. Although all three networks had systems in place to collect data from the user perspective, none of them collected information about the coordinator's time required or about ease of system usage. They had only limited information about quality and cost.Measuring the performance of a telemedicine network is essential in understanding whether the network is working as intended and what effect it is having. Based on long-term field experience, the suggested framework is a practical tool that will permit organisations to assess the performance of

  12. Spreading of Excellence in SARNET Network on Severe Accidents: The Education and Training Programme

    Directory of Open Access Journals (Sweden)

    Sandro Paci

    2012-01-01

    Full Text Available The SARNET2 (severe accidents Research NETwork of Excellence project started in April 2009 for 4 years in the 7th Framework Programme (FP7 of the European Commission (EC, following a similar first project in FP6. Forty-seven organisations from 24 countries network their capacities of research in the severe accident (SA field inside SARNET to resolve the most important remaining uncertainties and safety issues on SA in water-cooled nuclear power plants (NPPs. The network includes a large majority of the European actors involved in SA research plus a few non-European relevant ones. The “Education and Training” programme in SARNET is a series of actions foreseen in this network for the “spreading of excellence.” It is focused on raising the competence level of Master and Ph.D. students and young researchers engaged in SA research and on organizing information/training courses for NPP staff or regulatory authorities (but also for researchers interested in SA management procedures.

  13. Comparative Analysis of Neural Network Training Methods in Real-time Radiotherapy

    Directory of Open Access Journals (Sweden)

    Nouri S.

    2017-03-01

    Full Text Available Background: The motions of body and tumor in some regions such as chest during radiotherapy treatments are one of the major concerns protecting normal tissues against high doses. By using real-time radiotherapy technique, it is possible to increase the accuracy of delivered dose to the tumor region by means of tracing markers on the body of patients. Objective: This study evaluates the accuracy of some artificial intelligence methods including neural network and those of combination with genetic algorithm as well as particle swarm optimization (PSO estimating tumor positions in real-time radiotherapy. Method: One hundred recorded signals of three external markers were used as input data. The signals from 3 markers thorough 10 breathing cycles of a patient treated via a cyber-knife for a lung tumor were used as data input. Then, neural network method and its combination with genetic or PSO algorithms were applied determining the tumor locations using MATLAB© software program. Results: The accuracies were obtained 0.8%, 12% and 14% in neural network, genetic and particle swarm optimization algorithms, respectively. Conclusion: The internal target volume (ITV should be determined based on the applied neural network algorithm on training steps.

  14. Long-term intensive gymnastic training induced changes in intra- and inter-network functional connectivity: an independent component analysis.

    Science.gov (United States)

    Huang, Huiyuan; Wang, Junjing; Seger, Carol; Lu, Min; Deng, Feng; Wu, Xiaoyan; He, Yuan; Niu, Chen; Wang, Jun; Huang, Ruiwang

    2018-01-01

    Long-term intensive gymnastic training can induce brain structural and functional reorganization. Previous studies have identified structural and functional network differences between world class gymnasts (WCGs) and non-athletes at the whole-brain level. However, it is still unclear how interactions within and between functional networks are affected by long-term intensive gymnastic training. We examined both intra- and inter-network functional connectivity of gymnasts relative to non-athletes using resting-state fMRI (R-fMRI). R-fMRI data were acquired from 13 WCGs and 14 non-athlete controls. Group-independent component analysis (ICA) was adopted to decompose the R-fMRI data into spatial independent components and associated time courses. An automatic component identification method was used to identify components of interest associated with resting-state networks (RSNs). We identified nine RSNs, the basal ganglia network (BG), sensorimotor network (SMN), cerebellum (CB), anterior and posterior default mode networks (aDMN/pDMN), left and right fronto-parietal networks (lFPN/rFPN), primary visual network (PVN), and extrastriate visual network (EVN). Statistical analyses revealed that the intra-network functional connectivity was significantly decreased within the BG, aDMN, lFPN, and rFPN, but increased within the EVN in the WCGs compared to the controls. In addition, the WCGs showed uniformly decreased inter-network functional connectivity between SMN and BG, CB, and PVN, BG and PVN, and pDMN and rFPN compared to the controls. We interpret this generally weaker intra- and inter-network functional connectivity in WCGs during the resting state as a result of greater efficiency in the WCGs' brain associated with long-term motor skill training.

  15. Tuning of spinal networks to frequency components of spike trains in individual afferents.

    Science.gov (United States)

    Koerber, H R; Seymour, A W; Mendell, L M

    1991-10-01

    Cord dorsum potentials (CDPs) evoked by primary afferent fiber stimulation reflect the response of postsynaptic dorsal horn neurons. The properties of these CDPs have been shown to vary in accordance with the type of primary afferent fiber stimulated. The purpose of the present study was to determine the relationships between frequency modulation of the afferent input trains, the amplitude modulation of the evoked CDPs, and the type of primary afferent stimulated. The somata of individual primary afferent fibers were impaled in the L7 dorsal root ganglion of alpha-chloralose-anesthetized cats. Action potentials (APs) were evoked in single identified afferents via the intracellular microelectrode while simultaneously recording the response of dorsal horn neurons as CDPs, or activity of individual target interneurons recorded extracellularly or intracellularly. APs were evoked in afferents using temporal patterns identical to the responses of selected afferents to natural stimulation of their receptive fields. Two such physiologically realistic trains, one recorded from a hair follicle and the other from a slowly adapting type 1 receptor, were chosen as standard test trains. Modulation of CDP amplitude in response to this frequency-modulated afferent activity varied according to the type of peripheral mechanoreceptor innervated. Dorsal horn networks driven by A beta afferents innervating hair follicles, rapidly adapting pad (Krause end bulb), and field receptors seemed "tuned" to amplify the onset of activity in single afferents. Networks driven by afferents innervating down hair follicles and pacinian corpuscles required more high-frequency activity to elicit their peak response. Dorsal horn networks driven by afferents innervating slowly adapting receptors including high-threshold mechanoreceptors exhibited some sensitivity to the instantaneous frequency, but in general they reproduced the activity in the afferent fiber much more faithfully. Responses of

  16. Primary health care service delivery networks for the prevention and management of type 2 diabetes: using social network methods to describe interorganisational collaboration in a rural setting.

    Science.gov (United States)

    McDonald, Julie; Jayasuriya, Rohan; Harris, Mark Fort

    2011-01-01

    Adults with type 2 diabetes or with behavioural risk factors require comprehensive and well coordinated responses from a range of health care providers who often work in different organisational settings. This study examines three types of collaborative links between organisations involved in a rural setting. Social network methods were employed using survey data on three types of links, and data was collected from a purposive sample of 17 organisations representing the major provider types. The analysis included a mix of unconfirmed and confirmed links, and network measures. General practices were the most influential provider group in initiating referrals, and they referred to the broadest range of organisations in the network. Team care arrangements formed a small part of the general practice referral network. They were used more for access to private sector allied health care providers and less for sharing care with public sector health services. Involvement in joint programs/activities was limited to public and non-government sector services, with no participation from the private sector. The patterns of interactions suggest that informal referral networks provide access to services and coordination of care for individual patients with diabetes. Two population subgroups would benefit from more proactive approaches to ensure equitable access to services and coordination of care across organisational boundaries: people with more complex health care needs and people at risk of developing diabetes.

  17. Co-trained convolutional neural networks for automated detection of prostate cancer in multi-parametric MRI.

    Science.gov (United States)

    Yang, Xin; Liu, Chaoyue; Wang, Zhiwei; Yang, Jun; Min, Hung Le; Wang, Liang; Cheng, Kwang-Ting Tim

    2017-12-01

    Multi-parameter magnetic resonance imaging (mp-MRI) is increasingly popular for prostate cancer (PCa) detection and diagnosis. However, interpreting mp-MRI data which typically contains multiple unregistered 3D sequences, e.g. apparent diffusion coefficient (ADC) and T2-weighted (T2w) images, is time-consuming and demands special expertise, limiting its usage for large-scale PCa screening. Therefore, solutions to computer-aided detection of PCa in mp-MRI images are highly desirable. Most recent advances in automated methods for PCa detection employ a handcrafted feature based two-stage classification flow, i.e. voxel-level classification followed by a region-level classification. This work presents an automated PCa detection system which can concurrently identify the presence of PCa in an image and localize lesions based on deep convolutional neural network (CNN) features and a single-stage SVM classifier. Specifically, the developed co-trained CNNs consist of two parallel convolutional networks for ADC and T2w images respectively. Each network is trained using images of a single modality in a weakly-supervised manner by providing a set of prostate images with image-level labels indicating only the presence of PCa without priors of lesions' locations. Discriminative visual patterns of lesions can be learned effectively from clutters of prostate and surrounding tissues. A cancer response map with each pixel indicating the likelihood to be cancerous is explicitly generated at the last convolutional layer of the network for each modality. A new back-propagated error E is defined to enforce both optimized classification results and consistent cancer response maps for different modalities, which help capture highly representative PCa-relevant features during the CNN feature learning process. The CNN features of each modality are concatenated and fed into a SVM classifier. For images which are classified to contain cancers, non-maximum suppression and adaptive

  18. High-Speed Rail Train Timetabling Problem: A Time-Space Network Based Method with an Improved Branch-and-Price Algorithm

    Directory of Open Access Journals (Sweden)

    Bisheng He

    2014-01-01

    Full Text Available A time-space network based optimization method is designed for high-speed rail train timetabling problem to improve the service level of the high-speed rail. The general time-space path cost is presented which considers both the train travel time and the high-speed rail operation requirements: (1 service frequency requirement; (2 stopping plan adjustment; and (3 priority of train types. Train timetabling problem based on time-space path aims to minimize the total general time-space path cost of all trains. An improved branch-and-price algorithm is applied to solve the large scale integer programming problem. When dealing with the algorithm, a rapid branching and node selection for branch-and-price tree and a heuristic train time-space path generation for column generation are adopted to speed up the algorithm computation time. The computational results of a set of experiments on China’s high-speed rail system are presented with the discussions about the model validation, the effectiveness of the general time-space path cost, and the improved branch-and-price algorithm.

  19. Training With Curved Laparoscopic Instruments in Single-Port Setting Improves Performance Using Straight Instruments: A Prospective Randomized Simulation Study.

    Science.gov (United States)

    Lukovich, Peter; Sionov, Valery Ben; Kakucs, Timea

    2016-01-01

    Lately single-port surgery is becoming a widespread procedure, but it is more difficult than conventional laparoscopy owing to the lack of triangulation. Although, these operations are also possible with standard laparoscopic instruments, curved instruments are being developed. The aims of the study were to identify the effect of training on a box trainer in single-port setting on the quality of acquired skills, and transferred with the straight and curved instruments for the basic laparoscopic tasks, and highlight the importance of a special laparoscopic training curriculum. A prospective study on a box trainer in single-port setting was conducted using 2 groups. Each group performed 2 tasks on the box trainer in single-port setting. Group-S used conventional straight laparoscopic instruments, and Group-C used curved laparoscopic instruments. Learning curves were obtained by daily measurements recorded in 7-day sessions. On the last day, the 2 groups changed instruments between each other. 1st Department of Surgery, Semmelweis University of Medicine from Budapest, Hungary, a university teaching hospital. In all, 20 fifth-year medical students were randomized into 2 groups. None of them had any laparoscopic or endoscopic experience. Participation was voluntary. Although Group-S performed all tasks significantly faster than Group-C on the first day, the difference proved to be nonsignificant on the last day. All participants achieved significantly shorter task completion time on the last day than on the first day, regardless of the instrument they used. Group-S showed improvement of 63.5%, and Group-C 69.0% improvement by the end of the session. After swapping the instruments, Group-S reached significantly higher task completion time with curved instruments, whereas Group-C showed further progression of 8.9% with straight instruments. Training with curved instruments in a single-port setting allows for a better acquisition of skills in a shorter period. For this

  20. Integrated Optimization of Service-Oriented Train Plan and Schedule on Intercity Rail Network with Varying Demand

    Directory of Open Access Journals (Sweden)

    Wenliang Zhou

    2015-01-01

    Full Text Available For a better service level of a train operating plan, we propose an integrated optimization method of train planning and train scheduling, which generally are optimized, respectively. Based on the cost analysis of both passengers travelling and enterprises operation, and the constraint analysis of trains operation, we construct a multiobjective function and build an integrated optimization model with the aim of reducing both passenger travel costs and enterprise operating costs. Then, a solving algorithm is established based on the simulated annealing algorithm. Finally, using as an example the Changzhutan intercity rail network, as an example we analyze the optimized results and the influence of the model parameters on the results.

  1. A multi-radio, multi-hop ad-hoc radio communication network for Communications-Based Train Control (CBTC)

    DEFF Research Database (Denmark)

    Farooq, Jahanzeb; Bro, Lars; Karstensen, Rasmus Thystrup

    2018-01-01

    Communications-Based Train Control (CBTC) is a modern signalling system that uses radio communication to transfer train control information between train and wayside. The trackside networks in these systems are mostly based on conventional infrastructure Wi-Fi (IEEE 802.11). It means a train has...... to continuously associate (i.e. perform handshake) with the trackside Wi-Fi Access Points (AP) as it moves, which incurs communication delays. Additionally, these APs are connected to the wayside infrastructure via optical fiber cables that incurs huge costs. This paper presents a novel design in which trackside...

  2. Sequential computation of elementary modes and minimal cut sets in genome-scale metabolic networks using alternate integer linear programming

    Energy Technology Data Exchange (ETDEWEB)

    Song, Hyun-Seob; Goldberg, Noam; Mahajan, Ashutosh; Ramkrishna, Doraiswami

    2017-03-27

    Elementary (flux) modes (EMs) have served as a valuable tool for investigating structural and functional properties of metabolic networks. Identification of the full set of EMs in genome-scale networks remains challenging due to combinatorial explosion of EMs in complex networks. It is often, however, that only a small subset of relevant EMs needs to be known, for which optimization-based sequential computation is a useful alternative. Most of the currently available methods along this line are based on the iterative use of mixed integer linear programming (MILP), the effectiveness of which significantly deteriorates as the number of iterations builds up. To alleviate the computational burden associated with the MILP implementation, we here present a novel optimization algorithm termed alternate integer linear programming (AILP). Results: Our algorithm was designed to iteratively solve a pair of integer programming (IP) and linear programming (LP) to compute EMs in a sequential manner. In each step, the IP identifies a minimal subset of reactions, the deletion of which disables all previously identified EMs. Thus, a subsequent LP solution subject to this reaction deletion constraint becomes a distinct EM. In cases where no feasible LP solution is available, IP-derived reaction deletion sets represent minimal cut sets (MCSs). Despite the additional computation of MCSs, AILP achieved significant time reduction in computing EMs by orders of magnitude. The proposed AILP algorithm not only offers a computational advantage in the EM analysis of genome-scale networks, but also improves the understanding of the linkage between EMs and MCSs.

  3. Evaluating the influence of goal setting on intravenous catheterization skill acquisition and transfer in a hybrid simulation training context.

    Science.gov (United States)

    Brydges, Ryan; Mallette, Claire; Pollex, Heather; Carnahan, Heather; Dubrowski, Adam

    2012-08-01

    Educators often simplify complex tasks by setting learning objectives that focus trainees on isolated skills rather than the holistic task. We designed 2 sets of learning objectives for intravenous catheterization using goal setting theory. We hypothesized that setting holistic goals related to technical, cognitive, and communication skills would result in superior holistic performance, whereas setting isolated goals related to technical skills would result in superior technical performance. We randomly assigned practicing health care professionals to set holistic (n = 14) or isolated (n = 15) goals. All watched an instructional video and studied a list of 9 goals specific to their group. Participants practiced independently in a hybrid simulation (standardized patient combined with an arm simulator). The first and the last practice trials were videotaped for analysis. One-week later, participants completed a transfer test in another hybrid simulation scenario. Blinded experts evaluated performance on all 3 trials using the Direct Observation of Procedural Skills tool. The holistic group scored higher than the isolated group on the holistic Direct Observation of Procedural Skills score for all 3 trials [mean (SD), 45.0 (9.16) vs. 38.4 (9.17); P = 0.01]. The isolated group did not perform better than the holistic group on the technical skills score [10.3 (2.73) vs. 11.6 (3.01); P = 0.11]. Our results suggest that asking learners to set holistic goals did not interfere with their attaining competent holistic and technical skills during hybrid simulation training. This exploratory trial provides preliminary evidence for how to consider integrating hybrid simulation into medical curricula and for the design of learning goals in simulation-based education.

  4. Neural network hydrological modelling: on questions of over-fitting, over-training and over-parameterisation

    Science.gov (United States)

    Abrahart, R. J.; Dawson, C. W.; Heppenstall, A. J.; See, L. M.

    2009-04-01

    The most critical issue in developing a neural network model is generalisation: how well will the preferred solution perform when it is applied to unseen datasets? The reported experiments used far-reaching sequences of model architectures and training periods to investigate the potential damage that could result from the impact of several interrelated items: (i) over-fitting - a machine learning concept related to exceeding some optimal architectural size; (ii) over-training - a machine learning concept related to the amount of adjustment that is applied to a specific model - based on the understanding that too much fine-tuning might result in a model that had accommodated random aspects of its training dataset - items that had no causal relationship to the target function; and (iii) over-parameterisation - a statistical modelling concept that is used to restrict the number of parameters in a model so as to match the information content of its calibration dataset. The last item in this triplet stems from an understanding that excessive computational complexities might permit an absurd and false solution to be fitted to the available material. Numerous feedforward multilayered perceptrons were trialled and tested. Two different methods of model construction were also compared and contrasted: (i) traditional Backpropagation of Error; and (ii) state-of-the-art Symbiotic Adaptive Neuro-Evolution. Modelling solutions were developed using the reported experimental set ups of Gaume & Gosset (2003). The models were applied to a near-linear hydrological modelling scenario in which past upstream and past downstream discharge records were used to forecast current discharge at the downstream gauging station [CS1: River Marne]; and a non-linear hydrological modelling scenario in which past river discharge measurements and past local meteorological records (precipitation and evaporation) were used to forecast current discharge at the river gauging station [CS2: Le Sauzay].

  5. Comparisons of complex network based models and real train flow model to analyze Chinese railway vulnerability

    International Nuclear Information System (INIS)

    Ouyang, Min; Zhao, Lijing; Hong, Liu; Pan, Zhezhe

    2014-01-01

    Recently numerous studies have applied complex network based models to study the performance and vulnerability of infrastructure systems under various types of attacks and hazards. But how effective are these models to capture their real performance response is still a question worthy of research. Taking the Chinese railway system as an example, this paper selects three typical complex network based models, including purely topological model (PTM), purely shortest path model (PSPM), and weight (link length) based shortest path model (WBSPM), to analyze railway accessibility and flow-based vulnerability and compare their results with those from the real train flow model (RTFM). The results show that the WBSPM can produce the train routines with 83% stations and 77% railway links identical to the real routines and can approach the RTFM the best for railway vulnerability under both single and multiple component failures. The correlation coefficient for accessibility vulnerability from WBSPM and RTFM under single station failures is 0.96 while it is 0.92 for flow-based vulnerability; under multiple station failures, where each station has the same failure probability fp, the WBSPM can produce almost identical vulnerability results with those from the RTFM under almost all failures scenarios when fp is larger than 0.62 for accessibility vulnerability and 0.86 for flow-based vulnerability

  6. Path selection rules for droplet trains in single-lane microfluidic networks

    Science.gov (United States)

    Amon, A.; Schmit, A.; Salkin, L.; Courbin, L.; Panizza, P.

    2013-07-01

    We investigate the transport of periodic trains of droplets through microfluidic networks having one inlet, one outlet, and nodes consisting of T junctions. Variations of the dilution of the trains, i.e., the distance between drops, reveal the existence of various hydrodynamic regimes characterized by the number of preferential paths taken by the drops. As the dilution increases, this number continuously decreases until only one path remains explored. Building on a continuous approach used to treat droplet traffic through a single asymmetric loop, we determine selection rules for the paths taken by the drops and we predict the variations of the fraction of droplets taking these paths with the parameters at play including the dilution. Our results show that as dilution decreases, the paths are selected according to the ascending order of their hydrodynamic resistance in the absence of droplets. The dynamics of these systems controlled by time-delayed feedback is complex: We observe a succession of periodic regimes separated by a wealth of bifurcations as the dilution is varied. In contrast to droplet traffic in single asymmetric loops, the dynamical behavior in networks of loops is sensitive to initial conditions because of extra degrees of freedom.

  7. A modified backpropagation algorithm for training neural networks on data with error bars

    International Nuclear Information System (INIS)

    Gernoth, K.A.; Clark, J.W.

    1994-08-01

    A method is proposed for training multilayer feedforward neural networks on data contaminated with noise. Specifically, we consider the case that the artificial neural system is required to learn a physical mapping when the available values of the target variable are subject to experimental uncertainties, but are characterized by error bars. The proposed method, based on maximum likelihood criterion for parameter estimation, involves simple modifications of the on-line backpropagation learning algorithm. These include incorporation of the error-bar assignments in a pattern-specific learning rate, together with epochal updating of a new measure of model accuracy that replaces the usual mean-square error. The extended backpropagation algorithm is successfully tested on two problems relevant to the modelling of atomic-mass systematics by neural networks. Provided the underlying mapping is reasonably smooth, neural nets trained with the new procedure are able to learn the true function to a good approximation even in the presence of high levels of Gaussian noise. (author). 26 refs, 2 figs, 5 tabs

  8. The impact of goal setting and goal orientation on performance during a clerkship surgical skills training program.

    Science.gov (United States)

    Gardner, Aimee K; Diesen, Diana L; Hogg, Deborah; Huerta, Sergio

    2016-02-01

    The purpose of this study was to integrate relevant goal-setting theory and to identify if trainees' goal orientations have an impact on the assigned goals-performance relationship. Trainees attended 1 of the 3 goal-training activities (do your best, performance, or learning goals) for knot tying (KT) and camera navigation (CN) during the 3rd-year clerkship rotation. Questionnaires and pretests and/or post-tests were completed. One twenty-seven 3rd-year medical students (age: 25 ± 2.6; 54% women) participated in the training program. Pretraining to post-training performance changes were significant for all groups on both tasks (P goals group (do your best: KTΔ = 2.14, CNΔ = 1.69; performance: KTΔ = 2.49, CNΔ = 2.24; learning: KTΔ = 3.04 CNΔ = 2.76). Correlations between goal orientations and improvement were examined, revealing a unique role of goal orientation for performance improvement. These data indicate that consideration of goal type and trainee goal orientation must be considered during curriculum development to maximize educational value. Copyright © 2016 Elsevier Inc. All rights reserved.

  9. Agenda Trending: Reciprocity and the Predictive Capacity of Social Networking Sites in Intermedia Agenda Setting across Topics over Time

    Directory of Open Access Journals (Sweden)

    Jacob Groshek

    2013-08-01

    Full Text Available In the contemporary converged media environment, agenda setting is being transformed by the dramatic growth of audiences that are simultaneously media users and producers. The study reported here addresses related gaps in the literature by first comparing the topical agendas of two leading traditional media outlets (New York Times and CNN with the most frequently shared stories and trending topics on two widely popular Social Networking Sites (Facebook and Twitter. Time-series analyses of the most prominent topics identify the extent to which traditional media sets the agenda for social media as well as reciprocal agenda-setting effects of social media topics entering traditional media agendas. In addition, this study examines social intermedia agenda setting topically and across time within social networking sites, and in so doing, adds a vital understanding of where traditional media, online uses, and social media content intersect around instances of focusing events, particularly elections. Findings identify core differences between certain traditional and social media agendas, but also within social media agendas that extend from uses examined here. Additional results further suggest important topical and event-oriented limitations upon the predictive capacit of social networking sites to shape traditional media agendas over time.

  10. Motor and psychosocial impact of robot-assisted gait training in a real-world rehabilitation setting: A pilot study.

    Directory of Open Access Journals (Sweden)

    Cira Fundarò

    Full Text Available In the last decade robotic devices have been applied in rehabilitation to overcome walking disability in neurologic diseases with promising results. Robot assisted gait training (RAGT using the Lokomat seems not only to improve gait parameters but also the perception of well-being. Data on the psychosocial patient-robot impact are limited, in particular in the real-world of RAGT, in the rehabilitation setting. During rehabilitation training, the Lokomat can be considered an "assistive device for movement". This allowed the use of the Psychosocial Impact of Assistive Device Scale- PIADS to describe patient interaction with the Lokomat. The primary aim of this pilot study was to evaluate the psychosocial impact of the Lokomat in an in-patient rehabilitation setting using the PIADS; secondary aims were to assess whether the psychosocial impact of RAGT is different between pathological sub-groups and if the Lokomat influenced functional variables (Functional Independence Measure scale-FIM and parameters provided by the Lokomat itself. Thirty-nine consecutive patients (69% males, 54.0±18.0 years eligible for Lokomat training, with etiologically heterogeneous walking disabilities (Parkinson's Disease, n = 10; Spinal Cord Injury, n = 21; Ictus Event, n = 8 were enrolled. Patients were assessed with the FIM before and after rehabilitation with Lokomat, and the PIADS was administered after the rehabilitative period with Lokomat. Overall the PIADS score was positive (35.8±21.6, as well as the three sub-scales, pertaining to "ability", "adaptability" and "self-esteem" (17.2±10.4, 8.9±5.5 and 10.1±6.6 respectively with no between-group differences. All patients significantly improved in gait measure and motor FIM scale (difference after-before treatment values: 11.7±9.8 and 11.2±10.3 respectively, increased treadmill speed (0.4 ± 0.2m/s, reduced body weight support (-14.0±9.5% and guidance force (-13.1 ± 10.7%. This pilot study indicates that

  11. Benefits, Barriers, and Motivators to Training Dietetic Interns in Clinical Settings: A Comparison between Preceptors and Nonpreceptors.

    Science.gov (United States)

    AbuSabha, Rayane; Muller, Colette; MacLasco, Jacqueline; George, Mary; Houghton, Erica; Helm, Alison

    2018-03-01

    The shortage of supervised practice sites in dietetics is associated with fewer numbers of preceptors available to supervise interns, especially in the clinical setting. To identify clinical dietitians' perceived benefits and challenges of training dietetic interns and to determine key motivators that would entice nonpreceptors to volunteer for the role. Registered dietitian nutritionists working in clinical settings completed a semi-structured, audiotaped interview followed by a brief questionnaire. Clinical dietitians working in hospitals, long-term care facilities, and outpatient clinics (n=100) participated: 54 preceptors and 46 nonpreceptors. Qualitative analysis was conducted using an iterative process to identify and code common themes. T tests were used to compare mean differences between the opinions of preceptors and nonpreceptors. Preceptors had approximately 5 more years of experience (mean=14.27±12.09 years) than nonpreceptors (mean=8.83±9.72 years) (Pmotivator for taking on interns. Incentive programs should be developed to entice nonpreceptors to take on interns. These programs should include extensive training on the preceptor role and how to alleviate the burden of time spent supervising interns and should provide a significant number of CPEUs to make the added workload worthwhile. Copyright © 2018 Academy of Nutrition and Dietetics. Published by Elsevier Inc. All rights reserved.

  12. The Effects of Martial Arts Training on Attentional Networks in Typical Adults.

    Science.gov (United States)

    Johnstone, Ashleigh; Marí-Beffa, Paloma

    2018-01-01

    There is substantial evidence that training in Martial Arts is associated with improvements in cognitive function in children; but little has been studied in healthy adults. Here, we studied the impact of extensive training in Martial Arts on cognitive control in adults. To do so, we used the Attention Network Test (ANT) to test two different groups of participants: with at least 2 years of Martial Arts experience, and with no experience with the sport. Participants were screened from a wider sample of over 500 participants who volunteered to participate. 48 participants were selected: 21 in the Martial Arts group (mean age = 19.68) and 27 in the Non-Martial Arts group (mean age = 19.63). The two groups were matched on a number of demographic variables that included Age and BMI, following the results of a previous pilot study where these factors were found to significantly impact the ANT measures. An effect of Martial Arts experience was found on the Alert network, but not the Orienting or Executive ones. More specifically, Martial Artists showed improved performance when alert had to be sustained endogenously, performing more like the control group when an exogenous cue was provided. This result was further confirmed by a negative correlation between number of years of Martial Arts experience and the costs due to the lack of an exogenous cue suggesting that the longer a person takes part in the sport, the better their endogenous alert is. Results are interpreted in the context of the impact of training a particular attentional state in specific neurocognitive pathways.

  13. The Effects of Martial Arts Training on Attentional Networks in Typical Adults

    Directory of Open Access Journals (Sweden)

    Ashleigh Johnstone

    2018-02-01

    Full Text Available There is substantial evidence that training in Martial Arts is associated with improvements in cognitive function in children; but little has been studied in healthy adults. Here, we studied the impact of extensive training in Martial Arts on cognitive control in adults. To do so, we used the Attention Network Test (ANT to test two different groups of participants: with at least 2 years of Martial Arts experience, and with no experience with the sport. Participants were screened from a wider sample of over 500 participants who volunteered to participate. 48 participants were selected: 21 in the Martial Arts group (mean age = 19.68 and 27 in the Non-Martial Arts group (mean age = 19.63. The two groups were matched on a number of demographic variables that included Age and BMI, following the results of a previous pilot study where these factors were found to significantly impact the ANT measures. An effect of Martial Arts experience was found on the Alert network, but not the Orienting or Executive ones. More specifically, Martial Artists showed improved performance when alert had to be sustained endogenously, performing more like the control group when an exogenous cue was provided. This result was further confirmed by a negative correlation between number of years of Martial Arts experience and the costs due to the lack of an exogenous cue suggesting that the longer a person takes part in the sport, the better their endogenous alert is. Results are interpreted in the context of the impact of training a particular attentional state in specific neurocognitive pathways.

  14. Low-Volume High-Intensity Interval Training in a Gym Setting Improves Cardio-Metabolic and Psychological Health.

    Directory of Open Access Journals (Sweden)

    Sam O Shepherd

    Full Text Available Within a controlled laboratory environment, high-intensity interval training (HIT elicits similar cardiovascular and metabolic benefits as traditional moderate-intensity continuous training (MICT. It is currently unclear how HIT can be applied effectively in a real-world environment.To investigate the hypothesis that 10 weeks of HIT, performed in an instructor-led, group-based gym setting, elicits improvements in aerobic capacity (VO2max, cardio-metabolic risk and psychological health which are comparable to MICT.Ninety physically inactive volunteers (42±11 y, 27.7±4.8 kg.m-2 were randomly assigned to HIT or MICT group exercise classes. HIT consisted of repeated sprints (15-60 seconds, >90% HRmax interspersed with periods of recovery cycling (≤25 min.session-1, 3 sessions.week-1. MICT participants performed continuous cycling (~70% HRmax, 30-45 min.session-1, 5 sessions.week-1. VO2max, markers of cardio-metabolic risk, and psychological health were assessed pre and post-intervention.Mean weekly training time was 55±10 (HIT and 128±44 min (MICT (p<0.05, with greater adherence to HIT (83±14% vs. 61±15% prescribed sessions attended, respectively; p<0.05. HIT improved VO2max, insulin sensitivity, reduced abdominal fat mass, and induced favourable changes in blood lipids (p<0.05. HIT also induced beneficial effects on health perceptions, positive and negative affect, and subjective vitality (p<0.05. No difference between HIT and MICT was seen for any of these variables.HIT performed in a real-world gym setting improves cardio-metabolic risk factors and psychological health in physically inactive adults. With a reduced time commitment and greater adherence than MICT, HIT offers a viable and effective exercise strategy to target the growing incidence of metabolic disease and psychological ill-being associated with physical inactivity.

  15. Effects of low-volume high-intensity interval training in a community setting: a pilot study.

    Science.gov (United States)

    Reljic, Dejan; Wittmann, Felix; Fischer, Joachim E

    2018-06-01

    High-intensity interval training (HIIT) is emerging as an effective and time-efficient exercise strategy for health promotion. However, most HIIT studies are conducted in laboratory settings and evidence regarding the efficacy of time-efficient "low-volume" HIIT is based mainly on demanding "all-out" protocols. Thus, the aim of this pilot study was to assess the feasibility and efficacy of two low-volume (≤ 30 min time-effort/week), non-all-out HIIT protocols, performed 2 ×/week over 8 weeks in a community-based fitness centre. Thirty-four sedentary men and women were randomised to either 2 × 4-min HIIT (2 × 4-HIIT) or 5 × 1-min HIIT (5 × 1-HIIT) at 85-95% maximal heart rate (HR max ), or an active control group performing moderate-intensity continuous training (MICT, 76 min/week) at 65-75% HR max . The exercise protocols were well tolerated and no adverse events occurred. 2 × 4-HIIT and 5 × 1-HIIT exhibited lower dropout rates (17 and 8 vs. 30%) than MICT. All training modes improved VO 2max (2 × 4-HIIT: + 20%, P HIIT: + 27%, P HIIT protocols required 60% less time commitment. Both HIIT protocols and MICT had positive impact on cholesterol profiles. Only 5 × 1-HIIT significantly improved waist circumference (P HIIT can be feasibly implemented in a community-based setting. Moreover, our data suggest that practical (non-all-out) HIIT that requires as little as 30 min/week, either performed as 2 × 4-HIIT or 5 × 1-HIIT, may induce significant improvements in VO 2max and cardiometabolic risk markers.

  16. Low-Volume High-Intensity Interval Training in a Gym Setting Improves Cardio-Metabolic and Psychological Health.

    Science.gov (United States)

    Shepherd, Sam O; Wilson, Oliver J; Taylor, Alexandra S; Thøgersen-Ntoumani, Cecilie; Adlan, Ahmed M; Wagenmakers, Anton J M; Shaw, Christopher S

    2015-01-01

    Within a controlled laboratory environment, high-intensity interval training (HIT) elicits similar cardiovascular and metabolic benefits as traditional moderate-intensity continuous training (MICT). It is currently unclear how HIT can be applied effectively in a real-world environment. To investigate the hypothesis that 10 weeks of HIT, performed in an instructor-led, group-based gym setting, elicits improvements in aerobic capacity (VO2max), cardio-metabolic risk and psychological health which are comparable to MICT. Ninety physically inactive volunteers (42±11 y, 27.7±4.8 kg.m-2) were randomly assigned to HIT or MICT group exercise classes. HIT consisted of repeated sprints (15-60 seconds, >90% HRmax) interspersed with periods of recovery cycling (≤25 min.session-1, 3 sessions.week-1). MICT participants performed continuous cycling (~70% HRmax, 30-45 min.session-1, 5 sessions.week-1). VO2max, markers of cardio-metabolic risk, and psychological health were assessed pre and post-intervention. Mean weekly training time was 55±10 (HIT) and 128±44 min (MICT) (pHIT (83±14% vs. 61±15% prescribed sessions attended, respectively; pHIT improved VO2max, insulin sensitivity, reduced abdominal fat mass, and induced favourable changes in blood lipids (pHIT also induced beneficial effects on health perceptions, positive and negative affect, and subjective vitality (pHIT and MICT was seen for any of these variables. HIT performed in a real-world gym setting improves cardio-metabolic risk factors and psychological health in physically inactive adults. With a reduced time commitment and greater adherence than MICT, HIT offers a viable and effective exercise strategy to target the growing incidence of metabolic disease and psychological ill-being associated with physical inactivity.

  17. Research on Linear Wireless Sensor Networks Used for Online Monitoring of Rolling Bearing in Freight Train

    International Nuclear Information System (INIS)

    Wang Nan; Meng Qingfeng; Zheng Bin; Li Tong; Ma Qinghai

    2011-01-01

    This paper presents a Wireless Sensor Networks (WSNs) technique for the purpose of on-line monitoring of rolling bearing in freight train. A new technical scheme including the arrangements of sensors, the design of sensor nodes and base station, routing protocols, signal acquirement, processing and transmission is described, and an on-line monitoring system is established. Considering the approximately linear arrangements of cars and the running state of freight train, a linear topology structure of WSNs is adopted and five linear routing protocols are discussed in detail as to obtain the desired minimum energy consumption of WSNs. By analysing the simulation results, an optimal multi-hop routing protocol named sub-section routing protocol according to equal distance is adopted, in which all sensor nodes are divided into different groups according to the equal transmission distance, the optimal transmission distance and number of hops of routing protocol are also studied. We know that the communication consumes significant power in WSNs, so, in order to save the limit power supply of WSNs, the data compression and coding scheme based on lifting integer wavelet and embedded zerotree wavelet (EZW) algorithms is studied to reduce the amounts of data transmitted. The experimental results of rolling bearing have been given at last to verify the effectiveness of data compression algorithm. The on-line monitoring system of rolling bearing in freight train will be applied to actual application in the near future.

  18. Using Wireless Sensor Networks and Trains as Data Mules to Monitor Slab Track Infrastructures.

    Science.gov (United States)

    Cañete, Eduardo; Chen, Jaime; Díaz, Manuel; Llopis, Luis; Reyna, Ana; Rubio, Bartolomé

    2015-06-26

    Recently, slab track systems have arisen as a safer and more sustainable option for high speed railway infrastructures, compared to traditional ballasted tracks. Integrating Wireless Sensor Networks within these infrastructures can provide structural health related data that can be used to evaluate their degradation and to not only detect failures but also to predict them. The design of such systems has to deal with a scenario of large areas with inaccessible zones, where neither Internet coverage nor electricity supply is guaranteed. In this paper we propose a monitoring system for slab track systems that measures vibrations and displacements in the track. Collected data is transmitted to passing trains, which are used as data mules to upload the information to a remote control center. On arrival at the station, the data is stored in a database, which is queried by an application in order to detect and predict failures. In this paper, different communication architectures are designed and tested to select the most suitable system meeting such requirements as efficiency, low cost and data accuracy. In addition, to ensure communication between the sensing devices and the train, the communication system must take into account parameters such as train speed, antenna coverage, band and frequency.

  19. Using Wireless Sensor Networks and Trains as Data Mules to Monitor Slab Track Infrastructures

    Directory of Open Access Journals (Sweden)

    Eduardo Cañete

    2015-06-01

    Full Text Available Recently, slab track systems have arisen as a safer and more sustainable option for high speed railway infrastructures, compared to traditional ballasted tracks. Integrating Wireless Sensor Networks within these infrastructures can provide structural health related data that can be used to evaluate their degradation and to not only detect failures but also to predict them. The design of such systems has to deal with a scenario of large areas with inaccessible zones, where neither Internet coverage nor electricity supply is guaranteed. In this paper we propose a monitoring system for slab track systems that measures vibrations and displacements in the track. Collected data is transmitted to passing trains, which are used as data mules to upload the information to a remote control center. On arrival at the station, the data is stored in a database, which is queried by an application in order to detect and predict failures. In this paper, different communication architectures are designed and tested to select the most suitable system meeting such requirements as efficiency, low cost and data accuracy. In addition, to ensure communication between the sensing devices and the train, the communication system must take into account parameters such as train speed, antenna coverage, band and frequency.

  20. Research on Linear Wireless Sensor Networks Used for Online Monitoring of Rolling Bearing in Freight Train

    Energy Technology Data Exchange (ETDEWEB)

    Wang Nan; Meng Qingfeng; Zheng Bin [Theory of Lubrication and Bearing Institute, Xi' an Jiaotong University Xi' an, 710049 (China); Li Tong; Ma Qinghai, E-mail: heroyoyu.2009@stu.xjtu.edu.cn [Xi' an Rail Bureau, Xi' an, 710054 (China)

    2011-07-19

    This paper presents a Wireless Sensor Networks (WSNs) technique for the purpose of on-line monitoring of rolling bearing in freight train. A new technical scheme including the arrangements of sensors, the design of sensor nodes and base station, routing protocols, signal acquirement, processing and transmission is described, and an on-line monitoring system is established. Considering the approximately linear arrangements of cars and the running state of freight train, a linear topology structure of WSNs is adopted and five linear routing protocols are discussed in detail as to obtain the desired minimum energy consumption of WSNs. By analysing the simulation results, an optimal multi-hop routing protocol named sub-section routing protocol according to equal distance is adopted, in which all sensor nodes are divided into different groups according to the equal transmission distance, the optimal transmission distance and number of hops of routing protocol are also studied. We know that the communication consumes significant power in WSNs, so, in order to save the limit power supply of WSNs, the data compression and coding scheme based on lifting integer wavelet and embedded zerotree wavelet (EZW) algorithms is studied to reduce the amounts of data transmitted. The experimental results of rolling bearing have been given at last to verify the effectiveness of data compression algorithm. The on-line monitoring system of rolling bearing in freight train will be applied to actual application in the near future.

  1. The effectiveness of three sets of school-based instructional materials and community training on the acquisition and generalization of community laundry skills by students with severe handicaps.

    Science.gov (United States)

    Morrow, S A; Bates, P E

    1987-01-01

    This study examined the effectiveness of three sets of school-based instructional materials and community training on acquisition and generalization of a community laundry skill by nine students with severe handicaps. School-based instruction involved artificial materials (pictures), simulated materials (cardboard replica of a community washing machine), and natural materials (modified home model washing machine). Generalization assessments were conducted at two different community laundromats, on two machines represented fully by the school-based instructional materials and two machines not represented fully by these materials. After three phases of school-based instruction, the students were provided ten community training trials in one laundromat setting and a final assessment was conducted in both the trained and untrained community settings. A multiple probe design across students was used to evaluate the effectiveness of the three types of school instruction and community training. After systematic training, most of the students increased their laundry performance with all three sets of school-based materials; however, generalization of these acquired skills was limited in the two community settings. Direct training in one of the community settings resulted in more efficient acquisition of the laundry skills and enhanced generalization to the untrained laundromat setting for most of the students. Results of this study are discussed in regard to the issue of school versus community-based instruction and recommendations are made for future research in this area.

  2. A Partnership Training Program in Breast Cancer Diagnosis: Concept Development of the Next Generation Diagnostic Breast Imaging Using Digital Image Library and Networking Techniques

    National Research Council Canada - National Science Library

    Chouikha, Mohamed F

    2004-01-01

    ...); and Georgetown University (Image Science and Information Systems, ISIS). In this partnership training program, we will train faculty and students in breast cancer imaging, digital image database library techniques and network communication strategy...

  3. Quantization and training of object detection networks with low-precision weights and activations

    Science.gov (United States)

    Yang, Bo; Liu, Jian; Zhou, Li; Wang, Yun; Chen, Jie

    2018-01-01

    As convolutional neural networks have demonstrated state-of-the-art performance in object recognition and detection, there is a growing need for deploying these systems on resource-constrained mobile platforms. However, the computational burden and energy consumption of inference for these networks are significantly higher than what most low-power devices can afford. To address these limitations, this paper proposes a method to train object detection networks with low-precision weights and activations. The probability density functions of weights and activations of each layer are first directly estimated using piecewise Gaussian models. Then, the optimal quantization intervals and step sizes for each convolution layer are adaptively determined according to the distribution of weights and activations. As the most computationally expensive convolutions can be replaced by effective fixed point operations, the proposed method can drastically reduce computation complexity and memory footprint. Performing on the tiny you only look once (YOLO) and YOLO architectures, the proposed method achieves comparable accuracy to their 32-bit counterparts. As an illustration, the proposed 4-bit and 8-bit quantized versions of the YOLO model achieve a mean average precision of 62.6% and 63.9%, respectively, on the Pascal visual object classes 2012 test dataset. The mAP of the 32-bit full-precision baseline model is 64.0%.

  4. Training and Validating a Deep Convolutional Neural Network for Computer-Aided Detection and Classification of Abnormalities on Frontal Chest Radiographs.

    Science.gov (United States)

    Cicero, Mark; Bilbily, Alexander; Colak, Errol; Dowdell, Tim; Gray, Bruce; Perampaladas, Kuhan; Barfett, Joseph

    2017-05-01

    Convolutional neural networks (CNNs) are a subtype of artificial neural network that have shown strong performance in computer vision tasks including image classification. To date, there has been limited application of CNNs to chest radiographs, the most frequently performed medical imaging study. We hypothesize CNNs can learn to classify frontal chest radiographs according to common findings from a sufficiently large data set. Our institution's research ethics board approved a single-center retrospective review of 35,038 adult posterior-anterior chest radiographs and final reports performed between 2005 and 2015 (56% men, average age of 56, patient type: 24% inpatient, 39% outpatient, 37% emergency department) with a waiver for informed consent. The GoogLeNet CNN was trained using 3 graphics processing units to automatically classify radiographs as normal (n = 11,702) or into 1 or more of cardiomegaly (n = 9240), consolidation (n = 6788), pleural effusion (n = 7786), pulmonary edema (n = 1286), or pneumothorax (n = 1299). The network's performance was evaluated using receiver operating curve analysis on a test set of 2443 radiographs with the criterion standard being board-certified radiologist interpretation. Using 256 × 256-pixel images as input, the network achieved an overall sensitivity and specificity of 91% with an area under the curve of 0.964 for classifying a study as normal (n = 1203). For the abnormal categories, the sensitivity, specificity, and area under the curve, respectively, were 91%, 91%, and 0.962 for pleural effusion (n = 782), 82%, 82%, and 0.868 for pulmonary edema (n = 356), 74%, 75%, and 0.850 for consolidation (n = 214), 81%, 80%, and 0.875 for cardiomegaly (n = 482), and 78%, 78%, and 0.861 for pneumothorax (n = 167). Current deep CNN architectures can be trained with modest-sized medical data sets to achieve clinically useful performance at detecting and excluding common pathology on chest radiographs.

  5. The Effectiveness of an Interactive Training Program in Developing a Set of Non-Cognitive Skills in Students at University of Petra

    Science.gov (United States)

    Gheith, Eman; Aljaberi, Nahil M.

    2017-01-01

    This study aimed to investigate the effectiveness of interactive training programs in developing a set of non-cognitive skills in students at the University of Petra. Furthermore, it sought to examine the impact of the sex, academic year, and university major variables on developing these skills in students who underwent the training program, as…

  6. Delivery and Evaluation of Training for School Nutrition Administrators and Managers on Meeting Special Food and Nutrition Needs of Students in the School Setting

    Science.gov (United States)

    Oakley, Charlotte B.; Knight, Kathy; Hobbs, Margie; Dodd, Lacy M.; Cole, Janie

    2011-01-01

    Purpose/Objectives: The purpose of this investigation was to complete a formal evaluation of a project that provided specialized training for school nutrition (SN) administrators and managers on meeting children's special dietary needs in the school setting. Methods: The training was provided as part of the "Eating Good and Moving Like We…

  7. Let’s Wiggle with 5-2-1-0: Curriculum Development for Training Childcare Providers to Promote Activity in Childcare Settings

    Directory of Open Access Journals (Sweden)

    Debra M. Vinci

    2016-01-01

    Full Text Available Overweight and obesity are increasing in preschool children in the US. Policy, systems, and environmental change interventions in childcare settings can improve obesity-related behaviors. The aim of this study was to develop and pilot an intervention to train childcare providers to promote physical activity (PA in childcare classrooms. An evidence scan, key informant (n=34 and focus group (n=20 interviews with childcare directors and staff, and environmental self-assessment of childcare facilities (n=22 informed the design of the training curriculum. Feedback from the interviews indicated that childcare providers believed in the importance of teaching children about PA and were supportive of training teachers to incorporate PA into classroom settings. The Promoting Physical Activity in Childcare Setting Curriculum was developed and training was implemented with 16 teachers. Participants reported a positive experience with the hands-on training and reported acquiring new knowledge that they intended to implement in their childcare settings. Our findings highlight the feasibility of working with childcare staff to develop PA training and curriculum. Next steps include evaluating the curriculum in additional childcare settings and childcare staff implementation of the curriculum to understand the effectiveness of the training on PA levels of children.

  8. Development and Operation of International Nuclear Education/Training Program and HRD Cooperation Network

    International Nuclear Information System (INIS)

    Lee, E. J.; Min, B. J.; Han, K. W.

    2006-12-01

    The primary result of the project is the establishment of a concept of International Nuclear R and D Academy that integrates the on-going long term activity for international nuclear education/training and a new activity to establish an international cooperation network for nuclear human resources development. For this, the 2007 WNU Summer Institute was hosted with the establishment of an MOU and subsequent preparations. Also, ANENT was promoted through development of a cyber platform for the ANENT web-portal, hosting the third ANENT Coordination Committee meeting, etc. Then a cooperation with universities in Vietnam was launched resulting in preparation of an MOU for the cooperation. Finally, a relevant system framework was established and required procedures were drafted especially for providing students from developing countries with long term education/training programs (e.g. MS and Ph D. courses). The international nuclear education/training programs have offered 13 courses to 182 people from 43 countries. The overall performance of the courses was evaluated to be outstanding. In parallel, the establishment of an MOU for the cooperation of KOICA-IAEA-KAERI courses to ensure their stable and systematic operation. Also, an effort was made to participate in FNCA. Atopia Hall of the International Nuclear Training and Education Center (INTEC) hosted 477 events (corresponding to 18,521 participants) and Nuri Hall (guesthouse) accommodated 4,616 people in 2006. This shows a steady increase of the use rate since the opening of the center, along with a continuous improvement of the equipment

  9. Development and Operation of International Nuclear Education/Training Program and HRD Cooperation Network

    Energy Technology Data Exchange (ETDEWEB)

    Lee, E J; Min, B J; Han, K W [and others

    2006-12-15

    The primary result of the project is the establishment of a concept of International Nuclear R and D Academy that integrates the on-going long term activity for international nuclear education/training and a new activity to establish an international cooperation network for nuclear human resources development. For this, the 2007 WNU Summer Institute was hosted with the establishment of an MOU and subsequent preparations. Also, ANENT was promoted through development of a cyber platform for the ANENT web-portal, hosting the third ANENT Coordination Committee meeting, etc. Then a cooperation with universities in Vietnam was launched resulting in preparation of an MOU for the cooperation. Finally, a relevant system framework was established and required procedures were drafted especially for providing students from developing countries with long term education/training programs (e.g. MS and Ph D. courses). The international nuclear education/training programs have offered 13 courses to 182 people from 43 countries. The overall performance of the courses was evaluated to be outstanding. In parallel, the establishment of an MOU for the cooperation of KOICA-IAEA-KAERI courses to ensure their stable and systematic operation. Also, an effort was made to participate in FNCA. Atopia Hall of the International Nuclear Training and Education Center (INTEC) hosted 477 events (corresponding to 18,521 participants) and Nuri Hall (guesthouse) accommodated 4,616 people in 2006. This shows a steady increase of the use rate since the opening of the center, along with a continuous improvement of the equipment.

  10. Generation and quality assessment of route choice sets in public transport networks by means of RP data analysis

    DEFF Research Database (Denmark)

    Larsen, Marie Karen; Nielsen, Otto Anker; Prato, Carlo Giacomo

    2010-01-01

    Literature in route choice modelling shows that a lot of attention has been devoted to route choices of car drivers, but much less attention has been dedicated to route choices of public transport users. As modelling route choice behaviour consists of generating relevant routes and estimating...... discrete choice models, this paper focuses on the issue of choice set generation in public transport networks. Specifically, this paper describes the generation of choice sets for users of the Greater Copenhagen public transport system by applying a doubly stochastic path generation algorithm...

  11. Proactive Approach for Safe Use of Antimicrobial Coatings in Healthcare Settings: Opinion of the COST Action Network AMiCI

    Directory of Open Access Journals (Sweden)

    Merja Ahonen

    2017-03-01

    Full Text Available Infections and infectious diseases are considered a major challenge to human health in healthcare units worldwide. This opinion paper was initiated by EU COST Action network AMiCI (AntiMicrobial Coating Innovations and focuses on scientific information essential for weighing the risks and benefits of antimicrobial surfaces in healthcare settings. Particular attention is drawn on nanomaterial-based antimicrobial surfaces in frequently-touched areas in healthcare settings and the potential of these nano-enabled coatings to induce (ecotoxicological hazard and antimicrobial resistance. Possibilities to minimize those risks e.g., at the level of safe-by-design are demonstrated.

  12. Agenda trending: reciprocity and the predictive capacity of social networking sites in intermedia agenda setting across topics over time

    OpenAIRE

    Groshek, Jacob; Groshek, Megan Clough

    2013-01-01

    In the contemporary converged media environment, agenda setting is being transformed by the dramatic growth of audiences that are simultaneously media users and producers. The study reported here addresses related gaps in the literature by first comparing the topical agendas of two leading traditional media outlets (New York Times and CNN) with the most frequently shared stories and trending topics on two widely popular Social Networking Sites (Facebook and Twitter). Time-series analyses of t...

  13. Exploring sets of molecules from patents and relationships to other active compounds in chemical space networks

    Science.gov (United States)

    Kunimoto, Ryo; Bajorath, Jürgen

    2017-09-01

    Patents from medicinal chemistry represent a rich source of novel compounds and activity data that appear only infrequently in the scientific literature. Moreover, patent information provides a primary focal point for drug discovery. Accordingly, text mining and image extraction approaches have become hot topics in patent analysis and repositories of patent data are being established. In this work, we have generated network representations using alternative similarity measures to systematically compare molecules from patents with other bioactive compounds, visualize similarity relationships, explore the chemical neighbourhood of patent molecules, and identify closely related compounds with different activities. The design of network representations that combine patent molecules and other bioactive compounds and view patent information in the context of current bioactive chemical space aids in the analysis of patents and further extends the use of molecular networks to explore structure-activity relationships.

  14. Transferring Pre-Trained Deep CNNs for Remote Scene Classification with General Features Learned from Linear PCA Network

    Directory of Open Access Journals (Sweden)

    Jie Wang

    2017-03-01

    Full Text Available Deep convolutional neural networks (CNNs have been widely used to obtain high-level representation in various computer vision tasks. However, in the field of remote sensing, there are not sufficient images to train a useful deep CNN. Instead, we tend to transfer successful pre-trained deep CNNs to remote sensing tasks. In the transferring process, generalization power of features in pre-trained deep CNNs plays the key role. In this paper, we propose two promising architectures to extract general features from pre-trained deep CNNs for remote scene classification. These two architectures suggest two directions for improvement. First, before the pre-trained deep CNNs, we design a linear PCA network (LPCANet to synthesize spatial information of remote sensing images in each spectral channel. This design shortens the spatial “distance” of target and source datasets for pre-trained deep CNNs. Second, we introduce quaternion algebra to LPCANet, which further shortens the spectral “distance” between remote sensing images and images used to pre-train deep CNNs. With five well-known pre-trained deep CNNs, experimental results on three independent remote sensing datasets demonstrate that our proposed framework obtains state-of-the-art results without fine-tuning and feature fusing. This paper also provides baseline for transferring fresh pretrained deep CNNs to other remote sensing tasks.

  15. The Role of Eif6 in Skeletal Muscle Homeostasis Revealed by Endurance Training Co-expression Networks

    Directory of Open Access Journals (Sweden)

    Kim Clarke

    2017-11-01

    Full Text Available Regular endurance training improves muscle oxidative capacity and reduces the risk of age-related disorders. Understanding the molecular networks underlying this phenomenon is crucial. Here, by exploiting the power of computational modeling, we show that endurance training induces profound changes in gene regulatory networks linking signaling and selective control of translation to energy metabolism and tissue remodeling. We discovered that knockdown of the mTOR-independent factor Eif6, which we predicted to be a key regulator of this process, affects mitochondrial respiration efficiency, ROS production, and exercise performance. Our work demonstrates the validity of a data-driven approach to understanding muscle homeostasis.

  16. Asian network for education in nuclear technology: An initiative to promote education and training in nuclear technology

    International Nuclear Information System (INIS)

    Kosilov, A.

    2006-01-01

    It has become increasingly clear that there is a need to consolidate the efforts of academia and industry in education and training. Partnerships of operating organizations with educational institutions and universities that provide qualified professionals for the nuclear industry should be assessed based upon medium and long term needs and strengthened where needed. In this regard the IAEA is taking the necessary action to initiate this kind of partnership through continuous networking. The paper describes the IAEA approach to promoting education and training through the Asian Network for Education in Nuclear Technology (ANENT). (author)

  17. Before the year 2000: Artificial neural networks may set the standard

    International Nuclear Information System (INIS)

    Michal, R.A.

    1994-01-01

    The use of artifical neural networks (ANNs) for monitoring of equipment and components in nuclear power plants could be commonplace before the turn of the century. Within five years, the relative inexpensiveness of neural networks could usher in a technology that will be used to detect incipient faults in machinery and increase effectiveness of maintenance scheduling. Working since November 1992 with the Electric Power Research Institute on research and development of the technology, SynEx and another Virginia-based company, A ampersand T, Inc., will later this year demonstrate prototype ANN systems at Consolidated Edison Company and New York State Electric Gas fossil fuel power plants. (Fossil fuel plants were chosen for the project because of easier access, as opposed to the security measures in place at nuclear facilities.) The demonstration will utilize sensors and the neural network systems to detect abnormal equipment behavior, sending signals back to centralized monitoring boards located in each plant's control room. The cost of the project, including research and development, will reach $1 million. However, the cost of installing a neural network at a nuclear plant within the next five years, according to Birdsall, could be as low as $10,000 to $15,000, with hopes of reducing the expenditure to just $5000

  18. Networks and landscapes: a framework for setting goals and evaluating performance at the large landscape scale

    Science.gov (United States)

    R Patrick Bixler; Shawn Johnson; Kirk Emerson; Tina Nabatchi; Melly Reuling; Charles Curtin; Michele Romolini; Morgan Grove

    2016-01-01

    The objective of large landscape conser vation is to mitigate complex ecological problems through interventions at multiple and overlapping scales. Implementation requires coordination among a diverse network of individuals and organizations to integrate local-scale conservation activities with broad-scale goals. This requires an understanding of the governance options...

  19. Secure Your Wireless Network: Going Wireless Comes with Its Own Special Set of Security Concerns

    Science.gov (United States)

    Bloomquist, Jane; Musa, Atif

    2004-01-01

    Imagine a completely wireless school, an open network in which all students and staff can roam around using laptops or handheld computers to browse the Internet, access files and applications on the school server, and communicate with each other and the world via e-mail. It's a great picture--and at some schools the future is already here. But…

  20. Pilot Integration of HIV Screening and Healthcare Settings with Multi- Component Social Network and Partner Testing for HIV Detection.

    Science.gov (United States)

    Rentz, Michael F; Ruffner, Andrew H; Ancona, Rachel M; Hart, Kimberly W; Kues, John R; Barczak, Christopher M; Lindsell, Christopher J; Fichtenbaum, Carl J; Lyons, Michael S

    2017-11-23

    Healthcare settings screen broadly for HIV. Public health settings use social network and partner testing ("Transmission Network Targeting (TNT)") to select high-risk individuals based on their contacts. HIV screening and TNT systems are not integrated, and healthcare settings have not implemented TNT. The study aimed to evaluate pilot implementation of multi-component, multi-venue TNT in conjunction with HIV screening by a healthcare setting. Our urban, academic health center implemented a TNT program in collaboration with the local health department for five months during 2011. High-risk or HIV positive patients of the infectious diseases clinic and emergency department HIV screening program were recruited to access social and partner networks via compensated peer-referral, testing of companions present with them, and partner notification services. Contacts became the next-generation index cases in a snowball recruitment strategy. The pilot TNT program yielded 485 HIV tests for 482 individuals through eight generations of recruitment with five (1.0%; 95% CI = 0.4%, 2.3%) new diagnoses. Of these, 246 (51.0%; 95% CI = 46.6%, 55.5%) reported that they had not been tested for HIV within the last 12 months and 383 (79.5%; 95% CI = 75.7%, 82.9%) had not been tested by the existing ED screening program within the last five years. TNT complements population screening by more directly targeting high-risk individuals and by expanding the population receiving testing. Information from existing healthcare services could be used to seed TNT programs, or TNT could be implemented within healthcare settings. Research evaluating multi-component, multi-venue HIV detection is necessary to maximize complementary approaches while minimizing redundancy. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.