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.

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.)

ENTERVISION: Research Training in 3D Digital Imaging for Cancer Radiation Therapy

CERN Multimedia

Dosanjh, M

2013-01-01

ENTERVISION, is a Marie Curie Initial Training Network project providing training for 12 Early - Stage Researchers and 4 Experienced Researchers in the field of online medical imaging for hadron therapy. It was established in response to the critical need for reinforcing research in online imaging and for training of highly skilled professionals, with the aim of early detection and more precise treatment of tumours.

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.

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…

Building capacity for public and population health research in Africa: the consortium for advanced research training in Africa (CARTA) model

Science.gov (United States)

Ezeh, Alex C.; Izugbara, Chimaraoke O.; Kabiru, Caroline W.; Fonn, Sharon; Kahn, Kathleen; Manderson, Lenore; Undieh, Ashiwel S.; Omigbodun, Akinyinka; Thorogood, Margaret

2010-01-01

Background Globally, sub-Saharan Africa bears the greatest burden of disease. Strengthened research capacity to understand the social determinants of health among different African populations is key to addressing the drivers of poor health and developing interventions to improve health outcomes and health systems in the region. Yet, the continent clearly lacks centers of research excellence that can generate a strong evidence base to address the region's socio-economic and health problems. Objective and program overview We describe the recently launched Consortium for Advanced Research Training in Africa (CARTA), which brings together a network of nine academic and four research institutions from West, East, Central, and Southern Africa, and select northern universities and training institutes. CARTA's program of activities comprises two primary, interrelated, and mutually reinforcing objectives: to strengthen research infrastructure and capacity at African universities; and to support doctoral training through the creation of a collaborative doctoral training program in population and public health. The ultimate goal of CARTA is to build local research capacity to understand the determinants of population health and effectively intervene to improve health outcomes and health systems. Conclusions CARTA's focus on the local production of networked and high-skilled researchers committed to working in sub-Saharan Africa, and on the concomitant increase in local research and training capacity of African universities and research institutes addresses the inability of existing programs to create a critical mass of well-trained and networked researchers across the continent. The initiative's goal of strengthening human resources and university-wide systems critical to the success and sustainability of research productivity in public and population health will rejuvenate institutional teaching, research, and administrative systems. PMID:21085517

Building capacity for public and population health research in Africa: the consortium for advanced research training in Africa (CARTA model

Directory of Open Access Journals (Sweden)

Alex C. Ezeh

2010-11-01

Full Text Available Background: Globally, sub-Saharan Africa bears the greatest burden of disease. Strengthened research capacity to understand the social determinants of health among different African populations is key to addressing the drivers of poor health and developing interventions to improve health outcomes and health systems in the region. Yet, the continent clearly lacks centers of research excellence that can generate a strong evidence base to address the region's socio-economic and health problems. Objective and program overview: We describe the recently launched Consortium for Advanced Research Training in Africa (CARTA, which brings together a network of nine academic and four research institutions from West, East, Central, and Southern Africa, and select northern universities and training institutes. CARTA's program of activities comprises two primary, interrelated, and mutually reinforcing objectives: to strengthen research infrastructure and capacity at African universities; and to support doctoral training through the creation of a collaborative doctoral training program in population and public health. The ultimate goal of CARTA is to build local research capacity to understand the determinants of population health and effectively intervene to improve health outcomes and health systems. Conclusions: CARTA's focus on the local production of networked and high-skilled researchers committed to working in sub-Saharan Africa, and on the concomitant increase in local research and training capacity of African universities and research institutes addresses the inability of existing programs to create a critical mass of well-trained and networked researchers across the continent. The initiative's goal of strengthening human resources and university-wide systems critical to the success and sustainability of research productivity in public and population health will rejuvenate institutional teaching, research, and administrative systems.

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.

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.

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.

Building up careers in translational neuroscience and mental health research: Education and training in the Centre for Biomedical Research in Mental Health.

Science.gov (United States)

Rapado-Castro, Marta; Pazos, Ángel; Fañanás, Lourdes; Bernardo, Miquel; Ayuso-Mateos, Jose Luis; Leza, Juan Carlos; Berrocoso, Esther; de Arriba, Jose; Roldán, Laura; Sanjuán, Julio; Pérez, Victor; Haro, Josep M; Palomo, Tomás; Valdizan, Elsa M; Micó, Juan Antonio; Sánchez, Manuel; Arango, Celso

2015-01-01

The number of large collaborative research networks in mental health is increasing. Training programs are an essential part of them. We critically review the specific implementation of a research training program in a translational Centre for Biomedical Research in Mental Health in order to inform the strategic integration of basic research into clinical practice to have a positive impact in the mental health system and society. Description of training activities, specific educational programs developed by the research network, and challenges on its implementation are examined. The Centre for Biomedical Research in Mental Health has focused on training through different activities which have led to the development of an interuniversity master's degree postgraduate program in mental health research, certified by the National Spanish Agency for Quality Evaluation and Accreditation. Consolidation of training programs within the Centre for Biomedical Research in Mental Health has considerably advanced the training of researchers to meet competency standards on research. The master's degree constitutes a unique opportunity to accomplish neuroscience and mental health research career-building within the official framework of university programs in Spain. Copyright © 2014 SEP y SEPB. Published by Elsevier España. All rights reserved.

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

Local Governance and ICT Research Network for Africa | IDRC ...

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

... promote principles of good governance, and encourage public participation and consultation. The African Training and Research Centre in Administration for Development (CAFRAD) will coordinate the network, ensuring effective implementation, a pan-African outlook and high-level dissemination of research results.

Training program attracts work and health researchers

DEFF Research Database (Denmark)

Skakon, Janne

2007-01-01

Each year in Canada, the costs of disability arising from work-related causes – including workers’ compensation and health-care costs – exceed $6.7 billion. Despite the significant financial and social impacts of worker injury and illness, only a small fraction of Canadian researchers are dedicated...... to examining work disability prevention issues. An innovative program that attracts international students, the Work Disability Prevention Canadian Institutes of Health Research (CIHR) Strategic Training Program, aims to build research capacity in young researchers and to create a strong network that examines...

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)

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 ...

Strengthening engineering research and training in Africa | IDRC ...

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

to develop mutually beneficial research and training activities. ... to inform how national or regional engineering systems operate in sub-Saharan Africa, ... developing networks and partnerships that have the potential for scaling-up activities ... from the collaborating institution in the case of Stream 1), in English or in French.

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.

Redes En Acción. Increasing Hispanic participation in cancer research, training, and awareness.

Science.gov (United States)

Ramirez, Amelie G; Talavera, Gregory A; Marti, Jose; Penedo, Frank J; Medrano, Martha A; Giachello, Aida L; Pérez-Stable, Eliseo J

2006-10-15

Hispanics are affected by many health care disparities. The National Cancer Institute (NCI), through its Special Populations Branch, is supporting networking and capacity-building activities designed to increase Hispanic participation and leadership in cancer research. Redes En Acción established a national network of cancer research centers, community-based organizations, and federal partners to facilitate opportunities for junior Hispanic scientists to participate in training and research projects on cancer control. Since 2000, Redes En Acción has established a network of more than 1800 Hispanic leaders involved in cancer research and education. The project has sustained 131 training positions and submitted 29 pilot projects to NCI for review, with 16 awards for a total of $800,000, plus an additional $8.8 million in competing grant funding based on pilot study results to date. Independent research has leveraged an additional $32 million in non-Redes funding, and together the national and regional network sites have participated in more than 1400 community and professional awareness events. In addition, the program conducted extensive national survey research that provided the basis for the Redes En Acción Latino Cancer Report, a national agenda on Hispanic cancer issues. Redes En Acción has increased participation in cancer control research, training, and awareness among Hispanic scientists and within Hispanic communities. Cancer 2006. (c) 2006 American Cancer Society.

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.

The Use of Underground Research Laboratories to Support Repository Development Programs. A Roadmap for the Underground Research Facilities Network.

Energy Technology Data Exchange (ETDEWEB)

MacKinnon, Robert J. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2015-10-26

Under the auspices of the International Atomic Energy Agency (IAEA), nationally developed underground research laboratories (URLs) and associated research institutions are being offered for use by other nations. These facilities form an Underground Research Facilities (URF) Network for training in and demonstration of waste disposal technologies and the sharing of knowledge and experience related to geologic repository development, research, and engineering. In order to achieve its objectives, the URF Network regularly sponsors workshops and training events related to the knowledge base that is transferable between existing URL programs and to nations with an interest in developing a new URL. This report describes the role of URLs in the context of a general timeline for repository development. This description includes identification of key phases and activities that contribute to repository development as a repository program evolves from an early research and development phase to later phases such as construction, operations, and closure. This information is cast in the form of a matrix with the entries in this matrix forming the basis of the URF Network roadmap that will be used to identify and plan future workshops and training events.

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.

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)

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.

Results of the Community Health Applied Research Network (CHARN) National Research Capacity Survey of Community Health Centers.

Science.gov (United States)

Song, Hui; Li, Vivian; Gillespie, Suzanne; Laws, Reesa; Massimino, Stefan; Nelson, Christine; Singal, Robbie; Wagaw, Fikirte; Jester, Michelle; Weir, Rosy Chang

2015-01-01

The mission of the Community Health Applied Research Network (CHARN) is to build capacity to carry out Patient-Centered Outcomes Research at community health centers (CHCs), with the ultimate goal to improve health care for vulnerable populations. The CHARN Needs Assessment Staff Survey investigates CHCs' involvement in research, as well as their need for research training and resources. Results will be used to guide future training. The survey was developed and implemented in partnership with CHARN CHCs. Data were collected across CHARN CHCs. Data analysis and reports were conducted by the CHARN data coordinating center (DCC). Survey results highlighted gaps in staff research training, and these gaps varied by staff role. There is considerable variation in research involvement, partnerships, and focus both within and across CHCs. Development of training programs to increase research capacity should be tailored to address the specific needs and roles of staff involved in research.

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

A new approach to mentoring for research careers: the National Research Mentoring Network.

Science.gov (United States)

Sorkness, Christine A; Pfund, Christine; Ofili, Elizabeth O; Okuyemi, Kolawole S; Vishwanatha, Jamboor K; Zavala, Maria Elena; Pesavento, Theresa; Fernandez, Mary; Tissera, Anthony; Deveci, Alp; Javier, Damaris; Short, Alexis; Cooper, Paige; Jones, Harlan; Manson, Spero; Buchwald, Dedra; Eide, Kristin; Gouldy, Andrea; Kelly, Erin; Langford, Nicole; McGee, Richard; Steer, Clifford; Unold, Thad; Weber-Main, Anne Marie; Báez, Adriana; Stiles, Jonathan; Pemu, Priscilla; Thompson, Winston; Gwathmey, Judith; Lawson, Kimberly; Johnson, Japera; Hall, Meldra; Paulsen, Douglas; Fouad, Mona; Smith, Ann; Luna, Rafael; Wilson, Donald; Adelsberger, Greg; Simenson, Drew; Cook, Abby; Feliu-Mojer, Monica; Harwood, Eileen; Jones, Amy; Branchaw, Janet; Thomas, Stephen; Butz, Amanda; Byars-Winston, Angela; House, Stephanie; McDaniels, Melissa; Quinn, Sandra; Rogers, Jenna; Spencer, Kim; Utzerath, Emily; Duplicate Of Weber-Main; Womack, Veronica

2017-01-01

Effective mentorship is critical to the success of early stage investigators, and has been linked to enhanced mentee productivity, self-efficacy, and career satisfaction. The mission of the National Research Mentoring Network (NRMN) is to provide all trainees across the biomedical, behavioral, clinical, and social sciences with evidence-based mentorship and professional development programming that emphasizes the benefits and challenges of diversity, inclusivity, and culture within mentoring relationships, and more broadly the research workforce. The purpose of this paper is to describe the structure and activities of NRMN. NRMN serves as a national training hub for mentors and mentees striving to improve their relationships by better aligning expectations, promoting professional development, maintaining effective communication, addressing equity and inclusion, assessing understanding, fostering independence, and cultivating ethical behavior. Training is offered in-person at institutions, regional training, or national meetings, as well as via synchronous and asynchronous platforms; the growing training demand is being met by a cadre of NRMN Master Facilitators. NRMN offers career stage-focused coaching models for grant writing, and other professional development programs. NRMN partners with diverse stakeholders from the NIH-sponsored Diversity Program Consortium (DPC), as well as organizations outside the DPC to work synergistically towards common diversity goals. NRMN offers a virtual portal to the Network and all NRMN program offerings for mentees and mentors across career development stages. NRMNet provides access to a wide array of mentoring experiences and resources including MyNRMN, Guided Virtual Mentorship Program, news, training calendar, videos, and workshops. National scale and sustainability are being addressed by NRMN "Coaches-in-Training" offerings for more senior researchers to implement coaching models across the nation. "Shark Tanks" provide

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.)

Community pharmacist participation in a practice-based research network: a report from the Medication Safety Research Network of Indiana (Rx-SafeNet).

Science.gov (United States)

Patel, Puja; Hemmeger, Heather; Kozak, Mary Ann; Gernant, Stephanie A; Snyder, Margie E

2015-01-01

To describe the experiences and opinions of pharmacists serving as site coordinators for the Medication Safety Research Network of Indiana (Rx-SafeNet). Retail chain, independent, and hospital/health system outpatient community pharmacies throughout Indiana, with a total of 127 pharmacy members represented by 26 site coordinators. Rx-SafeNet, a statewide practice-based research network (PBRN) formed in 2010 and administered by the Purdue University College of Pharmacy. Barriers and facilitators to participation in available research studies, confidence participating in research, and satisfaction with overall network communication. 22 of 26 site coordinators participated, resulting in an 85% response rate. Most (72.2%) of the respondents had received a doctor of pharmacy degree, and 13.6% had postgraduate year (PGY)1 residency training. The highest reported benefits of PBRN membership were an enhanced relationship with the Purdue University College of Pharmacy (81% agreed or strongly agreed) and enhanced professional development (80% agreed or strongly agreed). Time constraints were identified as the greatest potential barrier to network participation, reported by 62% of respondents. In addition, the majority (59%) of survey respondents identified no prior research experience. Last, respondents' confidence in performing research appeared to increase substantially after becoming network members, with 43% reporting a lack of confidence in engaging in research before joining the network compared with 90% reporting confidence after joining the network. In general, Rx-SafeNet site coordinators appeared to experience increased confidence in research engagement after joining the network. While respondents identified a number of benefits associated with network participation, concerns about potential time constraints remained a key barrier to participation. These findings will assist network leadership in identifying opportunities to positively increase member participation

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)

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

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....

IAEA Activities supporting education and training at research reactors

International Nuclear Information System (INIS)

Peld, N.D.; Ridikas, D.

2013-01-01

Full-text: Through the provision of neutrons for experiments and their historical association with universities, research reactors have played a prominent role in nuclear education and training of students, scientists and radiation workers. Today education and training remains the foremost application of research reactors, involving close to 160 facilities out of 246 operational. As part of its mandate to facilitate and expand the contribution of atomic energy to peace, health and prosperity throughout the world, the IAEA administers a number of activities intended to promote nuclear research and enable access to nuclear technology for peaceful purposes, one of which is the support of various education and training measures involving research reactors. In the last 5 years, education and training has formed one pillar for the creation of research reactor coalitions and networks to pool their resources and offer joint programmes, such as the on-going Group Fellowship Training Course. Conducted mainly through the Eastern European Research Reactor Initiative, this programme is a periodic sic week course for young scientists and engineers on nuclear techniques and administration jointly conducted at several member research reactor institutes. Organization of similar courses is under consideration in Latin America and the Asia-Pacific Region, also with support from the IAEA. Additionally, four research reactor institutes have begun offering practical education courses through virtual reactor experiments and operation known as the Internet Reactor Laboratory. Through little more than an internet connection and projection screens, university science departments can be connected regionally or bilaterally with the control room o a research reactor for various training activities. Finally, two publications are being prepared, namely Hands-On Training Courses Using Research Reactors and Accelerators, and Compendium on Education and training Based on Research Reactors. These

Guidelines for Biosafety Training Programs for Workers Assigned to BSL-3 Research Laboratories.

Science.gov (United States)

Homer, Lesley C; Alderman, T Scott; Blair, Heather Ann; Brocard, Anne-Sophie; Broussard, Elaine E; Ellis, Robert P; Frerotte, Jay; Low, Eleanor W; McCarthy, Travis R; McCormick, Jessica M; Newton, JeT'Aime M; Rogers, Francine C; Schlimgen, Ryan; Stabenow, Jennifer M; Stedman, Diann; Warfield, Cheryl; Ntiforo, Corrie A; Whetstone, Carol T; Zimmerman, Domenica; Barkley, Emmett

2013-03-01

The Guidelines for Biosafety Training Programs for Workers Assigned to BSL-3 Research Laboratories were developed by biosafety professionals who oversee training programs for the 2 national biocontainment laboratories (NBLs) and the 13 regional biocontainment laboratories (RBLs) that participate in the National Institute of Allergy and Infectious Diseases (NIAID) NBL/RBL Network. These guidelines provide a general training framework for biosafety level 3 (BSL-3) high-containment laboratories, identify key training concepts, and outline training methodologies designed to standardize base knowledge, understanding, and technical competence of laboratory personnel working in high-containment laboratories. Emphasis is placed on building a culture of risk assessment-based safety through competency training designed to enhance understanding and recognition of potential biological hazards as well as methods for controlling these hazards. These guidelines may be of value to other institutions and academic research laboratories that are developing biosafety training programs for BSL-3 research.

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.

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.

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.

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....

EUFAR training opportunities to advance European airborne research

Science.gov (United States)

Reusen, I.; Brenguier, J.-L.; Brown, P.; Wendish, M.

2009-04-01

EUFAR, EUropean Facilities for Airborne Research, is an FP7 project (http://www.eufar.net) funded by the European Commission with 33 partners that aims at providing and improving the access to European airborne facilities (i.e. aircraft, airborne instruments, data processing centres) for researchers in environmental and geo-sciences through Networking Activities, Transnational Access and Joint Research Activities. This paper reports on the training opportunities within EUFAR for European researchers. In EUFAR three types of training opportunities are offered: 1) Participate in training courses (ET-TC) 2) Join an existing field campaign (ET-EC) 3) Participate in the design of a new field campaign (ET-TA), in the frame of EUFAR Transnational Access and tutored by more experienced researchers. During the 4-year EUFAR project (2008-2012), 4 training courses covering the complete chain from acquisition to interpretation of airborne data and images will be organised during spring/summer for early-stage researchers as well as university lecturers (new in FP7 EUFAR) in airborne research. The training courses will have an equal focus on theory and practical training/demonstration and each training course will be accompanied by a "student" airborne field campaign. Participants will be trained by top-class scientists, aircraft and/or instrument operators and each participant will get the opportunity to design his/her own experiment and to participate to that flight experiment. Furthermore, researchers have the opportunity to join an existing field campaign and work with more experienced researchers, aircraft and/or instrument operators. The list of airborne field campaigns open to join and the eligibility criteria, can be consulted at the EUFAR website. Finally, researchers have the opportunity to participate in the design of a new field campaign in the frame of EUFAR Transnational Access (TA). TA provides access to either aircraft or instrumentation that are not otherwise

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.

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

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.

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.

Historical overview of the process of training of Senior Technician in Nursing in relation to research training

Directory of Open Access Journals (Sweden)

Lola Rosario Altamirano-Baquerizo

2016-07-01

Full Text Available The formation of the Senior Technician in Nursing has been studied by many researchers as a process that integrates knowledge, skills and attitudes for the development of techniques and nursing procedures to healthy individuals or patients, families and community in the various bodies of the Network Asistencial. This paper identifies the training needs of Technician in Nursing, as it has found some shortcomings that do not refer to little curricular and methodological intent of the research training of this professional in the Bolivarian Technological Institute of Ecuador. In addressing the characterization of the historical background of the formation of the Senior Technician in Nursing el-logical historical method to use as documentation reviewing educational programs and policies of vocational training the technical level in the Ecuadorian higher education was used.

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

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.

The Conundrum of Training and Capacity Building for People with Learning Disabilities Doing Research

Science.gov (United States)

Nind, Melanie; Chapman, Rohhss; Seale, Jane; Tilley, Liz

2016-01-01

Background: This study explores the training involved when people with learning disabilities take their place in the community as researchers. This was a theme in a recent UK seminar series where a network of researchers explored pushing the boundaries of participatory research. Method: Academics, researchers with learning disabilities, supporters…

[Cooperative Cardiovascular Disease Research Network (RECAVA)].

Science.gov (United States)

García-Dorado, David; Castro-Beiras, Alfonso; Díez, Javier; Gabriel, Rafael; Gimeno-Blanes, Juan R; Ortiz de Landázuri, Manuel; Sánchez, Pedro L; Fernández-Avilés, Francisco

2008-01-01

Today, cardiovascular disease is the principal cause of death and hospitalization in Spain, and accounts for an annual healthcare budget of more than 4000 million euros. Consequently, early diagnosis, effective prevention, and the optimum treatment of cardiovascular disease present a significant social and healthcare challenge for the country. In this context, combining all available resources to increase the efficacy and healthcare benefits of scientific research is a priority. This rationale prompted the establishment of the Spanish Cooperative Cardiovascular Disease Research Network, or RECAVA (Red Temática de Investigación Cooperativa en Enfermedades Cardiovasculares), 5 years ago. Since its foundation, RECAVA's activities have focused on achieving four objectives: a) to facilitate contacts between basic, clinical and epidemiological researchers; b) to promote the shared use of advanced technological facilities; c) to apply research results to clinical practice, and d) to train a new generation of translational cardiovascular researchers in Spain. At present, RECAVA consists of 41 research groups and seven shared technological facilities. RECAVA's research strategy is based on a scientific design matrix centered on the most important cardiovascular processes. The level of RECAVA's research activity is reflected in the fact that 28 co-authored articles were published in international journals during the first six months of 2007, with each involving contributions from at least two groups in the network. Finally, RECAVA also participates in the work of the Spanish National Center for Cardiovascular Research, or CNIC (Centro Nacional de Investigación Cardiovascular), and some established Biomedical Research Network Centers, or CIBER (Centros de Investigación Biomédica en RED), with the aim of consolidating the development of a dynamic multidisciplinary research framework that is capable of meeting the growing challenge that cardiovascular disease will present

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

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...

Building and strengthening capacity for cardiovascular research in Africa through technical training workshops: a report of the joint course on health research methods by the Clinical Research Education Networking and Consultancy and the Ivorian Society of Cardiology.

Science.gov (United States)

Dzekem, Bonaventure Suiru; Kacou, Jean Baptiste; Abanda, Martin; Kramoh, Euloge; Yapobi, Yves; Kingue, Samuel; Kengne, Andre Pascal; Dzudie, Anastase

Africa bears a quarter of the global burden of disease but contributes less than 2% of the global research publications on health, partially due to a lack of expertise and skills to carry out scientific research. We report on a short course on research methods organised by the Clinical Research Education Networking and Consultancy (CRENC) during the third international congress of the Ivorian Cardiac Society (SICARD) in Abidjan, Cote d'Ivoire. Results from the pre- and post-test evaluation during this course showed that African researchers could contribute more to scientific research and publications, provided adequate support and investment is geared towards the identification and training of motivated early-career scientists.

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)

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.

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.

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.

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.

Improving public health training and research capacity in Africa: a replicable model for linking training to health and socio-demographic surveillance data

Directory of Open Access Journals (Sweden)

Jill R. Williams

2010-08-01

Full Text Available Background: Research training for public health professionals is key to the future of public health and policy in Africa. A growing number of schools of public health are connected to health and socio-demographic surveillance system field sites in developing countries, in Africa and Asia in particular. Linking training programs with these sites provides important opportunities to improve training, build local research capacity, foreground local health priorities, and increase the relevance of research to local health policy. Objective: To increase research training capacity in public health programs by providing targeted training to students and increasing the accessibility of existing data. Design: This report is a case study of an approach to linking public health research and training at the University of the Witwatersrand. We discuss the development of a sample training database from the Agincourt Health and Socio-demographic Surveillance System in South Africa and outline a concordant transnational intensive short course on longitudinal data analysis offered by the University of the Witwatersrand and the University of Colorado-Boulder. This case study highlights ways common barriers to linking research and training can be overcome. Results and Conclusions: This collaborative effort demonstrates that linking training to ongoing data collection can improve student research, accelerate student training, and connect students to an international network of scholars. Importantly, the approach can be adapted to other partnerships between schools of public health and longitudinal research sites.

The Pediatric Emergency Care Applied Research Network: a history of multicenter collaboration in the United States.

Science.gov (United States)

Tzimenatos, Leah; Kim, Emily; Kuppermann, Nathan

2015-01-01

In this article, we review the history and progress of a large multicenter research network pertaining to emergency medical services for children. We describe the history, organization, infrastructure, and research agenda of the Pediatric Emergency Care Applied Research Network and highlight some of the important accomplishments since its inception. We also describe the network's strategy to grow its research portfolio, train new investigators, and study how to translate new evidence into practice. This strategy ensures not only the sustainability of the network in the future but the growth of research in emergency medical services for children in general.

Practice-based Research Network Research Good Practices (PRGPs): Summary of Recommendations.

Science.gov (United States)

Dolor, Rowena J; Campbell-Voytal, Kimberly; Daly, Jeanette; Nagykaldi, Zsolt J; O'Beirne, Maeve; Sterling, Pamela; Fagnan, Lyle J; Levy, Barcey; Michaels, LeAnn; Louks, Hannah A; Smith, Paul; Aspy, Cheryl B; Patterson, V Beth; Kano, Miria; Sussman, Andrew L; Williams, Robert; Neale, Anne Victoria

2015-12-01

Practice-based research networks (PBRNs) conduct research in community settings, which poses quality control challenges to the integrity of research, such as study implementation and data collection. A foundation for improving research processes within PBRNs is needed to ensure research integrity. Network directors and coordinators from seven U.S.-based PBRNs worked with a professional team facilitator during semiannual in-person meetings and monthly conference calls to produce content for a compendium of recommended research practices specific to the context of PBRNs. Participants were assigned to contribute content congruent with their expertise. Feedback on the draft document was obtained from attendees at the preconference workshop at the annual PBRN meeting in 2013. A revised document was circulated to additional PBRN peers prior to finalization. The PBRN Research Good Practices (PRGPs) document is organized into four chapters: (1) Building PBRN Infrastructure; (2) Study Development and Implementation; (3) Data Management, and (4) Dissemination Policies. Each chapter contains an introduction, detailed procedures for each section, and example resources with information links. The PRGPs is a PBRN-specific resource to facilitate PBRN management and staff training, to promote adherence to study protocols, and to increase validity and generalizability of study findings. © 2015 Wiley Periodicals, Inc.

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.

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.

The network researchers' network

DEFF Research Database (Denmark)

Henneberg, Stephan C.; Jiang, Zhizhong; Naudé, Peter

2009-01-01

The Industrial Marketing and Purchasing (IMP) Group is a network of academic researchers working in the area of business-to-business marketing. The group meets every year to discuss and exchange ideas, with a conference having been held every year since 1984 (there was no meeting in 1987). In thi......The Industrial Marketing and Purchasing (IMP) Group is a network of academic researchers working in the area of business-to-business marketing. The group meets every year to discuss and exchange ideas, with a conference having been held every year since 1984 (there was no meeting in 1987......). In this paper, based upon the papers presented at the 22 conferences held to date, we undertake a Social Network Analysis in order to examine the degree of co-publishing that has taken place between this group of researchers. We identify the different components in this database, and examine the large main...

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.

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

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

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.

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.

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...

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.

Health policy and systems research training: global status and recommendations for action

Science.gov (United States)

Tancred, Tara M; Schleiff, Meike; Peters, David H

2016-01-01

Abstract Objective To investigate the characteristics of health policy and systems research training globally and to identify recommendations for improvement and expansion. Methods We identified institutions offering health policy and systems research training worldwide. In 2014, we recruited participants from identified institutions for an online survey on the characteristics of the institutions and the courses given. Survey findings were explored during in-depth interviews with selected key informants. Findings The study identified several important gaps in health policy and systems research training. There were few courses in central and eastern Europe, the Middle East, North Africa or Latin America. Most (116/152) courses were instructed in English. Institutional support for courses was often lacking and many institutions lacked the critical mass of trained individuals needed to support doctoral and postdoctoral students. There was little consistency between institutions in definitions of the competencies required for health policy and systems research. Collaboration across disciplines to provide the range of methodological perspectives the subject requires was insufficient. Moreover, the lack of alternatives to on-site teaching may preclude certain student audiences such as policy-makers. Conclusion Training in health policy and systems research is important to improve local capacity to conduct quality research in this field. We provide six recommendations to improve the content, accessibility and reach of training. First, create a repository of information on courses. Second, establish networks to support training. Third, define competencies in health policy and systems research. Fourth, encourage multidisciplinary collaboration. Fifth, expand the geographical and language coverage of courses. Finally, consider alternative teaching formats. PMID:27429488

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.

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...

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

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.

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…

A research mentor training curriculum for clinical and translational researchers.

Science.gov (United States)

Pfund, Christine; House, Stephanie; Spencer, Kimberly; Asquith, Pamela; Carney, Paula; Masters, Kristyn S; McGee, Richard; Shanedling, Janet; Vecchiarelli, Stephanie; Fleming, Michael

2013-02-01

To design and evaluate a research mentor training curriculum for clinical and translational researchers. The resulting 8-hour curriculum was implemented as part of a national mentor training trial. The mentor training curriculum was implemented with 144 mentors at 16 academic institutions. Facilitators of the curriculum participated in a train-the-trainer workshop to ensure uniform delivery. The data used for this report were collected from participants during the training sessions through reflective writing, and following the last training session via confidential survey with a 94% response rate. A total of 88% of respondents reported high levels of satisfaction with the training experience, and 90% noted they would recommend the training to a colleague. Participants also reported significant learning gains across six mentoring competencies as well as specific impacts of the training on their mentoring practice. The data suggest the described research mentor training curriculum is an effective means of engaging research mentors to reflect upon and improve their research mentoring practices. The training resulted in high satisfaction, self-reported skill gains as well as behavioral changes of clinical and translational research mentors. Given success across 16 diverse sites, this training may serve as a national model. © 2012 Wiley Periodicals, Inc.

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.

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.

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.

Challenges in Measuring Benefit of Clinical Research Training Programs--the ASH Clinical Research Training Institute Example.

Science.gov (United States)

Sung, Lillian; Crowther, Mark; Byrd, John; Gitlin, Scott D; Basso, Joe; Burns, Linda

2015-12-01

The American Society of Hematology developed the Clinical Research Training Institute (CRTI) to address the lack of training in patient-oriented research among hematologists. As the program continues, we need to consider metrics for measuring the benefits of such a training program. This article addresses the benefits of clinical research training programs. The fundamental and key components are education and mentorship. However, there are several other benefits including promotion of collaboration, job and advancement opportunities, and promotion of work-life balance. The benefits of clinical research training programs need to be measured so that funders and society can judge if they are worth the investment in time and resources. Identification of elements that are important to program benefit is essential to measuring the benefit of the program as well as program planning. Future work should focus on the constructs which contribute to benefits of clinical research training programs such as CRTI.

Basic science research in urology training.

Science.gov (United States)

Eberli, D; Atala, A

2009-04-01

The role of basic science exposure during urology training is a timely topic that is relevant to urologic health and to the training of new physician scientists. Today, researchers are needed for the advancement of this specialty, and involvement in basic research will foster understanding of basic scientific concepts and the development of critical thinking skills, which will, in turn, improve clinical performance. If research education is not included in urology training, future urologists may not be as likely to contribute to scientific discoveries.Currently, only a minority of urologists in training are currently exposed to significant research experience. In addition, the number of physician-scientists in urology has been decreasing over the last two decades, as fewer physicians are willing to undertake a career in academics and perform basic research. However, to ensure that the field of urology is driving forward and bringing novel techniques to patients, it is clear that more research-trained urologists are needed. In this article we will analyse the current status of basic research in urology training and discuss the importance of and obstacles to successful addition of research into the medical training curricula. Further, we will highlight different opportunities for trainees to obtain significant research exposure in urology.

Training methods and facilities on reactor and simulators at the Grenoble Nuclear Research Centre

International Nuclear Information System (INIS)

Destot, M.; Siebert, S.

1987-01-01

Siloette is a CEA unit with a threshold vocation: operation of the Siloette 100 KW pool-type research reactor; basic training in reactor physics for nuclear power plant operators; and production of nuclear power plant simulators: PWR, GCR and more generally of all types of industrial unit simulators, thermal power plant, network, chemical plant, etc. From this experience, they would emphasize in particular the synergy arising from these complementary activities, the essential role of training in basic principles as a complement to operation training, and the ever-increasing importance of design ergonomics of the training means

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...

Researching attitudes in school training abstract

Directory of Open Access Journals (Sweden)

Julio Fernando Acosta Muñoz

2013-07-01

Full Text Available This work is a reflection article, product of the research referred to ‘Researching Attitudes of Young People in Research Training at the School’. The field of interest is focused on developing the contrast, of theoretical and critical type, facing the research training from the proposal of different research attitudes in the training processes of the school. Methodologically, it is constructed from the theoretical review of authors, exploring the problem at the same time. First the difficulties, expressed about the research training and the relationship of this type of education with traditional positivist view, are described. Within the text, it is proposed to visualize different attitudes in the scholar research training (childhood experience, self-knowledge, and the reflective and critical condition, based on the subjectivity of the classroom, placing the trainee as an object of reflection and action in his/her researcher process.

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.

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.

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

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.

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.)

INITIAL TRAINING OF RESEARCHERS

Directory of Open Access Journals (Sweden)

Karina Alejandra Cruz-Pallares

2015-07-01

Full Text Available The document presents results of a research that used as strategy a complementary training project with thirty-three students of a Bachelors Degree in Primary School 1997(DPS,1997 of an Education Faculty for the initial training of investigators, applied by four teachers members of the academic research group in Mexico; that develops through process of action research methodology. Highlighted in results is the strengthening of the competition of reading, understanding and writing scientific texts, which is analogous to the first feature of the graduate profile called intellectual skills. Among the conclusions it is emphasized that the initial training of teachers in a task that is quite interesting, challenging and complex, as is the educational complex phenomenon.

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...

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.

An evaluation of a data linkage training workshop for research ethics committees.

Science.gov (United States)

Tan, Kate M; Flack, Felicity S; Bear, Natasha L; Allen, Judy A

2015-03-04

In Australia research projects proposing the use of linked data require approval by a Human Research Ethics Committee (HREC). A sound evaluation of the ethical issues involved requires understanding of the basic mechanics of data linkage, the associated benefits and risks, and the legal context in which it occurs. The rapidly increasing number of research projects utilising linked data in Australia has led to an urgent need for enhanced capacity of HRECs to review research applications involving this emerging research methodology. The training described in this article was designed to respond to an identified need among the data linkage units in the Australian Population Health Research Network (PHRN) and HREC members in Australia. Five one-day face to face workshops were delivered in the study period to a total of 98 participants. Participants in the workshops represented all six categories of HREC membership composition listed in the National Health and Medical Research Centres' (NHMRC) National Statement on Ethical Conduct in Human Research. Participants were assessed at three time points, prior to the training (T1), immediately after the training (T2) and 8 to 17 months after the training (T3). Ninety participants completed the pre and post questionnaires; 58 of them completed the deferred questionnaire. Participants reported significant improvements in levels of knowledge, understanding and skills in each of the eight areas evaluated. The training was beneficial for those with prior experience in the area of ethics and data linkage as well as those with no prior exposure. Our preliminary work in this area demonstrates that the provision of intensive face to face ethics training in data linkage is feasible and has a significant impact on participant's confidence in reviewing HREC applications.

Excellence in Radiation Research for the 21st Century (EIRR21): Description of an Innovative Research Training Program

International Nuclear Information System (INIS)

P'ng, Christine; Ito, Emma; How, Christine; Bezjak, Andrea; Bristow, Rob; Catton, Pam; Fyles, Anthony; Gospodarowicz, Mary; Jaffray, David; Kelley, Shana; Wong Shun; Liu Feifei

2012-01-01

Purpose: To describe and assess an interdisciplinary research training program for graduate students, postdoctoral fellows, and clinical fellows focused on radiation medicine; funded by the Canadian Institutes for Health Research since 2003, the program entitled “Excellence in Radiation Research for the 21st Century” (EIRR21) aims to train the next generation of interdisciplinary radiation medicine researchers. Methods and Materials: Online surveys evaluating EIRR21 were sent to trainees (n=56), mentors (n=36), and seminar speakers (n=72). Face-to-face interviews were also conducted for trainee liaisons (n=4) and participants in the international exchange program (n=2). Results: Overall response rates ranged from 53% (mentors) to 91% (trainees). EIRR21 was well received by trainees, with the acquisition of several important skills related to their research endeavors. An innovative seminar series, entitled Brainstorm sessions, imparting “extracurricular” knowledge in intellectual property protection, commercialization strategies, and effective communication, was considered to be the most valuable component of the program. Networking with researchers in other disciplines was also facilitated owing to program participation. Conclusions: EIRR21 is an innovative training program that positively impacts the biomedical community and imparts valuable skill sets to foster success for the future generation of radiation medicine researchers.

Excellence in Radiation Research for the 21st Century (EIRR21): Description of an Innovative Research Training Program

Energy Technology Data Exchange (ETDEWEB)

P' ng, Christine [Radiation Medicine Program, University Health Network, Toronto, Ontario (Canada); Ito, Emma [Radiation Medicine Program, University Health Network, Toronto, Ontario (Canada); Ontario Cancer Institute, Toronto, Ontario (Canada); How, Christine [Ontario Cancer Institute, Toronto, Ontario (Canada); Department of Medical Biophysics, University of Toronto, Toronto, Ontario (Canada); Bezjak, Andrea [Radiation Medicine Program, University Health Network, Toronto, Ontario (Canada); Department of Radiation Oncology, University of Toronto, Toronto, Ontario (Canada); Bristow, Rob [Radiation Medicine Program, University Health Network, Toronto, Ontario (Canada); Ontario Cancer Institute, Toronto, Ontario (Canada); Department of Medical Biophysics, University of Toronto, Toronto, Ontario (Canada); Department of Radiation Oncology, University of Toronto, Toronto, Ontario (Canada); Catton, Pam; Fyles, Anthony; Gospodarowicz, Mary [Radiation Medicine Program, University Health Network, Toronto, Ontario (Canada); Department of Radiation Oncology, University of Toronto, Toronto, Ontario (Canada); Jaffray, David [Radiation Medicine Program, University Health Network, Toronto, Ontario (Canada); Department of Medical Biophysics, University of Toronto, Toronto, Ontario (Canada); Department of Radiation Oncology, University of Toronto, Toronto, Ontario (Canada); Kelley, Shana [Department of Biochemistry, University of Toronto, Toronto, Ontario (Canada); Wong Shun [Department of Radiation Oncology, University of Toronto, Toronto, Ontario (Canada); Odette Cancer Center, Toronto, Ontario (Canada); Liu Feifei, E-mail: Fei-Fei.Liu@rmp.uhn.on.ca [Radiation Medicine Program, University Health Network, Toronto, Ontario (Canada); Ontario Cancer Institute, Toronto, Ontario (Canada); Department of Medical Biophysics, University of Toronto, Toronto, Ontario (Canada); Department of Radiation Oncology, University of Toronto, Toronto, Ontario (Canada)

2012-08-01

Purpose: To describe and assess an interdisciplinary research training program for graduate students, postdoctoral fellows, and clinical fellows focused on radiation medicine; funded by the Canadian Institutes for Health Research since 2003, the program entitled 'Excellence in Radiation Research for the 21st Century' (EIRR21) aims to train the next generation of interdisciplinary radiation medicine researchers. Methods and Materials: Online surveys evaluating EIRR21 were sent to trainees (n=56), mentors (n=36), and seminar speakers (n=72). Face-to-face interviews were also conducted for trainee liaisons (n=4) and participants in the international exchange program (n=2). Results: Overall response rates ranged from 53% (mentors) to 91% (trainees). EIRR21 was well received by trainees, with the acquisition of several important skills related to their research endeavors. An innovative seminar series, entitled Brainstorm sessions, imparting 'extracurricular' knowledge in intellectual property protection, commercialization strategies, and effective communication, was considered to be the most valuable component of the program. Networking with researchers in other disciplines was also facilitated owing to program participation. Conclusions: EIRR21 is an innovative training program that positively impacts the biomedical community and imparts valuable skill sets to foster success for the future generation of radiation medicine researchers.

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.

An entrepreneurial training model to enhance undergraduate training in biomedical research.

Science.gov (United States)

Kamangar, Farin; Silver, Gillian; Hohmann, Christine; Hughes-Darden, Cleo; Turner-Musa, Jocelyn; Haines, Robert Trent; Jackson, Avis; Aguila, Nelson; Sheikhattari, Payam

2017-01-01

Undergraduate students who are interested in biomedical research typically work on a faculty member's research project, conduct one distinct task (e.g., running gels), and, step by step, enhance their skills. This "apprenticeship" model has been helpful in training many distinguished scientists over the years, but it has several potential drawbacks. For example, the students have limited autonomy, and may not understand the big picture, which may result in students giving up on their goals for a research career. Also, the model is costly and may greatly depend on a single mentor. The NIH Building Infrastructure Leading to Diversity (BUILD) Initiative has been established to fund innovative undergraduate research training programs and support institutional and faculty development of the recipient university. The training model at Morgan State University (MSU), namely " A S tudent- C entered En trepreneurship D evelopment training model" (ASCEND), is one of the 10 NIH BUILD-funded programs, and offers a novel, experimental "entrepreneurial" training approach. In the ASCEND training model, the students take the lead. They own the research, understand the big picture, and experience the entire scope of the research process, which we hypothesize will lead to a greater sense of self-efficacy and research competency, as well as an enhanced sense of science identity. They are also immersed in environments with substantial peer support, where they can exchange research ideas and share experiences. This is important for underrepresented minority students who might have fewer role models and less peer support in conducting research. In this article, we describe the MSU ASCEND entrepreneurial training model's components, rationale, and history, and how it may enhance undergraduate training in biomedical research that may be of benefit to other institutions. We also discuss evaluation methods, possible sustainability solutions, and programmatic challenges that can affect all

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.

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

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…

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.

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.

Enhancing adaptive capacity for restoring fire-dependent ecosystems: the Fire Learning Network's Prescribed Fire Training Exchanges

Directory of Open Access Journals (Sweden)

Andrew G. Spencer

2015-09-01

Full Text Available Prescribed fire is a critical tool for promoting restoration and increasing resilience in fire-adapted ecosystems, but there are barriers to its use, including a shortage of personnel with adequate ecological knowledge and operational expertise to implement prescribed fire across multijurisdictional landscapes. In the United States, recognized needs for both professional development and increased use of fire are not being met, often because of institutional limitations. The Fire Learning Network has been characterized as a multiscalar, collaborative network that works to enhance the adaptive capacity of fire management institutions, and this network developed the Prescribed Fire Training Exchanges (TREXs to address persistent challenges in increasing the capacity for prescribed fire implementation. Our research was designed to investigate where fire professionals face professional barriers, how the TREX addresses these, and in what ways the TREX may be contributing to the adaptive capacity of fire management institutions. We evaluated the training model using surveys, interviews, focus groups, and participant observation. We found that, although the training events cannot overcome all institutional barriers, they incorporate the key components of professional development in fire; foster collaboration, learning, and network building; and provide flexible opportunities with an emphasis on local context to train a variety of professionals with disparate needs. The strategy also offers an avenue for overcoming barriers faced by contingent and nonfederal fire professionals in attaining training and operational experience, thereby increasing the variety of actors and resources involved in fire management. Although it is an incremental step, the TREX is contributing to the adaptive capacity of institutions in social-ecological systems in which fire is a critical ecological process.

Summer research training for medical students: impact on research self-efficacy.

Science.gov (United States)

Black, Michelle L; Curran, Maureen C; Golshan, Shahrokh; Daly, Rebecca; Depp, Colin; Kelly, Carolyn; Jeste, Dilip V

2013-12-01

There is a well-documented shortage of physician researchers, and numerous training programs have been launched to facilitate development of new physician scientists. Short-term research training programs are the most practical form of research exposure for most medical students, and the summer between their first and second years of medical school is generally the longest period they can devote solely to research. The goal of short-term training programs is to whet the students' appetite for research and spark their interest in the field. Relatively little research has been done to test the effectiveness of short-term research training programs. In an effort to examine short-term effects of three different NIH-funded summer research training programs for medical students, we assessed the trainees' (N = 75) research self-efficacy prior to and after the programs using an 11-item scale. These hands-on training programs combined experiential, didactic, and mentoring elements. The students demonstrated a significant increase in their self-efficacy for research. Trainees' gender, ranking of their school, type of research, and specific content of research project did not predict improvement. Effect sizes for different types of items on the scale varied, with the largest gain seen in research methodology and communication of study findings. © 2013 Wiley Periodicals, Inc.

Broadening measures of success: results of a behavioral health translational research training program.

Science.gov (United States)

Baldwin, Julie A; Williamson, Heather J; Eaves, Emery R; Levin, Bruce L; Burton, Donna L; Massey, Oliver T

2017-07-24

While some research training programs have considered the importance of mentoring in inspiring professionals to engage in translational research, most evaluations emphasize outcomes specific to academic productivity as primary measures of training program success. The impact of such training or mentoring programs on stakeholders and local community organizations engaged in translational research efforts has received little attention. The purpose of this evaluation is to explore outcomes other than traditional academic productivity in a translational research graduate certificate program designed to pair graduate students and behavioral health professionals in collaborative service-learning projects. Semi-structured qualitative interviews with scholars, community mentors, and academic mentors were conducted regarding a translational research program to identify programmatic impacts. Interviews were transcribed and coded by the research team to identify salient themes related to programmatic outcomes. Results are framed using the Translational Research Impact Scale which is organized into three overarching domains of potential impact: (1) research-related impacts, (2) translational impacts, and (3) societal impacts. This evaluation demonstrates the program's impact in all three domains of the TRIS evaluation framework. Graduate certificate participants (scholars) reported that gaining experience in applied behavioral health settings added useful skills and expertise to their present careers and increased their interest in pursuing translational research. Scholars also described benefits resulting from networks gained through participation in the program, including valuable ties between the university and community behavioral health organizations. This evaluation of the outcomes of a graduate certificate program providing training in translational research highlights the need for more community-oriented and practice-based measures of success. Encouraging practitioner

International research networks in pharmaceuticals

DEFF Research Database (Denmark)

Cantner, Uwe; Rake, Bastian

2014-01-01

of scientific publications related to pharmaceutical research and applying social network analysis, we find that both the number of countries and their connectivity increase in almost all disease group specific networks. The cores of the networks consist of high income OECD countries and remain rather stable......Knowledge production and scientific research have become increasingly more collaborative and international, particularly in pharmaceuticals. We analyze this tendency in general and tie formation in international research networks on the country level in particular. Based on a unique dataset...... over time. Using network regression techniques to analyze the network dynamics our results indicate that accumulative advantages based on connectedness and multi-connectivity are positively related to changes in the countries' collaboration intensity whereas various indicators on similarity between...

Research, Boundaries, and Policy in Networked Learning

DEFF Research Database (Denmark)

This book presents cutting-edge, peer reviewed research on networked learning organized by three themes: policy in networked learning, researching networked learning, and boundaries in networked learning. The "policy in networked learning" section explores networked learning in relation to policy...... networks, spaces of algorithmic governance and more. The "boundaries in networked learning" section investigates frameworks of students' digital literacy practices, among other important frameworks in digital learning. Lastly, the "research in networked learning" section delves into new research methods...

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.

Encouraging student-driven clinical research in Germany: the CHIR-Net SIGMA network

Directory of Open Access Journals (Sweden)

Frey Pia-Elena

2017-11-01

Full Text Available Evidence should define and guide modern clinical care, yet many relevant questions in surgical practice remain unconfirmed by substantial data. Evidence-based medicine requires both the implementation of its principles in day-to-day work and the acquisition of new evidence preferably by randomized controlled trials and systematic reviews. Meaningful clinical research, however, is challenging to conduct, and its overall infrastructure in Germany was, until recently, considered poor compared to other leading countries. Although this has been significantly improved after the establishment of the Study Center of the German Surgical Society (SDGC and the surgical clinical trial network CHIR-Net, limited focus has been put on the training, teaching, and recruitment of medical students to become competent clinical researchers and clinician scientists. To ensure continuing comprehensive clinical research in surgery, CHIR-Net aims to establish a student-driven multicenter research network in Germany, which is embedded in both the national CHIR-Net and the pan-European and international frameworks. Student-Initiated German Medical Audits (SIGMA is a product of the strong collaboration between clinical scientists and medical trainees, enabling students to contribute to high-quality clinical trials. Additionally, participants are offered extensive training to support the next generation of research-active clinicians. Starting on 2018, SIGMA will perform its first multicenter observational study in Germany.

Rescuing policy in tourism network research

DEFF Research Database (Denmark)

Dredge, Dianne

2018-01-01

Networks provide a powerful lens to understand complex relational entanglements that are transforming social, economic and political life. Through a discussion of the various streams of network research in tourism, this paper argues that policy matters run across and throughout these strands....... Rather than arguing for increased interest in tourism policy network research as a separate subfield, the paper argues for deeper theoretical engagement with the policy dimension in tourism network research. Researchers adopting a network ontology could gain considerable insights and open up new lines...

Research team training: moving beyond job descriptions.

Science.gov (United States)

Nelson, LaRon E; Morrison-Beedy, Dianne

2008-08-01

Providing appropriate training to research team members is essential to the effective implementation and overall operation of a research project. It is important to identify job requirements beyond those listed in the job description in order to fully assess basic and supplementary training needs. Training needs should be identified prior to and during the conduct of the study. Methods for delivering the training must also be identified. This article describes the identification of training needs and methods in the design of a research team training program using examples from an HIV prevention intervention trial with adolescent girls.

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)

Heroin assisted treatment and research networks

DEFF Research Database (Denmark)

Houborg, Esben; Munksgaard, Rasmus

2015-01-01

Purpose – The purpose of this paper is to map research communities related to heroin-assisted treatment (HAT) and the scientific network they are part of to determine their structure and content. Design/methodology/approach – Co-authorship as the basis for conducting social network analysis....... In total, 11 research communities were constructed with different scientific content. HAT research communities are closely connected to medical, psychiatric, and epidemiological research and very loosely connected to social research. Originality/value – The first mapping of the collaborative network HAT...... researchers using social network methodology...

Collaborative field research and training in occupational health and ergonomics.

Science.gov (United States)

Kogi, K

1998-01-01

Networking collaborative research and training in Asian developing countries includes three types of joint activities: field studies of workplace potentials for better safety and health, intensive action training for improvement of working conditions in small enterprises, and action-oriented workshops on low-cost improvements for managers, workers, and farmers. These activities were aimed at identifying workable strategies for making locally adjusted improvements in occupational health and ergonomics. Many improvements have resulted as direct outcomes. Most these improvements were multifaceted, low-cost, and practicable using local skills. Three common features of these interactive processes seem important in facilitating realistic improvements: 1) voluntary approaches building on local achievements; 2) the use of practical methods for identifying multiple improvements; and 3) participatory steps for achieving low-cost results first. The effective use of group work tools is crucial. Stepwise training packages have thus proven useful for promoting local problem-solving interventions based on voluntary initiatives.

Basic science research in urology training

Directory of Open Access Journals (Sweden)

D Eberli

2009-01-01

In this article we will analyse the current status of basic research in urology training and discuss the importance of and obstacles to successful addition of research into the medical training curricula. Further, we will highlight different opportunities for trainees to obtain significant research exposure in urology.

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.

Catalyzing Interdisciplinary Research and Training: Initial Outcomes and Evolution of the Affinity Research Collaboratives Model.

Science.gov (United States)

Ravid, Katya; Seta, Francesca; Center, David; Waters, Gloria; Coleman, David

2017-10-01

Team science has been recognized as critical to solving increasingly complex biomedical problems and advancing discoveries in the prevention, diagnosis, and treatment of human disease. In 2009, the Evans Center for Interdisciplinary Biomedical Research (ECIBR) was established in the Department of Medicine at Boston University School of Medicine as a new organizational paradigm to promote interdisciplinary team science. The ECIBR is made up of affinity research collaboratives (ARCs), consisting of investigators from different departments and disciplines who come together to study biomedical problems that are relevant to human disease and not under interdisciplinary investigation at the university. Importantly, research areas are identified by investigators according to their shared interests. ARC proposals are evaluated by a peer review process, and collaboratives are funded annually for up to three years.Initial outcomes of the first 12 ARCs show the value of this model in fostering successful biomedical collaborations that lead to publications, extramural grants, research networking, and training. The most successful ARCs have been developed into more sustainable organizational entities, including centers, research cores, translational research projects, and training programs.To further expand team science at Boston University, the Interdisciplinary Biomedical Research Office was established in 2015 to more fully engage the entire university, not just the medical campus, in interdisciplinary research using the ARC mechanism. This approach to promoting team science may be useful to other academic organizations seeking to expand interdisciplinary research at their institutions.

Application of neural networks and its prospect. 1. General comment on application to nuclear fusion and plasma researches

International Nuclear Information System (INIS)

Takeda, Tatsuoki

2006-01-01

The back ground of application of neutral networks to R and D of scientific field and increasing of application fields are stated. A definition of neural networks, the kinds of neural networks and functions, error back propagation, and generalization are explained. An application of multi-layer neural networks to nuclear fusion and plasma researches are described by inverse problem, interpolation, time series prediction, and computerized tomography. Some examples of researches such as MHD of plasma from magnetic probe data of fusion reactor systems, parameter prediction of distribution of the impurity spectra and the charge exchange neutral particle energy spectra, disruption prediction, and residual minimization training neural network are commented. (S.Y.)

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 ...

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.

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.

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

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.

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...

Interventionist Research as a Network

DEFF Research Database (Denmark)

Boulus, Nina

2010-01-01

can be seen as network effects—they are produced, supported and enacted by the network. Hence, the capacity of the interventionist researcher to act in a particular role is neither located within the researcher nor the research project, but in particular socio-material arrangements. Accordingly, roles...

Neutrons for research and training

International Nuclear Information System (INIS)

Villa, M.; Bichler, M.; Hameed, F.; Jericha, E.; Steinhauser, G.; Sterba, J.H.; Boeck, H.

2008-01-01

The 250 kW TRIGA Mark-II reactor operates since March 1962 at the Atomic Institute in Vienna, Austria. Its main tasks are nuclear education and training in the fields of neutron- and solid state physics, nuclear technology, reactor safety, radiochemistry, radiation protection and dosimetry, and low temperature physics and fusion research. Academic research is carried out by students in the above mentioned fields co-ordinated and supervised by about 80 staff members with the aim of a master- or PhD degree in one of the above mentioned areas. During the past 15 years about 600 students graduated through the Atomic Institute. The paper focuses on the results in neutron- and solid state physics and the co-operation between the low power TRIGA reactor with high flux neutron sources in Europe. The use of the TRIGA reactor at the Atomic Institute in Vienna as an irradiation facility in neutron activation analysis has a remarkable history. Present research work includes the recent determination of the precise half-life of 182 Hf and the participation in an archaeological long-term research programme. The TRIGA reactor operated by the Atomic Institute is now the only nuclear facility in Austria. Although Austria follows a dedicated anti-nuclear policy, the Atomic Institute enjoys a relatively undisturbed nuclear freedom in its nuclear activities. This allows us to use the research reactor not only for academic training but also for international training courses especially in nuclear technology. The presentation will outline typical training programmes and summarizes the experience with international training courses. (authors)

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.

Neural network approaches to tracer identification as related to PIV research

International Nuclear Information System (INIS)

Seeley, C.H. Jr.

1992-12-01

Neural networks have become very powerful tools in many fields of interest. This thesis examines the application of neural networks to another rapidly growing field flow visualization. Flow visualization research is used to experimentally determine how fluids behave and to verify computational results obtained analytically. A form of flow visualization, particle image velocimetry (PIV). determines the flow movement by tracking neutrally buoyant particles suspended in the fluid. PIV research has begun to improve rapidly with the advent of digital imagers, which can quickly digitize an image into arrays of grey levels. These grey level arrays are analyzed to determine the location of the tracer particles. Once the particles positions have been determined across multiple image frames, it is possible to track their movements, and hence, the flow of the fluid. This thesis explores the potential of several different neural networks to identify the positions of the tracer particles. Among these networks are Backpropagation, Kohonen (counter-propagation), and Cellular. Each of these algorithms were employed in their basic form, and training and testing were performed on a synthetic grey level array. Modifications were then made to them in attempts to improve the results

Neural network approaches to tracer identification as related to PIV research

Energy Technology Data Exchange (ETDEWEB)

Seeley, C.H. Jr.

1992-12-01

Neural networks have become very powerful tools in many fields of interest. This thesis examines the application of neural networks to another rapidly growing field flow visualization. Flow visualization research is used to experimentally determine how fluids behave and to verify computational results obtained analytically. A form of flow visualization, particle image velocimetry (PIV). determines the flow movement by tracking neutrally buoyant particles suspended in the fluid. PIV research has begun to improve rapidly with the advent of digital imagers, which can quickly digitize an image into arrays of grey levels. These grey level arrays are analyzed to determine the location of the tracer particles. Once the particles positions have been determined across multiple image frames, it is possible to track their movements, and hence, the flow of the fluid. This thesis explores the potential of several different neural networks to identify the positions of the tracer particles. Among these networks are Backpropagation, Kohonen (counter-propagation), and Cellular. Each of these algorithms were employed in their basic form, and training and testing were performed on a synthetic grey level array. Modifications were then made to them in attempts to improve the results.

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...

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...

Training Research: Practical Recommendations for Maximum Impact

Science.gov (United States)

Beidas, Rinad S.; Koerner, Kelly; Weingardt, Kenneth R.; Kendall, Philip C.

2011-01-01

This review offers practical recommendations regarding research on training in evidence-based practices for mental health and substance abuse treatment. When designing training research, we recommend: (a) aligning with the larger dissemination and implementation literature to consider contextual variables and clearly defining terminology, (b) critically examining the implicit assumptions underlying the stage model of psychotherapy development, (c) incorporating research methods from other disciplines that embrace the principles of formative evaluation and iterative review, and (d) thinking about how technology can be used to take training to scale throughout all stages of a training research project. An example demonstrates the implementation of these recommendations. PMID:21380792

Using Social Network Research in HRM

DEFF Research Database (Denmark)

Kaše, Robert; King, Zella; Minbaeva, Dana

2013-01-01

; the impact of social networking sites on perceptions of relationships; and ethical issues in organizational network analysis, we propose specific suggestions to bring social network perspectives closer to HRM researchers and practitioners and rebalance our attention to people and to their relationships.......The article features a conversation between Rob Cross and Martin Kilduff about organizational network analysis in research and practice. It demonstrates the value of using social network perspectives in HRM. Drawing on the discussion about managing personal networks; managing the networks of others...

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...

An audit of food and beverage advertising on the Sydney metropolitan train network: regulation and policy implications

Directory of Open Access Journals (Sweden)

Emma Sainsbury

2017-05-01

Full Text Available Abstract Background Increased marketing of energy-dense, nutrient-poor foods has been identified as a driver of the global obesity epidemic and a priority area for preventative efforts. Local and international research has focused on the unhealthiness of television advertising, with limited research into the growing outdoor advertising industry. This study aimed to examine the extent of food and beverage advertising on the Sydney metropolitan train network, and to assess the nutritional quality of advertised products against the Australian Guide to Healthy Eating. Methods All 178 train stations on the Sydney metropolitan train network were surveyed in summer and winter. A survey tool was developed to collect information for all advertisements on and immediately surrounding the train station. Information included product, brand, location and advertisement format. Advertisements were coded by nutrition category, product subcategory and size. Chi-square, ANOVA and ANCOVA tests were conducted to test for differences in the amount of food and beverage advertising by season and area socioeconomic status (SES. Results Of 6931 advertisements identified, 1915 (27.6% were promoting a food or beverage. The majority of food and beverage advertisements were for unhealthy products; 84.3% were classified as discretionary, 8.0% core and 7.6% miscellaneous. Snack foods and sugar-sweetened beverages were the most frequently advertised products, regardless of season. Coca-Cola and PepsiCo were the largest advertisers on the network, contributing 10.9% and 6.5% of total advertisements respectively. There was no difference in the mean number of food and beverage advertisements by area SES, but the proportion of advertising that was for discretionary foods was highest in low SES areas (41.9%, p < 0.001. Conclusions The results indicate that, irrespective of season, food and beverage advertisements across the Sydney metropolitan train network are overwhelmingly for

An audit of food and beverage advertising on the Sydney metropolitan train network: regulation and policy implications.

Science.gov (United States)

Sainsbury, Emma; Colagiuri, Stephen; Magnusson, Roger

2017-05-22

Increased marketing of energy-dense, nutrient-poor foods has been identified as a driver of the global obesity epidemic and a priority area for preventative efforts. Local and international research has focused on the unhealthiness of television advertising, with limited research into the growing outdoor advertising industry. This study aimed to examine the extent of food and beverage advertising on the Sydney metropolitan train network, and to assess the nutritional quality of advertised products against the Australian Guide to Healthy Eating. All 178 train stations on the Sydney metropolitan train network were surveyed in summer and winter. A survey tool was developed to collect information for all advertisements on and immediately surrounding the train station. Information included product, brand, location and advertisement format. Advertisements were coded by nutrition category, product subcategory and size. Chi-square, ANOVA and ANCOVA tests were conducted to test for differences in the amount of food and beverage advertising by season and area socioeconomic status (SES). Of 6931 advertisements identified, 1915 (27.6%) were promoting a food or beverage. The majority of food and beverage advertisements were for unhealthy products; 84.3% were classified as discretionary, 8.0% core and 7.6% miscellaneous. Snack foods and sugar-sweetened beverages were the most frequently advertised products, regardless of season. Coca-Cola and PepsiCo were the largest advertisers on the network, contributing 10.9% and 6.5% of total advertisements respectively. There was no difference in the mean number of food and beverage advertisements by area SES, but the proportion of advertising that was for discretionary foods was highest in low SES areas (41.9%, p food and beverage advertisements across the Sydney metropolitan train network are overwhelmingly for unhealthy (discretionary) products. The results of this study highlight the inadequacy of Australia's voluntary self

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.

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

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.

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.

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.

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...

Strategic Research, Post-modern Universities and Research Training

NARCIS (Netherlands)

Rip, Arie

2004-01-01

The old division of labour between fundamental and applied or problem-oriented research has almost disappeared, and with it, the functional distinctions between universities, public labs and industrial and other private research. Doctoral research training can then also become diversified in terms

Quantitative Research Attitudes and Research Training Perceptions among Master's-Level Students

Science.gov (United States)

Steele, Janeé M.; Rawls, Glinda J.

2015-01-01

This study explored master's-level counseling students' (N = 804) perceptions of training in the Council for Accreditation of Counseling and Related Educational Programs (2009) Research and Program Evaluation standard, and their attitudes toward quantitative research. Training perceptions and quantitative research attitudes were low to moderate,…

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.

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.

Mapping training needs for dissemination and implementation research: lessons from a synthesis of existing D&I research training programs.

Science.gov (United States)

Chambers, David A; Proctor, Enola K; Brownson, Ross C; Straus, Sharon E

2017-09-01

With recent growth in the field of dissemination and implementation (D&I) research, multiple training programs have been developed to build capacity, including summer training institutes, graduate courses, degree programs, workshops, and conferences. While opportunities for D&I research training have expanded, course organizers acknowledge that available slots are insufficient to meet demand within the scientific and practitioner community. In addition, individual programs have struggled to best fit various needs of trainees, sometimes splitting coursework between specific D&I content and more introductory grant writing material. This article, stemming from a 2013 NIH workshop, reviews experiences across multiple training programs to align training needs, career stage and role, and availability of programs. We briefly review D&I needs and opportunities by career stage and role, discuss variations among existing training programs in format, mentoring relationships, and other characteristics, identify challenges of mapping needs of trainees to programs, and present recommendations for future D&I research training.

An audit of food and beverage advertising on the Sydney metropolitan train network: regulation and policy implications

OpenAIRE

Emma Sainsbury; Stephen Colagiuri; Roger Magnusson

2017-01-01

Background Increased marketing of energy-dense, nutrient-poor foods has been identified as a driver of the global obesity epidemic and a priority area for preventative efforts. Local and international research has focused on the unhealthiness of television advertising, with limited research into the growing outdoor advertising industry. This study aimed to examine the extent of food and beverage advertising on the Sydney metropolitan train network, and to assess the nutritional quality of adv...

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

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.

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.

Research Training in the Biomedical, Behavioral, and Clinical Research Sciences

Science.gov (United States)

National Academies Press, 2011

2011-01-01

Comprehensive research and a highly-trained workforce are essential for the improvement of health and health care both nationally and internationally. During the past 40 years the National Research Services Award (NRSA) Program has played a large role in training the workforce responsible for dramatic advances in the understanding of various…

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.

The challenges of cross-cultural research and teaching in family medicine: How can professional networks help?

Directory of Open Access Journals (Sweden)

Amanda Caroline Howe

2016-05-01

Full Text Available Modern medical training emphasizes the value of understanding the patient’s ideas, concerns and expectations, and the use of their personal perspective to assist communication, diagnosis, and uptake of all appropriate health and treatment options. This requires doctors to be ‘culturally sensitive’, which “… involves an awareness and acceptance of cultural differences, self-awareness, knowledge of a patient’s culture, and adaptation of skills”. Yet most of us work in one country, and often one community, for much of our professional careers. Those who enter into academic pursuits will similarly be constrained by our own backgrounds and experiences, even though universities and medical schools often attract a multicultural membership. We therefore rely on our professional training and networks to extend our scope and understanding of how cultural issues impact upon our research and its relevance to our discipline and curricula. This article uses a reflexive narrative approach to examine the role and value of international networks through the lens of one individual and one organisation. It explores the extent to which such networks assist cross cultural sensitivity, using examples from its networks, and how these can (and have impacted on greater cross-culturalism in our teaching and research outputs.

Mentoring and Training of Cancer-Related Health Disparities Researchers Committed to Community-Based Participatory Research.

Science.gov (United States)

Felder, Tisha M; Braun, Kathryn L; Brandt, Heather M; Khan, Samira; Tanjasiri, Sora; Friedman, Daniela B; Armstead, Cheryl A; Okuyemi, Kolawole S; Hébert, James R

2015-01-01

The National Cancer Institute's (NCI) Community Networks Program Centers (CNPCs) provide community-based participatory research (CBPR)-oriented mentoring and training to prepare early-stage/midcareer investigators and student trainees (trainees) in disparities reduction. This paper describes the academic, mentoring, training, and work-life balance experiences of CNPC-affiliated trainees. We used a collaborative and iterative process to develop a 57-item, web-based questionnaire completed by trainees from the 23 CNPCs between August 2012 and February 2013. Their CNPC mentors completed a 47-item questionnaire. Descriptive statistics were calculated. The final analytic sample included 189 of 269 individuals (70%) identified as active participants in CNPC research or training/mentoring. Mentors (n=45) were mostly non-Hispanic White (77.8%) and 48.9% were male. Mentors published a median of 6 (interquartile range [IQR], 3-12) first-authored and 15 (IQR, 6-25) senior authored manuscripts, and secured 15 (IQR, 11-29) grants from the National Institutes of Health (NIH) and other sources in the previous 5 years. Most trainees (n=144) were female (79.2%), 43.7% were underrepresented racial/ethnic minorities, and 36.8% were first-generation college graduates. Over the previous 5 years, trainees reported a median of 4 (IQR, 1-6) publications as first author and 4 (IQR, 2-8) as co-author; 27.1% reported having one or more NIH R01s. Trainees reported satisfaction with their CNPC mentor (79.1%) and confidence in demonstrating most CBPR competencies. The CNPC training program consists of a scientifically productive pool of mentors and trainees. Trainees reported rates of scholarly productivity comparable to other national training programs and provided insights into relationships with mentors, academic pressures, and professional-personal life balance.

Mentoring and Training of Cancer-Related Health Disparities Researchers Committed to Community-Based Participatory Research

Science.gov (United States)

Felder, Tisha M.; Braun, Kathryn L.; Brandt, Heather M.; Khan, Samira; Tanjasiri, Sora; Friedman, Daniela B.; Armstead, Cheryl A.; Okuyemi, Kolawole S.; Hébert, James R.

2015-01-01

Background and Objective The National Cancer Institute’s (NCI) Community Networks Program Centers (CNPCs) provide community-based participatory research (CBPR)-oriented mentoring and training to prepare early-stage/midcareer investigators and student trainees (trainees) in disparities reduction. This paper describes the academic, mentoring, training, and work–life balance experiences of CNPC-affiliated trainees. Methods We used a collaborative and iterative process to develop a 57-item, web-based questionnaire completed by trainees from the 23 CNPCs between August 2012 and February 2013. Their CNPC mentors completed a 47-item questionnaire. Descriptive statistics were calculated. Results The final analytic sample included 189 of 269 individuals (70%) identified as active participants in CNPC research or training/mentoring. Mentors (n = 45) were mostly non-Hispanic White (77.8%) and 48.9% were male. Mentors published a median of 6 (interquartile range [IQR], 3–12) first-authored and 15 (IQR, 6–25) senior authored manuscripts, and secured 15 (IQR, 11–29) grants from the National Institutes of Health (NIH) and other sources in the previous 5 years. Most trainees (n = 144) were female (79.2%), 43.7% were underrepresented racial/ethnic minorities, and 36.8% were first-generation college graduates. Over the previous 5 years, trainees reported a median of 4 (IQR, 1–6) publications as first author and 4 (IQR, 2–8) as co-author; 27.1% reported having one or more NIH R01s. Trainees reported satisfaction with their CNPC mentor (79.1%) and confidence in demonstrating most CBPR competencies. Conclusion The CNPC training program consists of a scientifically productive pool of mentors and trainees. Trainees reported rates of scholarly productivity comparable to other national training programs and provided insights into relationships with mentors, academic pressures, and professional–personal life balance. PMID:26213409

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.

Research training needs in Peruvian national TB/HIV programs.

Science.gov (United States)

Garcia, Patricia J; Cotrina, Armando; Gotuzzo, Eduardo; Gonzalez, Elsa; Buffardi, Anne L

2010-09-28

There are few published reports of research training needs assessments and research training programs. In an effort to expand this nascent field of study and to bridge the gap between research and practice, we sought to systematically assess the research training needs of health care professionals working at Peruvian governmental institutions leading HIV and tuberculosis (TB) control and among senior stakeholders in the field. Six institutional workshops were conducted with the participation of 161 mid-level health professionals from agencies involved in national HIV and TB control. At each workshop informants completed a structured questionnaire and participated in small and large group discussions. Additional data and institutional commitment was obtained through in-depth interviews from 32 senior managers and researchers from the Ministry of Health, academia and NGOs. Participants exhibited an overwhelming receptivity for additional research training, observing a gap between current levels of research training and their perceived importance. Specialized skills in obtaining funding, developing research protocols, particularly in operational, behavioral and prevention research were considered in greatest need. Beyond research training, participants identified broader social, economic and political factors as influential in infectious disease control. The needs assessment suggests that future training should focus on operational research techniques, rather than on clinical skill building or program implementation only. Strengthening health systems not only requires additional research training, but also adequate financial resources to implement research findings.

The European Network of Coloproctology: a strategy towards the European research and healthcare system.

Science.gov (United States)

Rubbini, Michele

2016-12-01

Many documents from the International Institutions point out that Health represents an engine of economic and social development. Based on these documents and concepts, the European Parliament decided to create a system of European Reference Networks as a synthesis of clinical and research activities, particularly in the field of rare diseases. This initiative, properly implemented, could be first step towards a new European health system. This article instead, wanting to deepen this perspective, postulates that the ERNs may also be related to widespread diseases, such as those of coloproctological interest, with the aim of setting up a European Network of Coloproctology (ENCP). Here are analyzed: (a) the documents related to ERNs and others related to research and training, the characteristics of the coloproctological diseases, and proposal of the ENCP; (b) a survey that involves 14 out of 25 of the National and Regional Representative of the European Society of Coloproctology. Hundred percent of the people interviewed agree to the ENCP project. The percentage of the approved proposed fields of activity of the ENCP are: Healthcare 71%, Research 100%, Training 86%, Support to legislation 78%, Professional Mobility 64%, Patient Database 71%, and Expenditure control 64%. From the analysis of the documents and the result of the survey, ERNs are appropriate not only in relation to rare diseases but also in those fields with higher diffusion and the creation of a European Network of Coloproctology is then postulated.

The European Judicial Training Network and its Role in the Strategy for the Europeanization of National Judges

Directory of Open Access Journals (Sweden)

Simone Benvenuti

2015-07-01

Full Text Available This article addresses the building of a European Judicial Training Framework (EJT, notably the establishment, organization and functioning of the European Judicial Training Network (EJTN. After describing the EJTN and retracing its distinctive features – co-operation, decentralization, complementarity, targeting –, the article underlines its peculiar function within EJT, which reflects the role of EJT itself in the strategy for Europeanization of national judges. It then concludes by pointing out and situating other strategic areas where important synergies with EJT for the purpose of judicial Europeanization can be strengthened, notably enhancement of transnational judicial networks and introduction of knowledge management tools in national systems. The article is based on the analysis of documents and scientific literature as well as on empirical research and semi-structured interviews conducted by the author in 2013 and 2014.

Action Research as a Network

DEFF Research Database (Denmark)

Boulus-Rødje, Nina

2012-01-01

This paper explores roles and interventions in IS action research. I draw upon a four-year research project about electronic medical records, conducted in close collaboration with a community partner. Following a self-reflexive stance, I trace the trajectory of the research engagement...... and the different roles I occupied. To better understand the complex nature of collaboration found within action research projects, I propose conceptualizing action research as a network. The network framework directs our attention to the collective production and the conditions through which roles...... this influences the researcher’s agency....

Genetic improvement of sugar cane for bioenergy: the Brazilian experience in network research with RIDESA

Directory of Open Access Journals (Sweden)

Luiz Alexandre Peternelli

2012-01-01

Full Text Available In this paper, it is presented RIDESA’s model for sugar cane breeding to ethanol, and its scientific, technological and human resources training contributions. RIDESA is an inter-university network for the development of sugar cane industry in Brazil, and was formed by a technical cooperation agreement between ten public universities. The model of network management is presented in this study, which involves, among other things, the public-private partnership (Universities-Mills for the development of cultivars. RIDESA has produced 59 cultivars since 1990 and is now responsible for 59% of the total area cultivated with this plant in Brazil. In the last five years, 286 agronomists were trained in breeding programs at universities that comprise RIDESA. In this same period, the network formed 35 professors, 24 doctors and 7 post-docs in researches with this crop. It is also presented a conceptual approach on methods of sugar cane breeding involving families and genome-wide selection.

Attitudes to research and research training among ophthalmologists and ophthalmology trainees in New Zealand.

Science.gov (United States)

Jayasundera, Thiran; Fisk, Michael; McGhee, Charles N J

2003-08-01

To determine the attitudes to research and research training among ophthalmologists and ophthalmology trainees in New Zealand. A structured, self-administered questionnaire was devised and after preliminary validation a postal survey was sent to all ophthalmologists and ophthalmology registrars and fellows in New Zealand. A total of 82 replies were received from 115 questionnaires sent out; a response rate of 71.3%. An overwhelming majority found research to have benefited their education, clinical practice and career; 67.1% of the respondents intended to do research in the future. Although a majority (56.4%) felt research to be beneficial to ophthalmology training, 42.3% felt research would be of limited or no benefit when selecting candidates for vocational training. However, 97.5% of respondents felt that ophthalmology trainees should undertake some form of research during training, with most supporting small studies or case reports (44.4%) or a short structured training course in research (42.0%). Interestingly, 86.6% felt that research methodology and data analysis should be taught in a structured fashion with most supporting courses or seminars of a few weeks duration during the vocational training period. Many ophthalmologists felt inadequately equipped or trained to mentor and supervise trainees undertaking research and 41.5% of consultant ophthalmologists felt further training to fulfil this role would be beneficial. This survey suggests that New Zealand ophthalmologists generally approve of and support a place for research, possibly of a more structured design, during ophthalmology training.

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.

Research Award: Networked Economies

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

Office 2004 Test Drive User

2015-08-06

year, paid, ... the areas of democracy, human rights and economic growth. ... Networked Economies is seeking a Research Award Recipient to explore research questions ... such as engineering or computer/information science;.

Research training needs in Peruvian national TB/HIV programs

Science.gov (United States)

2010-01-01

Background There are few published reports of research training needs assessments and research training programs. In an effort to expand this nascent field of study and to bridge the gap between research and practice, we sought to systematically assess the research training needs of health care professionals working at Peruvian governmental institutions leading HIV and tuberculosis (TB) control and among senior stakeholders in the field. Methods Six institutional workshops were conducted with the participation of 161 mid-level health professionals from agencies involved in national HIV and TB control. At each workshop informants completed a structured questionnaire and participated in small and large group discussions. Additional data and institutional commitment was obtained through in-depth interviews from 32 senior managers and researchers from the Ministry of Health, academia and NGOs. Results Participants exhibited an overwhelming receptivity for additional research training, observing a gap between current levels of research training and their perceived importance. Specialized skills in obtaining funding, developing research protocols, particularly in operational, behavioral and prevention research were considered in greatest need. Beyond research training, participants identified broader social, economic and political factors as influential in infectious disease control. Conclusions The needs assessment suggests that future training should focus on operational research techniques, rather than on clinical skill building or program implementation only. Strengthening health systems not only requires additional research training, but also adequate financial resources to implement research findings. PMID:20875140

Research training needs in Peruvian national TB/HIV programs

Directory of Open Access Journals (Sweden)

Gonzalez Elsa

2010-09-01

Full Text Available Abstract Background There are few published reports of research training needs assessments and research training programs. In an effort to expand this nascent field of study and to bridge the gap between research and practice, we sought to systematically assess the research training needs of health care professionals working at Peruvian governmental institutions leading HIV and tuberculosis (TB control and among senior stakeholders in the field. Methods Six institutional workshops were conducted with the participation of 161 mid-level health professionals from agencies involved in national HIV and TB control. At each workshop informants completed a structured questionnaire and participated in small and large group discussions. Additional data and institutional commitment was obtained through in-depth interviews from 32 senior managers and researchers from the Ministry of Health, academia and NGOs. Results Participants exhibited an overwhelming receptivity for additional research training, observing a gap between current levels of research training and their perceived importance. Specialized skills in obtaining funding, developing research protocols, particularly in operational, behavioral and prevention research were considered in greatest need. Beyond research training, participants identified broader social, economic and political factors as influential in infectious disease control. Conclusions The needs assessment suggests that future training should focus on operational research techniques, rather than on clinical skill building or program implementation only. Strengthening health systems not only requires additional research training, but also adequate financial resources to implement research findings.

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.

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.

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.

Using Network Science to Support Design Research

DEFF Research Database (Denmark)

Parraguez Ruiz, Pedro; Maier, Anja

2016-01-01

and societal impact. This chapter contributes to the use of network science in empirical studies of design organisations. It focuses on introducing a network-based perspective on the design process and in particular on making use of network science to support design research and practice. The main contribution...... of this chapter is an overview of the methodological challenges and core decision points when embarking on network-based design research, namely defining the overall research purpose and selecting network features. We furthermore highlight the potential for using archival data, the opportunities for navigating...

Barriers to Research and Implications for Training Counselors

Directory of Open Access Journals (Sweden)

James R Ruby

2013-03-01

Full Text Available Research is an important part of quality clinical practice in the field of counseling. This study addresses the constraints that produce a gap in master’s level practitioner research among counselors in Illinois. Ninety-nine master’s level clinicians responded to surveys and answered a series of questions regarding what constrains them from being more involved in research. These respondents provided valuable feedback regarding possible recommendations for training that might encourage increased research activity for future master’s level counselors. Training improvements such as mentored research activity and training in less complex research methods were indicated. Keywords: Clinical practice, Implications, Barriers to research, less complex research

Community-centred Networks and Networking among Companies, Educational and Cultural Institutions and Research

DEFF Research Database (Denmark)

Konnerup, Ulla; Dirckinck-Holmfeld, Lone

2010-01-01

This article presents visions for community-centred networks and networking among companies, educational and cultural institutions and research based on blended on- and off-line collaboration and communication. Our point of departure is the general vision of networking between government, industry...... and research as formulated in the Triple Helix Model (Etzkowitz 2008). The article draws on a case study of NoEL, a network on e-learning among business, educational and cultural institutions and research, all in all 21 partners from all around Denmark. Focus is how networks and networking change character......’ in Networked Learning, Wenger et al. 2009; The analysis concerns the participation structure and how the network activities connect local work practices and research, and how technology and online communication contribute to a change from participation in offline and physical network activities into online...

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.

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.

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.

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.

Research on Evolutionary Mechanism of Agile Supply Chain Network via Complex Network Theory

Directory of Open Access Journals (Sweden)

Nai-Ru Xu

2016-01-01

Full Text Available The paper establishes the evolutionary mechanism model of agile supply chain network by means of complex network theory which can be used to describe the growth process of the agile supply chain network and analyze the complexity of the agile supply chain network. After introducing the process and the suitability of taking complex network theory into supply chain network research, the paper applies complex network theory into the agile supply chain network research, analyzes the complexity of agile supply chain network, presents the evolutionary mechanism of agile supply chain network based on complex network theory, and uses Matlab to simulate degree distribution, average path length, clustering coefficient, and node betweenness. Simulation results show that the evolution result displays the scale-free property. It lays the foundations of further research on agile supply chain network based on complex network theory.

One Health research and training and government support for One Health in South Asia

Directory of Open Access Journals (Sweden)

Joanna S. McKenzie

2016-11-01

Full Text Available Introduction: Considerable advocacy, funding, training, and technical support have been provided to South Asian countries to strengthen One Health (OH collaborative approaches for controlling diseases with global human pandemic potential since the early 2000s. It is essential that the OH approach continues to be strengthened given South Asia is a hot spot for emerging and endemic zoonotic diseases. The objectives of this article are to describe OH research and training and capacity building activities and the important developments in government support for OH in these countries to identify current achievements and gaps. Materials and methods: A landscape analysis of OH research, training, and government support in South Asia was generated by searching peer-reviewed and grey literature for OH research publications and reports, a questionnaire survey of people potentially engaged in OH research in South Asia and the authors’ professional networks. Results: Only a small proportion of zoonotic disease research conducted in South Asia can be described as truly OH, with a significant lack of OH policy-relevant research. A small number of multisectoral OH research and OH capacity building programmes were conducted in the region. The governments of Bangladesh and Bhutan have established operational OH strategies, with variable progress institutionalising OH in other countries. Identified gaps were a lack of useful scientific information and of a collaborative culture for formulating and implementing integrated zoonotic disease control policies and the need for ongoing support for transdisciplinary OH research and policy-relevant capacity building programmes. Discussion: Overall we found a very small number of truly OH research and capacity building programmes in South Asia. Even though significant progress has been made in institutionalising OH in some South Asian countries, further behavioural, attitudinal, and institutional changes are required to

One Health research and training and government support for One Health in South Asia.

Science.gov (United States)

McKenzie, Joanna S; Dahal, Rojan; Kakkar, Manish; Debnath, Nitish; Rahman, Mahmudur; Dorjee, Sithar; Naeem, Khalid; Wijayathilaka, Tikiri; Sharma, Barun Kumar; Maidanwal, Nasir; Halimi, Asmatullah; Kim, Eunmi; Chatterjee, Pranab; Devleesschauwer, Brecht

2016-01-01

Considerable advocacy, funding, training, and technical support have been provided to South Asian countries to strengthen One Health (OH) collaborative approaches for controlling diseases with global human pandemic potential since the early 2000s. It is essential that the OH approach continues to be strengthened given South Asia is a hot spot for emerging and endemic zoonotic diseases. The objectives of this article are to describe OH research and training and capacity building activities and the important developments in government support for OH in these countries to identify current achievements and gaps. A landscape analysis of OH research, training, and government support in South Asia was generated by searching peer-reviewed and grey literature for OH research publications and reports, a questionnaire survey of people potentially engaged in OH research in South Asia and the authors' professional networks. Only a small proportion of zoonotic disease research conducted in South Asia can be described as truly OH, with a significant lack of OH policy-relevant research. A small number of multisectoral OH research and OH capacity building programmes were conducted in the region. The governments of Bangladesh and Bhutan have established operational OH strategies, with variable progress institutionalising OH in other countries. Identified gaps were a lack of useful scientific information and of a collaborative culture for formulating and implementing integrated zoonotic disease control policies and the need for ongoing support for transdisciplinary OH research and policy-relevant capacity building programmes. Overall we found a very small number of truly OH research and capacity building programmes in South Asia. Even though significant progress has been made in institutionalising OH in some South Asian countries, further behavioural, attitudinal, and institutional changes are required to strengthen OH research and training and implementation of sustainably effective

Research on NGN network control technology

Science.gov (United States)

Li, WenYao; Zhou, Fang; Wu, JianXue; Li, ZhiGuang

2004-04-01

Nowadays NGN (Next Generation Network) is the hotspot for discussion and research in IT section. The NGN core technology is the network control technology. The key goal of NGN is to realize the network convergence and evolution. Referring to overlay network model core on Softswitch technology, circuit switch network and IP network convergence realized. Referring to the optical transmission network core on ASTN/ASON, service layer (i.e. IP layer) and optical transmission convergence realized. Together with the distributing feature of NGN network control technology, on NGN platform, overview of combining Softswitch and ASTN/ASON control technology, the solution whether IP should be the NGN core carrier platform attracts general attention, and this is also a QoS problem on NGN end to end. This solution produces the significant practical meaning on equipment development, network deployment, network design and optimization, especially on realizing present network smooth evolving to the NGN. This is why this paper puts forward the research topic on the NGN network control technology. This paper introduces basics on NGN network control technology, then proposes NGN network control reference model, at the same time describes a realizable network structure of NGN. Based on above, from the view of function realization, NGN network control technology is discussed and its work mechanism is analyzed.

Systematic Approach to Research Training: Benefits for Counseling Practice.

Science.gov (United States)

Loughead, Teri A.; And Others

1991-01-01

Synthesizes developments concerning research training in graduate counselor education and presents a systematic approach for training master's and doctoral students in mental health counseling to assimilate, use, and perform research. Suggests diversity of research training strategies for implementation in counselor preparation programs.…

Advances in Artificial Neural Networks – Methodological Development and Application

Directory of Open Access Journals (Sweden)

Yanbo Huang

2009-08-01

Full Text Available Artificial neural networks as a major soft-computing technology have been extensively studied and applied during the last three decades. Research on backpropagation training algorithms for multilayer perceptron networks has spurred development of other neural network training algorithms for other networks such as radial basis function, recurrent network, feedback network, and unsupervised Kohonen self-organizing network. These networks, especially the multilayer perceptron network with a backpropagation training algorithm, have gained recognition in research and applications in various scientific and engineering areas. In order to accelerate the training process and overcome data over-fitting, research has been conducted to improve the backpropagation algorithm. Further, artificial neural networks have been integrated with other advanced methods such as fuzzy logic and wavelet analysis, to enhance the ability of data interpretation and modeling and to avoid subjectivity in the operation of the training algorithm. In recent years, support vector machines have emerged as a set of high-performance supervised generalized linear classifiers in parallel with artificial neural networks. A review on development history of artificial neural networks is presented and the standard architectures and algorithms of artificial neural networks are described. Furthermore, advanced artificial neural networks will be introduced with support vector machines, and limitations of ANNs will be identified. The future of artificial neural network development in tandem with support vector machines will be discussed in conjunction with further applications to food science and engineering, soil and water relationship for crop management, and decision support for precision agriculture. Along with the network structures and training algorithms, the applications of artificial neural networks will be reviewed as well, especially in the fields of agricultural and biological

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, ...

Exploring Practice-Research Networks for Critical Professional Learning

Science.gov (United States)

Appleby, Yvon; Hillier, Yvonne

2012-01-01

This paper discusses the contribution that practice-research networks can make to support critical professional development in the Learning and Skills sector in England. By practice-research networks we mean groups or networks which maintain a connection between research and professional practice. These networks stem from the philosophy of…

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.

Lymphatic Education & Research Network

Science.gov (United States)

Lymphatic Education & Research Network Donate Now Become a Supporting Member X Living with LYMPHEDEMA AND Lymphatic Disease FAQs About ... December 8, 2017 11.08.2017 The Lymphatic Education & Research Network… Read More > ASRM LE&RN Combined ...

Network Penetration Testing and Research

Science.gov (United States)

Murphy, Brandon F.

2013-01-01

This paper will focus the on research and testing done on penetrating a network for security purposes. This research will provide the IT security office new methods of attacks across and against a company's network as well as introduce them to new platforms and software that can be used to better assist with protecting against such attacks. Throughout this paper testing and research has been done on two different Linux based operating systems, for attacking and compromising a Windows based host computer. Backtrack 5 and BlackBuntu (Linux based penetration testing operating systems) are two different "attacker'' computers that will attempt to plant viruses and or NASA USRP - Internship Final Report exploits on a host Windows 7 operating system, as well as try to retrieve information from the host. On each Linux OS (Backtrack 5 and BlackBuntu) there is penetration testing software which provides the necessary tools to create exploits that can compromise a windows system as well as other operating systems. This paper will focus on two main methods of deploying exploits 1 onto a host computer in order to retrieve information from a compromised system. One method of deployment for an exploit that was tested is known as a "social engineering" exploit. This type of method requires interaction from unsuspecting user. With this user interaction, a deployed exploit may allow a malicious user to gain access to the unsuspecting user's computer as well as the network that such computer is connected to. Due to more advance security setting and antivirus protection and detection, this method is easily identified and defended against. The second method of exploit deployment is the method mainly focused upon within this paper. This method required extensive research on the best way to compromise a security enabled protected network. Once a network has been compromised, then any and all devices connected to such network has the potential to be compromised as well. With a compromised

Use of the Remote Access Virtual Environment Network (RAVEN) for coordinated IVA-EVA astronaut training and evaluation.

Science.gov (United States)

Cater, J P; Huffman, S D

1995-01-01

This paper presents a unique virtual reality training and assessment tool developed under a NASA grant, "Research in Human Factors Aspects of Enhanced Virtual Environments for Extravehicular Activity (EVA) Training and Simulation." The Remote Access Virtual Environment Network (RAVEN) was created to train and evaluate the verbal, mental and physical coordination required between the intravehicular (IVA) astronaut operating the Remote Manipulator System (RMS) arm and the EVA astronaut standing in foot restraints on the end of the RMS. The RAVEN system currently allows the EVA astronaut to approach the Hubble Space Telescope (HST) under control of the IVA astronaut and grasp, remove, and replace the Wide Field Planetary Camera drawer from its location in the HST. Two viewpoints, one stereoscopic and one monoscopic, were created all linked by Ethernet, that provided the two trainees with the appropriate training environments.

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

How well does early-career investigators' cardiovascular outcomes research training align with funded outcomes research?

Science.gov (United States)

Crowley, Matthew J; Al-Khatib, Sana M; Wang, Tracy Y; Khazanie, Prateeti; Kressin, Nancy R; Krumholz, Harlan M; Kiefe, Catarina I; Wells, Barbara L; O'Brien, Sean M; Peterson, Eric D; Sanders, Gillian D

2018-02-01

Outcomes research training programs should prepare trainees to successfully compete for research funding. We examined how early-career investigators' prior and desired training aligns with recently funded cardiovascular (CV) outcomes research. We (1) reviewed literature to identify 13 core competency areas in CV outcomes research; (2) surveyed early-career investigators to understand their prior and desired training in each competency area; (3) examined recently funded grants commonly pursued by early-career outcomes researchers to ascertain available funding in competency areas; and (4) analyzed alignment between investigator training and funded research in each competency area. We evaluated 185 survey responses from early-career investigators (response rate 28%) and 521 funded grants from 2010 to 2014. Respondents' prior training aligned with funded grants in the areas of clinical epidemiology, observational research, randomized controlled trials, and implementation/dissemination research. Funding in community-engaged research and health informatics was more common than prior training in these areas. Respondents' prior training in biostatistics and systematic review was more common than funded grants focusing on these specific areas. Respondents' desired training aligned similarly with funded grants, with some exceptions; for example, desired training in health economics/cost-effectiveness research was more common than funded grants in these areas. Restricting to CV grants (n=132) and National Heart, Lung, and Blood Institute-funded grants (n=170) produced similar results. Identifying mismatch between funded grants in outcomes research and early-career investigators' prior/desired training may help efforts to harmonize investigator interests, training, and funding. Our findings suggest a need for further consideration of how to best prepare early-career investigators for funding success. Copyright © 2017. Published by Elsevier Inc.

Advances in Artificial Neural Networks - Methodological Development and Application

Science.gov (United States)

Artificial neural networks as a major soft-computing technology have been extensively studied and applied during the last three decades. Research on backpropagation training algorithms for multilayer perceptron networks has spurred development of other neural network training algorithms for other ne...

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

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.

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.

Research Award: Informaon and Networks

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

Corey Piccioni

2013-08-07

Aug 7, 2013 ... IDRC's Informaon and Networks (I&N) program is seeking a Research ... The growth of networked technologies has created new opportunies for ... What role do collaborave technologies (e.g., social media) play in social ...

Regional Research Networking: A Stimulus to Research Collaboration and Research Productivity.

Science.gov (United States)

McElmurry, Beverly J.; Minckley, Barbara B.

1986-01-01

Models for collegial networking as a means of increasing the participants' scholarly productivity are presented. A Midwestern historical methodology research interest group is described as an example of the long-term benefits of forming networks of scholars. (MSE)

Water hammer research in networks

Directory of Open Access Journals (Sweden)

Anželika Jurkienė

2015-10-01

Full Text Available Formation of water hammer, its consequences and possible protection measures are rarely topics, however the problem is significant. Water hammer can form in water supply and pressurized sewage networks, for various reasons. The article presents short theory of water hammer and methodology for calculation of specific parameters. Research of water hammer was performed in real water supply and sewer networks of country. Simulation of water hammer was carried out by turning on and off water pumps in pumping station. Successful measurement of water hammer depends on accuracy of the measurement equipment, therefore during the research surge wave fluctuations were measured with especially high resolution pressure meters. Detailed analysis of water hammer and selection of protecting equipment hydraulic model of water supply network was created. Protection against water hammer helps to avoid breaking of the water network and extend operation time.

Research and training programmes

Directory of Open Access Journals (Sweden)

Daksha Patel

2007-03-01

Full Text Available Research is defined in the Oxford English Dictionary as “a systematic investigation and study of materials and sources in order to establish facts and reach new conclusions.”Research is embedded in the curricula of most postgraduate training programmes; students are expected to complete some form of original work towards a dissertation. This often evokes a range of reactions: “What is the purpose of this exercise? Why do I have to do research when I just want to do a job? Shouldn’t research rather be left to experts? I can’t do the course; I have no research background!”

ISS Microgravity Research Payload Training Methodology

Science.gov (United States)

Schlagheck, Ronald; Geveden, Rex (Technical Monitor)

2001-01-01

The NASA Microgravity Research Discipline has multiple categories of science payloads that are being planned and currently under development to operate on various ISS on-orbit increments. The current program includes six subdisciplines; Materials Science, Fluids Physics, Combustion Science, Fundamental Physics, Cellular Biology and Macromolecular Biotechnology. All of these experiment payloads will require the astronaut various degrees of crew interaction and science observation. With the current programs planning to build various facility class science racks, the crew will need to be trained on basic core operations as well as science background. In addition, many disciplines will use the Express Rack and the Microgravity Science Glovebox (MSG) to utilize the accommodations provided by these facilities for smaller and less complex type hardware. The Microgravity disciplines will be responsible to have a training program designed to maximize the experiment and hardware throughput as well as being prepared for various contingencies both with anomalies as well as unexpected experiment observations. The crewmembers will need various levels of training from simple tasks as power on and activate to extensive training on hardware mode change out to observing the cell growth of various types of tissue cultures. Sample replacement will be required for furnaces and combustion type modules. The Fundamental Physics program will need crew EVA support to provide module change out of experiment. Training will take place various research centers and hardware development locations. It is expected that onboard training through various methods and video/digital technology as well as limited telecommunication interaction. Since hardware will be designed to operate from a few weeks to multiple research increments, flexibility must be planned in the training approach and procedure skills to optimize the output as well as the equipment maintainability. Early increment lessons learned

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.

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

Integrating Research Skills Training into Non--Research Methods Courses

Science.gov (United States)

Woolf, Jules

2014-01-01

Research skills are a valued commodity by industry and university administrators. Despite the importance placed on these skills students typically dislike taking research method courses where these skills are learned. However, training in research skills does not necessarily have to be confined to these courses. In this study participants at a…

Charles Wagley's legacy of Interdisciplinary Graduate Research and Training Programs at the University of Florida

Directory of Open Access Journals (Sweden)

Marianne Schmink

Full Text Available When Charles Wagley moved from Columbia University to the University of Florida (UF in 1972, he established the Tropical South America Program. In this program he began an enduring legacy at UF of interdisciplinarity, collaborative research and training focused on the problems and solutions of tropical development, and support for students as future leaders. Reaching out to agricultural researchers and other social science disciplines, Wagley later co-founded and directed the Amazon Research and Training Program (ARTP, and remained active even after his retirement in 1983. The ARTP built on Wagley's strategy of supporting student research and building collaboration with partners in Latin America, and innovated in bringing in visiting professors from different disciplines, developing new interdisciplinary courses, and networking among Amazonian scholars in different countries. Wagley's most lasting contribution is the Tropical Conservation and Development (TCD program, which grew out of the ARTP to become an internationally-recognized interdisciplinary graduate program focused on the intersection between biodiversity conservation and the well-being of people in the tropical world. Drawing on participation from over 100 faculty affiliates in 27 academic units at UF, since 1980 the ARTP and TCD programs have trained over 400 graduate students from two dozen countries.

Research Ethics with Undergraduates in Summer Research Training Programs

Science.gov (United States)

Cheung, I.; Yalcin, K.

2016-02-01

Many undergraduate research training programs incorporate research ethics into their programs and some are required. Engaging students in conversations around challenging topics such as conflict of interest, cultural and gender biases, what is science and what is normative science can difficult in newly formed student cohorts. In addition, discussing topics with more distant impacts such as science and policy, intellectual property and authorship, can be difficult for students in their first research experience that have more immediate concerns about plagiarism, data manipulation, and the student/faculty relationship. Oregon State University's Research Experience for Undergraduates (REU) in Ocean Sciences: From Estuaries to the Deep Sea as one model for incorporating a research ethics component into summer undergraduate research training programs. Weaved into the 10-week REU program, undergraduate interns participate in a series of conversations and a faculty mentor panel focused on research ethics. Topics discussed are in a framework for sharing myths, knowledge and personal experiences on issues in research with ethical implications. The series follows guidelines and case studies outlined from the text, On Being A Scientist: Responsible Conduct In Research Committee on Science, Engineering, and Public Policy, National Academy of Sciences.

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...

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

Poverty-Related Diseases College: a virtual African-European network to build research capacity.

Science.gov (United States)

Dorlo, Thomas P C; Fernández, Carmen; Troye-Blomberg, Marita; de Vries, Peter J; Boraschi, Diana; Mbacham, Wilfred F

2016-01-01

The Poverty-Related Diseases College was a virtual African-European college and network that connected young African and European biomedical scientists working on poverty-related diseases. The aim of the Poverty-Related Diseases College was to build sustainable scientific capacity and international networks in poverty-related biomedical research in the context of the development of Africa. The Poverty-Related Diseases College consisted of three elective and mandatory training modules followed by a reality check in Africa and a science exchange in either Europe or the USA. In this analysis paper, we present our experience and evaluation, discuss the strengths and encountered weaknesses of the programme, and provide recommendations to policymakers and funders.

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...

A multi-radio, multi-hop ad-hoc radio communication network for Communications-Based Train Control (CBTC): Introducing frequency separation for train-to-trackside communication

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 conventionalinfrastructureWi-Fi(IEEE802.11).Itmeansatrain has to conti...

Social networks and research output

NARCIS (Netherlands)

Ductor, L.; Fafchamps, M.; Goyal, S.; van der Leij, M.J.

2014-01-01

We study how knowledge about the social network of an individual researcher - as embodied in his coauthor relations - helps us in developing a more accurate prediction of his future productivity. We find that incorporating information about coauthor networks leads to a modest improvement in the

The European network of excellence Emil

International Nuclear Information System (INIS)

2006-01-01

The network of excellence EMIL (European Molecular Imaging Laboratories ) is the only European network of excellence in molecular imaging for oncology. It was set up and is coordinated by the 'in vivo imaging of gene expression' group of CEA Orsay. Included in Priority Thematic Area 1 (life sciences, genomics and biotechnology for health) of the European Commission's 6. Framework Programme for Research and Technological Development (FP6), this five-year project (2004-2009) aims t o merge the leading European research teams in molecular imaging, in universities, research centres and small and medium enterprises, to focus on early diagnosis, prognosis and therapeutic evaluation of cancer. The EMIL network brings together 58 partners representing 43 bodies in 13 European countries, and integrates 6 technological facilities: Orsay (France), Turin (Italy), Cologne (Germany), Leiden (Netherlands), Milan (Italy) and Antwerpen (Belgium).The research and training activities of the EMIL network are based on 9 thematic working groups or 'work packages' (wp), forming a common activity programme including : Integration activities: creation of a network of technological and training facilities favouring the mobility of researchers and the integration of small and medium enterprises into the EMIL network. Dissemination of expertise activities: training, communication, common knowledge management and intellectual property rights. Research activities: a common research programme with a horizontal dimension, making use of methodological tools of physics, biology and chemistry necessary for the further development of molecular imaging (instrument techniques, molecular probes, biological engineering), and a vertical integrative dimension, bringing together cancer imaging applications (early diagnostic imaging, development of new therapies imaging for drug development). (author)

Creatiing a Collaborative Research Network for Scientists

Science.gov (United States)

Gunn, W.

2012-12-01

This abstract proposes a discussion of how professional science communication and scientific cooperation can become more efficient through the use of modern social network technology, using the example of Mendeley. Mendeley is a research workflow and collaboration tool which crowdsources real-time research trend information and semantic annotations of research papers in a central data store, thereby creating a "social research network" that is emergent from the research data added to the platform. We describe how Mendeley's model can overcome barriers for collaboration by turning research papers into social objects, making academic data publicly available via an open API, and promoting more efficient collaboration. Central to the success of Mendeley has been the creation of a tool that works for the researcher without the requirement of being part of an explicit social network. Mendeley automatically extracts metadata from research papers, and allows a researcher to annotate, tag and organize their research collection. The tool integrates with the paper writing workflow and provides advanced collaboration options, thus significantly improving researchers' productivity. By anonymously aggregating usage data, Mendeley enables the emergence of social metrics and real-time usage stats on top of the articles' abstract metadata. In this way a social network of collaborators, and people genuinely interested in content, emerges. By building this research network around the article as the social object, a social layer of direct relevance to academia emerges. As science, particularly Earth sciences with their large shared resources, become more and more global, the management and coordination of research is more and more dependent on technology to support these distributed collaborations.

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...

Innovative research of AD HOC network mobility model

Science.gov (United States)

Chen, Xin

2017-08-01

It is difficult for researchers of AD HOC network to conduct actual deployment during experimental stage as the network topology is changeable and location of nodes is unfixed. Thus simulation still remains the main research method of the network. Mobility model is an important component of AD HOC network simulation. It is used to describe the movement pattern of nodes in AD HOC network (including location and velocity, etc.) and decides the movement trail of nodes, playing as the abstraction of the movement modes of nodes. Therefore, mobility model which simulates node movement is an important foundation for simulation research. In AD HOC network research, mobility model shall reflect the movement law of nodes as truly as possible. In this paper, node generally refers to the wireless equipment people carry. The main research contents include how nodes avoid obstacles during movement process and the impacts of obstacles on the mutual relation among nodes, based on which a Node Self Avoiding Obstacle, i.e. NASO model is established in AD HOC network.

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...

Training research through EFL

Directory of Open Access Journals (Sweden)

Mardanshina Rimma M.

2016-01-01

Full Text Available In a globalized research market, developing students’ research skills by means of a foreign language is of particular importance. Students’ research work within the framework of the syllabus and extracurricular activities constitute the system of scientific work of students in a higher education institution. The potential of a foreign language in shaping the academic and research competence of students of Economics is revealed in the content and process aspects. Linguistics and economics as fields of scientific knowledge are reflected in the content aspect. Regarding the mode of training research, the emphasis is on reading strategies and activities aimed at fluent comprehension and handling professional and scientific information. Students’ scientific conference survey proves the potential of EFL in research activities and sheds the light on the new ways to develop research competence.

Network analysis to support research management: evidence from the Fiocruz Observatory in Science, Technology and Innovation in Health

Energy Technology Data Exchange (ETDEWEB)

Fonseca, B.; Sampaio, R.B.; Silva, M.V.; Dos Santos, P.X.

2016-07-01

Brazil has been encouraging the establishment of research networks to address strategic health issues in response to social demands, creating an urgent need to develop indicators for their evaluation. The Oswaldo Cruz Foundation (Fiocruz), a national research, training and production institution, has initiated the development of an “Observatory in Science, Technology and Innovation in Health” to monitor and evaluate research and technological development for the formulation of institutional policies. In this context, we are proposing the use of social network analysis to map cooperation in strategic areas of research, identify prominent researchers and support internal research networks. In this preliminary study, coauthorship analysis was used to map the cooperative relations of Fiocruz in tuberculosis (TB) research, an important public health issue for which diagnosis and adequate treatment are still challenging. Our findings suggest that Brazilian research organizations acting in TB research are embedded in highly connected networks. The large number of international organizations present in the Brazilian network reflects the global increase in scientific collaboration and Brazil’s engagement in international collaborative research efforts. Fiocruz frequent cooperation with high-income countries demonstrates its concern in benefiting from the access to facilities, funding, equipment and networks that are often limited in its research setting. Collaboration with high burden countries has to be strengthened, as it could improve access to local knowledge and better understanding of the disease in different endemic contexts. Centrality analysis consolidated information on the importance of Fiocruz in connecting TB research institutions in Brazil. Fiocruz Observatory intends to advance this analysis by looking into the mechanisms of collaboration, identifying priority themes and assessing comparative advantages of the network members, an important contribution

Targeting molecular networks for drug research

Directory of Open Access Journals (Sweden)

José Pedro Pinto

2014-06-01

Full Text Available The study of molecular networks has recently moved into the limelight of biomedical research. While it has certainly provided us with plenty of new insights into cellular mechanisms, the challenge now is how to modify or even restructure these networks. This is especially true for human diseases, which can be regarded as manifestations of distorted states of molecular networks. Of the possible interventions for altering networks, the use of drugs is presently the most feasible. In this mini-review, we present and discuss some exemplary approaches of how analysis of molecular interaction networks can contribute to pharmacology (e.g., by identifying new drug targets or prediction of drug side effects, as well as listing pointers to relevant resources and software to guide future research. We also outline recent progress in the use of drugs for in vitro reprogramming of cells, which constitutes an example par excellence for altering molecular interaction networks with drugs.

Research Award: Information and Networks

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

IDRC CRDI

... of networked technologies has created new opportunities for advancing human ... The I&N Research Awardee will ideally explore research questions centred ... Examples of questions include: ... engineering or computer/information science;.

SAGA advances in ShApes, Geometry, and Algebra : results from the Marie Curie initial training network

CERN Document Server

Muntingh, Georg

2014-01-01

This book summarizes research carried out in workshops of the SAGA project, an Initial Training Network exploring the interplay of Shapes, Algebra, Geometry and Algorithms. Written by a combination of young and experienced researchers, the book introduces new ideas in an established context. Among the central topics are approximate and sparse implicitization and surface parametrization; algebraic tools for geometric computing; algebraic geometry for computer aided design applications and problems with industrial applications. Readers will encounter new methods for the (approximate) transition between the implicit and parametric representation; new algebraic tools for geometric computing; new applications of isogeometric analysis, and will gain insight into the emerging research field situated between algebraic geometry and computer aided geometric design.

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.

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.

A research on the application of software defined networking in satellite network architecture

Science.gov (United States)

Song, Huan; Chen, Jinqiang; Cao, Suzhi; Cui, Dandan; Li, Tong; Su, Yuxing

2017-10-01

Software defined network is a new type of network architecture, which decouples control plane and data plane of traditional network, has the feature of flexible configurations and is a direction of the next generation terrestrial Internet development. Satellite network is an important part of the space-ground integrated information network, while the traditional satellite network has the disadvantages of difficult network topology maintenance and slow configuration. The application of SDN technology in satellite network can solve these problems that traditional satellite network faces. At present, the research on the application of SDN technology in satellite network is still in the stage of preliminary study. In this paper, we start with introducing the SDN technology and satellite network architecture. Then we mainly introduce software defined satellite network architecture, as well as the comparison of different software defined satellite network architecture and satellite network virtualization. Finally, the present research status and development trend of SDN technology in satellite network are analyzed.

CRM Assessment: Determining the Generalization of Rater Calibration Training. Summary of Research Report: Gold Standards Training

Science.gov (United States)

Baker, David P.

2002-01-01

The extent to which pilot instructors are trained to assess crew resource management (CRM) skills accurately during Line-Oriented Flight Training (LOFT) and Line Operational Evaluation (LOE) scenarios is critical. Pilot instructors must make accurate performance ratings to ensure that proper feedback is provided to flight crews and appropriate decisions are made regarding certification to fly the line. Furthermore, the Federal Aviation Administration's (FAA) Advanced Qualification Program (AQP) requires that instructors be trained explicitly to evaluate both technical and CRM performance (i.e., rater training) and also requires that proficiency and standardization of instructors be verified periodically. To address the critical need for effective pilot instructor training, the American Institutes for Research (AIR) reviewed the relevant research on rater training and, based on "best practices" from this research, developed a new strategy for training pilot instructors to assess crew performance. In addition, we explored new statistical techniques for assessing the effectiveness of pilot instructor training. The results of our research are briefly summarized below. This summary is followed by abstracts of articles and book chapters published under this grant.

[Researchers training in the context of the collaborative projects: experiences of Instituto de Medicina Tropical "Alexander von Humbolt", Universidad Peruana Cayetano Heredia].

Science.gov (United States)

Gotuzzo, Eduardo; González, Elsa; Verdonck, Kristien

2010-09-01

Research is a main element for human and social development. Under this point of view, it involves particular challenges and opportunities for the so-called "developing countries". An approach for those challenges and opportunities comes from the analysis of two interrelated activities; the training of new researchers and the research development with institutions or researchers which are external to the institution ("collaborative research"). Both activities are essential for the consolidation, widening and updating of the institutional capabilities for scientific production. We present here the experiences of the Instituto de Medicina Tropical "Alexander von Humboldt" of the Universidad Peruana Cayetano Heredia, in relation to the training of new researchers, we discuss the four elements we consider key for this process; the promotion of stimulating environments for research, the proactive identification of fellows, the complementary advice and networks consolidation; and we analyze three successful models of international collaboration for the training of new researchers under different institutional approaches.

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.

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

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.

The Application and Research of the GA-BP Neural Network Algorithm in the MBR Membrane Fouling

Directory of Open Access Journals (Sweden)

Chunqing Li

2014-01-01

Full Text Available It is one of the important issues in the field of today's sewage treatment of researching the MBR membrane flux prediction for membrane fouling. Firstly this paper used the principal component analysis method to achieve dimensionality and correlation of input variables and obtained the three major factors affecting membrane fouling most obvious: MLSS, total resistance, and operating pressure. Then it used the BP neural network to establish the system model of the MBR intelligent simulation, the relationship between three parameters, and membrane flux characterization of the degree of membrane fouling, because the BP neural network has slow training speed, is sensitive to the initial weights and the threshold, is easy to fall into local minimum points, and so on. So this paper used genetic algorithm to optimize the initial weights and the threshold of BP neural network and established the membrane fouling prediction model based on GA-BP network. As this research had shown, under the same conditions, the BP network model optimized by GA of MBR membrane fouling is better than that not optimized for prediction effect of membrane flux. It demonstrates that the GA-BP network model of MBR membrane fouling is more suitable for simulation of MBR membrane fouling process, comparing with the BP network.

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.

Connecting the Dots: Understanding the Flow of Research Knowledge within a Research Brokering Network

Science.gov (United States)

Rodway, Joelle

2015-01-01

Networks are frequently cited as an important knowledge mobilization strategy; however, there is little empirical research that considers how they connect research and practice. Taking a social network perspective, I explore how central office personnel find, understand and share research knowledge within a research brokering network. This mixed…

Solar Energy Innovation Network | Solar Research | NREL

Science.gov (United States)

Energy Innovation Network Solar Energy Innovation Network The Solar Energy Innovation Network grid. Text version The Solar Energy Innovation Network is a collaborative research effort administered (DOE) Solar Energy Technologies Office to develop and demonstrate new ways for solar energy to improve

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.

Genetic algorithm-based neural network for accidents diagnosis of research reactors on FPGA

International Nuclear Information System (INIS)

Ghuname, A.A.A.

2012-01-01

The Nuclear Research Reactors plants are expected to be operated with high levels of reliability, availability and safety. In order to achieve and maintain system stability and assure satisfactory and safe operation, there is increasing demand for automated systems to detect and diagnose such failures. Artificial Neural Networks (ANNs) are one of the most popular solutions because of their parallel structure, high speed, and their ability to give easy solution to complicated problems. The genetic algorithms (GAs) which are search algorithms (optimization techniques), in recent years, have been used to find the optimum construction of a neural network for definite application, as one of the advantages of its usage. Nowadays, Field Programmable Gate Arrays (FPGAs) are being an important implementation method of neural networks due to their high performance and they can easily be made parallel. The VHDL, which stands for VHSIC (Very High Speed Integrated Circuits) Hardware Description Language, have been used to describe the design behaviorally in addition to schematic and other description languages. The description of designs in synthesizable language such as VHDL make them reusable and be implemented in upgradeable systems like the Nuclear Research Reactors plants. In this thesis, the work was carried out through three main parts.In the first part, the Nuclear Research Reactors accident's pattern recognition is tackled within the artificial neural network approach. Such patterns are introduced initially without noise. And, to increase the reliability of such neural network, the noise ratio up to 50% was added for training in order to ensure the recognition of these patterns if it introduced with noise.The second part is concerned with the construction of Artificial Neural Networks (ANNs) using Genetic algorithms (GAs) for the nuclear accidents diagnosis. MATLAB ANNs toolbox and GAs toolbox are employed to optimize an ANN for this purpose. The results obtained show

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.

Research Data Management Training for Geographers: First Impressions

Directory of Open Access Journals (Sweden)

Kerstin Helbig

2016-03-01

Full Text Available Sharing and secondary analysis of data have become increasingly important for research. Especially in geography, the collection of digital data has grown due to technological changes. Responsible handling and proper documentation of research data have therefore become essential for funders, publishers and higher education institutions. To achieve this goal, universities offer support and training in research data management. This article presents the experiences of a pilot workshop in research data management, especially for geographers. A discipline-specific approach to research data management training is recommended. The focus of this approach increases researchers’ interest and allows for more specific guidance. The instructors identified problems and challenges of research data management for geographers. In regards to training, the communication of benefits and reaching the target groups seem to be the biggest challenges. Consequently, better incentive structures as well as communication channels have to be established.

Training Impact on Novice and Experienced Research Coordinators.

Science.gov (United States)

Behar-Horenstein, Linda S; Potter, JoNell Efantis; Prikhidko, Alena; Swords, Stephanie; Sonstein, Stephen; Kolb, H Robert

2017-12-01

Competency-based training and professional development is critical to the clinical research enterprise. Understanding research coordinators' perspectives is important for establishing a common core curriculum. The purpose of this study was to describe participants' perspectives regarding the impact of online and classroom training sessions. 27 participants among three institutions, completed a two-day classroom training session. 10 novice and seven experienced research coordinators participated in focus group interviews. Grounded theory revealed similarities in novice and experienced coordinator themes including Identifying Preferences for Instruction and Changing Self Perceptions. Differences, seen in experienced participants, focused on personal change, in the theme of Re-Assessing Skills. Infrastructure and cultural issues were evident in their theme, Promoting Leadership and Advocacy. Novice participants recommended ways to improve training via their theme of Making Programmatic Improvements. Participants reported a clear preference for classroom learning. Training played an influential role in changing participants' self-perceptions by validating their experiences. The findings provided guidance for developing a standardized curriculum. Training must be carefully tailored to the needs of participants while considering audience needs based on work experience, how technology can be used and offering content that is most urgently needed.

Research of convolutional neural networks for traffic sign recognition

OpenAIRE

Stadalnikas, Kasparas

2017-01-01

In this thesis the convolutional neural networks application for traffic sign recognition is analyzed. Thesis describes the basic operations, techniques that are commonly used to apply in the image classification using convolutional neural networks. Also, this paper describes the data sets used for traffic sign recognition, their problems affecting the final training results. The paper reviews most popular existing technologies – frameworks for developing the solution for traffic sign recogni...

Supporting Scientific Research with the Energy Sciences Network

CERN Multimedia

CERN. Geneva; Monga, Inder

2016-01-01

The Energy Sciences Network (ESnet) is a high-performance, unclassified national network built to support scientific research. Funded by the U.S. Department of Energy’s Office of Science (SC) and managed by Lawrence Berkeley National Laboratory, ESnet provides services to more than 40 DOE research sites, including the entire National Laboratory system, its supercomputing facilities, and its major scientific instruments. ESnet also connects to 140 research and commercial networks, permitting DOE-funded scientists to productively collaborate with partners around the world. ESnet Division Director (Interim) Inder Monga and ESnet Networking Engineer David Mitchell will present current ESnet projects and research activities which help support the HEP community. ESnet  helps support the CERN community by providing 100Gbps trans-Atlantic network transport for the LHCONE and LHCOPN services. ESnet is also actively engaged in researching connectivity to cloud computing resources for HEP workflows a...

The European network of excellence Emil

Energy Technology Data Exchange (ETDEWEB)

NONE

2006-07-01

The network of excellence EMIL (European Molecular Imaging Laboratories ) is the only European network of excellence in molecular imaging for oncology. It was set up and is coordinated by the 'in vivo imaging of gene expression' group of CEA Orsay. Included in Priority Thematic Area 1 (life sciences, genomics and biotechnology for health) of the European Commission's 6. Framework Programme for Research and Technological Development (FP6), this five-year project (2004-2009) aims t o merge the leading European research teams in molecular imaging, in universities, research centres and small and medium enterprises, to focus on early diagnosis, prognosis and therapeutic evaluation of cancer. The EMIL network brings together 58 partners representing 43 bodies in 13 European countries, and integrates 6 technological facilities: Orsay (France), Turin (Italy), Cologne (Germany), Leiden (Netherlands), Milan (Italy) and Antwerpen (Belgium).The research and training activities of the EMIL network are based on 9 thematic working groups or 'work packages' (wp), forming a common activity programme including : Integration activities: creation of a network of technological and training facilities favouring the mobility of researchers and the integration of small and medium enterprises into the EMIL network. Dissemination of expertise activities: training, communication, common knowledge management and intellectual property rights. Research activities: a common research programme with a horizontal dimension, making use of methodological tools of physics, biology and chemistry necessary for the further development of molecular imaging (instrument techniques, molecular probes, biological engineering), and a vertical integrative dimension, bringing together cancer imaging applications (early diagnostic imaging, development of new therapies imaging for drug development). (author)

The development of a TED-Ed online resident research training program.

Science.gov (United States)

Moreau, Katherine A; Pound, Catherine M; Peddle, Beth; Tokarewicz, Jaclyn; Eady, Kaylee

2014-01-01

Pediatric health research is important for improving the health and well-being of children and their families. To foster the development of physicians' research competencies, it is vital to integrate practical and context-specific research training into residency programs. To describe the development of a resident research training program at one tertiary care pediatric academic health sciences center in Ontario, Canada. We surveyed residents and pediatricians/research staff to establish the need and content for a resident research training program. Residents and resident research supervisors agreed or strongly agreed that research training is important for residents. However, few residents and supervisors believed that their academic health sciences center provided adequate training and resources to support resident research. As such, an online resident research training program was established. Residents and supervisors agreed that the program should focus on the following topics: 1) critically evaluating research literature, 2) writing a research proposal, 3) submitting an application for research funding, and 4) writing a manuscript. This highly accessible, context-specific, and inexpensive online program model may be of interest and benefit to other residency programs as a means to enhance residents' scholarly roles. A formal evaluation of the research training program is now underway.

The development of a TED-Ed online resident research training program

Directory of Open Access Journals (Sweden)

Katherine A. Moreau

2014-12-01

Full Text Available Background: Pediatric health research is important for improving the health and well-being of children and their families. To foster the development of physicians’ research competencies, it is vital to integrate practical and context-specific research training into residency programs. Purpose: To describe the development of a resident research training program at one tertiary care pediatric academic health sciences center in Ontario, Canada. Methods: We surveyed residents and pediatricians/research staff to establish the need and content for a resident research training program. Results: Residents and resident research supervisors agreed or strongly agreed that research training is important for residents. However, few residents and supervisors believed that their academic health sciences center provided adequate training and resources to support resident research. As such, an online resident research training program was established. Residents and supervisors agreed that the program should focus on the following topics: 1 critically evaluating research literature, 2 writing a research proposal, 3 submitting an application for research funding, and 4 writing a manuscript. Discussion: This highly accessible, context-specific, and inexpensive online program model may be of interest and benefit to other residency programs as a means to enhance residents’ scholarly roles. A formal evaluation of the research training program is now underway.

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.

The Nordic Health Promotion Research Network (NHPRN).

Science.gov (United States)

Ringsberg, Karin C

2015-08-01

The Nordic Health Promotion Research Network (NHPRN) was established in 2007 at the Nordic School of Public Health (NHV). This article aims to describe the foundation of the NHPRN, the development and the present status of the work of NHPRN. The NHPRN consists of about 50 senior and junior researchers from all Nordic countries. It is a working network that aims to develop the theoretical understanding of health promotion, to create research cooperation in health promotion from a Nordic perspective and to extend the scope of health promotion through education. Network members meet biannually to discuss and further develop research within the field and are also responsible for the Nordic conference on Health Promotion, organized every 3 years. The NHV hosted the network between 2007 and 2014; and the World Health Organisation (WHO) will assume this role in 2015. © 2015 the Nordic Societies of Public Health.

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.

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.

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.

Training of research reactor personnel

International Nuclear Information System (INIS)

Cherruau, F.

1980-01-01

Research reactor personnel operate the reactor and carry out the experiments. These two types of work entail different activities, and therefore different skills and competence, the number of relevant staff being basically a function of the size, complexity and versatility of the reactor. Training problems are often reactor-specific, but the present paper considers them from three different viewpoints: the training or retraining of new staff or of personnel already employed at an existing facility, and training of personnel responsible for the start-up and operation of a new reactor, according to whether local infrastructure and experience already exist or whether they have to be built up from scratch. On-the-spot experience seems to be an essential basis for sound training, but requires teaching abilities and aids often difficult to bring together, and the availability of instructors that does not always fit in smoothly with current operational and experimental tasks. (author)

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

Modeling management of research and education networks

NARCIS (Netherlands)

Galagan, D.V.

2004-01-01

Computer networks and their services have become an essential part of research and education. Nowadays every modern R&E institution must have a computer network and provide network services to its students and staff. In addition to its internal computer network, every R&E institution must have a

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.

Application of Decision Making and Team Training Research to Operational Training. A Translative Technique.

Science.gov (United States)

DECISION MAKING , * GROUP DYNAMICS, NAVAL TRAINING, TRANSFER OF TRAINING, SCIENTIFIC RESEARCH, CLASSIFICATION, PROBLEM SOLVING, MATHEMATICAL MODELS, SUBMARINES, SIMULATORS, PERFORMANCE(HUMAN), UNDERSEA WARFARE.

Scottish Stroke Research Network: the first three years.

Science.gov (United States)

McCormick, K; Langhorne, P; Graham, F E J; McFarlane, C

2010-08-01

Research networks were introduced in the UK to facilitate and improve clinical research and stroke was seen as a priority topic for local research network development. The Scottish Stroke Research Network (SSRN) is one of 11 stroke research networks in the UK. In this article we review the progress of the Scottish Stroke Research Network in the three years since inception. Between 2006-2009 the number of active hospital research sites has increased from 10 to 22 expanding to involve 20 stroke research nurses. There was a corresponding 58% increase in recruitment of participants into stroke studies, from 376 in 2006/07 to 594 in 2008/09. The majority (17/20) of our current studies are interventional. Data from one of these, the CLOTs trial (Clots in Legs Or sTocking after Stroke), demonstrates that the annual recruitment in Scotland increased from a median of 94 (range 6-122) patients per year in the six years before the SSRN, to 140 (135-158) patients per year after SSRN involvement. We currently screen about 50% of Scottish stroke patients and approximately 5% of Scottish stroke patients are participating in research studies that we support. The SSRN has made good progress in the first three years. Increasing the recruitment of screened patients remains a challenge.

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.

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.

Chain and network science: A research framework

NARCIS (Netherlands)

Omta, S.W.F.; Trienekens, J.H.; Beers, G.

2001-01-01

In this first article of the Journal on Chain and Network Science the base-line is set for a discussion on contents and scope of chain and network theory. Chain and network research is clustered into four main ‘streams’: Network theory, social capital theory, supply chain management and business

Stories in Networks and Networks in Stories: A Tri-Modal Model for Mixed-Methods Social Network Research on Teachers

Science.gov (United States)

Baker-Doyle, Kira J.

2015-01-01

Social network research on teachers and schools has risen exponentially in recent years as an innovative method to reveal the role of social networks in education. However, scholars are still exploring ways to incorporate traditional quantitative methods of Social Network Analysis (SNA) with qualitative approaches to social network research. This…

Training and research reactor facility longevity extension program

International Nuclear Information System (INIS)

Carriveau, G.W.

1991-01-01

Since 1943, over 550 training and research reactors have been in operation. According to statistics from the International Atomic Energy Agency, ∼325 training and research reactors are currently in service. This total includes a wide variety of designs covering a range of power and research capabilities located virtually around the world. A program has been established at General Atomics (GA) that is dedicated to the support of extended longevity of training and research reactor facilities. Aspects of this program include the following: (1) new instrumentation and control systems; (2) improved and upgraded nuclear monitoring and control channels; (3) facility testing, repair and upgrade services that include (a) pool or tank integrity, (b) cooling system, and (c) water purification system; (4) fuel element testing procedures and replacement; (5) control rod drive rebuilding and upgrades; (6) control and monitoring system calibration and repair service; (7) training services, including reactor operations, maintenance, instrumentation calibration, and repair; and (8) expanded or new uses such as neutron radiography and autoradiography, isotope production, nuclear medicine, activation analysis, and material properties modification

Federal Plan for Advanced Networking Research and Development

Data.gov (United States)

Networking and Information Technology Research and Development, Executive Office of the President — In the four decades since Federal research first enabled computers to send and receive data over networks, U.S. government research and development R and D in...

Maglev trains key underlying technologies

CERN Document Server

Liu, Zhigang; Li, Xiaolong

2015-01-01

The motion of the train depends on the traction of linear motors in the vehicle. This book describes a number of essential technologies that can ensure the safe operation of Maglev trains, such as suspension and orientation technologies, network control and diagnosis technologies. This book is intended for researchers, scientists, engineers and graduate students involved in the rail transit industry, train control and diagnosis, and Maglev technology.

AcademyHealth's Delivery System Science Fellowship: Training Embedded Researchers to Design, Implement, and Evaluate New Models of Care.

Science.gov (United States)

Kanani, Nisha; Hahn, Erin; Gould, Michael; Brunisholz, Kimberly; Savitz, Lucy; Holve, Erin

2017-07-01

AcademyHealth's Delivery System Science Fellowship (DSSF) provides a paid postdoctoral pragmatic learning experience to build capacity within learning healthcare systems to conduct research in applied settings. The fellowship provides hands-on training and professional leadership opportunities for researchers. Since its inception in 2012, the program has grown rapidly, with 16 health systems participating in the DSSF to date. In addition to specific projects conducted within health systems (and numerous publications associated with those initiatives), the DSSF has made several broader contributions to the field, including defining delivery system science, identifying a set of training objectives for researchers working in delivery systems, and developing a national collaborative network of care delivery organizations, operational leaders, and trainees. The DSSF is one promising approach to support higher-value care by promoting continuous learning and improvement in health systems. © 2017 Society of Hospital Medicine.

Training and Certification of Research Reactor Personnel

International Nuclear Information System (INIS)

Zarina Masood

2011-01-01

The safe operation of a research reactor requires that reactor personnel be fully trained and certified by the relevant authorities. Reactor operators at PUSPATI TRIGA Reactor underwent extensive training and are certified, ever since the reactor first started its operation in 1982. With the emphasis on enhancing reactor safety in recent years, reactor operator training and certification have also evolved. This paper discusses the changes that have to be implemented and the challenges encountered in developing a new training programme to be in line with the national standards. (author)

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.

Development of Safety Review Guidance for Research and Training Reactors

International Nuclear Information System (INIS)

Oh, Kju-Myeng; Shin, Dae-Soo; Ahn, Sang-Kyu; Lee, Hoon-Joo

2007-01-01

The KINS already issued the safety review guidance for pressurized LWRs. But the safety review guidance for research and training reactors were not developed. So, the technical standard including safety review guidance for domestic research and training reactors has been applied mutates mutandis to those of nuclear power plants. It is often difficult for the staff to effectively perform the safety review of applications for the permit by the licensee, based on peculiar safety review guidance. The NRC and NSC provide the safety review guidance for test and research reactors and European countries refer to IAEA safety requirements and guides. The safety review guide (SRG) of research and training reactors was developed considering descriptions of the NUREG- 1537 Part 2, previous experiences of safety review and domestic regulations for related facilities. This study provided the safety review guidance for research and training reactors and surveyed the difference of major acceptance criteria or characteristics between the SRG of pressurized light water reactor and research and training reactors

European Nuclear Education Network Association - Support for nuclear education, training and knowledge management

International Nuclear Information System (INIS)

Ghitescu, Petre

2009-01-01

Developed in 2002-2003 the FP5 EURATOM project 'European Nuclear Engineering Network - ENEN' aimed to establish the basis for conserving nuclear knowledge and expertise, to create an European Higher Education Area for nuclear disciplines and to facilitate the implementation of the Bologna declaration in the nuclear disciplines. In order to ensure the continuity of the achievements and results of the ENEN project, on 22 September 2003, the European Nuclear Higher Education Area was formalized by creating the European Nuclear Education Network Association. ENEN Association goals are oriented towards universities by developing a more harmonized approach for education in the nuclear sciences and engineering in Europe, integrating European education and training in nuclear safety and radiation protection and achieving a better cooperation and sharing of resources and capabilities at the national and international level. At the same time it is oriented towards the end-users (industries, regulatory bodies, research centers, universities) by creating a secure basis of knowledge and skills of value to the EU. It maintains an adequate supply of qualified human resources for design, construction, operation and maintenance of nuclear infrastructures and plants. Also it maintains the necessary competence and expertise for the continued safe use of nuclear energy and applications of radiation in industry and medicine. In 2004-2005, 35 partners continued and expanded the started in FP 5 ENEN Association activities with the FP6 project 'NEPTUNO- Nuclear Education Platform for Training and Universities Organizations'. Thus ENEN established and implemented the European Master of Science in Nuclear Engineering, expanded its activities from education to training, organized and coordinated training sessions and pilot courses and included in its activities the Knowledge Management. At present, the ENEN Association gathers 45 universities, 7 research centers and one multinational company

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

Directory of Open Access Journals (Sweden)

Regula Julia Leemann

2015-12-01

Full Text Available 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 basis. Drawing on a case study of four training networks in Switzerland and the theoretical framework of the sociology of conventions, this paper aims to understand the reasons for the slow dissemination and reluctant adoption of this promising form of organising VET in Switzerland. The results of the study show that the system of moving from one company to another creages a variety of free-rider constellations in the distribution of the collectively generated corporative benefits. This explains why companies are reluctant to participate in this model. For the network to be sustainable, the intermediary organisation has to address discontent arising from free-rider problems while taking into account that the solutions found are always tentative and will often result in new free-rider problems.

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

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

Research staff training in a multisite randomized clinical trial: Methods and recommendations from the Stimulant Reduction Intervention using Dosed Exercise (STRIDE) trial.

Science.gov (United States)

Walker, Robrina; Morris, David W; Greer, Tracy L; Trivedi, Madhukar H

2014-01-01

Descriptions of and recommendations for meeting the challenges of training research staff for multisite studies are limited despite the recognized importance of training on trial outcomes. The STRIDE (STimulant Reduction Intervention using Dosed Exercise) study is a multisite randomized clinical trial that was conducted at nine addiction treatment programs across the United States within the National Drug Abuse Treatment Clinical Trials Network (CTN) and evaluated the addition of exercise to addiction treatment as usual (TAU), compared to health education added to TAU, for individuals with stimulant abuse or dependence. Research staff administered a variety of measures that required a range of interviewing, technical, and clinical skills. In order to address the absence of information on how research staff are trained for multisite clinical studies, the current manuscript describes the conceptual process of training and certifying research assistants for STRIDE. Training was conducted using a three-stage process to allow staff sufficient time for distributive learning, practice, and calibration leading up to implementation of this complex study. Training was successfully implemented with staff across nine sites. Staff demonstrated evidence of study and procedural knowledge via quizzes and skill demonstration on six measures requiring certification. Overall, while the majority of staff had little to no experience in the six measures, all research assistants demonstrated ability to correctly and reliably administer the measures throughout the study. Practical recommendations are provided for training research staff and are particularly applicable to the challenges encountered with large, multisite trials.

Minority International Research Training Program: Global Collaboration in Nursing Research.

Science.gov (United States)

McElmurry, Beverly J.; Misner, Susan J.; Buseh, Aaron G.

2003-01-01

The Minority International Research Training Program pairs minority nursing students with faculty mentors at international sites for short-term research. A total of 26 undergraduate, 22 graduate, and 6 postdoctoral students have participated. Challenges include recruitment, orientation, and preparation of students; identification and preparation…

NIHR Clinical Research Networks: what they do and how they help paediatric research.

Science.gov (United States)

Lythgoe, Hanna; Price, Victoria; Poustie, Vanessa; Attar, Sabah; Hawcutt, Daniel; Preston, Jennifer; Beresford, Michael W

2017-08-01

This review provides paediatricians with an update on the new structure of the National Institute for Health Research's (NIHR) Clinical Research Network (CRN): Children and its role within the wider NIHR infrastructure. The network supports delivery of high-quality research within the NHS in England and supports researchers, through provision of staff and resources, with feasibility, site set-up, patient recruitment and study management. Since 2013, over 80% of commercial contract studies running within the UK sat within the UKCRN Portfolio. Of the diverse, increasing portfolio of studies supported by the network, many studies are interventional, with 33% being randomised controlled studies. Recruitment to studies supported by the network through the Children's Portfolio has consistently improved. Over 200 000 participants have been recruited to the Children's Portfolio studies to date, and there are currently approximately 500 studies open to recruitment. The CRN: Children has successfully involved patients and the public in all aspects of study design and delivery, including through the work of Generation R. Challenges remain in conducting paediatric research and the network is committed to supporting Children's research and further building on its achievements to date. Education and engagement of paediatricians within the network and research is important to further improving quality and delivery of paediatric research. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Action research in inter-organisational networks

DEFF Research Database (Denmark)

Goduscheit, René Chester; Rasmussen, Erik Stavnsager; Jørgensen, Jacob Høj

2007-01-01

Traditionally, the literature on action research has been aimed at intra-organisational issues. These studies have distinguished between two researcher roles: The problem-solver and the observer. This article addresses the distinct challenges of action research in inter-organisational projects....... In addition to the problem-solver and observer roles, the researcher in an inter-organisational setting can serve as a legitimiser of the project and manage to involve partners that in an ordinary business-to-business setting would not have participated. Based on an action research project in a Danish inter......-organisational network, this article discusses potential pitfalls in the legitimiser role. Lack of clarity in defining the researcher role and project ownership in relation to the funding organisation and the rest of the network can jeopardise the project and potentially the credibility of the researchers. The article...

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

The experiences of health services research and health services research training in Korea.

Science.gov (United States)

Moon, O R

1984-12-01

Early in the 1970s the Korean government recognized the necessity of Health Services Research (HSR). The law of the Korea Health Development Institute was promulgated in 1975, and a contribution from the Republic was combined with an Agency for International Development loan to field test low-cost health service strategies. A program to deploy Community Health Practitioners (CHPs), similar to family nurse practitioners or Medex has been demonstrated to be effective. The CHP training program grew from 9 in 1980 to 1343 in 1984. CHP's main functions are curative, preventive, educative, and administrative. They are selected registered nurses and/or midwives, where possible from serviced communities. They are trained in 24 weeks, including 12 weeks of clinical practice, in an anticipated recruiting post. CHPs help train village health volunteers (VHVs), who are literate women chosen by their communities. They work closely with the CHPs as a liaison with the village and in information gathering. An HSR orientation workshop held in Chuncheon in 1980, discussed role, policy, status, finance components, information systems, behavioral and manpower components, staff training, protocols for project development, HSR in the future and evaluation of the conference. In 1980, a National Workshop on Biomedical Research Methodology was also held, with World Health Organization and Korean consultants. Training of junior scientists would include introduction to scientific method, statement of problems, quantitative study technics, research proposals, and interpretation of results. The Korean Institute of Public Health sponsored a 1982 experts forum on the health care system, medical facilities, organizational management, financing and medical security, and health behavioral aspects. Training of trainers and lower level field workers, orientation of program managers, researchers, and communities themselves should all be training priorities. In future, CHPs should be refresher-trained

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…

The prevention research centers' managing epilepsy well network.

Science.gov (United States)

DiIorio, Colleen K; Bamps, Yvan A; Edwards, Ariele L; Escoffery, Cam; Thompson, Nancy J; Begley, Charles E; Shegog, Ross; Clark, Noreen M; Selwa, Linda; Stoll, Shelley C; Fraser, Robert T; Ciechanowski, Paul; Johnson, Erica K; Kobau, Rosemarie; Price, Patricia H

2010-11-01

The Managing Epilepsy Well (MEW) Network was created in 2007 by the Centers for Disease Control and Prevention's (CDC) Prevention Research Centers and Epilepsy Program to promote epilepsy self-management research and to improve the quality of life for people with epilepsy. MEW Network membership comprises four collaborating centers (Emory University, University of Texas Health Science Center at Houston, University of Michigan, and University of Washington), representatives from CDC, affiliate members, and community stakeholders. This article describes the MEW Network's background, mission statement, research agenda, and structure. Exploratory and intervention studies conducted by individual collaborating centers are described, as are Network collaborative projects, including a multisite depression prevention intervention and the development of a standard measure of epilepsy self-management. Communication strategies and examples of research translation programs are discussed. The conclusion outlines the Network's role in the future development and dissemination of evidence-based epilepsy self-management programs. Copyright © 2010 Elsevier Inc. All rights reserved.

Biopsychosocial Research Training in Breast Cancer

National Research Council Canada - National Science Library

Antoni, Michael

1998-01-01

.... Three others successfully defended their Master's theses. Training throughout YR 4 was closely coordinated with ongoing ACS-funded and NCI-funded biopsychosocial breast cancer research projects...

Is Participatory Action Research an innovative pedagogical alternative for training teachers as researchers? The training plan and evaluation for normal schools.

Science.gov (United States)

Paredes-Chi, Arely Anahy; Castillo-Burguete, María Teresa

2018-06-01

Normal schools in Mexico train teachers for basic level education. Classified as Higher Education Institutions, part of their mandate is to conduct scientific research to improve educational quality. Currently, normal school students can meet graduation requirements by either writing a thesis or reporting on professional practice using Participatory Action Research (PAR). Teachers at normal schools have only limited experience in conducting and supervising PAR projects. With the aim of analyzing the situation and addressing this paradox, we used PAR to develop a plan to train normal school teachers in application of PAR methodology. We present the training proposal and evaluate its results in a pilot phase. These suggest that PAR represents an innovative option for training teachers to conduct research and therefore fulfill part of their responsibilities at normal schools in Mexico. Changes in institutional culture and structure would be required for successful implementation of PAR in this context. Copyright © 2018 Elsevier Ltd. All rights reserved.

How Research Training Will Shape the Future of Dental, Oral, and Craniofacial Research.

Science.gov (United States)

D'Souza, Rena N; Colombo, John S

2017-09-01

This is a critical time in the history of the dental profession for it to fully embrace the responsibility to safeguard its reputation as a learned profession. In this golden era of scientific and technological advances, opportunities abound to create new diagnostics, preventions, treatments, and cures to improve oral health. Dental schools are the largest national resource entrusted with the responsibility to educate, train, and retain oral health researchers who can leverage such technologies and research opportunities that will benefit the profession at large as well as patients. This article reemphasizes the theme that research training and scholarship must be inextricably woven into the environment and culture in dental schools to ensure the future standing of the profession. An overview of the history of support provided by the National Institutes of Health and National Institute of Dental and Craniofacial Research for the training and career development of dentist-scientists is presented. In addition, new data on the outcomes of such investments are presented along with a comparison with other health professions. This overview underscores the need to expand the capacity of a well-trained cadre of oral health researchers through the reengineering of training programs. Such strategies will best prepare future graduates for team science, clinical trials, and translational research as well as other emerging opportunities. The urgent need for national organizations like the American Dental Association, American Dental Education Association, and American Association for Dental Research to create new alliances and novel initiatives to assist dental schools and universities in fulfilling their research mission is emphasized. To ignore such calls for action is to disavow a valuable legacy inherited by the dental profession. This article was written as part of the project "Advancing Dental Education in the 21 st Century."

The network evolves | IDRC - International Development Research ...

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

2011-07-08

Jul 8, 2011 ... For the 19 young scholars brought together by the Poverty Research Network, the rewards have been substantial. Lu Ming, who describes his experience with the group as “just fantastic,” likens the network to a bridge – connecting China to Canada, and linking researchers to each other and to scholars ...

Biological and Environmental Research Network Requirements

Energy Technology Data Exchange (ETDEWEB)

Balaji, V. [Princeton Univ., NJ (United States). Earth Science Grid Federation (ESGF); Boden, Tom [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Cowley, Dave [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Dart, Eli [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). ESNet; Dattoria, Vince [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). ESNet; Desai, Narayan [Argonne National Lab. (ANL), Argonne, IL (United States); Egan, Rob [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Foster, Ian [Argonne National Lab. (ANL), Argonne, IL (United States); Goldstone, Robin [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Gregurick, Susan [U.S. Dept. of Energy, Washington, DC (United States). Biological Systems Science Division; Houghton, John [U.S. Dept. of Energy, Washington, DC (United States). Biological and Environmental Research (BER) Program; Izaurralde, Cesar [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Johnston, Bill [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). ESNet; Joseph, Renu [U.S. Dept. of Energy, Washington, DC (United States). Climate and Environmental Sciences Division; Kleese-van Dam, Kerstin [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Lipton, Mary [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Monga, Inder [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). ESNet; Pritchard, Matt [British Atmospheric Data Centre (BADC), Oxon (United Kingdom); Rotman, Lauren [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). ESNet; Strand, Gary [National Center for Atmospheric Research (NCAR), Boulder, CO (United States); Stuart, Cory [Argonne National Lab. (ANL), Argonne, IL (United States); Tatusova, Tatiana [National Inst. of Health (NIH), Bethesda, MD (United States); Tierney, Brian [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). ESNet; Thomas, Brian [Univ. of California, Berkeley, CA (United States); Williams, Dean N. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Zurawski, Jason [Internet2, Washington, DC (United States)

2013-09-01

The Energy Sciences Network (ESnet) is the primary provider of network connectivity for the U.S. Department of Energy (DOE) Office of Science (SC), the single largest supporter of basic research in the physical sciences in the United States. In support of SC programs, ESnet regularly updates and refreshes its understanding of the networking requirements of the instruments, facilities, scientists, and science programs that it serves. This focus has helped ESnet be a highly successful enabler of scientific discovery for over 25 years. In November 2012, ESnet and the Office of Biological and Environmental Research (BER) of the DOE SC organized a review to characterize the networking requirements of the programs funded by the BER program office. Several key findings resulted from the review. Among them: 1) The scale of data sets available to science collaborations continues to increase exponentially. This has broad impact, both on the network and on the computational and storage systems connected to the network. 2) Many science collaborations require assistance to cope with the systems and network engineering challenges inherent in managing the rapid growth in data scale. 3) Several science domains operate distributed facilities that rely on high-performance networking for success. Key examples illustrated in this report include the Earth System Grid Federation (ESGF) and the Systems Biology Knowledgebase (KBase). This report expands on these points, and addresses others as well. The report contains a findings section as well as the text of the case studies discussed at the review.

A child abuse research network: Now what?

Science.gov (United States)

Lindberg, Daniel M; Scribano, Philip V

2017-08-01

As foundational work in preparation for a sustainable, multi-center network devoted to child abuse medical research, we recently used a combination of survey and modified Delphi methodologies to determine research priorities for future multi-center studies. Avoiding missed diagnoses, and improving selected/indicated prevention were the topics rated most highly in terms of research priority. Several constructive commentaries in this issue identify the key challenges which must be overcome to ensure a successful network. Indeed, as with the clinical work of child abuse pediatrics, a scientific network will also require constant collaboration within and outside the community of child abuse pediatricians, the wider medical community, and even non-medical professions. Copyright © 2017 Elsevier Ltd. All rights reserved.

NASA/DOD Aerospace Knowledge Diffusion Research Project. Report 35: The use of computer networks in aerospace engineering

Science.gov (United States)

Bishop, Ann P.; Pinelli, Thomas E.

1995-01-01

This research used survey research to explore and describe the use of computer networks by aerospace engineers. The study population included 2000 randomly selected U.S. aerospace engineers and scientists who subscribed to Aerospace Engineering. A total of 950 usable questionnaires were received by the cutoff date of July 1994. Study results contribute to existing knowledge about both computer network use and the nature of engineering work and communication. We found that 74 percent of mail survey respondents personally used computer networks. Electronic mail, file transfer, and remote login were the most widely used applications. Networks were used less often than face-to-face interactions in performing work tasks, but about equally with reading and telephone conversations, and more often than mail or fax. Network use was associated with a range of technical, organizational, and personal factors: lack of compatibility across systems, cost, inadequate access and training, and unwillingness to embrace new technologies and modes of work appear to discourage network use. The greatest positive impacts from networking appear to be increases in the amount of accurate and timely information available, better exchange of ideas across organizational boundaries, and enhanced work flexibility, efficiency, and quality. Involvement with classified or proprietary data and type of organizational structure did not distinguish network users from nonusers. The findings can be used by people involved in the design and implementation of networks in engineering communities to inform the development of more effective networking systems, services, and policies.

Training Program in Biostatistics for Breast Cancer Research

National Research Council Canada - National Science Library

Little, Roderick

1998-01-01

The current training program terminates in the summer of 1998. We had originally planned to develop a training program in biostatistics for cancer research for submission to the National Cancer Institute (Task 9...

Increasing Scalability of Researcher Network Extraction from the Web

Science.gov (United States)

Asada, Yohei; Matsuo, Yutaka; Ishizuka, Mitsuru

Social networks, which describe relations among people or organizations as a network, have recently attracted attention. With the help of a social network, we can analyze the structure of a community and thereby promote efficient communications within it. We investigate the problem of extracting a network of researchers from the Web, to assist efficient cooperation among researchers. Our method uses a search engine to get the cooccurences of names of two researchers and calculates the streangth of the relation between them. Then we label the relation by analyzing the Web pages in which these two names cooccur. Research on social network extraction using search engines as ours, is attracting attention in Japan as well as abroad. However, the former approaches issue too many queries to search engines to extract a large-scale network. In this paper, we propose a method to filter superfluous queries and facilitates the extraction of large-scale networks. By this method we are able to extract a network of around 3000-nodes. Our experimental results show that the proposed method reduces the number of queries significantly while preserving the quality of the network as compared to former methods.

Functional MRI neurofeedback training on connectivity between two regions induces long-lasting changes in intrinsic functional network

Directory of Open Access Journals (Sweden)

Fukuda eMegumi

2015-03-01

Full Text Available Motor or perceptual learning is known to influence functional connectivity between brain regions and induce short-term changes in the intrinsic functional networks revealed as correlations in slow blood-oxygen-level dependent (BOLD signal fluctuations. However, no cause-and-effect relationship has been elucidated between a specific change in connectivity and a long-term change in global networks. Here, we examine the hypothesis that functional connectivity (i.e. temporal correlation between two regions is increased and preserved for a long time when two regions are simultaneously activated or deactivated. Using the connectivity-neurofeedback training paradigm, subjects successfully learned to increase the correlation of activity between the lateral parietal and primary motor areas, regions that belong to different intrinsic networks and negatively correlated before training under the resting conditions. Furthermore, whole-brain hypothesis-free analysis as well as functional network analyses demonstrated that the correlation in the resting state between these areas as well as the correlation between the intrinsic networks that include the areas increased for at least two months. These findings indicate that the connectivity-neurofeedback training can cause long-term changes in intrinsic connectivity and that intrinsic networks can be shaped by experience-driven modulation of regional correlation.

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.

Training the next generation of psychotraumatologists

DEFF Research Database (Denmark)

Vallieres, Frederique; Hyland, Philip; Murphy, Jamie

2017-01-01

In this paper we present a description of a new, Horizon2020, Marie Curie Sklodowska Action funded, research and training program called CONTEXT, or the ‘COllaborative Network for Training and EXcellence in psychoTraumatology’. The three objectives of the program are put forward, each of which re...

EUFAR the key portal and network for airborne research in Europe

Science.gov (United States)

Gérard, Elisabeth; Brown, Philip

2017-04-01

Created in 2000 and supported by the EU Framework Programmes since then as an Integrating Activities' project, EUFAR (European Facility of Airborne Research in environmental and Geo-sciences) was born out of the necessity to create a central network and access point for the airborne research community in Europe. With the aim to support researchers by granting them access to aircraft and instrumentation most suited to the needs of researchers across Europe, not accessible in their home countries, EUFAR also provides technical support and training in the field of airborne research for the environmental and geosciences, and enables the sharing of expertise and harmonisation of research practices. Today, EUFAR2 (2014-2018) coordinates and facilitates transnational access to 19 instrumented aircraft and 5 remote-sensing instruments through the 14 operators who are part of EUFAR's current 24-partner European consortium. In addition, the current project supports networking and joint research activities focused on providing an enabling environment for and to promote airborne research. Examples of some of these recent activities will be shown EUFAR is currently seeking to establish itself as an AISBL (international non-profit association) to ensure its existence and operations beyond January 2018 when our present EC funding comes to an end. The objectives of the EUFAR AISBL will include continuing to develop the integration of the research aircraft community in Europe and also its links with other environmental research infrastructures, such as the community of research infrastructures under the umbrella of ENVRIplus. Another objective will be to continue to broaden access to research facilities beyond that supported solely by national funding streams so that EUFAR better approaches the status of a European open research infrastructure. Together with the implementation of an Open Access scheme by means of resource-sharing envisaged in late 2017, such a sustainable structure

Academic Social Networking Sites: Improves Research Visibility and Impact

OpenAIRE

Ebrahim, Nader Ale

2017-01-01

Researchers needs to remove many traditional obstacles to disseminate and outreach their research outputs. Academic social networking allows you to connect with other researchers in your field, share your publications, and get feedback on your non-peer-reviewed work. The academic social networking, making your work more widely discoverable and easily available. The two best known academic social networking are ResearchGate and Academia.edu. These sites offer an instant technique to monitor wh...

Social network analysis: Presenting an underused method for nursing research.

Science.gov (United States)

Parnell, James Michael; Robinson, Jennifer C

2018-06-01

This paper introduces social network analysis as a versatile method with many applications in nursing research. Social networks have been studied for years in many social science fields. The methods continue to advance but remain unknown to most nursing scholars. Discussion paper. English language and interpreted literature was searched from Ovid Healthstar, CINAHL, PubMed Central, Scopus and hard copy texts from 1965 - 2017. Social network analysis first emerged in nursing literature in 1995 and appears minimally through present day. To convey the versatility and applicability of social network analysis in nursing, hypothetical scenarios are presented. The scenarios are illustrative of three approaches to social network analysis and include key elements of social network research design. The methods of social network analysis are underused in nursing research, primarily because they are unknown to most scholars. However, there is methodological flexibility and epistemological versatility capable of supporting quantitative and qualitative research. The analytic techniques of social network analysis can add new insight into many areas of nursing inquiry, especially those influenced by cultural norms. Furthermore, visualization techniques associated with social network analysis can be used to generate new hypotheses. Social network analysis can potentially uncover findings not accessible through methods commonly used in nursing research. Social networks can be analysed based on individual-level attributes, whole networks and subgroups within networks. Computations derived from social network analysis may stand alone to answer a research question or incorporated as variables into robust statistical models. © 2018 John Wiley & Sons Ltd.

Comprehensive Oncologic Emergencies Research Network (CONCERN)

Science.gov (United States)

The Comprehensive Oncologic Emergencies Research Network (CONCERN) was established in March 2015 with the goal to accelerate knowledge generation, synthesis and translation of oncologic emergency medicine research through multi-center collaborations.

Agroecology in Europe: Research, Education, Collective Action Networks, and Alternative Food Systems

Directory of Open Access Journals (Sweden)

Alexander Wezel

2018-04-01

Full Text Available Agroecology is considered with different focus and weight in different parts of the world as a social and political movement, as science, and as practice. Despite its multitude of definitions, agroecology has begun in Europe to develop in different regional, national and continental networks of researchers, practitioners, advocates and movements. However, there is a lack of a comprehensive overview about these different developments and networks. Therefore, this paper attempts to document and provide a mapping of the development of European agroecology in its diverse forms. Through a literature review, interviews, active conference participation, and an extensive internet search we have collected information about the current state and development of agroecology in Europe. Agroecological research and higher education exist more in western and northern Europe, but farm schools and farmer-to-farmer training are also present in other regions. Today a large variety of topics are studied at research institutions. There is an increasing number of bottom-up agroecological initiatives and national or continental networks and movements. Important movements are around food sovereignty, access to land and seeds. Except for France, there are very few concrete policies for agroecology in Europe. Agroecology is increasingly linked to different fields of agri-food systems. This includes Community Supported Agriculture systems, but also agroecological territories, and some examples of labelling products. To amplify agroecology in Europe in the coming years, policy development will be crucial and proponents of agroecology must join forces and work hand-in-hand with the many stakeholders engaged in initiatives to develop more sustainable agriculture and food systems.

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.

Animating Research with Counseling Values: A Training Model to Address the Research-to-Practice Gap

Science.gov (United States)

Lee, Kristi A.; Dewell, John A.; Holmes, Courtney M.

2014-01-01

The persistent research-to-practice gap poses a problem for counselor education. The gap may be caused by conflicts between the humanistic values that guide much of counseling and the values that guide research training. In this article, the authors address historical concerns regarding research training for students and the conducting of research…

Education and Training on ISIS Research Reactor

International Nuclear Information System (INIS)

Foulon, F.; Badeau, G.; Lescop, B.; Wohleber, X.

2013-01-01

In the frame of academic and vocational programs the National Institute for Nuclear Science and Technology uses the ISIS research reactor as a major tool to ensure a practical and comprehensive understanding of the nuclear reactor physics, principles and operation. A large set of training courses have been developed on ISIS, optimising both the content of the courses and the pedagogical approach. Programs with duration ranging from 3 hours (introduction to reactor operation) to 24 hours (full program for the future operators of research reactors) are carried out on ISIS reactor. The reactor is operated about 350 hours/year for education and training, about 40 % of the courses being carried out in English. Thus, every year about 400 trainees attend training courses on ISIS reactor. We present here the ISIS research reactor and the practical courses that have been developed on ISIS reactor. Emphasis is given to the pedagogical method which is used to focus on the operational and safety aspects, both in normal and incidental operation. We will present the curricula of the academic and vocational courses in which the practical courses are integrated, the courses being targeted to a wide public, including operators of research reactors, engineers involved in the design and operation of nuclear reactors as well as staff of the regulatory body. We address the very positive impact of the courses on the development of the competences and skills of participants. Finally, we describe the Internet Reactor Laboratories (IRL) that are under development and will consist in broadcasting the training courses via internet to remote facilities or institutions

Education and Training on ISIS Research Reactor

Energy Technology Data Exchange (ETDEWEB)

Foulon, F.; Badeau, G.; Lescop, B.; Wohleber, X. [French Atomic Energy and Alternative Energies Commission, Paris (France)

2013-07-01

In the frame of academic and vocational programs the National Institute for Nuclear Science and Technology uses the ISIS research reactor as a major tool to ensure a practical and comprehensive understanding of the nuclear reactor physics, principles and operation. A large set of training courses have been developed on ISIS, optimising both the content of the courses and the pedagogical approach. Programs with duration ranging from 3 hours (introduction to reactor operation) to 24 hours (full program for the future operators of research reactors) are carried out on ISIS reactor. The reactor is operated about 350 hours/year for education and training, about 40 % of the courses being carried out in English. Thus, every year about 400 trainees attend training courses on ISIS reactor. We present here the ISIS research reactor and the practical courses that have been developed on ISIS reactor. Emphasis is given to the pedagogical method which is used to focus on the operational and safety aspects, both in normal and incidental operation. We will present the curricula of the academic and vocational courses in which the practical courses are integrated, the courses being targeted to a wide public, including operators of research reactors, engineers involved in the design and operation of nuclear reactors as well as staff of the regulatory body. We address the very positive impact of the courses on the development of the competences and skills of participants. Finally, we describe the Internet Reactor Laboratories (IRL) that are under development and will consist in broadcasting the training courses via internet to remote facilities or institutions.

Research Network on Regional Economic and Policy History

NARCIS (Netherlands)

Molema, A.M.; van der Zwet, Arno

2017-01-01

In the spring of 2017, the Research Network on Regional Economic and Policy History organised its inaugural workshop in London. The network aims to stimulate research in relation to regional economic development and planning challenges, by exploring the importance of historical approaches and

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

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.

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

The Healthy Aging Research Network: Modeling Collaboration for Community Impact.

Science.gov (United States)

Belza, Basia; Altpeter, Mary; Smith, Matthew Lee; Ory, Marcia G

2017-03-01

As the first Centers for Disease Control and Prevention (CDC) Prevention Research Centers Program thematic network, the Healthy Aging Research Network was established to better understand the determinants of healthy aging within older adult populations, identify interventions that promote healthy aging, and assist in translating research into sustainable community-based programs throughout the nation. To achieve these goals requires concerted efforts of a collaborative network of academic, community, and public health organizational partnerships. For the 2001-2014 Prevention Research Center funding cycles, the Healthy Aging Research Network conducted prevention research and promoted the wide use of practices known to foster optimal health. Organized around components necessary for successful collaborations (i.e., governance and infrastructure, shaping focus, community involvement, and evaluation and improvement), this commentary highlights exemplars that demonstrate the Healthy Aging Research Network's unique contributions to the field. The Healthy Aging Research Network's collaboration provided a means to collectively build capacity for practice and policy, reduce fragmentation and duplication in health promotion and aging research efforts, maximize the efficient use of existing resources and generate additional resources, and ultimately, create synergies for advancing the healthy aging agenda. This collaborative model was built upon a backbone organization (coordinating center); setting of common agendas and mutually reinforcing activities; and continuous communications. Given its successes, the Healthy Aging Research Network model could be used to create new and evaluate existing thematic networks to guide the translation of research into policy and practice. Copyright © 2016 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.

Southern African Development Research Network | IDRC ...

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

... to craft policies for fruitful integration into the global economy and inclusive growth. ... The grant will support a broad-based research network, the Southern Africa ... researchers based in regional institutions; transforming selected institutions ...

Transparent and accurate reporting increases reliability, utility, and impact of your research: reporting guidelines and the EQUATOR Network

Directory of Open Access Journals (Sweden)

Schulz Kenneth F

2010-04-01

Full Text Available Abstract Although current electronic methods of scientific publishing offer increased opportunities for publishing all research studies and describing them in sufficient detail, health research literature still suffers from many shortcomings. These shortcomings seriously undermine the value and utility of the literature and waste scarce resources invested in the research. In recent years there have been several positive steps aimed at improving this situation, such as a strengthening of journals' policies on research publication and the wide requirement to register clinical trials. The EQUATOR (Enhancing the QUAlity and Transparency Of health Research Network is an international initiative set up to advance high quality reporting of health research studies; it promotes good reporting practices including the wider implementation of reporting guidelines. EQUATOR provides free online resources http://www.equator-network.org supported by education and training activities and assists in the development of robust reporting guidelines. This paper outlines EQUATOR's goals and activities and offers suggestions for organizations and individuals involved in health research on how to strengthen research reporting.

Research on wind field algorithm of wind lidar based on BP neural network and grey prediction

Science.gov (United States)

Chen, Yong; Chen, Chun-Li; Luo, Xiong; Zhang, Yan; Yang, Ze-hou; Zhou, Jie; Shi, Xiao-ding; Wang, Lei

2018-01-01

This paper uses the BP neural network and grey algorithm to forecast and study radar wind field. In order to reduce the residual error in the wind field prediction which uses BP neural network and grey algorithm, calculating the minimum value of residual error function, adopting the residuals of the gray algorithm trained by BP neural network, using the trained network model to forecast the residual sequence, using the predicted residual error sequence to modify the forecast sequence of the grey algorithm. The test data show that using the grey algorithm modified by BP neural network can effectively reduce the residual value and improve the prediction precision.

AmeriFlux Measurement Network: Science Team Research

Energy Technology Data Exchange (ETDEWEB)

Law, B E

2012-12-12

Research involves analysis and field direction of AmeriFlux operations, and the PI provides scientific leadership of the AmeriFlux network. Activities include the coordination and quality assurance of measurements across AmeriFlux network sites, synthesis of results across the network, organizing and supporting the annual Science Team Meeting, and communicating AmeriFlux results to the scientific community and other users. Objectives of measurement research include (i) coordination of flux and biometric measurement protocols (ii) timely data delivery to the Carbon Dioxide Information and Analysis Center (CDIAC); and (iii) assurance of data quality of flux and ecosystem measurements contributed by AmeriFlux sites. Objectives of integration and synthesis activities include (i) integration of site data into network-wide synthesis products; and (ii) participation in the analysis, modeling and interpretation of network data products. Communications objectives include (i) organizing an annual meeting of AmeriFlux investigators for reporting annual flux measurements and exchanging scientific information on ecosystem carbon budgets; (ii) developing focused topics for analysis and publication; and (iii) developing data reporting protocols in support of AmeriFlux network goals.

Research collaboration in groups and networks: differences across academic fields.

Science.gov (United States)

Kyvik, Svein; Reymert, Ingvild

2017-01-01

The purpose of this paper is to give a macro-picture of collaboration in research groups and networks across all academic fields in Norwegian research universities, and to examine the relative importance of membership in groups and networks for individual publication output. To our knowledge, this is a new approach, which may provide valuable information on collaborative patterns in a particular national system, but of clear relevance to other national university systems. At the system level, conducting research in groups and networks are equally important, but there are large differences between academic fields. The research group is clearly most important in the field of medicine and health, while undertaking research in an international network is most important in the natural sciences. Membership in a research group and active participation in international networks are likely to enhance publication productivity and the quality of research.

The Impact of the Physical Activity Policy Research Network.

Science.gov (United States)

Manteiga, Alicia M; Eyler, Amy A; Valko, Cheryl; Brownson, Ross C; Evenson, Kelly R; Schmid, Thomas

2017-03-01

Lack of physical activity is one of the greatest challenges of the 21st century. The Physical Activity Policy Research Network (PAPRN) is a thematic network established in 2004 to identify determinants, implementation, and outcomes of policies that are effective in increasing physical activity. The purpose of this study is to describe the products of PAPRN and make recommendations for future research and best practices. A mixed methods approach was used to obtain both quantitative and qualitative data on the network. First, in 2014, PAPRN's dissemination products from 2004 to 2014 were extracted and reviewed, including 57 publications and 56 presentations. Next, semi-structured qualitative interviews were conducted with 25 key network participants from 17 locations around the U.S. The transcripts were transcribed and coded. The results of the interviews indicated that the research network addressed several components of its mission, including the identification of physical activity policies, determinants of these policies, and the process of policy implementation. However, research focusing on physical activity policy outcomes was limited. Best practices included collaboration between researchers and practitioners and involvement of practitioners in research design, data collection, and dissemination of results. PAPRN is an example of a productive research network and has contributed to both the process and content of physical activity policy research over the past decade. Future research should emphasize physical activity policy outcomes. Additionally, increased partnerships with practitioners for collaborative, cross-sectoral physical activity policy research should be developed. Copyright © 2016 American Journal of Preventive Medicine. All rights reserved.

Building research infrastructure in community health centers: a Community Health Applied Research Network (CHARN) report.

Science.gov (United States)

Likumahuwa, Sonja; Song, Hui; Singal, Robbie; Weir, Rosy Chang; Crane, Heidi; Muench, John; Sim, Shao-Chee; DeVoe, Jennifer E

2013-01-01

This article introduces the Community Health Applied Research Network (CHARN), a practice-based research network of community health centers (CHCs). Established by the Health Resources and Services Administration in 2010, CHARN is a network of 4 community research nodes, each with multiple affiliated CHCs and an academic center. The four nodes (18 individual CHCs and 4 academic partners in 9 states) are supported by a data coordinating center. Here we provide case studies detailing how CHARN is building research infrastructure and capacity in CHCs, with a particular focus on how community practice-academic partnerships were facilitated by the CHARN structure. The examples provided by the CHARN nodes include many of the building blocks of research capacity: communication capacity and "matchmaking" between providers and researchers; technology transfer; research methods tailored to community practice settings; and community institutional review board infrastructure to enable community oversight. We draw lessons learned from these case studies that we hope will serve as examples for other networks, with special relevance for community-based networks seeking to build research infrastructure in primary care settings.

Applying a Network-Lens to Hospitality Business Research: A New Research Agenda

Directory of Open Access Journals (Sweden)

Florian AUBKE

2014-06-01

Full Text Available Hospitality businesses are first and foremost places of social interaction. This paper argues for an inclusion of network methodology into the tool kit of hospitality researchers. This methodology focuses on the interaction of people rather than applying an actor-focused view, which currently seems dominant in hospitality research. Outside the field, a solid research basis has been formed, upon which hospitality researchers can build. The paper introduces the foundations of network theory and its applicability to the study of organizations. A brief methodological introduction is provided and potential applications and research topics relevant to the hospitality field are suggested.

Water hammer research in networks

OpenAIRE

Anželika Jurkienė; Mindaugas Rimeika

2015-01-01

Formation of water hammer, its consequences and possible protection measures are rarely topics, however the problem is significant. Water hammer can form in water supply and pressurized sewage networks, for various reasons. The article presents short theory of water hammer and methodology for calculation of specific parameters. Research of water hammer was performed in real water supply and sewer networks of country. Simulation of water hammer was carried out by turning on and off water pumps...

Research fellowship programs as a pathway for training independent clinical pharmacy scientists.

Science.gov (United States)

Mueller, Eric W; Bishop, Jeffrey R; Kanaan, Abir O; Kiser, Tyree H; Phan, Hanna; Yang, Katherine Y

2015-03-01

The American College of Clinical Pharmacy (ACCP) Research Affairs Committee published a commentary in 2013 on training clinical pharmacy scientists in the context of changes in economic, professional, political, and research environments. The commentary centered on the opportunities for pharmacists in clinical/translational research including strategies for ACCP, colleges of pharmacy, and the profession to increase the number and impact of clinical pharmacy scientists. A postdoctoral fellowship is cited as a current training pathway, capable of producing independent and productive pharmacy researchers. However, a decline in the number of programs, decreased funding availability, and variability in fellowship program activities and research focus have brought into question the relevance of this research training pathway to meet demand and opportunities. In response to these points, this commentary examines the state of research fellowship training including the current ACCP research fellowship review process, the need for standardization of research fellowship programs, and strategies to strengthen and promote research fellowships as relevant researcher training pathways. © 2015 Pharmacotherapy Publications, Inc.

Inr training programme in nuclear research

International Nuclear Information System (INIS)

Cretu, I.; Ionila, M.; Gyongyosi, E.; Dragan, E.; Petra, M.

2013-01-01

The field of scientific research goes through rapid changes to which organizations must dinamically and efficiently adapt, which leads to the need to develop a continuous learning process that should be the basis for a long-term operational performance. Thus, human resource management systems and continuous learning should be perfectly correlated/alligned with the organizational strategy and knowledge. The research institutes through the nature of their activity are constantly undergoing a transformation process by exploring new research areas which presumes ensuring competent human resources who have to continuously learn and improve. The «learning organization » concept represents a metaphor rooted in the search of a strategy for promoting the personal development of the individual within an organization through a continuous transformation. Learning is associated with the idea of continuous transformation based on the individual and organizational development. Within « learning organizations » the human development strategy occupies a central role in management strategies. It was learned that organizations which perform excellently depend on the employees committment, especially in the budget constraints environment. For this, the human resources have to be used at maximum capacity but this is possible only with an increased committment of the employee towards the organization. The purpose of this paper is to present the basic training programme for the new employees which is part of the training strategy which carry out activities in the nuclear field of SCN Pitesti. With the majority of the research personnel aged between 45 and 60 years old there is the risk of loosing the knowledge gained in this domain. The expertise gained by experienced experts in the institute nationally and internationally can be exploited through the knowledge transfer to the new employees by organizing training programmes. The knowledge transfer between generations is one of the

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.

Practical Qualitative Research Strategies: Training Interviewers and Coders.

Science.gov (United States)

Goodell, L Suzanne; Stage, Virginia C; Cooke, Natalie K

2016-09-01

The increased emphasis on incorporating qualitative methodologies into nutrition education development and evaluation underscores the importance of using rigorous protocols to enhance the trustworthiness of the findings. A 5-phase protocol for training qualitative research assistants (data collectors and coders) was developed as an approach to increase the consistency of the data produced. This training provides exposure to the core principles of qualitative research and then asks the research assistant to apply those principles through practice in a setting structured on critical reflection. Copyright © 2016 Society for Nutrition Education and Behavior. Published by Elsevier Inc. All rights reserved.

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.

Continuing training program in radiation protection in biological research centers

International Nuclear Information System (INIS)

Escudero, R.; Hidalgo, R.M.; Usera, F.; Macias, M.T.; Mirpuri, E.; Perez, J.; Sanchez, A.

2008-01-01

The use of ionizing radiation in biological research has many specific characteristics. A great variety of radioisotopic techniques involve unsealed radioactive sources, and their use not only carries a risk of irradiation, but also a significant risk of contamination. Moreover, a high proportion of researchers are in training and the labor mobility rate is therefore high. Furthermore, most newly incorporated personnel have little or no previous training in radiological protection, since most academic qualifications do not include training in this discipline. In a biological research center, in addition to personnel whose work is directly associated with the radioactive facility (scientific-technical personnel, operators, supervisors), there are also groups of support personnel The use of ionizing radiation in biological research has many specific characteristics. A great variety of radioisotopic techniques involve unsealed radioactive sources, and their use not only carries a risk of irradiation, but also a significant risk of contamination. Moreover, a high proportion of researchers are in training and the labor mobility rate is therefore high. Furthermore, most newly incorporated personnel have little or no previous training in radiological protection, since most academic qualifications do not include training in this discipline. In a biological research center, in addition to personnel whose work is directly associated with the radioactive facility (scientific-technical personnel, operators, supervisors), there are also groups of support personnel maintenance and instrumentation workers, cleaners, administrative personnel, etc. who are associated with the radioactive facility indirectly. These workers are affected by the work in the radioactive facility to varying degrees, and they therefore also require information and training in radiological protection tailored to their level of interaction with the installation. The aim of this study was to design a

Training physician investigators in medicine and public health research.

Science.gov (United States)

Gourevitch, Marc N; Jay, Melanie R; Goldfrank, Lewis R; Mendelsohn, Alan L; Dreyer, Benard P; Foltin, George L; Lipkin, Mack; Schwartz, Mark D

2012-07-01

We have described and evaluated the impact of a unique fellowship program designed to train postdoctoral, physician fellows in research at the interface of medicine and public health. We developed a rigorous curriculum in public health content and research methods and fostered linkages with research mentors and local public health agencies. Didactic training provided the foundation for fellows' mentored research initiatives, which addressed real-world challenges in advancing the health status of vulnerable urban populations. Two multidisciplinary cohorts (6 per cohort) completed this 2-year degree-granting program and engaged in diverse public health research initiatives on topics such as improving pediatric care outcomes through health literacy interventions, reducing hospital readmission rates among urban poor with multiple comorbidities, increasing cancer screening uptake, and broadening the reach of addiction screening and intervention. The majority of fellows (10/12) published their fellowship work and currently have a career focused in public health-related research or practice (9/12). A fellowship training program can prepare physician investigators for research careers that bridge the divide between medicine and public health.

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.

Conceptualizing and Advancing Research Networking Systems.

Science.gov (United States)

Schleyer, Titus; Butler, Brian S; Song, Mei; Spallek, Heiko

2012-03-01

Science in general, and biomedical research in particular, is becoming more collaborative. As a result, collaboration with the right individuals, teams, and institutions is increasingly crucial for scientific progress. We propose Research Networking Systems (RNS) as a new type of system designed to help scientists identify and choose collaborators, and suggest a corresponding research agenda. The research agenda covers four areas: foundations, presentation, architecture , and evaluation . Foundations includes project-, institution- and discipline-specific motivational factors; the role of social networks; and impression formation based on information beyond expertise and interests. Presentation addresses representing expertise in a comprehensive and up-to-date manner; the role of controlled vocabularies and folksonomies; the tension between seekers' need for comprehensive information and potential collaborators' desire to control how they are seen by others; and the need to support serendipitous discovery of collaborative opportunities. Architecture considers aggregation and synthesis of information from multiple sources, social system interoperability, and integration with the user's primary work context. Lastly, evaluation focuses on assessment of collaboration decisions, measurement of user-specific costs and benefits, and how the large-scale impact of RNS could be evaluated with longitudinal and naturalistic methods. We hope that this article stimulates the human-computer interaction, computer-supported cooperative work, and related communities to pursue a broad and comprehensive agenda for developing research networking systems.

Conceptualizing and Advancing Research Networking Systems

Science.gov (United States)

SCHLEYER, TITUS; BUTLER, BRIAN S.; SONG, MEI; SPALLEK, HEIKO

2013-01-01

Science in general, and biomedical research in particular, is becoming more collaborative. As a result, collaboration with the right individuals, teams, and institutions is increasingly crucial for scientific progress. We propose Research Networking Systems (RNS) as a new type of system designed to help scientists identify and choose collaborators, and suggest a corresponding research agenda. The research agenda covers four areas: foundations, presentation, architecture, and evaluation. Foundations includes project-, institution- and discipline-specific motivational factors; the role of social networks; and impression formation based on information beyond expertise and interests. Presentation addresses representing expertise in a comprehensive and up-to-date manner; the role of controlled vocabularies and folksonomies; the tension between seekers’ need for comprehensive information and potential collaborators’ desire to control how they are seen by others; and the need to support serendipitous discovery of collaborative opportunities. Architecture considers aggregation and synthesis of information from multiple sources, social system interoperability, and integration with the user’s primary work context. Lastly, evaluation focuses on assessment of collaboration decisions, measurement of user-specific costs and benefits, and how the large-scale impact of RNS could be evaluated with longitudinal and naturalistic methods. We hope that this article stimulates the human-computer interaction, computer-supported cooperative work, and related communities to pursue a broad and comprehensive agenda for developing research networking systems. PMID:24376309

Cognitive Radio Wireless Sensor Networks: Applications, Challenges and Research Trends

Science.gov (United States)

Joshi, Gyanendra Prasad; Nam, Seung Yeob; Kim, Sung Won

2013-01-01

A cognitive radio wireless sensor network is one of the candidate areas where cognitive techniques can be used for opportunistic spectrum access. Research in this area is still in its infancy, but it is progressing rapidly. The aim of this study is to classify the existing literature of this fast emerging application area of cognitive radio wireless sensor networks, highlight the key research that has already been undertaken, and indicate open problems. This paper describes the advantages of cognitive radio wireless sensor networks, the difference between ad hoc cognitive radio networks, wireless sensor networks, and cognitive radio wireless sensor networks, potential application areas of cognitive radio wireless sensor networks, challenges and research trend in cognitive radio wireless sensor networks. The sensing schemes suited for cognitive radio wireless sensor networks scenarios are discussed with an emphasis on cooperation and spectrum access methods that ensure the availability of the required QoS. Finally, this paper lists several open research challenges aimed at drawing the attention of the readers toward the important issues that need to be addressed before the vision of completely autonomous cognitive radio wireless sensor networks can be realized. PMID:23974152

Cognitive Radio Wireless Sensor Networks: Applications, Challenges and Research Trends

Directory of Open Access Journals (Sweden)

Gyanendra Prasad Joshi

2013-08-01

Full Text Available A cognitive radio wireless sensor network is one of the candidate areas where cognitive techniques can be used for opportunistic spectrum access. Research in this area is still in its infancy, but it is progressing rapidly. The aim of this study is to classify the existing literature of this fast emerging application area of cognitive radio wireless sensor networks, highlight the key research that has already been undertaken, and indicate open problems. This paper describes the advantages of cognitive radio wireless sensor networks, the difference between ad hoc cognitive radio networks, wireless sensor networks, and cognitive radio wireless sensor networks, potential application areas of cognitive radio wireless sensor networks, challenges and research trend in cognitive radio wireless sensor networks. The sensing schemes suited for cognitive radio wireless sensor networks scenarios are discussed with an emphasis on cooperation and spectrum access methods that ensure the availability of the required QoS. Finally, this paper lists several open research challenges aimed at drawing the attention of the readers toward the important issues that need to be addressed before the vision of completely autonomous cognitive radio wireless sensor networks can be realized.

Cognitive radio wireless sensor networks: applications, challenges and research trends.

Science.gov (United States)

Joshi, Gyanendra Prasad; Nam, Seung Yeob; Kim, Sung Won

2013-08-22

A cognitive radio wireless sensor network is one of the candidate areas where cognitive techniques can be used for opportunistic spectrum access. Research in this area is still in its infancy, but it is progressing rapidly. The aim of this study is to classify the existing literature of this fast emerging application area of cognitive radio wireless sensor networks, highlight the key research that has already been undertaken, and indicate open problems. This paper describes the advantages of cognitive radio wireless sensor networks, the difference between ad hoc cognitive radio networks, wireless sensor networks, and cognitive radio wireless sensor networks, potential application areas of cognitive radio wireless sensor networks, challenges and research trend in cognitive radio wireless sensor networks. The sensing schemes suited for cognitive radio wireless sensor networks scenarios are discussed with an emphasis on cooperation and spectrum access methods that ensure the availability of the required QoS. Finally, this paper lists several open research challenges aimed at drawing the attention of the readers toward the important issues that need to be addressed before the vision of completely autonomous cognitive radio wireless sensor networks can be realized.

The Evolution of the Personal Networks of Novice Librarian Researchers

Science.gov (United States)

Kennedy, Marie R.; Kennedy, David P.; Brancolini, Kristine R.

2017-01-01

This article describes for the first time the composition and structure of the personal networks of novice librarian researchers. We used social network analysis to observe if participating in the Institute for Research Design in Librarianship (IRDL) affected the development of the librarians' personal networks and how the networks changed over…

Advanced Scientific Computing Research Network Requirements: ASCR Network Requirements Review Final Report

Energy Technology Data Exchange (ETDEWEB)

Bacon, Charles [Argonne National Lab. (ANL), Argonne, IL (United States); Bell, Greg [ESnet, Berkeley, CA (United States); Canon, Shane [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Dart, Eli [ESnet, Berkeley, CA (United States); Dattoria, Vince [Dept. of Energy (DOE), Washington DC (United States). Office of Science. Advanced Scientific Computing Research (ASCR); Goodwin, Dave [Dept. of Energy (DOE), Washington DC (United States). Office of Science. Advanced Scientific Computing Research (ASCR); Lee, Jason [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Hicks, Susan [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Holohan, Ed [Argonne National Lab. (ANL), Argonne, IL (United States); Klasky, Scott [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Lauzon, Carolyn [Dept. of Energy (DOE), Washington DC (United States). Office of Science. Advanced Scientific Computing Research (ASCR); Rogers, Jim [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Shipman, Galen [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Skinner, David [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Tierney, Brian [ESnet, Berkeley, CA (United States)

2013-03-08

The Energy Sciences Network (ESnet) is the primary provider of network connectivity for the U.S. Department of Energy (DOE) Office of Science (SC), the single largest supporter of basic research in the physical sciences in the United States. In support of SC programs, ESnet regularly updates and refreshes its understanding of the networking requirements of the instruments, facilities, scientists, and science programs that it serves. This focus has helped ESnet to be a highly successful enabler of scientific discovery for over 25 years. In October 2012, ESnet and the Office of Advanced Scientific Computing Research (ASCR) of the DOE SC organized a review to characterize the networking requirements of the programs funded by the ASCR program office. The requirements identified at the review are summarized in the Findings section, and are described in more detail in the body of the report.

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

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.

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.

SARNET: Severe accident research network of excellence

International Nuclear Information System (INIS)

Albiol, Thierry; Haste, Tim; Dorsselaere, Jean-Pierre van

2007-01-01

51 organizations network in SARNET (Severe Accident Research NETwork of Excellence) their capacities of research in order to resolve the most important remaining uncertainties for enhancing, in regard of Severe Accidents (SA), the safety of existing and future Nuclear Power Plants (NPPs). This project, co-funded by the European Commission (EC), has been defined in order to optimise the use of the available means and to constitute sustainable research groups in the European Union. SARNET tackles the fragmentation that exists between the different R and D national programmes, in defining common research programmes and developing common computer tools and methodologies for safety assessment. SARNET comprises most of the actors involved in SA research in Europe (plus Canada). To reach these objectives, all the organizations networked in SARNET contribute to a so-called Joint Programme of Activities (JPA), which consists in: Implementing an advanced communication tool for accessing all project information, fostering exchange of information, and managing documents; Harmonizing and re-orienting the research programmes; Jointly analysing the experimental results provided by research programmes in order to elaborate a common understanding of relevant phenomena; Developing the ASTEC code (integral computer code used to predict the NPP behaviour during a postulated SA), which capitalizes in terms of physical models the knowledge produced within SARNET; Developing Scientific Databases, in which all the results of research programmes are stored in a common format (DATANET); Developing a common methodology for Probabilistic Safety Assessment (PSA) of NPPs; Developing courses and writing a text book on SA for students and researchers; Promoting personnel mobility between various European organizations. After the first period (2004-2008), co-funded by the EC, the network will progressively evolve toward self-sustainability. The bases for such an evolution, still under discussion

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,…

SARNET: Severe accident research network of excellence

International Nuclear Information System (INIS)

Albiol, T.; Van Dorsselaere, J. P.; Chaumont, B.; Haste, T.; Journeau, Ch.; Meyer, L.; Sehgal, Bal Raj; Schwinges, Bernd; Beraha, D.; Annunziato, A.; Zeyen, R.

2010-01-01

Fifty-one organisations network in SARNET (Severe Accident Research Network of Excellence) their research capacities in order to resolve the most important pending issues for enhancing, with regard to Severe Accidents (SA), the safety of existing and future Nuclear Power Plants (NPPs). This project. co-funded by the European Commission (EC) under the 6. Framework Programme, has been defined in order to optimise the use of the available means and to constitute sustainable research groups in the European Union. SARNET tackles the fragmentation that may exist between the different national R and D programmes, in defining common research programmes and developing common computer tools and methodologies for safety assessment. SARNET comprises most of the organisations involved in SA research in Europe, plus Canada. To reach these objectives, all the organisations networked in SARNET contributed to a joint Programme of Activities, which consisted of: Implementation of an advanced communication tool for accessing all project information, fostering exchange of information, and managing documents; Harmonization and re-orientation of the research programmes, and definition of new ones; Analysis of the experimental results provided by research programmes in order to elaborate a common understanding of relevant phenomena; Development of the ASTEC code (integral computer code used to predict the NPP behaviour during a postulated SA), which capitalizes in terms of physical models the knowledge produced within SARNET; Development of Scientific Databases in which all the results of research programmes are stored in a common format (DATANET); Development of a common methodology for Probabilistic Safety Assessment of NPPs; Development of short courses and writing a textbook on Severe Accidents for students and researchers; Promotion of personnel mobility amongst various European organisations. This paper presents the major achievements after four and a half years of operation of the

The research rotation: competency-based structured and novel approach to research training of internal medicine residents

Directory of Open Access Journals (Sweden)

Dimitrov Vihren

2006-10-01

Full Text Available Abstract Background In the United States, the Accreditation Council of graduate medical education (ACGME requires all accredited Internal medicine residency training programs to facilitate resident scholarly activities. However, clinical experience and medical education still remain the main focus of graduate medical education in many Internal Medicine (IM residency-training programs. Left to design the structure, process and outcome evaluation of the ACGME research requirement, residency-training programs are faced with numerous barriers. Many residency programs report having been cited by the ACGME residency review committee in IM for lack of scholarly activity by residents. Methods We would like to share our experience at Lincoln Hospital, an affiliate of Weill Medical College Cornell University New York, in designing and implementing a successful structured research curriculum based on ACGME competencies taught during a dedicated "research rotation". Results Since the inception of the research rotation in 2004, participation of our residents among scholarly activities has substantially increased. Our residents increasingly believe and appreciate that research is an integral component of residency training and essential for practice of medicine. Conclusion Internal medicine residents' outlook in research can be significantly improved using a research curriculum offered through a structured and dedicated research rotation. This is exemplified by the improvement noted in resident satisfaction, their participation in scholarly activities and resident research outcomes since the inception of the research rotation in our internal medicine training program.

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

The network researchers' network: A social network analysis of the IMP Group 1985-2006

DEFF Research Database (Denmark)

Henneberg, Stephan C. M.; Ziang, Zhizhong; Naudé, Peter

The Industrial Marketing and Purchasing (IMP) Group is a network of academic researchers working in the area of business-to-business marketing. The group meets every year to discuss and exchange ideas, with a conference having been held every year since 1984 (there was no meeting in 1987......). In this paper, based upon the papers presented at the 22 conferences held to date, we undertake a Social Network Analysis in order to examine the degree of co-publishing that has taken place between this group of researchers. We identify the different components in this database, and examine the large main...

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.

Association of learning styles with research self-efficacy: study of short-term research training program for medical students.

Science.gov (United States)

Dumbauld, Jill; Black, Michelle; Depp, Colin A; Daly, Rebecca; Curran, Maureen A; Winegarden, Babbi; Jeste, Dilip V

2014-12-01

With a growing need for developing future physician scientists, identifying characteristics of medical students who are likely to benefit from research training programs is important. This study assessed if specific learning styles of medical students, participating in federally funded short-term research training programs, were associated with research self-efficacy, a potential predictor of research career success. Seventy-five first-year medical students from 28 medical schools, selected to participate in two competitive NIH-supported summer programs for research training in aging, completed rating scales to evaluate learning styles at baseline, and research self-efficacy before and after training. We examined associations of individual learning styles (visual-verbal, sequential-global, sensing-intuitive, and active-reflective) with students' gender, ranking of medical school, and research self-efficacy. Research self-efficacy improved significantly following the training programs. Students with a verbal learning style reported significantly greater research self-efficacy at baseline, while visual, sequential, and intuitive learners demonstrated significantly greater increases in research self-efficacy from baseline to posttraining. No significant relationships were found between learning styles and students' gender or ranking of their medical school. Assessments of learning styles may provide useful information to guide future training endeavors aimed at developing the next generation of physician-scientists. © 2014 Wiley Periodicals, Inc.

U.S. NRC training for research and training reactor inspectors

International Nuclear Information System (INIS)

Sandquist, G.M.; Kunze, J.F.

2011-01-01

Currently, a large number of license activities (Early Site Permits, Combined Operating License, reactor certifications, etc.), are pending for review before the United States Nuclear Regulatory Commission (US NRC). Much of the senior staff at the NRC is now committed to these review and licensing actions. To address this additional workload, the NRC has recruited a large number of new Regulatory Staff for dealing with these and other regulatory actions such as the US Fleet of Research and Test Reactors (RTRs). These reactors pose unusual demands on Regulatory Staff since the US Fleet of RTRs, although few (32 Licensed RTRs as of 2010), they represent a broad range of reactor types, operations, and research and training aspects that nuclear reactor power plants (such as the 104 LWRs) do not pose. The US NRC must inspect and regulate all these entities. This paper addresses selected training topics and regulatory activities provided US NRC Inspectors for US RTRs. (author)

Methodological Issues in Leadership Training Research: In Pursuit of Causality

OpenAIRE

Martin, Robin; Epitropaki, O; O'Broin, Holly

2017-01-01

Leadership training has led to a large amount of research due to the belief that such training can lead to (or more precisely  cause) positive changes in followers’ behavior and work performance. This chapter describes some of the conditions necessary  for research to show a causal relationship between leadership training and outcomes. It then describes different research de‐ signs, employed in leadership training research, and considers the types of problems that can affect inferenc...