WorldWideScience

Sample records for advanced distributed learning

  1. Department of Defense Strategic Plan for Advanced Distributed Learning

    National Research Council Canada - National Science Library

    1999-01-01

    ...), requires the Secretary of Defense to develop a strategic plan for guiding and expanding distance learning initiatives within the Department of Defense, to include a provision for the expansion...

  2. Advancements in Distributed Learning (ADL) Environment in Support of Transformation

    Science.gov (United States)

    2017-01-01

    take a variety of forms, including Task Groups, Workshops, Symposia, Specialists’ Meetings, Lecture Series and Technical Courses . The content of this... MySQL and PHP Apache xAPI Extensive Application Program Interface viii STO-TR-HFM-212 HFM-212 Membership List Dr. Oleksandr BUROV Institute...in Paris, France, initiated a new management infrastructure for collaboration and integration of learning courses and technologies. This ADL effort

  3. Using External Collaborations To Advance Distributed Learning at the University of Pennsylvania.

    Science.gov (United States)

    Eleey, Michael; Comegno, Marsha

    1999-01-01

    Discusses distributed-learning technology and distance learning in higher education and describes initiatives at the University of Pennsylvania to collaborate with businesses and choose outsourcing for some functions. Reasons for outsourcing include a decentralized institutional structure, high initial costs, uncertainty about which techniques…

  4. Advanced Distribution Management System

    OpenAIRE

    Avazov, Artur; Sobinova, Lubov Anatolievna

    2016-01-01

    This article describes the advisability of using advanced distribution management systems in the electricity distribution networks area and considers premises of implementing ADMS within the Smart Grid era. Also, it gives the big picture of ADMS and discusses the ADMS advantages and functionalities.

  5. Advanced Distribution Management System

    Science.gov (United States)

    Avazov, Artur R.; Sobinova, Liubov A.

    2016-02-01

    This article describes the advisability of using advanced distribution management systems in the electricity distribution networks area and considers premises of implementing ADMS within the Smart Grid era. Also, it gives the big picture of ADMS and discusses the ADMS advantages and functionalities.

  6. Advanced Distribution Management System

    Directory of Open Access Journals (Sweden)

    Avazov Artur R.

    2016-01-01

    Full Text Available This article describes the advisability of using advanced distribution management systems in the electricity distribution networks area and considers premises of implementing ADMS within the Smart Grid era. Also, it gives the big picture of ADMS and discusses the ADMS advantages and functionalities.

  7. Editorial: Advanced learning technologies

    Directory of Open Access Journals (Sweden)

    Yu-Ju Lan

    2012-03-01

    Full Text Available Recent rapid development of advanced information technology brings high expectations of its potential to improvement and innovations in learning. This special issue is devoted to using some of the emerging technologies issues related to the topic of education and knowledge sharing, involving several cutting edge research outcomes from recent advancement of learning technologies. Advanced learning technologies are the composition of various related technologies and concepts such as mobile technologies and social media towards learner centered learning. This editorial note provides an overview of relevant issues discussed in this special issue.

  8. Advanced air distribution

    DEFF Research Database (Denmark)

    Melikov, Arsen Krikor

    2011-01-01

    The aim of total volume air distribution (TVAD) involves achieving uniform temperature and velocity in the occupied zone and environment designed for an average occupant. The supply of large amounts of clean and cool air are needed to maintain temperature and pollution concentration at acceptable...... levels in the entire space, leading to increased energy consumption and the use of large and costly HVAC and duct systems. The performance of desk installed PV combined with background TVAD used for room temperature control has been studied in an office building located in a hot and humid climate....... Ventilation in hospitals is essential to decrease the risk of airborne cross-infection. At present, mixing air distribution at a minimum of 12 ach is used in infection wards. Advanced air distribution has the potential to aid in achieving healthy, comfortable and productive indoor environments at levels...

  9. An Exploration of Advanced Distributed Learning Service Success Measures for Social Policy

    Science.gov (United States)

    2009-04-01

    and global access: 20 “We believe that all the hype surrounding the capabilities of information technologies have led us to develop a dangerous...System and Navy eLearning should be sought and made available. In addition, three existing concepts, already being used in other Air Force arenas, are

  10. Learning Networks Distributed Environment

    NARCIS (Netherlands)

    Martens, Harrie; Vogten, Hubert; Koper, Rob; Tattersall, Colin; Van Rosmalen, Peter; Sloep, Peter; Van Bruggen, Jan; Spoelstra, Howard

    2005-01-01

    Learning Networks Distributed Environment is a prototype of an architecture that allows the sharing and modification of learning materials through a number of transport protocols. The prototype implements a p2p protcol using JXTA.

  11. Advanced Learning Theories Applied to Leadership Development

    Science.gov (United States)

    2006-11-01

    Center for Army Leadership Technical Report 2006-2 Advanced Learning Theories Applied to Leadership Development Christina Curnow...2006 5a. CONTRACT NUMBER W91QF4-05-F-0026 5b. GRANT NUMBER 4. TITLE AND SUBTITLE Advanced Learning Theories Applied to Leadership Development 5c...ABSTRACT This report describes the development and implementation of an application of advanced learning theories to leadership development. A

  12. Semantic Coherence Facilitates Distributional Learning.

    Science.gov (United States)

    Ouyang, Long; Boroditsky, Lera; Frank, Michael C

    2017-04-01

    Computational models have shown that purely statistical knowledge about words' linguistic contexts is sufficient to learn many properties of words, including syntactic and semantic category. For example, models can infer that "postman" and "mailman" are semantically similar because they have quantitatively similar patterns of association with other words (e.g., they both tend to occur with words like "deliver," "truck," "package"). In contrast to these computational results, artificial language learning experiments suggest that distributional statistics alone do not facilitate learning of linguistic categories. However, experiments in this paradigm expose participants to entirely novel words, whereas real language learners encounter input that contains some known words that are semantically organized. In three experiments, we show that (a) the presence of familiar semantic reference points facilitates distributional learning and (b) this effect crucially depends both on the presence of known words and the adherence of these known words to some semantic organization. Copyright © 2016 Cognitive Science Society, Inc.

  13. Advanced smartgrids for distribution system operators

    CERN Document Server

    Boillot, Marc

    2014-01-01

    The dynamic of the Energy Transition is engaged in many region of the World. This is a real challenge for electric systems and a paradigm shift for existing distribution networks. With the help of "advanced" smart technologies, the Distribution System Operators will have a central role to integrate massively renewable generation, electric vehicle and demand response programs. Many projects are on-going to develop and assess advanced smart grids solutions, with already some lessons learnt. In the end, the Smart Grid is a mean for Distribution System Operators to ensure the quality and the secu

  14. Advanced Training Technologies and Learning Environments

    Science.gov (United States)

    Noor, Ahmed K. (Compiler); Malone, John B. (Compiler)

    1999-01-01

    This document contains the proceedings of the Workshop on Advanced Training Technologies and Learning Environments held at NASA Langley Research Center, Hampton, Virginia, March 9-10, 1999. The workshop was jointly sponsored by the University of Virginia's Center for Advanced Computational Technology and NASA. Workshop attendees were from NASA, other government agencies, industry, and universities. The objective of the workshop was to assess the status and effectiveness of different advanced training technologies and learning environments.

  15. Learning to Control Advanced Life Support Systems

    Science.gov (United States)

    Subramanian, Devika

    2004-01-01

    Advanced life support systems have many interacting processes and limited resources. Controlling and optimizing advanced life support systems presents unique challenges. In particular, advanced life support systems are nonlinear coupled dynamical systems and it is difficult for humans to take all interactions into account to design an effective control strategy. In this project. we developed several reinforcement learning controllers that actively explore the space of possible control strategies, guided by rewards from a user specified long term objective function. We evaluated these controllers using a discrete event simulation of an advanced life support system. This simulation, called BioSim, designed by Nasa scientists David Kortenkamp and Scott Bell has multiple, interacting life support modules including crew, food production, air revitalization, water recovery, solid waste incineration and power. They are implemented in a consumer/producer relationship in which certain modules produce resources that are consumed by other modules. Stores hold resources between modules. Control of this simulation is via adjusting flows of resources between modules and into/out of stores. We developed adaptive algorithms that control the flow of resources in BioSim. Our learning algorithms discovered several ingenious strategies for maximizing mission length by controlling the air and water recycling systems as well as crop planting schedules. By exploiting non-linearities in the overall system dynamics, the learned controllers easily out- performed controllers written by human experts. In sum, we accomplished three goals. We (1) developed foundations for learning models of coupled dynamical systems by active exploration of the state space, (2) developed and tested algorithms that learn to efficiently control air and water recycling processes as well as crop scheduling in Biosim, and (3) developed an understanding of the role machine learning in designing control systems for

  16. Distributed Wind Cost Reduction: Learning from Solar

    Energy Technology Data Exchange (ETDEWEB)

    Tegen, Suzanne

    2016-02-23

    The distributed wind energy industry can learn several lessons from the solar industry regarding reducing soft costs. Suzanne Tegen presented this overview at the 2016 Distributed Wind Energy Association Business Conference in Washington, D.C., on February 23, 2016.

  17. Arithmetic learning in advanced age.

    Science.gov (United States)

    Zamarian, Laura; Scherfler, Christoph; Kremser, Christian; Pertl, Marie-Theres; Gizewski, Elke; Benke, Thomas; Delazer, Margarete

    2018-01-01

    Acquisition of numerical knowledge and understanding of numerical information are crucial for coping with the changing demands of our digital society. In this study, we assessed arithmetic learning in older and younger individuals in a training experiment including brain imaging. In particular, we assessed age-related effects of training intensity, prior arithmetic competence, and neuropsychological variables on the acquisition of new arithmetic knowledge and on the transfer to new, unknown problems. Effects were assessed immediately after training and after 3 months. Behavioural results showed higher training effects for younger individuals than for older individuals and significantly better performance after 90 problem repetitions than after 30 repetitions in both age groups. A correlation analysis indicated that older adults with lower memory and executive functions at baseline could profit more from intensive training. Similarly, training effects in the younger group were higher for those individuals who had lower arithmetic competence and executive functions prior to intervention. In younger adults, successful transfer was associated with higher executive functions. Memory and set-shifting emerged as significant predictors of training effects in the older group. For the younger group, prior arithmetic competence was a significant predictor of training effects, while cognitive flexibility was a predictor of transfer effects. After training, a subgroup of participants underwent an MRI assessment. A voxel-based morphometry analysis showed a significant interaction between training effects and grey matter volume of the right middle temporal gyrus extending to the angular gyrus for the younger group relative to the older group. The reverse contrast (older group vs. younger group) did not yield any significant results. These results suggest that improvements in arithmetic competence are supported by temporo-parietal areas in the right hemisphere in younger

  18. Advancing Research on Undergraduate Science Learning

    Science.gov (United States)

    Singer, Susan Rundell

    2013-01-01

    This special issue of "Journal of Research in Science Teaching" reflects conclusions and recommendations in the "Discipline-Based Education Research" (DBER) report and makes a substantial contribution to advancing the field. Research on undergraduate science learning is currently a loose affiliation of related fields. The…

  19. A distributed algorithm for machine learning

    Science.gov (United States)

    Chen, Shihong

    2018-04-01

    This paper considers a distributed learning problem in which a group of machines in a connected network, each learning its own local dataset, aim to reach a consensus at an optimal model, by exchanging information only with their neighbors but without transmitting data. A distributed algorithm is proposed to solve this problem under appropriate assumptions.

  20. Distributed sensor coordination for advanced energy systems

    Energy Technology Data Exchange (ETDEWEB)

    Tumer, Kagan [Oregon State Univ., Corvallis, OR (United States). School of Mechanical, Industrial and Manufacturing Engineering

    2015-03-12

    coordination, and sensor network control in advanced power systems. Each application has specific needs, but they all share the one crucial underlying problem: how to ensure that the interactions of a large number of heterogenous agents lead to coordinated system behavior. This proposal describes a new paradigm that addresses that very issue in a systematic way. Key Results and Findings: All milestones have been completed. Our results demonstrate that by properly shaping agent objective functions, we can develop large (up to 10,000 devices) heterogeneous sensor networks with key desirable properties. The first milestone shows that properly choosing agent-specific objective functions increases system performance by up to 99.9% compared to global evaluations. The second milestone shows evolutionary algorithms learn excellent sensor network coordination policies prior to network deployment, and these policies can be refined online once the network is deployed. The third milestone shows the resulting sensor networks networks are extremely robust to sensor noise, where networks with up to 25% sensor noise are capable of providing measurements with errors on the order of 10⁻³. The fourth milestone shows the resulting sensor networks are extremely robust to sensor failure, with 25% of the sensors in the system failing resulting in no significant performance losses after system reconfiguration.

  1. Distribution-Independent Reliable Learning

    OpenAIRE

    Kanade, Varun; Thaler, Justin

    2014-01-01

    We study several questions in the reliable agnostic learning framework of Kalai et al. (2009), which captures learning tasks in which one type of error is costlier than others. A positive reliable classifier is one that makes no false positive errors. The goal in the positive reliable agnostic framework is to output a hypothesis with the following properties: (i) its false positive error rate is at most $\\epsilon$, (ii) its false negative error rate is at most $\\epsilon$ more than that of the...

  2. Fostering Self-Regulation in Distributed Learning

    Science.gov (United States)

    Terry, Krista P.; Doolittle, Peter

    2006-01-01

    Although much has been written about fostering self-regulated learning in traditional classroom settings, there has been little that addresses how to facilitate self-regulated learning skills in distributed and online environments. This article will examine some such strategies by specifically focusing on time management. Specific principles for…

  3. Theoretical Foundation for Advanced Distributed Learning Research

    National Research Council Canada - National Science Library

    Hays, Robert

    2001-01-01

    ... and constrained by system principles. The goal of this paper is to sensitize individuals working in all aspects of ADL systems to the power of a system view and to provide several examples of system methods...

  4. Learning theory of distributed spectral algorithms

    International Nuclear Information System (INIS)

    Guo, Zheng-Chu; Lin, Shao-Bo; Zhou, Ding-Xuan

    2017-01-01

    Spectral algorithms have been widely used and studied in learning theory and inverse problems. This paper is concerned with distributed spectral algorithms, for handling big data, based on a divide-and-conquer approach. We present a learning theory for these distributed kernel-based learning algorithms in a regression framework including nice error bounds and optimal minimax learning rates achieved by means of a novel integral operator approach and a second order decomposition of inverse operators. Our quantitative estimates are given in terms of regularity of the regression function, effective dimension of the reproducing kernel Hilbert space, and qualification of the filter function of the spectral algorithm. They do not need any eigenfunction or noise conditions and are better than the existing results even for the classical family of spectral algorithms. (paper)

  5. Distributed Collaborative Learning Communities Enabled by Information Communication Technology

    NARCIS (Netherlands)

    H.L. Alvarez (Heidi Lee)

    2006-01-01

    textabstractHow and why can Information Communication Technology (ICT) contribute to enhancing learning in distributed Collaborative Learning Communities (CLCs)? Drawing from relevant theories concerned with phenomenon of ICT enabled distributed collaborative learning, this book identifies gaps in

  6. Distributed Extreme Learning Machine for Nonlinear Learning over Network

    Directory of Open Access Journals (Sweden)

    Songyan Huang

    2015-02-01

    Full Text Available Distributed data collection and analysis over a network are ubiquitous, especially over a wireless sensor network (WSN. To our knowledge, the data model used in most of the distributed algorithms is linear. However, in real applications, the linearity of systems is not always guaranteed. In nonlinear cases, the single hidden layer feedforward neural network (SLFN with radial basis function (RBF hidden neurons has the ability to approximate any continuous functions and, thus, may be used as the nonlinear learning system. However, confined by the communication cost, using the distributed version of the conventional algorithms to train the neural network directly is usually prohibited. Fortunately, based on the theorems provided in the extreme learning machine (ELM literature, we only need to compute the output weights of the SLFN. Computing the output weights itself is a linear learning problem, although the input-output mapping of the overall SLFN is still nonlinear. Using the distributed algorithmto cooperatively compute the output weights of the SLFN, we obtain a distributed extreme learning machine (dELM for nonlinear learning in this paper. This dELM is applied to the regression problem and classification problem to demonstrate its effectiveness and advantages.

  7. Advanced feed water distributing system for WWER 440 steam generators

    Energy Technology Data Exchange (ETDEWEB)

    Matal, O.; Klinga, J. [Energovyzkum Ltd, Brno (Switzerland); Grazl, K. [Vitkovice s.c., Ostrava (Switzerland); Tischler, J.; Mihalik, M. [SEP Atomove Elektrarne Bohunice (Slovakia)

    1995-12-31

    The original designed feed water distributing system was replaced by an advanced one. The characteristics of both feed water distributing systems have been measured and evaluated. The paper deals with the problems of measurement and evaluation of both feed water distributing system characteristics and comparison of statistical data obtained. (orig.). 3 refs.

  8. Advanced feed water distributing system for WWER 440 steam generators

    Energy Technology Data Exchange (ETDEWEB)

    Matal, O; Klinga, J [Energovyzkum Ltd, Brno (Switzerland); Grazl, K [Vitkovice s.c., Ostrava (Switzerland); Tischler, J; Mihalik, M [SEP Atomove Elektrarne Bohunice (Slovakia)

    1996-12-31

    The original designed feed water distributing system was replaced by an advanced one. The characteristics of both feed water distributing systems have been measured and evaluated. The paper deals with the problems of measurement and evaluation of both feed water distributing system characteristics and comparison of statistical data obtained. (orig.). 3 refs.

  9. Advanced feed water distributing system for WWER 440 steam generators

    International Nuclear Information System (INIS)

    Matal, O.; Klinga, J.; Grazl, K.; Tischler, J.; Mihalik, M.

    1995-01-01

    The original designed feed water distributing system was replaced by an advanced one. The characteristics of both feed water distributing systems have been measured and evaluated. The paper deals with the problems of measurement and evaluation of both feed water distributing system characteristics and comparison of statistical data obtained. (orig.)

  10. Game-Theoretic Learning in Distributed Control

    KAUST Repository

    Marden, Jason R.

    2018-01-05

    In distributed architecture control problems, there is a collection of interconnected decision-making components that seek to realize desirable collective behaviors through local interactions and by processing local information. Applications range from autonomous vehicles to energy to transportation. One approach to control of such distributed architectures is to view the components as players in a game. In this approach, two design considerations are the components’ incentives and the rules that dictate how components react to the decisions of other components. In game-theoretic language, the incentives are defined through utility functions, and the reaction rules are online learning dynamics. This chapter presents an overview of this approach, covering basic concepts in game theory, special game classes, measures of distributed efficiency, utility design, and online learning rules, all with the interpretation of using game theory as a prescriptive paradigm for distributed control design.

  11. Recent Technology Advances in Distributed Engine Control

    Science.gov (United States)

    Culley, Dennis

    2017-01-01

    This presentation provides an overview of the work performed at NASA Glenn Research Center in distributed engine control technology. This is control system hardware technology that overcomes engine system constraints by modularizing control hardware and integrating the components over communication networks.

  12. Document Classification Using Distributed Machine Learning

    OpenAIRE

    Aydin, Galip; Hallac, Ibrahim Riza

    2018-01-01

    In this paper, we investigate the performance and success rates of Na\\"ive Bayes Classification Algorithm for automatic classification of Turkish news into predetermined categories like economy, life, health etc. We use Apache Big Data technologies such as Hadoop, HDFS, Spark and Mahout, and apply these distributed technologies to Machine Learning.

  13. Maximum Likelihood Learning of Conditional MTE Distributions

    DEFF Research Database (Denmark)

    Langseth, Helge; Nielsen, Thomas Dyhre; Rumí, Rafael

    2009-01-01

    We describe a procedure for inducing conditional densities within the mixtures of truncated exponentials (MTE) framework. We analyse possible conditional MTE specifications and propose a model selection scheme, based on the BIC score, for partitioning the domain of the conditioning variables....... Finally, experimental results demonstrate the applicability of the learning procedure as well as the expressive power of the conditional MTE distribution....

  14. Advanced Distribution Network Modelling with Distributed Energy Resources

    Science.gov (United States)

    O'Connell, Alison

    The addition of new distributed energy resources, such as electric vehicles, photovoltaics, and storage, to low voltage distribution networks means that these networks will undergo major changes in the future. Traditionally, distribution systems would have been a passive part of the wider power system, delivering electricity to the customer and not needing much control or management. However, the introduction of these new technologies may cause unforeseen issues for distribution networks, due to the fact that they were not considered when the networks were originally designed. This thesis examines different types of technologies that may begin to emerge on distribution systems, as well as the resulting challenges that they may impose. Three-phase models of distribution networks are developed and subsequently utilised as test cases. Various management strategies are devised for the purposes of controlling distributed resources from a distribution network perspective. The aim of the management strategies is to mitigate those issues that distributed resources may cause, while also keeping customers' preferences in mind. A rolling optimisation formulation is proposed as an operational tool which can manage distributed resources, while also accounting for the uncertainties that these resources may present. Network sensitivities for a particular feeder are extracted from a three-phase load flow methodology and incorporated into an optimisation. Electric vehicles are the focus of the work, although the method could be applied to other types of resources. The aim is to minimise the cost of electric vehicle charging over a 24-hour time horizon by controlling the charge rates and timings of the vehicles. The results demonstrate the advantage that controlled EV charging can have over an uncontrolled case, as well as the benefits provided by the rolling formulation and updated inputs in terms of cost and energy delivered to customers. Building upon the rolling optimisation, a

  15. Advanced Energy Storage Management in Distribution Network

    Energy Technology Data Exchange (ETDEWEB)

    Liu, Guodong [ORNL; Ceylan, Oguzhan [ORNL; Xiao, Bailu [ORNL; Starke, Michael R [ORNL; Ollis, T Ben [ORNL; King, Daniel J [ORNL; Irminger, Philip [ORNL; Tomsovic, Kevin [University of Tennessee, Knoxville (UTK)

    2016-01-01

    With increasing penetration of distributed generation (DG) in the distribution networks (DN), the secure and optimal operation of DN has become an important concern. In this paper, an iterative mixed integer quadratic constrained quadratic programming model to optimize the operation of a three phase unbalanced distribution system with high penetration of Photovoltaic (PV) panels, DG and energy storage (ES) is developed. The proposed model minimizes not only the operating cost, including fuel cost and purchasing cost, but also voltage deviations and power loss. The optimization model is based on the linearized sensitivity coefficients between state variables (e.g., node voltages) and control variables (e.g., real and reactive power injections of DG and ES). To avoid slow convergence when close to the optimum, a golden search method is introduced to control the step size and accelerate the convergence. The proposed algorithm is demonstrated on modified IEEE 13 nodes test feeders with multiple PV panels, DG and ES. Numerical simulation results validate the proposed algorithm. Various scenarios of system configuration are studied and some critical findings are concluded.

  16. Distributed Sensor Coordination for Advanced Energy Systems

    Energy Technology Data Exchange (ETDEWEB)

    Tumer, Kagan [Oregon State Univ., Corvallis, OR (United States)

    2013-07-31

    The ability to collect key system level information is critical to the safe, efficient and reliable operation of advanced energy systems. With recent advances in sensor development, it is now possible to push some level of decision making directly to computationally sophisticated sensors, rather than wait for data to arrive to a massive centralized location before a decision is made. This type of approach relies on networked sensors (called “agents” from here on) to actively collect and process data, and provide key control decisions to significantly improve both the quality/relevance of the collected data and the associating decision making. The technological bottlenecks for such sensor networks stem from a lack of mathematics and algorithms to manage the systems, rather than difficulties associated with building and deploying them. Indeed, traditional sensor coordination strategies do not provide adequate solutions for this problem. Passive data collection methods (e.g., large sensor webs) can scale to large systems, but are generally not suited to highly dynamic environments, such as advanced energy systems, where crucial decisions may need to be reached quickly and locally. Approaches based on local decisions on the other hand cannot guarantee that each agent performing its task (maximize an agent objective) will lead to good network wide solution (maximize a network objective) without invoking cumbersome coordination routines. There is currently a lack of algorithms that will enable self-organization and blend the efficiency of local decision making with the system level guarantees of global decision making, particularly when the systems operate in dynamic and stochastic environments. In this work we addressed this critical gap and provided a comprehensive solution to the problem of sensor coordination to ensure the safe, reliable, and robust operation of advanced energy systems. The differentiating aspect of the proposed work is in shifting the focus

  17. Recent advances in multiview distributed video coding

    Science.gov (United States)

    Dufaux, Frederic; Ouaret, Mourad; Ebrahimi, Touradj

    2007-04-01

    We consider dense networks of surveillance cameras capturing overlapped images of the same scene from different viewing directions, such a scenario being referred to as multi-view. Data compression is paramount in such a system due to the large amount of captured data. In this paper, we propose a Multi-view Distributed Video Coding approach. It allows for low complexity / low power consumption at the encoder side, and the exploitation of inter-view correlation without communications among the cameras. We introduce a combination of temporal intra-view side information and homography inter-view side information. Simulation results show both the improvement of the side information, as well as a significant gain in terms of coding efficiency.

  18. Lifelong Learning in Artistic Context Mediated by Advanced Technologies

    Science.gov (United States)

    Ferrari, Mirella

    2016-01-01

    This research starts by analysing the current state of artistic heritage in Italy and studying some examples in Europe: we try to investigate the scope of non-formal learning in artistic context, mediated by advanced technology. The framework within which we have placed our investigation is that of lifelong learning and lifedeep learning. The…

  19. Collaborative Learning in Advanced Supply Systems: The KLASS Pilot Project.

    Science.gov (United States)

    Rhodes, Ed; Carter, Ruth

    2003-01-01

    The Knowledge and Learning in Advanced Supply Systems (KLASS) project developed collaborative learning networks of suppliers in the British automotive and aerospace industries. Methods included face-to-face and distance learning, work toward National Vocational Qualifications, and diagnostic workshops for senior managers on improving quality,…

  20. Deep Learning for Distribution Channels' Management

    Directory of Open Access Journals (Sweden)

    Sabina-Cristiana NECULA

    2017-01-01

    Full Text Available This paper presents an experiment of using deep learning models for distribution channel management. We present an approach that combines self-organizing maps with artificial neural network with multiple hidden layers in order to identify the potential sales that might be addressed for channel distribution change/ management. Our study aims to highlight the evolution of techniques from simple features/learners to more complex learners and feature engineering or sampling techniques. This paper will allow researchers to choose best suited techniques and features to prepare their churn prediction models.

  1. Learning in Advance Selling with Heterogeneous Consumers

    OpenAIRE

    Oksana Loginova; X. Henry Wang; Chenhang Zeng

    2012-01-01

    The advance selling strategy is implemented when a firm offers consumers the opportunity to order its product in advance of the regular selling season. Advance selling reduces uncertainty for both the firm and the buyer and enables the firm to update its forecast of future demand. The distinctive feature of the present study of advance selling is that we divide consumers into two groups, experienced and inexperienced. Experienced consumers know their valuations of the product in advance, whil...

  2. Using Machine Learning to Advance Personality Assessment and Theory.

    Science.gov (United States)

    Bleidorn, Wiebke; Hopwood, Christopher James

    2018-05-01

    Machine learning has led to important advances in society. One of the most exciting applications of machine learning in psychological science has been the development of assessment tools that can powerfully predict human behavior and personality traits. Thus far, machine learning approaches to personality assessment have focused on the associations between social media and other digital records with established personality measures. The goal of this article is to expand the potential of machine learning approaches to personality assessment by embedding it in a more comprehensive construct validation framework. We review recent applications of machine learning to personality assessment, place machine learning research in the broader context of fundamental principles of construct validation, and provide recommendations for how to use machine learning to advance our understanding of personality.

  3. Distributed learning enhances relational memory consolidation.

    Science.gov (United States)

    Litman, Leib; Davachi, Lila

    2008-09-01

    It has long been known that distributed learning (DL) provides a mnemonic advantage over massed learning (ML). However, the underlying mechanisms that drive this robust mnemonic effect remain largely unknown. In two experiments, we show that DL across a 24 hr interval does not enhance immediate memory performance but instead slows the rate of forgetting relative to ML. Furthermore, we demonstrate that this savings in forgetting is specific to relational, but not item, memory. In the context of extant theories and knowledge of memory consolidation, these results suggest that an important mechanism underlying the mnemonic benefit of DL is enhanced memory consolidation. We speculate that synaptic strengthening mechanisms supporting long-term memory consolidation may be differentially mediated by the spacing of memory reactivation. These findings have broad implications for the scientific study of episodic memory consolidation and, more generally, for educational curriculum development and policy.

  4. JTEL Winter School for Advanced Technologically Enhanced Learning

    NARCIS (Netherlands)

    Glahn, Christian; Gruber, Marion

    2010-01-01

    Glahn, C., & Gruber, M. (2010). JTEL Winter School for Advanced Technologically Enhanced Learning. In ~mail. Das Magazin des Tiroler Bildungsinstituts, 01/10, März (p. 3-4). Innsbruck: Grillhof, Medienzentrum.

  5. Institute for Advanced Learning and Research names new executive director

    OpenAIRE

    Virginia Tech News

    2008-01-01

    Virginia Tech's Institute for Advanced Learning and Research has named Liam E. Leightley as executive director, effective Oct. 6, 2008, according to Mike Henderson, chair of the institute's board of trustees.

  6. Structure Learning in Power Distribution Networks

    Energy Technology Data Exchange (ETDEWEB)

    Deka, Deepjyoti [Univ. of Texas, Austin, TX (United States); Chertkov, Michael [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Backhaus, Scott N. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2015-01-13

    Traditionally power distribution networks are either not observable or only partially observable. This complicates development and implementation of new smart grid technologies, such as these related to demand response, outage detection and management, and improved load-monitoring. Here, inspired by proliferation of the metering technology, we discuss statistical estimation problems in structurally loopy but operationally radial distribution grids consisting in learning operational layout of the network from measurements, e.g. voltage data, which are either already available or can be made available with a relatively minor investment. Our newly suggested algorithms apply to a wide range of realistic scenarios. The algorithms are also computationally efficient – polynomial in time – which is proven theoretically and illustrated computationally on a number of test cases. The technique developed can be applied to detect line failures in real time as well as to understand the scope of possible adversarial attacks on the grid.

  7. Advanced Inverter Functions and Communication Protocols for Distribution Management

    Energy Technology Data Exchange (ETDEWEB)

    Nagarajan, Adarsh; Palmintier, Bryan; Baggu, Murali

    2016-05-05

    This paper aims at identifying the advanced features required by distribution management systems (DMS) service providers to bring inverter-connected distributed energy resources into use as an intelligent grid resource. This work explores the standard functions needed in the future DMS for enterprise integration of distributed energy resources (DER). The important DMS functionalities such as DER management in aggregate groups, including the discovery of capabilities, status monitoring, and dispatch of real and reactive power are addressed in this paper. It is intended to provide the industry with a point of reference for DER integration with other utility applications and to provide guidance to research and standards development organizations.

  8. Virtual learning environment for interactive engagement with advanced quantum mechanics

    Directory of Open Access Journals (Sweden)

    Mads Kock Pedersen

    2016-04-01

    Full Text Available A virtual learning environment can engage university students in the learning process in ways that the traditional lectures and lab formats cannot. We present our virtual learning environment StudentResearcher, which incorporates simulations, multiple-choice quizzes, video lectures, and gamification into a learning path for quantum mechanics at the advanced university level. StudentResearcher is built upon the experiences gathered from workshops with the citizen science game Quantum Moves at the high-school and university level, where the games were used extensively to illustrate the basic concepts of quantum mechanics. The first test of this new virtual learning environment was a 2014 course in advanced quantum mechanics at Aarhus University with 47 enrolled students. We found increased learning for the students who were more active on the platform independent of their previous performances.

  9. Virtual Learning Environment for Interactive Engagement with Advanced Quantum Mechanics

    Science.gov (United States)

    Pedersen, Mads Kock; Skyum, Birk; Heck, Robert; Müller, Romain; Bason, Mark; Lieberoth, Andreas; Sherson, Jacob F.

    2016-06-01

    A virtual learning environment can engage university students in the learning process in ways that the traditional lectures and lab formats cannot. We present our virtual learning environment StudentResearcher, which incorporates simulations, multiple-choice quizzes, video lectures, and gamification into a learning path for quantum mechanics at the advanced university level. StudentResearcher is built upon the experiences gathered from workshops with the citizen science game Quantum Moves at the high-school and university level, where the games were used extensively to illustrate the basic concepts of quantum mechanics. The first test of this new virtual learning environment was a 2014 course in advanced quantum mechanics at Aarhus University with 47 enrolled students. We found increased learning for the students who were more active on the platform independent of their previous performances.

  10. Collaborative learning through advanced Web2.0 practices

    DEFF Research Database (Denmark)

    Tambouris, Efthimios; Panopoulou, Eleni; Tarabanis, Konstantinos

    2010-01-01

    Latest advances in ICT have started impacting also the field of education and training. Social computing and Web2.0 technologies have brought vigorous opportunities for learning and have realised a shift of the web‟s role in learning from an information carrier to a facilitator for the creation...

  11. Advanced Learning Space as an Asset for Students with Disabilities

    Science.gov (United States)

    Císarová, Klára; Lamr, Marián; Vitvarová, Jana

    2015-01-01

    The paper describes an e-learning system called Advanced Learning Space that was developed at the Technical University of Liberec. The system provides a personalized virtual work space and promotes communication among students and their teachers. The core of the system is a module that can be used to automatically record, store and playback…

  12. The Strategies To Advance the Internationalization of Learning (SAIL) Program.

    Science.gov (United States)

    Ebert, Kenneth B.; Burnett, Jane

    This report documents the Strategies to Advance the Internalization of Learning (SAIL) program developed at Michigan State University (MSU) to promote international, comparative, and cross-cultural learning and cross-cultural understanding in the university community. A total of 350 foreign and U.S. students who had international experience…

  13. Computer-Assisted Foreign Language Teaching and Learning: Technological Advances

    Science.gov (United States)

    Zou, Bin; Xing, Minjie; Wang, Yuping; Sun, Mingyu; Xiang, Catherine H.

    2013-01-01

    Computer-Assisted Foreign Language Teaching and Learning: Technological Advances highlights new research and an original framework that brings together foreign language teaching, experiments and testing practices that utilize the most recent and widely used e-learning resources. This comprehensive collection of research will offer linguistic…

  14. Marshall ̶ Olkin Distributions : Advances in Theory and Applications

    CERN Document Server

    Durante, Fabrizio; Mulinacci, Sabrina

    2015-01-01

    This book presents the latest advances in the theory and practice of Marshall-Olkin distributions. These distributions have been increasingly applied in statistical practice in recent years, as they make it possible to describe interesting features of stochastic models like non-exchangeability, tail dependencies and the presence of a singular component. The book presents cutting-edge contributions in this research area, with a particular emphasis on financial and economic applications. It is recommended for researchers working in applied probability and statistics, as well as for practitioners interested in the use of stochastic models in economics. This volume collects selected contributions from the conference “Marshall-Olkin Distributions: Advances in Theory and Applications,” held in Bologna on October 2-3, 2013.

  15. Curriculum learning designs: teaching health assessment skills for advanced nursing practitioners through sustainable flexible learning.

    Science.gov (United States)

    Fitzgerald, Les; Wong, Pauline; Hannon, John; Solberg Tokerud, Marte; Lyons, Judith

    2013-10-01

    Innovative curriculum designs are vital for effective learning in contemporary nursing education where traditional modes of delivery are not adequate to meet the learning needs of postgraduate students. This instance of postgraduate teaching in a distributed learning environment offered the opportunity to design a flexible learning model for teaching advanced clinical skills. To present a sustainable model for flexible learning that enables specialist nurses to gain postgraduate qualifications without on-campus class attendance by teaching and assessing clinical health care skills in an authentic workplace setting. An action research methodology was used to gather evidence and report on the process of curriculum development of a core unit, Comprehensive Health Assessment (CHA), within 13 different postgraduate speciality courses. Qualitative data was collected from 27 teaching academics, 21 clinical specialist staff, and 7 hospital managers via interviews, focus groups and journal reflections. Evaluations from the initial iteration of CHA from 36 students were obtained. Data was analyzed to develop and evaluate the curriculum design of CHA. The key factors indicated by participants in the curriculum design process were coordination and structuring of teaching and assessment; integration of content development; working with technologies, balancing specialities and core knowledge; and managing induction and expectations. A set of recommendations emerged as a result of the action research process. These included: a constructive alignment approach to curriculum design; the production of a facilitator's guide that specifies expectations and unit information for academic and clinical education staff; an agreed template for content authors; and the inclusion of synchronous communication for real-time online tutoring. The highlight of the project was that it built curriculum design capabilities of clinicians and students which can sustain this alternative model of online

  16. Lifelong learning and advancement in a company: Experience from Serbia

    Directory of Open Access Journals (Sweden)

    Mllutinović Olivera

    2014-01-01

    Full Text Available Lifelong learning concept is the concept that brings humanism in both everyday and business life of people. It promotes education, learning, cooperation and advancement in people's lives. During last two decades it became obvious that it is important to implement this concept, particularly in the field of economy in order to achieve better economic results. The aim of this paper is to find out if there is an actual implementation of lifelong learning concept in Serbia. Besides that it will also show if there are instances of advancement for employees in the companies that are implementing lifelong learning concept. The paper contains empirical research that was conducted in 15 companies in Serbia, primarily state-owned. This research gathered the opinion of 492 individuals, both female and male, with every type of education possible in Serbia. By analyzing the given results, the authors of this paper will give a proposal for future improved implementation of lifelong learning concept in Serbia.

  17. Ambient Learning Displays - Distributed Mixed Reality Information Mash-ups to support Ubiquitous Learning

    NARCIS (Netherlands)

    Börner, Dirk

    2010-01-01

    Börner, D. (2010, 19-21 March). Ambient Learning Displays Distributed Mixed Reality Information Mash-ups to support Ubiquitous Learning. Presented at the IADIS International Conference Mobile Learning 2010, Porto, Portugal.

  18. The Dynamics of Learning and the Emergence of Distributed Adaption

    National Research Council Canada - National Science Library

    Crutchfield, James P

    2006-01-01

    .... The second goal was to adapt this single-agent learning theory and associated learning algorithms to the distributed setting in which a population of autonomous agents communicate to achieve a desired group task...

  19. Advanced airflow distribution methods for reducing exposure of indoor pollution

    DEFF Research Database (Denmark)

    Cao, Guangyu; Nielsen, Peter Vilhelm; Melikov, Arsen

    2017-01-01

    The adverse effect of various indoor pollutants on occupants’ health have been recognized. In public spaces flu viruses may spread from person to person by airflow generated by various traditional ventilation methods, like natural ventilation and mixing ventilation (MV Personalized ventilation (PV......) supplies clean air close to the occupant and directly into the breathing zone. Studies show that it improves the inhaled air quality and reduces the risk of airborne cross-infection in comparison with total volume (TV) ventilation. However, it is still challenging for PV and other advanced air distribution...... methods to reduce the exposure to gaseous and particulate pollutants under disturbed conditions and to ensure thermal comfort at the same time. The objective of this study is to analyse the performance of different advanced airflow distribution methods for protection of occupants from exposure to indoor...

  20. Advanced airflow distribution methods for reducing exposure of indoor pollution

    DEFF Research Database (Denmark)

    Cao, Guangyu; Nielsen, Peter Vilhelm; Melikov, Arsen Krikor

    methods to reduce the exposure to gaseous and particulate pollutants under disturbed conditions and to ensure thermal comfort at the same time. The objective of this study is to analyse the performance of different advanced airflow distribution methods for protection of occupants from exposure to indoor......The adverse effect of various indoor pollutants on occupants’ health have been recognized. In public spaces flu viruses may spread from person to person by airflow generated by various traditional ventilation methods, like natural ventilation and mixing ventilation (MV Personalized ventilation (PV......) supplies clean air close to the occupant and directly into the breathing zone. Studies show that it improves the inhaled air quality and reduces the risk of airborne cross-infection in comparison with total volume (TV) ventilation. However, it is still challenging for PV and other advanced air distribution...

  1. Recent Advances in Predictive (Machine) Learning

    Energy Technology Data Exchange (ETDEWEB)

    Friedman, J

    2004-01-24

    Prediction involves estimating the unknown value of an attribute of a system under study given the values of other measured attributes. In prediction (machine) learning the prediction rule is derived from data consisting of previously solved cases. Most methods for predictive learning were originated many years ago at the dawn of the computer age. Recently two new techniques have emerged that have revitalized the field. These are support vector machines and boosted decision trees. This paper provides an introduction to these two new methods tracing their respective ancestral roots to standard kernel methods and ordinary decision trees.

  2. Advanced logo a language for learning

    CERN Document Server

    Friendly, Michael

    1988-01-01

    Advanced Logo shows how LOGO can be used as a vehicle to promote problem solving skills among secondary students, college students, and instructors. The book demonstrates the wide range of educational domains that can be explored through LOGO including generative grammars, physical laws of motion and mechanics, artificial intelligence, robotics, and calculus.

  3. Integrating distributed Bayesian inference and reinforcement learning for sensor management

    NARCIS (Netherlands)

    Grappiolo, C.; Whiteson, S.; Pavlin, G.; Bakker, B.

    2009-01-01

    This paper introduces a sensor management approach that integrates distributed Bayesian inference (DBI) and reinforcement learning (RL). DBI is implemented using distributed perception networks (DPNs), a multiagent approach to performing efficient inference, while RL is used to automatically

  4. Distributional learning has immediate and long-lasting effects.

    Science.gov (United States)

    Escudero, Paola; Williams, Daniel

    2014-11-01

    Evidence of distributional learning, a statistical learning mechanism centered on relative frequency of exposure to different tokens, has mainly come from short-term learning and therefore does not ostensibly address the development of important learning processes. The present longitudinal study examines both short- and long-term effects of distributional learning of phonetic categories on non-native sound discrimination over a 12-month period. Two groups of listeners were exposed to a two-minute distribution of auditory stimuli in which the most frequently presented tokens either approximated or exaggerated the natural production of the speech sounds, whereas a control group listened to a piece of classical music for the same length of time. Discrimination by listeners in the two distribution groups improved immediately after the short exposure, replicating previous results. Crucially, this improvement was maintained after six and 12 months, demonstrating that distributional learning has long-lasting effects. Copyright © 2014 Elsevier B.V. All rights reserved.

  5. Distributional Language Learning: Mechanisms and Models of ategory Formation.

    Science.gov (United States)

    Aslin, Richard N; Newport, Elissa L

    2014-09-01

    In the past 15 years, a substantial body of evidence has confirmed that a powerful distributional learning mechanism is present in infants, children, adults and (at least to some degree) in nonhuman animals as well. The present article briefly reviews this literature and then examines some of the fundamental questions that must be addressed for any distributional learning mechanism to operate effectively within the linguistic domain. In particular, how does a naive learner determine the number of categories that are present in a corpus of linguistic input and what distributional cues enable the learner to assign individual lexical items to those categories? Contrary to the hypothesis that distributional learning and category (or rule) learning are separate mechanisms, the present article argues that these two seemingly different processes---acquiring specific structure from linguistic input and generalizing beyond that input to novel exemplars---actually represent a single mechanism. Evidence in support of this single-mechanism hypothesis comes from a series of artificial grammar-learning studies that not only demonstrate that adults can learn grammatical categories from distributional information alone, but that the specific patterning of distributional information among attested utterances in the learning corpus enables adults to generalize to novel utterances or to restrict generalization when unattested utterances are consistently absent from the learning corpus. Finally, a computational model of distributional learning that accounts for the presence or absence of generalization is reviewed and the implications of this model for linguistic-category learning are summarized.

  6. Advances in independent component analysis and learning machines

    CERN Document Server

    Bingham, Ella; Laaksonen, Jorma; Lampinen, Jouko

    2015-01-01

    In honour of Professor Erkki Oja, one of the pioneers of Independent Component Analysis (ICA), this book reviews key advances in the theory and application of ICA, as well as its influence on signal processing, pattern recognition, machine learning, and data mining. Examples of topics which have developed from the advances of ICA, which are covered in the book are: A unifying probabilistic model for PCA and ICA Optimization methods for matrix decompositions Insights into the FastICA algorithmUnsupervised deep learning Machine vision and image retrieval A review of developments in the t

  7. Advanced Learning Technologies and Learning Networks and Their Impact on Future Aerospace Workforce

    Science.gov (United States)

    Noor, Ahmed K. (Compiler)

    2003-01-01

    This document contains the proceedings of the training workshop on Advanced Learning Technologies and Learning Networks and their impact on Future Aerospace Workforce. The workshop was held at the Peninsula Workforce Development Center, Hampton, Virginia, April 2 3, 2003. The workshop was jointly sponsored by Old Dominion University and NASA. Workshop attendees came from NASA, other government agencies, industry, and universities. The objectives of the workshop were to: 1) provide broad overviews of the diverse activities related to advanced learning technologies and learning environments, and 2) identify future directions for research that have high potential for aerospace workforce development. Eighteen half-hour overviewtype presentations were made at the workshop.

  8. An Autonomous Mobile Agent-Based Distributed Learning Architecture-A Proposal and Analytical Analysis

    Directory of Open Access Journals (Sweden)

    I. Ahmed M. J. SADIIG

    2005-10-01

    Full Text Available An Autonomous Mobile Agent-Based Distributed Learning Architecture-A Proposal and Analytical Analysis Dr. I. Ahmed M. J. SADIIG Department of Electrical & Computer EngineeringInternational Islamic University GombakKuala Lumpur-MALAYSIA ABSTRACT The traditional learning paradigm invoving face-to-face interaction with students is shifting to highly data-intensive electronic learning with the advances in Information and Communication Technology. An important component of the e-learning process is the delivery of the learning contents to their intended audience over a network. A distributed learning system is dependent on the network for the efficient delivery of its contents to the user. However, as the demand of information provision and utilization increases on the Internet, the current information service provision and utilization methods are becoming increasingly inefficient. Although new technologies have been employed for efficient learning methodologies within the context of an e-learning environment, the overall efficiency of the learning system is dependent on the mode of distribution and utilization of its learning contents. It is therefore imperative to employ new techniques to meet the service demands of current and future e-learning systems. In this paper, an architecture based on autonomous mobile agents creating a Faded Information Field is proposed. Unlike the centralized information distribution in a conventional e-learning system, the information is decentralized in the proposed architecture resulting in increased efficiency of the overall system for distribution and utilization of system learning contents efficiently and fairly. This architecture holds the potential to address the heterogeneous user requirements as well as the changing conditions of the underlying network.

  9. Distribution Learning in Evolutionary Strategies and Restricted Boltzmann Machines

    DEFF Research Database (Denmark)

    Krause, Oswin

    The thesis is concerned with learning distributions in the two settings of Evolutionary Strategies (ESs) and Restricted Boltzmann Machines (RBMs). In both cases, the distributions are learned from samples, albeit with different goals. Evolutionary Strategies are concerned with finding an optimum ...

  10. Applying Distributed Learning Theory in Online Business Communication Courses.

    Science.gov (United States)

    Walker, Kristin

    2003-01-01

    Focuses on the critical use of technology in online formats that entail relatively new teaching media. Argues that distributed learning theory is valuable for teachers of online business communication courses for several reasons. Discusses the application of distributed learning theory to the teaching of business communication online. (SG)

  11. Distributed learning process: principles of design and implementation

    Directory of Open Access Journals (Sweden)

    G. N. Boychenko

    2016-01-01

    Full Text Available At the present stage, broad information and communication technologies (ICT usage in educational practices is one of the leading trends of global education system development. This trend has led to the instructional interaction models transformation. Scientists have developed the theory of distributed cognition (Salomon, G., Hutchins, E., and distributed education and training (Fiore, S. M., Salas, E., Oblinger, D. G., Barone, C. A., Hawkins, B. L.. Educational process is based on two separated in time and space sub-processes of learning and teaching which are aimed at the organization of fl exible interactions between learners, teachers and educational content located in different non-centralized places.The purpose of this design research is to fi nd a solution for the problem of formalizing distributed learning process design and realization that is signifi cant in instructional design. The solution to this problem should take into account specifi cs of distributed interactions between team members, which becomes collective subject of distributed cognition in distributed learning process. This makes it necessary to design roles and functions of the individual team members performing distributed educational activities. Personal educational objectives should be determined by decomposition of team objectives into functional roles of its members with considering personal and learning needs and interests of students.Theoretical and empirical methods used in the study: theoretical analysis of philosophical, psychological, and pedagogical literature on the issue, analysis of international standards in the e-learning domain; exploration on practical usage of distributed learning in academic and corporate sectors; generalization, abstraction, cognitive modelling, ontology engineering methods.Result of the research is methodology for design and implementation of distributed learning process based on the competency approach. Methodology proposed by

  12. Social networks and performance in distributed learning communities

    OpenAIRE

    Cadima, Rita; Ojeda Rodríguez, Jordi; Monguet Fierro, José María

    2012-01-01

    Social networks play an essential role in learning environments as a key channel for knowledge sharing and students' support. In distributed learning communities, knowledge sharing does not occur as spontaneously as when a working group shares the same physical space; knowledge sharing depends even more on student informal connections. In this study we analyse two distributed learning communities' social networks in order to understand how characteristics of the social structure can enhance s...

  13. Advances in learning analytics and educational data mining

    NARCIS (Netherlands)

    Vahdat, Mehrnoosh; Ghio, A; Oneto, L.; Anguita, D.; Funk, M.; Rauterberg, G.W.M.

    2015-01-01

    The growing interest in recent years towards Learning An- alytics (LA) and Educational Data Mining (EDM) has enabled novel ap- proaches and advancements in educational settings. The wide variety of research and practice in this context has enforced important possibilities and applications from

  14. An Investigation of Pronunciation Learning Strategies of Advanced EFL Learners

    Science.gov (United States)

    Hismanoglu, Murat

    2012-01-01

    This paper aims at investigating the kinds of strategies deployed by advanced EFL learners at English Language Teaching Department to learn or improve English pronunciation and revealing whether there are any significant differences between the strategies of successful pronunciation learners and those of unsuccessful pronunciation learners. After…

  15. Advanced, Analytic, Automated (AAA) Measurement of Engagement during Learning

    Science.gov (United States)

    D'Mello, Sidney; Dieterle, Ed; Duckworth, Angela

    2017-01-01

    It is generally acknowledged that engagement plays a critical role in learning. Unfortunately, the study of engagement has been stymied by a lack of valid and efficient measures. We introduce the advanced, analytic, and automated (AAA) approach to measure engagement at fine-grained temporal resolutions. The AAA measurement approach is grounded in…

  16. Council for the Advancement of Standards Learning and Developmental Outcomes

    Science.gov (United States)

    Council for the Advancement of Standards in Higher Education, 2008

    2008-01-01

    The Council for the Advancement of Standards in Higher Education (CAS) promotes standards to enhance opportunities for student learning and development from higher education programs and services. Responding to the increased shift in attention being paid by educators and their stakeholders from higher education inputs (i.e., standards and…

  17. Exploring a Century of Advancements in the Science of Learning

    Science.gov (United States)

    Murphy, P. Karen; Knight, Stephanie L.

    2016-01-01

    The past century has yielded a plethora of advancements in the science of learning, from expansions in the theoretical frames that undergird education research to cultural and contextual considerations in educational practice. The overarching purpose of this chapter is to explore and document the growth and development of the science of learning…

  18. Distributed electrochemical sensors: recent advances and barriers to market adoption.

    Science.gov (United States)

    Hoekstra, Rafael; Blondeau, Pascal; Andrade, Francisco J

    2018-07-01

    Despite predictions of their widespread application in healthcare and environmental monitoring, electrochemical sensors are yet to be distributed at scale, instead remaining largely confined to R&D labs. This contrasts sharply with the situation for physical sensors, which are now ubiquitous and seamlessly embedded in the mature ecosystem provided by electronics and connectivity protocols. Although chemical sensors could be integrated into the same ecosystem, there are fundamental issues with these sensors in the three key areas of analytical performance, usability, and affordability. Nevertheless, advances are being made in each of these fields, leading to hope that the deployment of automated and user-friendly low-cost electrochemical sensors is on the horizon. Here, we present a brief survey of key challenges and advances in the development of distributed electrochemical sensors for liquid samples, geared towards applications in healthcare and wellbeing, environmental monitoring, and homeland security. As will be seen, in many cases the analytical performance of the sensor is acceptable; it is usability that is the major barrier to commercial viability at this moment. Were this to be overcome, the issue of affordability could be addressed. Graphical Abstract ᅟ.

  19. Working Memory and Distributed Vocabulary Learning.

    Science.gov (United States)

    Atkins, Paul W. B.; Baddeley, Alan D.

    1998-01-01

    Tested the hypothesis that individual differences in immediate-verbal-memory span predict success in second-language vocabulary acquisition. In the two-session study, adult subjects learned 56 English-Finnish translations. Tested one week later, subjects were less likely to remember those words they had difficulty learning, even though they had…

  20. Games as Distributed Teaching and Learning Systems

    Science.gov (United States)

    Gee, Elisabeth; Gee, James Paul

    2017-01-01

    Background: Videogames and virtual worlds have frequently been studied as learning environments in isolation; that is, scholars have focused on understanding the features of games or virtual worlds as separate from or different than "real world" environments for learning. Although more recently, scholars have explored the teaching and…

  1. A Distributed Intelligent E-Learning System

    Science.gov (United States)

    Kristensen, Terje

    2016-01-01

    An E-learning system based on a multi-agent (MAS) architecture combined with the Dynamic Content Manager (DCM) model of E-learning, is presented. We discuss the benefits of using such a multi-agent architecture. Finally, the MAS architecture is compared with a pure service-oriented architecture (SOA). This MAS architecture may also be used within…

  2. Integration of advanced technologies to enhance problem-based learning over distance: Project TOUCH.

    Science.gov (United States)

    Jacobs, Joshua; Caudell, Thomas; Wilks, David; Keep, Marcus F; Mitchell, Steven; Buchanan, Holly; Saland, Linda; Rosenheimer, Julie; Lozanoff, Beth K; Lozanoff, Scott; Saiki, Stanley; Alverson, Dale

    2003-01-01

    Distance education delivery has increased dramatically in recent years as a result of the rapid advancement of communication technology. The National Computational Science Alliance's Access Grid represents a significant advancement in communication technology with potential for distance medical education. The purpose of this study is to provide an overview of the TOUCH project (Telehealth Outreach for Unified Community Health; http://hsc.unm.edu/touch) with special emphasis on the process of problem-based learning case development for distribution over the Access Grid. The objective of the TOUCH project is to use emerging Internet-based technology to overcome geographic barriers for delivery of tutorial sessions to medical students pursuing rotations at remote sites. The TOUCH project also is aimed at developing a patient simulation engine and an immersive virtual reality environment to achieve a realistic health care scenario enhancing the learning experience. A traumatic head injury case is developed and distributed over the Access Grid as a demonstration of the TOUCH system. Project TOUCH serves as an example of a computer-based learning system for developing and implementing problem-based learning cases within the medical curriculum, but this system should be easily applied to other educational environments and disciplines involving functional and clinical anatomy. Future phases will explore PC versions of the TOUCH cases for increased distribution. Copyright 2003 Wiley-Liss, Inc.

  3. Advances in Machine Learning and Data Mining for Astronomy

    Science.gov (United States)

    Way, Michael J.; Scargle, Jeffrey D.; Ali, Kamal M.; Srivastava, Ashok N.

    2012-03-01

    Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines, the material discussed in this text transcends traditional boundaries between various areas in the sciences and computer science. The book's introductory part provides context to issues in the astronomical sciences that are also important to health, social, and physical sciences, particularly probabilistic and statistical aspects of classification and cluster analysis. The next part describes a number of astrophysics case studies that leverage a range of machine learning and data mining technologies. In the last part, developers of algorithms and practitioners of machine learning and data mining show how these tools and techniques are used in astronomical applications. With contributions from leading astronomers and computer scientists, this book is a practical guide to many of the most important developments in machine learning, data mining, and statistics. It explores how these advances can solve current and future problems in astronomy and looks at how they could lead to the creation of entirely new algorithms within the data mining community.

  4. Advances in machine learning and data mining for astronomy

    CERN Document Server

    Way, Michael J

    2012-01-01

    Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines, the material discussed in this text transcends traditional boundaries between various areas in the sciences and computer science. The book's introductory part provides context to issues in the astronomical sciences that are also important to health

  5. Digital Skill Training Research: Preliminary Guidelines for Distributed Learning

    National Research Council Canada - National Science Library

    Childs, Jerry

    2001-01-01

    This task was aimed at the development of guidelines for distributed learning (DL). A matrix was generated to evaluate the effectiveness of various DL media for training representative knowledge/skill types...

  6. Distributed Emotions in the Design of Learning Technologies

    Science.gov (United States)

    Kim, Beaumie; Kim, Mi Song

    2010-01-01

    Learning is a social activity, which requires interactions with the environment, tools, people, and also ourselves (e.g., our previous experiences). Each interaction provides different meanings to learners, and the associated emotion affects their learning and performance. With the premise that emotion and cognition are distributed, the authors…

  7. Social Networks and Performance in Distributed Learning Communities

    Science.gov (United States)

    Cadima, Rita; Ojeda, Jordi; Monguet, Josep M.

    2012-01-01

    Social networks play an essential role in learning environments as a key channel for knowledge sharing and students' support. In distributed learning communities, knowledge sharing does not occur as spontaneously as when a working group shares the same physical space; knowledge sharing depends even more on student informal connections. In this…

  8. Evaluation of the information servicing in a distributed learning ...

    African Journals Online (AJOL)

    The authors' main idea is to organize a distributed learning environment (DLE) based on information and communication resources of global network in combination with the technologies for virtual reality and 3D simulation. In this reason a conceptual model of the DLE architecture and learning processes is defined, and ...

  9. Advanced, Analytic, Automated (AAA) Measurement of Engagement During Learning.

    Science.gov (United States)

    D'Mello, Sidney; Dieterle, Ed; Duckworth, Angela

    2017-01-01

    It is generally acknowledged that engagement plays a critical role in learning. Unfortunately, the study of engagement has been stymied by a lack of valid and efficient measures. We introduce the advanced, analytic, and automated (AAA) approach to measure engagement at fine-grained temporal resolutions. The AAA measurement approach is grounded in embodied theories of cognition and affect, which advocate a close coupling between thought and action. It uses machine-learned computational models to automatically infer mental states associated with engagement (e.g., interest, flow) from machine-readable behavioral and physiological signals (e.g., facial expressions, eye tracking, click-stream data) and from aspects of the environmental context. We present15 case studies that illustrate the potential of the AAA approach for measuring engagement in digital learning environments. We discuss strengths and weaknesses of the AAA approach, concluding that it has significant promise to catalyze engagement research.

  10. TF.Learn: TensorFlow's High-level Module for Distributed Machine Learning

    OpenAIRE

    Tang, Yuan

    2016-01-01

    TF.Learn is a high-level Python module for distributed machine learning inside TensorFlow. It provides an easy-to-use Scikit-learn style interface to simplify the process of creating, configuring, training, evaluating, and experimenting a machine learning model. TF.Learn integrates a wide range of state-of-art machine learning algorithms built on top of TensorFlow's low level APIs for small to large-scale supervised and unsupervised problems. This module focuses on bringing machine learning t...

  11. Distributed generation: remote power systems with advanced storage technologies

    International Nuclear Information System (INIS)

    Clark, Woodrow; Isherwood, William

    2004-01-01

    The paper discusses derived from an earlier hypothetical study of remote villiages. It considers the policy implications for communities who have their own local power resources rather than those distributed through transmission from distant sources such as dams, coal power plants or even renewables generation from wind farms, solar thermal or other resources. The issues today, post 911 and the energy crises in California, Northeast North America and Europe, signal the need for a new and different approach to energy supply(s), reliability and dissemination. Distributed generation (DG) as explored in the earlier paper appears to be one such approach that allows for local communities to become energy self-sufficient. Along with energy conservation, efficiency, and on-site generation, local power sources provide concrete definitions and understandings for heretofore ill defined concepts such as sustainability and eco-systems. The end result for any region and nation-state are 'agile energy systems' which use flexible DG, on-site generation and conservation systems meeting the needs of local communities. Now the challenge is to demonstrate and provide economic and policy structures for implementing new advanced technologies for local communities. For institutionalizing economically viable and sound environmental technologies then new finance mechanisms must be established that better reflect the true costs of clean energy distributed in local communities. For example, the aggregation of procurement contracts for on-site solar systems is far more cost effective than for each business owner, public building or household to purchase its own separate units. Thus mass purchasing contracts that are link technologies as hybrids can dramatically reduce costs. In short public-private partnerships can implement the once costly clean energy technologies into local DG systems

  12. Fusion barrier distributions - What have we learned?

    International Nuclear Information System (INIS)

    Hinde, D. J.; Dasgupta, M.

    1998-01-01

    The study of nuclear fusion received a strong impetus from the realisation that an experimental fusion barrier distribution could be determined from precisely measured fusion cross-sections. Experimental data for different reactions have shown in the fusion barrier distributions clear signatures of a range of nuclear excitations, for example the effects of static quadrupole and hexadecapole deformations, single- and double-phonon states, transfer of nucleons, and high-lying excited states. The improved understanding of fusion barrier distributions allows more reliable prediction of fusion angular momentum distributions, which aids interpretation of fission probabilities and fission anisotropies, and understanding of the population of super-deformed bands for nuclear structure studies. Studies of the relationship between the fusion barrier distribution and the extra-push energy should improve our understanding of the mechanism of the extra-push effect, and may help to predict new ways of forming very heavy or super-heavy nuclei

  13. Perspectives on Advanced Learning Technologies and Learning Networks and Future Aerospace Workforce Environments

    Science.gov (United States)

    Noor, Ahmed K. (Compiler)

    2003-01-01

    An overview of the advanced learning technologies is given in this presentation along with a brief description of their impact on future aerospace workforce development. The presentation is divided into five parts (see Figure 1). In the first part, a brief historical account of the evolution of learning technologies is given. The second part describes the current learning activities. The third part describes some of the future aerospace systems, as examples of high-tech engineering systems, and lists their enabling technologies. The fourth part focuses on future aerospace research, learning and design environments. The fifth part lists the objectives of the workshop and some of the sources of information on learning technologies and learning networks.

  14. Game-Theoretic Learning in Distributed Control

    KAUST Repository

    Marden, Jason R.; Shamma, Jeff S.

    2018-01-01

    from autonomous vehicles to energy to transportation. One approach to control of such distributed architectures is to view the components as players in a game. In this approach, two design considerations are the components’ incentives and the rules

  15. Distributed Leadership and Digital Collaborative Learning: A Synergistic Relationship?

    Science.gov (United States)

    Harris, Alma; Jones, Michelle; Baba, Suria

    2013-01-01

    This paper explores the synergy between distributed leadership and digital collaborative learning. It argues that distributed leadership offers an important theoretical lens for understanding and explaining how digital collaboration is best supported and led. Drawing upon evidence from two online educational platforms, the paper explores the…

  16. Cooperative Learning for Distributed In-Network Traffic Classification

    Science.gov (United States)

    Joseph, S. B.; Loo, H. R.; Ismail, I.; Andromeda, T.; Marsono, M. N.

    2017-04-01

    Inspired by the concept of autonomic distributed/decentralized network management schemes, we consider the issue of information exchange among distributed network nodes to network performance and promote scalability for in-network monitoring. In this paper, we propose a cooperative learning algorithm for propagation and synchronization of network information among autonomic distributed network nodes for online traffic classification. The results show that network nodes with sharing capability perform better with a higher average accuracy of 89.21% (sharing data) and 88.37% (sharing clusters) compared to 88.06% for nodes without cooperative learning capability. The overall performance indicates that cooperative learning is promising for distributed in-network traffic classification.

  17. Lessons Learned From Dynamic Simulations of Advanced Fuel Cycles

    International Nuclear Information System (INIS)

    Piet, Steven J.; Dixon, Brent W.; Jacobson, Jacob J.; Matthern, Gretchen E.; Shropshire, David E.

    2009-01-01

    Years of performing dynamic simulations of advanced nuclear fuel cycle options provide insights into how they could work and how one might transition from the current once-through fuel cycle. This paper summarizes those insights from the context of the 2005 objectives and goals of the Advanced Fuel Cycle Initiative (AFCI). Our intent is not to compare options, assess options versus those objectives and goals, nor recommend changes to those objectives and goals. Rather, we organize what we have learned from dynamic simulations in the context of the AFCI objectives for waste management, proliferation resistance, uranium utilization, and economics. Thus, we do not merely describe 'lessons learned' from dynamic simulations but attempt to answer the 'so what' question by using this context. The analyses have been performed using the Verifiable Fuel Cycle Simulation of Nuclear Fuel Cycle Dynamics (VISION). We observe that the 2005 objectives and goals do not address many of the inherently dynamic discriminators among advanced fuel cycle options and transitions thereof

  18. Advances in a Distributed Approach for Ocean Model Data Interoperability

    Directory of Open Access Journals (Sweden)

    Richard P. Signell

    2014-03-01

    Full Text Available An infrastructure for earth science data is emerging across the globe based on common data models and web services. As we evolve from custom file formats and web sites to standards-based web services and tools, data is becoming easier to distribute, find and retrieve, leaving more time for science. We describe recent advances that make it easier for ocean model providers to share their data, and for users to search, access, analyze and visualize ocean data using MATLAB® and Python®. These include a technique for modelers to create aggregated, Climate and Forecast (CF metadata convention datasets from collections of non-standard Network Common Data Form (NetCDF output files, the capability to remotely access data from CF-1.6-compliant NetCDF files using the Open Geospatial Consortium (OGC Sensor Observation Service (SOS, a metadata standard for unstructured grid model output (UGRID, and tools that utilize both CF and UGRID standards to allow interoperable data search, browse and access. We use examples from the U.S. Integrated Ocean Observing System (IOOS® Coastal and Ocean Modeling Testbed, a project in which modelers using both structured and unstructured grid model output needed to share their results, to compare their results with other models, and to compare models with observed data. The same techniques used here for ocean modeling output can be applied to atmospheric and climate model output, remote sensing data, digital terrain and bathymetric data.

  19. Advances in a distributed approach for ocean model data interoperability

    Science.gov (United States)

    Signell, Richard P.; Snowden, Derrick P.

    2014-01-01

    An infrastructure for earth science data is emerging across the globe based on common data models and web services. As we evolve from custom file formats and web sites to standards-based web services and tools, data is becoming easier to distribute, find and retrieve, leaving more time for science. We describe recent advances that make it easier for ocean model providers to share their data, and for users to search, access, analyze and visualize ocean data using MATLAB® and Python®. These include a technique for modelers to create aggregated, Climate and Forecast (CF) metadata convention datasets from collections of non-standard Network Common Data Form (NetCDF) output files, the capability to remotely access data from CF-1.6-compliant NetCDF files using the Open Geospatial Consortium (OGC) Sensor Observation Service (SOS), a metadata standard for unstructured grid model output (UGRID), and tools that utilize both CF and UGRID standards to allow interoperable data search, browse and access. We use examples from the U.S. Integrated Ocean Observing System (IOOS®) Coastal and Ocean Modeling Testbed, a project in which modelers using both structured and unstructured grid model output needed to share their results, to compare their results with other models, and to compare models with observed data. The same techniques used here for ocean modeling output can be applied to atmospheric and climate model output, remote sensing data, digital terrain and bathymetric data.

  20. Advances in Bayesian Model Based Clustering Using Particle Learning

    Energy Technology Data Exchange (ETDEWEB)

    Merl, D M

    2009-11-19

    Recent work by Carvalho, Johannes, Lopes and Polson and Carvalho, Lopes, Polson and Taddy introduced a sequential Monte Carlo (SMC) alternative to traditional iterative Monte Carlo strategies (e.g. MCMC and EM) for Bayesian inference for a large class of dynamic models. The basis of SMC techniques involves representing the underlying inference problem as one of state space estimation, thus giving way to inference via particle filtering. The key insight of Carvalho et al was to construct the sequence of filtering distributions so as to make use of the posterior predictive distribution of the observable, a distribution usually only accessible in certain Bayesian settings. Access to this distribution allows a reversal of the usual propagate and resample steps characteristic of many SMC methods, thereby alleviating to a large extent many problems associated with particle degeneration. Furthermore, Carvalho et al point out that for many conjugate models the posterior distribution of the static variables can be parametrized in terms of [recursively defined] sufficient statistics of the previously observed data. For models where such sufficient statistics exist, particle learning as it is being called, is especially well suited for the analysis of streaming data do to the relative invariance of its algorithmic complexity with the number of data observations. Through a particle learning approach, a statistical model can be fit to data as the data is arriving, allowing at any instant during the observation process direct quantification of uncertainty surrounding underlying model parameters. Here we describe the use of a particle learning approach for fitting a standard Bayesian semiparametric mixture model as described in Carvalho, Lopes, Polson and Taddy. In Section 2 we briefly review the previously presented particle learning algorithm for the case of a Dirichlet process mixture of multivariate normals. In Section 3 we describe several novel extensions to the original

  1. Fillers as Signs of Distributional Learning

    Science.gov (United States)

    Taelman, Helena; Durieux, Gert; Gillis, Steven

    2009-01-01

    A longitudinal analysis is presented of the fillers of a Dutch-speaking child between 1;10 and 2;7. Our analysis corroborates familiar regularities reported in the literature: most fillers resemble articles in shape and distribution, and are affected by rhythmic and positional constraints. A novel finding is the impact of the lexical environment:…

  2. Learning in the context of distribution drift

    Science.gov (United States)

    2017-05-09

    Figure 3 shows a heatmap of the pairwise drift in the joint distribution on the Landsat-8 French land usage satellite data. This data represents 10 meter...listed under the List of Publications. 1. White, C., Using Big Data for Smarter Decision Making. 2011, BI Research: Ashland, Or. 2. Cook , S., et al

  3. Creating Educational Technology Curricula for Advanced Studies in Learning Technology

    Directory of Open Access Journals (Sweden)

    Minoru Nakayama

    2016-08-01

    Full Text Available Curriculum design and content are key factors in the area of human resource development. To examine the possibility of using a collaboration of Human Computer Interaction (HCI and Educational Technology (ET to develop innovative improvements to the education system, the curricula of these two areas of study were lexically analyzed and compared. As a further example, the curriculum of a joint course in HCI and ET was also lexically analyzed and the contents were examined. These analyses can be used as references in the development of human resources for use in advanced learning environments.

  4. Distributed Economic Dispatch in Microgrids Based on Cooperative Reinforcement Learning.

    Science.gov (United States)

    Liu, Weirong; Zhuang, Peng; Liang, Hao; Peng, Jun; Huang, Zhiwu; Weirong Liu; Peng Zhuang; Hao Liang; Jun Peng; Zhiwu Huang; Liu, Weirong; Liang, Hao; Peng, Jun; Zhuang, Peng; Huang, Zhiwu

    2018-06-01

    Microgrids incorporated with distributed generation (DG) units and energy storage (ES) devices are expected to play more and more important roles in the future power systems. Yet, achieving efficient distributed economic dispatch in microgrids is a challenging issue due to the randomness and nonlinear characteristics of DG units and loads. This paper proposes a cooperative reinforcement learning algorithm for distributed economic dispatch in microgrids. Utilizing the learning algorithm can avoid the difficulty of stochastic modeling and high computational complexity. In the cooperative reinforcement learning algorithm, the function approximation is leveraged to deal with the large and continuous state spaces. And a diffusion strategy is incorporated to coordinate the actions of DG units and ES devices. Based on the proposed algorithm, each node in microgrids only needs to communicate with its local neighbors, without relying on any centralized controllers. Algorithm convergence is analyzed, and simulations based on real-world meteorological and load data are conducted to validate the performance of the proposed algorithm.

  5. Professional learning for distributed leadership:Primary headteachers’ perspectives

    OpenAIRE

    Torrance, Deirdre

    2015-01-01

    This article draws from a small-scale study of headteachers motivated to positively impact on the quality of pupil experience by involving all staff in a distributed perspective on leadership. Each headteacher perceived leadership as involving learned processes requiring support and experience, expending considerable effort in providing a fertile environment for learning about its practice. This perspective developed from their personal experience of challenging established leadership orthodo...

  6. Distributed Online Learning in Social Recommender Systems

    Science.gov (United States)

    Tekin, Cem; Zhang, Simpson; van der Schaar, Mihaela

    2014-08-01

    In this paper, we consider decentralized sequential decision making in distributed online recommender systems, where items are recommended to users based on their search query as well as their specific background including history of bought items, gender and age, all of which comprise the context information of the user. In contrast to centralized recommender systems, in which there is a single centralized seller who has access to the complete inventory of items as well as the complete record of sales and user information, in decentralized recommender systems each seller/learner only has access to the inventory of items and user information for its own products and not the products and user information of other sellers, but can get commission if it sells an item of another seller. Therefore the sellers must distributedly find out for an incoming user which items to recommend (from the set of own items or items of another seller), in order to maximize the revenue from own sales and commissions. We formulate this problem as a cooperative contextual bandit problem, analytically bound the performance of the sellers compared to the best recommendation strategy given the complete realization of user arrivals and the inventory of items, as well as the context-dependent purchase probabilities of each item, and verify our results via numerical examples on a distributed data set adapted based on Amazon data. We evaluate the dependence of the performance of a seller on the inventory of items the seller has, the number of connections it has with the other sellers, and the commissions which the seller gets by selling items of other sellers to its users.

  7. Distance-Learning for Advanced Military Education: Using Wargame Simulation Course as an Example

    Science.gov (United States)

    Keh, Huan-Chao; Wang, Kuei-Min; Wai, Shu-Shen; Huang, Jiung-yao; Hui, Lin; Wu, Ji-Jen

    2008-01-01

    Distance learning in advanced military education can assist officers around the world to become more skilled and qualified for future challenges. Through well-chosen technology, the efficiency of distance-learning can be improved significantly. In this paper we present the architecture of Advanced Military Education-Distance Learning (AME-DL)…

  8. Distributed deep learning networks among institutions for medical imaging.

    Science.gov (United States)

    Chang, Ken; Balachandar, Niranjan; Lam, Carson; Yi, Darvin; Brown, James; Beers, Andrew; Rosen, Bruce; Rubin, Daniel L; Kalpathy-Cramer, Jayashree

    2018-03-29

    Deep learning has become a promising approach for automated support for clinical diagnosis. When medical data samples are limited, collaboration among multiple institutions is necessary to achieve high algorithm performance. However, sharing patient data often has limitations due to technical, legal, or ethical concerns. In this study, we propose methods of distributing deep learning models as an attractive alternative to sharing patient data. We simulate the distribution of deep learning models across 4 institutions using various training heuristics and compare the results with a deep learning model trained on centrally hosted patient data. The training heuristics investigated include ensembling single institution models, single weight transfer, and cyclical weight transfer. We evaluated these approaches for image classification in 3 independent image collections (retinal fundus photos, mammography, and ImageNet). We find that cyclical weight transfer resulted in a performance that was comparable to that of centrally hosted patient data. We also found that there is an improvement in the performance of cyclical weight transfer heuristic with a high frequency of weight transfer. We show that distributing deep learning models is an effective alternative to sharing patient data. This finding has implications for any collaborative deep learning study.

  9. Optimizing Distributed Machine Learning for Large Scale EEG Data Set

    Directory of Open Access Journals (Sweden)

    M Bilal Shaikh

    2017-06-01

    Full Text Available Distributed Machine Learning (DML has gained its importance more than ever in this era of Big Data. There are a lot of challenges to scale machine learning techniques on distributed platforms. When it comes to scalability, improving the processor technology for high level computation of data is at its limit, however increasing machine nodes and distributing data along with computation looks as a viable solution. Different frameworks   and platforms are available to solve DML problems. These platforms provide automated random data distribution of datasets which miss the power of user defined intelligent data partitioning based on domain knowledge. We have conducted an empirical study which uses an EEG Data Set collected through P300 Speller component of an ERP (Event Related Potential which is widely used in BCI problems; it helps in translating the intention of subject w h i l e performing any cognitive task. EEG data contains noise due to waves generated by other activities in the brain which contaminates true P300Speller. Use of Machine Learning techniques could help in detecting errors made by P300 Speller. We are solving this classification problem by partitioning data into different chunks and preparing distributed models using Elastic CV Classifier. To present a case of optimizing distributed machine learning, we propose an intelligent user defined data partitioning approach that could impact on the accuracy of distributed machine learners on average. Our results show better average AUC as compared to average AUC obtained after applying random data partitioning which gives no control to user over data partitioning. It improves the average accuracy of distributed learner due to the domain specific intelligent partitioning by the user. Our customized approach achieves 0.66 AUC on individual sessions and 0.75 AUC on mixed sessions, whereas random / uncontrolled data distribution records 0.63 AUC.

  10. Occam factors and model independent Bayesian learning of continuous distributions

    International Nuclear Information System (INIS)

    Nemenman, Ilya; Bialek, William

    2002-01-01

    Learning of a smooth but nonparametric probability density can be regularized using methods of quantum field theory. We implement a field theoretic prior numerically, test its efficacy, and show that the data and the phase space factors arising from the integration over the model space determine the free parameter of the theory ('smoothness scale') self-consistently. This persists even for distributions that are atypical in the prior and is a step towards a model independent theory for learning continuous distributions. Finally, we point out that a wrong parametrization of a model family may sometimes be advantageous for small data sets

  11. MR PROSTATE SEGMENTATION VIA DISTRIBUTED DISCRIMINATIVE DICTIONARY (DDD) LEARNING.

    Science.gov (United States)

    Guo, Yanrong; Zhan, Yiqiang; Gao, Yaozong; Jiang, Jianguo; Shen, Dinggang

    2013-01-01

    Segmenting prostate from MR images is important yet challenging. Due to non-Gaussian distribution of prostate appearances in MR images, the popular active appearance model (AAM) has its limited performance. Although the newly developed sparse dictionary learning method[1, 2] can model the image appearance in a non-parametric fashion, the learned dictionaries still lack the discriminative power between prostate and non-prostate tissues, which is critical for accurate prostate segmentation. In this paper, we propose to integrate deformable model with a novel learning scheme, namely the Distributed Discriminative Dictionary ( DDD ) learning, which can capture image appearance in a non-parametric and discriminative fashion. In particular, three strategies are designed to boost the tissue discriminative power of DDD. First , minimum Redundancy Maximum Relevance (mRMR) feature selection is performed to constrain the dictionary learning in a discriminative feature space. Second , linear discriminant analysis (LDA) is employed to assemble residuals from different dictionaries for optimal separation between prostate and non-prostate tissues. Third , instead of learning the global dictionaries, we learn a set of local dictionaries for the local regions (each with small appearance variations) along prostate boundary, thus achieving better tissue differentiation locally. In the application stage, DDDs will provide the appearance cues to robustly drive the deformable model onto the prostate boundary. Experiments on 50 MR prostate images show that our method can yield a Dice Ratio of 88% compared to the manual segmentations, and have 7% improvement over the conventional AAM.

  12. Learning stochastic reward distributions in a speeded pointing task.

    Science.gov (United States)

    Seydell, Anna; McCann, Brian C; Trommershäuser, Julia; Knill, David C

    2008-04-23

    Recent studies have shown that humans effectively take into account task variance caused by intrinsic motor noise when planning fast hand movements. However, previous evidence suggests that humans have greater difficulty accounting for arbitrary forms of stochasticity in their environment, both in economic decision making and sensorimotor tasks. We hypothesized that humans can learn to optimize movement strategies when environmental randomness can be experienced and thus implicitly learned over several trials, especially if it mimics the kinds of randomness for which subjects might have generative models. We tested the hypothesis using a task in which subjects had to rapidly point at a target region partly covered by three stochastic penalty regions introduced as "defenders." At movement completion, each defender jumped to a new position drawn randomly from fixed probability distributions. Subjects earned points when they hit the target, unblocked by a defender, and lost points otherwise. Results indicate that after approximately 600 trials, subjects approached optimal behavior. We further tested whether subjects simply learned a set of stimulus-contingent motor plans or the statistics of defenders' movements by training subjects with one penalty distribution and then testing them on a new penalty distribution. Subjects immediately changed their strategy to achieve the same average reward as subjects who had trained with the second penalty distribution. These results indicate that subjects learned the parameters of the defenders' jump distributions and used this knowledge to optimally plan their hand movements under conditions involving stochastic rewards and penalties.

  13. Actively Encouraging Learning and Degree Persistence in Advanced Astrophysics Courses

    Science.gov (United States)

    McIntosh, Daniel H.

    2018-01-01

    The need to grow and diversify the STEM workforce remains a critical national challenge. Less than 40% of college students interested in STEM achieve a bachelor's degree. These numbers are even more dire for women and URMs, underscoring a serious concern about the country's ability to remain competitive in science and tech. A major factor is persistent performance gaps in rigorous 'gateway' and advanced STEM courses for majors from diverse backgrounds leading to discouragement, a sense of exclusion, and high dropout rates. Education research has clearly demonstrated that interactive-engagement (`active learning') strategies increase performance, boost confidence, and help build positive 'identity' in STEM. Likewise, the evidence shows that traditional science education practices do not help most students gain a genuine understanding of concepts nor the necessary skill set to succeed in their disciplines. Yet, lecture-heavy courses continue to dominate the higher-ed curriculum, thus, reinforcing the tired notion that only a small percentage of 'special' students have the inherent ability to achieve a STEM degree. In short, very capable students with less experience and confidence in science, who belong to groups that traditionally are less identified with STEM careers, are effectively and efficiently 'weeded out' by traditional education practices. I will share specific examples for how I successfully incorporate active learning in advanced astrophysics courses to encourage students from all backgrounds to synthesize complex ideas, build bedrock conceptual frameworks, gain technical communication skills, and achieve mastery learning outcomes all necessary to successfully complete rigorous degrees like astrophysics. By creating an inclusive and active learning experience in junior-level extragalactic and stellar interiors/atmospheres courses, I am helping students gain fluency in their chosen major and the ability to 'think like a scientist', both critical to

  14. Ambient Learning Displays - Distributed Mixed Reality Information Mash-ups to support Ubiquitous Learning

    NARCIS (Netherlands)

    Börner, Dirk

    2012-01-01

    Börner, D. (2012). Ambient Learning Displays - Distributed Mixed Reality Information Mash-ups to support Ubiquitous Learning. 2012 IEEE Seventh International Conference on Wireless, Mobile and Ubiquitous Technology in Education (pp. 337-338). March, 27-30, 2012, Takamatsu, Japan: IEEE Computer

  15. Learning to merge search results for efficient Distributed Information Retrieval

    NARCIS (Netherlands)

    Tjin-Kam-Jet, Kien; Hiemstra, Djoerd

    2010-01-01

    Merging search results from different servers is a major problem in Distributed Information Retrieval. We used Regression-SVM and Ranking-SVM which would learn a function that merges results based on information that is readily available: i.e. the ranks, titles, summaries and URLs contained in the

  16. Distributed Systems of Generalizing as the Basis of Workplace Learning

    Science.gov (United States)

    Virkkunen, Jaakko; Pihlaja, Juha

    2004-01-01

    This article proposes a new way of conceptualizing workplace learning as distributed systems of appropriation, development and the use of practice-relevant generalizations fixed within mediational artifacts. This article maintains that these systems change historically as technology and increasingly sophisticated forms of production develop.…

  17. Distributed Practice and Retrieval Practice in Primary School Vocabulary Learning

    NARCIS (Netherlands)

    N.A.M.C. Goossens (Nicole)

    2015-01-01

    markdownabstractThe aim of this thesis was to investigate whether particular memory strategies stemming from cognitive and educational psychology, enhance primary school vocabulary learning. Th e memory strategies investigated in this thesis were distributed practice and retrieval practice. Th e

  18. Structure Learning and Statistical Estimation in Distribution Networks - Part II

    Energy Technology Data Exchange (ETDEWEB)

    Deka, Deepjyoti [Univ. of Texas, Austin, TX (United States); Backhaus, Scott N. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Chertkov, Michael [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2015-02-13

    Limited placement of real-time monitoring devices in the distribution grid, recent trends notwithstanding, has prevented the easy implementation of demand-response and other smart grid applications. Part I of this paper discusses the problem of learning the operational structure of the grid from nodal voltage measurements. In this work (Part II), the learning of the operational radial structure is coupled with the problem of estimating nodal consumption statistics and inferring the line parameters in the grid. Based on a Linear-Coupled(LC) approximation of AC power flows equations, polynomial time algorithms are designed to identify the structure and estimate nodal load characteristics and/or line parameters in the grid using the available nodal voltage measurements. Then the structure learning algorithm is extended to cases with missing data, where available observations are limited to a fraction of the grid nodes. The efficacy of the presented algorithms are demonstrated through simulations on several distribution test cases.

  19. Recent Advances in Airframe-Propulsion Concepts with Distributed Propulsion

    OpenAIRE

    Isikveren , A.T.; Seitz , A.; Bijewitz , J.; Hornung , M.; Mirzoyan , A.; Isyanov , A.; Godard , J.L.; Stückl , S.; Van Toor , J.

    2014-01-01

    International audience; This paper discusses design and integration associated with distributed propulsion as a means of providing motive power for future aircraft concepts. The technical work reflects activities performed within a European Commission funded Framework 7 project entitled Distributed Propulsion and Ultra-high By-Pass Rotor Study at Aircraft Level, or, DisPURSAL. In this instance, the approach of distributed propulsion includes one unique solution that integrates the fuselage wi...

  20. Learning Curves of Virtual Mastoidectomy in Distributed and Massed Practice.

    Science.gov (United States)

    Andersen, Steven Arild Wuyts; Konge, Lars; Cayé-Thomasen, Per; Sørensen, Mads Sølvsten

    2015-10-01

    Repeated and deliberate practice is crucial in surgical skills training, and virtual reality (VR) simulation can provide self-directed training of basic surgical skills to meet the individual needs of the trainee. Assessment of the learning curves of surgical procedures is pivotal in understanding skills acquisition and best-practice implementation and organization of training. To explore the learning curves of VR simulation training of mastoidectomy and the effects of different practice sequences with the aim of proposing the optimal organization of training. A prospective trial with a 2 × 2 design was conducted at an academic teaching hospital. Participants included 43 novice medical students. Of these, 21 students completed time-distributed practice from October 14 to November 29, 2013, and a separate group of 19 students completed massed practice on May 16, 17, or 18, 2014. Data analysis was performed from June 6, 2014, to March 3, 2015. Participants performed 12 repeated virtual mastoidectomies using a temporal bone surgical simulator in either a distributed (practice blocks spaced in time) or massed (all practice in 1 day) training program with randomization for simulator-integrated tutoring during the first 5 sessions. Performance was assessed using a modified Welling Scale for final product analysis by 2 blinded senior otologists. Compared with the 19 students in the massed practice group, the 21 students in the distributed practice group were older (mean age, 25.1 years), more often male (15 [62%]), and had slightly higher mean gaming frequency (2.3 on a 1-5 Likert scale). Learning curves were established and distributed practice was found to be superior to massed practice, reported as mean end score (95% CI) of 15.7 (14.4-17.0) in distributed practice vs. 13.0 (11.9-14.1) with massed practice (P = .002). Simulator-integrated tutoring accelerated the initial performance, with mean score for tutored sessions of 14.6 (13.9-15.2) vs. 13.4 (12.8-14.0) for

  1. Semi-Tandem Electric Distributed Wing Zip Aviation Advanced Concept

    Data.gov (United States)

    National Aeronautics and Space Administration — This project aims to develop a unique distributed electric propulsion approach that provides breakthrough capability improvements across conventional take-off and...

  2. Machine learning of network metrics in ATLAS Distributed Data Management

    CERN Document Server

    AUTHOR|(INSPIRE)INSPIRE-00218873; The ATLAS collaboration; Toler, Wesley; Vamosi, Ralf; Bogado Garcia, Joaquin Ignacio

    2017-01-01

    The increasing volume of physics data poses a critical challenge to the ATLAS experiment. In anticipation of high luminosity physics, automation of everyday data management tasks has become necessary. Previously many of these tasks required human decision-making and operation. Recent advances in hardware and software have made it possible to entrust more complicated duties to automated systems using models trained by machine learning algorithms. In this contribution we show results from one of our ongoing automation efforts that focuses on network metrics. First, we describe our machine learning framework built atop the ATLAS Analytics Platform. This framework can automatically extract and aggregate data, train models with various machine learning algorithms, and eventually score the resulting models and parameters. Second, we use these models to forecast metrics relevant for network-aware job scheduling and data brokering. We show the characteristics of the data and evaluate the forecasting accuracy of our m...

  3. Machine learning of network metrics in ATLAS Distributed Data Management

    Science.gov (United States)

    Lassnig, Mario; Toler, Wesley; Vamosi, Ralf; Bogado, Joaquin; ATLAS Collaboration

    2017-10-01

    The increasing volume of physics data poses a critical challenge to the ATLAS experiment. In anticipation of high luminosity physics, automation of everyday data management tasks has become necessary. Previously many of these tasks required human decision-making and operation. Recent advances in hardware and software have made it possible to entrust more complicated duties to automated systems using models trained by machine learning algorithms. In this contribution we show results from one of our ongoing automation efforts that focuses on network metrics. First, we describe our machine learning framework built atop the ATLAS Analytics Platform. This framework can automatically extract and aggregate data, train models with various machine learning algorithms, and eventually score the resulting models and parameters. Second, we use these models to forecast metrics relevant for networkaware job scheduling and data brokering. We show the characteristics of the data and evaluate the forecasting accuracy of our models.

  4. Nonintrusive Load Monitoring Based on Advanced Deep Learning and Novel Signature

    Directory of Open Access Journals (Sweden)

    Jihyun Kim

    2017-01-01

    Full Text Available Monitoring electricity consumption in the home is an important way to help reduce energy usage. Nonintrusive Load Monitoring (NILM is existing technique which helps us monitor electricity consumption effectively and costly. NILM is a promising approach to obtain estimates of the electrical power consumption of individual appliances from aggregate measurements of voltage and/or current in the distribution system. Among the previous studies, Hidden Markov Model (HMM based models have been studied very much. However, increasing appliances, multistate of appliances, and similar power consumption of appliances are three big issues in NILM recently. In this paper, we address these problems through providing our contributions as follows. First, we proposed state-of-the-art energy disaggregation based on Long Short-Term Memory Recurrent Neural Network (LSTM-RNN model and additional advanced deep learning. Second, we proposed a novel signature to improve classification performance of the proposed model in multistate appliance case. We applied the proposed model on two datasets such as UK-DALE and REDD. Via our experimental results, we have confirmed that our model outperforms the advanced model. Thus, we show that our combination between advanced deep learning and novel signature can be a robust solution to overcome NILM’s issues and improve the performance of load identification.

  5. Learning and remembering strategies of novice and advanced jazz dancers for skill level appropriate dance routines.

    Science.gov (United States)

    Poon, P P; Rodgers, W M

    2000-06-01

    This study examined the influence of the challenge level of to-be-learned stimulus on learning strategies in novice and advanced dancers. In Study 1, skill-level appropriate dance routines were developed for novice and advanced jazz dancers. In Study 2, 8 novice and 9 advanced female jazz dancers attempted to learn and remember the two routines in mixed model factorial design, with one between-participants factor: skill level (novice or advanced) and two within-participants factors: routine (easy or difficult) and performance (immediate or delayed). Participants were interviewed regarding the strategies used to learn and remember the routines. Results indicated that advanced performers used atypical learning strategies for insufficiently challenging stimuli, which may reflect characteristics of the stimuli rather than the performer. The qualitative data indicate a clear preference of novice and advanced performers for spatial compatibility of stimuli and response.

  6. Advanced Machine Learning Emulators of Radiative Transfer Models

    Science.gov (United States)

    Camps-Valls, G.; Verrelst, J.; Martino, L.; Vicent, J.

    2017-12-01

    Physically-based model inversion methodologies are based on physical laws and established cause-effect relationships. A plethora of remote sensing applications rely on the physical inversion of a Radiative Transfer Model (RTM), which lead to physically meaningful bio-geo-physical parameter estimates. The process is however computationally expensive, needs expert knowledge for both the selection of the RTM, its parametrization and the the look-up table generation, as well as its inversion. Mimicking complex codes with statistical nonlinear machine learning algorithms has become the natural alternative very recently. Emulators are statistical constructs able to approximate the RTM, although at a fraction of the computational cost, providing an estimation of uncertainty, and estimations of the gradient or finite integral forms. We review the field and recent advances of emulation of RTMs with machine learning models. We posit Gaussian processes (GPs) as the proper framework to tackle the problem. Furthermore, we introduce an automatic methodology to construct emulators for costly RTMs. The Automatic Gaussian Process Emulator (AGAPE) methodology combines the interpolation capabilities of GPs with the accurate design of an acquisition function that favours sampling in low density regions and flatness of the interpolation function. We illustrate the good capabilities of our emulators in toy examples, leaf and canopy levels PROSPECT and PROSAIL RTMs, and for the construction of an optimal look-up-table for atmospheric correction based on MODTRAN5.

  7. Improving the Effectiveness of Army Distributed Learning: A Research and Policy Agenda

    National Research Council Canada - National Science Library

    Straus, Susan G; Galegher, Jolene; Shanley, Michael G; Moini, Joy S

    2006-01-01

    .... The Army Distributed Learning Program, or TADLP, is a comprehensive program that is implementing DL through digital training facilities, courseware, learning management systems, and other strategies...

  8. Learning Multirobot Hose Transportation and Deployment by Distributed Round-Robin Q-Learning.

    Directory of Open Access Journals (Sweden)

    Borja Fernandez-Gauna

    Full Text Available Multi-Agent Reinforcement Learning (MARL algorithms face two main difficulties: the curse of dimensionality, and environment non-stationarity due to the independent learning processes carried out by the agents concurrently. In this paper we formalize and prove the convergence of a Distributed Round Robin Q-learning (D-RR-QL algorithm for cooperative systems. The computational complexity of this algorithm increases linearly with the number of agents. Moreover, it eliminates environment non sta tionarity by carrying a round-robin scheduling of the action selection and execution. That this learning scheme allows the implementation of Modular State-Action Vetoes (MSAV in cooperative multi-agent systems, which speeds up learning convergence in over-constrained systems by vetoing state-action pairs which lead to undesired termination states (UTS in the relevant state-action subspace. Each agent's local state-action value function learning is an independent process, including the MSAV policies. Coordination of locally optimal policies to obtain the global optimal joint policy is achieved by a greedy selection procedure using message passing. We show that D-RR-QL improves over state-of-the-art approaches, such as Distributed Q-Learning, Team Q-Learning and Coordinated Reinforcement Learning in a paradigmatic Linked Multi-Component Robotic System (L-MCRS control problem: the hose transportation task. L-MCRS are over-constrained systems with many UTS induced by the interaction of the passive linking element and the active mobile robots.

  9. New advances in the statistical parton distributions approach*

    Directory of Open Access Journals (Sweden)

    Soffer Jacques

    2016-01-01

    Full Text Available The quantum statistical parton distributions approach proposed more than one decade ago is revisited by considering a larger set of recent and accurate Deep Inelastic Scattering experimental results. It enables us to improve the description of the data by means of a new determination of the parton distributions. This global next-to-leading order QCD analysis leads to a good description of several structure functions, involving unpolarized parton distributions and helicity distributions, in terms of a rather small number of free parameters. There are many serious challenging issues. The predictions of this theoretical approach will be tested for single-jet production and charge asymmetry in W± production in p̄p and pp collisions up to LHC energies, using recent data and also for forthcoming experimental results.

  10. Requirements for Self-Stabilization of Distributed Advanced Battle Managers

    National Research Council Canada - National Science Library

    Auguston, Mikhail; Cook, Thomas S; Michael, James B; Shing, Man-Tak; Tummala, Harsha; Wijesekera, Duminda; Xie, Geoffrey G

    2006-01-01

    In this report, we formalize the self-stabilization problem as it pertains to the C2BMC, in addition to highlighting some of key features of the C2BMC that distinguish it from general-purpose distributed systems...

  11. Distribution-Preserving Stratified Sampling for Learning Problems.

    Science.gov (United States)

    Cervellera, Cristiano; Maccio, Danilo

    2017-06-09

    The need for extracting a small sample from a large amount of real data, possibly streaming, arises routinely in learning problems, e.g., for storage, to cope with computational limitations, obtain good training/test/validation sets, and select minibatches for stochastic gradient neural network training. Unless we have reasons to select the samples in an active way dictated by the specific task and/or model at hand, it is important that the distribution of the selected points is as similar as possible to the original data. This is obvious for unsupervised learning problems, where the goal is to gain insights on the distribution of the data, but it is also relevant for supervised problems, where the theory explains how the training set distribution influences the generalization error. In this paper, we analyze the technique of stratified sampling from the point of view of distances between probabilities. This allows us to introduce an algorithm, based on recursive binary partition of the input space, aimed at obtaining samples that are distributed as much as possible as the original data. A theoretical analysis is proposed, proving the (greedy) optimality of the procedure together with explicit error bounds. An adaptive version of the algorithm is also introduced to cope with streaming data. Simulation tests on various data sets and different learning tasks are also provided.

  12. Advancing Higher Education with Mobile Learning Technologies: Cases, Trends, and Inquiry-Based Methods

    Science.gov (United States)

    Keengwe, Jared, Ed.; Maxfield, Marian B., Ed.

    2015-01-01

    Rapid advancements in technology are creating new opportunities for educators to enhance their classroom techniques with digital learning resources. Once used solely outside of the classroom, smartphones, tablets, and e-readers are becoming common in many school settings. "Advancing Higher Education with Mobile Learning Technologies: Cases,…

  13. Grid Monitoring and Advanced Control of Distributed Power Generation Systems

    DEFF Research Database (Denmark)

    Timbus, Adrian Vasile

    . As an example, the latest published grid codes stress the ability of distributed generators, especially wind turbines, to stay connected during short grid disturbances and in addition to provide active/reactive power control at the point of common coupling. Based on the above facts, the need for improving...... reported in some countries creating concerns about power system stability. This leads to a continuous evolution of grid interconnection requirements towards a better controllability of generated power and an enhanced contribution of distributed power generation systems to power system stability...... and adding more features to the control of distributed power generation systems (DPGS) arises. As a consequence, this thesis focuses on grid monitoring methods and possible approaches in control in order to obtain a more reliable and  exible power generation system during normal and faulty grid conditions...

  14. Learning Curves of Virtual Mastoidectomy in Distributed and Massed Practice

    DEFF Research Database (Denmark)

    Andersen, Steven Arild Wuyts; Konge, Lars; Cayé-Thomasen, Per

    2015-01-01

    IMPORTANCE: Repeated and deliberate practice is crucial in surgical skills training, and virtual reality (VR) simulation can provide self-directed training of basic surgical skills to meet the individual needs of the trainee. Assessment of the learning curves of surgical procedures is pivotal...... in understanding skills acquisition and best-practice implementation and organization of training. OBJECTIVE: To explore the learning curves of VR simulation training of mastoidectomy and the effects of different practice sequences with the aim of proposing the optimal organization of training. DESIGN, SETTING...... plateaued on a score of 16.0 (15.3-16.7) at approximately the ninth repetition, but the individual learning curves were highly variable. CONCLUSIONS AND RELEVANCE: Novices can acquire basic mastoidectomy competencies with self-directed VR simulation training. Training should be organized with distributed...

  15. Reinforcement Learning in Distributed Domains: Beyond Team Games

    Science.gov (United States)

    Wolpert, David H.; Sill, Joseph; Turner, Kagan

    2000-01-01

    Distributed search algorithms are crucial in dealing with large optimization problems, particularly when a centralized approach is not only impractical but infeasible. Many machine learning concepts have been applied to search algorithms in order to improve their effectiveness. In this article we present an algorithm that blends Reinforcement Learning (RL) and hill climbing directly, by using the RL signal to guide the exploration step of a hill climbing algorithm. We apply this algorithm to the domain of a constellations of communication satellites where the goal is to minimize the loss of importance weighted data. We introduce the concept of 'ghost' traffic, where correctly setting this traffic induces the satellites to act to optimize the world utility. Our results indicated that the bi-utility search introduced in this paper outperforms both traditional hill climbing algorithms and distributed RL approaches such as team games.

  16. Making Improvements to The Army Distributed Learning Program

    Science.gov (United States)

    2012-01-01

    ing focuses on leadership and management as well as technical skills, and involves the creation of global virtual teams. e training often deals...develop and distribute knowledge via a dynamic, global knowledge network called the Battle Command Knowledge System with a purpose of providing...Levels of Interactivity,” paper presented at 2006 dL Workshop, March 14, 2006. Wexler, S., et al., E-Learning 2.0., Santa Rosa, Calif.: e ELearning

  17. Advanced Strategy Guideline. Air Distribution Basics and Duct Design

    Energy Technology Data Exchange (ETDEWEB)

    Arlan Burdick

    2011-12-01

    This report discusses considerations for designing an air distribution system for an energy efficient house that requires less air volume to condition the space. Considering the HVAC system early in the design process will allow adequate space for equipment and ductwork and can result in cost savings.

  18. Advances in architectural concepts to support distributed systems design

    NARCIS (Netherlands)

    Ferreira Pires, Luis; Vissers, C.A.; van Sinderen, Marten J.

    1993-01-01

    This paper presents and discusses some architectural concepts for distributed systems design. These concepts are derived from an analysis of limitations of some currently available standard design languages. We conclude that language design should be based upon the careful consideration of

  19. Fast distributed strategic learning for global optima in queueing access games

    KAUST Repository

    Tembine, Hamidou

    2014-01-01

    In this paper we examine combined fully distributed payoff and strategy learning (CODIPAS) in a queue-aware access game over a graph. The classical strategic learning analysis relies on vanishing or small learning rate and uses stochastic

  20. Foundational Report Series: Advanced Distribution Management Systems for Grid Modernization

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Jianhui [Argonne National Lab. (ANL), Argonne, IL (United States)

    2015-09-01

    This report describes the application functions for distribution management systems (DMS). The application functions are those surveyed by the IEEE Power and Energy Society’s Task Force on Distribution Management Systems. The description of each DMS application includes functional requirements and the key features and characteristics in current and future deployments, as well as a summary of the major benefits provided by each function to stakeholders — from customers to shareholders. Due consideration is paid to the fact that the realizable benefits of each function may differ by type of utility, whether investor-owned, cooperative, or municipal. This report is sufficient to define the functional requirements of each application for system procurement (request-for-proposal [RFP]) purposes and for developing preliminary high-level use cases for those functions. However, it should not be considered a design document that will enable a vendor or software developer to design and build actual DMS applications.

  1. A Concept Plane using electric distributed propulsion Evaluation of advanced power architecture

    OpenAIRE

    Ridel , M.; Paluch , B.; Doll , C.; Donjat , D.; Hermetz , J.; Guigon , A.; Schmollgruber , P.; Atinault , O.; Choy , P.; Le Tallec , P.; Dessornes , O.; Lefebvre , T.

    2015-01-01

    International audience; Starting from electrical distributed propulsion system concept, the ONERA’s engineers demonstrated the viability of an all electrical aircraft for a small business aircraft. This paper describes the advanced power architecture considering energy conversion and power distribution. The design of this advanced power architecture requires the multi-physic integration of different domains as flight performances, safety and environmental requirements (thermal, electric, elec...

  2. Differently Structured Advance Organizers Lead to Different Initial Schemata and Learning Outcomes

    Science.gov (United States)

    Gurlitt, Johannes; Dummel, Sebastian; Schuster, Silvia; Nuckles, Matthias

    2012-01-01

    Does the specific structure of advance organizers influence learning outcomes? In the first experiment, 48 psychology students were randomly assigned to three differently structured advance organizers: a well-structured, a well-structured and key-concept emphasizing, and a less structured advance organizer. These were followed by a sorting task, a…

  3. Virtual Learning Environment for Interactive Engagement with Advanced Quantum Mechanics

    Science.gov (United States)

    Pedersen, Mads Kock; Skyum, Birk; Heck, Robert; Müller, Romain; Bason, Mark; Lieberoth, Andreas; Sherson, Jacob F.

    2016-01-01

    A virtual learning environment can engage university students in the learning process in ways that the traditional lectures and lab formats cannot. We present our virtual learning environment "StudentResearcher," which incorporates simulations, multiple-choice quizzes, video lectures, and gamification into a learning path for quantum…

  4. Advancing Learner Autonomy in TEFL via Collaborative Learning

    Science.gov (United States)

    Jacobs, George M.; Shan, Tan Hui

    2015-01-01

    The present paper begins by situating learner autonomy and collaborative learning as part of a larger paradigm shift towards student-centred learning. Next are brief discussions of learner autonomy and how learner autonomy links with collaborative learning. In the main part of the paper, four central principles of collaborative learning are…

  5. Advanced Strategy Guideline: Air Distribution Basics and Duct Design

    Energy Technology Data Exchange (ETDEWEB)

    Burdick, A.

    2011-12-01

    This report discusses considerations for designing an air distribution system for an energy efficient house that requires less air volume to condition the space. Considering the HVAC system early in the design process will allow adequate space for equipment and ductwork and can result in cost savings. Principles discussed that will maximize occupant comfort include delivery of the proper amount of conditioned air for appropriate temperature mixing and uniformity without drafts, minimization of system noise, the impacts of pressure loss, efficient return air duct design, and supply air outlet placement, as well as duct layout, materials, and sizing.

  6. Advancement of magma fragmentation by inhomogeneous bubble distribution.

    Science.gov (United States)

    Kameda, M; Ichihara, M; Maruyama, S; Kurokawa, N; Aoki, Y; Okumura, S; Uesugi, K

    2017-12-01

    Decompression times reported in previous studies suggest that thoroughly brittle fragmentation is unlikely in actual explosive volcanic eruptions. What occurs in practice is brittle-like fragmentation, which is defined as the solid-like fracture of a material whose bulk rheological properties are close to those of a fluid. Through laboratory experiments and numerical simulation, the link between the inhomogeneous structure of bubbles and the development of cracks that may lead to brittle-like fragmentation was clearly demonstrated here. A rapid decompression test was conducted to simulate the fragmentation of a specimen whose pore morphology was revealed by X-ray microtomography. The dynamic response during decompression was observed by high-speed photography. Large variation was observed in the responses of the specimens even among specimens with equal bulk rheological properties. The stress fields of the specimens under decompression computed by finite element analysis shows that the presence of satellite bubbles beneath a large bubble induced the stress concentration. On the basis of the obtained results, a new mechanism for brittle-like fragmentation is proposed. In the proposed scenario, the second nucleation of bubbles near the fragmentation surface is an essential process for the advancement of fragmentation in an upward magma flow in a volcanic conduit.

  7. Advanced power supply and distribution systems for Columbus

    Science.gov (United States)

    Eggers, Gert

    1988-01-01

    The paper describes power supply and distribution systems to be used on unmanned/man-tended Columbus elements, capable of supplying 10 kW to 30 kW to a variety of users in low earth orbits (LEO's). For the definition of the Electrical Power System (EPS) challenging requirements as the provision of high power levels under hard LEO conditions, maintainability, commonality etc. are to be taken into account. These requirements are to be seen in conjunction with the Columbus IOC (initial operational capability) scenario stipulating that EPS hardware shall be used on the Polar Platform, the Pressurized Module attached to the U.S. Space Station and the Man-Tended Free Flier. According to the availability of European technologies, the baseline in the power generation area is a photovoltaic system which provides three regulated main buses (150 V d.c.) to the users. In order to maintain power supply during eclipse phases, nickel hydrogen batteries will be used for energy storage purposes with nickel cadmium as back-up solution. The power distribution system needs special attention. Due to the elevated voltage levels mechanical switch gear cannot be used any longer. It is to be replaced by solid state power controllers (SSPC). Because these devices show a totally different behaviour with regard to conventional relay contacts, new approaches in the area of switching and protection are necessary. In view of the crucial role of this new technology for the realization of medium voltage d.c. systems, it is of great importance for Columbus and, hence will receive adequate consideration in the paper. In order to cater for effective management and control of the power supply and distribution hardware, a so called power system internal data processing assembly (PINDAP) has been introduced in the EPS. PINDAP is the key to reduced dependence on ground stations (alleviated ground support requirements); it keeps crew involvement in the EPS control process to as minimum and provides

  8. Commentary on "Distributed Revisiting: An Analytic for Retention of Coherent Science Learning"

    Science.gov (United States)

    Hewitt, Jim

    2015-01-01

    The article, "Distributed Revisiting: An Analytic for Retention of Coherent Science Learning" is an interesting study that operates at the intersection of learning theory and learning analytics. The authors observe that the relationship between learning theory and research in the learning analytics field is constrained by several…

  9. Investigating the Statistical Distribution of Learning Coverage in MOOCs

    Directory of Open Access Journals (Sweden)

    Xiu Li

    2017-11-01

    Full Text Available Learners participating in Massive Open Online Courses (MOOC have a wide range of backgrounds and motivations. Many MOOC learners enroll in the courses to take a brief look; only a few go through the entire content, and even fewer are able to eventually obtain a certificate. We discovered this phenomenon after having examined 92 courses on both xuetangX and edX platforms. More specifically, we found that the learning coverage in many courses—one of the metrics used to estimate the learners’ active engagement with the online courses—observes a Zipf distribution. We apply the maximum likelihood estimation method to fit the Zipf’s law and test our hypothesis using a chi-square test. In the xuetangX dataset, the learning coverage in 53 of 76 courses fits Zipf’s law, but in all of 16 courses on the edX platform, the learning coverage rejects the Zipf’s law. The result from our study is expected to bring insight to the unique learning behavior on MOOC.

  10. The Novel Quantitative Technique for Assessment of Gait Symmetry Using Advanced Statistical Learning Algorithm

    Directory of Open Access Journals (Sweden)

    Jianning Wu

    2015-01-01

    Full Text Available The accurate identification of gait asymmetry is very beneficial to the assessment of at-risk gait in the clinical applications. This paper investigated the application of classification method based on statistical learning algorithm to quantify gait symmetry based on the assumption that the degree of intrinsic change in dynamical system of gait is associated with the different statistical distributions between gait variables from left-right side of lower limbs; that is, the discrimination of small difference of similarity between lower limbs is considered the reorganization of their different probability distribution. The kinetic gait data of 60 participants were recorded using a strain gauge force platform during normal walking. The classification method is designed based on advanced statistical learning algorithm such as support vector machine algorithm for binary classification and is adopted to quantitatively evaluate gait symmetry. The experiment results showed that the proposed method could capture more intrinsic dynamic information hidden in gait variables and recognize the right-left gait patterns with superior generalization performance. Moreover, our proposed techniques could identify the small significant difference between lower limbs when compared to the traditional symmetry index method for gait. The proposed algorithm would become an effective tool for early identification of the elderly gait asymmetry in the clinical diagnosis.

  11. The novel quantitative technique for assessment of gait symmetry using advanced statistical learning algorithm.

    Science.gov (United States)

    Wu, Jianning; Wu, Bin

    2015-01-01

    The accurate identification of gait asymmetry is very beneficial to the assessment of at-risk gait in the clinical applications. This paper investigated the application of classification method based on statistical learning algorithm to quantify gait symmetry based on the assumption that the degree of intrinsic change in dynamical system of gait is associated with the different statistical distributions between gait variables from left-right side of lower limbs; that is, the discrimination of small difference of similarity between lower limbs is considered the reorganization of their different probability distribution. The kinetic gait data of 60 participants were recorded using a strain gauge force platform during normal walking. The classification method is designed based on advanced statistical learning algorithm such as support vector machine algorithm for binary classification and is adopted to quantitatively evaluate gait symmetry. The experiment results showed that the proposed method could capture more intrinsic dynamic information hidden in gait variables and recognize the right-left gait patterns with superior generalization performance. Moreover, our proposed techniques could identify the small significant difference between lower limbs when compared to the traditional symmetry index method for gait. The proposed algorithm would become an effective tool for early identification of the elderly gait asymmetry in the clinical diagnosis.

  12. Advanced Power Electronics Interfaces for Distributed Energy Workshop Summary: August 24, 2006, Sacramento, California

    Energy Technology Data Exchange (ETDEWEB)

    Treanton, B.; Palomo, J.; Kroposki, B.; Thomas, H.

    2006-10-01

    The Advanced Power Electronics Interfaces for Distributed Energy Workshop, sponsored by the California Energy Commission Public Interest Energy Research program and organized by the National Renewable Energy Laboratory, was held Aug. 24, 2006, in Sacramento, Calif. The workshop provided a forum for industry stakeholders to share their knowledge and experience about technologies, manufacturing approaches, markets, and issues in power electronics for a range of distributed energy resources. It focused on the development of advanced power electronic interfaces for distributed energy applications and included discussions of modular power electronics, component manufacturing, and power electronic applications.

  13. Microgrid Controller and Advanced Distribution Management System Survey Report

    Energy Technology Data Exchange (ETDEWEB)

    Liu, Guodong [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Starke, Michael R. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Herron, Andrew N. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

    2016-07-01

    A microgrid controller, which serves as the heart of a microgrid, is responsible for optimally managing the distributed energy resources, energy storage systems, and responsive demand and for ensuring the microgrid is being operated in an efficient, reliable, and resilient way. As the market for microgrids has blossomed in recently years, many vendors have released their own microgrid controllers to meet the various needs of different microgrid clients. However, due to the absence of a recognized standard for such controllers, vendor-supported microgrid controllers have a range of functionalities that are significantly different from each other in many respects. As a result the current state of the industry has been difficult to assess. To remedy this situation the authors conducted a survey of the functions of microgrid controllers developed by vendors and national laboratories. This report presents a clear indication of the state of the microgrid-controller industry based on analysis of the survey results. The results demonstrate that US Department of Energy funded research in microgrid controllers is unique and not competing with that of industry.

  14. Advanced airflow distribution methods for reduction of personal exposure to indoor pollutants

    DEFF Research Database (Denmark)

    Cao, Guangyu; Kosonen, Risto; Melikov, Arsen

    2016-01-01

    The main objective of this study is to recognize possible airflow distribution methods to protect the occupants from exposure to various indoor pollutants. The fact of the increasing exposure of occupants to various indoor pollutants shows that there is an urgent need to develop advanced airflow ...... distribution methods to reduce indoor exposure to various indoor pollutants. This article presents some of the latest development of advanced airflow distribution methods to reduce indoor exposure in various types of buildings.......The main objective of this study is to recognize possible airflow distribution methods to protect the occupants from exposure to various indoor pollutants. The fact of the increasing exposure of occupants to various indoor pollutants shows that there is an urgent need to develop advanced airflow...

  15. Distributed interactive virtual environments for collaborative experiential learning and training independent of distance over Internet2.

    Science.gov (United States)

    Alverson, Dale C; Saiki, Stanley M; Jacobs, Joshua; Saland, Linda; Keep, Marcus F; Norenberg, Jeffrey; Baker, Rex; Nakatsu, Curtis; Kalishman, Summers; Lindberg, Marlene; Wax, Diane; Mowafi, Moad; Summers, Kenneth L; Holten, James R; Greenfield, John A; Aalseth, Edward; Nickles, David; Sherstyuk, Andrei; Haines, Karen; Caudell, Thomas P

    2004-01-01

    Medical knowledge and skills essential for tomorrow's healthcare professionals continue to change faster than ever before creating new demands in medical education. Project TOUCH (Telehealth Outreach for Unified Community Health) has been developing methods to enhance learning by coupling innovations in medical education with advanced technology in high performance computing and next generation Internet2 embedded in virtual reality environments (VRE), artificial intelligence and experiential active learning. Simulations have been used in education and training to allow learners to make mistakes safely in lieu of real-life situations, learn from those mistakes and ultimately improve performance by subsequent avoidance of those mistakes. Distributed virtual interactive environments are used over distance to enable learning and participation in dynamic, problem-based, clinical, artificial intelligence rules-based, virtual simulations. The virtual reality patient is programmed to dynamically change over time and respond to the manipulations by the learner. Participants are fully immersed within the VRE platform using a head-mounted display and tracker system. Navigation, locomotion and handling of objects are accomplished using a joy-wand. Distribution is managed via the Internet2 Access Grid using point-to-point or multi-casting connectivity through which the participants can interact. Medical students in Hawaii and New Mexico (NM) participated collaboratively in problem solving and managing of a simulated patient with a closed head injury in VRE; dividing tasks, handing off objects, and functioning as a team. Students stated that opportunities to make mistakes and repeat actions in the VRE were extremely helpful in learning specific principles. VRE created higher performance expectations and some anxiety among VRE users. VRE orientation was adequate but students needed time to adapt and practice in order to improve efficiency. This was also demonstrated successfully

  16. Learning multivariate distributions by competitive assembly of marginals.

    Science.gov (United States)

    Sánchez-Vega, Francisco; Younes, Laurent; Geman, Donald

    2013-02-01

    We present a new framework for learning high-dimensional multivariate probability distributions from estimated marginals. The approach is motivated by compositional models and Bayesian networks, and designed to adapt to small sample sizes. We start with a large, overlapping set of elementary statistical building blocks, or "primitives," which are low-dimensional marginal distributions learned from data. Each variable may appear in many primitives. Subsets of primitives are combined in a Lego-like fashion to construct a probabilistic graphical model; only a small fraction of the primitives will participate in any valid construction. Since primitives can be precomputed, parameter estimation and structure search are separated. Model complexity is controlled by strong biases; we adapt the primitives to the amount of training data and impose rules which restrict the merging of them into allowable compositions. The likelihood of the data decomposes into a sum of local gains, one for each primitive in the final structure. We focus on a specific subclass of networks which are binary forests. Structure optimization corresponds to an integer linear program and the maximizing composition can be computed for reasonably large numbers of variables. Performance is evaluated using both synthetic data and real datasets from natural language processing and computational biology.

  17. Structure Learning and Statistical Estimation in Distribution Networks - Part I

    Energy Technology Data Exchange (ETDEWEB)

    Deka, Deepjyoti [Univ. of Texas, Austin, TX (United States); Backhaus, Scott N. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Chertkov, Michael [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2015-02-13

    Traditionally power distribution networks are either not observable or only partially observable. This complicates development and implementation of new smart grid technologies, such as those related to demand response, outage detection and management, and improved load-monitoring. In this two part paper, inspired by proliferation of the metering technology, we discuss estimation problems in structurally loopy but operationally radial distribution grids from measurements, e.g. voltage data, which are either already available or can be made available with a relatively minor investment. In Part I, the objective is to learn the operational layout of the grid. Part II of this paper presents algorithms that estimate load statistics or line parameters in addition to learning the grid structure. Further, Part II discusses the problem of structure estimation for systems with incomplete measurement sets. Our newly suggested algorithms apply to a wide range of realistic scenarios. The algorithms are also computationally efficient – polynomial in time– which is proven theoretically and illustrated computationally on a number of test cases. The technique developed can be applied to detect line failures in real time as well as to understand the scope of possible adversarial attacks on the grid.

  18. Recommendation System Based On Association Rules For Distributed E-Learning Management Systems

    Science.gov (United States)

    Mihai, Gabroveanu

    2015-09-01

    Traditional Learning Management Systems are installed on a single server where learning materials and user data are kept. To increase its performance, the Learning Management System can be installed on multiple servers; learning materials and user data could be distributed across these servers obtaining a Distributed Learning Management System. In this paper is proposed the prototype of a recommendation system based on association rules for Distributed Learning Management System. Information from LMS databases is analyzed using distributed data mining algorithms in order to extract the association rules. Then the extracted rules are used as inference rules to provide personalized recommendations. The quality of provided recommendations is improved because the rules used to make the inferences are more accurate, since these rules aggregate knowledge from all e-Learning systems included in Distributed Learning Management System.

  19. A survey on control schemes for distributed solar collector fields. Part II: Advanced control approaches

    Energy Technology Data Exchange (ETDEWEB)

    Camacho, E.F.; Rubio, F.R. [Universidad de Sevilla, Escuela Superior de Ingenieros, Departamento de Ingenieria de Sistemas y Automatica, Camino de Los Descubrimientos s/n, E-41092 Sevilla (Spain); Berenguel, M. [Universidad de Almeria, Departamento de Lenguajes y Computacion, Area de Ingenieria de Sistemas y Automatica, Carretera Sacramento s/n, E-04120 La Canada, Almeria (Spain); Valenzuela, L. [Plataforma Solar de Almeria - CIEMAT, Carretera Senes s/n, P.O. Box 22, E-04200 Tabernas (Almeria) (Spain)

    2007-10-15

    This article presents a survey of the different advanced automatic control techniques that have been applied to control the outlet temperature of solar plants with distributed collectors during the last 25 years. A classification of the modeling and control approaches described in the first part of this survey is used to explain the main features of each strategy. The treated strategies range from classical advanced control strategies to those with few industrial applications. (author)

  20. Combining forces. Distributed Leadership and a professional learning community in primary and secondary education

    NARCIS (Netherlands)

    Hulsbos, Frank; Van Langevelde, Stefan; Evers, Arnoud

    2018-01-01

    This report describes an in depth case study of two good practice schools where a professional learning community and distributed leadership are highly developed. The goal of this study was to learn what conditions in the school support a professional learning community and distributed leadership.

  1. Strategies and Principles of Distributed Machine Learning on Big Data

    Directory of Open Access Journals (Sweden)

    Eric P. Xing

    2016-06-01

    Full Text Available The rise of big data has led to new demands for machine learning (ML systems to learn complex models, with millions to billions of parameters, that promise adequate capacity to digest massive datasets and offer powerful predictive analytics (such as high-dimensional latent features, intermediate representations, and decision functions thereupon. In order to run ML algorithms at such scales, on a distributed cluster with tens to thousands of machines, it is often the case that significant engineering efforts are required—and one might fairly ask whether such engineering truly falls within the domain of ML research. Taking the view that “big” ML systems can benefit greatly from ML-rooted statistical and algorithmic insights—and that ML researchers should therefore not shy away from such systems design—we discuss a series of principles and strategies distilled from our recent efforts on industrial-scale ML solutions. These principles and strategies span a continuum from application, to engineering, and to theoretical research and development of big ML systems and architectures, with the goal of understanding how to make them efficient, generally applicable, and supported with convergence and scaling guarantees. They concern four key questions that traditionally receive little attention in ML research: How can an ML program be distributed over a cluster? How can ML computation be bridged with inter-machine communication? How can such communication be performed? What should be communicated between machines? By exposing underlying statistical and algorithmic characteristics unique to ML programs but not typically seen in traditional computer programs, and by dissecting successful cases to reveal how we have harnessed these principles to design and develop both high-performance distributed ML software as well as general-purpose ML frameworks, we present opportunities for ML researchers and practitioners to further shape and enlarge the area

  2. Democratic Citizenship and Service Learning: Advancing the Caring Self.

    Science.gov (United States)

    Rhoads, Robert A.

    2000-01-01

    Discusses how service learning can promote the development of a "caring self" in college students by drawing on the ideas of John Dewey, George Herbert Mead, and contemporary critical theorists. Links this caring self to democratic citizenship and uses students' narratives to illustrate how it develops through service learning contexts.…

  3. Advances in Temporal Analysis in Learning and Instruction

    Science.gov (United States)

    Molenaar, Inge

    2014-01-01

    This paper focuses on a trend to analyse temporal characteristics of constructs important to learning and instruction. Different researchers have indicated that we should pay more attention to time in our research to enhance explanatory power and increase validity. Constructs formerly viewed as personal traits, such as self-regulated learning and…

  4. Advanced Level Biology Teachers' Attitudes towards Assessment and Their Engagement in Assessment for Learning

    Science.gov (United States)

    Bramwell-Lalor, Sharon; Rainford, Marcia

    2015-01-01

    This paper reports on a Mixed Methods study involving an investigation into the attitudes of advanced level biology teachers towards assessment and describes the teachers' experiences while being engaged in Assessment for Learning (AfL) practices such as sharing of learning objectives and peer- and self-assessment. Quantitative data were collected…

  5. INST7150 - Advanced Topics in Learning Object Design and Reuse, Fall 2005

    OpenAIRE

    Wiley, David

    2005-01-01

    This course is designed to help you understand and apply advanced topics in the design, creation, and reuse of learning objects. The course is structured around a practical, hands-on project using learning objects, intermingled with readings and discussion on a variety of topics.

  6. Gender Differences in the Use and Benefit of Advanced Learning Technologies for Mathematics

    Science.gov (United States)

    Arroyo, Ivon; Burleson, Winslow; Tai, Minghui; Muldner, Kasia; Woolf, Beverly Park

    2013-01-01

    We provide evidence of persistent gender effects for students using advanced adaptive technology while learning mathematics. This technology improves each gender's learning and affective predispositions toward mathematics, but specific features in the software help either female or male students. Gender differences were seen in the students' style…

  7. Integrating Project-Based Service-Learning into an Advanced Environmental Chemistry Course

    Science.gov (United States)

    Draper, Alison J.

    2004-01-01

    An active service-learning research work is conducted in the field of advanced environmental chemistry. Multiple projects are assigned to students, which promote individual learning skills, self-confidence as scientists, and a deep understanding of the environmental chemist's profession.

  8. Do Advance Organizers Facilitate Learning? A Review of Subsumption Theory.

    Science.gov (United States)

    McEneany, John E.

    1990-01-01

    A review of four studies conducted by Ausubel raises serious doubts about the efficacy of advance organizers under a variety of circumstances. In addition, this review questions the adequacy of definitions for two central notions of subsumption theory (discriminability and advance organizer). (IAH)

  9. Raising the Bar: Significant Advances and Future Needs for Promoting Learning for Students with Severe Disabilities

    Science.gov (United States)

    Spooner, Fred; Browder, Diane M.

    2015-01-01

    This essay describes major advances in educating students with severe disabilities. The authors propose that applied behavior analysis, the focus on functional life skills, and the promotion of academic content have been the major advances in the "how" and "what" of learning for this population. An increased focus on literacy,…

  10. A Distributed Simulation Facility to Support Human Factors Research in Advanced Air Transportation Technology

    Science.gov (United States)

    Amonlirdviman, Keith; Farley, Todd C.; Hansman, R. John, Jr.; Ladik, John F.; Sherer, Dana Z.

    1998-01-01

    A distributed real-time simulation of the civil air traffic environment developed to support human factors research in advanced air transportation technology is presented. The distributed environment is based on a custom simulation architecture designed for simplicity and flexibility in human experiments. Standard Internet protocols are used to create the distributed environment, linking all advanced cockpit simulator, all Air Traffic Control simulator, and a pseudo-aircraft control and simulation management station. The pseudo-aircraft control station also functions as a scenario design tool for coordinating human factors experiments. This station incorporates a pseudo-pilot interface designed to reduce workload for human operators piloting multiple aircraft simultaneously in real time. The application of this distributed simulation facility to support a study of the effect of shared information (via air-ground datalink) on pilot/controller shared situation awareness and re-route negotiation is also presented.

  11. Advanced Hydroinformatic Techniques for the Simulation and Analysis of Water Supply and Distribution Systems

    OpenAIRE

    Herrera, Manuel; Meniconi, Silvia; Alvisi, Stefano; Izquierdo, Joaquin

    2018-01-01

    This document is intended to be a presentation of the Special Issue “Advanced Hydroinformatic Techniques for the Simulation and Analysis of Water Supply and Distribution Systems”. The final aim of this Special Issue is to propose a suitable framework supporting insightful hydraulic mechanisms to aid the decision-making processes of water utility managers and practitioners. Its 18 peer-reviewed articles present as varied topics as: water distribution system design, optimization of network perf...

  12. Infrastructure and distributed learning methodology for privacy-preserving multi-centric rapid learning health care: euroCAT

    Directory of Open Access Journals (Sweden)

    Timo M. Deist

    2017-06-01

    The euroCAT infrastructure has been successfully implemented in five radiation clinics across three countries. SVM models can be learned on data distributed over all five clinics. Furthermore, the infrastructure provides a general framework to execute learning algorithms on distributed data. The ongoing expansion of the euroCAT network will facilitate machine learning in radiation oncology. The resulting access to larger datasets with sufficient variation will pave the way for generalizable prediction models and personalized medicine.

  13. Coupling between Metacognition and Emotions during STEM Learning with Advanced Learning Technologies: A Critical Analysis, Implications for Future Research, and Design of Learning Systems

    Science.gov (United States)

    Azevedo, Roger; Mudrick, Nicholas; Taub, Michelle; Wortha, Franz

    2017-01-01

    Metacognition and emotions play a critical role in learners' ability to monitor and regulate their learning about 21st-century skills related to science, technology, engineering, and mathematics (STEM) content while using advanced learning technologies (ALTs; e.g., intelligent tutoring systems, serious games, hypermedia, augmented reality). In…

  14. Integrated Multi-Scale Data Analytics and Machine Learning for the Distribution Grid and Building-to-Grid Interface

    Energy Technology Data Exchange (ETDEWEB)

    Stewart, Emma M. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Hendrix, Val [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Chertkov, Michael [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Deka, Deepjyoti [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2017-03-16

    This white paper introduces the application of advanced data analytics to the modernized grid. In particular, we consider the field of machine learning and where it is both useful, and not useful, for the particular field of the distribution grid and buildings interface. While analytics, in general, is a growing field of interest, and often seen as the golden goose in the burgeoning distribution grid industry, its application is often limited by communications infrastructure, or lack of a focused technical application. Overall, the linkage of analytics to purposeful application in the grid space has been limited. In this paper we consider the field of machine learning as a subset of analytical techniques, and discuss its ability and limitations to enable the future distribution grid and the building-to-grid interface. To that end, we also consider the potential for mixing distributed and centralized analytics and the pros and cons of these approaches. Machine learning is a subfield of computer science that studies and constructs algorithms that can learn from data and make predictions and improve forecasts. Incorporation of machine learning in grid monitoring and analysis tools may have the potential to solve data and operational challenges that result from increasing penetration of distributed and behind-the-meter energy resources. There is an exponentially expanding volume of measured data being generated on the distribution grid, which, with appropriate application of analytics, may be transformed into intelligible, actionable information that can be provided to the right actors – such as grid and building operators, at the appropriate time to enhance grid or building resilience, efficiency, and operations against various metrics or goals – such as total carbon reduction or other economic benefit to customers. While some basic analysis into these data streams can provide a wealth of information, computational and human boundaries on performing the analysis

  15. 78 FR 46621 - Status of the Office of New Reactors' Implementation of Electronic Distribution of Advanced...

    Science.gov (United States)

    2013-08-01

    ... availability of official agency records in the NRC's Agencywide Documents Access and Management System (ADAMS... and Management System (ADAMS): You may access publicly available documents online in the NRC Library... of Electronic Distribution of Advanced Reactor Correspondence AGENCY: Nuclear Regulatory Commission...

  16. Requirements for advanced decision support tools in future distribution network planning

    NARCIS (Netherlands)

    Grond, M.O.W.; Morren, J.; Slootweg, J.G.

    2013-01-01

    This paper describes the need and requirements for advanced decision support tools in future network planning from a distribution network operator perspective. The existing tools will no longer be satisfactory for future application due to present developments in the electricity sector that increase

  17. Advanced structures for grid Synchronization of power converters in distributed generation applications

    DEFF Research Database (Denmark)

    Luna, A.; Rocabert, J.; Candela, I.

    2012-01-01

    The Transmission System Operators are specially concerned about the Low Voltage Ride Through requirements of distributed generation power plants. Solutions based on the installation of STATCOMs and DVRs, as well as on advanced control functionalities for the existing power converters have contrib...

  18. Travel and Tourism Module. An Advanced-Level Option For Distribution and Marketing.

    Science.gov (United States)

    New York State Education Dept., Albany. Bureau of Occupational Education Curriculum Development.

    Intended as an advanced option for distributive education students in the twelfth grade, this travel and tourism module is designed to cover a minimum of ten weeks or a maximum of twenty weeks. Introductory material includes information on employment demands, administrative considerations, course format, teaching suggestions, expected outcomes,…

  19. A Database for Decision-Making in Training and Distributed Learning Technology

    National Research Council Canada - National Science Library

    Stouffer, Virginia

    1998-01-01

    .... A framework for incorporating data about distributed learning courseware into the existing training database was devised and a plan for a national electronic courseware redistribution network was recommended...

  20. Advancing Affect Modeling via Preference Learning and Unsupervised Feature Extraction

    DEFF Research Database (Denmark)

    Martínez, Héctor Pérez

    strategies (error functions and training algorithms) for artificial neural networks are examined across synthetic and psycho-physiological datasets, and compared against support vector machines and Cohen’s method. Results reveal the best training strategies for neural networks and suggest their superiority...... difficulties, ordinal reports such as rankings and ratings can yield more reliable affect annotations than alternative tools. This thesis explores preference learning methods to automatically learn computational models from ordinal annotations of affect. In particular, an extensive collection of training...... over the other examined methods. The second challenge addressed in this thesis refers to the extraction of relevant information from physiological modalities. Deep learning is proposed as an automatic approach to extract input features for models of affect from physiological signals. Experiments...

  1. WOMEN AND ADVANCEMENT IN NEUROPSYCHOLOGY:REAL-LIFE LESSONS LEARNED

    Science.gov (United States)

    Hilsabeck, Robin C.; Martin, Eileen M.

    2013-01-01

    The number of women in neuropsychology has been increasing over the past 20 years while the number of women in senior and leadership positions within neuropsychology has not. The field of neuropsychology has much to gain by facilitating the advancement of women into leadership roles, including access to some of the brightest and creative minds in the field. The purpose of this article is to offer practical advice about how to overcome barriers and advance within neuropsychology. Suggestions for professional organizations, women, and mentors of women are provided that will likely benefit trainees and junior colleagues regardless of their gender. PMID:18841516

  2. Women and advancement in neuropsychology: real-life lessons learned.

    Science.gov (United States)

    Hilsabeck, Robin C; Martin, Eileen M

    2010-04-01

    The number of women in neuropsychology has been increasing over the past 20 years while the number of women in senior and leadership positions within neuropsychology has not. The field of neuropsychology has much to gain by facilitating the advancement of women into leadership roles, including access to some of the brightest and creative minds in the field. The purpose of this article is to offer practical advice about how to overcome barriers and advance within neuropsychology. Suggestions for professional organizations, women, and mentors of women are provided that will likely benefit trainees and junior colleagues regardless of their gender.

  3. Smart-DS: Synthetic Models for Advanced, Realistic Testing: Distribution Systems and Scenarios

    Energy Technology Data Exchange (ETDEWEB)

    Krishnan, Venkat K [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Palmintier, Bryan S [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Hodge, Brian S [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Hale, Elaine T [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Elgindy, Tarek [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Bugbee, Bruce [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Rossol, Michael N [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Lopez, Anthony J [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Krishnamurthy, Dheepak [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Vergara, Claudio [MIT; Domingo, Carlos Mateo [IIT Comillas; Postigo, Fernando [IIT Comillas; de Cuadra, Fernando [IIT Comillas; Gomez, Tomas [IIT Comillas; Duenas, Pablo [MIT; Luke, Max [MIT; Li, Vivian [MIT; Vinoth, Mohan [GE Grid Solutions; Kadankodu, Sree [GE Grid Solutions

    2017-08-09

    The National Renewable Energy Laboratory (NREL) in collaboration with Massachusetts Institute of Technology (MIT), Universidad Pontificia Comillas (Comillas-IIT, Spain) and GE Grid Solutions, is working on an ARPA-E GRID DATA project, titled Smart-DS, to create: 1) High-quality, realistic, synthetic distribution network models, and 2) Advanced tools for automated scenario generation based on high-resolution weather data and generation growth projections. Through these advancements, the Smart-DS project is envisioned to accelerate the development, testing, and adoption of advanced algorithms, approaches, and technologies for sustainable and resilient electric power systems, especially in the realm of U.S. distribution systems. This talk will present the goals and overall approach of the Smart-DS project, including the process of creating the synthetic distribution datasets using reference network model (RNM) and the comprehensive validation process to ensure network realism, feasibility, and applicability to advanced use cases. The talk will provide demonstrations of early versions of synthetic models, along with the lessons learnt from expert engagements to enhance future iterations. Finally, the scenario generation framework, its development plans, and co-ordination with GRID DATA repository teams to house these datasets for public access will also be discussed.

  4. Advanced prototyping tools for project- and problem-based learning

    DEFF Research Database (Denmark)

    Teodorescu, Remus; Bech, Michael Møller; Holm, Allan J.

    2002-01-01

    A new approach in prototyping for project- and problem-based learning is achieved by using the new Total Development Environment concept introduced by dSPACE that allows a full visual block-oriented programming of dynamic real-time systems to be achieved  using the Matlab/Simulink environment...

  5. Writing to Learn Statistics in an Advanced Placement Statistics Course

    Science.gov (United States)

    Northrup, Christian Glenn

    2012-01-01

    This study investigated the use of writing in a statistics classroom to learn if writing provided a rich description of problem-solving processes of students as they solved problems. Through analysis of 329 written samples provided by students, it was determined that writing provided a rich description of problem-solving processes and enabled…

  6. Advanced EFL Apologies: What Remains To Be Learned?

    Science.gov (United States)

    Cohen, Andrew D.; And Others

    A study of the structure of the speech act known as an apology looked at the differences in linguistic strategies used by advanced nonnative English language learners and native speakers in apology behavior, and whether the differences result from the severity of the offense or the familiarity of the interlocutors. An apology is seen as consisting…

  7. Strategy to Promote Active Learning of an Advanced Research Method

    Science.gov (United States)

    McDermott, Hilary J.; Dovey, Terence M.

    2013-01-01

    Research methods courses aim to equip students with the knowledge and skills required for research yet seldom include practical aspects of assessment. This reflective practitioner report describes and evaluates an innovative approach to teaching and assessing advanced qualitative research methods to final-year psychology undergraduate students. An…

  8. Advanced Displays and Natural User Interfaces to Support Learning

    Science.gov (United States)

    Martin-SanJose, Juan-Fernando; Juan, M. -Carmen; Mollá, Ramón; Vivó, Roberto

    2017-01-01

    Advanced displays and natural user interfaces (NUI) are a very suitable combination for developing systems to provide an enhanced and richer user experience. This combination can be appropriate in several fields and has not been extensively exploited. One of the fields that this combination is especially suitable for is education. Nowadays,…

  9. Using Micro-Synchrophasor Data for Advanced Distribution Grid Planning and Operations Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Stewart, Emma [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Kiliccote, Sila [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); McParland, Charles [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Roberts, Ciaran [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

    2014-07-01

    This report reviews the potential for distribution-grid phase-angle data that will be available from new micro-synchrophasors (µPMUs) to be utilized in existing distribution-grid planning and operations analysis. This data could augment the current diagnostic capabilities of grid analysis software, used in both planning and operations for applications such as fault location, and provide data for more accurate modeling of the distribution system. µPMUs are new distribution-grid sensors that will advance measurement and diagnostic capabilities and provide improved visibility of the distribution grid, enabling analysis of the grid’s increasingly complex loads that include features such as large volumes of distributed generation. Large volumes of DG leads to concerns on continued reliable operation of the grid, due to changing power flow characteristics and active generation, with its own protection and control capabilities. Using µPMU data on change in voltage phase angle between two points in conjunction with new and existing distribution-grid planning and operational tools is expected to enable model validation, state estimation, fault location, and renewable resource/load characterization. Our findings include: data measurement is outstripping the processing capabilities of planning and operational tools; not every tool can visualize a voltage phase-angle measurement to the degree of accuracy measured by advanced sensors, and the degree of accuracy in measurement required for the distribution grid is not defined; solving methods cannot handle the high volumes of data generated by modern sensors, so new models and solving methods (such as graph trace analysis) are needed; standardization of sensor-data communications platforms in planning and applications tools would allow integration of different vendors’ sensors and advanced measurement devices. In addition, data from advanced sources such as µPMUs could be used to validate models to improve

  10. Supporting Collective Inquiry: A Technology Framework for Distributed Learning

    Science.gov (United States)

    Tissenbaum, Michael

    This design-based study describes the implementation and evaluation of a technology framework to support smart classrooms and Distributed Technology Enhanced Learning (DTEL) called SAIL Smart Space (S3). S3 is an open-source technology framework designed to support students engaged in inquiry investigations as a knowledge community. To evaluate the effectiveness of S3 as a generalizable technology framework, a curriculum named PLACE (Physics Learning Across Contexts and Environments) was developed to support two grade-11 physics classes (n = 22; n = 23) engaged in a multi-context inquiry curriculum based on the Knowledge Community and Inquiry (KCI) pedagogical model. This dissertation outlines three initial design studies that established a set of design principles for DTEL curricula, and related technology infrastructures. These principles guided the development of PLACE, a twelve-week inquiry curriculum in which students drew upon their community-generated knowledge base as a source of evidence for solving ill-structured physics problems based on the physics of Hollywood movies. During the culminating smart classroom activity, the S3 framework played a central role in orchestrating student activities, including managing the flow of materials and students using real-time data mining and intelligent agents that responded to emergent class patterns. S3 supported students' construction of knowledge through the use individual, collective and collaborative scripts and technologies, including tablets and interactive large-format displays. Aggregate and real-time ambient visualizations helped the teacher act as a wondering facilitator, supporting students in their inquiry where needed. A teacher orchestration tablet gave the teacher some control over the flow of the scripted activities, and alerted him to critical moments for intervention. Analysis focuses on S3's effectiveness in supporting students' inquiry across multiple learning contexts and scales of time, and in

  11. The effect of pre-course e-learning prior to advanced life support training: a randomised controlled trial.

    Science.gov (United States)

    Perkins, Gavin D; Fullerton, James N; Davis-Gomez, Nicole; Davies, Robin P; Baldock, Catherine; Stevens, Harry; Bullock, Ian; Lockey, Andrew S

    2010-07-01

    The role of e-learning in contemporary healthcare education is quickly developing. The aim of this study was to examine the relationship between the use of an e-learning simulation programme (Microsim, Laerdal, UK) prior to attending an Advanced Life Support (ALS) course and the subsequent relationship to candidate performance. An open label, multi-centre randomised controlled study was conducted. The control group received a course manual and pre-course MCQ four weeks prior to the face to face course. The intervention group in addition received the Microsim programme on a CD. The primary outcome was performance during a simulated cardiac arrest at the end of the course. Secondary outcomes were performance during multiple choice exams, resuscitation skills assessments and feedback to Microsim programme. 572 participants were randomised (287 Microsim, 285 control). There were no significant differences in the primary outcome (performance during a standard cardiac arrest simulation) or secondary outcomes. User evaluations were favorable. 79% would recommend it to colleagues. 9% stated Microsim could replace the entire ALS course, 25% parts. Over 70% of participants' perceived that Microsim improved their understanding of the key learning domains of the ALS course. Distributing Microsim to healthcare providers prior to attending an ALS courses did not improve either cognitive or psychomotor skills performance during cardiac arrest simulation testing. The challenge that lies ahead is to identify the optimal way to use e-learning as part of a blended approach to learning for this type of training programme.

  12. Advanced Steel Microstructural Classification by Deep Learning Methods.

    Science.gov (United States)

    Azimi, Seyed Majid; Britz, Dominik; Engstler, Michael; Fritz, Mario; Mücklich, Frank

    2018-02-01

    The inner structure of a material is called microstructure. It stores the genesis of a material and determines all its physical and chemical properties. While microstructural characterization is widely spread and well known, the microstructural classification is mostly done manually by human experts, which gives rise to uncertainties due to subjectivity. Since the microstructure could be a combination of different phases or constituents with complex substructures its automatic classification is very challenging and only a few prior studies exist. Prior works focused on designed and engineered features by experts and classified microstructures separately from the feature extraction step. Recently, Deep Learning methods have shown strong performance in vision applications by learning the features from data together with the classification step. In this work, we propose a Deep Learning method for microstructural classification in the examples of certain microstructural constituents of low carbon steel. This novel method employs pixel-wise segmentation via Fully Convolutional Neural Network (FCNN) accompanied by a max-voting scheme. Our system achieves 93.94% classification accuracy, drastically outperforming the state-of-the-art method of 48.89% accuracy. Beyond the strong performance of our method, this line of research offers a more robust and first of all objective way for the difficult task of steel quality appreciation.

  13. e-Learning in Advanced Life Support-What factors influence assessment outcome?

    Science.gov (United States)

    Thorne, C J; Lockey, A S; Kimani, P K; Bullock, I; Hampshire, S; Begum-Ali, S; Perkins, G D

    2017-05-01

    To establish variables which are associated with favourable Advanced Life Support (ALS) course assessment outcomes, maximising learning effect. Between 1 January 2013 and 30 June 2014, 8218 individuals participated in a Resuscitation Council (UK) e-learning Advanced Life Support (e-ALS) course. Participants completed 5-8h of online e-learning prior to attending a one day face-to-face course. e-Learning access data were collected through the Learning Management System (LMS). All participants were assessed by a multiple choice questionnaire (MCQ) before and after the face-to-face aspect alongside a practical cardiac arrest simulation (CAS-Test). Participant demographics and assessment outcomes were analysed. The mean post e-learning MCQ score was 83.7 (SD 7.3) and the mean post-course MCQ score was 87.7 (SD 7.9). The first attempt CAS-Test pass rate was 84.6% and overall pass rate 96.6%. Participants with previous ALS experience, ILS experience, or who were a core member of the resuscitation team performed better in the post-course MCQ, CAS-Test and overall assessment. Median time spent on the e-learning was 5.2h (IQR 3.7-7.1). There was a large range in the degree of access to e-learning content. Increased time spent accessing e-learning had no effect on the overall result (OR 0.98, P=0.367) on simulated learning outcome. Clinical experience through membership of cardiac arrest teams and previous ILS or ALS training were independent predictors of performance on the ALS course whilst time spent accessing e-learning materials did not affect course outcomes. This supports the blended approach to e-ALS which allows participants to tailor their e-learning experience to their specific needs. Copyright © 2017 Elsevier B.V. All rights reserved.

  14. Advanced methods in NDE using machine learning approaches

    Science.gov (United States)

    Wunderlich, Christian; Tschöpe, Constanze; Duckhorn, Frank

    2018-04-01

    Machine learning (ML) methods and algorithms have been applied recently with great success in quality control and predictive maintenance. Its goal to build new and/or leverage existing algorithms to learn from training data and give accurate predictions, or to find patterns, particularly with new and unseen similar data, fits perfectly to Non-Destructive Evaluation. The advantages of ML in NDE are obvious in such tasks as pattern recognition in acoustic signals or automated processing of images from X-ray, Ultrasonics or optical methods. Fraunhofer IKTS is using machine learning algorithms in acoustic signal analysis. The approach had been applied to such a variety of tasks in quality assessment. The principal approach is based on acoustic signal processing with a primary and secondary analysis step followed by a cognitive system to create model data. Already in the second analysis steps unsupervised learning algorithms as principal component analysis are used to simplify data structures. In the cognitive part of the software further unsupervised and supervised learning algorithms will be trained. Later the sensor signals from unknown samples can be recognized and classified automatically by the algorithms trained before. Recently the IKTS team was able to transfer the software for signal processing and pattern recognition to a small printed circuit board (PCB). Still, algorithms will be trained on an ordinary PC; however, trained algorithms run on the Digital Signal Processor and the FPGA chip. The identical approach will be used for pattern recognition in image analysis of OCT pictures. Some key requirements have to be fulfilled, however. A sufficiently large set of training data, a high signal-to-noise ratio, and an optimized and exact fixation of components are required. The automated testing can be done subsequently by the machine. By integrating the test data of many components along the value chain further optimization including lifetime and durability

  15. Student Difficulties in Learning Density: A Distributed Cognition Perspective

    Science.gov (United States)

    Xu, Lihua; Clarke, David

    2012-08-01

    Density has been reported as one of the most difficult concepts for secondary school students (e.g. Smith et al. 1997). Discussion about the difficulties of learning this concept has been largely focused on the complexity of the concept itself or student misconceptions. Few, if any, have investigated how the concept of density was constituted in classroom interactions, and what consequences these interactions have for individual students' conceptual understanding. This paper reports a detailed analysis of two lessons on density in a 7th Grade Australian science classroom, employing the theory of Distributed Cognition (Hollan et al. 1999; Hutchins 1995). The analysis demonstrated that student understanding of density was shaped strongly by the public classroom discussion on the density of two metal blocks. It also revealed the ambiguities associated with the teacher demonstration and the student practical work. These ambiguities contributed to student difficulties with the concept of density identified in this classroom. The results of this study suggest that deliberate effort is needed to establish shared understanding not only about the purpose of the activities, but also about the meaning of scientific language and the utility of tools. It also suggests the importance of appropriate employment of instructional resources in order to facilitate student scientific understanding.

  16. Developing Structured-Learning Exercises for a Community Advanced Pharmacy Practice Experience

    OpenAIRE

    Thomas, Renee Ahrens

    2006-01-01

    The recent growth in the number of pharmacy schools across the nation has resulted in the need for high-quality community advanced pharmacy practice experience (APPE) sites. A vital part of a student's education, these APPEs should be structured and formalized to provide an environment conducive to student learning. This paper discusses how to use a calendar, structured-learning activities, and scheduled evaluations to develop students' knowledge, skills, and abilities in a community pharmacy...

  17. Developing structured-learning exercises for a community advanced pharmacy practice experience.

    Science.gov (United States)

    Thomas, Renee Ahrens

    2006-02-15

    The recent growth in the number of pharmacy schools across the nation has resulted in the need for high-quality community advanced pharmacy practice experience (APPE) sites. A vital part of a student's education, these APPEs should be structured and formalized to provide an environment conducive to student learning. This paper discusses how to use a calendar, structured-learning activities, and scheduled evaluations to develop students' knowledge, skills, and abilities in a community pharmacy setting.

  18. Modelling Digital Knowledge Transfer: Nurse Supervisors Transforming Learning at Point of Care to Advance Nursing Practice

    Directory of Open Access Journals (Sweden)

    Carey Mather

    2017-05-01

    Full Text Available Limited adoption of mobile technology for informal learning and continuing professional development within Australian healthcare environments has been explained primarily as an issue of insufficient digital and ehealth literacy of healthcare professionals. This study explores nurse supervisors’ use of mobile technology for informal learning and continuing professional development both for their own professional practice, and in their role in modelling digital knowledge transfer, by facilitating the learning and teaching of nursing students in the workplace. A convenience sample of 27 nurse supervisors involved with guiding and supporting undergraduate nurses participated in one of six focus groups held in two states of Australia. Expanding knowledge emerged as the key theme of importance to this group of clinicians. Although nurse supervisors regularly browsed Internet sources for learning and teaching purposes, a mixed understanding of the mobile learning activities that could be included as informal learning or part of formal continuing professional development was detected. Participants need educational preparation and access to mobile learning opportunities to improve and maintain their digital and ehealth literacy to appropriately model digital professionalism with students. Implementation of mobile learning at point of care to enable digital knowledge transfer, augment informal learning for students and patients, and support continuing professional development opportunities is necessary. Embedding digital and ehealth literacy within nursing curricula will promote mobile learning as a legitimate nursing function and advance nursing practice.

  19. Active-learning implementation in an advanced elective course on infectious diseases.

    Science.gov (United States)

    Hidayat, Levita; Patel, Shreya; Veltri, Keith

    2012-06-18

    To describe the development, implementation, and assessment of an advanced elective course on infectious diseases using active-learning strategies. Pedagogy for active learning was incorporated by means of mini-lecture, journal club, and debate with follow-up discussion. Forty-eight students were enrolled in this 4-week elective course, in which 30% of course time was allocated for active-learning exercises. All activities were fundamentally designed as a stepwise approach in complementing each active-learning exercise. Achievement of the course learning objectives was assessed using a 5-point Likert scale survey instrument. Students' awareness of the significance of antimicrobial resistance was improved (p ≤ 0.05). Students' ability to critically evaluate the infectious-disease literature and its application in informed clinical judgments was also enhanced through these active-learning exercises (p ≤ 0.05). Students agreed that active learning should be part of the pharmacy curriculum and that active-learning exercises improved their critical-thinking, literature-evaluation, and self-learning skills. An elective course using active-learning strategies allowed students to combine information gained from the evaluation of infectious-disease literature, critical thinking, and informed clinical judgment. This blended approach ultimately resulted in an increased knowledge and awareness of infectious diseases.

  20. Advanced Machine learning Algorithm Application for Rotating Machine Health Monitoring

    Energy Technology Data Exchange (ETDEWEB)

    Kanemoto, Shigeru; Watanabe, Masaya [The University of Aizu, Aizuwakamatsu (Japan); Yusa, Noritaka [Tohoku University, Sendai (Japan)

    2014-08-15

    The present paper tries to evaluate the applicability of conventional sound analysis techniques and modern machine learning algorithms to rotating machine health monitoring. These techniques include support vector machine, deep leaning neural network, etc. The inner ring defect and misalignment anomaly sound data measured by a rotating machine mockup test facility are used to verify the above various kinds of algorithms. Although we cannot find remarkable difference of anomaly discrimination performance, some methods give us the very interesting eigen patterns corresponding to normal and abnormal states. These results will be useful for future more sensitive and robust anomaly monitoring technology.

  1. Advanced Machine learning Algorithm Application for Rotating Machine Health Monitoring

    International Nuclear Information System (INIS)

    Kanemoto, Shigeru; Watanabe, Masaya; Yusa, Noritaka

    2014-01-01

    The present paper tries to evaluate the applicability of conventional sound analysis techniques and modern machine learning algorithms to rotating machine health monitoring. These techniques include support vector machine, deep leaning neural network, etc. The inner ring defect and misalignment anomaly sound data measured by a rotating machine mockup test facility are used to verify the above various kinds of algorithms. Although we cannot find remarkable difference of anomaly discrimination performance, some methods give us the very interesting eigen patterns corresponding to normal and abnormal states. These results will be useful for future more sensitive and robust anomaly monitoring technology

  2. Advanced Markov chain Monte Carlo methods learning from past samples

    CERN Document Server

    Liang, Faming; Carrol, Raymond J

    2010-01-01

    This book provides comprehensive coverage of simulation of complex systems using Monte Carlo methods. Developing algorithms that are immune to the local trap problem has long been considered as the most important topic in MCMC research. Various advanced MCMC algorithms which address this problem have been developed include, the modified Gibbs sampler, the methods based on auxiliary variables and the methods making use of past samples. The focus of this book is on the algorithms that make use of past samples. This book includes the multicanonical algorithm, dynamic weighting, dynamically weight

  3. Electronic learning in advanced resuscitation training: The perspective of the candidate.

    Science.gov (United States)

    Lockey, Andrew S; Dyal, Laura; Kimani, Peter K; Lam, Jenny; Bullock, Ian; Buck, Dominic; Davies, Robin P; Perkins, Gavin D

    2015-12-01

    Studies have shown that blended approaches combining e-learning with face-to-face training reduces costs whilst maintaining similar learning outcomes. The preferences in learning approach for healthcare providers to this new style of learning have not been comprehensively studied. The aim of this study is to evaluate the acceptability of blended learning to advanced resuscitation training. Participants taking part in the traditional and blended electronic advanced life support (e-ALS) courses were invited to complete a written evaluation of the course. Participants' views were captured on a 6-point Likert scale and in free text written comments covering the content, delivery and organisation of the course. Proportional-odds cumulative logit models were used to compare quantitative responses. Thematic analysis was used to synthesise qualitative feedback. 2848 participants from 31 course centres took part in the study (2008-2010). Candidates consistently scored content delivered face-to-face over the same content delivered over the e-learning platform. Candidates valued practical hands on training which included simulation highly. Within the e-ALS group, a common theme was a feeling of "time pressure" and they "preferred the face-to-face teaching". However, others felt that e-ALS "suited their learning style", was "good for those recertifying", and allowed candidates to "use the learning materials at their own pace". The e-ALS course was well received by most, but not all participants. The majority felt the e-learning module was beneficial. There was universal agreement that the face-to-face training was invaluable. Individual learning styles of the candidates affected their reaction to the course materials. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  4. Advanced model for expansion of natural gas distribution networks based on geographic information systems

    Energy Technology Data Exchange (ETDEWEB)

    Ramirez-Rosado, I.J.; Fernandez-Jimenez, L.A.; Garcia-Garrido, E.; Zorzano-Santamaria, P.; Zorzano-Alba, E. [La Rioja Univ., La Rioja (Spain). Dept. of Electrical Engineering; Miranda, V.; Montneiro, C. [Porto Univ., Porto (Portugal). Faculty of Engineering]|[Inst. de Engenharia de Sistemas e Computadores do Porto, Porto (Portugal)

    2005-07-01

    An advanced geographic information system (GIS) model of natural gas distribution networks was presented. The raster-based model was developed to evaluate costs associated with the expansion of electrical networks due to increased demand in the La Rioja region of Spain. The model was also used to evaluate costs associated with maintenance and amortization of the already existing distribution network. Expansion costs of the distribution network were modelled in various demand scenarios. The model also considered a variety of technical factors associated with pipeline length and topography. Soil and slope data from previous pipeline projects were used to estimate real costs per unit length of pipeline. It was concluded that results obtained by the model will be used by planners to select zones where expansion is economically feasible. 4 refs., 5 figs.

  5. Advanced air distribution: Improving health and comfort while reducing energy use

    DEFF Research Database (Denmark)

    Melikov, Arsen Krikor

    2015-01-01

    -quality indoor environments at the same time as low-energy consumption. Advanced air distribution, designed to supply clean air where, when, and as much as needed, makes it possible to efficiently achieve thermal comfort, control exposure to contaminants, provide high-quality air for breathing and minimizing......Indoor environment affects the health, comfort, and performance of building occupants. The energy used for heating, cooling, ventilating, and air conditioning of buildings is substantial. Ventilation based on total volume air distribution in spaces is not always an efficient way to provide high...... the risk of airborne cross-infection while reducing energy use. This study justifies the need for improving the present air distribution design in occupied spaces, and in general the need for a paradigm shift from the design of collective environments to the design of individually controlled environments...

  6. Reading Authentic EFL Text Using Visualization and Advance Organizers in a Multimedia Learning Environment

    Science.gov (United States)

    Lin, Huifen; Chen, Tsuiping

    2007-01-01

    The purpose of this experimental study was to compare the effects of different types of computer-generated visuals (static versus animated) and advance organizers (descriptive versus question) in enhancing comprehension and retention of a content-based lesson for learning English as a Foreign Language (EFL). Additionally, the study investigated…

  7. Equilibrium II: Acids and Bases. Independent Learning Project for Advanced Chemistry (ILPAC). Unit P3.

    Science.gov (United States)

    Inner London Education Authority (England).

    This unit on equilibrium is one of 10 first year units produced by the Independent Learning Project for Advanced Chemistry (ILPAC). The unit, which consists of two levels, focuses on the application of equilibrium principles to equilibria involving weak acids and bases, including buffer solutions and indicators. Level one uses Le Chatelier's…

  8. Equilibrium I: Principles. Independent Learning Project for Advanced Chemistry (ILPAC). Unit P2.

    Science.gov (United States)

    Inner London Education Authority (England).

    This unit on the principles of equilibrium is one of 10 first year units produced by the Independent Learning Project for Advanced Chemistry (ILPAC). The unit consists of two levels. After a treatment of non-mathematical aspects in level one (the idea of a reversible reaction, characteristics of an equilibrium state, the Le Chatelier's principle),…

  9. Developing Emotion-Aware, Advanced Learning Technologies: A Taxonomy of Approaches and Features

    Science.gov (United States)

    Harley, Jason M.; Lajoie, Susanne P.; Frasson, Claude; Hall, Nathan C.

    2017-01-01

    A growing body of work on intelligent tutoring systems, affective computing, and artificial intelligence in education is exploring creative, technology-driven approaches to enhance learners' experience of adaptive, positively-valenced emotions while interacting with advanced learning technologies. Despite this, there has been no published work to…

  10. Book Review "Advances on remote laboratories and e-learning experiences"

    Directory of Open Access Journals (Sweden)

    Jesús A. del Alamo

    2007-08-01

    Full Text Available Book Review "Advances on remote laboratories and e-learning experiences", book editors: Luís Gomes and Javier García-Zubía, University of Deusto, Spain. Reviewed by Jesús A. del Alamo, Massachusetts Institute of Technology, M.I.T.

  11. Effects of Advance Organizer Instruction on Preschool Children's Learning of Musical Concepts.

    Science.gov (United States)

    Lawton, Joseph T.; Johnson, Ann

    1992-01-01

    Presents results of a study of the effects of advance organizer instruction on preschool children's learning of the musical concepts of dynamics, pitch, tempo, and rhythm. Reports that three modes and three methods of presentation were evaluated. Concludes that, although results did vary with mode, the method of presentation had no significant…

  12. Hydrocarbons. Independent Learning Project for Advanced Chemistry (ILPAC). Unit O1.

    Science.gov (United States)

    Inner London Education Authority (England).

    This unit on hydrocarbons is one of 10 first year units produced by the Independent Learning Project for Advanced Chemistry (ILPAC). The unit is divided into sections dealing with alkanes, alkenes, alkynes, arenes, and several aspects of the petroleum industry. Two experiments, exercises (with answers), and pre- and post-tests are included.…

  13. The Gaseous State. Independent Learning Project for Advanced Chemistry (ILPAC). Unit P1.

    Science.gov (United States)

    Inner London Education Authority (England).

    This unit on the gaseous state is one of 10 first year units produced by the Independent Learning Project for Advanced Chemistry (ILPAC). The unit consists of two levels. Level one deals with the distinctive characteristics of gases, then considers the gas laws, in particular the ideal gas equation and its applications. Level two concentrates on…

  14. Abstract: Use of E-Learning to Advance Nursing Education in Rwanda

    African Journals Online (AJOL)

    As of 2011, there were 6,970 nurses in Rwanda with 90% trained at the high school level. The Ministry of Health has set the goal of upgrading high school trained nurses to a diploma by 2020. To assist in educating nurses and advancing their education, E-Learning (distance education) has been adopted as a model to ...

  15. Lights, Camera, Action: Advancing Learning, Research, and Program Evaluation through Video Production in Educational Leadership Preparation

    Science.gov (United States)

    Friend, Jennifer; Militello, Matthew

    2015-01-01

    This article analyzes specific uses of digital video production in the field of educational leadership preparation, advancing a three-part framework that includes the use of video in (a) teaching and learning, (b) research methods, and (c) program evaluation and service to the profession. The first category within the framework examines videos…

  16. Advanced Cell Classifier: User-Friendly Machine-Learning-Based Software for Discovering Phenotypes in High-Content Imaging Data.

    Science.gov (United States)

    Piccinini, Filippo; Balassa, Tamas; Szkalisity, Abel; Molnar, Csaba; Paavolainen, Lassi; Kujala, Kaisa; Buzas, Krisztina; Sarazova, Marie; Pietiainen, Vilja; Kutay, Ulrike; Smith, Kevin; Horvath, Peter

    2017-06-28

    High-content, imaging-based screens now routinely generate data on a scale that precludes manual verification and interrogation. Software applying machine learning has become an essential tool to automate analysis, but these methods require annotated examples to learn from. Efficiently exploring large datasets to find relevant examples remains a challenging bottleneck. Here, we present Advanced Cell Classifier (ACC), a graphical software package for phenotypic analysis that addresses these difficulties. ACC applies machine-learning and image-analysis methods to high-content data generated by large-scale, cell-based experiments. It features methods to mine microscopic image data, discover new phenotypes, and improve recognition performance. We demonstrate that these features substantially expedite the training process, successfully uncover rare phenotypes, and improve the accuracy of the analysis. ACC is extensively documented, designed to be user-friendly for researchers without machine-learning expertise, and distributed as a free open-source tool at www.cellclassifier.org. Copyright © 2017 Elsevier Inc. All rights reserved.

  17. Distributed Open and Distance Learning: How Does E-Learning Fit? LSDA Reports.

    Science.gov (United States)

    Fletcher, Mick

    The distinctions between types of open and distance learning broadly equate to the concept of learning at a time, place, and pace that best suits the learner. Distance learning refers to geography, whereas open learning refers to time. Flexible learning is a generic term referring either to geography or time. Combining these distinctions allows…

  18. Active Learning through Materials Development: A Project for the Advanced L2 Classroom

    Directory of Open Access Journals (Sweden)

    Katrina Daly Thompson

    2008-01-01

    Full Text Available Building on the notion of active learning, the assumption that students learn more when given opportunities to practice using their skills and to receive feedback on their performance, this article de-scribes a project undertaken in an Advanced (third-year Swahili course in which students were given the opportunity to develop L2 materials for computer-mediated peer instruction. The article exam-ines the goals, design and results of the project in light of the litera-ture on active learning and learner autonomy, and suggests how the project might be improved in order to serve as a model for other Ad-vanced L2 courses.

  19. Recent Advances in Conotoxin Classification by Using Machine Learning Methods.

    Science.gov (United States)

    Dao, Fu-Ying; Yang, Hui; Su, Zhen-Dong; Yang, Wuritu; Wu, Yun; Hui, Ding; Chen, Wei; Tang, Hua; Lin, Hao

    2017-06-25

    Conotoxins are disulfide-rich small peptides, which are invaluable peptides that target ion channel and neuronal receptors. Conotoxins have been demonstrated as potent pharmaceuticals in the treatment of a series of diseases, such as Alzheimer's disease, Parkinson's disease, and epilepsy. In addition, conotoxins are also ideal molecular templates for the development of new drug lead compounds and play important roles in neurobiological research as well. Thus, the accurate identification of conotoxin types will provide key clues for the biological research and clinical medicine. Generally, conotoxin types are confirmed when their sequence, structure, and function are experimentally validated. However, it is time-consuming and costly to acquire the structure and function information by using biochemical experiments. Therefore, it is important to develop computational tools for efficiently and effectively recognizing conotoxin types based on sequence information. In this work, we reviewed the current progress in computational identification of conotoxins in the following aspects: (i) construction of benchmark dataset; (ii) strategies for extracting sequence features; (iii) feature selection techniques; (iv) machine learning methods for classifying conotoxins; (v) the results obtained by these methods and the published tools; and (vi) future perspectives on conotoxin classification. The paper provides the basis for in-depth study of conotoxins and drug therapy research.

  20. Learning to attain an advanced level of professional responsibility.

    Science.gov (United States)

    Ter Maten-Speksnijder, Ada; Grypdonck, Mieke; Pool, Aart; Meurs, Pauline; Van Staa, AnneLoes

    2015-08-01

    After graduation, nurse practitioner students are expected to be capable of providing complex, evidence-based nursing care independently, combined with standardized medical care. The students who follow work-study programs have to develop their competencies in a healthcare environment dominated by efficiency policies. This study aims to explore nurse practitioner students' perceptions of their professional responsibility for patient care. This qualitative interpretative study entails a content analysis of 46 reflective case studies written by nurse practitioner students. The students felt responsible for the monitoring of patients' health status, attending to psychosocial problems, emphasizing compliance, and optimizing the family's role as informal caregivers. At the same time, students struggled to understand the complexities of their patients' needs, and they had difficulty applying their knowledge and skills to complex medical, psychological, and social problems. The students' perceptions of their new responsibility were characterized by a strong focus on curative care, while psychosocial components of health and illness concerns were often overlooked. The students experienced difficulties in meeting the criteria of advanced practice nursing described in the Dutch competency framework. Copyright © 2015 Elsevier Ltd. All rights reserved.

  1. Lessons learned from the design and implementation of distributed post-WIMP user interfaces

    OpenAIRE

    Seifried, Thomas; Jetter, Hans-Christian; Haller, Michael; Reiterer, Harald

    2011-01-01

    Creating novel user interfaces that are “natural” and distributed is challenging for designers and developers. “Natural” interaction techniques are barely standardized and in combination with distributed UIs additional technical difficulties arise. In this paper we present the lessons we have learned in developing several natural and distributed user interfaces and propose design patterns to support development of such applications.

  2. The Quintessence of Traditional Chinese Medicine: Syndrome and Its Distribution among Advanced Cancer Patients with Constipation

    Directory of Open Access Journals (Sweden)

    Chung-Wah Cheng

    2012-01-01

    Full Text Available Constipation is a common problem in advanced cancer patients; however, specific clinical guidelines on traditional Chinese medicine (TCM syndrome (Zhang are not yet available. In this cross-sectional study, the TCM syndromes distribution and their common symptoms and signs among 225 constipated advanced cancer patients were determined. Results showed that 127 patients (56.4% and 7 patients (3.1% were in deficient and excessive patterns, respectively, while 91 patients (40.4% were in deficiency-excess complex. The distributions of the five syndromes were: Qi deficiency (93.3%, Qi stagnation (40.0%, blood (Yin deficiency (28.9%, Yang deficiency (22.2%, and excess heat (5.8%. Furthermore, age, functional status, and level of blood haemoglobin were factors related to the type of TCM syndrome. A TCM prescription with the functions on replenishing the Deficiency, redirecting the flow of Qi stagnation and moistening the dryness caused by the blood (Yin deficiency can be made for the treatment of advance cancer patients with constipation. Robust trials are urgently needed for further justifying its efficacy and safety in evidence-based approaches.

  3. Museum Signage as Distributed Mediation to Encourage Family Learning

    Science.gov (United States)

    Kim, Kyungyoun

    2009-01-01

    Many prior studies conducted in museums have focused primarily on exhibits as the main objects for learning. Less progress has been made in studying signage as another meaning-making tool in museums. The present study was designed to understand the role of signage in family learning by answering the following research questions, "How does signage…

  4. Distributed Scaffolding: Synergy in Technology-Enhanced Learning Environments

    Science.gov (United States)

    Ustunel, Hale H.; Tokel, Saniye Tugba

    2018-01-01

    When technology is employed challenges increase in learning environments. Kim et al. ("Sci Educ" 91(6):1010-1030, 2007) presented a pedagogical framework that provides a valid technology-enhanced learning environment. The purpose of the present design-based study was to investigate the micro context dimension of this framework and to…

  5. Scaling up machine learning: parallel and distributed approaches

    National Research Council Canada - National Science Library

    Bekkerman, Ron; Bilenko, Mikhail; Langford, John

    2012-01-01

    ... presented in the book cover a range of parallelization platforms from FPGAs and GPUs to multi-core systems and commodity clusters; concurrent programming frameworks that include CUDA, MPI, MapReduce, and DryadLINQ; and various learning settings: supervised, unsupervised, semi-supervised, and online learning. Extensive coverage of parallelizat...

  6. Learning general phonological rules from distributional information: a computational model.

    Science.gov (United States)

    Calamaro, Shira; Jarosz, Gaja

    2015-04-01

    Phonological rules create alternations in the phonetic realizations of related words. These rules must be learned by infants in order to identify the phonological inventory, the morphological structure, and the lexicon of a language. Recent work proposes a computational model for the learning of one kind of phonological alternation, allophony (Peperkamp, Le Calvez, Nadal, & Dupoux, 2006). This paper extends the model to account for learning of a broader set of phonological alternations and the formalization of these alternations as general rules. In Experiment 1, we apply the original model to new data in Dutch and demonstrate its limitations in learning nonallophonic rules. In Experiment 2, we extend the model to allow it to learn general rules for alternations that apply to a class of segments. In Experiment 3, the model is further extended to allow for generalization by context; we argue that this generalization must be constrained by linguistic principles. Copyright © 2014 Cognitive Science Society, Inc.

  7. Collaborative Learning: Cognitive and Computational Approaches. Advances in Learning and Instruction Series.

    Science.gov (United States)

    Dillenbourg, Pierre, Ed.

    Intended to illustrate the benefits of collaboration between scientists from psychology and computer science, namely machine learning, this book contains the following chapters, most of which are co-authored by scholars from both sides: (1) "Introduction: What Do You Mean by 'Collaborative Learning'?" (Pierre Dillenbourg); (2)…

  8. Recent machine learning advancements in sensor-based mobility analysis: Deep learning for Parkinson's disease assessment.

    Science.gov (United States)

    Eskofier, Bjoern M; Lee, Sunghoon I; Daneault, Jean-Francois; Golabchi, Fatemeh N; Ferreira-Carvalho, Gabriela; Vergara-Diaz, Gloria; Sapienza, Stefano; Costante, Gianluca; Klucken, Jochen; Kautz, Thomas; Bonato, Paolo

    2016-08-01

    The development of wearable sensors has opened the door for long-term assessment of movement disorders. However, there is still a need for developing methods suitable to monitor motor symptoms in and outside the clinic. The purpose of this paper was to investigate deep learning as a method for this monitoring. Deep learning recently broke records in speech and image classification, but it has not been fully investigated as a potential approach to analyze wearable sensor data. We collected data from ten patients with idiopathic Parkinson's disease using inertial measurement units. Several motor tasks were expert-labeled and used for classification. We specifically focused on the detection of bradykinesia. For this, we compared standard machine learning pipelines with deep learning based on convolutional neural networks. Our results showed that deep learning outperformed other state-of-the-art machine learning algorithms by at least 4.6 % in terms of classification rate. We contribute a discussion of the advantages and disadvantages of deep learning for sensor-based movement assessment and conclude that deep learning is a promising method for this field.

  9. Optimization of time distribution for studying the course modules on advanced training of health care administrators

    Directory of Open Access Journals (Sweden)

    Dorovskaya A.l.

    2015-06-01

    Full Text Available The research objective is rational (optimal time management in studying the course modules on Advanced Training of Health Care Administrators. Materials and methods. We conducted expert survey of 73 healthcare administrators from medical organizations of Saratov region. Branch-and-bound method was used for rescheduling the educational program. Results. Both direct and inverse problems have been solved. The direct one refers to time distribution for each module of the advanced Training of Healthcare Administrators course so that the total score is maximum and each module is marked not lower than "satisfactory". The inverse one resulted in achieving minimal time characteristics for varieties of average score. Conclusion. The offered approach allows to solve problems of managing time given for education.

  10. The prominent role of the cerebellum in the learning, origin and advancement of culture.

    Science.gov (United States)

    Vandervert, Larry

    2016-01-01

    Vandervert described how, in collaboration with the cerebral cortex, unconscious learning of cerebellar internal models leads to enhanced executive control in working memory in expert music performance and in scientific discovery. Following Vandervert's arguments, it is proposed that since music performance and scientific discovery, two pillars of cultural learning and advancement, are learned through in cerebellar internal models, it is reasonable that additional if not all components of culture may be learned in the same way. Within this perspective strong evidence is presented that argues that the learning, maintenance, and advancement of culture are accomplished primarily by recently-evolved (the last million or so years) motor/cognitive functions of the cerebellum and not primarily by the cerebral cortex as previously assumed. It is suggested that the unconscious cerebellar mechanism behind the origin and learning of culture greatly expands Ito's conception of the cerebellum as "a brain for an implicit self." Through the mechanism of predictive sequence detection in cerebellar internal models related to the body, other persons, or the environment, it is shown how individuals can unconsciously learn the elements of culture and yet, at the same time, be in social sync with other members of culture. Further, this predictive, cerebellar mechanism of socialization toward the norms of culture is hypothesized to be diminished among children who experience excessive television viewing, which results in lower grades, poor socialization, and diminished executive control. It is concluded that the essential components of culture are learned and sustained not by the cerebral cortex alone as many traditionally believe, but are learned through repetitious improvements in prediction and control by internal models in the cerebellum. From this perspective, the following new explanations of culture are discussed: (1) how culture can be learned unconsciously but yet be socially

  11. Fast distributed strategic learning for global optima in queueing access games

    KAUST Repository

    Tembine, Hamidou

    2014-08-24

    In this paper we examine combined fully distributed payoff and strategy learning (CODIPAS) in a queue-aware access game over a graph. The classical strategic learning analysis relies on vanishing or small learning rate and uses stochastic approximation tool to derive steady states and invariant sets of the underlying learning process. Here, the stochastic approximation framework does not apply due to non-vanishing learning rate. We propose a direct proof of convergence of the process. Interestingly, the convergence time to one of the global optima is almost surely finite and we explicitly characterize the convergence time. We show that pursuit-based CODIPAS learning is much faster than the classical learning algorithms in games. We extend the methodology to coalitional learning and proves a very fast formation of coalitions for queue-aware access games where the action space is dynamically changing depending on the location of the user over a graph.

  12. Scaling up machine learning: parallel and distributed approaches

    National Research Council Canada - National Science Library

    Bekkerman, Ron; Bilenko, Mikhail; Langford, John

    2012-01-01

    .... Demand for parallelizing learning algorithms is highly task-specific: in some settings it is driven by the enormous dataset sizes, in others by model complexity or by real-time performance requirements...

  13. A Parameter Communication Optimization Strategy for Distributed Machine Learning in Sensors.

    Science.gov (United States)

    Zhang, Jilin; Tu, Hangdi; Ren, Yongjian; Wan, Jian; Zhou, Li; Li, Mingwei; Wang, Jue; Yu, Lifeng; Zhao, Chang; Zhang, Lei

    2017-09-21

    In order to utilize the distributed characteristic of sensors, distributed machine learning has become the mainstream approach, but the different computing capability of sensors and network delays greatly influence the accuracy and the convergence rate of the machine learning model. Our paper describes a reasonable parameter communication optimization strategy to balance the training overhead and the communication overhead. We extend the fault tolerance of iterative-convergent machine learning algorithms and propose the Dynamic Finite Fault Tolerance (DFFT). Based on the DFFT, we implement a parameter communication optimization strategy for distributed machine learning, named Dynamic Synchronous Parallel Strategy (DSP), which uses the performance monitoring model to dynamically adjust the parameter synchronization strategy between worker nodes and the Parameter Server (PS). This strategy makes full use of the computing power of each sensor, ensures the accuracy of the machine learning model, and avoids the situation that the model training is disturbed by any tasks unrelated to the sensors.

  14. IPAD 2: Advances in Distributed Data Base Management for CAD/CAM

    Science.gov (United States)

    Bostic, S. W. (Compiler)

    1984-01-01

    The Integrated Programs for Aerospace-Vehicle Design (IPAD) Project objective is to improve engineering productivity through better use of computer-aided design and manufacturing (CAD/CAM) technology. The focus is on development of technology and associated software for integrated company-wide management of engineering information. The objectives of this conference are as follows: to provide a greater awareness of the critical need by U.S. industry for advancements in distributed CAD/CAM data management capability; to present industry experiences and current and planned research in distributed data base management; and to summarize IPAD data management contributions and their impact on U.S. industry and computer hardware and software vendors.

  15. 'Oorja' in India: Assessing a large-scale commercial distribution of advanced biomass stoves to households.

    Science.gov (United States)

    Thurber, Mark C; Phadke, Himani; Nagavarapu, Sriniketh; Shrimali, Gireesh; Zerriffi, Hisham

    2014-04-01

    Replacing traditional stoves with advanced alternatives that burn more cleanly has the potential to ameliorate major health problems associated with indoor air pollution in developing countries. With a few exceptions, large government and charitable programs to distribute advanced stoves have not had the desired impact. Commercially-based distributions that seek cost recovery and even profits might plausibly do better, both because they encourage distributors to supply and promote products that people want and because they are based around properly-incentivized supply chains that could more be scalable, sustainable, and replicable. The sale in India of over 400,000 "Oorja" stoves to households from 2006 onwards represents the largest commercially-based distribution of a gasification-type advanced biomass stove. BP's Emerging Consumer Markets (ECM) division and then successor company First Energy sold this stove and the pelletized biomass fuel on which it operates. We assess the success of this effort and the role its commercial aspect played in outcomes using a survey of 998 households in areas of Maharashtra and Karnataka where the stove was sold as well as detailed interviews with BP and First Energy staff. Statistical models based on this data indicate that Oorja purchase rates were significantly influenced by the intensity of Oorja marketing in a region as well as by pre-existing stove mix among households. The highest rate of adoption came from LPG-using households for which Oorja's pelletized biomass fuel reduced costs. Smoke- and health-related messages from Oorja marketing did not significantly influence the purchase decision, although they did appear to affect household perceptions about smoke. By the time of our survey, only 9% of households that purchased Oorja were still using the stove, the result in large part of difficulties First Energy encountered in developing a viable supply chain around low-cost procurement of "agricultural waste" to make

  16. Distributional learning aids linguistic category formation in school-age children.

    Science.gov (United States)

    Hall, Jessica; Owen VAN Horne, Amanda; Farmer, Thomas

    2018-05-01

    The goal of this study was to determine if typically developing children could form grammatical categories from distributional information alone. Twenty-seven children aged six to nine listened to an artificial grammar which contained strategic gaps in its distribution. At test, we compared how children rated novel sentences that fit the grammar to sentences that were ungrammatical. Sentences could be distinguished only through the formation of categories of words with shared distributional properties. Children's ratings revealed that they could discriminate grammatical and ungrammatical sentences. These data lend support to the hypothesis that distributional learning is a potential mechanism for learning grammatical categories in a first language.

  17. eLearning in education and advanced training in neuroradiology: introduction of a web-based teaching and learning application.

    Science.gov (United States)

    Zajaczek, J E W; Götz, F; Kupka, T; Behrends, M; Haubitz, B; Donnerstag, F; Rodt, T; Walter, G F; Matthies, H K; Becker, H

    2006-09-01

    New information technologies offer the possibility of major improvements in the professional education and advanced training of physicians. The web-based, multimedia teaching and learning application Schoolbook has been created and utilized for neuroradiology. Schoolbook is technically based as a content management system and is realized in a LAMP environment. The content is generated with the help of the developed system and stored in a database. The layout is defined by a PHP application, and the webpages are generated from the system. Schoolbook is realized as an authoring tool so that it can be integrated into daily practice. This enables the teacher to autonomously process the content into the web-based application which is used for lectures, seminars and self-study. A multimedia case library is the central building block of Schoolbook for neuroradiology, whereby the learner is provided with original diagnostic and therapeutic data from numerous individual cases. The user can put individual emphasis on key learning points as there are various ways to work with the case histories. Besides the case-based way of teaching and learning, a systematically structured way of dealing with the content is available. eLearning offers various opportunities for teaching and learning in academic and scientific as well as in economic contexts. Web-based applications such as Schoolbook may be beneficial not only for basic university education but also for the realization of international educational programmes such as the European Master of Medical Science with a major in neuroradiology.

  18. eLearning in education and advanced training in neuroradiology: introduction of a web-based teaching and learning application

    Energy Technology Data Exchange (ETDEWEB)

    Zajaczek, J.E.W. [Hannover Medical School, Department of Neuroradiology (OE 8210), Hannover (Germany); Hannover Medical School, Department of Medical Informatics, Hannover (Germany); Goetz, F.; Haubitz, B.; Donnerstag, F.; Becker, H. [Hannover Medical School, Department of Neuroradiology (OE 8210), Hannover (Germany); Kupka, T.; Behrends, M.; Matthies, H.K. [Hannover Medical School, Department of Medical Informatics, Hannover (Germany); Rodt, T. [Hannover Medical School, Department of Neurosurgery, Hannover (Germany); Walter, G.F. [Medical University of Graz, Graz (Austria)

    2006-09-15

    New information technologies offer the possibility of major improvements in the professional education and advanced training of physicians. The web-based, multimedia teaching and learning application Schoolbook has been created and utilized for neuroradiology. Schoolbook is technically based as a content management system and is realized in a LAMP environment. The content is generated with the help of the developed system and stored in a database. The layout is defined by a PHP application, and the webpages are generated from the system. Schoolbook is realized as an authoring tool so that it can be integrated into daily practice. This enables the teacher to autonomously process the content into the web-based application which is used for lectures, seminars and self-study. A multimedia case library is the central building block of Schoolbook for neuroradiology, whereby the learner is provided with original diagnostic and therapeutic data from numerous individual cases. The user can put individual emphasis on key learning points as there are various ways to work with the case histories. Besides the case-based way of teaching and learning, a systematically structured way of dealing with the content is available. eLearning offers various opportunities for teaching and learning in academic and scientific as well as in economic contexts. Web-based applications such as Schoolbook may be beneficial not only for basic university education but also for the realization of international educational programmes such as the European Master of Medical Science with a major in neuroradiology. (orig.)

  19. eLearning in education and advanced training in neuroradiology: introduction of a web-based teaching and learning application

    International Nuclear Information System (INIS)

    Zajaczek, J.E.W.; Goetz, F.; Haubitz, B.; Donnerstag, F.; Becker, H.; Kupka, T.; Behrends, M.; Matthies, H.K.; Rodt, T.; Walter, G.F.

    2006-01-01

    New information technologies offer the possibility of major improvements in the professional education and advanced training of physicians. The web-based, multimedia teaching and learning application Schoolbook has been created and utilized for neuroradiology. Schoolbook is technically based as a content management system and is realized in a LAMP environment. The content is generated with the help of the developed system and stored in a database. The layout is defined by a PHP application, and the webpages are generated from the system. Schoolbook is realized as an authoring tool so that it can be integrated into daily practice. This enables the teacher to autonomously process the content into the web-based application which is used for lectures, seminars and self-study. A multimedia case library is the central building block of Schoolbook for neuroradiology, whereby the learner is provided with original diagnostic and therapeutic data from numerous individual cases. The user can put individual emphasis on key learning points as there are various ways to work with the case histories. Besides the case-based way of teaching and learning, a systematically structured way of dealing with the content is available. eLearning offers various opportunities for teaching and learning in academic and scientific as well as in economic contexts. Web-based applications such as Schoolbook may be beneficial not only for basic university education but also for the realization of international educational programmes such as the European Master of Medical Science with a major in neuroradiology. (orig.)

  20. Can Individualized Learning Plans in an advanced clinical experience course for fourth year medical students foster Self-Directed Learning?

    Science.gov (United States)

    Chitkara, Maribeth B; Satnick, Daniel; Lu, Wei-Hsin; Fleit, Howard; Go, Roderick A; Chandran, Latha

    2016-09-01

    Residency programs have utilized Individualized Learning Plans (ILPs) to customize resident education while undergraduate medical education has not done so in a meaningful way. We discuss the use of ILPs within a fourth year medical school course to facilitate self-directed learning (SDL). At Stony Brook University School of Medicine, an ILP component was added to the Advanced Clinical Experience (ACE) course for fourth year students. Each completed an ILP outlining personal learning goals and strategies to achieve them. An adaptation of the Motivated Strategies for Learning Questionnaire (MSLQ) (Duncan T and McKeachie W, Educ Psych 40(2):117-128, 2005 and Cook DA et al., Med Ed 45:1230-1240, 2011) was used to measure success of ILPs in improving SDL. Qualitative data analysis was conducted on the ILPs and self-reflections. Forty-eight students participated. Two of the four SDL sub-domains identified on the MSLQ showed improvement; self-efficacy (p = .001) and self-regulation (p = .002). 'Medical Knowledge' was the competency most frequently identified as an area of concentration (90 %) and professionalism was selected least frequently (4 %). A higher percentage (83 %) of students who reported complete achievement of their ILP goals also reported feeling better prepared for entering residency. ILPs improve SDL strategies among medical students and may serve as useful tools to help shape future learning goals as they transition to residency training.

  1. Editorial: Advances in Health Education Applying E-Learning, Simulations and Distance Technologies

    Directory of Open Access Journals (Sweden)

    Andre W. Kushniruk

    2011-03-01

    Full Text Available This special issue of the KM&EL international journal is dedicated to coverage of novel advances in health professional education applying e-Learning, simulations and distance education technologies. Modern healthcare is beginning to be transformed through the emergence of new information technologies and rapid advances in health informatics. Advances such as electronic health record systems (EHRs, clinical decision support systems and other advanced information systems such as public health surveillance systems are rapidly being deployed worldwide. The education of health professionals such as medical, nursing and allied health professionals will require an improved understanding of these technologies and how they will transform their healthcare practice. However, currently there is a lack of integration of knowledge and skills related to such technology in health professional education. In this issue of the journal we present articles that describe a set of novel approaches to integrating essential health information technology into the education of health professionals, as well as the use of advanced information technologies and e-Learning approaches for improving health professional education. The approaches range from use of simulations to development of novel Web-based platforms for allowing students to interact with the technologies and healthcare practices that are rapidly changing healthcare.

  2. Students’ perception of the learning environment in a distributed medical programme

    Directory of Open Access Journals (Sweden)

    Kiran Veerapen

    2010-09-01

    Full Text Available Background : The learning environment of a medical school has a significant impact on students’ achievements and learning outcomes. The importance of equitable learning environments across programme sites is implicit in distributed undergraduate medical programmes being developed and implemented. Purpose : To study the learning environment and its equity across two classes and three geographically separate sites of a distributed medical programme at the University of British Columbia Medical School that commenced in 2004. Method : The validated Dundee Ready Educational Environment Survey was sent to all students in their 2nd and 3rd year (classes graduating in 2009 and 2008 of the programme. The domains of the learning environment surveyed were: students’ perceptions of learning, students’ perceptions of teachers, students’ academic self-perceptions, students’ perceptions of the atmosphere, and students’ social self-perceptions. Mean scores, frequency distribution of responses, and inter- and intrasite differences were calculated. Results : The perception of the global learning environment at all sites was more positive than negative. It was characterised by a strongly positive perception of teachers. The work load and emphasis on factual learning were perceived negatively. Intersite differences within domains of the learning environment were more evident in the pioneer class (2008 of the programme. Intersite differences consistent across classes were largely related to on-site support for students. Conclusions : Shared strengths and weaknesses in the learning environment at UBC sites were evident in areas that were managed by the parent institution, such as the attributes of shared faculty and curriculum. A greater divergence in the perception of the learning environment was found in domains dependent on local arrangements and social factors that are less amenable to central regulation. This study underlines the need for ongoing

  3. Students' perception of the learning environment in a distributed medical programme.

    Science.gov (United States)

    Veerapen, Kiran; McAleer, Sean

    2010-09-24

    The learning environment of a medical school has a significant impact on students' achievements and learning outcomes. The importance of equitable learning environments across programme sites is implicit in distributed undergraduate medical programmes being developed and implemented. To study the learning environment and its equity across two classes and three geographically separate sites of a distributed medical programme at the University of British Columbia Medical School that commenced in 2004. The validated Dundee Ready Educational Environment Survey was sent to all students in their 2nd and 3rd year (classes graduating in 2009 and 2008) of the programme. The domains of the learning environment surveyed were: students' perceptions of learning, students' perceptions of teachers, students' academic self-perceptions, students' perceptions of the atmosphere, and students' social self-perceptions. Mean scores, frequency distribution of responses, and inter- and intrasite differences were calculated. The perception of the global learning environment at all sites was more positive than negative. It was characterised by a strongly positive perception of teachers. The work load and emphasis on factual learning were perceived negatively. Intersite differences within domains of the learning environment were more evident in the pioneer class (2008) of the programme. Intersite differences consistent across classes were largely related to on-site support for students. Shared strengths and weaknesses in the learning environment at UBC sites were evident in areas that were managed by the parent institution, such as the attributes of shared faculty and curriculum. A greater divergence in the perception of the learning environment was found in domains dependent on local arrangements and social factors that are less amenable to central regulation. This study underlines the need for ongoing comparative evaluation of the learning environment at the distributed sites and

  4. MACHINE LEARNING FOR THE SELF-ORGANIZATION OF DISTRIBUTED SYSTEMS IN ECONOMIC APPLICATIONS

    OpenAIRE

    Jerzy Balicki; Waldemar Korłub

    2017-01-01

    In this paper, an application of machine learning to the problem of self-organization of distributed systems has been discussed with regard to economic applications, with particular emphasis on supervised neural network learning to predict stock investments and some ratings of companies. In addition, genetic programming can play an important role in the preparation and testing of several financial information systems. For this reason, machine learning applications have been discussed because ...

  5. Instructional Designers' Media Selection Practices for Distributed Problem-Based Learning Environments

    Science.gov (United States)

    Fells, Stephanie

    2012-01-01

    The design of online or distributed problem-based learning (dPBL) is a nascent, complex design problem. Instructional designers are challenged to effectively unite the constructivist principles of problem-based learning (PBL) with appropriate media in order to create quality dPBL environments. While computer-mediated communication (CMC) tools and…

  6. A Critical Analysis of Job-Embedded Professional Learning within a Distributed Leadership Framework

    Science.gov (United States)

    Campoli, Ashley Jimerson

    2011-01-01

    Leadership style and professional learning have been linked to student achievement. Studies have linked leadership styles such as distributed leadership to job-embedded professional learning. However, research is mixed when these two constructs are related to student achievement. This study evaluated the relationship between distributed…

  7. The Osseus platform: a prototype for advanced web-based distributed simulation

    Science.gov (United States)

    Franceschini, Derrick; Riecken, Mark

    2016-05-01

    Recent technological advances in web-based distributed computing and database technology have made possible a deeper and more transparent integration of some modeling and simulation applications. Despite these advances towards true integration of capabilities, disparate systems, architectures, and protocols will remain in the inventory for some time to come. These disparities present interoperability challenges for distributed modeling and simulation whether the application is training, experimentation, or analysis. Traditional approaches call for building gateways to bridge between disparate protocols and retaining interoperability specialists. Challenges in reconciling data models also persist. These challenges and their traditional mitigation approaches directly contribute to higher costs, schedule delays, and frustration for the end users. Osseus is a prototype software platform originally funded as a research project by the Defense Modeling & Simulation Coordination Office (DMSCO) to examine interoperability alternatives using modern, web-based technology and taking inspiration from the commercial sector. Osseus provides tools and services for nonexpert users to connect simulations, targeting the time and skillset needed to successfully connect disparate systems. The Osseus platform presents a web services interface to allow simulation applications to exchange data using modern techniques efficiently over Local or Wide Area Networks. Further, it provides Service Oriented Architecture capabilities such that finer granularity components such as individual models can contribute to simulation with minimal effort.

  8. Distributed Learning, Recognition, and Prediction by ART and ARTMAP Neural Networks.

    Science.gov (United States)

    Carpenter, Gail A.

    1997-11-01

    A class of adaptive resonance theory (ART) models for learning, recognition, and prediction with arbitrarily distributed code representations is introduced. Distributed ART neural networks combine the stable fast learning capabilities of winner-take-all ART systems with the noise tolerance and code compression capabilities of multilayer perceptrons. With a winner-take-all code, the unsupervised model dART reduces to fuzzy ART and the supervised model dARTMAP reduces to fuzzy ARTMAP. With a distributed code, these networks automatically apportion learned changes according to the degree of activation of each coding node, which permits fast as well as slow learning without catastrophic forgetting. Distributed ART models replace the traditional neural network path weight with a dynamic weight equal to the rectified difference between coding node activation and an adaptive threshold. Thresholds increase monotonically during learning according to a principle of atrophy due to disuse. However, monotonic change at the synaptic level manifests itself as bidirectional change at the dynamic level, where the result of adaptation resembles long-term potentiation (LTP) for single-pulse or low frequency test inputs but can resemble long-term depression (LTD) for higher frequency test inputs. This paradoxical behavior is traced to dual computational properties of phasic and tonic coding signal components. A parallel distributed match-reset-search process also helps stabilize memory. Without the match-reset-search system, dART becomes a type of distributed competitive learning network.

  9. Museum signage as distributed mediation to encourage family learning

    Science.gov (United States)

    Kim, Kyungyoun

    Many prior studies conducted in museums have focused primarily on exhibits as the main objects for learning. Less progress has been made in studying signage as another meaning-making tool in museums. The present study was designed to understand the role of signage in family learning by answering the following research questions, "How does signage about exhibit content or interaction strategies affect parents' and children's learning and their engagement?" and "What is the role of parent prior knowledge on parents' and children's learning and their engagement?" To address these questions, 45 parent-child dyads with children aged six to seven years were recruited to engage with two exhibits about cars. Fifteen parent-child dyads were assigned to each of three conditions, created by two different types of signage: (1) Content and interaction signage condition, (2) Content signage condition, and (3) No signage condition. In each condition, eight parents with low knowledge in the car domain and seven parents with high knowledge were recruited. Findings showed that parents and children learned and engaged differently across the three signage conditions. Both children and parents in the conditions with signage learned more than children and parents in the no signage condition. By using information from signage, parents in the two signage conditions were able to identify the content of the exhibit more quickly and to shape appropriate educational messages in their conversations with children. Findings also showed that parents with high knowledge were more likely to have the exhibit-focused engagement, which was often oriented to their own interpretation and not always beneficial for children's learning. However, by showing that parent-child dyads in the content and interaction signage condition were most likely to operate and observe the exhibit appropriately and most likely to describe evidence and make appropriate inferences, this study suggested that the interaction

  10. READING AUTHENTIC EFL TEXT USING VISUALIZATION AND ADVANCE ORGANIZERS IN A MULTIMEDIA LEARNING ENVIRONMENT

    OpenAIRE

    Tsuiping Chen; Huifen Lin

    2007-01-01

    The purpose of this experimental study was to compare the effects of different types of computer-generated visuals (static versus animated) and advance organizers (descriptive versus question) in enhancing comprehension and retention of a content-based lesson for learning English as a Foreign Language (EFL). Additionally, the study investigated the interactive effect of students’ existing reading proficiency level and the above-mentioned treatments on their reading comprehension achievement. ...

  11. A Distributed System for Learning Programming On-Line

    Science.gov (United States)

    Verdu, Elena; Regueras, Luisa M.; Verdu, Maria J.; Leal, Jose P.; de Castro, Juan P.; Queiros, Ricardo

    2012-01-01

    Several Web-based on-line judges or on-line programming trainers have been developed in order to allow students to train their programming skills. However, their pedagogical functionalities in the learning of programming have not been clearly defined. EduJudge is a project which aims to integrate the "UVA On-line Judge", an existing…

  12. Animated pedagogical agents: do they advance student motivation and learning in an inquiry learning environment?

    NARCIS (Netherlands)

    van der Meij, Hans; van der Meij, Jan; Harmsen, Ruth

    2012-01-01

    Student behavior in inquiry learning environments has often been found to be in need of (meta)cognitive support. Two pilots revealed that students might also benefit from motivational support in such an environment. An experiment with 61 junior high school students (ages 14-16) compared three

  13. Animated Pedagogical Agents: Do they advance student motivation and learning in an inquiry learning environment?

    NARCIS (Netherlands)

    van der Meij, Hans; van der Meij, Jan; Harmsen, R.

    2012-01-01

    Student behavior in inquiry learning environments has often been found to be in need of (meta)cognitive support. Two pilots revealed that students might also benefit from motivational support in such an environment. An experiment with 61 junior high school students (ages 14-16) compared three

  14. Load Segmentation for Convergence of Distribution Automation and Advanced Metering Infrastructure Systems

    Science.gov (United States)

    Pamulaparthy, Balakrishna; KS, Swarup; Kommu, Rajagopal

    2014-12-01

    Distribution automation (DA) applications are limited to feeder level today and have zero visibility outside of the substation feeder and reaching down to the low-voltage distribution network level. This has become a major obstacle in realizing many automated functions and enhancing existing DA capabilities. Advanced metering infrastructure (AMI) systems are being widely deployed by utilities across the world creating system-wide communications access to every monitoring and service point, which collects data from smart meters and sensors in short time intervals, in response to utility needs. DA and AMI systems convergence provides unique opportunities and capabilities for distribution grid modernization with the DA system acting as a controller and AMI system acting as feedback to DA system, for which DA applications have to understand and use the AMI data selectively and effectively. In this paper, we propose a load segmentation method that helps the DA system to accurately understand and use the AMI data for various automation applications with a suitable case study on power restoration.

  15. Two-Dimensional Key Table-Based Group Key Distribution in Advanced Metering Infrastructure

    Directory of Open Access Journals (Sweden)

    Woong Go

    2014-01-01

    Full Text Available A smart grid provides two-way communication by using the information and communication technology. In order to establish two-way communication, the advanced metering infrastructure (AMI is used in the smart grid as the core infrastructure. This infrastructure consists of smart meters, data collection units, maintenance data management systems, and so on. However, potential security problems of the AMI increase owing to the application of the public network. This is because the transmitted information is electricity consumption data for charging. Thus, in order to establish a secure connection to transmit electricity consumption data, encryption is necessary, for which key distribution is required. Further, a group key is more efficient than a pairwise key in the hierarchical structure of the AMI. Therefore, we propose a group key distribution scheme using a two-dimensional key table through the analysis result of the sensor network group key distribution scheme. The proposed scheme has three phases: group key predistribution, selection of group key generation element, and generation of group key.

  16. Learning Grammatical Categories from Distributional Cues: Flexible Frames for Language Acquisition

    Science.gov (United States)

    St. Clair, Michelle C.; Monaghan, Padraic; Christiansen, Morten H.

    2010-01-01

    Numerous distributional cues in the child's environment may potentially assist in language learning, but what cues are useful to the child and when are these cues utilised? We propose that the most useful source of distributional cue is a flexible frame surrounding the word, where the language learner integrates information from the preceding and…

  17. Some Technical Implications of Distributed Cognition on the Design on Interactive Learning Environments.

    Science.gov (United States)

    Dillenbourg, Pierre

    1996-01-01

    Maintains that diagnosis, explanation, and tutoring, the functions of an interactive learning environment, are collaborative processes. Examines how human-computer interaction can be improved using a distributed cognition framework. Discusses situational and distributed knowledge theories and provides a model on how they can be used to redesign…

  18. Distributed practice and retrieval practice in primary school vocabulary learning: A multi-classroom study

    NARCIS (Netherlands)

    Goossens, Nicole; Camp, Gino; Verkoeijen, Peter; Tabbers, Huib; Bouwmeester, Samantha; Zwaan, Rolf

    2018-01-01

    Distributed practice and retrieval practice are promising learning strategies to use in education. We examined the effects of these strategies in primary school vocabulary lessons. Grades 2, 3, 4, and 6 children performed exercises that were part of the regular curriculum. For the distributed

  19. Problem-Based Learning and Problem-Solving Tools: Synthesis and Direction for Distributed Education Environments.

    Science.gov (United States)

    Friedman, Robert S.; Deek, Fadi P.

    2002-01-01

    Discusses how the design and implementation of problem-solving tools used in programming instruction are complementary with both the theories of problem-based learning (PBL), including constructivism, and the practices of distributed education environments. Examines how combining PBL, Web-based distributed education, and a problem-solving…

  20. IMPLEMENTATION OF MULTIAGENT REINFORCEMENT LEARNING MECHANISM FOR OPTIMAL ISLANDING OPERATION OF DISTRIBUTION NETWORK

    DEFF Research Database (Denmark)

    Saleem, Arshad; Lind, Morten

    2008-01-01

    among electric power utilities to utilize modern information and communication technologies (ICT) in order to improve the automation of the distribution system. In this paper we present our work for the implementation of a dynamic multi-agent based distributed reinforcement learning mechanism...

  1. Deep learning beyond cats and dogs: recent advances in diagnosing breast cancer with deep neural networks.

    Science.gov (United States)

    Burt, Jeremy R; Torosdagli, Neslisah; Khosravan, Naji; RaviPrakash, Harish; Mortazi, Aliasghar; Tissavirasingham, Fiona; Hussein, Sarfaraz; Bagci, Ulas

    2018-04-10

    Deep learning has demonstrated tremendous revolutionary changes in the computing industry and its effects in radiology and imaging sciences have begun to dramatically change screening paradigms. Specifically, these advances have influenced the development of computer-aided detection and diagnosis (CAD) systems. These technologies have long been thought of as "second-opinion" tools for radiologists and clinicians. However, with significant improvements in deep neural networks, the diagnostic capabilities of learning algorithms are approaching levels of human expertise (radiologists, clinicians etc.), shifting the CAD paradigm from a "second opinion" tool to a more collaborative utility. This paper reviews recently developed CAD systems based on deep learning technologies for breast cancer diagnosis, explains their superiorities with respect to previously established systems, defines the methodologies behind the improved achievements including algorithmic developments, and describes remaining challenges in breast cancer screening and diagnosis. We also discuss possible future directions for new CAD models that continue to change as artificial intelligence algorithms evolve.

  2. What do listeners learn from exposure to a vowel distribution? An analysis of listening strategies in distributional learning

    NARCIS (Netherlands)

    Wanrooij, K.; Escudero, P.; Raijmakers, M.E.J.

    2013-01-01

    This study first confirms the previous finding that Spanish learners improve their perception of a difficult Dutch vowel contrast through listening to a frequency distribution of the vowels involved in the contrast, a technique also known as distributional training. Secondly, it is demonstrated that

  3. Adult Learning, Economic Growth and the Distribution of Income

    Directory of Open Access Journals (Sweden)

    Peter J. Stauvermann

    2018-02-01

    Full Text Available Technological change causes three consequences: it guarantees economic growth, it requires employees to acquire more skills and human capital, and it increases inequality if employees are not capable adapting to new technologies. The second consequence makes it almost necessary for employees to learn during their whole working life, thereby accelerating technological change. Accordingly, the OECD (the Organization for Economic Co-operation and Development and many governments supports the idea of lifelong learning, but it remains unclear how to finance the education of adult students who are working efficiently. In this paper, we use an overlapping generation model with human capital accumulation and inequality to derive a mechanism which reduces income inequality and provides an incentive for all adults to invest more in education. As a consequence, the growth rate of per capita income will increase and income inequality will be reduced.

  4. Distributed Learning and Information Dynamics In Networked Autonomous Systems

    Science.gov (United States)

    2015-11-20

    problem, while balancing uncertainty, sensor information, and information about other agents. We used three variants of a warehouse task to show that a...Communication- Efficient Sparse Learning”, ICML 2014 workshop on New Learning Frameworks and Models for Big Data , December 2014. (to appear) 44. A. Bellet... data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this

  5. New Tools and Metrics for Evaluating Army Distributed Learning

    Science.gov (United States)

    2011-01-01

    courseware. Designing DL to provide for more opportunities for interaction with instructors and peers is likely to increase student engagement in IMI...toward blended learning may achieve these goals. Student engagement may also be fostered to the extent that the course pro- vides sufficient numbers of... student engagement . • Design and implement DL in ways that provide greater opportunities to interact with instructors and peers. • Enforce policy of

  6. Stability Analysis of an Advanced Persistent Distributed Denial-of-Service Attack Dynamical Model

    Directory of Open Access Journals (Sweden)

    Chunming Zhang

    2018-01-01

    Full Text Available The advanced persistent distributed denial-of-service (APDDoS attack is a fairly significant threat to cybersecurity. Formulating a mathematical model for accurate prediction of APDDoS attack is important. However, the dynamical model of APDDoS attack has barely been reported. This paper first proposes a novel dynamical model of APDDoS attack to understand the mechanisms of APDDoS attack. Then, the attacked threshold of this model is calculated. The global stability of attack-free and attacked equilibrium are both proved. The influences of the model’s parameters on attacked equilibrium are discussed. Eventually, the main conclusions of the theoretical analysis are examined through computer simulations.

  7. Centralized and Distributed Solutions for Fast Muting Adaptation in LTE-Advanced HetNets

    DEFF Research Database (Denmark)

    Soret, Beatriz; Pedersen, Klaus I.

    2015-01-01

    Enhanced Intercell Interference Coordination (eICIC) is known to provide promising performance benefits for LTE-Advanced Heterogeneous Networks. The use of eICIC facilitates more flexible inter-layer load balancing by means of small cell Range Extension (RE) and Almost Blank Subframes (ABS). Even...... though the eICIC configuration (RE and ABS) ideally should be instantaneously adapted to follow the fluctuations of the traffic and the channel conditions over time, previous studies have focused on slow intercell coordination. In this paper, we investigate fast dynamic eICIC solutions for centralized....... Two different fast muting adaptation algorithms are derived, and it is shown how those can be appplied to both the centralized and the distributed architecture. Performance results with bursty traffic show that the fast dynamic adaptation provides significant gains, both in 5%-ile and 50%-ile user...

  8. Advances in snow cover distributed modelling via ensemble simulations and assimilation of satellite data

    Science.gov (United States)

    Revuelto, J.; Dumont, M.; Tuzet, F.; Vionnet, V.; Lafaysse, M.; Lecourt, G.; Vernay, M.; Morin, S.; Cosme, E.; Six, D.; Rabatel, A.

    2017-12-01

    Nowadays snowpack models show a good capability in simulating the evolution of snow in mountain areas. However singular deviations of meteorological forcing and shortcomings in the modelling of snow physical processes, when accumulated on time along a snow season, could produce large deviations from real snowpack state. The evaluation of these deviations is usually assessed with on-site observations from automatic weather stations. Nevertheless the location of these stations could strongly influence the results of these evaluations since local topography may have a marked influence on snowpack evolution. Despite the evaluation of snowpack models with automatic weather stations usually reveal good results, there exist a lack of large scale evaluations of simulations results on heterogeneous alpine terrain subjected to local topographic effects.This work firstly presents a complete evaluation of the detailed snowpack model Crocus over an extended mountain area, the Arve upper catchment (western European Alps). This catchment has a wide elevation range with a large area above 2000m a.s.l. and/or glaciated. The evaluation compares results obtained with distributed and semi-distributed simulations (the latter nowadays used on the operational forecasting). Daily observations of the snow covered area from MODIS satellite sensor, seasonal glacier surface mass balance evolution measured in more than 65 locations and the galciers annual equilibrium line altitude from Landsat/Spot/Aster satellites, have been used for model evaluation. Additionally the latest advances in producing ensemble snowpack simulations for assimilating satellite reflectance data over extended areas will be presented. These advances comprises the generation of an ensemble of downscaled high-resolution meteorological forcing from meso-scale meteorological models and the application of a particle filter scheme for assimilating satellite observations. Despite the results are prefatory, they show a good

  9. 45 CFR 2516.600 - How are funds for school-based service-learning programs distributed?

    Science.gov (United States)

    2010-10-01

    ... 45 Public Welfare 4 2010-10-01 2010-10-01 false How are funds for school-based service-learning... (Continued) CORPORATION FOR NATIONAL AND COMMUNITY SERVICE SCHOOL-BASED SERVICE-LEARNING PROGRAMS Distribution of Funds § 2516.600 How are funds for school-based service-learning programs distributed? (a) Of...

  10. 45 CFR 2517.600 - How are funds for community-based service-learning programs distributed?

    Science.gov (United States)

    2010-10-01

    ... 45 Public Welfare 4 2010-10-01 2010-10-01 false How are funds for community-based service-learning... (Continued) CORPORATION FOR NATIONAL AND COMMUNITY SERVICE COMMUNITY-BASED SERVICE-LEARNING PROGRAMS Distribution of Funds § 2517.600 How are funds for community-based service-learning programs distributed? All...

  11. Noise Residual Learning for Noise Modeling in Distributed Video Coding

    DEFF Research Database (Denmark)

    Luong, Huynh Van; Forchhammer, Søren

    2012-01-01

    Distributed video coding (DVC) is a coding paradigm which exploits the source statistics at the decoder side to reduce the complexity at the encoder. The noise model is one of the inherently difficult challenges in DVC. This paper considers Transform Domain Wyner-Ziv (TDWZ) coding and proposes...

  12. Using Group Projects to Assess the Learning of Sampling Distributions

    Science.gov (United States)

    Neidigh, Robert O.; Dunkelberger, Jake

    2012-01-01

    In an introductory business statistics course, student groups used sample data to compare a set of sample means to the theoretical sampling distribution. Each group was given a production measurement with a population mean and standard deviation. The groups were also provided an excel spreadsheet with 40 sample measurements per week for 52 weeks…

  13. The Effects of Advance Graphic Organizers Strategy Intervention on Academic Achievement, Self Efficacy, and Motivation to Learn Social Studies in Learning Disabled Second Year Prep Students

    Science.gov (United States)

    Eissa, Mourad Ali

    2012-01-01

    This study investigated the effect of using advance graphic organizers on academic achievement, self efficacy, and motivation to learn social studies in learning disabled second year prep students. A total of 60 students identified with LD were invited to participate. The sample was randomly divided into two groups; experimental (n = 30, 23 boys,…

  14. Learning with distribution of optimized features for recognizing common CT imaging signs of lung diseases

    Science.gov (United States)

    Ma, Ling; Liu, Xiabi; Fei, Baowei

    2017-01-01

    Common CT imaging signs of lung diseases (CISLs) are defined as the imaging signs that frequently appear in lung CT images from patients. CISLs play important roles in the diagnosis of lung diseases. This paper proposes a novel learning method, namely learning with distribution of optimized feature (DOF), to effectively recognize the characteristics of CISLs. We improve the classification performance by learning the optimized features under different distributions. Specifically, we adopt the minimum spanning tree algorithm to capture the relationship between features and discriminant ability of features for selecting the most important features. To overcome the problem of various distributions in one CISL, we propose a hierarchical learning method. First, we use an unsupervised learning method to cluster samples into groups based on their distribution. Second, in each group, we use a supervised learning method to train a model based on their categories of CISLs. Finally, we obtain multiple classification decisions from multiple trained models and use majority voting to achieve the final decision. The proposed approach has been implemented on a set of 511 samples captured from human lung CT images and achieves a classification accuracy of 91.96%. The proposed DOF method is effective and can provide a useful tool for computer-aided diagnosis of lung diseases on CT images.

  15. Book Review ~ Advancing Online Learning in Asia. Editors: David Murphy, Namin Shin, and Weiyuan Zhang

    Directory of Open Access Journals (Sweden)

    Insung Jung

    2004-08-01

    Full Text Available The Internet, high-speed electronic communications, and computers have transformed the way we teach and learn. With the development of these new information and communication technologies, the idea of online education has been adopted in many developed, and more recently in developing countries, to bring wider opportunities to people in the form of increased access to flexible and interactive, open and distance learning systems. As stated in the Introduction of “Advancing Online Learning in Asia” edited by Murphy, Shin, and Zhang, online education is now everywhere and it “is changing the ways in which educational institutions interact with their students, for both traditional and distance education universities.” By examining recent developments of online education in Asia from multiple perspectives, this book has a potential to be an invaluable resource to educators. Taking cases from the Asian region in which online learning was introduced, implemented, and experienced, this book presents the cases from a number of perspectives, especially from student perspectives, and addresses pedagogical and technical issues faced by online educators. The breadth of the articles in this book provides a wide range of online learning cases and varied perspectives, which should clearly appeal to educators, researchers, administrators, and policy makers in online education.

  16. Supervised learning of probability distributions by neural networks

    Science.gov (United States)

    Baum, Eric B.; Wilczek, Frank

    1988-01-01

    Supervised learning algorithms for feedforward neural networks are investigated analytically. The back-propagation algorithm described by Werbos (1974), Parker (1985), and Rumelhart et al. (1986) is generalized by redefining the values of the input and output neurons as probabilities. The synaptic weights are then varied to follow gradients in the logarithm of likelihood rather than in the error. This modification is shown to provide a more rigorous theoretical basis for the algorithm and to permit more accurate predictions. A typical application involving a medical-diagnosis expert system is discussed.

  17. Blending work-integrated learning with distance education in an Australian radiation therapy advanced practice curriculum

    International Nuclear Information System (INIS)

    Matthews, Kristie; Wright, Caroline; Osborne, Catherine

    2014-01-01

    Advanced practice for radiation therapists has been a part of the international landscape for several years; however formal implementation into the Australian health care system is yet to happen. Despite this, three short course radiation therapy advanced practitioner programs have been established by an Australian tertiary institution in response to clinical service needs at several organisations. This paper describes the rationale for curriculum design and development of the program materials, the small-scale implementation of the programs at pilot sites, and the evolution of the curriculum to be available to registered radiation therapists nationally. Each program has been designed around a specific clinical role, where flexibility of delivery to busy practitioners was central to the decision to offer them via distance education. The curriculum comprises theoretical units of study which run in parallel to and underpin clinical practice units, where advanced competence in the specific area of practice is overseen by an experienced radiation oncologist mentor. Given the nature of the disparate clinical services requiring an advanced radiation therapy practitioner, the workplace learning component of the course is individually negotiated at a local level. Outcomes suggest that the flexible clinically based training underpinned by a distance education academic curriculum is able to support the development of advanced radiation therapy practitioners responsive to local service need, and ultimately may improve the patient experience

  18. Strategic Decision-Making Learning from Label Distributions: An Approach for Facial Age Estimation.

    Science.gov (United States)

    Zhao, Wei; Wang, Han

    2016-06-28

    Nowadays, label distribution learning is among the state-of-the-art methodologies in facial age estimation. It takes the age of each facial image instance as a label distribution with a series of age labels rather than the single chronological age label that is commonly used. However, this methodology is deficient in its simple decision-making criterion: the final predicted age is only selected at the one with maximum description degree. In many cases, different age labels may have very similar description degrees. Consequently, blindly deciding the estimated age by virtue of the highest description degree would miss or neglect other valuable age labels that may contribute a lot to the final predicted age. In this paper, we propose a strategic decision-making label distribution learning algorithm (SDM-LDL) with a series of strategies specialized for different types of age label distribution. Experimental results from the most popular aging face database, FG-NET, show the superiority and validity of all the proposed strategic decision-making learning algorithms over the existing label distribution learning and other single-label learning algorithms for facial age estimation. The inner properties of SDM-LDL are further explored with more advantages.

  19. The distribution of advanced glycation end products and their receptor in the gastrointestinal tract in the rats

    DEFF Research Database (Denmark)

    Chen, Pengmin; Zhao, Jingbo; Gregersen, Hans

    2012-01-01

    To investigate the distribution of advanced glycation end products (AGEs) and their receptor (RAGE) in the gastrointestinal (GI) tract to provide a basis for further study of the association between AGE/RAGE and diabetic GI dysfunction. METHODS: The distribution of AGEs [N epsilon-(carboxymethyl)......To investigate the distribution of advanced glycation end products (AGEs) and their receptor (RAGE) in the gastrointestinal (GI) tract to provide a basis for further study of the association between AGE/RAGE and diabetic GI dysfunction. METHODS: The distribution of AGEs [N epsilon......-(carboxymethyl) lysine and N epsilon-(carboxyethyl) lysine] and RAGE were detected in the esopha-geal, gastric, duodenal, jejunal, ileal, colonic and rectal tissues of normal adult Wistar rats using immunohistochemistry. RESULTS: In the esophagus, AGEs and RAGE were mainly distributed in striated muscle cells...

  20. The potential of blended learning in education and training for advanced civilian and military trauma care.

    Science.gov (United States)

    Sonesson, Linda; Boffard, Kenneth; Lundberg, Lars; Rydmark, Martin; Karlgren, Klas

    2018-01-01

    In the field of advanced care of the complex trauma patient, there is an emerging need for focused education and training. However, several hospitals do not support further education and training in this field, and the challenge of releasing time for physicians and nurses is well-known. Educational strategies using blended learning, which combines traditional classroom methods with modern computer-assisted methods and media, have not yet been widely used. This study analysed the educational challenges and areas for improvement, according to senior physicians and nurses, and investigated the potential use of blended learning. The setting was an international course, Definitive Surgical Trauma Care (DSTC) - Military Version, part of a programme which prepares health professionals for work during extreme conditions. The sample consisted of senior physicians and nurses, participating in the course in September 2015. A survey was completed, interviews were performed and a post-course survey was conducted 18 months later in March 2017. The most difficult aspect of learning how to manage the complex trauma patient, was the lack of real practice. Even though the respondents were knowledgeable in advanced trauma, they lacked personal experience in managing complex trauma cases. Cases presented during the course represented significantly greater complexity of injury compared to those usually seen in hospitals and during military deployment. The following educational challenges were identified from the study: (1) Lack of experience and knowledge of advanced trauma care. (2) Lack of the use of blended learning as support for education and training. (3) Limited time available for preparation and reflection in the education and training process. (4) Lack of support for such education and training from home hospitals. (5) The unfulfilled requirement for multidisciplinary team-training in the military medical environment. Educational strategies and methods, such as blended

  1. A PKI Approach for Deploying Modern Secure Distributed E-Learning and M-Learning Environments

    Science.gov (United States)

    Kambourakis, Georgios; Kontoni, Denise-Penelope N.; Rouskas, Angelos; Gritzalis, Stefanos

    2007-01-01

    While public key cryptography is continuously evolving and its installed base is growing significantly, recent research works examine its potential use in e-learning or m-learning environments. Public key infrastructure (PKI) and attribute certificates (ACs) can provide the appropriate framework to effectively support authentication and…

  2. MACHINE LEARNING FOR THE SELF-ORGANIZATION OF DISTRIBUTED SYSTEMS IN ECONOMIC APPLICATIONS

    Directory of Open Access Journals (Sweden)

    Jerzy Balicki

    2017-03-01

    Full Text Available In this paper, an application of machine learning to the problem of self-organization of distributed systems has been discussed with regard to economic applications, with particular emphasis on supervised neural network learning to predict stock investments and some ratings of companies. In addition, genetic programming can play an important role in the preparation and testing of several financial information systems. For this reason, machine learning applications have been discussed because some software applications can be automatically constructed by genetic programming. To obtain a competitive advantage, machine learning can be used for the management of self-organizing cloud computing systems performing calculations for business. Also the use of selected economic self-organizing distributed systems has been described, including some testing methods of predicting borrower reliability. Finally, some conclusions and directions for further research have been proposed.

  3. The Novel Quantitative Technique for Assessment of Gait Symmetry Using Advanced Statistical Learning Algorithm

    OpenAIRE

    Wu, Jianning; Wu, Bin

    2015-01-01

    The accurate identification of gait asymmetry is very beneficial to the assessment of at-risk gait in the clinical applications. This paper investigated the application of classification method based on statistical learning algorithm to quantify gait symmetry based on the assumption that the degree of intrinsic change in dynamical system of gait is associated with the different statistical distributions between gait variables from left-right side of lower limbs; that is, the discrimination of...

  4. What do we learn from transverse energy distributions?

    International Nuclear Information System (INIS)

    Baym, G.; Friedman, G.; Heiselberg, H.

    1990-01-01

    Transverse energy measurements provide a wealth of information on the physics of ultrarelativistic heavy-ion collisions. To begin with they tell one the degree to which the colliding nuclei transfer energy and stop each other. With information about the space-time dynamics of the collisions, transverse energy data enables one to estimate the evolving energy density in the collision volume, a quantity needed to assess the likelihood of quark-gluon plasma formation. Furthermore, since the transverse energy distribution is to a large extent determined by the geometry of the collision, transverse energy is a useful indicator of centrality of collisions. It also provides considerable evidence for the rescattering of secondaries in the medium. Finally, fluctuations in the tails of the transverse energy distributions are an important probe of the independence of or possible coherence among the nucleon-nucleon subcollisions that constitute the first stage of heavy-ion collisions. Since the experimental talks in this workshop have done a rather complete job of presenting the transverse energy data from both BNL and CERN, the focus, as the authors discuss these topics in turn, will be on the theoretical interpretation of the data

  5. Cognitive communication and cooperative hetnet coexistence selected advances on spectrum sensing, learning, and security approaches

    CERN Document Server

    Bader, Faouzi

    2014-01-01

    This book, written by experts from universities and major industrial research laboratories, is devoted to the very hot topic of cognitive radio and networking for cooperative coexistence of heterogeneous wireless networks. Selected highly relevant advanced research is presented on spectrum sensing and progress toward the realization of accurate radio environment mapping, biomimetic learning for self-organizing networks, security threats (with a special focus on primary user emulation attack), and cognition as a tool for green next-generation networks. The research activities covered include work undertaken within the framework of the European COST Action IC0902, which is geared towards the definition of a European platform for cognitive radio and networks. Communications engineers, R&D engineers, researchers, and students will all benefit from this complete reference on recent advances in wireless communications and the design and implementation of cognitive radio systems and networks.

  6. Evolution of the Pediatric Advanced Life Support course: enhanced learning with a new debriefing tool and Web-based module for Pediatric Advanced Life Support instructors.

    Science.gov (United States)

    Cheng, Adam; Rodgers, David L; van der Jagt, Élise; Eppich, Walter; O'Donnell, John

    2012-09-01

    To describe the history of the Pediatric Advanced Life Support course and outline the new developments in instructor training that will impact the way debriefing is conducted during Pediatric Advanced Life Support courses. The Pediatric Advanced Life Support course, first released by the American Heart Association in 1988, has seen substantial growth and change over the past few decades. Over that time, Pediatric Advanced Life Support has become the standard for resuscitation training for pediatric healthcare providers in North America. The incorporation of high-fidelity simulation-based learning into the most recent version of Pediatric Advanced Life Support has helped to enhance the realism of scenarios and cases, but has also placed more emphasis on the importance of post scenario debriefing. We developed two new resources: an online debriefing module designed to introduce a new model of debriefing and a debriefing tool for real-time use during Pediatric Advanced Life Support courses, to enhance and standardize the quality of debriefing by Pediatric Advanced Life Support instructors. In this article, we review the history of Pediatric Advanced Life Support and Pediatric Advanced Life Support instructor training and discuss the development and implementation of the new debriefing module and debriefing tool for Pediatric Advanced Life Support instructors. The incorporation of the debriefing module and debriefing tool into the 2011 Pediatric Advanced Life Support instructor materials will help both new and existing Pediatric Advanced Life Support instructors develop and enhance their debriefing skills with the intention of improving the acquisition of knowledge and skills for Pediatric Advanced Life Support students.

  7. Apoc Social: A Mobile Interactive and Social Learning Platform for Collaborative Solving of Advanced Problems in Organic Chemistry.

    Science.gov (United States)

    Sievertsen, Niels; Carreira, Erick M

    2018-02-01

    Mobile devices such as smartphones are carried in the pockets of university students around the globe and are increasingly cheap to come by. These portable devices have evolved into powerful and interconnected handheld computers, which, among other applications, can be used as advanced learning tools and providers of targeted, curated content. Herein, we describe Apoc Social (Advanced Problems in Organic Chemistry Social), a mobile application that assists both learning and teaching college-level organic chemistry both in the classroom and on the go. With more than 750 chemistry exercises available, Apoc Social facilitates collaborative learning through discussion boards and fosters enthusiasm for complex organic chemistry.

  8. Integrating Project-Based Service-Learning into an Advanced Environmental Chemistry Course

    Science.gov (United States)

    Draper, Alison J.

    2004-02-01

    In an advanced environmental chemistry course, the inclusion of semester-long scientific service projects successfully integrated the research process with course content. Each project involved a unique community-based environmental analysis in which students assessed an aspect of environmental health. The projects were due in small pieces at even intervals, and students worked independently or in pairs. Initially, students wrote a project proposal in which they chose and justified a project. Following a literature review of their topic, they drafted sampling and analysis plans using methods in the literature. Samples were collected and analyzed, and all students assembled scientific posters describing the results of their study. In the last week of the semester, the class traveled to a regional professional meeting to present the posters. In all, students found the experience valuable. They learned to be professional environmental chemists and learned the value of the discipline to community health. Students not only learned about their own project in depth, but they were inspired to learn textbook material, not for an exam, but because it helped them understand their own project. Finally, having a community to answer to at the end of the project motivated students to do careful work.

  9. READING AUTHENTIC EFL TEXT USING VISUALIZATION AND ADVANCE ORGANIZERS IN A MULTIMEDIA LEARNING ENVIRONMENT

    Directory of Open Access Journals (Sweden)

    Tsuiping Chen

    2007-02-01

    Full Text Available The purpose of this experimental study was to compare the effects of different types of computer-generated visuals (static versus animated and advance organizers (descriptive versus question in enhancing comprehension and retention of a content-based lesson for learning English as a Foreign Language (EFL. Additionally, the study investigated the interactive effect of students’ existing reading proficiency level and the above-mentioned treatments on their reading comprehension achievement. Students from two EFL reading sections (N = 115 were tested on their reading proficiency and then randomly assigned to one of four computer-based instructional modules—static visual alone, animation alone, animation plus descriptive advance organizer, and animation plus question advance organizer. Once having interacted with their respective instructional materials, students then took four criterion tests immediately afterward and again four weeks later. The results showed that the animation group outperformed the static visual group in one of the four tests, and that animation embedded with a question advance organizer had a marginal effect among the four treatments in facilitating the acquisition of L2 reading comprehension both for the immediate and the delayed posttests.

  10. A Self-Organizing Incremental Neural Network based on local distribution learning.

    Science.gov (United States)

    Xing, Youlu; Shi, Xiaofeng; Shen, Furao; Zhou, Ke; Zhao, Jinxi

    2016-12-01

    In this paper, we propose an unsupervised incremental learning neural network based on local distribution learning, which is called Local Distribution Self-Organizing Incremental Neural Network (LD-SOINN). The LD-SOINN combines the advantages of incremental learning and matrix learning. It can automatically discover suitable nodes to fit the learning data in an incremental way without a priori knowledge such as the structure of the network. The nodes of the network store rich local information regarding the learning data. The adaptive vigilance parameter guarantees that LD-SOINN is able to add new nodes for new knowledge automatically and the number of nodes will not grow unlimitedly. While the learning process continues, nodes that are close to each other and have similar principal components are merged to obtain a concise local representation, which we call a relaxation data representation. A denoising process based on density is designed to reduce the influence of noise. Experiments show that the LD-SOINN performs well on both artificial and real-word data. Copyright © 2016 Elsevier Ltd. All rights reserved.

  11. Developing clinical leaders: the impact of an action learning mentoring programme for advanced practice nurses.

    Science.gov (United States)

    Leggat, Sandra G; Balding, Cathy; Schiftan, Dan

    2015-06-01

    To determine whether a formal mentoring programme assists nurse practitioner candidates to develop competence in the clinical leadership competencies required in their advanced practice roles. Nurse practitioner candidates are required to show evidence of defined clinical leadership competencies when they apply for endorsement within the Australian health care system. Aiming to assist the candidates with the development or enhancement of these leadership skills, 18 nurse practitioner candidates participated in a mentoring programme that matched them with senior nurse mentors. A pre-postlongitudinal intervention study. Eighteen nurse practitioner candidates and 17 senior nurses participated in a voluntary mentoring programme that incorporated coaching and action learning over 18 months in 2012 and 2013. Participants completed a pen and paper questionnaire to document baseline measures of self-reported leadership practices prior to commencement of the programme and again at the end of the programme. The mentors and the nurse practitioner candidates qualitatively evaluated the programme as successful and quantitative data illustrated significant improvement in self-reported leadership practices among the nurse practitioner candidates. In particular, the nurse practitioner candidates reported greater competence in the transformational aspects of leadership, which is directly related to the nurse practitioner candidate clinical leadership standard. A formal, structured mentoring programme based on principles of action learning was successful in assisting Australian advanced practice nurses enhance their clinical leadership skills in preparation for formal endorsement as a nurse practitioner and for success in their advanced practice role. Mentoring can assist nurses to transition to new roles and develop knowledge and skills in clinical leadership essential for advanced practice roles. Nurse managers should make greater use of mentoring programmes to support nurses in

  12. Learning from the History of Distributed Query Processing

    DEFF Research Database (Denmark)

    Betz, Heiko; Gropengießer, Francis; Hose, Katja

    2012-01-01

    The vision of the Semantic Web has triggered the development of various new applications and opened up new directions in research. Recently, much effort has been put into the development of techniques for query processing over Linked Data. Being based upon techniques originally developed...... for distributed and federated databases, some of them inherit the same or similar problems. Thus, the goal of this paper is to point out pitfalls that the previous generation of researchers has already encountered and to introduce the Linked Data as a Service as an idea that has the potential to solve the problem...... in some scenarios. Hence, this paper discusses nine theses about Linked Data processing and sketches a research agenda for future endeavors in the area of Linked Data processing....

  13. Learning to read aloud: A neural network approach using sparse distributed memory

    Science.gov (United States)

    Joglekar, Umesh Dwarkanath

    1989-01-01

    An attempt to solve a problem of text-to-phoneme mapping is described which does not appear amenable to solution by use of standard algorithmic procedures. Experiments based on a model of distributed processing are also described. This model (sparse distributed memory (SDM)) can be used in an iterative supervised learning mode to solve the problem. Additional improvements aimed at obtaining better performance are suggested.

  14. DeepSpark: A Spark-Based Distributed Deep Learning Framework for Commodity Clusters

    OpenAIRE

    Kim, Hanjoo; Park, Jaehong; Jang, Jaehee; Yoon, Sungroh

    2016-01-01

    The increasing complexity of deep neural networks (DNNs) has made it challenging to exploit existing large-scale data processing pipelines for handling massive data and parameters involved in DNN training. Distributed computing platforms and GPGPU-based acceleration provide a mainstream solution to this computational challenge. In this paper, we propose DeepSpark, a distributed and parallel deep learning framework that exploits Apache Spark on commodity clusters. To support parallel operation...

  15. Distributed Generation Planning using Peer Enhanced Multi-objective Teaching-Learning based Optimization in Distribution Networks

    Science.gov (United States)

    Selvam, Kayalvizhi; Vinod Kumar, D. M.; Siripuram, Ramakanth

    2017-04-01

    In this paper, an optimization technique called peer enhanced teaching learning based optimization (PeTLBO) algorithm is used in multi-objective problem domain. The PeTLBO algorithm is parameter less so it reduced the computational burden. The proposed peer enhanced multi-objective based TLBO (PeMOTLBO) algorithm has been utilized to find a set of non-dominated optimal solutions [distributed generation (DG) location and sizing in distribution network]. The objectives considered are: real power loss and the voltage deviation subjected to voltage limits and maximum penetration level of DG in distribution network. Since the DG considered is capable of injecting real and reactive power to the distribution network the power factor is considered as 0.85 lead. The proposed peer enhanced multi-objective optimization technique provides different trade-off solutions in order to find the best compromise solution a fuzzy set theory approach has been used. The effectiveness of this proposed PeMOTLBO is tested on IEEE 33-bus and Indian 85-bus distribution system. The performance is validated with Pareto fronts and two performance metrics (C-metric and S-metric) by comparing with robust multi-objective technique called non-dominated sorting genetic algorithm-II and also with the basic TLBO.

  16. Subspace Learning via Local Probability Distribution for Hyperspectral Image Classification

    Directory of Open Access Journals (Sweden)

    Huiwu Luo

    2015-01-01

    Full Text Available The computational procedure of hyperspectral image (HSI is extremely complex, not only due to the high dimensional information, but also due to the highly correlated data structure. The need of effective processing and analyzing of HSI has met many difficulties. It has been evidenced that dimensionality reduction has been found to be a powerful tool for high dimensional data analysis. Local Fisher’s liner discriminant analysis (LFDA is an effective method to treat HSI processing. In this paper, a novel approach, called PD-LFDA, is proposed to overcome the weakness of LFDA. PD-LFDA emphasizes the probability distribution (PD in LFDA, where the maximum distance is replaced with local variance for the construction of weight matrix and the class prior probability is applied to compute the affinity matrix. The proposed approach increases the discriminant ability of the transformed features in low dimensional space. Experimental results on Indian Pines 1992 data indicate that the proposed approach significantly outperforms the traditional alternatives.

  17. Deep Learning for Detection of Object-Based Forgery in Advanced Video

    Directory of Open Access Journals (Sweden)

    Ye Yao

    2017-12-01

    Full Text Available Passive video forensics has drawn much attention in recent years. However, research on detection of object-based forgery, especially for forged video encoded with advanced codec frameworks, is still a great challenge. In this paper, we propose a deep learning-based approach to detect object-based forgery in the advanced video. The presented deep learning approach utilizes a convolutional neural network (CNN to automatically extract high-dimension features from the input image patches. Different from the traditional CNN models used in computer vision domain, we let video frames go through three preprocessing layers before being fed into our CNN model. They include a frame absolute difference layer to cut down temporal redundancy between video frames, a max pooling layer to reduce computational complexity of image convolution, and a high-pass filter layer to enhance the residual signal left by video forgery. In addition, an asymmetric data augmentation strategy has been established to get a similar number of positive and negative image patches before the training. The experiments have demonstrated that the proposed CNN-based model with the preprocessing layers has achieved excellent results.

  18. Foundational Report Series. Advanced Distribution management Systems for Grid Modernization (Importance of DMS for Distribution Grid Modernization)

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Jianhui [Argonne National Lab. (ANL), Argonne, IL (United States)

    2015-09-01

    Grid modernization is transforming the operation and management of electric distribution systems from manual, paper-driven business processes to electronic, computer-assisted decisionmaking. At the center of this business transformation is the distribution management system (DMS), which provides a foundation from which optimal levels of performance can be achieved in an increasingly complex business and operating environment. Electric distribution utilities are facing many new challenges that are dramatically increasing the complexity of operating and managing the electric distribution system: growing customer expectations for service reliability and power quality, pressure to achieve better efficiency and utilization of existing distribution system assets, and reduction of greenhouse gas emissions by accommodating high penetration levels of distributed generating resources powered by renewable energy sources (wind, solar, etc.). Recent “storm of the century” events in the northeastern United States and the lengthy power outages and customer hardships that followed have greatly elevated the need to make power delivery systems more resilient to major storm events and to provide a more effective electric utility response during such regional power grid emergencies. Despite these newly emerging challenges for electric distribution system operators, only a small percentage of electric utilities have actually implemented a DMS. This paper discusses reasons why a DMS is needed and why the DMS may emerge as a mission-critical system that will soon be considered essential as electric utilities roll out their grid modernization strategies.

  19. Teaching and learning in the operating theatre: a framework for trainers and advanced trainees in obstetrics and gynaecology.

    Science.gov (United States)

    Mukhopadhyay, S; China, S

    2010-04-01

    Surgical training of 'advanced trainees' in Obstetrics and Gynaecology currently occurs in a rather unstructured fashion. This is even more complicated by reduced training time of doctors necessitated by the European working time directive. Teaching and learning in theatre is a combination of art and science. This paper attempts to address the issues hampering effective theatre training and suggests ways to overcome them. The 'operating theatre' plan includes a needs assessment of trainees, goal setting and instructional methodologies. Various learning styles could potentially be adopted, although it might be difficult to choose a learning style suitable for a particular trainee. Additionally, team working skills and experiential learning need to be facilitated.

  20. Non-linear learning in online tutorial to enhance students’ knowledge on normal distribution application topic

    Science.gov (United States)

    Kartono; Suryadi, D.; Herman, T.

    2018-01-01

    This study aimed to analyze the enhancement of non-linear learning (NLL) in the online tutorial (OT) content to students’ knowledge of normal distribution application (KONDA). KONDA is a competence expected to be achieved after students studied the topic of normal distribution application in the course named Education Statistics. The analysis was performed by quasi-experiment study design. The subject of the study was divided into an experimental class that was given OT content in NLL model and a control class which was given OT content in conventional learning (CL) model. Data used in this study were the results of online objective tests to measure students’ statistical prior knowledge (SPK) and students’ pre- and post-test of KONDA. The statistical analysis test of a gain score of KONDA of students who had low and moderate SPK’s scores showed students’ KONDA who learn OT content with NLL model was better than students’ KONDA who learn OT content with CL model. Meanwhile, for students who had high SPK’s scores, the gain score of students who learn OT content with NLL model had relatively similar with the gain score of students who learn OT content with CL model. Based on those findings it could be concluded that the NLL model applied to OT content could enhance KONDA of students in low and moderate SPK’s levels. Extra and more challenging didactical situation was needed for students in high SPK’s level to achieve the significant gain score.

  1. Distributing Leadership to Establish Developing and Learning School Organisations in the Swedish Context

    Science.gov (United States)

    Liljenberg, Mette

    2015-01-01

    Leadership is considered to be significant for creating a developing and learning school organisation. In Sweden, distributed leadership and teacher teams are an "institutionalised practice"; despite this, sustainable school improvement is difficult to achieve. This article presents findings from a case study of three schools that…

  2. Framing and Enhancing Distributed Leadership in the Quality Management of Online Learning Environments in Higher Education

    Science.gov (United States)

    Holt, Dale; Palmer, Stuart; Gosper, Maree; Sankey, Michael; Allan, Garry

    2014-01-01

    This article reports on the findings of senior leadership interviews in a nationally funded project on distributed leadership in the quality management of online learning environments (OLEs) in higher education. Questions were framed around the development of an OLE quality management framework and the situation of the characteristics of…

  3. Planning for the Digital Classroom and Distributed Learning: Policies and Planning for Online Instructional Resources

    Science.gov (United States)

    McGee, Patricia; Diaz, Veronica

    2005-01-01

    In an era of state budget cuts and a tight economy, distributed learning is often seen as a way to address the needs of colleges and universities looking for additional revenue sources. Likewise, budding virtual universities, consortia, and corporate partnerships are now providing new ways for institutions to share resources across campuses. The…

  4. Transforming the Doctorate from Residential to Online: A Distributed PhD Learning Technologies

    Science.gov (United States)

    Jones, Greg; Warren, Scott J.; Ennis-Cole, Demetria; Knezek, Gerald; Lin, Lin; Norris, Cathie

    2014-01-01

    This article discusses a systemic change that expanded the doctorate in Learning Technologies at the University of North Texas to include a distributed option, delivered primarily online. It provides an overview of the development process from concept to initial implementation. The article examines the specific differences that make the online…

  5. Distribution of Feedback among Teacher and Students in Online Collaborative Learning in Small Groups

    Science.gov (United States)

    Coll, Cesar; Rochera, Maria Jose; de Gispert, Ines; Diaz-Barriga, Frida

    2013-01-01

    This study explores the characteristics and distribution of the feedback provided by the participants (a teacher and her students) in an activity organized inside a collaborative online learning environment. We analyse 853 submissions made by two groups of graduate students and their teacher (N1 = 629 & N2 = 224) involved in the collaborative…

  6. Classroom Audio Distribution in the Postsecondary Setting: A Story of Universal Design for Learning

    Science.gov (United States)

    Flagg-Williams, Joan B.; Bokhorst-Heng, Wendy D.

    2016-01-01

    Classroom Audio Distribution Systems (CADS) consist of amplification technology that enhances the teacher's, or sometimes the student's, vocal signal above the background noise in a classroom. Much research has supported the benefits of CADS for student learning, but most of it has focused on elementary school classrooms. This study investigated…

  7. Cross-Cultural Management Learning through Innovative Pedagogy: An Exploratory Study of Globally Distributed Student Teams

    Science.gov (United States)

    Bartel-Radic, Anne; Moos, J. Chris; Long, Suzanna K.

    2015-01-01

    This article presents an innovative pedagogy based on student participation in globally distributed project teams. The study questions the link between student learning of intercultural competence and the global teaming experience. Data was collected from 115 students participating in 22 virtual intercultural teams. Results revealed that students…

  8. Effect of tillage on water advance and distribution under surge and continuous furrow irrigation methods for cotton in Egypt

    NARCIS (Netherlands)

    Ismail, S.M.

    2006-01-01

    A field experiment was carried out to assess the effect of tillage on water advance and water distribution in the root zone area (0.5 m) under continuous and surge flow irrigation in a cotton field. The experiment was conducted at the Agriculture Experimental Station, Assiut University, Assiut,

  9. Measurements of the fast ion distribution during neutral beam injection and ion cyclotron heating in ATF [Advanced Toroidal Facility

    International Nuclear Information System (INIS)

    Wade, M.R.; Kwon, M.; Thomas, C.E.; Colchin, R.J.; England, A.C.; Gossett, J.M.; Horton, L.D.; Isler, R.C.; Lyon, J.F.; Rasmussen, D.A.; Rayburn, T.M.; Shepard, T.D.; Bell, G.L.; Fowler, R.H.; Morris, R.N.

    1990-01-01

    A neutral particle analyzer (NPA) with horizontal and vertical scanning capability has been used to make initial measurements of the fast ion distribution during neutral beam injection (NBI) and ion cyclotron heating (ICH) on the Advanced Toroidal Facility (ATF). These measurements are presented and compared with the results of modeling codes that predict the analyzer signals during these heating processes. 6 refs., 5 figs

  10. Online Distributed Learning Over Networks in RKH Spaces Using Random Fourier Features

    Science.gov (United States)

    Bouboulis, Pantelis; Chouvardas, Symeon; Theodoridis, Sergios

    2018-04-01

    We present a novel diffusion scheme for online kernel-based learning over networks. So far, a major drawback of any online learning algorithm, operating in a reproducing kernel Hilbert space (RKHS), is the need for updating a growing number of parameters as time iterations evolve. Besides complexity, this leads to an increased need of communication resources, in a distributed setting. In contrast, the proposed method approximates the solution as a fixed-size vector (of larger dimension than the input space) using Random Fourier Features. This paves the way to use standard linear combine-then-adapt techniques. To the best of our knowledge, this is the first time that a complete protocol for distributed online learning in RKHS is presented. Conditions for asymptotic convergence and boundness of the networkwise regret are also provided. The simulated tests illustrate the performance of the proposed scheme.

  11. Learning Based Approach for Optimal Clustering of Distributed Program's Call Flow Graph

    Science.gov (United States)

    Abofathi, Yousef; Zarei, Bager; Parsa, Saeed

    Optimal clustering of call flow graph for reaching maximum concurrency in execution of distributable components is one of the NP-Complete problems. Learning automatas (LAs) are search tools which are used for solving many NP-Complete problems. In this paper a learning based algorithm is proposed to optimal clustering of call flow graph and appropriate distributing of programs in network level. The algorithm uses learning feature of LAs to search in state space. It has been shown that the speed of reaching to solution increases remarkably using LA in search process, and it also prevents algorithm from being trapped in local minimums. Experimental results show the superiority of proposed algorithm over others.

  12. Foundational Report Series: Advanced Distribution Management Systems for Grid Modernization, DMS Integration of Distributed Energy Resources and Microgrids

    Energy Technology Data Exchange (ETDEWEB)

    Singh, Ravindra [Argonne National Lab. (ANL), Argonne, IL (United States); Reilly, James T. [Reilly Associates, Pittston, PA (United States); Wang, Jianhui [Argonne National Lab. (ANL), Argonne, IL (United States); Lu, Xiaonan [Argonne National Lab. (ANL), Argonne, IL (United States); Kang, Ning [Argonne National Lab. (ANL), Argonne, IL (United States)

    2017-03-01

    Deregulation of the electric utility industry, environmental concerns associated with traditional fossil fuel-based power plants, volatility of electric energy costs, Federal and State regulatory support of “green” energy, and rapid technological developments all support the growth of Distributed Energy Resources (DERs) in electric utility systems and ensure an important role for DERs in the smart grid and other aspects of modern utilities. DERs include distributed generation (DG) systems, such as renewables; controllable loads (also known as demand response); and energy storage systems. This report describes the role of aggregators of DERs in providing optimal services to distribution networks, through DER monitoring and control systems—collectively referred to as a Distributed Energy Resource Management System (DERMS)—and microgrids in various configurations.

  13. Advance distribution of misoprostol for prevention of postpartum hemorrhage (PPH) at home births in two districts of Liberia

    Science.gov (United States)

    2014-01-01

    Background A postpartum hemorrhage prevention program to increase uterotonic coverage for home and facility births was introduced in two districts of Liberia. Advance distribution of misoprostol was offered during antenatal care (ANC) and home visits. Feasibility, acceptability, effectiveness of distribution mechanisms and uterotonic coverage were evaluated. Methods Eight facilities were strengthened to provide PPH prevention with oxytocin, PPH management and advance distribution of misoprostol during ANC. Trained traditional midwives (TTMs) as volunteer community health workers (CHWs) provided education to pregnant women, and district reproductive health supervisors (DRHSs) distributed misoprostol during home visits. Data were collected through facility and DRHS registers. Postpartum interviews were conducted with a sample of 550 women who received advance distribution of misoprostol on place of delivery, knowledge, misoprostol use, and satisfaction. Results There were 1826 estimated deliveries during the seven-month implementation period. A total of 980 women (53.7%) were enrolled and provided misoprostol, primarily through ANC (78.2%). Uterotonic coverage rate of all deliveries was 53.5%, based on 97.7% oxytocin use at recorded facility vaginal births and 24.9% misoprostol use at home births. Among 550 women interviewed postpartum, 87.7% of those who received misoprostol and had a home birth took the drug. Sixty-three percent (63.0%) took it at the correct time, and 54.0% experienced at least one minor side effect. No serious adverse events reported among enrolled women. Facility-based deliveries appeared to increase during the program. Conclusions The program was moderately effective at achieving high uterotonic coverage of all births. Coverage of home births was low despite the use of two channels of advance distribution of misoprostol. Although ANC reached a greater proportion of women in late pregnancy than home visits, 46.3% of expected deliveries did not

  14. Advance distribution of misoprostol for prevention of postpartum hemorrhage (PPH) at home births in two districts of Liberia.

    Science.gov (United States)

    Smith, Jeffrey Michael; Baawo, Saye Dahn; Subah, Marion; Sirtor-Gbassie, Varwo; Howe, Cuallau Jabbeh; Ishola, Gbenga; Tehoungue, Bentoe Z; Dwivedi, Vikas

    2014-06-04

    A postpartum hemorrhage prevention program to increase uterotonic coverage for home and facility births was introduced in two districts of Liberia. Advance distribution of misoprostol was offered during antenatal care (ANC) and home visits. Feasibility, acceptability, effectiveness of distribution mechanisms and uterotonic coverage were evaluated. Eight facilities were strengthened to provide PPH prevention with oxytocin, PPH management and advance distribution of misoprostol during ANC. Trained traditional midwives (TTMs) as volunteer community health workers (CHWs) provided education to pregnant women, and district reproductive health supervisors (DRHSs) distributed misoprostol during home visits. Data were collected through facility and DRHS registers. Postpartum interviews were conducted with a sample of 550 women who received advance distribution of misoprostol on place of delivery, knowledge, misoprostol use, and satisfaction. There were 1826 estimated deliveries during the seven-month implementation period. A total of 980 women (53.7%) were enrolled and provided misoprostol, primarily through ANC (78.2%). Uterotonic coverage rate of all deliveries was 53.5%, based on 97.7% oxytocin use at recorded facility vaginal births and 24.9% misoprostol use at home births. Among 550 women interviewed postpartum, 87.7% of those who received misoprostol and had a home birth took the drug. Sixty-three percent (63.0%) took it at the correct time, and 54.0% experienced at least one minor side effect. No serious adverse events reported among enrolled women. Facility-based deliveries appeared to increase during the program. The program was moderately effective at achieving high uterotonic coverage of all births. Coverage of home births was low despite the use of two channels of advance distribution of misoprostol. Although ANC reached a greater proportion of women in late pregnancy than home visits, 46.3% of expected deliveries did not receive education or advance

  15. Combining advanced networked technology and pedagogical methods to improve collaborative distance learning.

    Science.gov (United States)

    Staccini, Pascal; Dufour, Jean-Charles; Raps, Hervé; Fieschi, Marius

    2005-01-01

    Making educational material be available on a network cannot be reduced to merely implementing hypermedia and interactive resources on a server. A pedagogical schema has to be defined to guide students for learning and to provide teachers with guidelines to prepare valuable and upgradeable resources. Components of a learning environment, as well as interactions between students and other roles such as author, tutor and manager, can be deduced from cognitive foundations of learning, such as the constructivist approach. Scripting the way a student will to navigate among information nodes and interact with tools to build his/her own knowledge can be a good way of deducing the features of the graphic interface related to the management of the objects. We defined a typology of pedagogical resources, their data model and their logic of use. We implemented a generic and web-based authoring and publishing platform (called J@LON for Join And Learn On the Net) within an object-oriented and open-source programming environment (called Zope) embedding a content management system (called Plone). Workflow features have been used to mark the progress of students and to trace the life cycle of resources shared by the teaching staff. The platform integrated advanced on line authoring features to create interactive exercises and support live courses diffusion. The platform engine has been generalized to the whole curriculum of medical studies in our faculty; it also supports an international master of risk management in health care and will be extent to all other continuous training diploma.

  16. Assessment of learning needs and the development of an educational programme for registered nurses in advanced midwifery and neonatology

    Directory of Open Access Journals (Sweden)

    AE Fichardt

    2000-09-01

    Full Text Available A key step in the development of any educational programme is learning needs assessment. This is however often neglected. The purpose of this research was to identify learning needs of potential students in order to develop a relevant educational programme for registered nurses in advanced midwifery and neonatology. A survey design was used, and the population of the study was the registered nurses in the Free State. Two thousand questionnaires were mailed to respondents, selected by means of simple random sampling. Advanced educational programmes emphasize the teaching of advanced knowledge and skills and accept that the students entering these programmes already have specific knowledge and skills included in the curricula for basic programmes. This is contrary to the findings of this study. The results underline the importance of learning needs assessment in the development of relevant educational programmes.

  17. Swedish students' and preceptors' perceptions of what students learn in a six-month advanced pharmacy practice experience.

    Science.gov (United States)

    Wallman, Andy; Sporrong, Sofia Kälvemark; Gustavsson, Maria; Lindblad, Asa Kettis; Johansson, Markus; Ring, Lena

    2011-12-15

    To identify what pharmacy students learn during the 6-month advanced pharmacy practice experience (APPE) in Sweden. Semi-structured interviews were conducted with 18 pharmacy APPE students and 17 pharmacist preceptors and analyzed in a qualitative directed content analysis using a defined workplace learning typology for categories. The Swedish APPE provides students with task performance skills for work at pharmacies and social and professional knowledge, such as teamwork, how to learn while in a work setting, self-evaluation, understanding of the pharmacist role, and decision making and problem solving skills. Many of these skills and knowledge are not accounted for in the curricula in Sweden. Using a workplace learning typology to identify learning outcomes, as in this study, could be useful for curricula development. Exploring the learning that takes place during the APPE in a pharmacy revealed a broad range of skills and knowledge that students acquire.

  18. A Data Flow Model to Solve the Data Distribution Changing Problem in Machine Learning

    Directory of Open Access Journals (Sweden)

    Shang Bo-Wen

    2016-01-01

    Full Text Available Continuous prediction is widely used in broad communities spreading from social to business and the machine learning method is an important method in this problem.When we use the machine learning method to predict a problem. We use the data in the training set to fit the model and estimate the distribution of data in the test set.But when we use machine learning to do the continuous prediction we get new data as time goes by and use the data to predict the future data, there may be a problem. As the size of the data set increasing over time, the distribution changes and there will be many garbage data in the training set.We should remove the garbage data as it reduces the accuracy of the prediction. The main contribution of this article is using the new data to detect the timeliness of historical data and remove the garbage data.We build a data flow model to describe how the data flow among the test set, training set, validation set and the garbage set and improve the accuracy of prediction. As the change of the data set, the best machine learning model will change.We design a hybrid voting algorithm to fit the data set better that uses seven machine learning models predicting the same problem and uses the validation set putting different weights on the learning models to give better model more weights. Experimental results show that, when the distribution of the data set changes over time, our time flow model can remove most of the garbage data and get a better result than the traditional method that adds all the data to the data set; our hybrid voting algorithm has a better prediction result than the average accuracy of other predict models

  19. Why Do Electricity Policy and Competitive Markets Fail to Use Advanced PV Systems to Improve Distribution Power Quality?

    Directory of Open Access Journals (Sweden)

    Mark P. McHenry

    2016-01-01

    Full Text Available The increasing pressure for network operators to meet distribution network power quality standards with increasing peak loads, renewable energy targets, and advances in automated distributed power electronics and communications is forcing policy-makers to understand new means to distribute costs and benefits within electricity markets. Discussions surrounding how distributed generation (DG exhibits active voltage regulation and power factor/reactive power control and other power quality capabilities are complicated by uncertainties of baseline local distribution network power quality and to whom and how costs and benefits of improved electricity infrastructure will be allocated. DG providing ancillary services that dynamically respond to the network characteristics could lead to major network improvements. With proper market structures renewable energy systems could greatly improve power quality on distribution systems with nearly no additional cost to the grid operators. Renewable DG does have variability challenges, though this issue can be overcome with energy storage, forecasting, and advanced inverter functionality. This paper presents real data from a large-scale grid-connected PV array with large-scale storage and explores effective mitigation measures for PV system variability. We discuss useful inverter technical knowledge for policy-makers to mitigate ongoing inflation of electricity network tariff components by new DG interconnection requirements or electricity markets which value power quality and control.

  20. TU-EF-207-03: Advances in Stationary Breast Tomosynthesis Using Distributed X-Ray Sources

    Energy Technology Data Exchange (ETDEWEB)

    Zhou, O. [The University of North Carolina at Chapel Hill (United States)

    2015-06-15

    mode due to lower photon fluence per projection. This may require fast-frame acquisition and symmetric or asymmetric pixel binning in some systems. Recent studies investigated the performance of increased conversion layer thickness for contrast-enhanced imaging of the breast in dual-energy acquisition mode. In other direct conversion detectors operating in the avalanche mode, sensitivities close to the single photon response are also explored for mammography and breast tomosynthesis. The potential advantages and challenges of this approach are described. Dedicated breast CT brings x-ray imaging of the breast to true tomographic 3D imaging. It can eliminate the tissue superposition problem and does not require physical compression of the breast. Using cone beam geometry and a flat-panel detector, several hundred projections are acquired and reconstructed to near isotropic voxels. Multiplanar reconstruction facilitates viewing the breast volume in any desired orientation. Ongoing clinical studies, the current state-of-the art, and research to advance the technology are described. Learning Objectives: To understand the ongoing developments in x-ray imaging of the breast To understand the approaches and applications of spectral mammography To understand the potential advantages of distributed x-ray source arrays for digital breast tomosynthesis To understand the ongoing developments in detector technology for digital mammography and breast tomosynthesis To understand the current state-of-the-art for dedicated cone-beam breast CT and research to advance the technology. Research collaboration with Koning Corporation.

  1. TU-EF-207-03: Advances in Stationary Breast Tomosynthesis Using Distributed X-Ray Sources

    International Nuclear Information System (INIS)

    Zhou, O.

    2015-01-01

    mode due to lower photon fluence per projection. This may require fast-frame acquisition and symmetric or asymmetric pixel binning in some systems. Recent studies investigated the performance of increased conversion layer thickness for contrast-enhanced imaging of the breast in dual-energy acquisition mode. In other direct conversion detectors operating in the avalanche mode, sensitivities close to the single photon response are also explored for mammography and breast tomosynthesis. The potential advantages and challenges of this approach are described. Dedicated breast CT brings x-ray imaging of the breast to true tomographic 3D imaging. It can eliminate the tissue superposition problem and does not require physical compression of the breast. Using cone beam geometry and a flat-panel detector, several hundred projections are acquired and reconstructed to near isotropic voxels. Multiplanar reconstruction facilitates viewing the breast volume in any desired orientation. Ongoing clinical studies, the current state-of-the art, and research to advance the technology are described. Learning Objectives: To understand the ongoing developments in x-ray imaging of the breast To understand the approaches and applications of spectral mammography To understand the potential advantages of distributed x-ray source arrays for digital breast tomosynthesis To understand the ongoing developments in detector technology for digital mammography and breast tomosynthesis To understand the current state-of-the-art for dedicated cone-beam breast CT and research to advance the technology. Research collaboration with Koning Corporation

  2. Lessons Learned During Cryogenic Optical Testing of the Advanced Mirror System Demonstrators (AMSDs)

    Science.gov (United States)

    Hadaway, James; Reardon, Patrick; Geary, Joseph; Robinson, Brian; Stahl, Philip; Eng, Ron; Kegley, Jeff

    2004-01-01

    Optical testing in a cryogenic environment presents a host of challenges above and beyond those encountered during room temperature testing. The Advanced Mirror System Demonstrators (AMSDs) are 1.4 m diameter, ultra light-weight (mA2), off-axis parabolic segments. They are required to have 250 nm PV & 50 nm RMS surface figure error or less at 35 K. An optical testing system, consisting of an Instantaneous Phase Interferometer (PI), a diffractive null corrector (DNC), and an Absolute Distance Meter (ADM), was used to measure the surface figure & radius-of-curvature of these mirrors at the operational temperature within the X-Ray Calibration Facility (XRCF) at Marshall Space Flight Center (MSFC). The Ah4SD program was designed to improve the technology related to the design, fabrication, & testing of such mirrors in support of NASA s James Webb Space Telescope (JWST). This paper will describe the lessons learned during preparation & cryogenic testing of the AMSDs.

  3. Consensus-based distributed cooperative learning from closed-loop neural control systems.

    Science.gov (United States)

    Chen, Weisheng; Hua, Shaoyong; Zhang, Huaguang

    2015-02-01

    In this paper, the neural tracking problem is addressed for a group of uncertain nonlinear systems where the system structures are identical but the reference signals are different. This paper focuses on studying the learning capability of neural networks (NNs) during the control process. First, we propose a novel control scheme called distributed cooperative learning (DCL) control scheme, by establishing the communication topology among adaptive laws of NN weights to share their learned knowledge online. It is further proved that if the communication topology is undirected and connected, all estimated weights of NNs can converge to small neighborhoods around their optimal values over a domain consisting of the union of all state orbits. Second, as a corollary it is shown that the conclusion on the deterministic learning still holds in the decentralized adaptive neural control scheme where, however, the estimated weights of NNs just converge to small neighborhoods of the optimal values along their own state orbits. Thus, the learned controllers obtained by DCL scheme have the better generalization capability than ones obtained by decentralized learning method. A simulation example is provided to verify the effectiveness and advantages of the control schemes proposed in this paper.

  4. Foundational Report Series: Advanced Distribution Management Systems for Grid Modernization, Implementation Strategy for a Distribution Management System

    Energy Technology Data Exchange (ETDEWEB)

    Singh, Ravindra [Argonne National Lab. (ANL), Argonne, IL (United States); Reilly, James T. [Reilly Associates, Pittston, PA (United States); Wang, Jianhui [Argonne National Lab. (ANL), Argonne, IL (United States)

    2017-03-01

    Electric distribution utilities encounter many challenges to successful deployment of Distribution Management Systems (DMSs). The key challenges are documented in this report, along with suggestions for overcoming them. This report offers a recommended list of activities for implementing a DMS. It takes a strategic approach to implementing DMS from a project management perspective. The project management strategy covers DMS planning, procurement, design, building, testing, Installation, commissioning, and system integration issues and solutions. It identifies the risks that are associated with implementation and suggests strategies for utilities to use to mitigate them or avoid them altogether. Attention is given to common barriers to successful DMS implementation. This report begins with an overview of the implementation strategy for a DMS and proceeds to put forward a basic approach for procuring hardware and software for a DMS; designing the interfaces with external corporate computing systems such as EMS, GIS, OMS, and AMI; and implementing a complete solution.

  5. Assessing Understanding of Sampling Distributions and Differences in Learning amongst Different Learning Styles

    Science.gov (United States)

    Beeman, Jennifer Leigh Sloan

    2013-01-01

    Research has found that students successfully complete an introductory course in statistics without fully comprehending the underlying theory or being able to exhibit statistical reasoning. This is particularly true for the understanding about the sampling distribution of the mean, a crucial concept for statistical inference. This study…

  6. Optimizing Music Learning: Exploring How Blocked and Interleaved Practice Schedules Affect Advanced Performance.

    Science.gov (United States)

    Carter, Christine E; Grahn, Jessica A

    2016-01-01

    Repetition is the most commonly used practice strategy by musicians. Although blocks of repetition continue to be suggested in the pedagogical literature, work in the field of cognitive psychology suggests that repeated events receive less processing, thereby reducing the potential for long-term learning. Motor skill learning and sport psychology research offer an alternative. Instead of using a blocked practice schedule, with practice completed on one task before moving on to the next task, an interleaved schedule can be used, in which practice is frequently alternated between tasks. This frequent alternation involves more effortful processing, resulting in increased long-term learning. The finding that practicing in an interleaved schedule leads to better retention than practicing in a blocked schedule has been labeled the "contextual interference effect." While the effect has been observed across a wide variety of fields, few studies have researched this phenomenon in a music-learning context, despite the broad potential for application to music practice. This study compared the effects of blocked and interleaved practice schedules on advanced clarinet performance in an ecologically valid context. Ten clarinetists were given one concerto exposition and one technical excerpt to practice in a blocked schedule (12 min per piece) and a second concerto exposition and technical excerpt to practice in an interleaved schedule (3 min per piece, alternating until a total of 12 min of practice were completed on each piece). Participants sight-read the four pieces prior to practice and performed them at the end of practice and again one day later. The sight-reading and two performance run-throughs of each piece were recorded and given to three professional clarinetists to rate using a percentage scale. Overall, whenever there was a ratings difference between the conditions, pieces practiced in the interleaved schedule were rated better than those in the blocked schedule

  7. Optimizing Music Learning: Exploring How Blocked and Interleaved Practice Schedules Affect Advanced Performance

    Science.gov (United States)

    Carter, Christine E.; Grahn, Jessica A.

    2016-01-01

    Repetition is the most commonly used practice strategy by musicians. Although blocks of repetition continue to be suggested in the pedagogical literature, work in the field of cognitive psychology suggests that repeated events receive less processing, thereby reducing the potential for long-term learning. Motor skill learning and sport psychology research offer an alternative. Instead of using a blocked practice schedule, with practice completed on one task before moving on to the next task, an interleaved schedule can be used, in which practice is frequently alternated between tasks. This frequent alternation involves more effortful processing, resulting in increased long-term learning. The finding that practicing in an interleaved schedule leads to better retention than practicing in a blocked schedule has been labeled the “contextual interference effect.” While the effect has been observed across a wide variety of fields, few studies have researched this phenomenon in a music-learning context, despite the broad potential for application to music practice. This study compared the effects of blocked and interleaved practice schedules on advanced clarinet performance in an ecologically valid context. Ten clarinetists were given one concerto exposition and one technical excerpt to practice in a blocked schedule (12 min per piece) and a second concerto exposition and technical excerpt to practice in an interleaved schedule (3 min per piece, alternating until a total of 12 min of practice were completed on each piece). Participants sight-read the four pieces prior to practice and performed them at the end of practice and again one day later. The sight-reading and two performance run-throughs of each piece were recorded and given to three professional clarinetists to rate using a percentage scale. Overall, whenever there was a ratings difference between the conditions, pieces practiced in the interleaved schedule were rated better than those in the blocked schedule

  8. Optimizing music learning: Exploring how blocked and interleaved practice schedules affect advanced performance

    Directory of Open Access Journals (Sweden)

    Christine E Carter

    2016-08-01

    Full Text Available Repetition is the most commonly used practice strategy by musicians. Although blocks of repetition continue to be suggested in the pedagogical literature, work in the field of cognitive psychology suggests that repeated events receive less processing, thereby reducing the potential for long-term learning. Motor skill learning and sport psychology research offer an alternative. Instead of using a blocked practice schedule, with practice completed on one task before moving on to the next task, an interleaved schedule can be used, in which practice is frequently alternated between tasks. This frequent alternation involves more effortful processing, resulting in increased long-term learning. The finding that practicing in an interleaved schedule leads to better retention than practicing in a blocked schedule has been labeled the contextual interference effect. While the effect has been observed across a wide variety of fields, few studies have researched this phenomenon in a music-learning context, despite the broad potential for application to music practice. This study compared the effects of blocked and interleaved practice schedules on advanced clarinet performance in an ecologically valid context. Ten clarinetists were given one concerto exposition and one technical excerpt to practice in a blocked schedule (twelve minutes per piece and a second concerto exposition and technical excerpt to practice in an interleaved schedule (three minutes per piece, alternating until a total of twelve minutes of practice were completed on each piece. Participants sight-read the four pieces prior to practice and performed them at the end of practice and again one day later. The sight-reading and two performance run-throughs of each piece were recorded and given to three professional clarinetists to rate using a percentage scale. Overall, whenever there was a ratings difference between the conditions, pieces practiced in the interleaved schedule were rated

  9. Simultaneous allocation of distributed resources using improved teaching learning based optimization

    International Nuclear Information System (INIS)

    Kanwar, Neeraj; Gupta, Nikhil; Niazi, K.R.; Swarnkar, Anil

    2015-01-01

    Highlights: • Simultaneous allocation of distributed energy resources in distribution networks. • Annual energy loss reduction is optimized using a multi-level load profile. • A new penalty factor approach is suggested to check node voltage deviations. • An improved TLBO is proposed by suggesting several modifications in standard TLBO. • An intelligent search is proposed to enhance the performance of solution technique. - Abstract: Active and reactive power flow in distribution networks can be effectively controlled by optimally placing distributed resources like shunt capacitors and distributed generators. This paper presents improved variant of Teaching Learning Based Optimization (TLBO) to efficiently and effectively deal with the problem of simultaneous allocation of these distributed resources in radial distribution networks while considering multi-level load scenario. Several algorithm specific modifications are suggested in the standard form of TLBO to cope against the intrinsic flaws of this technique. In addition, an intelligent search approach is proposed to restrict the problem search space without loss of diversity. This enhances the overall performance of the proposed method. The proposed method is investigated on IEEE 33-bus, 69-bus and 83-bus test distribution systems showing promising results

  10. Probabilistic Analysis of Passive Safety System Reliability in Advanced Small Modular Reactors: Methodologies and Lessons Learned

    Energy Technology Data Exchange (ETDEWEB)

    Grabaskas, David; Bucknor, Matthew; Brunett, Acacia; Grelle, Austin

    2015-06-28

    Many advanced small modular reactor designs rely on passive systems to fulfill safety functions during accident sequences. These systems depend heavily on boundary conditions to induce a motive force, meaning the system can fail to operate as intended due to deviations in boundary conditions, rather than as the result of physical failures. Furthermore, passive systems may operate in intermediate or degraded modes. These factors make passive system operation difficult to characterize with a traditional probabilistic framework that only recognizes discrete operating modes and does not allow for the explicit consideration of time-dependent boundary conditions. Argonne National Laboratory has been examining various methodologies for assessing passive system reliability within a probabilistic risk assessment for a station blackout event at an advanced small modular reactor. This paper describes the most promising options: mechanistic techniques, which share qualities with conventional probabilistic methods, and simulation-based techniques, which explicitly account for time-dependent processes. The primary intention of this paper is to describe the strengths and weaknesses of each methodology and highlight the lessons learned while applying the two techniques while providing high-level results. This includes the global benefits and deficiencies of the methods and practical problems encountered during the implementation of each technique.

  11. Reflection as a Deliberative and Distributed Practice: Assessing Neuro-Enhancement Technologies via Mutual Learning Exercises (MLEs).

    Science.gov (United States)

    Zwart, Hub; Brenninkmeijer, Jonna; Eduard, Peter; Krabbenborg, Lotte; Laursen, Sheena; Revuelta, Gema; Toonders, Winnie

    2017-01-01

    In 1968, Jürgen Habermas claimed that, in an advanced technological society, the emancipatory force of knowledge can only be regained by actively recovering the 'forgotten experience of reflection'. In this article, we argue that, in the contemporary situation, critical reflection requires a deliberative ambiance, a process of mutual learning, a consciously organised process of deliberative and distributed reflection. And this especially applies, we argue, to critical reflection concerning a specific subset of technologies which are actually oriented towards optimising human cognition (neuro-enhancement). In order to create a deliberative ambiance, fostering critical upstream reflection on emerging technologies, we developed (in the context of a European 7 th Framework Programme project on neuro-enhancement and responsible research and innovation, called NERRI) the concept of a mutual learning exercise (MLE). Building on a number of case studies, we analyse what an MLE involves, both practically and conceptually, focussing on key aspects such as ambiance and expertise, the role of 'genres of the imagination' and the profiles of various 'subcultures of debate'. Ideally, an MLE becomes a contemporary version of the Socratic agora, providing a stage where multiple and sometimes unexpected voices and perspectives mutually challenge each other, in order to strength-en the societal robustness and responsiveness of emerg-ing technologies.

  12. Logarithmic distributions prove that intrinsic learning is Hebbian [version 2; referees: 2 approved

    Directory of Open Access Journals (Sweden)

    Gabriele Scheler

    2017-10-01

    Full Text Available In this paper, we present data for the lognormal distributions of spike rates, synaptic weights and intrinsic excitability (gain for neurons in various brain areas, such as auditory or visual cortex, hippocampus, cerebellum, striatum, midbrain nuclei. We find a remarkable consistency of heavy-tailed, specifically lognormal, distributions for rates, weights and gains in all brain areas examined. The difference between strongly recurrent and feed-forward connectivity (cortex vs. striatum and cerebellum, neurotransmitter (GABA (striatum or glutamate (cortex or the level of activation (low in cortex, high in Purkinje cells and midbrain nuclei turns out to be irrelevant for this feature. Logarithmic scale distribution of weights and gains appears to be a general, functional property in all cases analyzed. We then created a generic neural model to investigate adaptive learning rules that create and maintain lognormal distributions. We conclusively demonstrate that not only weights, but also intrinsic gains, need to have strong Hebbian learning in order to produce and maintain the experimentally attested distributions. This provides a solution to the long-standing question about the type of plasticity exhibited by intrinsic excitability.

  13. Healthy outcomes for teens project: diabetes prevention through distributed interactive learning.

    Science.gov (United States)

    Castelli, Darla M; Goss, David; Scherer, Jane; Chapman-Novakofski, Karen

    2011-03-01

    This study assessed whether distributed interactive learning via web-based modules and grounded in schema and social cognitive theory (treatment group, n = 101) would increase knowledge about diabetes prevention in adolescents from three middle schools to a greater extent than the control group (n = 80) and examined whether the school environment used to convey the education had an effect. The treatment group showed substantially greater increases in overall and individual modular content knowledge, with 72 voluntarily choosing to retake evaluations that significantly improved their scores. The treatment (t[3.8], β ≥ 0.30, P school, pull out from physical education, or health education curriculum) (t[3.41], β ≥ 0.24, P learning was more effective than its passive counterpart, and a more structured delivery enhanced knowledge, as did opportunities to self-regulate learning. Attention to these process components will facilitate effective interventions by educators in schools.

  14. Empirical Centroid Fictitious Play: An Approach For Distributed Learning In Multi-Agent Games

    OpenAIRE

    Swenson, Brian; Kar, Soummya; Xavier, Joao

    2013-01-01

    The paper is concerned with distributed learning in large-scale games. The well-known fictitious play (FP) algorithm is addressed, which, despite theoretical convergence results, might be impractical to implement in large-scale settings due to intense computation and communication requirements. An adaptation of the FP algorithm, designated as the empirical centroid fictitious play (ECFP), is presented. In ECFP players respond to the centroid of all players' actions rather than track and respo...

  15. Advanced Power Electronic Interfaces for Distributed Energy Systems Part 1: Systems and Topologies

    Energy Technology Data Exchange (ETDEWEB)

    Kramer, W.; Chakraborty, S.; Kroposki, B.; Thomas, H.

    2008-03-01

    This report summarizes power electronic interfaces for DE applications and the topologies needed for advanced power electronic interfaces. It focuses on photovoltaic, wind, microturbine, fuel cell, internal combustion engine, battery storage, and flywheel storage systems.

  16. Non-Intrusive, Distributed Gas Sensing Technology for Advanced Spacesuits, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — Advances in spacesuits are required to support the ISS and future human exploration. Spacesuit development and ground-based testing tasks require sensing and...

  17. Developing and Validating a Survival Prediction Model for NSCLC Patients Through Distributed Learning Across 3 Countries.

    Science.gov (United States)

    Jochems, Arthur; Deist, Timo M; El Naqa, Issam; Kessler, Marc; Mayo, Chuck; Reeves, Jackson; Jolly, Shruti; Matuszak, Martha; Ten Haken, Randall; van Soest, Johan; Oberije, Cary; Faivre-Finn, Corinne; Price, Gareth; de Ruysscher, Dirk; Lambin, Philippe; Dekker, Andre

    2017-10-01

    Tools for survival prediction for non-small cell lung cancer (NSCLC) patients treated with chemoradiation or radiation therapy are of limited quality. In this work, we developed a predictive model of survival at 2 years. The model is based on a large volume of historical patient data and serves as a proof of concept to demonstrate the distributed learning approach. Clinical data from 698 lung cancer patients, treated with curative intent with chemoradiation or radiation therapy alone, were collected and stored at 2 different cancer institutes (559 patients at Maastro clinic (Netherlands) and 139 at Michigan university [United States]). The model was further validated on 196 patients originating from The Christie (United Kingdon). A Bayesian network model was adapted for distributed learning (the animation can be viewed at https://www.youtube.com/watch?v=ZDJFOxpwqEA). Two-year posttreatment survival was chosen as the endpoint. The Maastro clinic cohort data are publicly available at https://www.cancerdata.org/publication/developing-and-validating-survival-prediction-model-nsclc-patients-through-distributed, and the developed models can be found at www.predictcancer.org. Variables included in the final model were T and N category, age, performance status, and total tumor dose. The model has an area under the curve (AUC) of 0.66 on the external validation set and an AUC of 0.62 on a 5-fold cross validation. A model based on the T and N category performed with an AUC of 0.47 on the validation set, significantly worse than our model (PLearning the model in a centralized or distributed fashion yields a minor difference on the probabilities of the conditional probability tables (0.6%); the discriminative performance of the models on the validation set is similar (P=.26). Distributed learning from federated databases allows learning of predictive models on data originating from multiple institutions while avoiding many of the data-sharing barriers. We believe that

  18. Effects of Multimedia Annotations on Incidental Vocabulary Learning and Reading Comprehension of Advanced Learners of English as a Foreign Language

    Science.gov (United States)

    Akbulut, Yavuz

    2007-01-01

    The study investigates immediate and delayed effects of different hypermedia glosses on incidental vocabulary learning and reading comprehension of advanced foreign language learners. Sixty-nine freshman TEFL students studying at a Turkish university were randomly assigned to three types of annotations: (a) definitions of words, (b) definitions…

  19. A role for distributed processing in advanced nuclear materials control and accountability systems

    International Nuclear Information System (INIS)

    Tisinger, R.M.; Whitty, W.J.; Ford, W.; Strittmatter, R.B.

    1986-01-01

    Networking and distributed processing hardware and software have the potential of greatly enhancing nuclear materials control and account-ability (MCandA) systems, both from safeguards and process operations perspectives while allowing timely integrated safeguards activities and enhanced computer security at reasonable cost. A hierarchical distributed system is proposed consisting of groups of terminals and instruments in plant production and support areas connected to microprocessors that are connected to either larger microprocessors or minicomputers. The structuring and development of a limited distributed MCandA prototype system, including human engineering concepts, are described. Implications of integrated safeguards and computer security concepts to the distributed system design are discussed

  20. Distribution of language-related Cntnap2 protein in neural circuits critical for vocal learning.

    Science.gov (United States)

    Condro, Michael C; White, Stephanie A

    2014-01-01

    Variants of the contactin associated protein-like 2 (Cntnap2) gene are risk factors for language-related disorders including autism spectrum disorder, specific language impairment, and stuttering. Songbirds are useful models for study of human speech disorders due to their shared capacity for vocal learning, which relies on similar cortico-basal ganglia circuitry and genetic factors. Here we investigate Cntnap2 protein expression in the brain of the zebra finch, a songbird species in which males, but not females, learn their courtship songs. We hypothesize that Cntnap2 has overlapping functions in vocal learning species, and expect to find protein expression in song-related areas of the zebra finch brain. We further expect that the distribution of this membrane-bound protein may not completely mirror its mRNA distribution due to the distinct subcellular localization of the two molecular species. We find that Cntnap2 protein is enriched in several song control regions relative to surrounding tissues, particularly within the adult male, but not female, robust nucleus of the arcopallium (RA), a cortical song control region analogous to human layer 5 primary motor cortex. The onset of this sexually dimorphic expression coincides with the onset of sensorimotor learning in developing males. Enrichment in male RA appears due to expression in projection neurons within the nucleus, as well as to additional expression in nerve terminals of cortical projections to RA from the lateral magnocellular nucleus of the nidopallium. Cntnap2 protein expression in zebra finch brain supports the hypothesis that this molecule affects neural connectivity critical for vocal learning across taxonomic classes. Copyright © 2013 Wiley Periodicals, Inc.

  1. Response monitoring using quantitative ultrasound methods and supervised dictionary learning in locally advanced breast cancer

    Science.gov (United States)

    Gangeh, Mehrdad J.; Fung, Brandon; Tadayyon, Hadi; Tran, William T.; Czarnota, Gregory J.

    2016-03-01

    A non-invasive computer-aided-theragnosis (CAT) system was developed for the early assessment of responses to neoadjuvant chemotherapy in patients with locally advanced breast cancer. The CAT system was based on quantitative ultrasound spectroscopy methods comprising several modules including feature extraction, a metric to measure the dissimilarity between "pre-" and "mid-treatment" scans, and a supervised learning algorithm for the classification of patients to responders/non-responders. One major requirement for the successful design of a high-performance CAT system is to accurately measure the changes in parametric maps before treatment onset and during the course of treatment. To this end, a unified framework based on Hilbert-Schmidt independence criterion (HSIC) was used for the design of feature extraction from parametric maps and the dissimilarity measure between the "pre-" and "mid-treatment" scans. For the feature extraction, HSIC was used to design a supervised dictionary learning (SDL) method by maximizing the dependency between the scans taken from "pre-" and "mid-treatment" with "dummy labels" given to the scans. For the dissimilarity measure, an HSIC-based metric was employed to effectively measure the changes in parametric maps as an indication of treatment effectiveness. The HSIC-based feature extraction and dissimilarity measure used a kernel function to nonlinearly transform input vectors into a higher dimensional feature space and computed the population means in the new space, where enhanced group separability was ideally obtained. The results of the classification using the developed CAT system indicated an improvement of performance compared to a CAT system with basic features using histogram of intensity.

  2. Deformable segmentation of 3D MR prostate images via distributed discriminative dictionary and ensemble learning

    International Nuclear Information System (INIS)

    Guo, Yanrong; Shao, Yeqin; Gao, Yaozong; Price, True; Oto, Aytekin; Shen, Dinggang

    2014-01-01

    Purpose: Automatic prostate segmentation from MR images is an important task in various clinical applications such as prostate cancer staging and MR-guided radiotherapy planning. However, the large appearance and shape variations of the prostate in MR images make the segmentation problem difficult to solve. Traditional Active Shape/Appearance Model (ASM/AAM) has limited accuracy on this problem, since its basic assumption, i.e., both shape and appearance of the targeted organ follow Gaussian distributions, is invalid in prostate MR images. To this end, the authors propose a sparse dictionary learning method to model the image appearance in a nonparametric fashion and further integrate the appearance model into a deformable segmentation framework for prostate MR segmentation. Methods: To drive the deformable model for prostate segmentation, the authors propose nonparametric appearance and shape models. The nonparametric appearance model is based on a novel dictionary learning method, namely distributed discriminative dictionary (DDD) learning, which is able to capture fine distinctions in image appearance. To increase the differential power of traditional dictionary-based classification methods, the authors' DDD learning approach takes three strategies. First, two dictionaries for prostate and nonprostate tissues are built, respectively, using the discriminative features obtained from minimum redundancy maximum relevance feature selection. Second, linear discriminant analysis is employed as a linear classifier to boost the optimal separation between prostate and nonprostate tissues, based on the representation residuals from sparse representation. Third, to enhance the robustness of the authors' classification method, multiple local dictionaries are learned for local regions along the prostate boundary (each with small appearance variations), instead of learning one global classifier for the entire prostate. These discriminative dictionaries are located on

  3. Deformable segmentation of 3D MR prostate images via distributed discriminative dictionary and ensemble learning

    Science.gov (United States)

    Guo, Yanrong; Gao, Yaozong; Shao, Yeqin; Price, True; Oto, Aytekin; Shen, Dinggang

    2014-01-01

    Purpose: Automatic prostate segmentation from MR images is an important task in various clinical applications such as prostate cancer staging and MR-guided radiotherapy planning. However, the large appearance and shape variations of the prostate in MR images make the segmentation problem difficult to solve. Traditional Active Shape/Appearance Model (ASM/AAM) has limited accuracy on this problem, since its basic assumption, i.e., both shape and appearance of the targeted organ follow Gaussian distributions, is invalid in prostate MR images. To this end, the authors propose a sparse dictionary learning method to model the image appearance in a nonparametric fashion and further integrate the appearance model into a deformable segmentation framework for prostate MR segmentation. Methods: To drive the deformable model for prostate segmentation, the authors propose nonparametric appearance and shape models. The nonparametric appearance model is based on a novel dictionary learning method, namely distributed discriminative dictionary (DDD) learning, which is able to capture fine distinctions in image appearance. To increase the differential power of traditional dictionary-based classification methods, the authors' DDD learning approach takes three strategies. First, two dictionaries for prostate and nonprostate tissues are built, respectively, using the discriminative features obtained from minimum redundancy maximum relevance feature selection. Second, linear discriminant analysis is employed as a linear classifier to boost the optimal separation between prostate and nonprostate tissues, based on the representation residuals from sparse representation. Third, to enhance the robustness of the authors' classification method, multiple local dictionaries are learned for local regions along the prostate boundary (each with small appearance variations), instead of learning one global classifier for the entire prostate. These discriminative dictionaries are located on different

  4. Deformable segmentation of 3D MR prostate images via distributed discriminative dictionary and ensemble learning.

    Science.gov (United States)

    Guo, Yanrong; Gao, Yaozong; Shao, Yeqin; Price, True; Oto, Aytekin; Shen, Dinggang

    2014-07-01

    Automatic prostate segmentation from MR images is an important task in various clinical applications such as prostate cancer staging and MR-guided radiotherapy planning. However, the large appearance and shape variations of the prostate in MR images make the segmentation problem difficult to solve. Traditional Active Shape/Appearance Model (ASM/AAM) has limited accuracy on this problem, since its basic assumption, i.e., both shape and appearance of the targeted organ follow Gaussian distributions, is invalid in prostate MR images. To this end, the authors propose a sparse dictionary learning method to model the image appearance in a nonparametric fashion and further integrate the appearance model into a deformable segmentation framework for prostate MR segmentation. To drive the deformable model for prostate segmentation, the authors propose nonparametric appearance and shape models. The nonparametric appearance model is based on a novel dictionary learning method, namely distributed discriminative dictionary (DDD) learning, which is able to capture fine distinctions in image appearance. To increase the differential power of traditional dictionary-based classification methods, the authors' DDD learning approach takes three strategies. First, two dictionaries for prostate and nonprostate tissues are built, respectively, using the discriminative features obtained from minimum redundancy maximum relevance feature selection. Second, linear discriminant analysis is employed as a linear classifier to boost the optimal separation between prostate and nonprostate tissues, based on the representation residuals from sparse representation. Third, to enhance the robustness of the authors' classification method, multiple local dictionaries are learned for local regions along the prostate boundary (each with small appearance variations), instead of learning one global classifier for the entire prostate. These discriminative dictionaries are located on different patches of the

  5. Deformable segmentation of 3D MR prostate images via distributed discriminative dictionary and ensemble learning

    Energy Technology Data Exchange (ETDEWEB)

    Guo, Yanrong; Shao, Yeqin [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599 (United States); Gao, Yaozong; Price, True [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599 and Department of Computer Science, University of North Carolina at Chapel Hill, North Carolina 27599 (United States); Oto, Aytekin [Department of Radiology, Section of Urology, University of Chicago, Illinois 60637 (United States); Shen, Dinggang, E-mail: dgshen@med.unc.edu [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599 and Department of Brain and Cognitive Engineering, Korea University, Seoul 136-713 (Korea, Republic of)

    2014-07-15

    Purpose: Automatic prostate segmentation from MR images is an important task in various clinical applications such as prostate cancer staging and MR-guided radiotherapy planning. However, the large appearance and shape variations of the prostate in MR images make the segmentation problem difficult to solve. Traditional Active Shape/Appearance Model (ASM/AAM) has limited accuracy on this problem, since its basic assumption, i.e., both shape and appearance of the targeted organ follow Gaussian distributions, is invalid in prostate MR images. To this end, the authors propose a sparse dictionary learning method to model the image appearance in a nonparametric fashion and further integrate the appearance model into a deformable segmentation framework for prostate MR segmentation. Methods: To drive the deformable model for prostate segmentation, the authors propose nonparametric appearance and shape models. The nonparametric appearance model is based on a novel dictionary learning method, namely distributed discriminative dictionary (DDD) learning, which is able to capture fine distinctions in image appearance. To increase the differential power of traditional dictionary-based classification methods, the authors' DDD learning approach takes three strategies. First, two dictionaries for prostate and nonprostate tissues are built, respectively, using the discriminative features obtained from minimum redundancy maximum relevance feature selection. Second, linear discriminant analysis is employed as a linear classifier to boost the optimal separation between prostate and nonprostate tissues, based on the representation residuals from sparse representation. Third, to enhance the robustness of the authors' classification method, multiple local dictionaries are learned for local regions along the prostate boundary (each with small appearance variations), instead of learning one global classifier for the entire prostate. These discriminative dictionaries are located on

  6. SMART-DS: Synthetic Models for Advanced, Realistic Testing: Distribution Systems and Scenarios

    Energy Technology Data Exchange (ETDEWEB)

    Palmintier, Bryan: Hodge, Bri-Mathias

    2017-01-26

    This presentation provides a Smart-DS project overview and status update for the ARPA-e GRID DATA program meeting 2017, including distribution systems, models, and scenarios, as well as opportunities for GRID DATA collaborations.

  7. Advances in Distributed Operations and Mission Activity Planning for Mars Surface Exploration

    Science.gov (United States)

    Fox, Jason M.; Norris, Jeffrey S.; Powell, Mark W.; Rabe, Kenneth J.; Shams, Khawaja

    2006-01-01

    A centralized mission activity planning system for any long-term mission, such as the Mars Exploration Rover Mission (MER), is completely infeasible due to budget and geographic constraints. A distributed operations system is key to addressing these constraints; therefore, future system and software engineers must focus on the problem of how to provide a secure, reliable, and distributed mission activity planning system. We will explain how Maestro, the next generation mission activity planning system, with its heavy emphasis on portability and distributed operations has been able to meet these design challenges. MER has been an excellent proving ground for Maestro's new approach to distributed operations. The backend that has been developed for Maestro could benefit many future missions by reducing the cost of centralized operations system architecture.

  8. Teacher learning about probabilistic reasoning in relation to teaching it in an Advanced Certificate in Education (ACE programme

    Directory of Open Access Journals (Sweden)

    Faaiz Gierdien

    2008-02-01

    Full Text Available I report on what teachers in an Advanced Certificate in Education (ACE in-service programme learned about probabilistic reasoning in relation to teaching it. I worked 'on the inside' using my practice as a site for studying teaching and learning. The teachers were from three different towns in the Northern Cape province and had limited teaching contact time, as is the nature of ACE programmes. Findings revealed a complicated picture, where some teachers were prepared to consider influences of their intuitive probabilistic reasoning on formal probabilistic reasoning when it came to teaching. It was, however, the 'genuineness' of teacher learning which was the issue that the findings have to address. Therefore a speculative, hopeful strategy for affecting teacher learning in mathematics teacher education practice is to sustain disequilibrium between dichotomies such as formal and intuitive probabilistic reasoning, which has analogies in content and pedagogy, and subject matter and method.

  9. Advancing the skill set of SCM graduates – An active learning approach

    NARCIS (Netherlands)

    Scholten, Kirstin; Dubois, Anna

    2017-01-01

    Purpose Drawing on a novel approach to active learning in supply chain management, the purpose of this paper is to describe and analyze how the students’ learning process as well as their learning outcomes are influenced by the learning and teaching contexts. Design/methodology/approach A case study

  10. Learning Styles among Students in an Advanced Soil Management Class: Impact on Students' Performance

    Science.gov (United States)

    Eudoxie, Gaius D.

    2011-01-01

    Learning styles represent an integral component of the learning environment, which has been shown to differ across institutions and disciplines. To identify learner preferences within a discipline would aid in evaluating instructional resources geared toward active learning. The learning profiles of second-year soil science students (n = 62) were…

  11. Advancing the skill set of SCM graduates – An active learning approach

    NARCIS (Netherlands)

    Scholten, Kirstin; Dubois, Anna

    Purpose Drawing on a novel approach to active learning in supply chain management, the purpose of this paper is to describe and analyze how the students’ learning process as well as their learning outcomes are influenced by the learning and teaching contexts. Design/methodology/approach A case study

  12. The effectiveness of advance organiser model on students' academic achievement in learning work and energy

    Science.gov (United States)

    Gidena, Asay; Gebeyehu, Desta

    2017-11-01

    The purpose of this study was to investigate the effectiveness of the advance organiser model (AOM) on students' academic achievement in learning work and energy. The design of the study was quasi-experimental pretest-posttest nonequivalent control groups. The total population of the study was 139 students of three sections in Endabaguna preparatory school in Tigray Region, Ethiopia. Two sections with equivalent means on the pretest were taken to participate in the study purposely and one section assigned as the experimental group and the other section assigned as the control group randomly. The experimental group was taught using the lesson plan based on the AOM, and the control group was taught using the lesson plan based on the conventional teaching method. Pretest and posttest were administered before and after the treatment, respectively. Independent sample t-test was used to analyse the data at the probability level of 0.05. The findings of the study showed that the AOM was more effective than the conventional teaching method with effect size of 0.49. This model was also effective to teach male and female students and objectives namely understanding and application. However, both methods were equally important to teach work and energy under the objective knowledge level.

  13. Lessons learned from testing the quality cost model of Advanced Practice Nursing (APN) transitional care.

    Science.gov (United States)

    Brooten, Dorothy; Naylor, Mary D; York, Ruth; Brown, Linda P; Munro, Barbara Hazard; Hollingsworth, Andrea O; Cohen, Susan M; Finkler, Steven; Deatrick, Janet; Youngblut, JoAnne M

    2002-01-01

    To describe the development, testing, modification, and results of the Quality Cost Model of Advanced Practice Nurses (APNs) Transitional Care on patient outcomes and health care costs in the United States over 22 years, and to delineate what has been learned for nursing education, practice, and further research. The Quality Cost Model of APN Transitional Care. Review of published results of seven randomized clinical trials with very low birth-weight (VLBW) infants; women with unplanned cesarean births, high risk pregnancies, and hysterectomy surgery; elders with cardiac medical and surgical diagnoses and common diagnostic related groups (DRGs); and women with high risk pregnancies in which half of physician prenatal care was substituted with APN care. Ongoing work with the model is linking the process of APN care with the outcomes and costs of care. APN intervention has consistently resulted in improved patient outcomes and reduced health care costs across groups. Groups with APN providers were rehospitalized for less time at less cost, reflecting early detection and intervention. Optimal number and timing of postdischarge home visits and telephone contacts by the APNs and patterns of rehospitalizations and acute care visits varied by group. To keep people well over time, APNs must have depth of knowledge and excellent clinical and interpersonal skills that are the hallmark of specialist practice, an in-depth understanding of systems and how to work within them, and sufficient patient contact to effect positive outcomes at low cost.

  14. Advanced Communication and Control for Distributed Energy Resource Integration: Phase 2 Scientific Report

    Energy Technology Data Exchange (ETDEWEB)

    BPL Global

    2008-09-30

    The objective of this research project is to demonstrate sensing, communication, information and control technologies to achieve a seamless integration of multivendor distributed energy resource (DER) units at aggregation levels that meet individual user requirements for facility operations (residential, commercial, industrial, manufacturing, etc.) and further serve as resource options for electric and natural gas utilities. The fully demonstrated DER aggregation system with embodiment of communication and control technologies will lead to real-time, interactive, customer-managed service networks to achieve greater customer value. Work on this Advanced Communication and Control Project (ACCP) consists of a two-phase approach for an integrated demonstration of communication and control technologies to achieve a seamless integration of DER units to reach progressive levels of aggregated power output. Phase I involved design and proof-of-design, and Phase II involves real-world demonstration of the Phase I design architecture. The scope of work for Phase II of this ACCP involves demonstrating the Phase I design architecture in large scale real-world settings while integrating with the operations of one or more electricity supplier feeder lines. The communication and control architectures for integrated demonstration shall encompass combinations of software and hardware components, including: sensors, data acquisition and communication systems, remote monitoring systems, metering (interval revenue, real-time), local and wide area networks, Web-based systems, smart controls, energy management/information systems with control and automation of building energy loads, and demand-response management with integration of real-time market pricing. For Phase II, BPL Global shall demonstrate the Phase I design for integrating and controlling the operation of more than 10 DER units, dispersed at various locations in one or more Independent System Operator (ISO) Control Areas, at

  15. Homepage to distribute the anatomy learning contents including Visible Korean products, comics, and books.

    Science.gov (United States)

    Chung, Beom Sun; Chung, Min Suk

    2018-03-01

    The authors have operated the homepage (http://anatomy.co.kr) to provide the learning contents of anatomy. From the homepage, sectioned images, volume models, and surface models-all Visible Korean products-can be downloaded. The realistic images can be interactively manipulated, which will give rise to the interest in anatomy. The various anatomy comics (learning comics, comic strips, plastination comics, etc.) are approachable. Visitors can obtain the regional anatomy book with concise contents, mnemonics, and schematics as well as the simplified dissection manual and the pleasant anatomy essay. Medical students, health allied professional students, and even laypeople are expected to utilize the easy and comforting anatomy contents. It is hoped that other anatomists successively produce and distribute their own informative contents.

  16. Homepage to distribute the anatomy learning contents including Visible Korean products, comics, and books

    Science.gov (United States)

    Chung, Beom Sun

    2018-01-01

    The authors have operated the homepage (http://anatomy.co.kr) to provide the learning contents of anatomy. From the homepage, sectioned images, volume models, and surface models—all Visible Korean products—can be downloaded. The realistic images can be interactively manipulated, which will give rise to the interest in anatomy. The various anatomy comics (learning comics, comic strips, plastination comics, etc.) are approachable. Visitors can obtain the regional anatomy book with concise contents, mnemonics, and schematics as well as the simplified dissection manual and the pleasant anatomy essay. Medical students, health allied professional students, and even laypeople are expected to utilize the easy and comforting anatomy contents. It is hoped that other anatomists successively produce and distribute their own informative contents. PMID:29644104

  17. Two-Dimensional Key Table-Based Group Key Distribution in Advanced Metering Infrastructure

    OpenAIRE

    Woong Go; Jin Kawk

    2014-01-01

    A smart grid provides two-way communication by using the information and communication technology. In order to establish two-way communication, the advanced metering infrastructure (AMI) is used in the smart grid as the core infrastructure. This infrastructure consists of smart meters, data collection units, maintenance data management systems, and so on. However, potential security problems of the AMI increase owing to the application of the public network. This is because the transmitted in...

  18. Can pilots still fly? Role distribution and hybrid interaction in advanced automated aircraft

    OpenAIRE

    Weyer, Johannes

    2015-01-01

    Recent accidents of commercial airplanes have raised the question once more whether pilots can rely on automation in order to fly advanced aircraft safely. Although the issue of human-machine interaction in aviation has been investigated frequently, profound knowledge about pilots’ perceptions and attitudes is fragmentary and partly out-dated. The paper at hand presents the results of a pilot survey, which has been guided by a collaborative perspective of human-automation decision-making. It ...

  19. Development of advanced methods for planning electric energy distribution systems. Final report

    Energy Technology Data Exchange (ETDEWEB)

    Goenen, T.; Foote, B.L.; Thompson, J.C.; Fagan, J.E.

    1979-10-01

    An extensive search was made for the identification and collection of reports published in the open literature which describes distribution planning methods and techniques. In addition, a questionnaire has been prepared and sent to a large number of electric power utility companies. A large number of these companies were visited and/or their distribution planners interviewed for the identification and description of distribution system planning methods and techniques used by these electric power utility companies and other commercial entities. Distribution systems planning models were reviewed and a set of new mixed-integer programming models were developed for the optimal expansion of the distribution systems. The models help the planner to select: (1) optimum substation locations; (2) optimum substation expansions; (3) optimum substation transformer sizes; (4) optimum load transfers between substations; (5) optimum feeder routes and sizes subject to a set of specified constraints. The models permit following existing right-of-ways and avoid areas where feeders and substations cannot be constructed. The results of computer runs were analyzed for adequacy in serving projected loads within regulation limits for both normal and emergency operation.

  20. A hybrid approach to advancing quantitative prediction of tissue distribution of basic drugs in human

    International Nuclear Information System (INIS)

    Poulin, Patrick; Ekins, Sean; Theil, Frank-Peter

    2011-01-01

    A general toxicity of basic drugs is related to phospholipidosis in tissues. Therefore, it is essential to predict the tissue distribution of basic drugs to facilitate an initial estimate of that toxicity. The objective of the present study was to further assess the original prediction method that consisted of using the binding to red blood cells measured in vitro for the unbound drug (RBCu) as a surrogate for tissue distribution, by correlating it to unbound tissue:plasma partition coefficients (Kpu) of several tissues, and finally to predict volume of distribution at steady-state (V ss ) in humans under in vivo conditions. This correlation method demonstrated inaccurate predictions of V ss for particular basic drugs that did not follow the original correlation principle. Therefore, the novelty of this study is to provide clarity on the actual hypotheses to identify i) the impact of pharmacological mode of action on the generic correlation of RBCu-Kpu, ii) additional mechanisms of tissue distribution for the outlier drugs, iii) molecular features and properties that differentiate compounds as outliers in the original correlation analysis in order to facilitate its applicability domain alongside the properties already used so far, and finally iv) to present a novel and refined correlation method that is superior to what has been previously published for the prediction of human V ss of basic drugs. Applying a refined correlation method after identifying outliers would facilitate the prediction of more accurate distribution parameters as key inputs used in physiologically based pharmacokinetic (PBPK) and phospholipidosis models.

  1. Bayesian analysis of general failure data from an ageing distribution: advances in numerical methods

    International Nuclear Information System (INIS)

    Procaccia, H.; Villain, B.; Clarotti, C.A.

    1996-01-01

    EDF and ENEA carried out a joint research program for developing the numerical methods and computer codes needed for Bayesian analysis of component-lives in the case of ageing. Early results of this study were presented at ESREL'94. Since then the following further steps have been gone: input data have been generalized to the case that observed lives are censored both on the right and on the left; allowable life distributions are Weibull and gamma - their parameters are both unknown and can be statistically dependent; allowable priors are histograms relative to different parametrizations of the life distribution of concern; first-and-second-order-moments of the posterior distributions can be computed. In particular the covariance will give some important information about the degree of the statistical dependence between the parameters of interest. An application of the code to the appearance of a stress corrosion cracking in a tube of the PWR Steam Generator system is presented. (authors)

  2. Using the learning management evaluation model for advancing to life skills of lower secondary students in the 21st century

    Science.gov (United States)

    Kansaart, Preecha; Suikraduang, Arun; Panya, Piyatida

    2018-01-01

    The aims of this research study were to develop the Learning Management Evaluation Model (LMEM) for advancing to lower secondary students of their life skills in the 21st century with the Research & Development process technique. The research procedures were administered of four steps that composed of analyze, the synthetic indicator to assess learning to advance to their life skills in the 21st century by the 4-educational experts were interviewed. The LMEM model was developed by the information from the first draft format and the educational experts to check a suitability and feasibility of the draft assessment form with a technical symposium multipath characteristics to find consensus dimensional (Multi-Attribute Consensus Reaching: MACR) by 12 specialists who provided the instruction in the form of Assessment and Evaluation Guide (AEG) was brought to five the number of professionals who ensure the proper coverage, a clear assessment of the manual before using the AEG. The LMEM model was to trial at an experiment with different schools in the Secondary Educational Office Area 26 (Maha Sarakham) whereas taught at the upper secondary educational school with the sample consisted of 7 schools with the purposive sampling was selected. Assessing the LMEM model was evaluated the based on the evaluation criteria of the educational development. The assessor was related to the trial consisted of 35 evaluators. Using the interview form with the rubric score and a five rating scale level was analyzed; the qualitative and quantitative data were used. It has found that: The LMEM evaluation model of learning to advance to life skills of students in the 21st century was a chart structure that ties together of 6 relevant components of the evaluation such as; the purpose of the assessment, the evaluation focused assessment methods, the evaluator, the evaluation technique, and the evaluation criteria. The evaluation targets were to assess the management of learning, the factors

  3. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

    OpenAIRE

    Abadi, Martín; Agarwal, Ashish; Barham, Paul; Brevdo, Eugene; Chen, Zhifeng; Citro, Craig; Corrado, Greg S.; Davis, Andy; Dean, Jeffrey; Devin, Matthieu; Ghemawat, Sanjay; Goodfellow, Ian; Harp, Andrew; Irving, Geoffrey; Isard, Michael

    2016-01-01

    TensorFlow is an interface for expressing machine learning algorithms, and an implementation for executing such algorithms. A computation expressed using TensorFlow can be executed with little or no change on a wide variety of heterogeneous systems, ranging from mobile devices such as phones and tablets up to large-scale distributed systems of hundreds of machines and thousands of computational devices such as GPU cards. The system is flexible and can be used to express a wide variety of algo...

  4. Numerical and machine learning simulation of parametric distributions of groundwater residence time in streams and wells

    Science.gov (United States)

    Starn, J. J.; Belitz, K.; Carlson, C.

    2017-12-01

    Groundwater residence-time distributions (RTDs) are critical for assessing susceptibility of water resources to contamination. This novel approach for estimating regional RTDs was to first simulate groundwater flow using existing regional digital data sets in 13 intermediate size watersheds (each an average of 7,000 square kilometers) that are representative of a wide range of glacial systems. RTDs were simulated with particle tracking. We refer to these models as "general models" because they are based on regional, as opposed to site-specific, digital data. Parametric RTDs were created from particle RTDs by fitting 1- and 2-component Weibull, gamma, and inverse Gaussian distributions, thus reducing a large number of particle travel times to 3 to 7 parameters (shape, location, and scale for each component plus a mixing fraction) for each modeled area. The scale parameter of these distributions is related to the mean exponential age; the shape parameter controls departure from the ideal exponential distribution and is partly a function of interaction with bedrock and with drainage density. Given the flexible shape and mathematical similarity of these distributions, any of them are potentially a good fit to particle RTDs. The 1-component gamma distribution provided a good fit to basin-wide particle RTDs. RTDs at monitoring wells and streams often have more complicated shapes than basin-wide RTDs, caused in part by heterogeneity in the model, and generally require 2-component distributions. A machine learning model was trained on the RTD parameters using features derived from regionally available watershed characteristics such as recharge rate, material thickness, and stream density. RTDs appeared to vary systematically across the landscape in relation to watershed features. This relation was used to produce maps of useful metrics with respect to risk-based thresholds, such as the time to first exceedance, time to maximum concentration, time above the threshold

  5. Advanced characterization techniques of nonuniform indium distribution within InGaN/GaN heterostructures grown by MOCVD

    International Nuclear Information System (INIS)

    Lu, D.; Florescu, D.I.; Lee, D.S.; Ramer, J.C.; Parekh, A.; Merai, V.; Li, S.; Begarney, M.J.; Armour, E.A.; Gardner, J.J.

    2005-01-01

    Nonuniform indium distribution within InGaN/GaN single quantum well (SQW) structures with nanoscale islands grown by metalorganic chemical vapor deposition (MOCVD) have been characterized by advanced characterization techniques. Robinson backscattered electron (BSE) measurements show cluster-like BSE contrast of high brightness regions, which are not centered at small dark pits in a SQW structure of spiral growth mode. By comparing with the secondary electron (SE) images, the bright cluster areas from the BSE images were found to have higher indium content compared to the surrounding dark areas. Temperature dependant photoluminescence (PL) measurement shows typical ''S-shape'' curve, which shows good correlation with nonuniform indium distribution from BSE measurement. Optical evaluation of the samples show increased PL slope efficiency of the spiral mode SQW, which can be attributed to the presence of Indium inhomogeneities. (copyright 2005 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim) (orig.)

  6. Military Transformation: Progress and Challenges for DOD's Advanced Distributed Learning Programs

    National Research Council Canada - National Science Library

    Ensign, John

    2003-01-01

    ...) programs such as Internet-based training as critical to achieving the department's training and overarching transformation goals and to deliver the highest quality training cost effectively anytime...

  7. Military Transformation: Progress and Challenges for DOD's Advanced Distributed Learning Programs

    National Research Council Canada - National Science Library

    Ensign, John

    2003-01-01

    The Department of Defense (DOD) spends more than $17 billion annually for military schools that offer nearly 30,000 military training courses to almost 3 million military personnel and DOD civilians, much of it to maintain readiness...

  8. ‘Oorja’ in India: Assessing a large-scale commercial distribution of advanced biomass stoves to households

    Science.gov (United States)

    Thurber, Mark C.; Phadke, Himani; Nagavarapu, Sriniketh; Shrimali, Gireesh; Zerriffi, Hisham

    2015-01-01

    Replacing traditional stoves with advanced alternatives that burn more cleanly has the potential to ameliorate major health problems associated with indoor air pollution in developing countries. With a few exceptions, large government and charitable programs to distribute advanced stoves have not had the desired impact. Commercially-based distributions that seek cost recovery and even profits might plausibly do better, both because they encourage distributors to supply and promote products that people want and because they are based around properly-incentivized supply chains that could more be scalable, sustainable, and replicable. The sale in India of over 400,000 “Oorja” stoves to households from 2006 onwards represents the largest commercially-based distribution of a gasification-type advanced biomass stove. BP's Emerging Consumer Markets (ECM) division and then successor company First Energy sold this stove and the pelletized biomass fuel on which it operates. We assess the success of this effort and the role its commercial aspect played in outcomes using a survey of 998 households in areas of Maharashtra and Karnataka where the stove was sold as well as detailed interviews with BP and First Energy staff. Statistical models based on this data indicate that Oorja purchase rates were significantly influenced by the intensity of Oorja marketing in a region as well as by pre-existing stove mix among households. The highest rate of adoption came from LPG-using households for which Oorja's pelletized biomass fuel reduced costs. Smoke- and health-related messages from Oorja marketing did not significantly influence the purchase decision, although they did appear to affect household perceptions about smoke. By the time of our survey, only 9% of households that purchased Oorja were still using the stove, the result in large part of difficulties First Energy encountered in developing a viable supply chain around low-cost procurement of “agricultural waste” to

  9. Knowledge exchange and learning from failures in distributed environments: The role of contractor relationship management and work characteristics

    International Nuclear Information System (INIS)

    Gressgård, Leif Jarle; Hansen, Kåre

    2015-01-01

    Learning from failures is vital for improvement of safety performance, reliability, and resilience in organizations. In order for such learning to take place in distributed environments, knowledge has to be shared among organizational members at different locations and units. This paper reports on a study conducted in the context of drilling and well operations on the Norwegian Continental Shelf, which represents a high-risk distributed organizational environment. The study investigates the relationships between organizations' abilities to learn from failures, knowledge exchange within and between organizational units, quality of contractor relationship management, and work characteristics. The results show that knowledge exchange between units is the most important predictor of perceived ability to learn from failures. Contractor relationship management, leadership involvement, role clarity, and empowerment are also important factors for failure-based learning, both directly and through increased knowledge exchange. The results of the study enhance our understanding of how abilities to learn from failures can be improved in distributed environments where similar work processes take place at different locations and involve employees from several companies. Theoretical contributions and practical implications are discussed. - Highlights: • We investigate factors affecting failure-based learning in distributed environments. • Knowledge exchange between units is the most important predictor. • Contractor relationship management is positively related to knowledge exchange. • Leadership involvement, role clarity, and empowerment are significant variables. • Respondents from an operator firm and eight contractors are included in the study

  10. The Differences across Distributed Leadership Practices by School Position According to the Comprehensive Assessment of Leadership for Learning (CALL)

    Science.gov (United States)

    Blitz, Mark H.; Modeste, Marsha

    2015-01-01

    The Comprehensive Assessment of Leadership for Learning (CALL) is a multi-source assessment of distributed instructional leadership. As part of the validation of CALL, researchers examined differences between teacher and leader ratings in assessing distributed leadership practices. The authors utilized a t-test for equality of means for the…

  11. Gravity Spy: integrating advanced LIGO detector characterization, machine learning, and citizen science

    Science.gov (United States)

    Zevin, M; Coughlin, S; Bahaadini, S; Besler, E; Rohani, N; Allen, S; Cabero, M; Crowston, K; Katsaggelos, A K; Larson, S L; Lee, T K; Lintott, C; Littenberg, T B; Lundgren, A; Østerlund, C; Smith, J R; Trouille, L; Kalogera, V

    2018-01-01

    With the first direct detection of gravitational waves, the advanced laser interferometer gravitational-wave observatory (LIGO) has initiated a new field of astronomy by providing an alternative means of sensing the universe. The extreme sensitivity required to make such detections is achieved through exquisite isolation of all sensitive components of LIGO from non-gravitational-wave disturbances. Nonetheless, LIGO is still susceptible to a variety of instrumental and environmental sources of noise that contaminate the data. Of particular concern are noise features known as glitches, which are transient and non-Gaussian in their nature, and occur at a high enough rate so that accidental coincidence between the two LIGO detectors is non-negligible. Glitches come in a wide range of time-frequency-amplitude morphologies, with new morphologies appearing as the detector evolves. Since they can obscure or mimic true gravitational-wave signals, a robust characterization of glitches is paramount in the effort to achieve the gravitational-wave detection rates that are predicted by the design sensitivity of LIGO. This proves a daunting task for members of the LIGO Scientific Collaboration alone due to the sheer amount of data. In this paper we describe an innovative project that combines crowdsourcing with machine learning to aid in the challenging task of categorizing all of the glitches recorded by the LIGO detectors. Through the Zooniverse platform, we engage and recruit volunteers from the public to categorize images of time-frequency representations of glitches into pre-identified morphological classes and to discover new classes that appear as the detectors evolve. In addition, machine learning algorithms are used to categorize images after being trained on human-classified examples of the morphological classes. Leveraging the strengths of both classification methods, we create a combined method with the aim of improving the efficiency and accuracy of each individual

  12. Gravity Spy: integrating advanced LIGO detector characterization, machine learning, and citizen science

    International Nuclear Information System (INIS)

    Zevin, M; Coughlin, S; Larson, S L; Trouille, L; Kalogera, V; Bahaadini, S; Besler, E; Rohani, N; Katsaggelos, A K; Allen, S; Cabero, M; Lundgren, A; Crowston, K; Østerlund, C; Lee, T K; Lintott, C; Littenberg, T B; Smith, J R

    2017-01-01

    With the first direct detection of gravitational waves, the advanced laser interferometer gravitational-wave observatory (LIGO) has initiated a new field of astronomy by providing an alternative means of sensing the universe. The extreme sensitivity required to make such detections is achieved through exquisite isolation of all sensitive components of LIGO from non-gravitational-wave disturbances. Nonetheless, LIGO is still susceptible to a variety of instrumental and environmental sources of noise that contaminate the data. Of particular concern are noise features known as glitches , which are transient and non-Gaussian in their nature, and occur at a high enough rate so that accidental coincidence between the two LIGO detectors is non-negligible. Glitches come in a wide range of time-frequency-amplitude morphologies, with new morphologies appearing as the detector evolves. Since they can obscure or mimic true gravitational-wave signals, a robust characterization of glitches is paramount in the effort to achieve the gravitational-wave detection rates that are predicted by the design sensitivity of LIGO. This proves a daunting task for members of the LIGO Scientific Collaboration alone due to the sheer amount of data. In this paper we describe an innovative project that combines crowdsourcing with machine learning to aid in the challenging task of categorizing all of the glitches recorded by the LIGO detectors. Through the Zooniverse platform, we engage and recruit volunteers from the public to categorize images of time-frequency representations of glitches into pre-identified morphological classes and to discover new classes that appear as the detectors evolve. In addition, machine learning algorithms are used to categorize images after being trained on human-classified examples of the morphological classes. Leveraging the strengths of both classification methods, we create a combined method with the aim of improving the efficiency and accuracy of each individual

  13. Advanced Parkinson’s disease effect on goal-directed and habitual processes involved in visuomotor associative learning

    Directory of Open Access Journals (Sweden)

    Fadila eHadj-Bouziane

    2013-01-01

    Full Text Available The present behavioral study readdresses the question of habit learning in Parkinson's disease. Patients were early onset, non-demented, dopa-responsive, candidates for surgical treatment, similar to those we found earlier as suffering greater dopamine depletion in the putamen than in the caudate nucleus. The task was the same conditional associative learning task as that used previously in monkeys and healthy humans to unveil the striatum involvement in habit learning. Sixteen patients and 20 age- and education-matched healthy control subjects learned sets of 3 visuo-motor associations between complex patterns and joystick displacements during two testing sessions separated by a few hours. We distinguished errors preceding versus following the first correct response to compare patients' performance during the earliest phase of learning dominated by goal-directed actions with that observed later on, when responses start to become habitual. The disease significantly retarded both learning phases, especially in patients under sixty years of age. However, only the late phase deficit was disease severity-dependent and persisted on the second testing session. These findings provide the first corroboration in Parkinson patients of two ideas well-established in the animal literature. The first is the idea that associating visual stimuli to motor acts is a form of habit learning that engages the striatum. It is confirmed here by the global impairment in visuo-motor learning induced by Parkinson's disease. The second idea is that goal-directed behaviors are predominantly caudate-dependent whereas habitual responses are primarily putamen-dependent. At the advanced Parkinson's disease stages tested here, dopamine depletion is greater in the putamen than in the caudate nucleus. Accordingly, the late phase of learning corresponding to the emergence of habitual responses was more vulnerable to the disease than the early phase dominated by goal

  14. Editorial: Advanced Learning Technologies, Performance Technologies, Open Contents, and Standards - Some Papers from the Best Papers of the Conference ICCE C3 2009

    Directory of Open Access Journals (Sweden)

    Fanny Klett (IEEE Fellow

    2010-09-01

    Full Text Available This special issue deals with several cutting edge research outcomes from recent advancement of learning technologies. Advanced learning technologies are the composition of various related technologies and concepts such as i internet technologies and mobile technologies, ii human and organizational performance/knowledge management, and iii underlying trends toward open technology, open content and open education. This editorial note describes the overview of these topics related to the advanced learning technologies to provide the common framework for the accepted papers in this special issue.

  15. Advanced electrical power, distribution and control for the Space Transportation System

    Science.gov (United States)

    Hansen, Irving G.; Brandhorst, Henry W., Jr.

    1990-01-01

    High frequency power distribution and management is a technology ready state of development. As such, a system employs the fewest power conversion steps, and employs zero current switching for those steps. It results in the most efficiency, and lowest total parts system count when equivalent systems are compared. The operating voltage and frequency are application specific trade off parameters. However, a 20 kHz Hertz system is suitable for wide range systems.

  16. Alchemical and structural distribution based representation for universal quantum machine learning

    Science.gov (United States)

    Faber, Felix A.; Christensen, Anders S.; Huang, Bing; von Lilienfeld, O. Anatole

    2018-06-01

    We introduce a representation of any atom in any chemical environment for the automatized generation of universal kernel ridge regression-based quantum machine learning (QML) models of electronic properties, trained throughout chemical compound space. The representation is based on Gaussian distribution functions, scaled by power laws and explicitly accounting for structural as well as elemental degrees of freedom. The elemental components help us to lower the QML model's learning curve, and, through interpolation across the periodic table, even enable "alchemical extrapolation" to covalent bonding between elements not part of training. This point is demonstrated for the prediction of covalent binding in single, double, and triple bonds among main-group elements as well as for atomization energies in organic molecules. We present numerical evidence that resulting QML energy models, after training on a few thousand random training instances, reach chemical accuracy for out-of-sample compounds. Compound datasets studied include thousands of structurally and compositionally diverse organic molecules, non-covalently bonded protein side-chains, (H2O)40-clusters, and crystalline solids. Learning curves for QML models also indicate competitive predictive power for various other electronic ground state properties of organic molecules, calculated with hybrid density functional theory, including polarizability, heat-capacity, HOMO-LUMO eigenvalues and gap, zero point vibrational energy, dipole moment, and highest vibrational fundamental frequency.

  17. Event-Triggered Distributed Control of Nonlinear Interconnected Systems Using Online Reinforcement Learning With Exploration.

    Science.gov (United States)

    Narayanan, Vignesh; Jagannathan, Sarangapani

    2017-09-07

    In this paper, a distributed control scheme for an interconnected system composed of uncertain input affine nonlinear subsystems with event triggered state feedback is presented by using a novel hybrid learning scheme-based approximate dynamic programming with online exploration. First, an approximate solution to the Hamilton-Jacobi-Bellman equation is generated with event sampled neural network (NN) approximation and subsequently, a near optimal control policy for each subsystem is derived. Artificial NNs are utilized as function approximators to develop a suite of identifiers and learn the dynamics of each subsystem. The NN weight tuning rules for the identifier and event-triggering condition are derived using Lyapunov stability theory. Taking into account, the effects of NN approximation of system dynamics and boot-strapping, a novel NN weight update is presented to approximate the optimal value function. Finally, a novel strategy to incorporate exploration in online control framework, using identifiers, is introduced to reduce the overall cost at the expense of additional computations during the initial online learning phase. System states and the NN weight estimation errors are regulated and local uniformly ultimately bounded results are achieved. The analytical results are substantiated using simulation studies.

  18. Acquiring and processing verb argument structure: distributional learning in a miniature language.

    Science.gov (United States)

    Wonnacott, Elizabeth; Newport, Elissa L; Tanenhaus, Michael K

    2008-05-01

    Adult knowledge of a language involves correctly balancing lexically-based and more language-general patterns. For example, verb argument structures may sometimes readily generalize to new verbs, yet with particular verbs may resist generalization. From the perspective of acquisition, this creates significant learnability problems, with some researchers claiming a crucial role for verb semantics in the determination of when generalization may and may not occur. Similarly, there has been debate regarding how verb-specific and more generalized constraints interact in sentence processing and on the role of semantics in this process. The current work explores these issues using artificial language learning. In three experiments using languages without semantic cues to verb distribution, we demonstrate that learners can acquire both verb-specific and verb-general patterns, based on distributional information in the linguistic input regarding each of the verbs as well as across the language as a whole. As with natural languages, these factors are shown to affect production, judgments and real-time processing. We demonstrate that learners apply a rational procedure in determining their usage of these different input statistics and conclude by suggesting that a Bayesian perspective on statistical learning may be an appropriate framework for capturing our findings.

  19. A Takagi-Sugeno fuzzy power-distribution method for a prototypical advanced reactor considering pump degradation

    Energy Technology Data Exchange (ETDEWEB)

    Yuan, Yue [Institute of Nuclear and New Energy Technology, Tsinghua University, Collaborative Innovation Center of Advanced Nuclear Energy Technology, Key Laboratory of Advanced Reactor Engineering and Safety of Ministry of Education, Beijing (China); Coble, Jamie [Dept. of Nuclear Engineering, University of Tennessee, Knoxville (United States)

    2017-08-15

    Advanced reactor designs often feature longer operating cycles between refueling and new concepts of operation beyond traditional baseload electricity production. Owing to this increased complexity, traditional proportional–integral control may not be sufficient across all potential operating regimes. The prototypical advanced reactor (PAR) design features two independent reactor modules, each connected to a single dedicated steam generator that feeds a common balance of plant for electricity generation and process heat applications. In the current research, the PAR is expected to operate in a load-following manner to produce electricity to meet grid demand over a 24-hour period. Over the operational lifetime of the PAR system, primary and intermediate sodium pumps are expected to degrade in performance. The independent operation of the two reactor modules in the PAR may allow the system to continue operating under degraded pump performance by shifting the power production between reactor modules in order to meet overall load demands. This paper proposes a Takagi–Sugeno (T–S) fuzzy logic-based power distribution system. Two T–S fuzzy power distribution controllers have been designed and tested. Simulation shows that the devised T–S fuzzy controllers provide improved performance over traditional controls during daily load-following operation under different levels of pump degradation.

  20. A Takagi–Sugeno fuzzy power-distribution method for a prototypical advanced reactor considering pump degradation

    Directory of Open Access Journals (Sweden)

    Yue Yuan

    2017-08-01

    Full Text Available Advanced reactor designs often feature longer operating cycles between refueling and new concepts of operation beyond traditional baseload electricity production. Owing to this increased complexity, traditional proportional–integral control may not be sufficient across all potential operating regimes. The prototypical advanced reactor (PAR design features two independent reactor modules, each connected to a single dedicated steam generator that feeds a common balance of plant for electricity generation and process heat applications. In the current research, the PAR is expected to operate in a load-following manner to produce electricity to meet grid demand over a 24-hour period. Over the operational lifetime of the PAR system, primary and intermediate sodium pumps are expected to degrade in performance. The independent operation of the two reactor modules in the PAR may allow the system to continue operating under degraded pump performance by shifting the power production between reactor modules in order to meet overall load demands. This paper proposes a Takagi–Sugeno (T–S fuzzy logic-based power distribution system. Two T–S fuzzy power distribution controllers have been designed and tested. Simulation shows that the devised T–S fuzzy controllers provide improved performance over traditional controls during daily load-following operation under different levels of pump degradation.

  1. Global assessment of soil organic carbon stocks and spatial distribution of histosols: the Machine Learning approach

    Science.gov (United States)

    Hengl, Tomislav

    2016-04-01

    Preliminary results of predicting distribution of soil organic soils (Histosols) and soil organic carbon stock (in tonnes per ha) using global compilations of soil profiles (about 150,000 points) and covariates at 250 m spatial resolution (about 150 covariates; mainly MODIS seasonal land products, SRTM DEM derivatives, climatic images, lithological and land cover and landform maps) are presented. We focus on using a data-driven approach i.e. Machine Learning techniques that often require no knowledge about the distribution of the target variable or knowledge about the possible relationships. Other advantages of using machine learning are (DOI: 10.1371/journal.pone.0125814): All rules required to produce outputs are formalized. The whole procedure is documented (the statistical model and associated computer script), enabling reproducible research. Predicted surfaces can make use of various information sources and can be optimized relative to all available quantitative point and covariate data. There is more flexibility in terms of the spatial extent, resolution and support of requested maps. Automated mapping is also more cost-effective: once the system is operational, maintenance and production of updates are an order of magnitude faster and cheaper. Consequently, prediction maps can be updated and improved at shorter and shorter time intervals. Some disadvantages of automated soil mapping based on Machine Learning are: Models are data-driven and any serious blunders or artifacts in the input data can propagate to order-of-magnitude larger errors than in the case of expert-based systems. Fitting machine learning models is at the order of magnitude computationally more demanding. Computing effort can be even tens of thousands higher than if e.g. linear geostatistics is used. Many machine learning models are fairly complex often abstract and any interpretation of such models is not trivial and require special multidimensional / multivariable plotting and data mining

  2. Dynamic Modeling of Learning in Emerging Energy Industries: The Example of Advanced Biofuels in the United States: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Vimmerstedt, Laura J.; Bush, Brian W.; Peterson, Steven O.

    2015-09-03

    This paper (and its supplemental model) presents novel approaches to modeling interactions and related policies among investment, production, and learning in an emerging competitive industry. New biomass-to-biofuels pathways are being developed and commercialized to support goals for U.S. advanced biofuel use, such as those in the Energy Independence and Security Act of 2007. We explore the impact of learning rates and techno-economics in a learning model excerpted from the Biomass Scenario Model (BSM), developed by the U.S. Department of Energy and the National Renewable Energy Laboratory to explore the impact of biofuel policy on the evolution of the biofuels industry. The BSM integrates investment, production, and learning among competing biofuel conversion options that are at different stages of industrial development. We explain the novel methods used to simulate the impact of differing assumptions about mature industry techno-economics and about learning rates while accounting for the different maturity levels of various conversion pathways. A sensitivity study shows that the parameters studied (fixed capital investment, process yield, progress ratios, and pre-commercial investment) exhibit highly interactive effects, and the system, as modeled, tends toward market dominance of a single pathway due to competition and learning dynamics.

  3. Equity by Design: Using Peer-Mediated Learning to Advance Equity for All Students

    Science.gov (United States)

    Tan, Paulo; Macey, Erin M.; Thorius, Kathleen A. K.; Simon, Marsha

    2013-01-01

    The use of peer-mediated learning has emerged as a promising practice to transform the classroom experiences of both students with disabilities and their non-disabled peers. This brief summarizes the best practices for implementing peer-mediated learning and advocates situating peer-mediated learning in inclusive, interdependent learning…

  4. Game Changer for Online Learning Driven by Advances in Web Technology

    Science.gov (United States)

    Kaul, Manfred; Kless, André; Bonne, Thorsten; Rieke, Almut

    2017-01-01

    Almost unnoticed by the e-learning community, the underlying technology of the WWW is undergoing massive technological changes on all levels these days. In this paper we draw the attention to the emerging game changer and discuss the consequences for online learning. In our e-learning project "Work & Study", funded by the German…

  5. Advanced technology for the reuse of learning objects in a course-management system

    NARCIS (Netherlands)

    Strijker, A.; Collis, Betty

    2005-01-01

    The creation, labelling, use, and re-use of learning objects is an important area of development involving learning technology. In the higher education context, instructors typically use a course management system (CMS) to organize and manage their own learning objects. The needs and practices of

  6. Developing media and information literacy education to improve foreign language learning : working with Internet resources at advanced levels

    Directory of Open Access Journals (Sweden)

    Joanna Górecka

    2011-01-01

    Full Text Available The aim of the paper is to discuss the relevance of media and information education in language learning at advanced levels. The present paper is based on the empirical data obtained during the action-research conducted with the Romance philology students attending the course of French as a foreign language. The main object of the research is to establish to what degree an oral argumentation task, preceded by the task of planning the discussion on Wiki is considered to be a learning situation by students themselves. The research focuses on a selected aspect of the learning process, namely, on the use of media resources while negotiating the discussion outline and specifically, while negotiating its topic, objectives and its cognitive value. The principal conclusions indicate 1 that the task scenario should be based on the critical and dialogical approach to media and 2 that this kind of instruction can reinforce the argumentative dimension of the discussion.

  7. Study on applying Augmented Reality for effective learning of School Curriculum of Advanced Level in Sri Lanka

    Directory of Open Access Journals (Sweden)

    B.M.Terrence Chandike

    2015-08-01

    Full Text Available Advanced Level examination is the most critical examination that determines the future of the students in Sri Lanka as it is the path for the higher education. In Sri Lanka students are sitting for Advanced Level examination in Art Commerce Bio or Maths streams. The objective of this study was to develop a framework with Augmented Reality applications to be used in Advanced Level Bio science subject as a supportive learning technique evaluating the performances of the students with developed Augmented Reality AR applications running on the computer. The study was carried out using the Advanced Level students in Gampaha educational region. AR application was developed using Java language and with available supportive tools. As this application was based on markers another tool was used to create markers. An open ended questionnaire was provided to the selected students to identify the feasibility and students perception on the developed AR application for the students. A question paper was designed based on the subject area of the developed AR application to evaluate the student performances. After developing the AR application it was provided to the half of the selected student sampleone class and the other half of the students another class was exposed to normal existing learning procedure. Thereafter both student samples were exposed to the common examination to assess the performances. AR application helped to enhance the students performances by increasing the student interest better understanding memorizing ability and increasing the pass rate of the students.

  8. Advanced Machine Learning for Classification, Regression, and Generation in Jet Physics

    CERN Multimedia

    CERN. Geneva

    2017-01-01

    There is a deep connection between machine learning and jet physics - after all, jets are defined by unsupervised learning algorithms. Jet physics has been a driving force for studying modern machine learning in high energy physics. Domain specific challenges require new techniques to make full use of the algorithms. A key focus is on understanding how and what the algorithms learn. Modern machine learning techniques for jet physics are demonstrated for classification, regression, and generation. In addition to providing powerful baseline performance, we show how to train complex models directly on data and to generate sparse stacked images with non-uniform granularity.

  9. Guidelines for Implementing Advanced Distribution Management Systems-Requirements for DMS Integration with DERMS and Microgrids

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Jianhui [Argonne National Lab. (ANL), Argonne, IL (United States); Chen, Chen [Argonne National Lab. (ANL), Argonne, IL (United States); Lu, Xiaonan [Argonne National Lab. (ANL), Argonne, IL (United States)

    2015-08-01

    This guideline focuses on the integration of DMS with DERMS and microgrids connected to the distribution grid by defining generic and fundamental design and implementation principles and strategies. It starts by addressing the current status, objectives, and core functionalities of each system, and then discusses the new challenges and the common principles of DMS design and implementation for integration with DERMS and microgrids to realize enhanced grid operation reliability and quality power delivery to consumers while also achieving the maximum energy economics from the DER and microgrid connections.

  10. Facilitated Learning to Advance Geriatrics: Increasing the Capacity of Nurse Faculty to Teach Students About Caring for Older Adults.

    Science.gov (United States)

    Krichbaum, Kathleen; Kaas, Merrie J; Wyman, Jean F; Van Son, Catherine R

    2015-06-01

    The Facilitated Learning to Advance Geriatrics program (FLAG) was designed to increase the numbers of nurse faculty in prelicensure programs with basic knowledge about aging and teaching effectiveness to prepare students to provide safe, high quality care for older adults. Using a framework to improve transfer of learning, FLAG was designed to include: (a) a workshop to increase basic knowledge of aging and common geriatric syndromes, and effective use of evidence-based teaching/learning strategies; (b) a year-long mentoring program to support application of workshop learning and leading change in participants' schools to ensure that geriatrics is a priority. Both formative and summative evaluation methods were used, and included self-assessment of objectives, program satisfaction, and teaching self-efficacy. FLAG achieved its overall purpose by enrolling 152 participants from 19 states including 23 faculty from associate degree programs and 102 from baccalaureate programs. Self-rated teaching effectiveness improved significantly from pre- to post-workshop each year. Achievement of learning objectives was rated highly as was satisfaction. Transfer of learning was evidenced by implementation of educational projects in home schools supported by mentoring. The FLAG program provided opportunities for nurse educators to learn to teach geriatrics more effectively and to transfer learning to their work environment. Future FLAG programs will be offered in a shortened format, incorporating online content and strategies, adding other health professionals to the audience with the same goal of increasing the knowledge and abilities of educators to prepare learners to provide competent care for older adults. © The Author 2015. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  11. [Advance to the research of the climate factor effect on the distribution of plague].

    Science.gov (United States)

    Zhang, A P; Wei, R J; Xiong, H M; Wang, Z Y

    2016-05-01

    Plague is an anthropozoonotic disease caused by the Yersinia pestis ,which developed by many factors including local climate factors. In recent years, more and more studies on the effects of climate on plague were conducted. According to the researches, climate factors (mainly the rainfall and temperature) affected the development and distribution of plague by influencing the abundance of plague host animals and fleas index. The climate also affected the epidemic dynamics and the scope of plague. There were significant differences existing in the influence of climate on the palgue developed in the north and south China. In the two different plague epidemic systems, the solitary Daurian ground squirrel-flea-plague and the social Mongolian gerbil-flea-plague, the obvious population differences existed among the responses of the host animal to the climate changes. Although the internal relationship between the rainfall, the flea index, the density of rodents and the plague supported the nutritional cascade hypothesis, it can not prove that there is a clear causality between the occurrence of plague and rainfall. So the influence of climate factors on plague distribution can only be used for early forecasting and warning of the plague.

  12. Application of Learning Methods to Local Electric Field Distributions in Defected Dielectric Materials

    Science.gov (United States)

    Ferris, Kim; Jones, Dumont

    2014-03-01

    Local electric fields reflect the structural and dielectric fluctuations in a semiconductor, and affect the material performance both for electron transport and carrier lifetime properties. In this paper, we use the LOCALF methodology with periodic boundary conditions to examine the local electric field distributions and its perturbations for II-VI (CdTe, Cd(1-x)Zn(x)Te) semiconductors, containing Te inclusions and small fluctuations in the local dielectric susceptibility. With inclusion of the induced-field term, the electric field distribution shows enhancements and diminishments compared to the macroscopic applied field, reflecting the microstructure characteristics of the dielectric. Learning methods are applied to these distributions to assess the spatial extent of the perturbation, and determine an electric field defined defect size as compared to its physical dimension. Critical concentrations of defects are assessed in terms of defect formation energies. This work was supported by the US Department of Homeland Security, Domestic Nuclear Detection Office, under competitively awarded contract/IAA HSHQDC-08-X-00872-e. This support does not constitute an express or implied endorsement on the part of the Gov't.

  13. Boosting up quantum key distribution by learning statistics of practical single-photon sources

    International Nuclear Information System (INIS)

    Adachi, Yoritoshi; Yamamoto, Takashi; Koashi, Masato; Imoto, Nobuyuki

    2009-01-01

    We propose a simple quantum-key-distribution (QKD) scheme for practical single-photon sources (SPSs), which works even with a moderate suppression of the second-order correlation g (2) of the source. The scheme utilizes a passive preparation of a decoy state by monitoring a fraction of the signal via an additional beam splitter and a detector at the sender's side to monitor photon-number splitting attacks. We show that the achievable distance increases with the precision with which the sub-Poissonian tendency is confirmed in higher photon-number distribution of the source, rather than with actual suppression of the multiphoton emission events. We present an example of the secure key generation rate in the case of a poor SPS with g (2) =0.19, in which no secure key is produced with the conventional QKD scheme, and show that learning the photon-number distribution up to several numbers is sufficient for achieving almost the same distance as that of an ideal SPS.

  14. Foundational Report Series: Advanced Distribution Management Systems for Grid Modernization, Business Case Calculations for DMS

    Energy Technology Data Exchange (ETDEWEB)

    Lu, Xiaonan [Argonne National Lab. (ANL), Argonne, IL (United States); Singh, Ravindra [Argonne National Lab. (ANL), Argonne, IL (United States); Wang, Jianhui [Argonne National Lab. (ANL), Argonne, IL (United States); Reilly, James T. [Reilly Associates, Pittson, PA (United States)

    2017-01-01

    Distribution Management System (DMS) applications require a substantial commitment of technical and financial resources. In order to proceed beyond limited-scale demonstration projects, utilities must have a clear understanding of the business case for committing these resources that recognizes the total cost of ownership. Many of the benefits provided by investments in DMSs do not translate easily into monetary terms, making cost-benefit calculations difficult. For example, Fault Location Isolation and Service Restoration (FLISR) can significantly reduce customer outage duration and improve reliability. However, there is no well-established and universally-accepted procedure for converting these benefits into monetary terms that can be compared directly to investment costs. This report presents a methodology to analyze the benefits and costs of DMS applications as fundamental to the business case.

  15. Management of complex multi-reservoir water distribution systems using advanced control theoretic tools and techniques

    CERN Document Server

    Chmielowski, Wojciech Z

    2013-01-01

    This study discusses issues of optimal water management in a complex distribution system. The main elements of the water-management system under consideration are retention reservoirs, among which water transfers are possible, and a network of connections between these reservoirs and water treatment plants (WTPs). System operation optimisation involves determining the proper water transport routes and their flow volumes from the retention reservoirs to the WTPs, and the volumes of possible transfers among the reservoirs, taking into account transport-related delays for inflows, outflows and water transfers in the system. Total system operation costs defined by an assumed quality coefficient should be minimal. An analytical solution of the optimisation task so formulated has been obtained as a result of using Pontriagin’s maximum principle with reference to the quality coefficient assumed. Stable start and end conditions in reservoir state trajectories have been assumed. The researchers have taken into accou...

  16. Impact of body fat distribution on neoadjuvant chemotherapy outcomes in advanced breast cancer patients

    International Nuclear Information System (INIS)

    Iwase, Toshiaki; Sangai, Takafumi; Nagashima, Takeshi; Sakakibara, Masahiro; Sakakibara, Junta; Hayama, Shouko; Ishigami, Emi; Masuda, Takahito; Miyazaki, Masaru

    2015-01-01

    Obesity is known to decrease the efficacy of neoadjuvant chemotherapy (NAC) against breast cancer; however, the relationship between actual body composition and NAC outcomes remains unknown. Therefore, we determined the effect of body composition on NAC outcomes. A total of 172 advanced breast cancer patients who underwent surgery after NAC were retrospectively analyzed. Body composition parameters including abdominal circumference (AC), subcutaneous fat area (SFA), visceral fat area (VFA), and skeletal muscle area (SMA) were calculated using computed tomography volume-analyzing software. VFA/SFA ratio was used to evaluate visceral obesity. The associations of body composition parameters with pathological complete remission (pCR) and survival were analyzed. AC, SFA, and VFA were significantly correlated with body mass index (BMI) (all P < 0.05; r = 0.82, r = 0.71, and r = 0.78, respectively). AC, SFA, and VFA increased significantly and SMA decreased significantly after menopause (all P < 0.05). VFA/SFA ratio increased significantly after menopause, even though BMI remained unchanged. Body composition parameters were not associated with pCR. Distant disease-free survival (DDFS) was significantly worse in the high VFA group than in the low VFA group (P < 0.05). Furthermore, in the high VFA group, postmenopausal patients had significantly shorter DDFS than premenopausal patients (P < 0.05). VFA was independently associated with DDFS in the multivariate analysis (P < 0.05). High visceral fat is associated with worse NAC outcomes in breast cancer patients, especially postmenopausal patients. Interventions targeting visceral fat accumulation will likely improve NAC outcomes

  17. Pro deep learning with TensorFlow a mathematical approach to advanced artificial intelligence in Python

    CERN Document Server

    Pattanayak, Santanu

    2017-01-01

    Deploy deep learning solutions in production with ease using TensorFlow. You'll also develop the mathematical understanding and intuition required to invent new deep learning architectures and solutions on your own. Pro Deep Learning with TensorFlow provides practical, hands-on expertise so you can learn deep learning from scratch and deploy meaningful deep learning solutions. This book will allow you to get up to speed quickly using TensorFlow and to optimize different deep learning architectures. All of the practical aspects of deep learning that are relevant in any industry are emphasized in this book. You will be able to use the prototypes demonstrated to build new deep learning applications. The code presented in the book is available in the form of iPython notebooks and scripts which allow you to try out examples and extend them in interesting ways. You will be equipped with the mathematical foundation and scientific knowledge to pursue research in this field and give back to the community.

  18. Mind map learning for advanced engineering study: case study in system dynamics

    Science.gov (United States)

    Woradechjumroen, Denchai

    2018-01-01

    System Dynamics (SD) is one of the subjects that were use in learning Automatic Control Systems in dynamic and control field. Mathematical modelling and solving skills of students for engineering systems are expecting outcomes of the course which can be further used to efficiently study control systems and mechanical vibration; however, the fundamental of the SD includes strong backgrounds in Dynamics and Differential Equations, which are appropriate to the students in governmental universities that have strong skills in Mathematics and Scientifics. For private universities, students are weak in the above subjects since they obtained high vocational certificate from Technical College or Polytechnic School, which emphasize the learning contents in practice. To enhance their learning for improving their backgrounds, this paper applies mind maps based problem based learning to relate the essential relations of mathematical and physical equations. With the advantages of mind maps, each student is assigned to design individual mind maps for self-leaning development after they attend the class and learn overall picture of each chapter from the class instructor. Four problems based mind maps learning are assigned to each student. Each assignment is evaluated via mid-term and final examinations, which are issued in terms of learning concepts and applications. In the method testing, thirty students are tested and evaluated via student learning backgrounds in the past. The result shows that well-design mind maps can improve learning performance based on outcome evaluation. Especially, mind maps can reduce time-consuming and reviewing for Mathematics and Physics in SD significantly.

  19. Multi-Time Step Service Restoration for Advanced Distribution Systems and Microgrids

    International Nuclear Information System (INIS)

    Chen, Bo; Chen, Chen; Wang, Jianhui; Butler-Purry, Karen L.

    2017-01-01

    Modern power systems are facing increased risk of disasters that can cause extended outages. The presence of remote control switches (RCSs), distributed generators (DGs), and energy storage systems (ESS) provides both challenges and opportunities for developing post-fault service restoration methodologies. Inter-temporal constraints of DGs, ESS, and loads under cold load pickup (CLPU) conditions impose extra complexity on problem formulation and solution. In this paper, a multi-time step service restoration methodology is proposed to optimally generate a sequence of control actions for controllable switches, ESSs, and dispatchable DGs to assist the system operator with decision making. The restoration sequence is determined to minimize the unserved customers by energizing the system step by step without violating operational constraints at each time step. The proposed methodology is formulated as a mixed-integer linear programming (MILP) model and can adapt to various operation conditions. Furthermore, the proposed method is validated through several case studies that are performed on modified IEEE 13-node and IEEE 123-node test feeders.

  20. Conventional and conformal technique of external beam radiotherapy in locally advanced cervical cancer: Dose distribution, tumor response, and side effects

    Science.gov (United States)

    Mutrikah, N.; Winarno, H.; Amalia, T.; Djakaria, M.

    2017-08-01

    The objective of this study was to compare conventional and conformal techniques of external beam radiotherapy (EBRT) in terms of the dose distribution, tumor response, and side effects in the treatment of locally advanced cervical cancer patients. A retrospective cohort study was conducted on cervical cancer patients who underwent EBRT before brachytherapy in the Radiotherapy Department of Cipto Mangunkusumo Hospital. The prescribed dose distribution, tumor response, and acute side effects of EBRT using conventional and conformal techniques were investigated. In total, 51 patients who underwent EBRT using conventional techniques (25 cases using Cobalt-60 and 26 cases using a linear accelerator (LINAC)) and 29 patients who underwent EBRT using conformal techniques were included in the study. The distribution of the prescribed dose in the target had an impact on the patient’s final response to EBRT. The complete response rate of patients to conformal techniques was significantly greater (58%) than that of patients to conventional techniques (42%). No severe acute local side effects were seen in any of the patients (Radiation Therapy Oncology Group (RTOG) grades 3-4). The distribution of the dose and volume to the gastrointestinal tract affected the proportion of mild acute side effects (RTOG grades 1-2). The urinary bladder was significantly greater using conventional techniques (Cobalt-60/LINAC) than using conformal techniques at 72% and 78% compared to 28% and 22%, respectively. The use of conformal techniques in pelvic radiation therapy is suggested in radiotherapy centers with CT simulators and 3D Radiotherapy Treatment Planning Systems (RTPSs) to decrease some uncertainties in radiotherapy planning. The use of AP/PA pelvic radiation techniques with Cobalt-60 should be limited in body thicknesses equal to or less than 18 cm. When using conformal techniques, delineation should be applied in the small bowel, as it is considered a critical organ according to RTOG

  1. A Distributed Multi-Agent System for Collaborative Information Management and Learning

    Science.gov (United States)

    Chen, James R.; Wolfe, Shawn R.; Wragg, Stephen D.; Koga, Dennis (Technical Monitor)

    2000-01-01

    In this paper, we present DIAMS, a system of distributed, collaborative agents to help users access, manage, share and exchange information. A DIAMS personal agent helps its owner find information most relevant to current needs. It provides tools and utilities for users to manage their information repositories with dynamic organization and virtual views. Flexible hierarchical display is integrated with indexed query search-to support effective information access. Automatic indexing methods are employed to support user queries and communication between agents. Contents of a repository are kept in object-oriented storage to facilitate information sharing. Collaboration between users is aided by easy sharing utilities as well as automated information exchange. Matchmaker agents are designed to establish connections between users with similar interests and expertise. DIAMS agents provide needed services for users to share and learn information from one another on the World Wide Web.

  2. A Distributed Algorithm for the Cluster-Based Outlier Detection Using Unsupervised Extreme Learning Machines

    Directory of Open Access Journals (Sweden)

    Xite Wang

    2017-01-01

    Full Text Available Outlier detection is an important data mining task, whose target is to find the abnormal or atypical objects from a given dataset. The techniques for detecting outliers have a lot of applications, such as credit card fraud detection and environment monitoring. Our previous work proposed the Cluster-Based (CB outlier and gave a centralized method using unsupervised extreme learning machines to compute CB outliers. In this paper, we propose a new distributed algorithm for the CB outlier detection (DACB. On the master node, we collect a small number of points from the slave nodes to obtain a threshold. On each slave node, we design a new filtering method that can use the threshold to efficiently speed up the computation. Furthermore, we also propose a ranking method to optimize the order of cluster scanning. At last, the effectiveness and efficiency of the proposed approaches are verified through a plenty of simulation experiments.

  3. Side Information and Noise Learning for Distributed Video Coding using Optical Flow and Clustering

    DEFF Research Database (Denmark)

    Luong, Huynh Van; Rakêt, Lars Lau; Huang, Xin

    2012-01-01

    Distributed video coding (DVC) is a coding paradigm which exploits the source statistics at the decoder side to reduce the complexity at the encoder. The coding efficiency of DVC critically depends on the quality of side information generation and accuracy of noise modeling. This paper considers...... Transform Domain Wyner-Ziv (TDWZ) coding and proposes using optical flow to improve side information generation and clustering to improve noise modeling. The optical flow technique is exploited at the decoder side to compensate weaknesses of block based methods, when using motion-compensation to generate...... side information frames. Clustering is introduced to capture cross band correlation and increase local adaptivity in the noise modeling. This paper also proposes techniques to learn from previously decoded (WZ) frames. Different techniques are combined by calculating a number of candidate soft side...

  4. Book Review ~ Distance Education and Distributed Learning. Editors: Editors: Charalambos Vrasidas and Gene V. Glass

    Directory of Open Access Journals (Sweden)

    Ramesh C. Sharma

    2004-08-01

    Full Text Available As universities and educational institutions around the globe strive to adopt and expand the use of information technologies in their teaching/ learning offerings, this book, Distance Education and Distributed Learning, will help those engaged in coming to grips with this fundamental paradigm shift taking place in education. This book addresses a wide range of issues related to distance education and online technologies. In the broadest sense, today’s technology-driven changes in distance education will help make students and teachers more aware of social justice and equity through the use of technology used to solve real life problems irrespective of time and space, culture and ability to participate. Distance education has changed over the years, and even more so since the introduction of Web-based technologies. Today, the trend is towards globalization and collaboration among educational institutions. Distance educators and students now have access to emerging opportunities to engage in higher quality education irrespective of time and space.

  5. Distributed Cerebellar Motor Learning; a Spike-Timing-Dependent Plasticity Model

    Directory of Open Access Journals (Sweden)

    Niceto Rafael Luque

    2016-03-01

    Full Text Available Deep cerebellar nuclei neurons receive both inhibitory (GABAergic synaptic currents from Purkinje cells (within the cerebellar cortex and excitatory (glutamatergic synaptic currents from mossy fibres. Those two deep cerebellar nucleus inputs are thought to be also adaptive, embedding interesting properties in the framework of accurate movements. We show that distributed spike-timing-dependent plasticity mechanisms (STDP located at different cerebellar sites (parallel fibres to Purkinje cells, mossy fibres to deep cerebellar nucleus cells, and Purkinje cells to deep cerebellar nucleus cells in close-loop simulations provide an explanation for the complex learning properties of the cerebellum in motor learning. Concretely, we propose a new mechanistic cerebellar spiking model. In this new model, deep cerebellar nuclei embed a dual functionality: deep cerebellar nuclei acting as a gain adaptation mechanism and as a facilitator for the slow memory consolidation at mossy fibres to deep cerebellar nucleus synapses. Equipping the cerebellum with excitatory (e-STDP and inhibitory (i-STDP mechanisms at deep cerebellar nuclei afferents allows the accommodation of synaptic memories that were formed at parallel fibres to Purkinje cells synapses and then transferred to mossy fibres to deep cerebellar nucleus synapses. These adaptive mechanisms also contribute to modulate the deep-cerebellar-nucleus-output firing rate (output gain modulation towards optimising its working range.

  6. Generation and Validation of Spatial Distribution of Hourly Wind Speed Time-Series using Machine Learning

    International Nuclear Information System (INIS)

    Veronesi, F; Grassi, S

    2016-01-01

    Wind resource assessment is a key aspect of wind farm planning since it allows to estimate the long term electricity production. Moreover, wind speed time-series at high resolution are helpful to estimate the temporal changes of the electricity generation and indispensable to design stand-alone systems, which are affected by the mismatch of supply and demand. In this work, we present a new generalized statistical methodology to generate the spatial distribution of wind speed time-series, using Switzerland as a case study. This research is based upon a machine learning model and demonstrates that statistical wind resource assessment can successfully be used for estimating wind speed time-series. In fact, this method is able to obtain reliable wind speed estimates and propagate all the sources of uncertainty (from the measurements to the mapping process) in an efficient way, i.e. minimizing computational time and load. This allows not only an accurate estimation, but the creation of precise confidence intervals to map the stochasticity of the wind resource for a particular site. The validation shows that machine learning can minimize the bias of the wind speed hourly estimates. Moreover, for each mapped location this method delivers not only the mean wind speed, but also its confidence interval, which are crucial data for planners. (paper)

  7. [Possible evolutionary mechanisms of 'culture' in animals: The hypothesis of distributed social learning].

    Science.gov (United States)

    Reznikova, Zh I; Panteleeva, S N

    2015-01-01

    There is a plethora of works on the origin and genesis of behavioral traditions in different animal species. Nevertheless, it still remains unclear as for which factors facilitate and which factors hinder the spreading those forms of behavior that are new for a population. Here, we present an analytical review on the topic, considering also the results of studies on 'culture' in animals and analyzing contradictions that arise when attempting to clarify the ethological mechanisms of cultural succession. The hypothesis of 'distributed social learning' is formulated, meaning that for spreading of complex behavioral stereotypes in a population the presence of few carriers of consistent stereotypes is enough under the condition that the rest of animals carry incomplete genetic programmes that start up these stereotypes. Existence of 'dormant' fragments of such programmes determines an inborn predisposition of their bearer to perform a certain sequence of acts. To complete the consistent stereotype, the simplest forms of social learning ('social alleviation') turn to be enough. The hypothesis is examined at the behavioral level and supported by experimental data obtained when studying the scenarios of hunting behavior development in ants Myrmica rubra L. It makes possible to explain the spreading of behavioral models in animal communities in a simpler way than cultural succession.

  8. Generation and Validation of Spatial Distribution of Hourly Wind Speed Time-Series using Machine Learning

    Science.gov (United States)

    Veronesi, F.; Grassi, S.

    2016-09-01

    Wind resource assessment is a key aspect of wind farm planning since it allows to estimate the long term electricity production. Moreover, wind speed time-series at high resolution are helpful to estimate the temporal changes of the electricity generation and indispensable to design stand-alone systems, which are affected by the mismatch of supply and demand. In this work, we present a new generalized statistical methodology to generate the spatial distribution of wind speed time-series, using Switzerland as a case study. This research is based upon a machine learning model and demonstrates that statistical wind resource assessment can successfully be used for estimating wind speed time-series. In fact, this method is able to obtain reliable wind speed estimates and propagate all the sources of uncertainty (from the measurements to the mapping process) in an efficient way, i.e. minimizing computational time and load. This allows not only an accurate estimation, but the creation of precise confidence intervals to map the stochasticity of the wind resource for a particular site. The validation shows that machine learning can minimize the bias of the wind speed hourly estimates. Moreover, for each mapped location this method delivers not only the mean wind speed, but also its confidence interval, which are crucial data for planners.

  9. Learner support in a distributed learning environment:the use of WWW-based teachware packages

    Directory of Open Access Journals (Sweden)

    Christian Langenbach

    1997-01-01

    Full Text Available Various forms of education and training, based on the classical Computer Based Training (CBT, have become well established within companies and in higher education. With regard to the flexibility in terms of time and location, as well as in terms of incurred cost, they are far superior to traditional forms of instruction. A relatively recent development which has not yet gained widespread acceptance is the use of the World Wide Web (WWW as a basis and platform for distributed computer assisted teaching and learning. In addition to the advantages outlined with regard to classical CBT, the WWW offers the two advantages of world wide availablity as well as ease of promptly updating courses, but also adds many possibilities of collaborative learning. Using the Java programming language and the ever improving means of online presentation it has become possible to realize sophisticated WWW-based teachware packages which are comparable in terms of appearance and functionality to classical CBT applications created with dedicated authoring systems.

  10. Development, Evaluation and Use of a Student Experience Survey in Undergraduate Science Laboratories: The Advancing Science by Enhancing Learning in the Laboratory Student Laboratory Learning Experience Survey

    Science.gov (United States)

    Barrie, Simon C.; Bucat, Robert B.; Buntine, Mark A.; Burke da Silva, Karen; Crisp, Geoffrey T.; George, Adrian V.; Jamie, Ian M.; Kable, Scott H.; Lim, Kieran F.; Pyke, Simon M.; Read, Justin R.; Sharma, Manjula D.; Yeung, Alexandra

    2015-07-01

    Student experience surveys have become increasingly popular to probe various aspects of processes and outcomes in higher education, such as measuring student perceptions of the learning environment and identifying aspects that could be improved. This paper reports on a particular survey for evaluating individual experiments that has been developed over some 15 years as part of a large national Australian study pertaining to the area of undergraduate laboratories-Advancing Science by Enhancing Learning in the Laboratory. This paper reports on the development of the survey instrument and the evaluation of the survey using student responses to experiments from different institutions in Australia, New Zealand and the USA. A total of 3153 student responses have been analysed using factor analysis. Three factors, motivation, assessment and resources, have been identified as contributing to improved student attitudes to laboratory activities. A central focus of the survey is to provide feedback to practitioners to iteratively improve experiments. Implications for practitioners and researchers are also discussed.

  11. Exploring the Role of Distributed Learning in Distance Education at Allama Iqbal Open University: Academic Challenges at Postgraduate Level

    Directory of Open Access Journals (Sweden)

    Qadir BUKHSH

    2015-01-01

    Full Text Available Distributed learning is derived from the concept of distributed resources. Different institutions around the globe connected through network and the learners are diverse, located in the different cultures and communities. Distributed learning provides global standards of quality to all learners through synchronous and asynchronous communications and provides the opportunity of flexible and independent learning with equity, low cost educational services and has become the first choice of the dispersed learners around the globe. The present study was undertaken to investigate the challenges faced by the Faculty Members of Department of Business Administration and Computer Science at Allama Iqbal Open University Islamabad Pakistan. 25 Faculty Members were taken as sample of the study from both Departments (100% Sampling. The study was qualitative in nature and interview was the data collection tool. Data was analyzed by thematic analysis technique. The major challenges faced by the Faculty Members were as: bandwidth, synchronous learning activities, irregularity of the learners, feedback on individual work, designing and managing the learning activities, quality issues and training to use the network for teaching learning activities

  12. Undertaking an Ecological Approach to Advance Game-Based Learning: A Case Study

    Science.gov (United States)

    Shah, Mamta; Foster, Aroutis

    2014-01-01

    Systematic incorporation of digital games in schools is largely unexplored. This case study explored the ecological conditions necessary for implementing a game-based learning course by examining the interaction between three domains (the innovator, the innovation, and the context). From January-April 2012, one in-service teacher learned and…

  13. Intersecting Work and Learning: Assembling Advanced Liberal Regimes of Governing Workers in Australia

    Science.gov (United States)

    Reich, Ann

    2008-01-01

    Much had been written over the past few years on the intersections of work and learning. This article suggests that the analysis of the intersections of work and learning can benefit greatly from understanding the ways in which governing workers as individuals and populations has changed in Western liberal democracies in the latter part of the…

  14. Contact, Attitude and Motivation in the Learning of Catalan at Advanced Levels

    Science.gov (United States)

    Hamilton, Colleen; Serrano, Raquel

    2015-01-01

    The theoretical complexity of current understandings of second language (L2) identity has brought the study of language learning motivations from basic concepts of intrinsic, integrative and instrumental motives to a more dynamic construct that interacts with background factors, learning contexts and proficiency levels. This cross-sectional study…

  15. Cooperative Learning in the Advanced Algebra and Trigonometry Mathematics High School Classroom

    Science.gov (United States)

    Jozsa, Alison

    2017-01-01

    Over the past three decades, researchers have found cooperative learning to have positive effects on student achievement in various subject areas and levels in education. However, there are limited studies on the impact of cooperative learning on student achievement in the area of high school mathematics. This study examined the impact of…

  16. Development and Deployment of a Library of Industrially Focused Advanced Immersive VR Learning Environments

    Science.gov (United States)

    Cameron, Ian; Crosthwaite, Caroline; Norton, Christine; Balliu, Nicoleta; Tadé, Moses; Hoadley, Andrew; Shallcross, David; Barton, Geoff

    2008-01-01

    This work presents a unique education resource for both process engineering students and the industry workforce. The learning environment is based around spherical imagery of real operating plants coupled with interactive embedded activities and content. This Virtual Reality (VR) learning tool has been developed by applying aspects of relevant…

  17. Advancing Research in Second Language Writing through Computational Tools and Machine Learning Techniques: A Research Agenda

    Science.gov (United States)

    Crossley, Scott A.

    2013-01-01

    This paper provides an agenda for replication studies focusing on second language (L2) writing and the use of natural language processing (NLP) tools and machine learning algorithms. Specifically, it introduces a range of the available NLP tools and machine learning algorithms and demonstrates how these could be used to replicate seminal studies…

  18. Second Language Learning: Investigating Domain-Specific Adaptation in Advanced L2 Production

    NARCIS (Netherlands)

    Kerz, E.; Wiechmann, D.

    2016-01-01

    Usage-based (UB) accounts conceive of language learning as continuous, locally contingent construction learning, i.e., a lifelong process of developing and honing the repertoire of constructional patterns geared to the optimization of a language user’s communicative ability across a wide range of

  19. Earning While Learning: Maintaining Income While Upgrading Skills. Advancement for Low-Wage Workers

    Science.gov (United States)

    Prince, Heath

    2004-01-01

    Drawing on innovative workforce development efforts around the country, Jobs for the Future (JJF) publications, tool kits, and other resources respond to the challenges to advancement for low-wage workers. Occasional papers in the series Advancement for Low-Wage Workers seeks to elevate discussion of this issue within and outside the workforce…

  20. Relation of Opportunity to Learn Advanced Math to the Educational Attainment of Rural Youth

    Science.gov (United States)

    Irvin, Matthew; Byun, Soo-yong; Smiley, Whitney S.; Hutchins, Bryan C.

    2017-01-01

    Our study examined the relation of advanced math course taking to the educational attainment of rural youth. We used data from the Educational Longitudinal Study of 2002. Regression analyses demonstrated that when previous math achievement is accounted for, rural students take advanced math at a significantly lower rate than urban students.…

  1. The Effects of Advance Organizers and Within-Text Questions on the Learning of a Taxonomy of Concepts. Technical Report No. 357.

    Science.gov (United States)

    Bernard, Michael E.

    This study, presented in three parts, investigated the effects of a group of single-concept instructional variables on the learning at an advanced level of attainment of taxonomy of behavior management concepts. The effects of presenting advance organizers and inserting within-text questions was also examined. The influence of the single-concept…

  2. Community of inquiry model: advancing distance learning in nurse anesthesia education.

    Science.gov (United States)

    Pecka, Shannon L; Kotcherlakota, Suhasini; Berger, Ann M

    2014-06-01

    The number of distance education courses offered by nurse anesthesia programs has increased substantially. Emerging distance learning trends must be researched to ensure high-quality education for student registered nurse anesthetists. However, research to examine distance learning has been hampered by a lack of theoretical models. This article introduces the Community of Inquiry model for use in nurse anesthesia education. This model has been used for more than a decade to guide and research distance learning in higher education. A major strength of this model learning. However, it lacks applicability to the development of higher order thinking for student registered nurse anesthetists. Thus, a new derived Community of Inquiry model was designed to improve these students' higher order thinking in distance learning. The derived model integrates Bloom's revised taxonomy into the original Community of Inquiry model and provides a means to design, evaluate, and research higher order thinking in nurse anesthesia distance education courses.

  3. Distribution of involved regional lymph nodes in recurrent and locally advanced breast cancer and its impact on target definition

    International Nuclear Information System (INIS)

    Chen Jian; Ma Jinli; Zhang Shengjian; Yang Zhaozhi; Cai Gang; Feng Yan; Guo Xiaomao; Chen Jiayi

    2011-01-01

    Objective: The frequency and the anatomic distribution of involved regional nodes in recurrent and locally advanced breast cancer were analyzed, in order to evaluate the rational of conventional regional node radiation technique and provide evidence for target definition of breast cancer . Methods: Patients with recurrent or locally advanced breast cancer who were treated in our hospital from August 2003 to December 2009 were included in this study. 111 patients had contrast enhanced chest CT images of the whole regional nodes before treatment. The regional nodes were categorized into 8 anatomical substructures including medial and lateral supraclavicular nodes ( SC-M, SC-L), axilla nodes ( ALN )- I , II , III, infra clavicular nodes (IFN), Rotter's nodes (RN) and internal mammary nodes (IMN). The frequency of involvement and anatomical distribution of the involved nodes on CT images were analyzed. Results: A total of 111 patients were enrolled this study and 199 anatomical substructures with involved nodes were identified. The frequency of involvement were : SC-M 33, SC-L 21, ALN- I 30, ALN-II 25, ALN-III + IFN 35, RN 27, IMN 28. Supraclavicular region and axilla were the most frequently involved area (72.3%). The average depth of the SC-M and SC-L nodes was 33.48 mm ± 10. 57 mm and 45.62 mm ±20. 45 mm, and 51.5% and 71.4% of the SC-M and SC-L nodes were located more than 3 cm deep from the skin. The axilla nodes were located cranial and caudal to the axillary vein in 5 and 20 locally advanced breast cancer patients and in 64 and 28 patients who received prior axillary dissection. The majority of involved IMN was located within the first 3 intercostal spaces (26/28). The average distance between the center of involved IMN and chest skin was 24. 23 mm ± 10. 28 mm. The average distance between the center of involved IMN and midline of the body was 29. 38 mm ±6. 7 mm. The center of involved IMN was 6.19 mm ±5.73 mm lateral and 5.73 mm ± 4. 56 mm posterior to

  4. EMR-based medical knowledge representation and inference via Markov random fields and distributed representation learning.

    Science.gov (United States)

    Zhao, Chao; Jiang, Jingchi; Guan, Yi; Guo, Xitong; He, Bin

    2018-05-01

    Electronic medical records (EMRs) contain medical knowledge that can be used for clinical decision support (CDS). Our objective is to develop a general system that can extract and represent knowledge contained in EMRs to support three CDS tasks-test recommendation, initial diagnosis, and treatment plan recommendation-given the condition of a patient. We extracted four kinds of medical entities from records and constructed an EMR-based medical knowledge network (EMKN), in which nodes are entities and edges reflect their co-occurrence in a record. Three bipartite subgraphs (bigraphs) were extracted from the EMKN, one to support each task. One part of the bigraph was the given condition (e.g., symptoms), and the other was the condition to be inferred (e.g., diseases). Each bigraph was regarded as a Markov random field (MRF) to support the inference. We proposed three graph-based energy functions and three likelihood-based energy functions. Two of these functions are based on knowledge representation learning and can provide distributed representations of medical entities. Two EMR datasets and three metrics were utilized to evaluate the performance. As a whole, the evaluation results indicate that the proposed system outperformed the baseline methods. The distributed representation of medical entities does reflect similarity relationships with respect to knowledge level. Combining EMKN and MRF is an effective approach for general medical knowledge representation and inference. Different tasks, however, require individually designed energy functions. Copyright © 2018 Elsevier B.V. All rights reserved.

  5. DISTRIBUTED LEADERSHIP COLLABORATION FACTORS TO SUPPORT IDEA GENERATION IN COMPUTER-SUPPORTED COLLABORATIVE e-LEARNING

    Directory of Open Access Journals (Sweden)

    Niki Lambropoulos

    2011-01-01

    Full Text Available This paper aims to identify, discuss and analyze students’ collaboration factors related to distributed leadership (DL, which correlates with interaction quality evident in idea generation. Scripting computer-supported collaborative e-learning (CSCeL activities based on DL can scaffold students’ interactions that support collaboration and promote idea generation. Furthermore, the associated tools can facilitate collaboration via scripting and shed light on students’ interactions and dialogical sequences. Such detailed planning can result in effective short e-courses. In this case study, 21 MSc students’ teams worked on a DL project within a 2-day e-course at the IT Institute (ITIN, France. The research methods involved a self-reported questionnaire; the Non-Negative Matrix Factorization (NNMF algorithm with qualitative analysis; and outcomes from the Social Network Analysis (SNA tools implemented within the forums. The results indicated that scripting DL based on the identified distributed leadership attributes can support values such as collaboration and can be useful in supporting idea generation in short e-courses.

  6. Online learning of a Dirichlet process mixture of Beta-Liouville distributions via variational inference.

    Science.gov (United States)

    Fan, Wentao; Bouguila, Nizar

    2013-11-01

    A large class of problems can be formulated in terms of the clustering process. Mixture models are an increasingly important tool in statistical pattern recognition and for analyzing and clustering complex data. Two challenging aspects that should be addressed when considering mixture models are how to choose between a set of plausible models and how to estimate the model's parameters. In this paper, we address both problems simultaneously within a unified online nonparametric Bayesian framework that we develop to learn a Dirichlet process mixture of Beta-Liouville distributions (i.e., an infinite Beta-Liouville mixture model). The proposed infinite model is used for the online modeling and clustering of proportional data for which the Beta-Liouville mixture has been shown to be effective. We propose a principled approach for approximating the intractable model's posterior distribution by a tractable one-which we develop-such that all the involved mixture's parameters can be estimated simultaneously and effectively in a closed form. This is done through variational inference that enjoys important advantages, such as handling of unobserved attributes and preventing under or overfitting; we explain that in detail. The effectiveness of the proposed work is evaluated on three challenging real applications, namely facial expression recognition, behavior modeling and recognition, and dynamic textures clustering.

  7. DLTAP: A Network-efficient Scheduling Method for Distributed Deep Learning Workload in Containerized Cluster Environment

    Directory of Open Access Journals (Sweden)

    Qiao Wei

    2017-01-01

    Full Text Available Deep neural networks (DNNs have recently yielded strong results on a range of applications. Training these DNNs using a cluster of commodity machines is a promising approach since training is time consuming and compute-intensive. Furthermore, putting DNN tasks into containers of clusters would enable broader and easier deployment of DNN-based algorithms. Toward this end, this paper addresses the problem of scheduling DNN tasks in the containerized cluster environment. Efficiently scheduling data-parallel computation jobs like DNN over containerized clusters is critical for job performance, system throughput, and resource utilization. It becomes even more challenging with the complex workloads. We propose a scheduling method called Deep Learning Task Allocation Priority (DLTAP which performs scheduling decisions in a distributed manner, and each of scheduling decisions takes aggregation degree of parameter sever task and worker task into account, in particularly, to reduce cross-node network transmission traffic and, correspondingly, decrease the DNN training time. We evaluate the DLTAP scheduling method using a state-of-the-art distributed DNN training framework on 3 benchmarks. The results show that the proposed method can averagely reduce 12% cross-node network traffic, and decrease the DNN training time even with the cluster of low-end servers.

  8. Developing a Simulation-Based Mastery Learning Curriculum: Lessons From 11 Years of Advanced Cardiac Life Support.

    Science.gov (United States)

    Barsuk, Jeffrey H; Cohen, Elaine R; Wayne, Diane B; Siddall, Viva J; McGaghie, William C

    2016-02-01

    Curriculum development in medical education should follow a planned, systematic approach fitted to the needs and conditions of a local institutional environment and its learners. This article describes the development and maintenance of a simulation-based medical education curriculum on advanced cardiac life support skills and its transformation to a mastery learning program. Curriculum development used the Kern 6-step model involving problem identification and general needs assessment, targeted needs assessment, goals and objectives, educational strategies, implementation, and evaluation and feedback. Curriculum maintenance and enhancement and dissemination are also addressed. Transformation of the simulation-based medical education curriculum to a mastery learning program was accomplished after a 2-year phase-in trial. A series of studies spanning 11 years was performed to adjust the curriculum, improve checklist outcome measures, and evaluate curriculum effects as learning outcomes among internal medicine residents and improved patient care practices. We anticipate wide adoption of the mastery learning model for skill and knowledge acquisition and maintenance in medical education settings.

  9. Progress and Lessons Learned in Transuranic Waste Disposition at The Department of Energy's Advanced Mixed Waste Treatment Project

    International Nuclear Information System (INIS)

    J.D. Mousseau; S.C. Raish; F.M. Russo

    2006-01-01

    This paper provides an overview of the Department of Energy's (DOE) Advanced Mixed Waste Treatment Project (AMWTP) located at the Idaho National Laboratory (INL) and operated by Bechtel BWXT Idaho, LLC(BBWI) It describes the results to date in meeting the 6,000-cubic-meter Idaho Settlement Agreement milestone that was due December 31, 2005. The paper further describes lessons that have been learned from the project in the area of transuranic (TRU) waste processing and waste certification. Information contained within this paper would be beneficial to others who manage TRU waste for disposal at the Waste Isolation Pilot Plant (WIPP)

  10. Intrusion detection system using Online Sequence Extreme Learning Machine (OS-ELM) in advanced metering infrastructure of smart grid.

    Science.gov (United States)

    Li, Yuancheng; Qiu, Rixuan; Jing, Sitong

    2018-01-01

    Advanced Metering Infrastructure (AMI) realizes a two-way communication of electricity data through by interconnecting with a computer network as the core component of the smart grid. Meanwhile, it brings many new security threats and the traditional intrusion detection method can't satisfy the security requirements of AMI. In this paper, an intrusion detection system based on Online Sequence Extreme Learning Machine (OS-ELM) is established, which is used to detecting the attack in AMI and carrying out the comparative analysis with other algorithms. Simulation results show that, compared with other intrusion detection methods, intrusion detection method based on OS-ELM is more superior in detection speed and accuracy.

  11. Observations on the 2016 World Congress on Continuing Professional Development: Advancing Learning and Care in the Health Professions.

    Science.gov (United States)

    Turco, Mary G; Baron, Robert B

    2016-01-01

    The 2016 World Congress on Continuing Professional Development: Advancing Learning and Care in the Health Professions took place in San Diego, California, March 17-19, 2016. Hosts were the Association for Hospital Medical Education (AHME), Alliance for Continuing Education in the Health Professionals (ACEhp), and Society for Academic Continuing Medical Education (SACME). The target audience was the international community working to improve medical (CME), nursing (CNE), pharmacy (CPE), and interprofessional (CIPE) continuing education (CE) and continuing professional development (CPD). Goals included: addressing patients' concerns and needs; advancing global medical and interprofessional health sciences education; utilizing learning to address health disparities; and promoting international cooperation. The five keynote speakers were: patient advocate Alicia Cole ("Why What We Do Matters: The Patients Voice"); linguist Lorelei Lingard ("Myths about Healthcare Teamwork and Their Implications for How We Understand Competence"); futurist and philosopher Alex Jadad ("What Do We Need to Protect at All Costs in the 21st Century?"); ethicist and change agent Zeke Emanuel ("Learn to Change: Teaching Toward a Shifting Healthcare Horizon"); and technology innovator Stephen Downes ("From Individual to Community: The Learning Is in the Doing"). Organizers announced the new Dave Davis Distinguished Award for Excellence in Mentorship in Continuing Professional Development to honor the career of David Davis, MD, in CME/CPD scholarship in Canada, the United States, and beyond. Participants valued the emphasis on interprofessional education and practice, the importance of integrating the patient voice, the effectiveness of flipped classroom methods, and the power of collective competency theories. Attendee-respondents encouraged Congress planners to continue to strive for a broad global audience and themes of international interest.

  12. An advanced teaching scheme for integrating problem-based learning in control education

    Science.gov (United States)

    Juuso, Esko K.

    2018-03-01

    Engineering education needs to provide both theoretical knowledge and problem-solving skills. Many topics can be presented in lectures and computer exercises are good tools in teaching the skills. Learning by doing is combined with lectures to provide additional material and perspectives. The teaching scheme includes lectures, computer exercises, case studies, seminars and reports organized as a problem-based learning process. In the gradually refining learning material, each teaching method has its own role. The scheme, which has been used in teaching two 4th year courses, is beneficial for overall learning progress, especially in bilingual courses. The students become familiar with new perspectives and are ready to use the course material in application projects.

  13. Application of Advances in Learning Theory and Philosophy of Science to the Improvement of Chemistry Teaching.

    Science.gov (United States)

    Novak, Joseph D.

    1984-01-01

    Discusses seven key concepts in Ausubel's learning theory which function to guide research and teaching. Also discusses concept mapping and Gowins Vee, providing examples of how they are used in chemistry instruction. (JN)

  14. Building a Governance Strategy for CER: The Patient Outcomes Research to Advance Learning (PORTAL) Network Experience.

    Science.gov (United States)

    Paolino, Andrea R; McGlynn, Elizabeth A; Lieu, Tracy; Nelson, Andrew F; Prausnitz, Stephanie; Horberg, Michael A; Arterburn, David E; Gould, Michael K; Laws, Reesa L; Steiner, John F

    2016-01-01

    The Patient Outcomes Research to Advance Learning (PORTAL) Network was established with funding from the Patient-Centered Outcomes Research Institute (PCORI) in 2014. The PORTAL team adapted governance structures and processes from past research network collaborations. We will review and outline the structures and processes of the PORTAL governance approach and describe how proactively focusing on priority areas helped us to facilitate an ambitious research agenda. For years a variety of funders have supported large-scale infrastructure grants to promote the use of clinical datasets to answer important comparative effectiveness research (CER) questions. These awards have provided the impetus for health care systems to join forces in creating clinical data research networks. Often, these scientific networks do not develop governance processes proactively or systematically, and address issues only as problems arise. Even if network leaders and collaborators foresee the need to develop governance approaches, they may underestimate the time and effort required to develop sound processes. The resulting delays can impede research progress. Because the PORTAL sites had built trust and a foundation of collaboration by participating with one another in past research networks, essential elements of effective governance such as guiding principles, decision making processes, project governance, data governance, and stakeholders in governance were familiar to PORTAL investigators. This trust and familiarity enabled the network to rapidly prioritize areas that required sound governance approaches: responding to new research opportunities, creating a culture of trust and collaboration, conducting individual studies, within the broader network, assigning responsibility and credit to scientific investigators, sharing data while protecting privacy/security, and allocating resources. The PORTAL Governance Document, complete with a Toolkit of Appendices is included for reference and

  15. Lessons learned from the tokamak Advanced Reactor Innovation and Evaluation Study (ARIES)

    International Nuclear Information System (INIS)

    Krakowski, R.A.; Bathke, C.G.; Miller, R.L.; Werley, K.A.

    1994-01-01

    Lessons from the four-year ARIES (Advanced Reactor Innovation and Evaluation Study) investigation of a number of commercial magnetic-fusion-energy (MFE) power-plant embodiments of the tokamak are summarized. These lessons apply to physics, engineering and technology, and environmental, safety, and health (ES ampersand H) characteristics of projected tokamak power plants. Summarized herein are the composite conclusions and lessons developed in the course of four conceptual tokamak power-plant designs. A general conclusion from this extensive investigation of the commercial potential of tokamak power plants is the need for combined, symbiotic advances in both physics, engineering, and materials before economic competitiveness with developing advanced energy sources can be realized. Advances in materials are also needed for the exploitation of environmental advantages otherwise inherent in fusion power

  16. Advanced motion control for next-generation precision mechatronics: Challenges for control, identification, and learning

    NARCIS (Netherlands)

    Oomen, Tom

    2017-01-01

    Manufacturing equipment and scientific instruments, including wafer scanners, printers, microscopes, and medical imaging scanners, require accurate and fast motions. Increasing requirements necessitate enhanced control performance. The aim of this paper is to identify several challenges for advanced

  17. Lessons learned from the Tokamak Advanced Reactor Innovation and Evaluation Study (ARIES)

    International Nuclear Information System (INIS)

    Krakowski, R.A.; Bathke, C.G.; Miller, R.L.; Werley, K.A.

    1994-01-01

    Lessons from the four-year ARIES (Advanced Reactor Innovation and Evaluation Study) investigation of a number of commercial magnetic-fusion-energy (MFE) power-plant embodiments of the tokamak are summarized. These lessons apply to physics, engineering and technology, and environmental, safety and health (ES ampersand H) characteristics of projected tokamak power plants. A general conclusion from this extensive investigation of the commercial potential of tokamak power plants is the need for combined, symbiotic advances relative to present understanding in physics, engineering, and materials before economic competitiveness with developing advanced energy sources can be realized. Advanced tokamak plasmas configured in the second-stability regime that achieve both high β and bootstrap fractions near unity through strong profile control offer high promise in this regard

  18. Prior Knowledge or Advance Organizers as Effective Variables in Chemical Learning

    Science.gov (United States)

    Fensham, P. J.; West, L. H. T.

    1976-01-01

    This report describes an attempt to apply a critical empirical test to some predictions from Ausubel's theory concerning the subsuming role of advance organizers. Alternative explanations are proposed and subsequent predictions tested. (BT)

  19. Advances in Patient Classification for Traditional Chinese Medicine: A Machine Learning Perspective

    Directory of Open Access Journals (Sweden)

    Changbo Zhao

    2015-01-01

    data analyzed by different computational methods, we present the overview for four subfields of TCM diagnosis, respectively. For each subfield, we design a rectangular reference list with applications in the horizontal direction and machine learning algorithms in the longitudinal direction. According to the current development of objective TCM diagnosis for patient classification, a discussion of the research issues around machine learning techniques with applications to TCM diagnosis is given to facilitate the further research for TCM patient classification.

  20. Assessing Faculty Experiences with and Perceptions of an Internal Quality Assurance Process for Undergraduate Distributed Learning Courses: A Pilot Study

    Science.gov (United States)

    Rucker, Ryan; Edwards, Karen; Frass, Lydia R.

    2015-01-01

    To ensure that online courses match traditional classes' quality, some institutions are implementing internal standards for online course design and quality review. The University of South Carolina created the Distributed Learning Quality Review program, based on "Quality Matters'" standards. It was designed to be faculty-guided, as…

  1. Distributed Problem-Based Learning in Social Economy: A Study of the Use of a Structured Method for Education.

    Science.gov (United States)

    Bjorck, Ulric

    Students' use of distributed Problem-Based Learning (dPBL) in university courses in social economy was studied. A sociocultural framework was used to analyze the actions of students focusing on their mastery of dPBL. The main data material consisted of messages written in an asynchronous conferencing system by 50 Swedish college students in 2…

  2. Use of the Semantic Web to solve some basic problems in Education: Increase flexible, distributed lifelong learning, decrease teacher's workload

    NARCIS (Netherlands)

    Koper, Rob

    2003-01-01

    Please refer to: Koper, R. (2004). Use of the Semantic Web to Solve Some Basic Problems in Education: Increase Flexible, Distributed Lifelong Learning, Decrease Teacher's Workload. Journal of Interactive Media in Education, 2004 (6). Special Issue on the Educational Semantic Web. ISSN:1365-893X [

  3. Distribution

    Science.gov (United States)

    John R. Jones

    1985-01-01

    Quaking aspen is the most widely distributed native North American tree species (Little 1971, Sargent 1890). It grows in a great diversity of regions, environments, and communities (Harshberger 1911). Only one deciduous tree species in the world, the closely related Eurasian aspen (Populus tremula), has a wider range (Weigle and Frothingham 1911)....

  4. Using an academic-community partnership model and blended learning to advance community health nursing pedagogy.

    Science.gov (United States)

    Ezeonwu, Mabel; Berkowitz, Bobbie; Vlasses, Frances R

    2014-01-01

    This article describes a model of teaching community health nursing that evolved from a long-term partnership with a community with limited existing health programs. The partnership supported RN-BSN students' integration in the community and resulted in reciprocal gains for faculty, students and community members. Community clients accessed public health services as a result of the partnership. A blended learning approach that combines face-to-face interactions, service learning and online activities was utilized to enhance students' learning. Following classroom sessions, students actively participated in community-based educational process through comprehensive health needs assessments, planning and implementation of disease prevention and health promotion activities for community clients. Such active involvement in an underserved community deepened students' awareness of the fundamentals of community health practice. Students were challenged to view public health from a broader perspective while analyzing the impacts of social determinants of health on underserved populations. Through asynchronous online interactions, students synthesized classroom and community activities through critical thinking. This paper describes a model for teaching community health nursing that informs students' learning through blended learning, and meets the demands for community health nursing services delivery. © 2013 Wiley Periodicals, Inc.

  5. Confronting the challenges of digital media and learning: Advancing the debate on education, youth and citizenship

    Directory of Open Access Journals (Sweden)

    Wellington de Oliveira

    2016-01-01

    Full Text Available Our discussion in this paper is focused on digital media and education as powerful means for creating more opportunities for more youth to engage in learning that is relevant to their lives and prepares them for success and good life in school, the workplace, and their community. We will discuss how new media builds up a new social reality at school and how new media influences the configuration of the subjectivity of students and the implications of learning and development in newer forms of digital environments for issues like democracy, citizenship and ethics as debated in the DIGIT-M-ED Project.

  6. Intellectual Amplification through Reflection and Didactic Change in Distributed Collaborative Learning

    DEFF Research Database (Denmark)

    Sorensen, Elsebeth K.

    Presented at the Conference on Computer Supported Collaborative Learning, CSCL99, Stanford University, California, December 11-18, 1999 Presented at the Conference on Computer Supported Collaborative Learning, CSCL99, Stanford University, California, December 11-18, 1999...

  7. Simulating and stimulating performance: Introducing distributed simulation to enhance musical learning and performance

    Directory of Open Access Journals (Sweden)

    Aaron eWilliamon

    2014-02-01

    Full Text Available Musicians typically rehearse far away from their audiences and in practice rooms that differ significantly from the concert venues in which they aspire to perform. Due to the high costs and inaccessibility of such venues, much current international music training lacks repeated exposure to realistic performance situations, with students learning all too late (or not at all how to manage performance stress and the demands of their audiences. Virtual environments have been shown to be an effective training tool in the fields of medicine and sport, offering practitioners access to real-life performance scenarios but with lower risk of negative evaluation and outcomes. The aim of this research was to design and test the efficacy of simulated performance environments in which conditions of real performance could be recreated. Advanced violin students (n=11 were recruited to perform in two simulations: a solo recital with a small virtual audience and an audition situation with three expert virtual judges. Each simulation contained back-stage and on-stage areas, life-sized interactive virtual observers, and pre- and post-performance protocols designed to match those found at leading international performance venues. Participants completed a questionnaire on their experiences of using the simulations. Results show that both simulated environments offered realistic experience of performance contexts and were rated particularly useful for developing performance skills. For a subset of 7 violinists, state anxiety and electrocardiographic data were collected during the simulated audition and an actual audition with real judges. Results display comparable levels of reported state anxiety and patterns of heart rate variability in both situations, suggesting that responses to the simulated audition closely approximate those of a real audition. The findings are discussed in relation to their implications, both generalizable and individual-specific, for

  8. Simulating and stimulating performance: introducing distributed simulation to enhance musical learning and performance.

    Science.gov (United States)

    Williamon, Aaron; Aufegger, Lisa; Eiholzer, Hubert

    2014-01-01

    Musicians typically rehearse far away from their audiences and in practice rooms that differ significantly from the concert venues in which they aspire to perform. Due to the high costs and inaccessibility of such venues, much current international music training lacks repeated exposure to realistic performance situations, with students learning all too late (or not at all) how to manage performance stress and the demands of their audiences. Virtual environments have been shown to be an effective training tool in the fields of medicine and sport, offering practitioners access to real-life performance scenarios but with lower risk of negative evaluation and outcomes. The aim of this research was to design and test the efficacy of simulated performance environments in which conditions of "real" performance could be recreated. Advanced violin students (n = 11) were recruited to perform in two simulations: a solo recital with a small virtual audience and an audition situation with three "expert" virtual judges. Each simulation contained back-stage and on-stage areas, life-sized interactive virtual observers, and pre- and post-performance protocols designed to match those found at leading international performance venues. Participants completed a questionnaire on their experiences of using the simulations. Results show that both simulated environments offered realistic experience of performance contexts and were rated particularly useful for developing performance skills. For a subset of 7 violinists, state anxiety and electrocardiographic data were collected during the simulated audition and an actual audition with real judges. Results display comparable levels of reported state anxiety and patterns of heart rate variability in both situations, suggesting that responses to the simulated audition closely approximate those of a real audition. The findings are discussed in relation to their implications, both generalizable and individual-specific, for performance training.

  9. Stimulating learning-by-doing in advanced biofuels: effectiveness of alternative policies

    International Nuclear Information System (INIS)

    Chen Xiaoguang; Khanna, Madhu; Yeh, Sonia

    2012-01-01

    This letter examines the effectiveness of various biofuel and climate policies in reducing future processing costs of cellulosic biofuels due to learning-by-doing. These policies include a biofuel production mandate alone and supplementing the biofuel mandate with other policies, namely a national low carbon fuel standard, a cellulosic biofuel production tax credit or a carbon price policy. We find that the binding biofuel targets considered here can reduce the unit processing cost of cellulosic ethanol by about 30% to 70% between 2015 and 2035 depending on the assumptions about learning rates and initial costs of biofuel production. The cost in 2035 is more sensitive to the speed with which learning occurs and less sensitive to uncertainty in the initial production cost. With learning rates of 5–10%, cellulosic biofuels will still be at least 40% more expensive than liquid fossil fuels in 2035. The addition of supplementary low carbon/tax credit policies to the mandate that enhance incentives for cellulosic biofuels can achieve similar reductions in these costs several years earlier than the mandate alone; the extent of these incentives differs across policies and different kinds of cellulosic biofuels. (letter)

  10. Advancing Teacher Technology Education Using Open-Ended Learning Environments as Research and Training Platforms

    Science.gov (United States)

    Poitras, Eric; Doleck, Tenzin; Huang, Lingyun; Li, Shan; Lajoie, Susanne

    2017-01-01

    A primary concern of teacher technology education is for pre-service teachers to develop a sophisticated mental model of the affordances of technology that facilitates both teaching and learning with technology. One of the main obstacles to developing the requisite technological pedagogical content knowledge is the inherent challenge faced by…

  11. Local markets for global health technologies: lessons learned from advancing 6 new products.

    Science.gov (United States)

    Matthias, Dipika Mathur; Taylor, Catharine H; Sen, Debjeet; Metzler, Mutsumi

    2014-05-01

    Key components to support local institutional and consumer markets are: supply chain, finance, clinical use, and consumer use. Key lessons learned: (1) Build supply and demand simultaneously. (2) Support a lead organization to drive the introduction process. (3) Plan for scale up from the start. (4) Profitability for the private sector is an absolute.

  12. Local markets for global health technologies: lessons learned from advancing 6 new products

    OpenAIRE

    Matthias, Dipika Mathur; Taylor, Catharine H; Sen, Debjeet; Metzler, Mutsumi

    2014-01-01

    Key components to support local institutional and consumer markets are: supply chain, finance, clinical use, and consumer use. Key lessons learned: (1) Build supply and demand simultaneously. (2) Support a lead organization to drive the introduction process. (3) Plan for scale up from the start. (4) Profitability for the private sector is an absolute.

  13. Advancing MCH Interdisciplinary/Interprofessional Leadership Training and Practice Through a Learning Collaborative.

    Science.gov (United States)

    McHugh, Meaghan C; Margolis, Lewis H; Rosenberg, Angela; Humphreys, Elizabeth

    2016-11-01

    Purpose The Interdisciplinary Leadership Learning Collaborative (ILLC), under the sponsorship of AUCD and the Maternal and Child Health Bureau, brought together six teams, composed of 14 MCHB and UCEDD training programs to enhance their leadership training. Description Using adult learning principles, interactive training methods, and skill-focused learning, the ILLC built upon the evidence-based Interdisciplinary Leadership Development Program of the University of North Carolina at Chapel Hill. The program began with a 4-day on-site intensive and then continued through monthly conference calls, a mid-term on-site workshop, and a summary virtual workshop to present programmatic accomplishments and share plans for sustainability. Coaching/consultation for the teams around particular challenges was also part of the program. Assessment All teams reported enhancements in intentional leadership training, threading of leadership concepts across clinical, didactic, and workshop settings, and new collaborative partnerships for leadership training. Teams also identified a number of strategies to increase sustainability of their intentional leadership training efforts. Conclusion for Practice The learning collaborative is a productive model to address the growing need for interdisciplinary MCH leaders.

  14. Advances in the Use of Neuroscience Methods in Research on Learning and Instruction

    Science.gov (United States)

    De Smedt, Bert

    2014-01-01

    Cognitive neuroscience offers a series of tools and methodologies that allow researchers in the field of learning and instruction to complement and extend the knowledge they have accumulated through decades of behavioral research. The appropriateness of these methods depends on the research question at hand. Cognitive neuroscience methods allow…

  15. Comparison of Advanced Machine Learning Tools for Disruption Prediction and Disruption Studies

    Czech Academy of Sciences Publication Activity Database

    Odstrčil, Michal; Murari, A.; Mlynář, Jan

    2013-01-01

    Roč. 41, č. 7 (2013), s. 1751-1759 ISSN 0093-3813 R&D Projects: GA ČR GAP205/10/2055 Institutional support: RVO:61389021 Keywords : Learning Machines * Support Vector Machines * Neural Network * ASDEX Upgrade * JET * Disruption mitigation * Tokamaks * ITER Subject RIV: BL - Plasma and Gas Discharge Physics Impact factor: 0.950, year: 2013

  16. 78 FR 28717 - Advancing Pay Equality in the Federal Government and Learning From Successful Practices

    Science.gov (United States)

    2013-05-15

    ...) affect the compensation of similarly situated men and women, and to promote gender pay equality in the... gender pay equality; and (e) any best practices the agency has employed to improve gender pay equality... Equality in the Federal Government and Learning From Successful Practices Memorandum for the Heads of...

  17. Online virtual-patient cases versus traditional problem-based learning in advanced pharmacy practice experiences.

    Science.gov (United States)

    Al-Dahir, Sara; Bryant, Kendrea; Kennedy, Kathleen B; Robinson, Donna S

    2014-05-15

    To evaluate the efficacy of faculty-led problem-based learning (PBL) vs online simulated-patient case in fourth-year (P4) pharmacy students. Fourth-year pharmacy students were randomly assigned to participate in either online branched-case learning using a virtual simulation platform or a small-group discussion. Preexperience and postexperience student assessments and a survey instrument were completed. While there were no significant differences in the preexperience test scores between the groups, there was a significant increase in scores in both the virtual-patient group and the PBL group between the preexperience and postexperience tests. The PBL group had higher postexperience test scores (74.8±11.7) than did the virtual-patient group (66.5±13.6) (p=0.001). The PBL method demonstrated significantly greater improvement in postexperience test scores than did the virtual-patient method. Both were successful learning methods, suggesting that a diverse approach to simulated patient cases may reach more student learning styles.

  18. Development of an Advanced, Automatic, Ultrasonic NDE Imaging System via Adaptive Learning Network Signal Processing Techniques

    Science.gov (United States)

    1981-03-13

    UNCLASSIFIED SECURITY CLAS,:FtfC ’i OF TH*!’ AGC W~ct P- A* 7~9r1) 0. ABSTRACT (continued) onuing in concert with a sophisticated detector has...and New York, 1969. Whalen, M.F., L.J. O’Brien, and A.N. Mucciardi, "Application of Adaptive Learning Netowrks for the Characterization of Two

  19. The Hawaii Teleschool: An Evaluation of Distance Learning for Advanced Placement Calculus Instruction in "Paradise."

    Science.gov (United States)

    Barker, Bruce O.; Bannon, James

    This paper describes the Hawaii Interactive Television System (HITS) program and provides an evaluation of the first year of broadcasts for the advanced placement (AP) calculus course. HITS allows two-way video-audio interaction among origination sites, but the configuration used by the Department of Education for its Teleschool program is the…

  20. An Exploration of Learners' Conceptions of Language, Culture, and Learning in Advanced-Level Spanish Courses

    Science.gov (United States)

    Drewelow, Isabelle; Mitchell, Claire

    2015-01-01

    This article reports on an exploratory study, which examines learners' rating of culture in relation to other concepts in advanced Spanish courses and their justification of the ratings attributed. Open-ended responses, elicited from a questionnaire completed by 179 respondents, were analysed line by line using an interpretive approach. Data…

  1. Just-in-Time Teaching: A Tool for Enhancing Student Engagement in Advanced Foreign Language Learning

    Science.gov (United States)

    Abreu, Laurel; Knouse, Stephanie

    2014-01-01

    Scholars have indicated a need for further research on effective pedagogical strategies designed for advanced foreign language courses in the postsecondary setting, especially in light of decreased enrollments at this level and the elimination of foreign language programs altogether in some institutions (Paesani & Allen, 2012). This article…

  2. Discovery Learning and Teaching with Electronic Corpora in an Advanced German Grammar Course

    Science.gov (United States)

    Vyatkina, Nina

    2013-01-01

    This study describes the design and implementation of a usage-based and corpus-based advanced German grammar course. Teaching materials for the course included DWDS, or "Digitales Worterbuch der deutschen Sprache": a large, representative, free and publicly available corpus of contemporary German texts. The article outlines specific…

  3. STRUCTURED LEARNING AND TRAINING ENVIRONMENTS--A PREPARATION LABORATORY FOR ADVANCED MAMMALIAN PHYSIOLOGY.

    Science.gov (United States)

    FIEL, NICHOLAS J.; JOHNSTON, RAYMOND F.

    A PREPARATION LABORATORY WAS DESIGNED TO FAMILIARIZE STUDENTS IN ADVANCED MAMMALIAN PHYSIOLOGY WITH LABORATORY SKILLS AND TECHNIQUES AND THUS SHORTEN THE TIME THEY SPEND IN SETTING UP ACTUAL EXPERIMENTS. THE LABORATORY LASTS 30 MINUTES, IS FLEXIBLE AND SIMPLE OF OPERATION, AND DOES NOT REQUIRE A PROFESSOR'S PRESENCE. THE BASIC TRAINING UNIT IS THE…

  4. Distance Learning in Advanced Military Education: Analysis of Joint Operations Course in the Taiwan Military

    Science.gov (United States)

    Tung, Ming-Chih; Huang, Jiung-yao; Keh, Huan-Chao; Wai, Shu-shen

    2009-01-01

    High-ranking officers require advanced military education in war tactics for future combat. However, line officers rarely have time to take such courses on campus. The conventional solution to this problem used to take the inefficient correspondence courses. Whereas Internet technologies progress, online course is the current trend for military…

  5. Learning to Facilitate Advance Care Planning: The Novice Social Worker's Experience

    Science.gov (United States)

    Washington, Karla; Bowland, Sharon; Mueggenburg, Kay; Pederson, Margaret; Otten, Sheila; Renn, Tanya

    2014-01-01

    Professional leaders have identified clear roles for social workers involved in advance care planning (ACP), a facilitated process whereby individuals identify their preferences for future medical care; yet information about effective teaching practices in this area is scant. This study reports on the experiences of 14 social workers who…

  6. The Construction of New Political Identities through the Internationally Distributed English Learning Textbooks

    Science.gov (United States)

    Varzande, Mohsen

    2015-01-01

    Today, English education is very important but language learning has long been challenged since learning a second language is not only the mastery of its forms but also a process of identity construction and self-positioning in the second language. A review of recent studies shows that the cultural effects of learning English in the…

  7. Grade Distribution Digests: A Novel Tool to Enhance Teaching and Student Learning in Laboratory Practicals

    Science.gov (United States)

    Arthur, Peter G.; Zareie, Reza; Kirkwood, Paul; Ludwig, Martha; Attwood, Paul V.

    2018-01-01

    Assessment is a central component of course curriculums and is used to certify student learning, but it can also be used as a tool to improve teaching and learning. Many laboratory courses are structured such that there is only a grade for a particular laboratory, which limits the insights that can be gained in student learning. We developed a…

  8. Prediction of plasma-induced damage distribution during silicon nitride etching using advanced three-dimensional voxel model

    Energy Technology Data Exchange (ETDEWEB)

    Kuboi, Nobuyuki, E-mail: Nobuyuki.Kuboi@jp.sony.com; Tatsumi, Tetsuya; Kinoshita, Takashi; Shigetoshi, Takushi; Fukasawa, Masanaga; Komachi, Jun; Ansai, Hisahiro [Device and Material Research Group, RDS Platform, Sony Corporation, 4-14-1 Asahi-cho, Atsugi, Kanagawa 243-0014 (Japan)

    2015-11-15

    The authors modeled SiN film etching with hydrofluorocarbon (CH{sub x}F{sub y}/Ar/O{sub 2}) plasma considering physical (ion bombardment) and chemical reactions in detail, including the reactivity of radicals (C, F, O, N, and H), the area ratio of Si dangling bonds, the outflux of N and H, the dependence of the H/N ratio on the polymer layer, and generation of by-products (HCN, C{sub 2}N{sub 2}, NH, HF, OH, and CH, in addition to CO, CF{sub 2}, SiF{sub 2}, and SiF{sub 4}) as ion assistance process parameters for the first time. The model was consistent with the measured C-F polymer layer thickness, etch rate, and selectivity dependence on process variation for SiN, SiO{sub 2}, and Si film etching. To analyze the three-dimensional (3D) damage distribution affected by the etched profile, the authors developed an advanced 3D voxel model that can predict the time-evolution of the etched profile and damage distribution. The model includes some new concepts for gas transportation in the pattern using a fluid model and the property of voxels called “smart voxels,” which contain details of the history of the etching situation. Using this 3D model, the authors demonstrated metal–oxide–semiconductor field-effect transistor SiN side-wall etching that consisted of the main-etch step with CF{sub 4}/Ar/O{sub 2} plasma and an over-etch step with CH{sub 3}F/Ar/O{sub 2} plasma under the assumption of a realistic process and pattern size. A large amount of Si damage induced by irradiated hydrogen occurred in the source/drain region, a Si recess depth of 5 nm was generated, and the dislocated Si was distributed in a 10 nm deeper region than the Si recess, which was consistent with experimental data for a capacitively coupled plasma. An especially large amount of Si damage was also found at the bottom edge region of the metal–oxide–semiconductor field-effect transistors. Furthermore, our simulation results for bulk fin-type field-effect transistor side-wall etching

  9. Detecting epileptic seizure with different feature extracting strategies using robust machine learning classification techniques by applying advance parameter optimization approach.

    Science.gov (United States)

    Hussain, Lal

    2018-06-01

    Epilepsy is a neurological disorder produced due to abnormal excitability of neurons in the brain. The research reveals that brain activity is monitored through electroencephalogram (EEG) of patients suffered from seizure to detect the epileptic seizure. The performance of EEG detection based epilepsy require feature extracting strategies. In this research, we have extracted varying features extracting strategies based on time and frequency domain characteristics, nonlinear, wavelet based entropy and few statistical features. A deeper study was undertaken using novel machine learning classifiers by considering multiple factors. The support vector machine kernels are evaluated based on multiclass kernel and box constraint level. Likewise, for K-nearest neighbors (KNN), we computed the different distance metrics, Neighbor weights and Neighbors. Similarly, the decision trees we tuned the paramours based on maximum splits and split criteria and ensemble classifiers are evaluated based on different ensemble methods and learning rate. For training/testing tenfold Cross validation was employed and performance was evaluated in form of TPR, NPR, PPV, accuracy and AUC. In this research, a deeper analysis approach was performed using diverse features extracting strategies using robust machine learning classifiers with more advanced optimal options. Support Vector Machine linear kernel and KNN with City block distance metric give the overall highest accuracy of 99.5% which was higher than using the default parameters for these classifiers. Moreover, highest separation (AUC = 0.9991, 0.9990) were obtained at different kernel scales using SVM. Additionally, the K-nearest neighbors with inverse squared distance weight give higher performance at different Neighbors. Moreover, to distinguish the postictal heart rate oscillations from epileptic ictal subjects, and highest performance of 100% was obtained using different machine learning classifiers.

  10. The efficacy of self-directed modules for clinical learning: advanced competencies in entry-level physical therapy education.

    Science.gov (United States)

    Peck, Kirk; Paschal, Karen; Black, Lisa; Nelson, Kelly

    2014-01-01

    Prior to graduation, students often express an interest to advance clinical and professional skills in teaching, research, administration, and various niche practice areas. The acquisition of advanced education in selected areas of practice is believed to improve employment opportunities, accelerate career advancement including eligibility for professional certifications, and contribute to personal satisfaction in the profession. The purpose of this paper is to (1) describe an innovative model of education, the Directed Practice Experience (DPE) elective, that incorporates a student-initiated learning process designed to achieve student-identified professional goals, and (2) report the outcomes for graduates who have completed the DPE in an entry-level program in physical therapy education. Students who met select criteria were eligible to complete a DPE. Applicants designed a 4- to 6-week clinical education experience consisting of stated rationale for personal and professional growth, examples of leadership and service, and self-directed objectives that are beyond entry-level expectations as measured by the revised Physical Therapist Clinical Performance Instrument, version 2006. Twenty-six students have completed DPEs since 2005. Fifty percent resulted in new academic partnerships. At least 25% of graduates now serve as clinical instructors for the entry-level program. Those who participated in DPEs have also completed post-graduate residencies, attained ABPTS Board certifications, authored peer-reviewed publications, and taught in both PT and residency programs. The DPE model allows qualified students to acquire advanced personal skills and knowledge prior to graduation in areas of professional practice that exceed entry-level expectations. The model is applicable to all CAPTE accredited physical therapy education programs and is especially beneficial for academic programs desiring to form new community partnerships for student clinical education.

  11. Promoting Probabilistic Programming System (PPS) Development in Probabilistic Programming for Advancing Machine Learning (PPAML)

    Science.gov (United States)

    2018-03-01

    invested in the future developments of PPSs. 3.0 METHODS , ASSUMPTIONS, AND PROCEDURES Section 3 describes the methods for each of the primary areas of...approaches for solving machine learning problems of interest to defense, science , and the economy. Within DoD, there are different needs for ...Datasets include social network data and vaccination statistics . Those data have different characteristics (e.g., percentages for CDC regional

  12. Anomaly Detection in Log Data using Graph Databases and Machine Learning to Defend Advanced Persistent Threats

    OpenAIRE

    Schindler, Timo

    2018-01-01

    Advanced Persistent Threats (APTs) are a main impendence in cyber security of computer networks. In 2015, a successful breach remains undetected 146 days on average, reported by [Fi16].With our work we demonstrate a feasible and fast way to analyse real world log data to detect breaches or breach attempts. By adapting well-known kill chain mechanisms and a combine of a time series database and an abstracted graph approach, it is possible to create flexible attack profiles. Using this approach...

  13. Faculty Development Program Models to Advance Teaching and Learning Within Health Science Programs

    Science.gov (United States)

    Lancaster, Jason W.; Stein, Susan M.; MacLean, Linda Garrelts; Van Amburgh, Jenny

    2014-01-01

    Within health science programs there has been a call for more faculty development, particularly for teaching and learning. The primary objectives of this review were to describe the current landscape for faculty development programs for teaching and learning and make recommendations for the implementation of new faculty development programs. A thorough search of the pertinent health science databases was conducted, including the Education Resource Information Center (ERIC), MEDLINE, and EMBASE, and faculty development books and relevant information found were reviewed in order to provide recommendations for best practices. Faculty development for teaching and learning comes in a variety of forms, from individuals charged to initiate activities to committees and centers. Faculty development has been effective in improving faculty perceptions on the value of teaching, increasing motivation and enthusiasm for teaching, increasing knowledge and behaviors, and disseminating skills. Several models exist that can be implemented to support faculty teaching development. Institutions need to make informed decisions about which plan could be most successfully implemented in their college or school. PMID:24954939

  14. Advances in Patient Classification for Traditional Chinese Medicine: A Machine Learning Perspective

    Science.gov (United States)

    Zhao, Changbo; Li, Guo-Zheng; Wang, Chengjun; Niu, Jinling

    2015-01-01

    As a complementary and alternative medicine in medical field, traditional Chinese medicine (TCM) has drawn great attention in the domestic field and overseas. In practice, TCM provides a quite distinct methodology to patient diagnosis and treatment compared to western medicine (WM). Syndrome (ZHENG or pattern) is differentiated by a set of symptoms and signs examined from an individual by four main diagnostic methods: inspection, auscultation and olfaction, interrogation, and palpation which reflects the pathological and physiological changes of disease occurrence and development. Patient classification is to divide patients into several classes based on different criteria. In this paper, from the machine learning perspective, a survey on patient classification issue will be summarized on three major aspects of TCM: sign classification, syndrome differentiation, and disease classification. With the consideration of different diagnostic data analyzed by different computational methods, we present the overview for four subfields of TCM diagnosis, respectively. For each subfield, we design a rectangular reference list with applications in the horizontal direction and machine learning algorithms in the longitudinal direction. According to the current development of objective TCM diagnosis for patient classification, a discussion of the research issues around machine learning techniques with applications to TCM diagnosis is given to facilitate the further research for TCM patient classification. PMID:26246834

  15. Faculty development program models to advance teaching and learning within health science programs.

    Science.gov (United States)

    Lancaster, Jason W; Stein, Susan M; MacLean, Linda Garrelts; Van Amburgh, Jenny; Persky, Adam M

    2014-06-17

    Within health science programs there has been a call for more faculty development, particularly for teaching and learning. The primary objectives of this review were to describe the current landscape for faculty development programs for teaching and learning and make recommendations for the implementation of new faculty development programs. A thorough search of the pertinent health science databases was conducted, including the Education Resource Information Center (ERIC), MEDLINE, and EMBASE, and faculty development books and relevant information found were reviewed in order to provide recommendations for best practices. Faculty development for teaching and learning comes in a variety of forms, from individuals charged to initiate activities to committees and centers. Faculty development has been effective in improving faculty perceptions on the value of teaching, increasing motivation and enthusiasm for teaching, increasing knowledge and behaviors, and disseminating skills. Several models exist that can be implemented to support faculty teaching development. Institutions need to make informed decisions about which plan could be most successfully implemented in their college or school.

  16. Advances in Patient Classification for Traditional Chinese Medicine: A Machine Learning Perspective.

    Science.gov (United States)

    Zhao, Changbo; Li, Guo-Zheng; Wang, Chengjun; Niu, Jinling

    2015-01-01

    As a complementary and alternative medicine in medical field, traditional Chinese medicine (TCM) has drawn great attention in the domestic field and overseas. In practice, TCM provides a quite distinct methodology to patient diagnosis and treatment compared to western medicine (WM). Syndrome (ZHENG or pattern) is differentiated by a set of symptoms and signs examined from an individual by four main diagnostic methods: inspection, auscultation and olfaction, interrogation, and palpation which reflects the pathological and physiological changes of disease occurrence and development. Patient classification is to divide patients into several classes based on different criteria. In this paper, from the machine learning perspective, a survey on patient classification issue will be summarized on three major aspects of TCM: sign classification, syndrome differentiation, and disease classification. With the consideration of different diagnostic data analyzed by different computational methods, we present the overview for four subfields of TCM diagnosis, respectively. For each subfield, we design a rectangular reference list with applications in the horizontal direction and machine learning algorithms in the longitudinal direction. According to the current development of objective TCM diagnosis for patient classification, a discussion of the research issues around machine learning techniques with applications to TCM diagnosis is given to facilitate the further research for TCM patient classification.

  17. [Challenges and opportunities: contributions of the Advanced Practice Nurse in the chronicity. Learning from experiences].

    Science.gov (United States)

    Appleby, Christine; Camacho-Bejarano, Rafaela

    2014-01-01

    Undoubtedly, our society is facing new economic, political, demographic, social and cultural challenges that require healthcare services able to meet the growing health needs of the population, especially in dealing with chronic conditions. In this new context, some countries such as the United Kingdom have made a firm commitment to develop new models for chronic patients care based on the introduction of new figures of Advanced Practice Nurses, which includes 4 cornerstones of professional practice: advanced clinical skills, clinical management, teaching and research. The implementation of this new figures implies a redefinition of professional competencies and has its own accreditation system and a specific catalogue of services adapted to the population requirements, in order to provide chronic care support from Primary Care settings. This trajectory allows us analysing the process of design and implementation of these new models and the organizational structure where it is integrated. In Spain, there are already experiences in some regions such as Andalucia and the Basque Country, focused on the creation of new advanced nursing roles. At present, it is necessary to consider suitable strategic proposals for the complete development of these models and to achieve the best results in terms of overall health and quality of life of patients with chronic conditions, improving the quality of services and cost-effectiveness through a greater cohesion and performance of healthcare teams towards the sustainability of healthcare services and patient satisfaction. Copyright © 2013 Elsevier España, S.L. All rights reserved.

  18. Design Pattern Mining Using Distributed Learning Automata and DNA Sequence Alignment

    Science.gov (United States)

    Esmaeilpour, Mansour; Naderifar, Vahideh; Shukur, Zarina

    2014-01-01

    Context Over the last decade, design patterns have been used extensively to generate reusable solutions to frequently encountered problems in software engineering and object oriented programming. A design pattern is a repeatable software design solution that provides a template for solving various instances of a general problem. Objective This paper describes a new method for pattern mining, isolating design patterns and relationship between them; and a related tool, DLA-DNA for all implemented pattern and all projects used for evaluation. DLA-DNA achieves acceptable precision and recall instead of other evaluated tools based on distributed learning automata (DLA) and deoxyribonucleic acid (DNA) sequences alignment. Method The proposed method mines structural design patterns in the object oriented source code and extracts the strong and weak relationships between them, enabling analyzers and programmers to determine the dependency rate of each object, component, and other section of the code for parameter passing and modular programming. The proposed model can detect design patterns better that available other tools those are Pinot, PTIDEJ and DPJF; and the strengths of their relationships. Results The result demonstrate that whenever the source code is build standard and non-standard, based on the design patterns, then the result of the proposed method is near to DPJF and better that Pinot and PTIDEJ. The proposed model is tested on the several source codes and is compared with other related models and available tools those the results show the precision and recall of the proposed method, averagely 20% and 9.6% are more than Pinot, 27% and 31% are more than PTIDEJ and 3.3% and 2% are more than DPJF respectively. Conclusion The primary idea of the proposed method is organized in two following steps: the first step, elemental design patterns are identified, while at the second step, is composed to recognize actual design patterns. PMID:25243670

  19. Design pattern mining using distributed learning automata and DNA sequence alignment.

    Directory of Open Access Journals (Sweden)

    Mansour Esmaeilpour

    Full Text Available CONTEXT: Over the last decade, design patterns have been used extensively to generate reusable solutions to frequently encountered problems in software engineering and object oriented programming. A design pattern is a repeatable software design solution that provides a template for solving various instances of a general problem. OBJECTIVE: This paper describes a new method for pattern mining, isolating design patterns and relationship between them; and a related tool, DLA-DNA for all implemented pattern and all projects used for evaluation. DLA-DNA achieves acceptable precision and recall instead of other evaluated tools based on distributed learning automata (DLA and deoxyribonucleic acid (DNA sequences alignment. METHOD: The proposed method mines structural design patterns in the object oriented source code and extracts the strong and weak relationships between them, enabling analyzers and programmers to determine the dependency rate of each object, component, and other section of the code for parameter passing and modular programming. The proposed model can detect design patterns better that available other tools those are Pinot, PTIDEJ and DPJF; and the strengths of their relationships. RESULTS: The result demonstrate that whenever the source code is build standard and non-standard, based on the design patterns, then the result of the proposed method is near to DPJF and better that Pinot and PTIDEJ. The proposed model is tested on the several source codes and is compared with other related models and available tools those the results show the precision and recall of the proposed method, averagely 20% and 9.6% are more than Pinot, 27% and 31% are more than PTIDEJ and 3.3% and 2% are more than DPJF respectively. CONCLUSION: The primary idea of the proposed method is organized in two following steps: the first step, elemental design patterns are identified, while at the second step, is composed to recognize actual design patterns.

  20. Design pattern mining using distributed learning automata and DNA sequence alignment.

    Science.gov (United States)

    Esmaeilpour, Mansour; Naderifar, Vahideh; Shukur, Zarina

    2014-01-01

    Over the last decade, design patterns have been used extensively to generate reusable solutions to frequently encountered problems in software engineering and object oriented programming. A design pattern is a repeatable software design solution that provides a template for solving various instances of a general problem. This paper describes a new method for pattern mining, isolating design patterns and relationship between them; and a related tool, DLA-DNA for all implemented pattern and all projects used for evaluation. DLA-DNA achieves acceptable precision and recall instead of other evaluated tools based on distributed learning automata (DLA) and deoxyribonucleic acid (DNA) sequences alignment. The proposed method mines structural design patterns in the object oriented source code and extracts the strong and weak relationships between them, enabling analyzers and programmers to determine the dependency rate of each object, component, and other section of the code for parameter passing and modular programming. The proposed model can detect design patterns better that available other tools those are Pinot, PTIDEJ and DPJF; and the strengths of their relationships. The result demonstrate that whenever the source code is build standard and non-standard, based on the design patterns, then the result of the proposed method is near to DPJF and better that Pinot and PTIDEJ. The proposed model is tested on the several source codes and is compared with other related models and available tools those the results show the precision and recall of the proposed method, averagely 20% and 9.6% are more than Pinot, 27% and 31% are more than PTIDEJ and 3.3% and 2% are more than DPJF respectively. The primary idea of the proposed method is organized in two following steps: the first step, elemental design patterns are identified, while at the second step, is composed to recognize actual design patterns.

  1. Analysis of DC properties and current distribution in TFAS ITER conductor samples using high Jc Nb3Sn advanced strands

    International Nuclear Information System (INIS)

    Zani, L.; Ciazynski, D.; Torre, A.; Bruzzone, P.; Stepanov, B.; Dewittler, R.; Staehli, F.

    2007-01-01

    Two full-size conductor samples using advanced Nb 3 Sn strands were tested in the SULTAN facility in 2005-2006 within (I,B,T) ranges close to the ITER operating conditions (B MAX ∼ 12 T, T ∼ 5 K). Each sample includes two conductor legs, connected together by a twin-box joint in their lower part. The conductor design is the same for the four legs, similar to that of the ITER Toroidal Field Model Coil, but each leg uses specific strands newly developed and industrially produced to reach higher J c performances than in previous samples. In addition to classical voltage taps and temperature sensors, the sample instrumentation included Hall probe (HP) heads positioned so as to discriminate current distribution between conductor main sub-cables (petals). In a first simple approach, we analyse the results supposing that the conductor drives a uniform current among strands. The model is mainly based on geometrical considerations associated with a global approach on strand mechanical behavior. In a second part, we model the conductor in a more realistic way with different currents shared between main sub-cables. Taking into account various geometrical aspects (spiral trajectories, precise self-field maps...) the current in all petals are reconstructed with help of HP's signals, expected to experience self-field from CICC's. The mechanical aspects are also tentatively considered (electromagnetic load, bending strain...). Global results for both samples are shown, and possible inaccuracies due to geometrical parameters (petals positioning) are discussed. Those data are then injected into a Matlab program for electrical and geometrical CICC modeling (derived from the previous ENSIC code from CEA) and compared with dedicated experimental runs. Results are finally commented on the basis of overall consistency with HP's signals. (authors)

  2. Intrusion detection system using Online Sequence Extreme Learning Machine (OS-ELM in advanced metering infrastructure of smart grid.

    Directory of Open Access Journals (Sweden)

    Yuancheng Li

    Full Text Available Advanced Metering Infrastructure (AMI realizes a two-way communication of electricity data through by interconnecting with a computer network as the core component of the smart grid. Meanwhile, it brings many new security threats and the traditional intrusion detection method can't satisfy the security requirements of AMI. In this paper, an intrusion detection system based on Online Sequence Extreme Learning Machine (OS-ELM is established, which is used to detecting the attack in AMI and carrying out the comparative analysis with other algorithms. Simulation results show that, compared with other intrusion detection methods, intrusion detection method based on OS-ELM is more superior in detection speed and accuracy.

  3. Interconnection of Distributed Energy Resources

    Energy Technology Data Exchange (ETDEWEB)

    Reiter, Emerson [National Renewable Energy Lab. (NREL), Golden, CO (United States)

    2017-04-19

    This is a presentation on interconnection of distributed energy resources, including the relationships between different aspects of interconnection, best practices and lessons learned from different areas of the U.S., and an update on technical advances and standards for interconnection.

  4. Distributed scheduling for autonomous vehicles by reinforcement learning; Kyoka gakushu ni yoru mujin hansosha no bunsangata scheduling

    Energy Technology Data Exchange (ETDEWEB)

    Unoki, T.; Suetake, N. [Oki Electric Industry Co. Ltd., Tokyo (Japan)

    1997-08-20

    In this paper, we propose an autonomous vehicle scheduling schema in large physical distribution terminals publicly used as the next generation wide area physical distribution bases. This schema uses Learning Automaton for vehicles scheduling based on Contract Net Protocol, in order to obtain useful emergent behaviors of agents in the system based on the local decision-making of each agent. The state of the automaton is updated at each instant on the basis of new information that includes the arrival estimation time of vehicles. Each agent estimates the arrival time of vehicles by using Bayesian learning process. Using traffic simulation, we evaluate the schema in various simulated environments. The result shows the advantage of the schema over when each agent provides the same criteria from the top down, and each agent voluntarily generates criteria via interactions with the environment, playing an individual role in tie system. 22 refs., 5 figs., 2 tabs.

  5. ATLAAS: an automatic decision tree-based learning algorithm for advanced image segmentation in positron emission tomography.

    Science.gov (United States)

    Berthon, Beatrice; Marshall, Christopher; Evans, Mererid; Spezi, Emiliano

    2016-07-07

    Accurate and reliable tumour delineation on positron emission tomography (PET) is crucial for radiotherapy treatment planning. PET automatic segmentation (PET-AS) eliminates intra- and interobserver variability, but there is currently no consensus on the optimal method to use, as different algorithms appear to perform better for different types of tumours. This work aimed to develop a predictive segmentation model, trained to automatically select and apply the best PET-AS method, according to the tumour characteristics. ATLAAS, the automatic decision tree-based learning algorithm for advanced segmentation is based on supervised machine learning using decision trees. The model includes nine PET-AS methods and was trained on a 100 PET scans with known true contour. A decision tree was built for each PET-AS algorithm to predict its accuracy, quantified using the Dice similarity coefficient (DSC), according to the tumour volume, tumour peak to background SUV ratio and a regional texture metric. The performance of ATLAAS was evaluated for 85 PET scans obtained from fillable and printed subresolution sandwich phantoms. ATLAAS showed excellent accuracy across a wide range of phantom data and predicted the best or near-best segmentation algorithm in 93% of cases. ATLAAS outperformed all single PET-AS methods on fillable phantom data with a DSC of 0.881, while the DSC for H&N phantom data was 0.819. DSCs higher than 0.650 were achieved in all cases. ATLAAS is an advanced automatic image segmentation algorithm based on decision tree predictive modelling, which can be trained on images with known true contour, to predict the best PET-AS method when the true contour is unknown. ATLAAS provides robust and accurate image segmentation with potential applications to radiation oncology.

  6. Disentangling the Influence of Salience and Familiarity on Infant Word Learning: Methodological Advances

    Directory of Open Access Journals (Sweden)

    Heather eBortfeld

    2013-04-01

    Full Text Available The initial stages of language learning involve a critical interaction between infants’ environmental experience and their developing brains. The past several decades of research have produced important behavioral evidence of the many factors influencing this process, both on the part of the child and on the part of the environment that the child is in. The application of neurophysiological techniques to the study of early development has been augmenting these findings at a rapid pace. While the result is an accrual of data bridging the gap between brain and behavior, much work remains to make the link between behavioral evidence of infants' emerging sensitivities and neurophysiological evidence of changes in how their brains process information. Here we review the background behavioral data on how salience and familiarity in the auditory signal shape initial language learning. We follow this with a summary of more recent evidence of changes in infants’ brain activity in response to specific aspects of speech. Our goal is to examine language learning through the lens of brain/environment interactions, ultimately focusing on changes in cortical processing of speech across the first year of life. We will ground our examination of recent brain data in the two auditory features initially outlined: salience and familiarity. Our own and others' findings on the influence of these two features reveal that they are key parameters in infants’ emerging recognition of structure in the speech signal. Importantly, the evidence we review makes the critical link between behavioral and brain data. We discuss the importance of future work that makes this bridge as a means of moving the study of language development solidly into the domain of brain science.

  7. An open-source solution for advanced imaging flow cytometry data analysis using machine learning.

    Science.gov (United States)

    Hennig, Holger; Rees, Paul; Blasi, Thomas; Kamentsky, Lee; Hung, Jane; Dao, David; Carpenter, Anne E; Filby, Andrew

    2017-01-01

    Imaging flow cytometry (IFC) enables the high throughput collection of morphological and spatial information from hundreds of thousands of single cells. This high content, information rich image data can in theory resolve important biological differences among complex, often heterogeneous biological samples. However, data analysis is often performed in a highly manual and subjective manner using very limited image analysis techniques in combination with conventional flow cytometry gating strategies. This approach is not scalable to the hundreds of available image-based features per cell and thus makes use of only a fraction of the spatial and morphometric information. As a result, the quality, reproducibility and rigour of results are limited by the skill, experience and ingenuity of the data analyst. Here, we describe a pipeline using open-source software that leverages the rich information in digital imagery using machine learning algorithms. Compensated and corrected raw image files (.rif) data files from an imaging flow cytometer (the proprietary .cif file format) are imported into the open-source software CellProfiler, where an image processing pipeline identifies cells and subcellular compartments allowing hundreds of morphological features to be measured. This high-dimensional data can then be analysed using cutting-edge machine learning and clustering approaches using "user-friendly" platforms such as CellProfiler Analyst. Researchers can train an automated cell classifier to recognize different cell types, cell cycle phases, drug treatment/control conditions, etc., using supervised machine learning. This workflow should enable the scientific community to leverage the full analytical power of IFC-derived data sets. It will help to reveal otherwise unappreciated populations of cells based on features that may be hidden to the human eye that include subtle measured differences in label free detection channels such as bright-field and dark-field imagery

  8. Advancing Continuous Predictive Analytics Monitoring: Moving from Implementation to Clinical Action in a Learning Health System.

    Science.gov (United States)

    Keim-Malpass, Jessica; Kitzmiller, Rebecca R; Skeeles-Worley, Angela; Lindberg, Curt; Clark, Matthew T; Tai, Robert; Calland, James Forrest; Sullivan, Kevin; Randall Moorman, J; Anderson, Ruth A

    2018-06-01

    In the intensive care unit, clinicians monitor a diverse array of data inputs to detect early signs of impending clinical demise or improvement. Continuous predictive analytics monitoring synthesizes data from a variety of inputs into a risk estimate that clinicians can observe in a streaming environment. For this to be useful, clinicians must engage with the data in a way that makes sense for their clinical workflow in the context of a learning health system (LHS). This article describes the processes needed to evoke clinical action after initiation of continuous predictive analytics monitoring in an LHS. Copyright © 2018 Elsevier Inc. All rights reserved.

  9. Prenatal alcohol exposure modifies glucocorticoid receptor subcellular distribution in the medial prefrontal cortex and impairs frontal cortex-dependent learning.

    Directory of Open Access Journals (Sweden)

    Andrea M Allan

    Full Text Available Prenatal alcohol exposure (PAE has been shown to impair learning, memory and executive functioning in children. Perseveration, or the failure to respond adaptively to changing contingencies, is a hallmark on neurobehavioral assessment tasks for human fetal alcohol spectrum disorder (FASD. Adaptive responding is predominantly a product of the medial prefrontal cortex (mPFC and is regulated by corticosteroids. In our mouse model of PAE we recently reported deficits in hippocampal formation-dependent learning and memory and a dysregulation of hippocampal formation glucocorticoid receptor (GR subcellular distribution. Here, we examined the effect of PAE on frontal cortical-dependent behavior, as well as mPFC GR subcellular distribution and the levels of regulators of intracellular GR transport. PAE mice displayed significantly reduced response flexibility in a Y-maze reversal learning task. While the levels of total nuclear GR were reduced in PAE mPFC, levels of GR phosphorylated at serines 203, 211 and 226 were not significantly changed. Cytosolic, but not nuclear, MR levels were elevated in the PAE mPFC. The levels of critical GR trafficking proteins, FKBP51, Hsp90, cyclophilin 40, dynamitin and dynein intermediate chain, were altered in PAE mice, in favor of the exclusion of GR from the nucleus, indicating dysregulation of GR trafficking. Our findings suggest that there may be a link between a deficit in GR nuclear localization and frontal cortical learning deficits in prenatal alcohol-exposed mice.

  10. Advances in industrial biopharmaceutical batch process monitoring: Machine-learning methods for small data problems.

    Science.gov (United States)

    Tulsyan, Aditya; Garvin, Christopher; Ündey, Cenk

    2018-04-06

    Biopharmaceutical manufacturing comprises of multiple distinct processing steps that require effective and efficient monitoring of many variables simultaneously in real-time. The state-of-the-art real-time multivariate statistical batch process monitoring (BPM) platforms have been in use in recent years to ensure comprehensive monitoring is in place as a complementary tool for continued process verification to detect weak signals. This article addresses a longstanding, industry-wide problem in BPM, referred to as the "Low-N" problem, wherein a product has a limited production history. The current best industrial practice to address the Low-N problem is to switch from a multivariate to a univariate BPM, until sufficient product history is available to build and deploy a multivariate BPM platform. Every batch run without a robust multivariate BPM platform poses risk of not detecting potential weak signals developing in the process that might have an impact on process and product performance. In this article, we propose an approach to solve the Low-N problem by generating an arbitrarily large number of in silico batches through a combination of hardware exploitation and machine-learning methods. To the best of authors' knowledge, this is the first article to provide a solution to the Low-N problem in biopharmaceutical manufacturing using machine-learning methods. Several industrial case studies from bulk drug substance manufacturing are presented to demonstrate the efficacy of the proposed approach for BPM under various Low-N scenarios. © 2018 Wiley Periodicals, Inc.

  11. Transfer and Multi-task Learning in QSAR Modeling: Advances and Challenges

    Directory of Open Access Journals (Sweden)

    Rodolfo S. Simões

    2018-02-01

    Full Text Available Medicinal chemistry projects involve some steps aiming to develop a new drug, such as the analysis of biological targets related to a given disease, the discovery and the development of drug candidates for these targets, performing parallel biological tests to validate the drug effectiveness and side effects. Approaches as quantitative study of activity-structure relationships (QSAR involve the construction of predictive models that relate a set of descriptors of a chemical compound series and its biological activities with respect to one or more targets in the human body. Datasets used to perform QSAR analyses are generally characterized by a small number of samples and this makes them more complex to build accurate predictive models. In this context, transfer and multi-task learning techniques are very suitable since they take information from other QSAR models to the same biological target, reducing efforts and costs for generating new chemical compounds. Therefore, this review will present the main features of transfer and multi-task learning studies, as well as some applications and its potentiality in drug design projects.

  12. Learning by Computer Simulation Does Not Lead to Better Test Performance on Advanced Cardiac Life Support Than Textbook Study.

    Science.gov (United States)

    Kim, Jong Hoon; Kim, Won Oak; Min, Kyeong Tae; Yang, Jong Yoon; Nam, Yong Taek

    2002-01-01

    For an effective acquisition and the practical application of rapidly increasing amounts of information, computer-based learning has already been introduced in medical education. However, there have been few studies that compare this innovative method to traditional learning methods in studying advanced cardiac life support (ACLS). Senior medical students were randomized to computer simulation and a textbook study. Each group studied ACLS for 150 minutes. Tests were done one week before, immediately after, and one week after the study period. Testing consisted of 20 questions. All questions were formulated in such a way that there was a single best answer. Each student also completed a questionnaire designed to assess computer skills as well as satisfaction with and benefit from the study materials. Test scores improved after both textbook study and computer simulation study in both groups but the improvement in scores was significantly higher for the textbook group only immediately after the study. There was no significant difference between groups in their computer skill and satisfaction with the study materials. The textbook group reported greater benefit from study materials than did the computer simulation group. Studying ACLS with a hard copy textbook may be more effective than computer simulation for the acquisition of simple information during a brief period. However, the difference in effectiveness is likely transient.

  13. Students' satisfaction to hybrid problem-based learning format for basic life support/advanced cardiac life support teaching.

    Science.gov (United States)

    Chilkoti, Geetanjali; Mohta, Medha; Wadhwa, Rachna; Saxena, Ashok Kumar; Sharma, Chhavi Sarabpreet; Shankar, Neelima

    2016-11-01

    Students are exposed to basic life support (BLS) and advanced cardiac life support (ACLS) training in the first semester in some medical colleges. The aim of this study was to compare students' satisfaction between lecture-based traditional method and hybrid problem-based learning (PBL) in BLS/ACLS teaching to undergraduate medical students. We conducted a questionnaire-based, cross-sectional survey among 118 1 st -year medical students from a university medical college in the city of New Delhi, India. We aimed to assess the students' satisfaction between lecture-based and hybrid-PBL method in BLS/ACLS teaching. Likert 5-point scale was used to assess students' satisfaction levels between the two teaching methods. Data were collected and scores regarding the students' satisfaction levels between these two teaching methods were analysed using a two-sided paired t -test. Most students preferred hybrid-PBL format over traditional lecture-based method in the following four aspects; learning and understanding, interest and motivation, training of personal abilities and being confident and satisfied with the teaching method ( P < 0.05). Implementation of hybrid-PBL format along with the lecture-based method in BLS/ACLS teaching provided high satisfaction among undergraduate medical students.

  14. Global faculty development: lessons learned from the Foundation for Advancement of International Medical Education and Research (FAIMER) initiatives.

    Science.gov (United States)

    Burdick, William P

    2014-08-01

    Foundation for Advancement of International Medical Education and Research (FAIMER) faculty development programs have operated since 2001 and are designed to overcome many of the challenges inherent in global health collaborations, including alignment with local needs, avoiding persistent dependency, and development of trust. FAIMER fellowship programs, developed for midcareer faculty members in all health professions from around the world, share goals of strengthening knowledge and skills in education leadership, education methods, and project management and evaluation. Building community is another explicit goal that allows participants to support and learn from each other.The author recommends several practices for successful international collaborations based on 13 years of experience with FAIMER fellowships. These include using authentic education projects to maintain alignment with local needs and apply newly acquired knowledge and skills, teaching leadership across cultures with careful communication and adaptation of concepts to local environments, cultivating a strong field of health professions education to promote diffusion of ideas and advocate for policy change, intentionally promoting field development and leadership to reduce dependency, giving generously of time and resources, learning from others as much as teaching others, and recognizing that effective partnerships revolve around personal relationships to build trust. These strategies have enabled the FAIMER fellowship programs to stay aligned with local needs, reduce dependency, and maintain trust.

  15. 25 Years of DECOVALEX - Research Advances and Lessons Learned from an International Model Comparison Initiative

    Science.gov (United States)

    Birkholzer, J. T.

    2017-12-01

    This presentation provides an overview of an international research and model comparison collaboration (DECOVALEX) for advancing the understanding and modeling of coupled thermo-hydro-mechanical-chemical (THMC) processes in geological systems. Prediction of these coupled effects is an essential part of the performance and safety assessment of geologic disposal systems for radioactive waste and spent nuclear fuel, and is also relevant for a range of other sub-surface engineering activities. DECOVALEX research activities have been supported by a large number of radioactive-waste-management organizations and regulatory authorities. Research teams from more than a dozen international partner organizations have participated in the comparative modeling evaluation of complex field and laboratory experiments in the UK, Switzerland, Japan, France and Sweden. Together, these tasks (1) have addressed a wide range of relevant issues related to engineered and natural system behavior in argillaceous, crystalline and other host rocks, (2) have yielded in-depth knowledge of coupled THM and THMC processes associated with nuclear waste repositories and wider geo-engineering applications, and (3) have advanced the capability, as well as demonstrated the suitability, of numerical simulation models for quantitative analysis.

  16. [Implementation of bedside training and advanced objective structured clinical examination (OSCE) trial to learn and confirm about pharmacy clinical skills].

    Science.gov (United States)

    Tokunaga, Jin; Takamura, Norito; Ogata, Kenji; Setoguchi, Nao; Sato, Keizo

    2013-01-01

    Bedside training for fourth-year students, as well as seminars in hospital pharmacy (vital sign seminars) for fifth-year students at the Department of Pharmacy of Kyushu University of Health and Welfare have been implemented using patient training models and various patient simulators. The introduction of simulation-based pharmaceutical education, where no patients are present, promotes visually, aurally, and tactilely simulated learning regarding the evaluation of vital signs and implementation of physical assessment when disease symptoms are present or adverse effects occur. A patient simulator also promotes the creation of training programs for emergency and critical care, with which basic as well as advanced life support can be practiced. In addition, an advanced objective structured clinical examination (OSCE) trial has been implemented to evaluate skills regarding vital signs and physical assessments. Pharmacists are required to examine vital signs and conduct physical assessment from a pharmaceutical point of view. The introduction of these pharmacy clinical skills will improve the efficacy of drugs, work for the prevention or early detection of adverse effects, and promote the appropriate use of drugs. It is considered that simulation-based pharmaceutical education is essential to understand physical assessment, and such education will ideally be applied and developed according to on-site practices.

  17. Developing the role of Swedish advanced practice nurse (APN) through a blended learning master's program: Consequences of knowledge organisation.

    Science.gov (United States)

    Bergström, Peter; Lindh, Viveca

    2018-01-01

    This paper reports on a research study conducted with a group of nurses in Sweden enrolled in a newly developed blended learning master's programme to become advanced practice nurses (APNs). As background, the paper presents the regional needs the programme is intended to address and describes how the programme was designed. The aim was to understand how, from students' perspective, the nurse master's programme structured knowledge for their future position as APNs. The research question focuses on how the master's programme prepares students by meeting their diverse needs for knowledge. Empirical material was collected at two times during the students' first and second years of study through semi-structured qualitative interviews. The findings highlight the process in which these master's students gained a more advanced identity of becoming APNs. This process demonstrates how students perceive their current position as nurses based on a discourse of knowledge in relation to the practical and theoretical knowledge they encounter in the master's programme. This article concludes by recommending that attention should be paid to developing APN role models in the current Swedish healthcare system. Copyright © 2017 Elsevier Ltd. All rights reserved.

  18. Lessons from Zhu Xi’s Views on Inquiry and Learning for Contemporary Advanced Humanities Education and Research

    Directory of Open Access Journals (Sweden)

    Kirill Ole THOMPSON

    2017-06-01

    Full Text Available We are bearing witness to the rapid rise of a brave new world of education as flashy websites and interactive software replace individual study and classroom lectures. The expansion of college lecture halls has been stretched thin with video lessons and distance learning, and the siren call of massive open online courses (MOOCs by star Ivy League professors renders the traditional classroom barren in the eyes of savvy students who have the system pegged. Several questions arise in this context. Can the students of today receive a college education in the full sense? Does learning still have the same quality without close interactions with teachers and classmates in small to medium sized classrooms? Does research hold the same significance today when much of the work is done and so much information supplied by computers? What lessons do Zhu Xi’s teachings on inquiry and learning have for this educational world of e-texts and cyber-lessons? While not a Luddite tract, the present study raises questions and concerns about the goals and conduct of higher education today which, as Heisenberg avers, should not only aim at transmitting knowledge understood in set ways, but also at inculcating new ways of thinking and understanding. A college education, as Zhu Xi holds for “advanced learning”, is as much about cultivating a thoughtful, responsible person as producing a professional expert. Such education should include cultivating a student’s sensitivity, logic, and judgment, as well as knowledge about humanity, society, and the world. It is often forgotten that such sensitivity, logic, knowledge, and commitment not only make the student more thoughtful and responsible, in short more self-conscious, but also give her additional perspectives and enhance her professional expertise.

  19. Pathogenesis-based treatments in primary Sjogren's syndrome using artificial intelligence and advanced machine learning techniques: a systematic literature review.

    Science.gov (United States)

    Foulquier, Nathan; Redou, Pascal; Le Gal, Christophe; Rouvière, Bénédicte; Pers, Jacques-Olivier; Saraux, Alain

    2018-05-17

    Big data analysis has become a common way to extract information from complex and large datasets among most scientific domains. This approach is now used to study large cohorts of patients in medicine. This work is a review of publications that have used artificial intelligence and advanced machine learning techniques to study physio pathogenesis-based treatments in pSS. A systematic literature review retrieved all articles reporting on the use of advanced statistical analysis applied to the study of systemic autoimmune diseases (SADs) over the last decade. An automatic bibliography screening method has been developed to perform this task. The program called BIBOT was designed to fetch and analyze articles from the pubmed database using a list of keywords and Natural Language Processing approaches. The evolution of trends in statistical approaches, sizes of cohorts and number of publications over this period were also computed in the process. In all, 44077 abstracts were screened and 1017 publications were analyzed. The mean number of selected articles was 101.0 (S.D. 19.16) by year, but increased significantly over the time (from 74 articles in 2008 to 138 in 2017). Among them only 12 focused on pSS but none of them emphasized on the aspect of pathogenesis-based treatments. To conclude, medicine progressively enters the era of big data analysis and artificial intelligence, but these approaches are not yet used to describe pSS-specific pathogenesis-based treatment. Nevertheless, large multicentre studies are investigating this aspect with advanced algorithmic tools on large cohorts of SADs patients.

  20. A transfer-learning approach to image segmentation across scanners by maximizing distribution similarity

    DEFF Research Database (Denmark)

    van Opbroek, Annegreet; Ikram, M. Arfan; Vernooij, Meike W.

    2013-01-01

    Many successful methods for biomedical image segmentation are based on supervised learning, where a segmentation algorithm is trained based on manually labeled training data. For supervised-learning algorithms to perform well, this training data has to be representative for the target data. In pr...