WorldWideScience

Sample records for learning track efficiency

  1. An efficient incremental learning mechanism for tracking concept drift in spam filtering.

    Directory of Open Access Journals (Sweden)

    Jyh-Jian Sheu

    Full Text Available This research manages in-depth analysis on the knowledge about spams and expects to propose an efficient spam filtering method with the ability of adapting to the dynamic environment. We focus on the analysis of email's header and apply decision tree data mining technique to look for the association rules about spams. Then, we propose an efficient systematic filtering method based on these association rules. Our systematic method has the following major advantages: (1 Checking only the header sections of emails, which is different from those spam filtering methods at present that have to analyze fully the email's content. Meanwhile, the email filtering accuracy is expected to be enhanced. (2 Regarding the solution to the problem of concept drift, we propose a window-based technique to estimate for the condition of concept drift for each unknown email, which will help our filtering method in recognizing the occurrence of spam. (3 We propose an incremental learning mechanism for our filtering method to strengthen the ability of adapting to the dynamic environment.

  2. Efficient Online Subspace Learning With an Indefinite Kernel for Visual Tracking and Recognition

    NARCIS (Netherlands)

    Liwicki, Stephan; Zafeiriou, Stefanos; Tzimiropoulos, Georgios; Pantic, Maja

    2012-01-01

    We propose an exact framework for online learning with a family of indefinite (not positive) kernels. As we study the case of nonpositive kernels, we first show how to extend kernel principal component analysis (KPCA) from a reproducing kernel Hilbert space to Krein space. We then formulate an

  3. Tracking by Machine Learning Methods

    CERN Document Server

    Jofrehei, Arash

    2015-01-01

    Current track reconstructing methods start with two points and then for each layer loop through all possible hits to find proper hits to add to that track. Another idea would be to use this large number of already reconstructed events and/or simulated data and train a machine on this data to find tracks given hit pixels. Training time could be long but real time tracking is really fast Simulation might not be as realistic as real data but tacking has been done for that with 100 percent efficiency while by using real data we would probably be limited to current efficiency.

  4. Can Tracking Improve Learning?

    Science.gov (United States)

    Duflo, Esther; Dupas, Pascaline; Kremer, Michael

    2009-01-01

    Tracking students into different classrooms according to their prior academic performance is controversial among both scholars and policymakers. If teachers find it easier to teach a homogeneous group of students, tracking could enhance school effectiveness and raise test scores of both low- and high-ability students. If students benefit from…

  5. Adaptive and accelerated tracking-learning-detection

    Science.gov (United States)

    Guo, Pengyu; Li, Xin; Ding, Shaowen; Tian, Zunhua; Zhang, Xiaohu

    2013-08-01

    An improved online long-term visual tracking algorithm, named adaptive and accelerated TLD (AA-TLD) based on Tracking-Learning-Detection (TLD) which is a novel tracking framework has been introduced in this paper. The improvement focuses on two aspects, one is adaption, which makes the algorithm not dependent on the pre-defined scanning grids by online generating scale space, and the other is efficiency, which uses not only algorithm-level acceleration like scale prediction that employs auto-regression and moving average (ARMA) model to learn the object motion to lessen the detector's searching range and the fixed number of positive and negative samples that ensures a constant retrieving time, but also CPU and GPU parallel technology to achieve hardware acceleration. In addition, in order to obtain a better effect, some TLD's details are redesigned, which uses a weight including both normalized correlation coefficient and scale size to integrate results, and adjusts distance metric thresholds online. A contrastive experiment on success rate, center location error and execution time, is carried out to show a performance and efficiency upgrade over state-of-the-art TLD with partial TLD datasets and Shenzhou IX return capsule image sequences. The algorithm can be used in the field of video surveillance to meet the need of real-time video tracking.

  6. Efficient Learning Design

    DEFF Research Database (Denmark)

    Godsk, Mikkel

    This paper presents the current approach to implementing educational technology with learning design at the Faculty of Science and Technology, Aarhus University, by introducing the concept of ‘efficient learning design’. The underlying hypothesis is that implementing learning design is more than...... engaging educators in the design process and developing teaching and learning, it is a shift in educational practice that potentially requires a stakeholder analysis and ultimately a business model for the deployment. What is most important is to balance the institutional, educator, and student...... perspectives and to consider all these in conjunction in order to obtain a sustainable, efficient learning design. The approach to deploying learning design in terms of the concept of efficient learning design, the catalyst for educational development, i.e. the learning design model and how it is being used...

  7. Nextgen Navy eLearning Tracking

    Science.gov (United States)

    2014-12-01

    ELEARNING TRACKING by William E. Miller December 2014 Thesis Advisor: Man-Tak Shing Co-Advisor: Arijit Das THIS PAGE INTENTIONALLY LEFT......Navy’s eLearning (NeL) computer-based learning system relies on a Learning Management System (LMS) for content delivery and tracking learning

  8. Learning efficient correlated equilibria

    KAUST Repository

    Borowski, Holly P.; Marden, Jason R.; Shamma, Jeff S.

    2014-01-01

    The majority of distributed learning literature focuses on convergence to Nash equilibria. Correlated equilibria, on the other hand, can often characterize more efficient collective behavior than even the best Nash equilibrium. However, there are no existing distributed learning algorithms that converge to specific correlated equilibria. In this paper, we provide one such algorithm which guarantees that the agents' collective joint strategy will constitute an efficient correlated equilibrium with high probability. The key to attaining efficient correlated behavior through distributed learning involves incorporating a common random signal into the learning environment.

  9. Learning efficient correlated equilibria

    KAUST Repository

    Borowski, Holly P.

    2014-12-15

    The majority of distributed learning literature focuses on convergence to Nash equilibria. Correlated equilibria, on the other hand, can often characterize more efficient collective behavior than even the best Nash equilibrium. However, there are no existing distributed learning algorithms that converge to specific correlated equilibria. In this paper, we provide one such algorithm which guarantees that the agents\\' collective joint strategy will constitute an efficient correlated equilibrium with high probability. The key to attaining efficient correlated behavior through distributed learning involves incorporating a common random signal into the learning environment.

  10. ENERGY EFFICIENT TRACKING SYSTEM USING WIRELESS SENSORS

    OpenAIRE

    Thankaselvi Kumaresan; Sheryl Mathias; Digja Khanvilkar; Prof. Smita Dange

    2014-01-01

    One of the most important applications of wireless sensor networks (WSNs) is surveillance system, which is used to track moving targets. WSN is composed of a large number of low cost sensors which operate on the power derived from batteries. Energy efficiency is an important issue in WSN, which determines the network lifetime. Due to the need for continuous monitoring with 100% efficiency, keeping all the sensor nodes active permanently leads to fast draining of batteries. Hen...

  11. Efficiency calibration of solid track spark auto counter

    International Nuclear Information System (INIS)

    Wang Mei; Wen Zhongwei; Lin Jufang; Liu Rong; Jiang Li; Lu Xinxin; Zhu Tonghua

    2008-01-01

    The factors influencing detection efficiency of solid track spark auto counter were analyzed, and the best etch condition and parameters of charge were also reconfirmed. With small plate fission ionization chamber, the efficiency of solid track spark auto counter at various experiment assemblies was re-calibrated. The efficiency of solid track spark auto counter at various experimental conditions was obtained. (authors)

  12. Robust Visual Tracking Via Consistent Low-Rank Sparse Learning

    KAUST Repository

    Zhang, Tianzhu; Liu, Si; Ahuja, Narendra; Yang, Ming-Hsuan; Ghanem, Bernard

    2014-01-01

    and the low-rank minimization problem for learning joint sparse representations can be efficiently solved by a sequence of closed form update operations. We evaluate the proposed CLRST algorithm against 14 state-of-the-art tracking methods on a set of 25

  13. Energy Efficiency Adult Tracking Report - Final

    Energy Technology Data Exchange (ETDEWEB)

    Gibson-Grant, Amy [Ad Council, NY (United States)

    2014-09-30

    Postwave tracking study for the Energy Efficiency Adult Campaign This study serves as measure of key metrics among the campaign’s target audience, homeowners age 25+. Key measures include: Awareness of messages relating to the broad issue; Recognition of the PSAs; Relevant attitudes, including interest, ease of taking energy efficient steps, and likelihood to act; Relevant knowledge, including knowledge of light bulb alternatives and energy efficient options; and Relevant behaviors, including specific energy-saving behaviors mentioned within the PSAs. Wave 1: May 27 – June 7, 2011 Wave 2: May 29 – June 8, 2012 Wave 3: May 29 – June 19, 2014 General market sample of adults 25+ who own their homes W1 sample: n = 704; W2: n=701; W3: n=806 Online Survey Panel Methodology Study was fielded by Lightspeed Research among their survey panel. Sample is US Census representative of US homeowners by race/ethnicity, income, age, region, and family status. At least 30% of respondents were required to have not updated major appliances in their home in the past 5 years (dishwasher, stove, refrigerator, washer, or dryer).

  14. Nonlinear Motion Tracking by Deep Learning Architecture

    Science.gov (United States)

    Verma, Arnav; Samaiya, Devesh; Gupta, Karunesh K.

    2018-03-01

    In the world of Artificial Intelligence, object motion tracking is one of the major problems. The extensive research is being carried out to track people in crowd. This paper presents a unique technique for nonlinear motion tracking in the absence of prior knowledge of nature of nonlinear path that the object being tracked may follow. We achieve this by first obtaining the centroid of the object and then using the centroid as the current example for a recurrent neural network trained using real-time recurrent learning. We have tweaked the standard algorithm slightly and have accumulated the gradient for few previous iterations instead of using just the current iteration as is the norm. We show that for a single object, such a recurrent neural network is highly capable of approximating the nonlinearity of its path.

  15. Low-rank sparse learning for robust visual tracking

    KAUST Repository

    Zhang, Tianzhu

    2012-01-01

    In this paper, we propose a new particle-filter based tracking algorithm that exploits the relationship between particles (candidate targets). By representing particles as sparse linear combinations of dictionary templates, this algorithm capitalizes on the inherent low-rank structure of particle representations that are learned jointly. As such, it casts the tracking problem as a low-rank matrix learning problem. This low-rank sparse tracker (LRST) has a number of attractive properties. (1) Since LRST adaptively updates dictionary templates, it can handle significant changes in appearance due to variations in illumination, pose, scale, etc. (2) The linear representation in LRST explicitly incorporates background templates in the dictionary and a sparse error term, which enables LRST to address the tracking drift problem and to be robust against occlusion respectively. (3) LRST is computationally attractive, since the low-rank learning problem can be efficiently solved as a sequence of closed form update operations, which yield a time complexity that is linear in the number of particles and the template size. We evaluate the performance of LRST by applying it to a set of challenging video sequences and comparing it to 6 popular tracking methods. Our experiments show that by representing particles jointly, LRST not only outperforms the state-of-the-art in tracking accuracy but also significantly improves the time complexity of methods that use a similar sparse linear representation model for particles [1]. © 2012 Springer-Verlag.

  16. Methodologies for tracking learning paths

    DEFF Research Database (Denmark)

    Frølunde, Lisbeth; Gilje, Øystein; Lindstrand, Fredrik

    2009-01-01

    filmmakers: what furthers their interest and/or hinders it, and what learning patterns emerge. The aim of this article is to present and discuss issues regarding the methodology and meth- ods of the study, such as developing a relationship with interviewees when conducting inter- views online (using MSN). We...... suggest two considerations about using online interviews: how the interviewees value the given subject of conversation and their familiarity with being online. The benefit of getting online communication with the young filmmakers offers ease, because it is both practical and appropriates a meeting...

  17. Real-time probabilistic covariance tracking with efficient model update.

    Science.gov (United States)

    Wu, Yi; Cheng, Jian; Wang, Jinqiao; Lu, Hanqing; Wang, Jun; Ling, Haibin; Blasch, Erik; Bai, Li

    2012-05-01

    The recently proposed covariance region descriptor has been proven robust and versatile for a modest computational cost. The covariance matrix enables efficient fusion of different types of features, where the spatial and statistical properties, as well as their correlation, are characterized. The similarity between two covariance descriptors is measured on Riemannian manifolds. Based on the same metric but with a probabilistic framework, we propose a novel tracking approach on Riemannian manifolds with a novel incremental covariance tensor learning (ICTL). To address the appearance variations, ICTL incrementally learns a low-dimensional covariance tensor representation and efficiently adapts online to appearance changes of the target with only O(1) computational complexity, resulting in a real-time performance. The covariance-based representation and the ICTL are then combined with the particle filter framework to allow better handling of background clutter, as well as the temporary occlusions. We test the proposed probabilistic ICTL tracker on numerous benchmark sequences involving different types of challenges including occlusions and variations in illumination, scale, and pose. The proposed approach demonstrates excellent real-time performance, both qualitatively and quantitatively, in comparison with several previously proposed trackers.

  18. Geometric Hypergraph Learning for Visual Tracking

    OpenAIRE

    Du, Dawei; Qi, Honggang; Wen, Longyin; Tian, Qi; Huang, Qingming; Lyu, Siwei

    2016-01-01

    Graph based representation is widely used in visual tracking field by finding correct correspondences between target parts in consecutive frames. However, most graph based trackers consider pairwise geometric relations between local parts. They do not make full use of the target's intrinsic structure, thereby making the representation easily disturbed by errors in pairwise affinities when large deformation and occlusion occur. In this paper, we propose a geometric hypergraph learning based tr...

  19. Energy Efficient Hybrid Dual Axis Solar Tracking System

    Directory of Open Access Journals (Sweden)

    Rashid Ahammed Ferdaus

    2014-01-01

    Full Text Available This paper describes the design and implementation of an energy efficient solar tracking system from a normal mechanical single axis to a hybrid dual axis. For optimizing the solar tracking mechanism electromechanical systems were evolved through implementation of different evolutional algorithms and methodologies. To present the tracker, a hybrid dual-axis solar tracking system is designed, built, and tested based on both the solar map and light sensor based continuous tracking mechanism. These light sensors also compare the darkness and cloudy and sunny conditions assisting daily tracking. The designed tracker can track sun’s apparent position at different months and seasons; thereby the electrical controlling device requires a real time clock device for guiding the tracking system in seeking solar position for the seasonal motion. So the combination of both of these tracking mechanisms made the designed tracker a hybrid one. The power gain and system power consumption are compared with a static and continuous dual axis solar tracking system. It is found that power gain of hybrid dual axis solar tracking system is almost equal to continuous dual axis solar tracking system, whereas the power saved in system operation by the hybrid tracker is 44.44% compared to the continuous tracking system.

  20. Energy-efficient Trajectory Tracking for Mobile Devices

    DEFF Research Database (Denmark)

    Kjærgaard, Mikkel Baun; Bhattacharya, Sourav; Blunck, Henrik

    2011-01-01

    Emergent location-aware applications often require tracking trajectories of mobile devices over a long period of time. To be useful, the tracking has to be energy-efficient to avoid having a major impact on the battery life of the mobile de vice. Furthermore, when trajectory information needs to ...

  1. Robust visual tracking via multi-task sparse learning

    KAUST Repository

    Zhang, Tianzhu

    2012-06-01

    In this paper, we formulate object tracking in a particle filter framework as a multi-task sparse learning problem, which we denote as Multi-Task Tracking (MTT). Since we model particles as linear combinations of dictionary templates that are updated dynamically, learning the representation of each particle is considered a single task in MTT. By employing popular sparsity-inducing p, q mixed norms (p D; 1), we regularize the representation problem to enforce joint sparsity and learn the particle representations together. As compared to previous methods that handle particles independently, our results demonstrate that mining the interdependencies between particles improves tracking performance and overall computational complexity. Interestingly, we show that the popular L 1 tracker [15] is a special case of our MTT formulation (denoted as the L 11 tracker) when p q 1. The learning problem can be efficiently solved using an Accelerated Proximal Gradient (APG) method that yields a sequence of closed form updates. As such, MTT is computationally attractive. We test our proposed approach on challenging sequences involving heavy occlusion, drastic illumination changes, and large pose variations. Experimental results show that MTT methods consistently outperform state-of-the-art trackers. © 2012 IEEE.

  2. A visual tracking method based on deep learning without online model updating

    Science.gov (United States)

    Tang, Cong; Wang, Yicheng; Feng, Yunsong; Zheng, Chao; Jin, Wei

    2018-02-01

    The paper proposes a visual tracking method based on deep learning without online model updating. In consideration of the advantages of deep learning in feature representation, deep model SSD (Single Shot Multibox Detector) is used as the object extractor in the tracking model. Simultaneously, the color histogram feature and HOG (Histogram of Oriented Gradient) feature are combined to select the tracking object. In the process of tracking, multi-scale object searching map is built to improve the detection performance of deep detection model and the tracking efficiency. In the experiment of eight respective tracking video sequences in the baseline dataset, compared with six state-of-the-art methods, the method in the paper has better robustness in the tracking challenging factors, such as deformation, scale variation, rotation variation, illumination variation, and background clutters, moreover, its general performance is better than other six tracking methods.

  3. Robust Visual Tracking Via Consistent Low-Rank Sparse Learning

    KAUST Repository

    Zhang, Tianzhu

    2014-06-19

    Object tracking is the process of determining the states of a target in consecutive video frames based on properties of motion and appearance consistency. In this paper, we propose a consistent low-rank sparse tracker (CLRST) that builds upon the particle filter framework for tracking. By exploiting temporal consistency, the proposed CLRST algorithm adaptively prunes and selects candidate particles. By using linear sparse combinations of dictionary templates, the proposed method learns the sparse representations of image regions corresponding to candidate particles jointly by exploiting the underlying low-rank constraints. In addition, the proposed CLRST algorithm is computationally attractive since temporal consistency property helps prune particles and the low-rank minimization problem for learning joint sparse representations can be efficiently solved by a sequence of closed form update operations. We evaluate the proposed CLRST algorithm against 14 state-of-the-art tracking methods on a set of 25 challenging image sequences. Experimental results show that the CLRST algorithm performs favorably against state-of-the-art tracking methods in terms of accuracy and execution time.

  4. Robust visual tracking via multi-task sparse learning

    KAUST Repository

    Zhang, Tianzhu; Ghanem, Bernard; Liu, Si; Ahuja, Narendra

    2012-01-01

    In this paper, we formulate object tracking in a particle filter framework as a multi-task sparse learning problem, which we denote as Multi-Task Tracking (MTT). Since we model particles as linear combinations of dictionary templates

  5. Measurement of the track reconstruction efficiency at LHCb

    CERN Document Server

    Aaij, Roel; Adinolfi, Marco; Affolder, Anthony; Ajaltouni, Ziad; Akar, Simon; Albrecht, Johannes; Alessio, Federico; Alexander, Michael; Ali, Suvayu; Alkhazov, Georgy; Alvarez Cartelle, Paula; Alves Jr, Antonio; Amato, Sandra; Amerio, Silvia; Amhis, Yasmine; An, Liupan; Anderlini, Lucio; Anderson, Jonathan; Andreassen, Rolf; Andreotti, Mirco; Andrews, Jason; Appleby, Robert; Aquines Gutierrez, Osvaldo; Archilli, Flavio; Artamonov, Alexander; Artuso, Marina; Aslanides, Elie; Auriemma, Giulio; Baalouch, Marouen; Bachmann, Sebastian; Back, John; Badalov, Alexey; Baldini, Wander; Barlow, Roger; Barschel, Colin; Barsuk, Sergey; Barter, William; Batozskaya, Varvara; Battista, Vincenzo; Bay, Aurelio; Beaucourt, Leo; Beddow, John; Bedeschi, Franco; Bediaga, Ignacio; Belogurov, Sergey; Belous, Konstantin; Belyaev, Ivan; Ben-Haim, Eli; Bencivenni, Giovanni; Benson, Sean; Benton, Jack; Berezhnoy, Alexander; Bernet, Roland; Bettler, Marc-Olivier; van Beuzekom, Martinus; Bien, Alexander; Bifani, Simone; Bird, Thomas; Bizzeti, Andrea; Bjørnstad, Pål Marius; Blake, Thomas; Blanc, Frédéric; Blouw, Johan; Blusk, Steven; Bocci, Valerio; Bondar, Alexander; Bondar, Nikolay; Bonivento, Walter; Borghi, Silvia; Borgia, Alessandra; Borsato, Martino; Bowcock, Themistocles; Bowen, Espen Eie; Bozzi, Concezio; Brambach, Tobias; van den Brand, Johannes; Bressieux, Joël; Brett, David; Britsch, Markward; Britton, Thomas; Brodzicka, Jolanta; Brook, Nicholas; Brown, Henry; Bursche, Albert; Busetto, Giovanni; Buytaert, Jan; Cadeddu, Sandro; Calabrese, Roberto; Calvi, Marta; Calvo Gomez, Miriam; Campana, Pierluigi; Campora Perez, Daniel; Carbone, Angelo; Carboni, Giovanni; Cardinale, Roberta; Cardini, Alessandro; Carson, Laurence; Carvalho Akiba, Kazuyoshi; Casse, Gianluigi; Cassina, Lorenzo; Castillo Garcia, Lucia; Cattaneo, Marco; Cauet, Christophe; Cenci, Riccardo; Charles, Matthew; Charpentier, Philippe; Chen, Shanzhen; Cheung, Shu-Faye; Chiapolini, Nicola; Chrzaszcz, Marcin; Ciba, Krzystof; Cid Vidal, Xabier; Ciezarek, Gregory; Clarke, Peter; Clemencic, Marco; Cliff, Harry; Closier, Joel; Coco, Victor; Cogan, Julien; Cogneras, Eric; Collins, Paula; Comerma-Montells, Albert; Contu, Andrea; Cook, Andrew; Coombes, Matthew; Coquereau, Samuel; Corti, Gloria; Corvo, Marco; Counts, Ian; Couturier, Benjamin; Cowan, Greig; Craik, Daniel Charles; Cruz Torres, Melissa Maria; Cunliffe, Samuel; Currie, Robert; D'Ambrosio, Carmelo; Dalseno, Jeremy; David, Pascal; David, Pieter; Davis, Adam; De Bruyn, Kristof; De Capua, Stefano; De Cian, Michel; De Miranda, Jussara; De Paula, Leandro; De Silva, Weeraddana; De Simone, Patrizia; Decamp, Daniel; Deckenhoff, Mirko; Del Buono, Luigi; Déléage, Nicolas; Derkach, Denis; Deschamps, Olivier; Dettori, Francesco; Di Canto, Angelo; Dijkstra, Hans; Donleavy, Stephanie; Dordei, Francesca; Dorigo, Mirco; Dosil Suárez, Alvaro; Dossett, David; Dovbnya, Anatoliy; Dreimanis, Karlis; Dujany, Giulio; Dupertuis, Frederic; Durante, Paolo; Dzhelyadin, Rustem; Dziurda, Agnieszka; Dzyuba, Alexey; Easo, Sajan; Egede, Ulrik; Egorychev, Victor; Eidelman, Semen; Eisenhardt, Stephan; Eitschberger, Ulrich; Ekelhof, Robert; Eklund, Lars; El Rifai, Ibrahim; Elsasser, Christian; Ely, Scott; Esen, Sevda; Evans, Hannah Mary; Evans, Timothy; Falabella, Antonio; Färber, Christian; Farinelli, Chiara; Farley, Nathanael; Farry, Stephen; Fay, Robert; Ferguson, Dianne; Fernandez Albor, Victor; Ferreira Rodrigues, Fernando; Ferro-Luzzi, Massimiliano; Filippov, Sergey; Fiore, Marco; Fiorini, Massimiliano; Firlej, Miroslaw; Fitzpatrick, Conor; Fiutowski, Tomasz; Fontana, Marianna; Fontanelli, Flavio; Forty, Roger; Francisco, Oscar; Frank, Markus; Frei, Christoph; Frosini, Maddalena; Fu, Jinlin; Furfaro, Emiliano; Gallas Torreira, Abraham; Galli, Domenico; Gallorini, Stefano; Gambetta, Silvia; Gandelman, Miriam; Gandini, Paolo; Gao, Yuanning; García Pardiñas, Julián; Garofoli, Justin; Garra Tico, Jordi; Garrido, Lluis; Gaspar, Clara; Gauld, Rhorry; Gavardi, Laura; Gavrilov, Gennadii; Gersabeck, Evelina; Gersabeck, Marco; Gershon, Timothy; Ghez, Philippe; Gianelle, Alessio; Giani', Sebastiana; Gibson, Valerie; Giubega, Lavinia-Helena; Gligorov, V.V.; Göbel, Carla; Golubkov, Dmitry; Golutvin, Andrey; Gomes, Alvaro; Gotti, Claudio; Grabalosa Gándara, Marc; Graciani Diaz, Ricardo; Granado Cardoso, Luis Alberto; Graugés, Eugeni; Graziani, Giacomo; Grecu, Alexandru; Greening, Edward; Gregson, Sam; Griffith, Peter; Grillo, Lucia; Grünberg, Oliver; Gui, Bin; Gushchin, Evgeny; Guz, Yury; Gys, Thierry; Hadjivasiliou, Christos; Haefeli, Guido; Haen, Christophe; Haines, Susan; Hall, Samuel; Hamilton, Brian; Hampson, Thomas; Han, Xiaoxue; Hansmann-Menzemer, Stephanie; Harnew, Neville; Harnew, Samuel; Harrison, Jonathan; He, Jibo; Head, Timothy; Heijne, Veerle; Hennessy, Karol; Henrard, Pierre; Henry, Louis; Hernando Morata, Jose Angel; van Herwijnen, Eric; Heß, Miriam; Hicheur, Adlène; Hill, Donal; Hoballah, Mostafa; Hombach, Christoph; Hulsbergen, Wouter; Hunt, Philip; Hussain, Nazim; Hutchcroft, David; Hynds, Daniel; Idzik, Marek; Ilten, Philip; Jacobsson, Richard; Jaeger, Andreas; Jalocha, Pawel; Jans, Eddy; Jaton, Pierre; Jawahery, Abolhassan; Jing, Fanfan; John, Malcolm; Johnson, Daniel; Jones, Christopher; Joram, Christian; Jost, Beat; Jurik, Nathan; Kaballo, Michael; Kandybei, Sergii; Kanso, Walaa; Karacson, Matthias; Karbach, Moritz; Karodia, Sarah; Kelsey, Matthew; Kenyon, Ian; Ketel, Tjeerd; Khanji, Basem; Khurewathanakul, Chitsanu; Klaver, Suzanne; Klimaszewski, Konrad; Kochebina, Olga; Kolpin, Michael; Komarov, Ilya; Koopman, Rose; Koppenburg, Patrick; Korolev, Mikhail; Kozlinskiy, Alexandr; Kravchuk, Leonid; Kreplin, Katharina; Kreps, Michal; Krocker, Georg; Krokovny, Pavel; Kruse, Florian; Kucewicz, Wojciech; Kucharczyk, Marcin; Kudryavtsev, Vasily; Kurek, Krzysztof; Kvaratskheliya, Tengiz; La Thi, Viet Nga; Lacarrere, Daniel; Lafferty, George; Lai, Adriano; Lambert, Dean; Lambert, Robert W; Lanfranchi, Gaia; Langenbruch, Christoph; Langhans, Benedikt; Latham, Thomas; Lazzeroni, Cristina; Le Gac, Renaud; van Leerdam, Jeroen; Lees, Jean-Pierre; Lefèvre, Regis; Leflat, Alexander; Lefrançois, Jacques; Leo, Sabato; Leroy, Olivier; Lesiak, Tadeusz; Leverington, Blake; Li, Yiming; Likhomanenko, Tatiana; Liles, Myfanwy; Lindner, Rolf; Linn, Christian; Lionetto, Federica; Liu, Bo; Lohn, Stefan; Longstaff, Iain; Lopes, Jose; Lopez-March, Neus; Lowdon, Peter; Lu, Haiting; Lucchesi, Donatella; Luo, Haofei; Lupato, Anna; Luppi, Eleonora; Lupton, Oliver; Machefert, Frederic; Machikhiliyan, Irina V; Maciuc, Florin; Maev, Oleg; Malde, Sneha; Manca, Giulia; Mancinelli, Giampiero; Maratas, Jan; Marchand, Jean François; Marconi, Umberto; Marin Benito, Carla; Marino, Pietro; Märki, Raphael; Marks, Jörg; Martellotti, Giuseppe; Martens, Aurelien; Martín Sánchez, Alexandra; Martinelli, Maurizio; Martinez Santos, Diego; Martinez Vidal, Fernando; Martins Tostes, Danielle; Massafferri, André; Matev, Rosen; Mathe, Zoltan; Matteuzzi, Clara; Mazurov, Alexander; McCann, Michael; McCarthy, James; McNab, Andrew; McNulty, Ronan; McSkelly, Ben; Meadows, Brian; Meier, Frank; Meissner, Marco; Merk, Marcel; Milanes, Diego Alejandro; Minard, Marie-Noelle; Moggi, Niccolò; Molina Rodriguez, Josue; Monteil, Stephane; Morandin, Mauro; Morawski, Piotr; Mordà, Alessandro; Morello, Michael Joseph; Moron, Jakub; Morris, Adam Benjamin; Mountain, Raymond; Muheim, Franz; Müller, Katharina; Mussini, Manuel; Muster, Bastien; Naik, Paras; Nakada, Tatsuya; Nandakumar, Raja; Nasteva, Irina; Needham, Matthew; Neri, Nicola; Neubert, Sebastian; Neufeld, Niko; Neuner, Max; Nguyen, Anh Duc; Nguyen, Thi-Dung; Nguyen-Mau, Chung; Nicol, Michelle; Niess, Valentin; Niet, Ramon; Nikitin, Nikolay; Nikodem, Thomas; Novoselov, Alexey; O'Hanlon, Daniel Patrick; Oblakowska-Mucha, Agnieszka; Obraztsov, Vladimir; Oggero, Serena; Ogilvy, Stephen; Okhrimenko, Oleksandr; Oldeman, Rudolf; Onderwater, Gerco; Orlandea, Marius; Otalora Goicochea, Juan Martin; Owen, Patrick; Oyanguren, Maria Arantza; Pal, Bilas Kanti; Palano, Antimo; Palombo, Fernando; Palutan, Matteo; Panman, Jacob; Papanestis, Antonios; Pappagallo, Marco; Pappalardo, Luciano; Parkes, Christopher; Parkinson, Christopher John; Passaleva, Giovanni; Patel, Girish; Patel, Mitesh; Patrignani, Claudia; Pazos Alvarez, Antonio; Pearce, Alex; Pellegrino, Antonio; Pepe Altarelli, Monica; Perazzini, Stefano; Perez Trigo, Eliseo; Perret, Pascal; Perrin-Terrin, Mathieu; Pescatore, Luca; Pesen, Erhan; Petridis, Konstantin; Petrolini, Alessandro; Picatoste Olloqui, Eduardo; Pietrzyk, Boleslaw; Pilař, Tomas; Pinci, Davide; Pistone, Alessandro; Playfer, Stephen; Plo Casasus, Maximo; Polci, Francesco; Poluektov, Anton; Polycarpo, Erica; Popov, Alexander; Popov, Dmitry; Popovici, Bogdan; Potterat, Cédric; Price, Eugenia; Prisciandaro, Jessica; Pritchard, Adrian; Prouve, Claire; Pugatch, Valery; Puig Navarro, Albert; Punzi, Giovanni; Qian, Wenbin; Rachwal, Bartolomiej; Rademacker, Jonas; Rakotomiaramanana, Barinjaka; Rama, Matteo; Rangel, Murilo; Raniuk, Iurii; Rauschmayr, Nathalie; Raven, Gerhard; Reichert, Stefanie; Reid, Matthew; dos Reis, Alberto; Ricciardi, Stefania; Richards, Sophie; Rihl, Mariana; Rinnert, Kurt; Rives Molina, Vincente; Roa Romero, Diego; Robbe, Patrick; Rodrigues, Ana Barbara; Rodrigues, Eduardo; Rodriguez Perez, Pablo; Roiser, Stefan; Romanovsky, Vladimir; Romero Vidal, Antonio; Rotondo, Marcello; Rouvinet, Julien; Ruf, Thomas; Ruffini, Fabrizio; Ruiz, Hugo; Ruiz Valls, Pablo; Saborido Silva, Juan Jose; Sagidova, Naylya; Sail, Paul; Saitta, Biagio; Salustino Guimaraes, Valdir; Sanchez Mayordomo, Carlos; Sanmartin Sedes, Brais; Santacesaria, Roberta; Santamarina Rios, Cibran; Santovetti, Emanuele; Sarti, Alessio; Satriano, Celestina; Satta, Alessia; Saunders, Daniel Martin; Savrie, Mauro; Savrina, Darya; Schiller, Manuel; Schindler, Heinrich; Schlupp, Maximilian; Schmelling, Michael; Schmidt, Burkhard; Schneider, Olivier; Schopper, Andreas; Schune, Marie Helene; Schwemmer, Rainer; Sciascia, Barbara; Sciubba, Adalberto; Seco, Marcos; Semennikov, Alexander; Sepp, Indrek; Serra, Nicola; Serrano, Justine; Sestini, Lorenzo; Seyfert, Paul; Shapkin, Mikhail; Shapoval, Illya; Shcheglov, Yury; Shears, Tara; Shekhtman, Lev; Shevchenko, Vladimir; Shires, Alexander; Silva Coutinho, Rafael; Simi, Gabriele; Sirendi, Marek; Skidmore, Nicola; Skwarnicki, Tomasz; Smith, Anthony; Smith, Edmund; Smith, Eluned; Smith, Jackson; Smith, Mark; Snoek, Hella; Sokoloff, Michael; Soler, Paul; Soomro, Fatima; Souza, Daniel; Souza De Paula, Bruno; Spaan, Bernhard; Sparkes, Ailsa; Spradlin, Patrick; Sridharan, Srikanth; Stagni, Federico; Stahl, Marian; Stahl, Sascha; Steinkamp, Olaf; Stenyakin, Oleg; Stevenson, Scott; Stoica, Sabin; Stone, Sheldon; Storaci, Barbara; Stracka, Simone; Straticiuc, Mihai; Straumann, Ulrich; Stroili, Roberto; Subbiah, Vijay Kartik; Sun, Liang; Sutcliffe, William; Swientek, Krzysztof; Swientek, Stefan; Syropoulos, Vasileios; Szczekowski, Marek; Szczypka, Paul; Szilard, Daniela; Szumlak, Tomasz; T'Jampens, Stephane; Teklishyn, Maksym; Tellarini, Giulia; Teubert, Frederic; Thomas, Christopher; Thomas, Eric; van Tilburg, Jeroen; Tisserand, Vincent; Tobin, Mark; Tolk, Siim; Tomassetti, Luca; Tonelli, Diego; Topp-Joergensen, Stig; Torr, Nicholas; Tournefier, Edwige; Tourneur, Stephane; Tran, Minh Tâm; Tresch, Marco; Tsaregorodtsev, Andrei; Tsopelas, Panagiotis; Tuning, Niels; Ubeda Garcia, Mario; Ukleja, Artur; Ustyuzhanin, Andrey; Uwer, Ulrich; Vagnoni, Vincenzo; Valenti, Giovanni; Vallier, Alexis; Vazquez Gomez, Ricardo; Vazquez Regueiro, Pablo; Vázquez Sierra, Carlos; Vecchi, Stefania; Velthuis, Jaap; Veltri, Michele; Veneziano, Giovanni; Vesterinen, Mika; Viaud, Benoit; Vieira, Daniel; Vieites Diaz, Maria; Vilasis-Cardona, Xavier; Vollhardt, Achim; Volyanskyy, Dmytro; Voong, David; Vorobyev, Alexey; Vorobyev, Vitaly; Voß, Christian; Voss, Helge; de Vries, Jacco; Waldi, Roland; Wallace, Charlotte; Wallace, Ronan; Walsh, John; Wandernoth, Sebastian; Wang, Jianchun; Ward, David; Watson, Nigel; Websdale, David; Whitehead, Mark; Wicht, Jean; Wiedner, Dirk; Wilkinson, Guy; Williams, Matthew; Williams, Mike; Wilson, Fergus; Wimberley, Jack; Wishahi, Julian; Wislicki, Wojciech; Witek, Mariusz; Wormser, Guy; Wotton, Stephen; Wright, Simon; Wu, Suzhi; Wyllie, Kenneth; Xie, Yuehong; Xing, Zhou; Xu, Zhirui; Yang, Zhenwei; Yuan, Xuhao; Yushchenko, Oleg; Zangoli, Maria; Zavertyaev, Mikhail; Zhang, Liming; Zhang, Wen Chao; Zhang, Yanxi; Zhelezov, Alexey; Zhokhov, Anatoly; Zhong, Liang; Zvyagin, Alexander

    2015-02-12

    The determination of track reconstruction efficiencies at LHCb using $J/\\psi\\rightarrow\\mu^{+}\\mu^{-}$ decays is presented. Efficiencies above $95\\%$ are found for the data taking periods in 2010, 2011, and 2012. The ratio of the track reconstruction efficiency of muons in data and simulation is compatible with unity and measured with an uncertainty of $0.8\\,\\%$ for data taking in 2010, and at a precision of $0.4\\,\\%$ for data taking in 2011 and 2012. For hadrons an additional $1.4\\,\\%$ uncertainty due to material interactions is assumed. This result is crucial for accurate cross section and branching fraction measurements in LHCb.

  6. Measurement of the track reconstruction efficiency at LHCb

    International Nuclear Information System (INIS)

    Collaboration, The LHCb

    2015-01-01

    The determination of track reconstruction efficiencies at LHCb using J/ψ→μ + μ - decays is presented. Efficiencies above 95% are found for the data taking periods in 2010, 2011, and 2012. The ratio of the track reconstruction efficiency of muons in data and simulation is compatible with unity and measured with an uncertainty of 0.8 % for data taking in 2010, and at a precision of 0.4 % for data taking in 2011 and 2012. For hadrons an additional 1.4 % uncertainty due to material interactions is assumed. This result is crucial for accurate cross section and branching fraction measurements in LHCb

  7. Robust visual tracking via structured multi-task sparse learning

    KAUST Repository

    Zhang, Tianzhu

    2012-11-09

    In this paper, we formulate object tracking in a particle filter framework as a structured multi-task sparse learning problem, which we denote as Structured Multi-Task Tracking (S-MTT). Since we model particles as linear combinations of dictionary templates that are updated dynamically, learning the representation of each particle is considered a single task in Multi-Task Tracking (MTT). By employing popular sparsity-inducing lp,q mixed norms (specifically p∈2,∞ and q=1), we regularize the representation problem to enforce joint sparsity and learn the particle representations together. As compared to previous methods that handle particles independently, our results demonstrate that mining the interdependencies between particles improves tracking performance and overall computational complexity. Interestingly, we show that the popular L1 tracker (Mei and Ling, IEEE Trans Pattern Anal Mach Intel 33(11):2259-2272, 2011) is a special case of our MTT formulation (denoted as the L11 tracker) when p=q=1. Under the MTT framework, some of the tasks (particle representations) are often more closely related and more likely to share common relevant covariates than other tasks. Therefore, we extend the MTT framework to take into account pairwise structural correlations between particles (e.g. spatial smoothness of representation) and denote the novel framework as S-MTT. The problem of learning the regularized sparse representation in MTT and S-MTT can be solved efficiently using an Accelerated Proximal Gradient (APG) method that yields a sequence of closed form updates. As such, S-MTT and MTT are computationally attractive. We test our proposed approach on challenging sequences involving heavy occlusion, drastic illumination changes, and large pose variations. Experimental results show that S-MTT is much better than MTT, and both methods consistently outperform state-of-the-art trackers. © 2012 Springer Science+Business Media New York.

  8. Efficiency of a concentric matrix track detector surface scanning

    International Nuclear Information System (INIS)

    Bek-Uzarov, Dj.; Nikezic, D.; Kostic, D.; Krstic, D.; Cuknic, O.

    1995-01-01

    Heavy particle ionizing radiation track counting on the surface of a solid state round surface detector is made using the microscope and scanning step by step by a round field of vision. The whole solid state detector surface could not be fully or completely covered by round fields of visions. Therefore detector surface could be divided on the two parts, the larger surface, being under fields of vision, really scanned and no scanned missed or omitted surface. The ratio between omitted and scanned surfaces is so called track scanning efficiency. The knowledge of really counted, or scanned surface is a important value for evaluating the real surface track density an exposed solid state track detector. In the paper a matrix of a concentric field of vision made around the first microscope field of vision placed in center of the round disc of the scanned track detector is proposed. In a such scanning matrix the real scanned surface could be easy calculated and by the microscope scanning made as well. By this way scanned surface is very precisely obtained as well. Precise knowledge of scanned and omitted surface allows to obtain more precise scanning efficiency factor as well as real surface track density, the main parameter in solid state track detection measurements. (author)

  9. Demonstrating EnTracked a System for Energy-Efficient Position Tracking for Mobile Devices

    DEFF Research Database (Denmark)

    Kjærgaard, Mikkel Baun; Jensen, Jakob Langdal; Godsk, Torben

    An important feature of a modern mobile device is that it can position itself. Not only for use on the device but also for remote applications that require tracking of the device. To be useful, such position tracking has to be energy-efficient to avoid having a major impact on the battery life...... of the mobile device. To address this challenge we have build a system named EnTracked that, based on the estimation and prediction of system conditions and mobility, schedules position updates to both minimize energy consumption and optimize robustness. In this demonstration we would like to show how...

  10. Optical efficiency for fission fragment track counting in Muscovite solid state track recorders

    International Nuclear Information System (INIS)

    Roberts, J.H.; Ruddy, F.H.; Gold, R.

    1984-01-01

    In order to determine absolute fission rates from thin actinide deposits placed in direct contact with Muscovite Solid State Track Recorders, it is necessary to know the efficiency with which fission fragment tracks are recorded. In this paper, a redetermination of the 'optical efficiency', i.e. the fraction of fission events recorded and observed in the Muscovite is reported. The value obtained from a well-calibrated thin deposit of 252 Cf and Muscovite etched about 90 min. in 49% HF at room temperature, is 0.9875 +- 0.0085. Manual counting was used. Preliminary results from a deposit of 242 Pu are also reported, along with preliminary comparisons of track counting with an automated system. Reasons for the discrepancy of the optical efficiency reported here with an earlier measurement are also reported. (author)

  11. Optical efficiency for fission-fragment track counting in Muscovite Solid-State Track Recorders

    International Nuclear Information System (INIS)

    Roberts, J.H.; Ruddy, F.H.; Gold, R.

    1983-07-01

    In order to determine absolute fission rates from thin actinide deposits placed in direct contact with Muscovite Solid-State Track Recorders, it is necessary to know the efficiency with which fission-fragment tracks are recorded. In this paper, a redetermination of the optical efficiency, i.e., the fraction of fission events recorded and observed in the Muscovite, is reported. The value obtained from a well-calibrated thin deposit of 252 Cf and Muscovite etched about 90 min. in 49% HF at room temperature, is 0.9875 +- 0.0085. Manual counting was used. Preliminary results from a deposit of 242 Pu are also reported, along with preliminary comparisons of track counting with an automated system. Reasons for the discrepancy of the optical efficiency reported here with an earlier measurement are also reported. 5 references, 1 figure, 3 tables

  12. Neural network fusion capabilities for efficient implementation of tracking algorithms

    Science.gov (United States)

    Sundareshan, Malur K.; Amoozegar, Farid

    1997-03-01

    The ability to efficiently fuse information of different forms to facilitate intelligent decision making is one of the major capabilities of trained multilayer neural networks that is now being recognized. While development of innovative adaptive control algorithms for nonlinear dynamical plants that attempt to exploit these capabilities seems to be more popular, a corresponding development of nonlinear estimation algorithms using these approaches, particularly for application in target surveillance and guidance operations, has not received similar attention. We describe the capabilities and functionality of neural network algorithms for data fusion and implementation of tracking filters. To discuss details and to serve as a vehicle for quantitative performance evaluations, the illustrative case of estimating the position and velocity of surveillance targets is considered. Efficient target- tracking algorithms that can utilize data from a host of sensing modalities and are capable of reliably tracking even uncooperative targets executing fast and complex maneuvers are of interest in a number of applications. The primary motivation for employing neural networks in these applications comes from the efficiency with which more features extracted from different sensor measurements can be utilized as inputs for estimating target maneuvers. A system architecture that efficiently integrates the fusion capabilities of a trained multilayer neural net with the tracking performance of a Kalman filter is described. The innovation lies in the way the fusion of multisensor data is accomplished to facilitate improved estimation without increasing the computational complexity of the dynamical state estimator itself.

  13. Correlation Filter Learning Toward Peak Strength for Visual Tracking.

    Science.gov (United States)

    Sui, Yao; Wang, Guanghui; Zhang, Li

    2018-04-01

    This paper presents a novel visual tracking approach to correlation filter learning toward peak strength of correlation response. Previous methods leverage all features of the target and the immediate background to learn a correlation filter. Some features, however, may be distractive to tracking, like those from occlusion and local deformation, resulting in unstable tracking performance. This paper aims at solving this issue and proposes a novel algorithm to learn the correlation filter. The proposed approach, by imposing an elastic net constraint on the filter, can adaptively eliminate those distractive features in the correlation filtering. A new peak strength metric is proposed to measure the discriminative capability of the learned correlation filter. It is demonstrated that the proposed approach effectively strengthens the peak of the correlation response, leading to more discriminative performance than previous methods. Extensive experiments on a challenging visual tracking benchmark demonstrate that the proposed tracker outperforms most state-of-the-art methods.

  14. Robust visual tracking via structured multi-task sparse learning

    KAUST Repository

    Zhang, Tianzhu; Ghanem, Bernard; Liu, Si; Ahuja, Narendra

    2012-01-01

    In this paper, we formulate object tracking in a particle filter framework as a structured multi-task sparse learning problem, which we denote as Structured Multi-Task Tracking (S-MTT). Since we model particles as linear combinations of dictionary

  15. Eye-tracking research in computer-mediated language learning

    NARCIS (Netherlands)

    Michel, Marije; Smith, Bryan

    2017-01-01

    Though eye-tracking technology has been used in reading research for over 100 years, researchers have only recently begun to use it in studies of computer-assisted language learning (CALL). This chapter provides an overview of eye-tracking research to date, which is relevant to computer-mediated

  16. Particle filters for object tracking: enhanced algorithm and efficient implementations

    International Nuclear Information System (INIS)

    Abd El-Halym, H.A.

    2010-01-01

    Object tracking and recognition is a hot research topic. In spite of the extensive research efforts expended, the development of a robust and efficient object tracking algorithm remains unsolved due to the inherent difficulty of the tracking problem. Particle filters (PFs) were recently introduced as a powerful, post-Kalman filter, estimation tool that provides a general framework for estimation of nonlinear/ non-Gaussian dynamic systems. Particle filters were advanced for building robust object trackers capable of operation under severe conditions (small image size, noisy background, occlusions, fast object maneuvers ..etc.). The heavy computational load of the particle filter remains a major obstacle towards its wide use.In this thesis, an Excitation Particle Filter (EPF) is introduced for object tracking. A new likelihood model is proposed. It depends on multiple functions: position likelihood; gray level intensity likelihood and similarity likelihood. Also, we modified the PF as a robust estimator to overcome the well-known sample impoverishment problem of the PF. This modification is based on re-exciting the particles if their weights fall below a memorized weight value. The proposed enhanced PF is implemented in software and evaluated. Its results are compared with a single likelihood function PF tracker, Particle Swarm Optimization (PSO) tracker, a correlation tracker, as well as, an edge tracker. The experimental results demonstrated the superior performance of the proposed tracker in terms of accuracy, robustness, and occlusion compared with other methods Efficient novel hardware architectures of the Sample Important Re sample Filter (SIRF) and the EPF are implemented. Three novel hardware architectures of the SIRF for object tracking are introduced. The first architecture is a two-step sequential PF machine, where particle generation, weight calculation and normalization are carried out in parallel during the first step followed by a sequential re

  17. Multiple Learning Tracks: For Training Multinational Managers

    Science.gov (United States)

    Harvey, Michael G.; Kerin, Roger A.

    1977-01-01

    The problem of identifying and training college students to be effective multinational marketing managers is investigated in three parts: (1) Identification of multinational manager attributes, (2) selection of multinational managers, and (3) multiple "track" training programs. (TA)

  18. 5G technologies boosting efficient mobile learning

    Directory of Open Access Journals (Sweden)

    Leligou Helen C.

    2017-01-01

    Full Text Available The needs for education, learning and training proliferate primarily due to the facts that economy becomes more and more knowledge based (mandating continuous lifelong learning and people migrate among countries, which introduces the need for learning other languages, for training on different skills and learning about the new cultural and societal framework. Given that in parallel, time schedules continuously become tighter, learning through mobile devices continuously gains in popularity as it allows for learning anytime, anywhere. To increase the learning efficiency, personalisation (in terms of selecting the learning content, type and presentation and adaptation of the learning experience in real time based on the experienced affect state are key instruments. All these user requirements challenge the current network architectures and technologies. In this paper, we investigate the requirements implied by efficient mobile learning scenarios and we explore how 5G technologies currently under design/testing/validation and standardisation meet these requirements.

  19. Visual object tracking by correlation filters and online learning

    Science.gov (United States)

    Zhang, Xin; Xia, Gui-Song; Lu, Qikai; Shen, Weiming; Zhang, Liangpei

    2018-06-01

    Due to the complexity of background scenarios and the variation of target appearance, it is difficult to achieve high accuracy and fast speed for object tracking. Currently, correlation filters based trackers (CFTs) show promising performance in object tracking. The CFTs estimate the target's position by correlation filters with different kinds of features. However, most of CFTs can hardly re-detect the target in the case of long-term tracking drifts. In this paper, a feature integration object tracker named correlation filters and online learning (CFOL) is proposed. CFOL estimates the target's position and its corresponding correlation score using the same discriminative correlation filter with multi-features. To reduce tracking drifts, a new sampling and updating strategy for online learning is proposed. Experiments conducted on 51 image sequences demonstrate that the proposed algorithm is superior to the state-of-the-art approaches.

  20. Tracking industrial energy efficiency and CO2 emissions

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2007-06-25

    Industry accounts for about one-third of global energy demand. Most of that energy is used to produce raw materials: chemicals, iron and steel, non-metallic minerals, pulp and paper and non-ferrous metals. Just how efficiently is this energy put to work? This question was on the minds of the G8 leaders at their summit in Gleneagles in 2005, when they set a 'Plan of Action for Climate Change, Clean Energy and Sustainable Development'. They called upon the International Energy Agency to provide information and advice in a number of areas including special attention to the industrial sector. Tracking Industrial Energy Efficiency and CO2 Emissions responds to the G8 request. This major new analysis shows how industrial energy efficiency has improved dramatically over the last 25 years. Yet important opportunities for additional gains remain, which is evident when the efficiencies of different countries are compared. This analysis identifies the leaders and the laggards. It explains clearly a complex issue for non-experts. With new statistics, groundbreaking methodologies, thorough analysis and advice, and substantial industry consultation, this publication equips decision makers in the public and private sectors with the essential information that is needed to reshape energy use in manufacturing in a more sustainable manner.

  1. Tracking industrial energy efficiency and CO2 emissions

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2007-06-25

    Industry accounts for about one-third of global energy demand. Most of that energy is used to produce raw materials: chemicals, iron and steel, non-metallic minerals, pulp and paper and non-ferrous metals. Just how efficiently is this energy put to work? This question was on the minds of the G8 leaders at their summit in Gleneagles in 2005, when they set a 'Plan of Action for Climate Change, Clean Energy and Sustainable Development'. They called upon the International Energy Agency to provide information and advice in a number of areas including special attention to the industrial sector. Tracking Industrial Energy Efficiency and CO2 Emissions responds to the G8 request. This major new analysis shows how industrial energy efficiency has improved dramatically over the last 25 years. Yet important opportunities for additional gains remain, which is evident when the efficiencies of different countries are compared. This analysis identifies the leaders and the laggards. It explains clearly a complex issue for non-experts. With new statistics, groundbreaking methodologies, thorough analysis and advice, and substantial industry consultation, this publication equips decision makers in the public and private sectors with the essential information that is needed to reshape energy use in manufacturing in a more sustainable manner.

  2. Learning to Play Efficient Coarse Correlated Equilibria

    KAUST Repository

    Borowski, Holly P.

    2018-03-10

    The majority of the distributed learning literature focuses on convergence to Nash equilibria. Coarse correlated equilibria, on the other hand, can often characterize more efficient collective behavior than even the best Nash equilibrium. However, there are no existing distributed learning algorithms that converge to specific coarse correlated equilibria. In this paper, we provide one such algorithm, which guarantees that the agents’ collective joint strategy will constitute an efficient coarse correlated equilibrium with high probability. The key to attaining efficient correlated behavior through distributed learning involves incorporating a common random signal into the learning environment.

  3. Statistical tracking of tree-like tubular structures with efficient branching detection in 3D medical image data

    DEFF Research Database (Denmark)

    Wang, X.; Heimann, T.; Lo, P.

    2012-01-01

    to their robustness against image noise and pathological changes. However, most tracking methods are limited to a specific application and do not support branching structures efficiently. In this work, we present a novel statistical tracking approach for the extraction of different types of tubular structures...... with ringlike cross-sections. Domain-specific knowledge is learned from training data sets and integrated into the tracking process by simple adaption of parameters. In addition, an efficient branching detection algorithm is presented. This approach was evaluated by extracting coronary arteries from 32 CTA data...... for the tracking of coronary arteries were achieved. For the extraction of airway trees, 51.3% of the total tree length, 53.6% of the total number of branches and a 4.98% false positive rate were attained. In both experiments, our approach is comparable to state-of-the-art methods....

  4. Systematic studies on the reconstruction efficiency and accuracy of a track drift chamber

    International Nuclear Information System (INIS)

    Appelshaeuser, H.

    1993-02-01

    The author has studied the reconstruction efficiency of the NA35-TPC by means of a simulation of the tracks by means of laser radiation. He has obtained results on the diffusion constant and the track resolution. (HSI) [de

  5. Multiple instance learning tracking method with local sparse representation

    KAUST Repository

    Xie, Chengjun

    2013-10-01

    When objects undergo large pose change, illumination variation or partial occlusion, most existed visual tracking algorithms tend to drift away from targets and even fail in tracking them. To address this issue, in this study, the authors propose an online algorithm by combining multiple instance learning (MIL) and local sparse representation for tracking an object in a video system. The key idea in our method is to model the appearance of an object by local sparse codes that can be formed as training data for the MIL framework. First, local image patches of a target object are represented as sparse codes with an overcomplete dictionary, where the adaptive representation can be helpful in overcoming partial occlusion in object tracking. Then MIL learns the sparse codes by a classifier to discriminate the target from the background. Finally, results from the trained classifier are input into a particle filter framework to sequentially estimate the target state over time in visual tracking. In addition, to decrease the visual drift because of the accumulative errors when updating the dictionary and classifier, a two-step object tracking method combining a static MIL classifier with a dynamical MIL classifier is proposed. Experiments on some publicly available benchmarks of video sequences show that our proposed tracker is more robust and effective than others. © The Institution of Engineering and Technology 2013.

  6. Understanding Learning Style by Eye Tracking in Slide Video Learning

    Science.gov (United States)

    Cao, Jianxia; Nishihara, Akinori

    2012-01-01

    More and more videos are now being used in e-learning context. For improving learning effect, to understand how students view the online video is important. In this research, we investigate how students deploy their attention when they learn through interactive slide video in the aim of better understanding observers' learning style. Felder and…

  7. Visual Vehicle Tracking Based on Deep Representation and Semisupervised Learning

    Directory of Open Access Journals (Sweden)

    Yingfeng Cai

    2017-01-01

    Full Text Available Discriminative tracking methods use binary classification to discriminate between the foreground and background and have achieved some useful results. However, the use of labeled training samples is insufficient for them to achieve accurate tracking. Hence, discriminative classifiers must use their own classification results to update themselves, which may lead to feedback-induced tracking drift. To overcome these problems, we propose a semisupervised tracking algorithm that uses deep representation and transfer learning. Firstly, a 2D multilayer deep belief network is trained with a large amount of unlabeled samples. The nonlinear mapping point at the top of this network is subtracted as the feature dictionary. Then, this feature dictionary is utilized to transfer train and update a deep tracker. The positive samples for training are the tracked vehicles, and the negative samples are the background images. Finally, a particle filter is used to estimate vehicle position. We demonstrate experimentally that our proposed vehicle tracking algorithm can effectively restrain drift while also maintaining the adaption of vehicle appearance. Compared with similar algorithms, our method achieves a better tracking success rate and fewer average central-pixel errors.

  8. Tracking orthographic learning in children with different types of dyslexia

    Directory of Open Access Journals (Sweden)

    Hua-Chen eWang

    2014-07-01

    Full Text Available Previous studies have found that children with reading difficulties need more exposures to acquire the representations needed to support fluent reading than typically developing readers (e.g., Ehri & Saltmarsh, 1995. Building on existing orthographic learning paradigms, we report on an investigation of orthographic learning in poor readers using a new learning task tracking both the accuracy (untimed exposure duration and fluency (200ms exposure duration of learning novel words over trials. In study 1, we used the paradigm to examine orthographic learning in children with specific poor reader profiles (9 with a surface profile, 9 a phonological profile and 9 age-matched controls. Both profiles showed improvement over the learning cycles, but the children with surface profile showed impaired orthographic learning in spelling and orthographic choice tasks. Study 2 explored predictors of orthographic learning in a group of 91 poor readers using the same outcome measures as in Study 1. Consistent with earlier findings in typically developing readers, phonological decoding skill predicted orthographic learning. Moreover, orthographic knowledge significantly predicted orthographic learning over and beyond phonological decoding. The two studies provide insights into how poor readers learn novel words, and how their learning process may be compromised by less proficient orthographic and/or phonological skills.

  9. Adaptive learning compressive tracking based on Markov location prediction

    Science.gov (United States)

    Zhou, Xingyu; Fu, Dongmei; Yang, Tao; Shi, Yanan

    2017-03-01

    Object tracking is an interdisciplinary research topic in image processing, pattern recognition, and computer vision which has theoretical and practical application value in video surveillance, virtual reality, and automatic navigation. Compressive tracking (CT) has many advantages, such as efficiency and accuracy. However, when there are object occlusion, abrupt motion and blur, similar objects, and scale changing, the CT has the problem of tracking drift. We propose the Markov object location prediction to get the initial position of the object. Then CT is used to locate the object accurately, and the classifier parameter adaptive updating strategy is given based on the confidence map. At the same time according to the object location, extract the scale features, which is able to deal with object scale variations effectively. Experimental results show that the proposed algorithm has better tracking accuracy and robustness than current advanced algorithms and achieves real-time performance.

  10. Discriminative object tracking via sparse representation and online dictionary learning.

    Science.gov (United States)

    Xie, Yuan; Zhang, Wensheng; Li, Cuihua; Lin, Shuyang; Qu, Yanyun; Zhang, Yinghua

    2014-04-01

    We propose a robust tracking algorithm based on local sparse coding with discriminative dictionary learning and new keypoint matching schema. This algorithm consists of two parts: the local sparse coding with online updated discriminative dictionary for tracking (SOD part), and the keypoint matching refinement for enhancing the tracking performance (KP part). In the SOD part, the local image patches of the target object and background are represented by their sparse codes using an over-complete discriminative dictionary. Such discriminative dictionary, which encodes the information of both the foreground and the background, may provide more discriminative power. Furthermore, in order to adapt the dictionary to the variation of the foreground and background during the tracking, an online learning method is employed to update the dictionary. The KP part utilizes refined keypoint matching schema to improve the performance of the SOD. With the help of sparse representation and online updated discriminative dictionary, the KP part are more robust than the traditional method to reject the incorrect matches and eliminate the outliers. The proposed method is embedded into a Bayesian inference framework for visual tracking. Experimental results on several challenging video sequences demonstrate the effectiveness and robustness of our approach.

  11. Learning to Play Efficient Coarse Correlated Equilibria

    KAUST Repository

    Borowski, Holly P.; Marden, Jason R.; Shamma, Jeff S.

    2018-01-01

    The majority of the distributed learning literature focuses on convergence to Nash equilibria. Coarse correlated equilibria, on the other hand, can often characterize more efficient collective behavior than even the best Nash equilibrium. However

  12. On Improving the Energy Efficiency and Robustness of Position Tracking for Mobile Devices

    DEFF Research Database (Denmark)

    Kjærgaard, Mikkel Baun

    An important feature of a modern mobile device is that it can position itself and support remote position tracking. To be useful, such position tracking has to be energy-efficient to avoid having a major impact on the battery life of the mobile device. Furthermore, tracking has to robustly deliver...... of different mobile devices....

  13. Adaptive Trajectory Tracking Control using Reinforcement Learning for Quadrotor

    Directory of Open Access Journals (Sweden)

    Wenjie Lou

    2016-02-01

    Full Text Available Inaccurate system parameters and unpredicted external disturbances affect the performance of non-linear controllers. In this paper, a new adaptive control algorithm under the reinforcement framework is proposed to stabilize a quadrotor helicopter. Based on a command-filtered non-linear control algorithm, adaptive elements are added and learned by policy-search methods. To predict the inaccurate system parameters, a new kernel-based regression learning method is provided. In addition, Policy learning by Weighting Exploration with the Returns (PoWER and Return Weighted Regression (RWR are utilized to learn the appropriate parameters for adaptive elements in order to cancel the effect of external disturbance. Furthermore, numerical simulations under several conditions are performed, and the ability of adaptive trajectory-tracking control with reinforcement learning are demonstrated.

  14. Tracking truck flows with programmable mobile devices for drayage efficiency analysis: [research brief].

    Science.gov (United States)

    2016-05-01

    The purpose of this project is to design and experiment on a technology to track, organize, : extract and analyze data on port drayage activities from which a clear understanding of drayage : efficiency can be gained. Drayage efficiency may point to ...

  15. Statistical tracking of tree-like tubular structures with efficient branching detection in 3D medical image data

    International Nuclear Information System (INIS)

    Wang, X; Heimann, T; Meinzer, H P; Wegner, I; Lo, P; Sumkauskaite, M; Puderbach, M; De Bruijne, M

    2012-01-01

    The segmentation of tree-like tubular structures such as coronary arteries and airways is an essential step for many 3D medical imaging applications. Statistical tracking techniques for the extraction of elongated structures have received considerable attention in recent years due to their robustness against image noise and pathological changes. However, most tracking methods are limited to a specific application and do not support branching structures efficiently. In this work, we present a novel statistical tracking approach for the extraction of different types of tubular structures with ringlike cross-sections. Domain-specific knowledge is learned from training data sets and integrated into the tracking process by simple adaption of parameters. In addition, an efficient branching detection algorithm is presented. This approach was evaluated by extracting coronary arteries from 32 CTA data sets and distal airways from 20 CT scans. These data sets were provided by the organizers of the workshop ‘3D Segmentation in the Clinic: A Grand Challenge II-Coronary Artery Tracking (CAT08)’ and ‘Extraction of Airways from CT 2009 (EXACT’09)’. On average, 81.5% overlap and 0.51 mm accuracy for the tracking of coronary arteries were achieved. For the extraction of airway trees, 51.3% of the total tree length, 53.6% of the total number of branches and a 4.98% false positive rate were attained. In both experiments, our approach is comparable to state-of-the-art methods. (paper)

  16. Object tracking by occlusion detection via structured sparse learning

    KAUST Repository

    Zhang, Tianzhu

    2013-06-01

    Sparse representation based methods have recently drawn much attention in visual tracking due to good performance against illumination variation and occlusion. They assume the errors caused by image variations can be modeled as pixel-wise sparse. However, in many practical scenarios these errors are not truly pixel-wise sparse but rather sparsely distributed in a structured way. In fact, pixels in error constitute contiguous regions within the object\\'s track. This is the case when significant occlusion occurs. To accommodate for non-sparse occlusion in a given frame, we assume that occlusion detected in previous frames can be propagated to the current one. This propagated information determines which pixels will contribute to the sparse representation of the current track. In other words, pixels that were detected as part of an occlusion in the previous frame will be removed from the target representation process. As such, this paper proposes a novel tracking algorithm that models and detects occlusion through structured sparse learning. We test our tracker on challenging benchmark sequences, such as sports videos, which involve heavy occlusion, drastic illumination changes, and large pose variations. Experimental results show that our tracker consistently outperforms the state-of-the-art. © 2013 IEEE.

  17. Occlusion detection via structured sparse learning for robust object tracking

    KAUST Repository

    Zhang, Tianzhu

    2014-01-01

    Sparse representation based methods have recently drawn much attention in visual tracking due to good performance against illumination variation and occlusion. They assume the errors caused by image variations can be modeled as pixel-wise sparse. However, in many practical scenarios, these errors are not truly pixel-wise sparse but rather sparsely distributed in a structured way. In fact, pixels in error constitute contiguous regions within the object’s track. This is the case when significant occlusion occurs. To accommodate for nonsparse occlusion in a given frame, we assume that occlusion detected in previous frames can be propagated to the current one. This propagated information determines which pixels will contribute to the sparse representation of the current track. In other words, pixels that were detected as part of an occlusion in the previous frame will be removed from the target representation process. As such, this paper proposes a novel tracking algorithm that models and detects occlusion through structured sparse learning. We test our tracker on challenging benchmark sequences, such as sports videos, which involve heavy occlusion, drastic illumination changes, and large pose variations. Extensive experimental results show that our proposed tracker consistently outperforms the state-of-the-art trackers.

  18. EnTracked: Energy-Efficient Robust Position Tracking for Mobile Devices

    DEFF Research Database (Denmark)

    Kjærgaard, Mikkel Baun; Jensen, Jakob Langdal; Godsk, Torben

    2009-01-01

    conditions and mobility, schedules position updates to both minimize energy consumption and optimize robustness. The realized system tracks pedestrian targets equipped with GPS-enabled devices. The system is configurable to realize different trade-offs between energy consumption and robustness. We provide...... of the mobile device. Furthermore, tracking has to robustly deliver position updates when faced with changing conditions such as delays due to positioning and communication, and changing positioning accuracy. This work proposes EnTracked --- a system that, based on the estimation and prediction of system...... extensive experimental results by profiling how devices consume power, by emulation on collected data and by validation in several real-world deployments. Results from this profiling show how a device consumes power while tracking its position. Results from the emulation indicate that the system can...

  19. Efficient Sample Tracking With OpenLabFramework

    DEFF Research Database (Denmark)

    List, Markus; Schmidt, Steffen; Trojnar, Jakub

    2014-01-01

    of samples created and need to be replaced with state-of-the-art laboratory information management systems. Such systems have been developed in large numbers, but they are often limited to specific research domains and types of data. One domain so far neglected is the management of libraries of vector clones...... and genetically engineered cell lines. OpenLabFramework is a newly developed web-application for sample tracking, particularly laid out to fill this gap, but with an open architecture allowing it to be extended for other biological materials and functional data. Its sample tracking mechanism is fully customizable...

  20. Decrease in gamma-band activity tracks sequence learning

    Science.gov (United States)

    Madhavan, Radhika; Millman, Daniel; Tang, Hanlin; Crone, Nathan E.; Lenz, Fredrick A.; Tierney, Travis S.; Madsen, Joseph R.; Kreiman, Gabriel; Anderson, William S.

    2015-01-01

    Learning novel sequences constitutes an example of declarative memory formation, involving conscious recall of temporal events. Performance in sequence learning tasks improves with repetition and involves forming temporal associations over scales of seconds to minutes. To further understand the neural circuits underlying declarative sequence learning over trials, we tracked changes in intracranial field potentials (IFPs) recorded from 1142 electrodes implanted throughout temporal and frontal cortical areas in 14 human subjects, while they learned the temporal-order of multiple sequences of images over trials through repeated recall. We observed an increase in power in the gamma frequency band (30–100 Hz) in the recall phase, particularly in areas within the temporal lobe including the parahippocampal gyrus. The degree of this gamma power enhancement decreased over trials with improved sequence recall. Modulation of gamma power was directly correlated with the improvement in recall performance. When presenting new sequences, gamma power was reset to high values and decreased again after learning. These observations suggest that signals in the gamma frequency band may play a more prominent role during the early steps of the learning process rather than during the maintenance of memory traces. PMID:25653598

  1. Efficient Ways to Learn Weather Radar Polarimetry

    Science.gov (United States)

    Cao, Qing; Yeary, M. B.; Zhang, Guifu

    2012-01-01

    The U.S. weather radar network is currently being upgraded with dual-polarization capability. Weather radar polarimetry is an interdisciplinary area of engineering and meteorology. This paper presents efficient ways to learn weather radar polarimetry through several basic and practical topics. These topics include: 1) hydrometeor scattering model…

  2. The impact of a sustainability constraint on the mean-tracking error efficient frontier

    NARCIS (Netherlands)

    Boudt, K.M.R.; Cornelissen, J.; Croux, C.

    2013-01-01

    Most socially responsible investment funds combine a sustainability objective with a tracking error constraint. We characterize the impact of a sustainability constraint on the mean-tracking error efficient frontier and illustrate this on a universe of US stocks for the period 2003-2010. © 2013

  3. Possibilities of increasing the efficiency of track-bound haulage in roadways, particularly diesel locomotive haulage

    Energy Technology Data Exchange (ETDEWEB)

    Arnold, H; Zubiller, H

    1977-12-15

    This paper discusses ways of increasing the efficiency of monorail transport systems by changes in the design of trains and tracks. It is important that the slope of roadways should be as even as possible, thus avoiding steep sections. The tracks should be kept clean, and critical parts should be covered to protect them from dirt. (In German)

  4. Efficient Tracking of Moving Objects with Precision Guarantees

    DEFF Research Database (Denmark)

    Civilis, Alminas; Jensen, Christian Søndergaard; Nenortaite, Jovita

    2004-01-01

    Sustained advances in wireless communications, geo-positioning, and consumer electronics pave the way to a kind of location-based service that relies on the tracking of the continuously changing positions of an entire population of service users. This type of service is characterized by large...... an object is moving. Empirical performance studies based on a real road network and GPS logs from cars are reported....

  5. An Efficient Approach for Node Localisation and Tracking in Wireless Sensor Networks

    CSIR Research Space (South Africa)

    Mwila, Martin

    2014-08-01

    Full Text Available -1 An Efficient Approach for Node Localisation and Tracking in Wireless Sensor Networks Martin K. Mwila Submitted in partial fulfilment of the requirements for the degree Magister Technologiae: Electrical Engineering in the Department of Electrical Engineering...

  6. Note: Fast neutron efficiency in CR-39 nuclear track detectors

    Energy Technology Data Exchange (ETDEWEB)

    Cavallaro, S. [Dipartimento di Fisica ed Astronomia,Università di Catania, Via S. Sofia 44, 95123 Catania, Italy and INFN-LNS, Via S. Sofia 42, 95123 Catania (Italy)

    2015-03-15

    CR-39 samples are commonly employed for fast neutron detection in fusion reactors and in inertial confinement fusion experiments. The literature reported efficiencies are strongly depending on experimental conditions and, in some cases, highly dispersed. The present note analyses the dependence of efficiency as a function of various parameters and experimental conditions in both the radiator-assisted and the stand-alone CR-39 configurations. Comparisons of literature experimental data with Monte Carlo calculations and optimized efficiency values are shown and discussed.

  7. A Mobile Service Oriented Multiple Object Tracking Augmented Reality Architecture for Education and Learning Experiences

    Science.gov (United States)

    Rattanarungrot, Sasithorn; White, Martin; Newbury, Paul

    2014-01-01

    This paper describes the design of our service-oriented architecture to support mobile multiple object tracking augmented reality applications applied to education and learning scenarios. The architecture is composed of a mobile multiple object tracking augmented reality client, a web service framework, and dynamic content providers. Tracking of…

  8. Energy Efficiency Tracking in Thai Manufacturing Sector by Decomposition Technique

    Directory of Open Access Journals (Sweden)

    Wongsapai Wongkot

    2016-01-01

    Full Text Available This paper presents an analysis of energy saving and changes in energy intensities in Thai manufacturing sector by Logarithmic Mean Divisia Index decomposition technique. This method includes three effects consists of the energy intensity effect, the structural effect and the effect of the economic growth on the energy consumption in Thailand by using the 25-year annual data from 1990 to 2014, carried out in four phases; (i before National Energy Conservation law, (ii during the effect of the law, (iii Transition period of the law from first to second version, and (iv during the effect of the law (No.2. We found that the most effective intensity effect is in the third phase due to the effect of the implementation of new energy efficient equipment from the second phase by enforcement of the law, especially in non-metallic sector, while the first phase illustrates the lowest intensity effect due to the energy conservation law had not been occurred. However, due to the highest economic growth of the country and change from agricultural to industrial development direction, the first phase presents the most effective structural effect, then this effect continuously decreased by time. We also conclude that the energy conservation law have direct effect to energy efficiency of the country however, strictly individual regulation which have target to enforce to energy intensive industries is still required for sustainable energy efficiency improvement.

  9. Labour-efficient in vitro lymphocyte population tracking and fate prediction using automation and manual review.

    Directory of Open Access Journals (Sweden)

    Rajib Chakravorty

    Full Text Available Interest in cell heterogeneity and differentiation has recently led to increased use of time-lapse microscopy. Previous studies have shown that cell fate may be determined well in advance of the event. We used a mixture of automation and manual review of time-lapse live cell imaging to track the positions, contours, divisions, deaths and lineage of 44 B-lymphocyte founders and their 631 progeny in vitro over a period of 108 hours. Using this data to train a Support Vector Machine classifier, we were retrospectively able to predict the fates of individual lymphocytes with more than 90% accuracy, using only time-lapse imaging captured prior to mitosis or death of 90% of all cells. The motivation for this paper is to explore the impact of labour-efficient assistive software tools that allow larger and more ambitious live-cell time-lapse microscopy studies. After training on this data, we show that machine learning methods can be used for realtime prediction of individual cell fates. These techniques could lead to realtime cell culture segregation for purposes such as phenotype screening. We were able to produce a large volume of data with less effort than previously reported, due to the image processing, computer vision, tracking and human-computer interaction tools used. We describe the workflow of the software-assisted experiments and the graphical interfaces that were needed. To validate our results we used our methods to reproduce a variety of published data about lymphocyte populations and behaviour. We also make all our data publicly available, including a large quantity of lymphocyte spatio-temporal dynamics and related lineage information.

  10. Labour-efficient in vitro lymphocyte population tracking and fate prediction using automation and manual review.

    Science.gov (United States)

    Chakravorty, Rajib; Rawlinson, David; Zhang, Alan; Markham, John; Dowling, Mark R; Wellard, Cameron; Zhou, Jie H S; Hodgkin, Philip D

    2014-01-01

    Interest in cell heterogeneity and differentiation has recently led to increased use of time-lapse microscopy. Previous studies have shown that cell fate may be determined well in advance of the event. We used a mixture of automation and manual review of time-lapse live cell imaging to track the positions, contours, divisions, deaths and lineage of 44 B-lymphocyte founders and their 631 progeny in vitro over a period of 108 hours. Using this data to train a Support Vector Machine classifier, we were retrospectively able to predict the fates of individual lymphocytes with more than 90% accuracy, using only time-lapse imaging captured prior to mitosis or death of 90% of all cells. The motivation for this paper is to explore the impact of labour-efficient assistive software tools that allow larger and more ambitious live-cell time-lapse microscopy studies. After training on this data, we show that machine learning methods can be used for realtime prediction of individual cell fates. These techniques could lead to realtime cell culture segregation for purposes such as phenotype screening. We were able to produce a large volume of data with less effort than previously reported, due to the image processing, computer vision, tracking and human-computer interaction tools used. We describe the workflow of the software-assisted experiments and the graphical interfaces that were needed. To validate our results we used our methods to reproduce a variety of published data about lymphocyte populations and behaviour. We also make all our data publicly available, including a large quantity of lymphocyte spatio-temporal dynamics and related lineage information.

  11. A parallel spatiotemporal saliency and discriminative online learning method for visual target tracking in aerial videos.

    Science.gov (United States)

    Aghamohammadi, Amirhossein; Ang, Mei Choo; A Sundararajan, Elankovan; Weng, Ng Kok; Mogharrebi, Marzieh; Banihashem, Seyed Yashar

    2018-01-01

    Visual tracking in aerial videos is a challenging task in computer vision and remote sensing technologies due to appearance variation difficulties. Appearance variations are caused by camera and target motion, low resolution noisy images, scale changes, and pose variations. Various approaches have been proposed to deal with appearance variation difficulties in aerial videos, and amongst these methods, the spatiotemporal saliency detection approach reported promising results in the context of moving target detection. However, it is not accurate for moving target detection when visual tracking is performed under appearance variations. In this study, a visual tracking method is proposed based on spatiotemporal saliency and discriminative online learning methods to deal with appearance variations difficulties. Temporal saliency is used to represent moving target regions, and it was extracted based on the frame difference with Sauvola local adaptive thresholding algorithms. The spatial saliency is used to represent the target appearance details in candidate moving regions. SLIC superpixel segmentation, color, and moment features can be used to compute feature uniqueness and spatial compactness of saliency measurements to detect spatial saliency. It is a time consuming process, which prompted the development of a parallel algorithm to optimize and distribute the saliency detection processes that are loaded into the multi-processors. Spatiotemporal saliency is then obtained by combining the temporal and spatial saliencies to represent moving targets. Finally, a discriminative online learning algorithm was applied to generate a sample model based on spatiotemporal saliency. This sample model is then incrementally updated to detect the target in appearance variation conditions. Experiments conducted on the VIVID dataset demonstrated that the proposed visual tracking method is effective and is computationally efficient compared to state-of-the-art methods.

  12. A parallel spatiotemporal saliency and discriminative online learning method for visual target tracking in aerial videos

    Science.gov (United States)

    2018-01-01

    Visual tracking in aerial videos is a challenging task in computer vision and remote sensing technologies due to appearance variation difficulties. Appearance variations are caused by camera and target motion, low resolution noisy images, scale changes, and pose variations. Various approaches have been proposed to deal with appearance variation difficulties in aerial videos, and amongst these methods, the spatiotemporal saliency detection approach reported promising results in the context of moving target detection. However, it is not accurate for moving target detection when visual tracking is performed under appearance variations. In this study, a visual tracking method is proposed based on spatiotemporal saliency and discriminative online learning methods to deal with appearance variations difficulties. Temporal saliency is used to represent moving target regions, and it was extracted based on the frame difference with Sauvola local adaptive thresholding algorithms. The spatial saliency is used to represent the target appearance details in candidate moving regions. SLIC superpixel segmentation, color, and moment features can be used to compute feature uniqueness and spatial compactness of saliency measurements to detect spatial saliency. It is a time consuming process, which prompted the development of a parallel algorithm to optimize and distribute the saliency detection processes that are loaded into the multi-processors. Spatiotemporal saliency is then obtained by combining the temporal and spatial saliencies to represent moving targets. Finally, a discriminative online learning algorithm was applied to generate a sample model based on spatiotemporal saliency. This sample model is then incrementally updated to detect the target in appearance variation conditions. Experiments conducted on the VIVID dataset demonstrated that the proposed visual tracking method is effective and is computationally efficient compared to state-of-the-art methods. PMID:29438421

  13. Combination of various data analysis techniques for efficient track reconstruction in very high multiplicity events

    Science.gov (United States)

    Siklér, Ferenc

    2017-08-01

    A novel combination of established data analysis techniques for reconstructing charged-particles in high energy collisions is proposed. It uses all information available in a collision event while keeping competing choices open as long as possible. Suitable track candidates are selected by transforming measured hits to a binned, three- or four-dimensional, track parameter space. It is accomplished by the use of templates taking advantage of the translational and rotational symmetries of the detectors. Track candidates and their corresponding hits, the nodes, form a usually highly connected network, a bipartite graph, where we allow for multiple hit to track assignments, edges. In order to get a manageable problem, the graph is cut into very many minigraphs by removing a few of its vulnerable components, edges and nodes. Finally the hits are distributed among the track candidates by exploring a deterministic decision tree. A depth-limited search is performed maximizing the number of hits on tracks, and minimizing the sum of track-fit χ2. Simplified but realistic models of LHC silicon trackers including the relevant physics processes are used to test and study the performance (efficiency, purity, timing) of the proposed method in the case of single or many simultaneous proton-proton collisions (high pileup), and for single heavy-ion collisions at the highest available energies.

  14. Exploiting Best-Match Equations for Efficient Reinforcement Learning

    NARCIS (Netherlands)

    van Seijen, Harm; Whiteson, Shimon; van Hasselt, Hado; Wiering, Marco

    This article presents and evaluates best-match learning, a new approach to reinforcement learning that trades off the sample efficiency of model-based methods with the space efficiency of model-free methods. Best-match learning works by approximating the solution to a set of best-match equations,

  15. Tracking Porters: Learning the Craft of Techno-Anthropology.

    Science.gov (United States)

    Bruun, Maja Hojer; Krause-Jensen, Jakob; Saltofte, Margit

    2015-01-01

    Anthropology attempts to gain insight into people's experiential life-worlds through long-term fieldwork. The quality of anthropological knowledge production, however, does not depend solely on the duration of the stay in the field, but also on a particular way of seeing social situations. The anthropological perspective is grounded in socio-cultural theory and forged by a distinct relativist or contextualist epistemological stance. The point is to understand events, concepts and phenomena from the insiders' point of view and to show how this view relates to the particular social and cultural context. In this chapter, we argue that although anthropology has its specific methodology - including a myriad of ethnographic data-gathering tools, techniques, analytical approaches and theories - it must first and foremost be understood as a craft. Anthropology as craft requires a specific 'anthropological sensibility' that differs from the standardized procedures of normal science. To establish our points we use an example of problem-based project work conducted by a group of Techno-Anthropology students at Aalborg University, we focus on key aspects of this craft and how the students began to learn it: For two weeks the students followed the work of a group of porters. Drawing on anthropological concepts and research strategies the students gained crucial insights about the potential effects of using tracking technologies in the hospital.

  16. Real time eye tracking using Kalman extended spatio-temporal context learning

    Science.gov (United States)

    Munir, Farzeen; Minhas, Fayyaz ul Amir Asfar; Jalil, Abdul; Jeon, Moongu

    2017-06-01

    Real time eye tracking has numerous applications in human computer interaction such as a mouse cursor control in a computer system. It is useful for persons with muscular or motion impairments. However, tracking the movement of the eye is complicated by occlusion due to blinking, head movement, screen glare, rapid eye movements, etc. In this work, we present the algorithmic and construction details of a real time eye tracking system. Our proposed system is an extension of Spatio-Temporal context learning through Kalman Filtering. Spatio-Temporal Context Learning offers state of the art accuracy in general object tracking but its performance suffers due to object occlusion. Addition of the Kalman filter allows the proposed method to model the dynamics of the motion of the eye and provide robust eye tracking in cases of occlusion. We demonstrate the effectiveness of this tracking technique by controlling the computer cursor in real time by eye movements.

  17. Improving STEM Undergraduate Education with Efficient Learning Design

    DEFF Research Database (Denmark)

    Godsk, Mikkel

    2018-01-01

    The project investigates the potential of Learning Design for efficiently improving STEM undergraduate education with technology. In order to investigate this potential, the project consists of two main studies at Aarhus University: a study of the perspectives of the main stakeholders on Learning...... Design uptake. The project concludes that it is possible to improve STEM undergraduate education with Learning Design for technology-enhanced learning efficiently and that Efficient Learning Design provides a useful concept for qualifying educational decisions....... provided by technology-enhanced learning based on Learning Design, and in particular students’ learning was of a high common interest. However, only the educators were directly interested in Learning Design and its support for design, reuse in their practice and to inform pedagogy. A holistic concept...

  18. Ego-Motion and Tracking for Continuous Object Learning: A Brief Survey

    Science.gov (United States)

    2017-09-01

    past research related to the tasks of ego-motion estimation and object tracking from the viewpoint of their role in continuous object learning...in visual object tracking, competitions are held each year to identify the most accurate and robust tracking implementations. Over recent competitions...information should they share) or vice versa? These are just some of the questions that must be addressed in future research toward continuous object

  19. Efficient Photovoltaic System Maximum Power Point Tracking Using a New Technique

    Directory of Open Access Journals (Sweden)

    Mehdi Seyedmahmoudian

    2016-03-01

    Full Text Available Partial shading is an unavoidable condition which significantly reduces the efficiency and stability of a photovoltaic (PV system. When partial shading occurs the system has multiple-peak output power characteristics. In order to track the global maximum power point (GMPP within an appropriate period a reliable technique is required. Conventional techniques such as hill climbing and perturbation and observation (P&O are inadequate in tracking the GMPP subject to this condition resulting in a dramatic reduction in the efficiency of the PV system. Recent artificial intelligence methods have been proposed, however they have a higher computational cost, slower processing time and increased oscillations which results in further instability at the output of the PV system. This paper proposes a fast and efficient technique based on Radial Movement Optimization (RMO for detecting the GMPP under partial shading conditions. The paper begins with a brief description of the behavior of PV systems under partial shading conditions followed by the introduction of the new RMO-based technique for GMPP tracking. Finally, results are presented to demonstration the performance of the proposed technique under different partial shading conditions. The results are compared with those of the PSO method, one of the most widely used methods in the literature. Four factors, namely convergence speed, efficiency (power loss reduction, stability (oscillation reduction and computational cost, are considered in the comparison with the PSO technique.

  20. Efficient and Adaptive Node Selection for Target Tracking in Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    Juan Feng

    2016-01-01

    Full Text Available In target tracking wireless sensor network, choosing the proper working nodes can not only minimize the number of active nodes, but also satisfy the tracking reliability requirement. However, most existing works focus on selecting sensor nodes which are the nearest to the target for tracking missions and they did not consider the correlation of the location of the sensor nodes so that these approaches can not meet all the goals of the network. This work proposes an efficient and adaptive node selection approach for tracking a target in a distributed wireless sensor network. The proposed approach combines the distance-based node selection strategy and particle filter prediction considering the spatial correlation of the different sensing nodes. Moreover, a joint distance weighted measurement is proposed to estimate the information utility of sensing nodes. Experimental results show that EANS outperformed the state-of-the-art approaches by reducing the energy cost and computational complexity as well as guaranteeing the tracking accuracy.

  1. Efficiency and Privacy Enhancement for a Track and Trace System of RFID-Based Supply Chains

    Directory of Open Access Journals (Sweden)

    Xunjun Chen

    2015-06-01

    Full Text Available One of the major applications of Radio Frequency Identification (RFID technology is in supply chain management as it promises to provide real-time visibility based on the function of track and trace. However, such an RFID-based track and trace system raises new security and privacy challenges due to the restricted resource of tags. In this paper, we refine three privacy related models (i.e., the privacy, path unlinkability, and tag unlinkability of RFID-based track and trace systems, and clarify the relations among these privacy models. Specifically, we have proven that privacy is equivalent to path unlinkability and tag unlinkability implies privacy. Our results simplify the privacy concept and protocol design for RFID-based track and trace systems. Furthermore, we propose an efficient track and trace scheme, Tracker+, which allows for authentic and private identification of RFID-tagged objects in supply chains. In the Tracker+, no computational ability is required for tags, but only a few bytes of storage (such as EPC Class 1 Gen 2 tags are needed to store the tag state. Indeed, Tracker+ reduces the memory requirements for each tag by one group element compared to the Tracker presented in other literature. Moreover, Tracker+ provides privacy against supply chain inside attacks.

  2. Using location tracking data to assess efficiency in established clinical workflows.

    Science.gov (United States)

    Meyer, Mark; Fairbrother, Pamela; Egan, Marie; Chueh, Henry; Sandberg, Warren S

    2006-01-01

    Location tracking systems are becoming more prevalent in clinical settings yet applications still are not common. We have designed a system to aid in the assessment of clinical workflow efficiency. Location data is captured from active RFID tags and processed into usable data. These data are stored and presented visually with trending capability over time. The system allows quick assessments of the impact of process changes on workflow, and isolates areas for improvement.

  3. Geometric efficiency calculations for solid state track detectors (SSTD) in radon measurements

    International Nuclear Information System (INIS)

    Gil, L.R.; Marques, A.; Rivera, A.

    1992-01-01

    Geometric efficiencies for SSTD cut into rectangular pieces are calculated by simulation technique. The procedure involves introducing a sampling volume that depends on α-ray ranges in air which has to be used in converting observed number of tracks into activity concentrations. A quick procedure for computing ranges in air at different meteorological conditions is also included. (author). 6 refs, 5 figs, 2 tabs

  4. Off-policy integral reinforcement learning optimal tracking control for continuous-time chaotic systems

    International Nuclear Information System (INIS)

    Wei Qing-Lai; Song Rui-Zhuo; Xiao Wen-Dong; Sun Qiu-Ye

    2015-01-01

    This paper estimates an off-policy integral reinforcement learning (IRL) algorithm to obtain the optimal tracking control of unknown chaotic systems. Off-policy IRL can learn the solution of the HJB equation from the system data generated by an arbitrary control. Moreover, off-policy IRL can be regarded as a direct learning method, which avoids the identification of system dynamics. In this paper, the performance index function is first given based on the system tracking error and control error. For solving the Hamilton–Jacobi–Bellman (HJB) equation, an off-policy IRL algorithm is proposed. It is proven that the iterative control makes the tracking error system asymptotically stable, and the iterative performance index function is convergent. Simulation study demonstrates the effectiveness of the developed tracking control method. (paper)

  5. Multiple instance learning tracking method with local sparse representation

    KAUST Repository

    Xie, Chengjun; Tan, Jieqing; Chen, Peng; Zhang, Jie; Helg, Lei

    2013-01-01

    as training data for the MIL framework. First, local image patches of a target object are represented as sparse codes with an overcomplete dictionary, where the adaptive representation can be helpful in overcoming partial occlusion in object tracking. Then MIL

  6. Low-rank sparse learning for robust visual tracking

    KAUST Repository

    Zhang, Tianzhu; Ghanem, Bernard; Liu, Si; Ahuja, Narendra

    2012-01-01

    In this paper, we propose a new particle-filter based tracking algorithm that exploits the relationship between particles (candidate targets). By representing particles as sparse linear combinations of dictionary templates, this algorithm

  7. An efficient and accurate 3D displacements tracking strategy for digital volume correlation

    KAUST Repository

    Pan, Bing

    2014-07-01

    Owing to its inherent computational complexity, practical implementation of digital volume correlation (DVC) for internal displacement and strain mapping faces important challenges in improving its computational efficiency. In this work, an efficient and accurate 3D displacement tracking strategy is proposed for fast DVC calculation. The efficiency advantage is achieved by using three improvements. First, to eliminate the need of updating Hessian matrix in each iteration, an efficient 3D inverse compositional Gauss-Newton (3D IC-GN) algorithm is introduced to replace existing forward additive algorithms for accurate sub-voxel displacement registration. Second, to ensure the 3D IC-GN algorithm that converges accurately and rapidly and avoid time-consuming integer-voxel displacement searching, a generalized reliability-guided displacement tracking strategy is designed to transfer accurate and complete initial guess of deformation for each calculation point from its computed neighbors. Third, to avoid the repeated computation of sub-voxel intensity interpolation coefficients, an interpolation coefficient lookup table is established for tricubic interpolation. The computational complexity of the proposed fast DVC and the existing typical DVC algorithms are first analyzed quantitatively according to necessary arithmetic operations. Then, numerical tests are performed to verify the performance of the fast DVC algorithm in terms of measurement accuracy and computational efficiency. The experimental results indicate that, compared with the existing DVC algorithm, the presented fast DVC algorithm produces similar precision and slightly higher accuracy at a substantially reduced computational cost. © 2014 Elsevier Ltd.

  8. An efficient and accurate 3D displacements tracking strategy for digital volume correlation

    Science.gov (United States)

    Pan, Bing; Wang, Bo; Wu, Dafang; Lubineau, Gilles

    2014-07-01

    Owing to its inherent computational complexity, practical implementation of digital volume correlation (DVC) for internal displacement and strain mapping faces important challenges in improving its computational efficiency. In this work, an efficient and accurate 3D displacement tracking strategy is proposed for fast DVC calculation. The efficiency advantage is achieved by using three improvements. First, to eliminate the need of updating Hessian matrix in each iteration, an efficient 3D inverse compositional Gauss-Newton (3D IC-GN) algorithm is introduced to replace existing forward additive algorithms for accurate sub-voxel displacement registration. Second, to ensure the 3D IC-GN algorithm that converges accurately and rapidly and avoid time-consuming integer-voxel displacement searching, a generalized reliability-guided displacement tracking strategy is designed to transfer accurate and complete initial guess of deformation for each calculation point from its computed neighbors. Third, to avoid the repeated computation of sub-voxel intensity interpolation coefficients, an interpolation coefficient lookup table is established for tricubic interpolation. The computational complexity of the proposed fast DVC and the existing typical DVC algorithms are first analyzed quantitatively according to necessary arithmetic operations. Then, numerical tests are performed to verify the performance of the fast DVC algorithm in terms of measurement accuracy and computational efficiency. The experimental results indicate that, compared with the existing DVC algorithm, the presented fast DVC algorithm produces similar precision and slightly higher accuracy at a substantially reduced computational cost.

  9. Theoretical determination of the neutron detection efficiency of plastic track detectors. Pt. 1

    International Nuclear Information System (INIS)

    Pretzsch, G.

    1982-01-01

    A theoretical model to determine the neutron detection efficiency of organic solid state nuclear track detectors without external radiator is described. The model involves the following calculation steps: production of heavy charged particles within the detector volume, characterization of the charged particles by appropriate physical quantities, application of suitable registration criteria, formation of etch pits. The etch pits formed are described by means of a distribution function which is doubly differential in both diameter and depth of the etch pits. The distribution function serves as the input value for the calculation of the detection efficiency. The detection efficiency is defined as the measured effect per neutron fluence. Hence it depends on the evaluation technique considered. The calculation of the distribution function is carried out for cellulose triacetate. The determination of the concrete detection efficiency using the light microscope and light transmission measurements as the evaluation technique will be described in further publications. (orig.)

  10. Workplace Learning - How We Keep Track of Relevant Information

    OpenAIRE

    Bischoff, Kerstin; Herder, Eelco; Nejdl, Wolfgang

    2007-01-01

    At the workplace, learning is often a by-product of working on complex projects, requiring self-steered, need-driven and goal-oriented retrieval of information just in time from documents or peers. The personal desktop provides one rich source for learning material and for adaptation of learning resources. Data within that personal information space enables learning from previous experience, sharing tacit and explicit knowledge, and allows for establishing context and context-aware delivery o...

  11. Workplace Learning - How We Keep Track of Relevant Information

    NARCIS (Netherlands)

    Bischoff, Kerstin; Herder, Eelco; Nejdl, Wolfgang

    2007-01-01

    At the workplace, learning is often a by-product of working on complex projects, requiring self-steered, need-driven and goal-oriented retrieval of information just in time from documents or peers. The personal desktop provides one rich source for learning material and for adaptation of learning

  12. Learning based particle filtering object tracking for visible-light systems.

    Science.gov (United States)

    Sun, Wei

    2015-10-01

    We propose a novel object tracking framework based on online learning scheme that can work robustly in challenging scenarios. Firstly, a learning-based particle filter is proposed with color and edge-based features. We train a. support vector machine (SVM) classifier with object and background information and map the outputs into probabilities, then the weight of particles in a particle filter can be calculated by the probabilistic outputs to estimate the state of the object. Secondly, the tracking loop starts with Lucas-Kanade (LK) affine template matching and follows by learning-based particle filter tracking. Lucas-Kanade method estimates errors and updates object template in the positive samples dataset, and learning-based particle filter tracker will start if the LK tracker loses the object. Finally, SVM classifier evaluates every tracked appearance to update the training set or restart the tracking loop if necessary. Experimental results show that our method is robust to challenging light, scale and pose changing, and test on eButton image sequence also achieves satisfactory tracking performance.

  13. Jointly Feature Learning and Selection for Robust Tracking via a Gating Mechanism.

    Directory of Open Access Journals (Sweden)

    Bineng Zhong

    Full Text Available To achieve effective visual tracking, a robust feature representation composed of two separate components (i.e., feature learning and selection for an object is one of the key issues. Typically, a common assumption used in visual tracking is that the raw video sequences are clear, while real-world data is with significant noise and irrelevant patterns. Consequently, the learned features may be not all relevant and noisy. To address this problem, we propose a novel visual tracking method via a point-wise gated convolutional deep network (CPGDN that jointly performs the feature learning and feature selection in a unified framework. The proposed method performs dynamic feature selection on raw features through a gating mechanism. Therefore, the proposed method can adaptively focus on the task-relevant patterns (i.e., a target object, while ignoring the task-irrelevant patterns (i.e., the surrounding background of a target object. Specifically, inspired by transfer learning, we firstly pre-train an object appearance model offline to learn generic image features and then transfer rich feature hierarchies from an offline pre-trained CPGDN into online tracking. In online tracking, the pre-trained CPGDN model is fine-tuned to adapt to the tracking specific objects. Finally, to alleviate the tracker drifting problem, inspired by an observation that a visual target should be an object rather than not, we combine an edge box-based object proposal method to further improve the tracking accuracy. Extensive evaluation on the widely used CVPR2013 tracking benchmark validates the robustness and effectiveness of the proposed method.

  14. Efficient tuning in supervised machine learning

    NARCIS (Netherlands)

    Koch, Patrick

    2013-01-01

    The tuning of learning algorithm parameters has become more and more important during the last years. With the fast growth of computational power and available memory databases have grown dramatically. This is very challenging for the tuning of parameters arising in machine learning, since the

  15. An Efficient Implementation of Track-Oriented Multiple Hypothesis Tracker Using Graphical Model Approaches

    Directory of Open Access Journals (Sweden)

    Jinping Sun

    2017-01-01

    Full Text Available The multiple hypothesis tracker (MHT is currently the preferred method for addressing data association problem in multitarget tracking (MTT application. MHT seeks the most likely global hypothesis by enumerating all possible associations over time, which is equal to calculating maximum a posteriori (MAP estimate over the report data. Despite being a well-studied method, MHT remains challenging mostly because of the computational complexity of data association. In this paper, we describe an efficient method for solving the data association problem using graphical model approaches. The proposed method uses the graph representation to model the global hypothesis formation and subsequently applies an efficient message passing algorithm to obtain the MAP solution. Specifically, the graph representation of data association problem is formulated as a maximum weight independent set problem (MWISP, which translates the best global hypothesis formation into finding the maximum weight independent set on the graph. Then, a max-product belief propagation (MPBP inference algorithm is applied to seek the most likely global hypotheses with the purpose of avoiding a brute force hypothesis enumeration procedure. The simulation results show that the proposed MPBP-MHT method can achieve better tracking performance than other algorithms in challenging tracking situations.

  16. KDE-Track: An Efficient Dynamic Density Estimator for Data Streams

    KAUST Repository

    Qahtan, Abdulhakim Ali Ali; Wang, Suojin; Zhang, Xiangliang

    2016-01-01

    Recent developments in sensors, global positioning system devices and smart phones have increased the availability of spatiotemporal data streams. Developing models for mining such streams is challenged by the huge amount of data that cannot be stored in the memory, the high arrival speed and the dynamic changes in the data distribution. Density estimation is an important technique in stream mining for a wide variety of applications. The construction of kernel density estimators is well studied and documented. However, existing techniques are either expensive or inaccurate and unable to capture the changes in the data distribution. In this paper, we present a method called KDE-Track to estimate the density of spatiotemporal data streams. KDE-Track can efficiently estimate the density function with linear time complexity using interpolation on a kernel model, which is incrementally updated upon the arrival of new samples from the stream. We also propose an accurate and efficient method for selecting the bandwidth value for the kernel density estimator, which increases its accuracy significantly. Both theoretical analysis and experimental validation show that KDE-Track outperforms a set of baseline methods on the estimation accuracy and computing time of complex density structures in data streams.

  17. KDE-Track: An Efficient Dynamic Density Estimator for Data Streams

    KAUST Repository

    Qahtan, Abdulhakim Ali Ali

    2016-11-08

    Recent developments in sensors, global positioning system devices and smart phones have increased the availability of spatiotemporal data streams. Developing models for mining such streams is challenged by the huge amount of data that cannot be stored in the memory, the high arrival speed and the dynamic changes in the data distribution. Density estimation is an important technique in stream mining for a wide variety of applications. The construction of kernel density estimators is well studied and documented. However, existing techniques are either expensive or inaccurate and unable to capture the changes in the data distribution. In this paper, we present a method called KDE-Track to estimate the density of spatiotemporal data streams. KDE-Track can efficiently estimate the density function with linear time complexity using interpolation on a kernel model, which is incrementally updated upon the arrival of new samples from the stream. We also propose an accurate and efficient method for selecting the bandwidth value for the kernel density estimator, which increases its accuracy significantly. Both theoretical analysis and experimental validation show that KDE-Track outperforms a set of baseline methods on the estimation accuracy and computing time of complex density structures in data streams.

  18. Investigating the Efficiency of Scenario Based Learning and Reflective Learning Approaches in Teacher Education

    Science.gov (United States)

    Hursen, Cigdem; Fasli, Funda Gezer

    2017-01-01

    The main purpose of this research is to investigate the efficiency of scenario based learning and reflective learning approaches in teacher education. The impact of applications of scenario based learning and reflective learning on prospective teachers' academic achievement and views regarding application and professional self-competence…

  19. Robust Visual Tracking via Online Discriminative and Low-Rank Dictionary Learning.

    Science.gov (United States)

    Zhou, Tao; Liu, Fanghui; Bhaskar, Harish; Yang, Jie

    2017-09-12

    In this paper, we propose a novel and robust tracking framework based on online discriminative and low-rank dictionary learning. The primary aim of this paper is to obtain compact and low-rank dictionaries that can provide good discriminative representations of both target and background. We accomplish this by exploiting the recovery ability of low-rank matrices. That is if we assume that the data from the same class are linearly correlated, then the corresponding basis vectors learned from the training set of each class shall render the dictionary to become approximately low-rank. The proposed dictionary learning technique incorporates a reconstruction error that improves the reliability of classification. Also, a multiconstraint objective function is designed to enable active learning of a discriminative and robust dictionary. Further, an optimal solution is obtained by iteratively computing the dictionary, coefficients, and by simultaneously learning the classifier parameters. Finally, a simple yet effective likelihood function is implemented to estimate the optimal state of the target during tracking. Moreover, to make the dictionary adaptive to the variations of the target and background during tracking, an online update criterion is employed while learning the new dictionary. Experimental results on a publicly available benchmark dataset have demonstrated that the proposed tracking algorithm performs better than other state-of-the-art trackers.

  20. Online multi-modal robust non-negative dictionary learning for visual tracking.

    Science.gov (United States)

    Zhang, Xiang; Guan, Naiyang; Tao, Dacheng; Qiu, Xiaogang; Luo, Zhigang

    2015-01-01

    Dictionary learning is a method of acquiring a collection of atoms for subsequent signal representation. Due to its excellent representation ability, dictionary learning has been widely applied in multimedia and computer vision. However, conventional dictionary learning algorithms fail to deal with multi-modal datasets. In this paper, we propose an online multi-modal robust non-negative dictionary learning (OMRNDL) algorithm to overcome this deficiency. Notably, OMRNDL casts visual tracking as a dictionary learning problem under the particle filter framework and captures the intrinsic knowledge about the target from multiple visual modalities, e.g., pixel intensity and texture information. To this end, OMRNDL adaptively learns an individual dictionary, i.e., template, for each modality from available frames, and then represents new particles over all the learned dictionaries by minimizing the fitting loss of data based on M-estimation. The resultant representation coefficient can be viewed as the common semantic representation of particles across multiple modalities, and can be utilized to track the target. OMRNDL incrementally learns the dictionary and the coefficient of each particle by using multiplicative update rules to respectively guarantee their non-negativity constraints. Experimental results on a popular challenging video benchmark validate the effectiveness of OMRNDL for visual tracking in both quantity and quality.

  1. An Energy-Efficient Sleep Strategy for Target Tracking Sensor Networks

    Directory of Open Access Journals (Sweden)

    Juan FENG

    2014-02-01

    Full Text Available Energy efficiency is very important for sensor networks since sensor nodes have limited energy supply from battery. So far, many researches have been focused on this issue, while less emphasis was placed on the optimal sleep time of each node. This paper proposed an adaptive energy conservation strategy for target tracking based on a grid network structure, where each node autonomously determines when and if to sleep. It allows sensor nodes far away from targets to sleep to save energy and guarantee the tracking accuracy. The proposed approach extend network lifetime by adopting an adaptive sleep scheduling scheme that combines the local power management (PM and the adaptive coordinate PM strategies to schedule the activities of sensor nodes. And each node can choose an optimal sleep time so as to make system adaptive and energy-efficient. We show the performance of our approach in terms of energy drop, comparing it to a naive approach, dynamic PM with fixed sleep time and the coordinate PM strategies. From the experimental results, it is readily seen that the efficiency of the proposed approach.

  2. PhD on Track – designing learning for PhD students

    Directory of Open Access Journals (Sweden)

    Gunhild Austrheim

    2013-12-01

    Full Text Available Three years ago we started the project "Information Management for Knowledge Creation". The project was initiated to create online information literacy modules for PhD students. The result of our endeavours, PhD on Track, will be launched in May 2013. The initial stage of the project was mapping out the information behaviour of PhD students, as well as what services they require from the library through a literature review and a focus group study. The findings of these inquiries formed the knowledge base from which we developed our information literacy modules. Our paper will focus on the interaction between content production and user testing when creating PhD on Track. Methods: User testing has been employed throughout the production stage. We have tested navigation and organisation of the web site, content and usability. The project team have conducted expert testing. Analysis: The results from our user testing have played an important part in decisions concerning content production. Our working hypothesis was that the PhD students would want an encyclopaedic website, a place to quickly find answers. However, the user tests revealed that PhD students understood and expected the website to be learning modules. Conclusions: The PhD students in the tests agreed that a site such as this would be useful, especially to new PhD students. They also liked the design, but had some qualms with the level of information. They preferred shorter text, but with more depth. The students would likewise have preferred more practical examples, more illustrations and more discipline specific information. The current content of PhD on Track reflects the feedback from the user testing. We have retained initial ideas such as one section for reviewing and discovering research literature and one section for publishing PhD research work. In addition, we have included more practical examples to indicate efficient workflows or relevant actions in context. Illustrations

  3. Differential theory of learning for efficient neural network pattern recognition

    Science.gov (United States)

    Hampshire, John B., II; Vijaya Kumar, Bhagavatula

    1993-09-01

    We describe a new theory of differential learning by which a broad family of pattern classifiers (including many well-known neural network paradigms) can learn stochastic concepts efficiently. We describe the relationship between a classifier's ability to generate well to unseen test examples and the efficiency of the strategy by which it learns. We list a series of proofs that differential learning is efficient in its information and computational resource requirements, whereas traditional probabilistic learning strategies are not. The proofs are illustrated by a simple example that lends itself to closed-form analysis. We conclude with an optical character recognition task for which three different types of differentially generated classifiers generalize significantly better than their probabilistically generated counterparts.

  4. Efficient learning strategy of Chinese characters based on network approach.

    Directory of Open Access Journals (Sweden)

    Xiaoyong Yan

    Full Text Available We develop an efficient learning strategy of Chinese characters based on the network of the hierarchical structural relations between Chinese characters. A more efficient strategy is that of learning the same number of useful Chinese characters in less effort or time. We construct a node-weighted network of Chinese characters, where character usage frequencies are used as node weights. Using this hierarchical node-weighted network, we propose a new learning method, the distributed node weight (DNW strategy, which is based on a new measure of nodes' importance that considers both the weight of the nodes and its location in the network hierarchical structure. Chinese character learning strategies, particularly their learning order, are analyzed as dynamical processes over the network. We compare the efficiency of three theoretical learning methods and two commonly used methods from mainstream Chinese textbooks, one for Chinese elementary school students and the other for students learning Chinese as a second language. We find that the DNW method significantly outperforms the others, implying that the efficiency of current learning methods of major textbooks can be greatly improved.

  5. Development of Efficient Authoring Software for e-Learning Contents

    Science.gov (United States)

    Kozono, Kazutake; Teramoto, Akemi; Akiyama, Hidenori

    The contents creation in e-Learning system becomes an important problem. The contents of e-Learning should include figure and voice media for a high-level educational effect. However, the use of figure and voice complicates the operation of authoring software considerably. A new authoring software, which can build e-Learning contents efficiently, has been developed to solve this problem. This paper reports development results of the authoring software.

  6. Occlusion detection via structured sparse learning for robust object tracking

    KAUST Repository

    Zhang, Tianzhu; Ghanem, Bernard; Xu, Changsheng; Ahuja, Narendra

    2014-01-01

    occlusion through structured sparse learning. We test our tracker on challenging benchmark sequences, such as sports videos, which involve heavy occlusion, drastic illumination changes, and large pose variations. Extensive experimental results show that our

  7. Object tracking by occlusion detection via structured sparse learning

    KAUST Repository

    Zhang, Tianzhu; Ghanem, Bernard; Xu, Changsheng; Ahuja, Narendra

    2013-01-01

    occlusion through structured sparse learning. We test our tracker on challenging benchmark sequences, such as sports videos, which involve heavy occlusion, drastic illumination changes, and large pose variations. Experimental results show that our tracker

  8. Efficient abstraction selection in reinforcement learning

    NARCIS (Netherlands)

    Seijen, H. van; Whiteson, S.; Kester, L.

    2013-01-01

    This paper introduces a novel approach for abstraction selection in reinforcement learning problems modelled as factored Markov decision processes (MDPs), for which a state is described via a set of state components. In abstraction selection, an agent must choose an abstraction from a set of

  9. Thermodynamic efficiency of learning a rule in neural networks

    Science.gov (United States)

    Goldt, Sebastian; Seifert, Udo

    2017-11-01

    Biological systems have to build models from their sensory input data that allow them to efficiently process previously unseen inputs. Here, we study a neural network learning a binary classification rule for these inputs from examples provided by a teacher. We analyse the ability of the network to apply the rule to new inputs, that is to generalise from past experience. Using stochastic thermodynamics, we show that the thermodynamic costs of the learning process provide an upper bound on the amount of information that the network is able to learn from its teacher for both batch and online learning. This allows us to introduce a thermodynamic efficiency of learning. We analytically compute the dynamics and the efficiency of a noisy neural network performing online learning in the thermodynamic limit. In particular, we analyse three popular learning algorithms, namely Hebbian, Perceptron and AdaTron learning. Our work extends the methods of stochastic thermodynamics to a new type of learning problem and might form a suitable basis for investigating the thermodynamics of decision-making.

  10. Efficiency improvement of the maximum power point tracking for PV systems using support vector machine technique

    International Nuclear Information System (INIS)

    Kareim, Ameer A; Mansor, Muhamad Bin

    2013-01-01

    The aim of this paper is to improve efficiency of maximum power point tracking (MPPT) for PV systems. The Support Vector Machine (SVM) was proposed to achieve the MPPT controller. The theoretical, the perturbation and observation (P and O), and incremental conductance (IC) algorithms were used to compare with proposed SVM algorithm. MATLAB models for PV module, theoretical, SVM, P and O, and IC algorithms are implemented. The improved MPPT uses the SVM method to predict the optimum voltage of the PV system in order to extract the maximum power point (MPP). The SVM technique used two inputs which are solar radiation and ambient temperature of the modeled PV module. The results show that the proposed SVM technique has less Root Mean Square Error (RMSE) and higher efficiency than P and O and IC methods.

  11. Efficiency of Photovoltaic Maximum Power Point Tracking Controller Based on a Fuzzy Logic

    Directory of Open Access Journals (Sweden)

    Ammar Al-Gizi

    2017-07-01

    Full Text Available This paper examines the efficiency of a fuzzy logic control (FLC based maximum power point tracking (MPPT of a photovoltaic (PV system under variable climate conditions and connected load requirements. The PV system including a PV module BP SX150S, buck-boost DC-DC converter, MPPT, and a resistive load is modeled and simulated using Matlab/Simulink package. In order to compare the performance of FLC-based MPPT controller with the conventional perturb and observe (P&O method at different irradiation (G, temperature (T and connected load (RL variations – rising time (tr, recovering time, total average power and MPPT efficiency topics are calculated. The simulation results show that the FLC-based MPPT method can quickly track the maximum power point (MPP of the PV module at the transient state and effectively eliminates the power oscillation around the MPP of the PV module at steady state, hence more average power can be extracted, in comparison with the conventional P&O method.

  12. An Energy-Efficient Target Tracking Framework in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Zhijun Yu

    2009-01-01

    Full Text Available This study devises and evaluates an energy-efficient distributed collaborative signal and information processing framework for acoustic target tracking in wireless sensor networks. The distributed processing algorithm is based on mobile agent computing paradigm and sequential Bayesian estimation. At each time step, the short detection reports of cluster members will be collected by cluster head, and a sensor node with the highest signal-to-noise ratio (SNR is chosen there as reference node for time difference of arrive (TDOA calculation. During the mobile agent migration, the target state belief is transmitted among nodes and updated using the TDOA measurement of these fusion nodes one by one. The computing and processing burden is evenly distributed in the sensor network. To decrease the wireless communications, we propose to represent the belief by parameterized methods such as Gaussian approximation or Gaussian mixture model approximation. Furthermore, we present an attraction force function to handle the mobile agent migration planning problem, which is a combination of the node residual energy, useful information, and communication cost. Simulation examples demonstrate the estimation effectiveness and energy efficiency of the proposed distributed collaborative target tracking framework.

  13. Efficient model learning methods for actor-critic control.

    Science.gov (United States)

    Grondman, Ivo; Vaandrager, Maarten; Buşoniu, Lucian; Babuska, Robert; Schuitema, Erik

    2012-06-01

    We propose two new actor-critic algorithms for reinforcement learning. Both algorithms use local linear regression (LLR) to learn approximations of the functions involved. A crucial feature of the algorithms is that they also learn a process model, and this, in combination with LLR, provides an efficient policy update for faster learning. The first algorithm uses a novel model-based update rule for the actor parameters. The second algorithm does not use an explicit actor but learns a reference model which represents a desired behavior, from which desired control actions can be calculated using the inverse of the learned process model. The two novel methods and a standard actor-critic algorithm are applied to the pendulum swing-up problem, in which the novel methods achieve faster learning than the standard algorithm.

  14. A fast track path improves access to palliative care for people with learning disabilities.

    Science.gov (United States)

    Whitington, Jane; Ma, Peng

    People with learning disabilities often experience inequalities in accessing general health services. This group, their families and carers need access to effective palliative care when facing a life limiting illness. This article describes the development and implementation of a fast track referral pathway for people with learning disabilities at St Francis Hospice in Essex. Our aim is to share this pathway so others can replicate the collaborative working to improve access to palliative care services for this group.

  15. Instructional Suggestions Supporting Science Learning in Digital Environments Based on a Review of Eye-Tracking Studies

    Science.gov (United States)

    Yang, Fang-Ying; Tsai, Meng-Jung; Chiou, Guo-Li; Lee, Silvia Wen-Yu; Chang, Cheng-Chieh; Chen, Li-Ling

    2018-01-01

    The main purpose of this study was to provide instructional suggestions for supporting science learning in digital environments based on a review of eye tracking studies in e-learning related areas. Thirty-three eye-tracking studies from 2005 to 2014 were selected from the Social Science Citation Index (SSCI) database for review. Through a…

  16. Learning or Lurking?: Tracking the "Invisible" Online Student.

    Science.gov (United States)

    Beaudoin, Michael F.

    2002-01-01

    This case study of inactive, or invisible, students enrolled in an online graduate course identifies how much time is spent in course-related activity, what the reasons are for students' invisibility, and if their preferred learning styles influence online behavior. Preliminary analysis of grades indicate that grades are better for high-visibility…

  17. Tracking the Eye Movement of Four Years Old Children Learning Chinese Words

    Science.gov (United States)

    Lin, Dan; Chen, Guangyao; Liu, Yingyi; Liu, Jiaxin; Pan, Jue; Mo, Lei

    2018-01-01

    Storybook reading is the major source of literacy exposure for beginning readers. The present study tracked 4-year-old Chinese children's eye movements while they were reading simulated storybook pages. Their eye-movement patterns were examined in relation to their word learning gains. The same reading list, consisting of 20 two-character Chinese…

  18. An Eye-Tracking Study of Learning from Science Text with Concrete and Abstract Illustrations

    Science.gov (United States)

    Mason, Lucia; Pluchino, Patrik; Tornatora, Maria Caterina; Ariasi, Nicola

    2013-01-01

    This study investigated the online process of reading and the offline learning from an illustrated science text. The authors examined the effects of using a concrete or abstract picture to illustrate a text and adopted eye-tracking methodology to trace text and picture processing. They randomly assigned 59 eleventh-grade students to 3 reading…

  19. Human tracking in thermal images using adaptive particle filters with online random forest learning

    Science.gov (United States)

    Ko, Byoung Chul; Kwak, Joon-Young; Nam, Jae-Yeal

    2013-11-01

    This paper presents a fast and robust human tracking method to use in a moving long-wave infrared thermal camera under poor illumination with the existence of shadows and cluttered backgrounds. To improve the human tracking performance while minimizing the computation time, this study proposes an online learning of classifiers based on particle filters and combination of a local intensity distribution (LID) with oriented center-symmetric local binary patterns (OCS-LBP). Specifically, we design a real-time random forest (RF), which is the ensemble of decision trees for confidence estimation, and confidences of the RF are converted into a likelihood function of the target state. First, the target model is selected by the user and particles are sampled. Then, RFs are generated using the positive and negative examples with LID and OCS-LBP features by online learning. The learned RF classifiers are used to detect the most likely target position in the subsequent frame in the next stage. Then, the RFs are learned again by means of fast retraining with the tracked object and background appearance in the new frame. The proposed algorithm is successfully applied to various thermal videos as tests and its tracking performance is better than those of other methods.

  20. Self-directed learning skills in air-traffic control training; An eye-tracking approach

    NARCIS (Netherlands)

    Van Meeuwen, Ludo; Brand-Gruwel, Saskia; Van Merriënboer, Jeroen; Bock, Jeano; Kirschner, Paul A.

    2011-01-01

    Van Meeuwen, L. W., Brand-Gruwel, S., De Bock, J. J. P. R., Kirschner, P. A., & Van Merriënboer, J. J. G. (2010, September). Self-directed Learning Skills in Air-traffic Control Training; An Eye-tracking Approach. Paper presented at the European Association for Aviation Psychology, Budapest.

  1. Compromised NMDA/Glutamate Receptor Expression in Dopaminergic Neurons Impairs Instrumental Learning, But Not Pavlovian Goal Tracking or Sign Tracking

    Science.gov (United States)

    James, Alex S; Pennington, Zachary T; Tran, Phu; Jentsch, James David

    2015-01-01

    Two theories regarding the role for dopamine neurons in learning include the concepts that their activity serves as a (1) mechanism that confers incentive salience onto rewards and associated cues and/or (2) contingency teaching signal reflecting reward prediction error. While both theories are provocative, the causal role for dopamine cell activity in either mechanism remains controversial. In this study mice that either fully or partially lacked NMDARs in dopamine neurons exclusively, as well as appropriate controls, were evaluated for reward-related learning; this experimental design allowed for a test of the premise that NMDA/glutamate receptor (NMDAR)-mediated mechanisms in dopamine neurons, including NMDA-dependent regulation of phasic discharge activity of these cells, modulate either the instrumental learning processes or the likelihood of pavlovian cues to become highly motivating incentive stimuli that directly attract behavior. Loss of NMDARs in dopamine neurons did not significantly affect baseline dopamine utilization in the striatum, novelty evoked locomotor behavior, or consumption of a freely available, palatable food solution. On the other hand, animals lacking NMDARs in dopamine cells exhibited a selective reduction in reinforced lever responses that emerged over the course of instrumental learning. Loss of receptor expression did not, however, influence the likelihood of an animal acquiring a pavlovian conditional response associated with attribution of incentive salience to reward-paired cues (sign tracking). These data support the view that reductions in NMDAR signaling in dopamine neurons affect instrumental reward-related learning but do not lend support to hypotheses that suggest that the behavioral significance of this signaling includes incentive salience attribution.

  2. Quantized Iterative Learning Consensus Tracking of Digital Networks With Limited Information Communication.

    Science.gov (United States)

    Xiong, Wenjun; Yu, Xinghuo; Chen, Yao; Gao, Jie

    2017-06-01

    This brief investigates the quantized iterative learning problem for digital networks with time-varying topologies. The information is first encoded as symbolic data and then transmitted. After the data are received, a decoder is used by the receiver to get an estimate of the sender's state. Iterative learning quantized communication is considered in the process of encoding and decoding. A sufficient condition is then presented to achieve the consensus tracking problem in a finite interval using the quantized iterative learning controllers. Finally, simulation results are given to illustrate the usefulness of the developed criterion.

  3. Nonmyopic Sensor Scheduling and its Efficient Implementation for Target Tracking Applications

    Directory of Open Access Journals (Sweden)

    Morrell Darryl

    2006-01-01

    Full Text Available We propose two nonmyopic sensor scheduling algorithms for target tracking applications. We consider a scenario where a bearing-only sensor is constrained to move in a finite number of directions to track a target in a two-dimensional plane. Both algorithms provide the best sensor sequence by minimizing a predicted expected scheduler cost over a finite time-horizon. The first algorithm approximately computes the scheduler costs based on the predicted covariance matrix of the tracker error. The second algorithm uses the unscented transform in conjunction with a particle filter to approximate covariance-based costs or information-theoretic costs. We also propose the use of two branch-and-bound-based optimal pruning algorithms for efficient implementation of the scheduling algorithms. We design the first pruning algorithm by combining branch-and-bound with a breadth-first search and a greedy-search; the second pruning algorithm combines branch-and-bound with a uniform-cost search. Simulation results demonstrate the advantage of nonmyopic scheduling over myopic scheduling and the significant savings in computational and memory resources when using the pruning algorithms.

  4. A novel robust and efficient algorithm for charge particle tracking in high background flux

    International Nuclear Information System (INIS)

    Fanelli, C; Cisbani, E; Dotto, A Del

    2015-01-01

    The high luminosity that will be reached in the new generation of High Energy Particle and Nuclear physics experiments implies large high background rate and large tracker occupancy, representing therefore a new challenge for particle tracking algorithms. For instance, at Jefferson Laboratory (JLab) (VA,USA), one of the most demanding experiment in this respect, performed with a 12 GeV electron beam, is characterized by a luminosity up to 10 39 cm -2 s -1 . To this scope, Gaseous Electron Multiplier (GEM) based trackers are under development for a new spectrometer that will operate at these high rates in the Hall A of JLab. Within this context, we developed a new tracking algorithm, based on a multistep approach: (i) all hardware - time and charge - information are exploited to minimize the number of hits to associate; (ii) a dedicated Neural Network (NN) has been designed for a fast and efficient association of the hits measured by the GEM detector; (iii) the measurements of the associated hits are further improved in resolution through the application of Kalman filter and Rauch- Tung-Striebel smoother. The algorithm is shortly presented along with a discussion of the promising first results. (paper)

  5. An Energy-Efficient Target-Tracking Strategy for Mobile Sensor Networks.

    Science.gov (United States)

    Mahboubi, Hamid; Masoudimansour, Walid; Aghdam, Amir G; Sayrafian-Pour, Kamran

    2017-02-01

    In this paper, an energy-efficient strategy is proposed for tracking a moving target in an environment with obstacles, using a network of mobile sensors. Typically, the most dominant sources of energy consumption in a mobile sensor network are sensing, communication, and movement. The proposed algorithm first divides the field into a grid of sufficiently small cells. The grid is then represented by a graph whose edges are properly weighted to reflect the energy consumption of sensors. The proposed technique searches for near-optimal locations for the sensors in different time instants to route information from the target to destination, using a shortest path algorithm. Simulations confirm the efficacy of the proposed algorithm.

  6. Semi-Supervised Tensor-Based Graph Embedding Learning and Its Application to Visual Discriminant Tracking.

    Science.gov (United States)

    Hu, Weiming; Gao, Jin; Xing, Junliang; Zhang, Chao; Maybank, Stephen

    2017-01-01

    An appearance model adaptable to changes in object appearance is critical in visual object tracking. In this paper, we treat an image patch as a two-order tensor which preserves the original image structure. We design two graphs for characterizing the intrinsic local geometrical structure of the tensor samples of the object and the background. Graph embedding is used to reduce the dimensions of the tensors while preserving the structure of the graphs. Then, a discriminant embedding space is constructed. We prove two propositions for finding the transformation matrices which are used to map the original tensor samples to the tensor-based graph embedding space. In order to encode more discriminant information in the embedding space, we propose a transfer-learning- based semi-supervised strategy to iteratively adjust the embedding space into which discriminative information obtained from earlier times is transferred. We apply the proposed semi-supervised tensor-based graph embedding learning algorithm to visual tracking. The new tracking algorithm captures an object's appearance characteristics during tracking and uses a particle filter to estimate the optimal object state. Experimental results on the CVPR 2013 benchmark dataset demonstrate the effectiveness of the proposed tracking algorithm.

  7. Automatic Association of Chats and Video Tracks for Activity Learning and Recognition in Aerial Video Surveillance

    Directory of Open Access Journals (Sweden)

    Riad I. Hammoud

    2014-10-01

    Full Text Available We describe two advanced video analysis techniques, including video-indexed by voice annotations (VIVA and multi-media indexing and explorer (MINER. VIVA utilizes analyst call-outs (ACOs in the form of chat messages (voice-to-text to associate labels with video target tracks, to designate spatial-temporal activity boundaries and to augment video tracking in challenging scenarios. Challenging scenarios include low-resolution sensors, moving targets and target trajectories obscured by natural and man-made clutter. MINER includes: (1 a fusion of graphical track and text data using probabilistic methods; (2 an activity pattern learning framework to support querying an index of activities of interest (AOIs and targets of interest (TOIs by movement type and geolocation; and (3 a user interface to support streaming multi-intelligence data processing. We also present an activity pattern learning framework that uses the multi-source associated data as training to index a large archive of full-motion videos (FMV. VIVA and MINER examples are demonstrated for wide aerial/overhead imagery over common data sets affording an improvement in tracking from video data alone, leading to 84% detection with modest misdetection/false alarm results due to the complexity of the scenario. The novel use of ACOs and chat Sensors 2014, 14 19844 messages in video tracking paves the way for user interaction, correction and preparation of situation awareness reports.

  8. Automatic association of chats and video tracks for activity learning and recognition in aerial video surveillance.

    Science.gov (United States)

    Hammoud, Riad I; Sahin, Cem S; Blasch, Erik P; Rhodes, Bradley J; Wang, Tao

    2014-10-22

    We describe two advanced video analysis techniques, including video-indexed by voice annotations (VIVA) and multi-media indexing and explorer (MINER). VIVA utilizes analyst call-outs (ACOs) in the form of chat messages (voice-to-text) to associate labels with video target tracks, to designate spatial-temporal activity boundaries and to augment video tracking in challenging scenarios. Challenging scenarios include low-resolution sensors, moving targets and target trajectories obscured by natural and man-made clutter. MINER includes: (1) a fusion of graphical track and text data using probabilistic methods; (2) an activity pattern learning framework to support querying an index of activities of interest (AOIs) and targets of interest (TOIs) by movement type and geolocation; and (3) a user interface to support streaming multi-intelligence data processing. We also present an activity pattern learning framework that uses the multi-source associated data as training to index a large archive of full-motion videos (FMV). VIVA and MINER examples are demonstrated for wide aerial/overhead imagery over common data sets affording an improvement in tracking from video data alone, leading to 84% detection with modest misdetection/false alarm results due to the complexity of the scenario. The novel use of ACOs and chat Sensors 2014, 14 19844 messages in video tracking paves the way for user interaction, correction and preparation of situation awareness reports.

  9. Efficient HIK SVM learning for image classification.

    Science.gov (United States)

    Wu, Jianxin

    2012-10-01

    Histograms are used in almost every aspect of image processing and computer vision, from visual descriptors to image representations. Histogram intersection kernel (HIK) and support vector machine (SVM) classifiers are shown to be very effective in dealing with histograms. This paper presents contributions concerning HIK SVM for image classification. First, we propose intersection coordinate descent (ICD), a deterministic and scalable HIK SVM solver. ICD is much faster than, and has similar accuracies to, general purpose SVM solvers and other fast HIK SVM training methods. We also extend ICD to the efficient training of a broader family of kernels. Second, we show an important empirical observation that ICD is not sensitive to the C parameter in SVM, and we provide some theoretical analyses to explain this observation. ICD achieves high accuracies in many problems, using its default parameters. This is an attractive property for practitioners, because many image processing tasks are too large to choose SVM parameters using cross-validation.

  10. Comparing Efficiency of Web Based Learning Contents on Different Media

    Directory of Open Access Journals (Sweden)

    Julija Lapuh Bele

    2009-11-01

    Full Text Available The purpose of the research was to find out what kind of multimedia learning materials gave the most efficient and effective results with regards to learning time and knowledge gained. Different web based learning materials were used as regards presentation mode: static pictures, animations with online text and animations with narrated text. Although the research results showed that learners from WBL contents with static graphics learnt less time than learners from animations, we did not find significant differences in learning time between experimental groups. However, we proved significant differences between three experimental groups in terms of gained knowledge. The learners using learning materials with static graphics performed worse than learners using materials with animations. Furthermore, we did not prove significant differences in gained knowledge between groups that learnt from audio animations and the animations with online text.

  11. Freezing and sleeping: Tracking experts that learn by evolving past posteriors

    NARCIS (Netherlands)

    Koolen, W.M.; van Erven, T.; van Erp, M.; Stehouwer, H.; van Zaanen, M.

    2009-01-01

    A problem posed by Freund is how to efficiently track a small pool of experts out of a much larger set. This problem was solved when Bousquet and Warmuth introduced their mixing past posteriors (MPP) algorithm in 2001. In Freund’s problem the experts would normally be considered black boxes.

  12. On the Design of Energy-Efficient Location Tracking Mechanism in Location-Aware Computing

    Directory of Open Access Journals (Sweden)

    MoonBae Song

    2005-01-01

    Full Text Available The battery, in contrast to other hardware, is not governed by Moore's Law. In location-aware computing, power is a very limited resource. As a consequence, recently, a number of promising techniques in various layers have been proposed to reduce the energy consumption. The paper considers the problem of minimizing the energy used to track the location of mobile user over a wireless link in mobile computing. Energy-efficient location update protocol can be done by reducing the number of location update messages as possible and switching off as long as possible. This can be achieved by the concept of mobility-awareness we propose. For this purpose, this paper proposes a novel mobility model, called state-based mobility model (SMM to provide more generalized framework for both describing the mobility and updating location information of complexly moving objects. We also introduce the state-based location update protocol (SLUP based on this mobility model. An extensive experiment on various synthetic datasets shows that the proposed method improves the energy efficiency by 2 ∼ 3 times with the additional 10% of imprecision cost.

  13. Three-dimensional tracking for efficient fire fighting in complex situations

    Science.gov (United States)

    Akhloufi, Moulay; Rossi, Lucile

    2009-05-01

    Each year, hundred millions hectares of forests burn causing human and economic losses. For efficient fire fighting, the personnel in the ground need tools permitting the prediction of fire front propagation. In this work, we present a new technique for automatically tracking fire spread in three-dimensional space. The proposed approach uses a stereo system to extract a 3D shape from fire images. A new segmentation technique is proposed and permits the extraction of fire regions in complex unstructured scenes. It works in the visible spectrum and combines information extracted from YUV and RGB color spaces. Unlike other techniques, our algorithm does not require previous knowledge about the scene. The resulting fire regions are classified into different homogenous zones using clustering techniques. Contours are then extracted and a feature detection algorithm is used to detect interest points like local maxima and corners. Extracted points from stereo images are then used to compute the 3D shape of the fire front. The resulting data permits to build the fire volume. The final model is used to compute important spatial and temporal fire characteristics like: spread dynamics, local orientation, heading direction, etc. Tests conducted on the ground show the efficiency of the proposed scheme. This scheme is being integrated with a fire spread mathematical model in order to predict and anticipate the fire behaviour during fire fighting. Also of interest to fire-fighters, is the proposed automatic segmentation technique that can be used in early detection of fire in complex scenes.

  14. Computationally Efficient Automatic Coast Mode Target Tracking Based on Occlusion Awareness in Infrared Images.

    Science.gov (United States)

    Kim, Sohyun; Jang, Gwang-Il; Kim, Sungho; Kim, Junmo

    2018-03-27

    This paper proposes the automatic coast mode tracking of centroid trackers for infrared images to overcome the target occlusion status. The centroid tracking method, using only the brightness information of an image, is still widely used in infrared imaging tracking systems because it is difficult to extract meaningful features from infrared images. However, centroid trackers are likely to lose the track because they are highly vulnerable to screened status by the clutter or background. Coast mode, one of the tracking modes, maintains the servo slew rate with the tracking rate right before the loss of track. The proposed automatic coast mode tracking method makes decisions regarding entering coast mode by the prediction of target occlusion and tries to re-lock the target and resume the tracking after blind time. This algorithm comprises three steps. The first step is the prediction process of the occlusion by checking both matters which have target-likelihood brightness and which may screen the target despite different brightness. The second step is the process making inertial tracking commands to the servo. The last step is the process of re-locking a target based on the target modeling of histogram ratio. The effectiveness of the proposed algorithm is addressed by presenting experimental results based on computer simulation with various test imagery sequences compared to published tracking algorithms. The proposed algorithm is tested under a real environment with a naval electro-optical tracking system (EOTS) and airborne EO/IR system.

  15. Computationally Efficient Automatic Coast Mode Target Tracking Based on Occlusion Awareness in Infrared Images

    Directory of Open Access Journals (Sweden)

    Sohyun Kim

    2018-03-01

    Full Text Available This paper proposes the automatic coast mode tracking of centroid trackers for infrared images to overcome the target occlusion status. The centroid tracking method, using only the brightness information of an image, is still widely used in infrared imaging tracking systems because it is difficult to extract meaningful features from infrared images. However, centroid trackers are likely to lose the track because they are highly vulnerable to screened status by the clutter or background. Coast mode, one of the tracking modes, maintains the servo slew rate with the tracking rate right before the loss of track. The proposed automatic coast mode tracking method makes decisions regarding entering coast mode by the prediction of target occlusion and tries to re-lock the target and resume the tracking after blind time. This algorithm comprises three steps. The first step is the prediction process of the occlusion by checking both matters which have target-likelihood brightness and which may screen the target despite different brightness. The second step is the process making inertial tracking commands to the servo. The last step is the process of re-locking a target based on the target modeling of histogram ratio. The effectiveness of the proposed algorithm is addressed by presenting experimental results based on computer simulation with various test imagery sequences compared to published tracking algorithms. The proposed algorithm is tested under a real environment with a naval electro-optical tracking system (EOTS and airborne EO/IR system.

  16. Tracking Active Learning in the Medical School Curriculum: A Learning-Centered Approach

    Science.gov (United States)

    McCoy, Lise; Pettit, Robin K; Kellar, Charlyn; Morgan, Christine

    2018-01-01

    Background: Medical education is moving toward active learning during large group lecture sessions. This study investigated the saturation and breadth of active learning techniques implemented in first year medical school large group sessions. Methods: Data collection involved retrospective curriculum review and semistructured interviews with 20 faculty. The authors piloted a taxonomy of active learning techniques and mapped learning techniques to attributes of learning-centered instruction. Results: Faculty implemented 25 different active learning techniques over the course of 9 first year courses. Of 646 hours of large group instruction, 476 (74%) involved at least 1 active learning component. Conclusions: The frequency and variety of active learning components integrated throughout the year 1 curriculum reflect faculty familiarity with active learning methods and their support of an active learning culture. This project has sparked reflection on teaching practices and facilitated an evolution from teacher-centered to learning-centered instruction. PMID:29707649

  17. Tracking Active Learning in the Medical School Curriculum: A Learning-Centered Approach.

    Science.gov (United States)

    McCoy, Lise; Pettit, Robin K; Kellar, Charlyn; Morgan, Christine

    2018-01-01

    Medical education is moving toward active learning during large group lecture sessions. This study investigated the saturation and breadth of active learning techniques implemented in first year medical school large group sessions. Data collection involved retrospective curriculum review and semistructured interviews with 20 faculty. The authors piloted a taxonomy of active learning techniques and mapped learning techniques to attributes of learning-centered instruction. Faculty implemented 25 different active learning techniques over the course of 9 first year courses. Of 646 hours of large group instruction, 476 (74%) involved at least 1 active learning component. The frequency and variety of active learning components integrated throughout the year 1 curriculum reflect faculty familiarity with active learning methods and their support of an active learning culture. This project has sparked reflection on teaching practices and facilitated an evolution from teacher-centered to learning-centered instruction.

  18. An Eye-tracking Study of Notational, Informational, and Emotional Aspects of Learning Analytics Representations

    DEFF Research Database (Denmark)

    Vatrapu, Ravi; Reimann, Peter; Bull, Susan

    2013-01-01

    This paper presents an eye-tracking study of notational, informational, and emotional aspects of nine different notational systems (Skill Meters, Smilies, Traffic Lights, Topic Boxes, Collective Histograms, Word Clouds, Textual Descriptors, Table, and Matrix) and three different information states...... (Weak, Average, & Strong) used to represent student's learning. Findings from the eye-tracking study show that higher emotional activation was observed for the metaphorical notations of traffic lights and smilies and collective representations. Mean view time was higher for representations...... of the "average" informational learning state. Qualitative data analysis of the think-aloud comments and post-study interview show that student participants reflected on the meaning-making opportunities and action-taking possibilities afforded by the representations. Implications for the design and evaluation...

  19. Incremental Structured Dictionary Learning for Video Sensor-Based Object Tracking

    Science.gov (United States)

    Xue, Ming; Yang, Hua; Zheng, Shibao; Zhou, Yi; Yu, Zhenghua

    2014-01-01

    To tackle robust object tracking for video sensor-based applications, an online discriminative algorithm based on incremental discriminative structured dictionary learning (IDSDL-VT) is presented. In our framework, a discriminative dictionary combining both positive, negative and trivial patches is designed to sparsely represent the overlapped target patches. Then, a local update (LU) strategy is proposed for sparse coefficient learning. To formulate the training and classification process, a multiple linear classifier group based on a K-combined voting (KCV) function is proposed. As the dictionary evolves, the models are also trained to timely adapt the target appearance variation. Qualitative and quantitative evaluations on challenging image sequences compared with state-of-the-art algorithms demonstrate that the proposed tracking algorithm achieves a more favorable performance. We also illustrate its relay application in visual sensor networks. PMID:24549252

  20. Incremental Structured Dictionary Learning for Video Sensor-Based Object Tracking

    Directory of Open Access Journals (Sweden)

    Ming Xue

    2014-02-01

    Full Text Available To tackle robust object tracking for video sensor-based applications, an online discriminative algorithm based on incremental discriminative structured dictionary learning (IDSDL-VT is presented. In our framework, a discriminative dictionary combining both positive, negative and trivial patches is designed to sparsely represent the overlapped target patches. Then, a local update (LU strategy is proposed for sparse coefficient learning. To formulate the training and classification process, a multiple linear classifier group based on a K-combined voting (KCV function is proposed. As the dictionary evolves, the models are also trained to timely adapt the target appearance variation. Qualitative and quantitative evaluations on challenging image sequences compared with state-of-the-art algorithms demonstrate that the proposed tracking algorithm achieves a more favorable performance. We also illustrate its relay application in visual sensor networks.

  1. Effective and efficient optics inspection approach using machine learning algorithms

    International Nuclear Information System (INIS)

    Abdulla, G.; Kegelmeyer, L.; Liao, Z.; Carr, W.

    2010-01-01

    The Final Optics Damage Inspection (FODI) system automatically acquires and utilizes the Optics Inspection (OI) system to analyze images of the final optics at the National Ignition Facility (NIF). During each inspection cycle up to 1000 images acquired by FODI are examined by OI to identify and track damage sites on the optics. The process of tracking growing damage sites on the surface of an optic can be made more effective by identifying and removing signals associated with debris or reflections. The manual process to filter these false sites is daunting and time consuming. In this paper we discuss the use of machine learning tools and data mining techniques to help with this task. We describe the process to prepare a data set that can be used for training and identifying hardware reflections in the image data. In order to collect training data, the images are first automatically acquired and analyzed with existing software and then relevant features such as spatial, physical and luminosity measures are extracted for each site. A subset of these sites is 'truthed' or manually assigned a class to create training data. A supervised classification algorithm is used to test if the features can predict the class membership of new sites. A suite of self-configuring machine learning tools called 'Avatar Tools' is applied to classify all sites. To verify, we used 10-fold cross correlation and found the accuracy was above 99%. This substantially reduces the number of false alarms that would otherwise be sent for more extensive investigation.

  2. Velocity Tracking Control of Wheeled Mobile Robots by Iterative Learning Control

    Directory of Open Access Journals (Sweden)

    Xiaochun Lu

    2016-05-01

    Full Text Available This paper presents an iterative learning control (ILC strategy to resolve the trajectory tracking problem of wheeled mobile robots (WMRs based on dynamic model. In the previous study of WMRs’ trajectory tracking, ILC was usually applied to the kinematical model of WMRs with the assumption that desired velocity can be tracked immediately. However, this assumption cannot be realized in the real world at all. The kinematic and dynamic models of WMRs are deduced in this chapter, and a novel combination of D-type ILC algorithm and dynamic model of WMR with random bounded disturbances are presented. To analyze the convergence of the algorithm, the method of contracting mapping, which shows that the designed controller can make the velocity tracking errors converge to zero completely when the iteration times tend to infinite, is adopted. Simulation results show the effectiveness of D-type ILC in the trajectory tracking problem of WMRs, demonstrating the effectiveness and robustness of the algorithm in the condition of random bounded disturbance. A comparative study conducted between D-type ILC and compound cosine function neural network (NN controller also demonstrates the effectiveness of the ILC strategy.

  3. Approximation methods for efficient learning of Bayesian networks

    CERN Document Server

    Riggelsen, C

    2008-01-01

    This publication offers and investigates efficient Monte Carlo simulation methods in order to realize a Bayesian approach to approximate learning of Bayesian networks from both complete and incomplete data. For large amounts of incomplete data when Monte Carlo methods are inefficient, approximations are implemented, such that learning remains feasible, albeit non-Bayesian. The topics discussed are: basic concepts about probabilities, graph theory and conditional independence; Bayesian network learning from data; Monte Carlo simulation techniques; and, the concept of incomplete data. In order to provide a coherent treatment of matters, thereby helping the reader to gain a thorough understanding of the whole concept of learning Bayesian networks from (in)complete data, this publication combines in a clarifying way all the issues presented in the papers with previously unpublished work.

  4. The "Tracked Roaming Transect" and distance sampling methods increase the efficiency of underwater visual censuses.

    Directory of Open Access Journals (Sweden)

    Alejo J Irigoyen

    Full Text Available Underwater visual census (UVC is the most common approach for estimating diversity, abundance and size of reef fishes in shallow and clear waters. Abundance estimation through UVC is particularly problematic in species occurring at low densities and/or highly aggregated because of their high variability at both spatial and temporal scales. The statistical power of experiments involving UVC techniques may be increased by augmenting the number of replicates or the area surveyed. In this work we present and test the efficiency of an UVC method based on diver towed GPS, the Tracked Roaming Transect (TRT, designed to maximize transect length (and thus the surveyed area with respect to diving time invested in monitoring, as compared to Conventional Strip Transects (CST. Additionally, we analyze the effect of increasing transect width and length on the precision of density estimates by comparing TRT vs. CST methods using different fixed widths of 6 and 20 m (FW3 and FW10, respectively and the Distance Sampling (DS method, in which perpendicular distance of each fish or group of fishes to the transect line is estimated by divers up to 20 m from the transect line. The TRT was 74% more time and cost efficient than the CST (all transect widths considered together and, for a given time, the use of TRT and/or increasing the transect width increased the precision of density estimates. In addition, since with the DS method distances of fishes to the transect line have to be estimated, and not measured directly as in terrestrial environments, errors in estimations of perpendicular distances can seriously affect DS density estimations. To assess the occurrence of distance estimation errors and their dependence on the observer's experience, a field experiment using wooden fish models was performed. We tested the precision and accuracy of density estimators based on fixed widths and the DS method. The accuracy of the estimates was measured comparing the actual

  5. Detection alpha particles and Cf-252 fission fragments with track solid detectors and with surface barrier detectors: efficiency determination

    International Nuclear Information System (INIS)

    Khouri, M.T.F.C.; Koskinas, M.F.; Andrade, C. de; Vilela, E.C.; Hinostroza, H.; Kaschiny, J.R.A.; Costa, M.S. da; Rizzo, P.; Santos, W.M.S.

    1990-01-01

    The technique of particle detection by solid track detectors, types of developing and analysis of results are presented. Efficiency measurements of alpha particle detection with Makrofol e and surface barrier detector are made. Detection of Cf-252 fission fragments is shown. (L.C.)

  6. Detection of alpha particles and Cf-252 fission fragments with solid track detectors and surface barrier detector. Efficiency calculation

    International Nuclear Information System (INIS)

    Khouri, M.T.F.C.; Koskinas, M.F.; Andrade, C. de; Vilela, E.C.; Hinostroza, H.; Kaschiny, J.E.A.; Costa, M.S. da; Rizzo, P.; Santos, W.M.S.

    1990-01-01

    A technique for particle detection by using track solid detector and also types of revealing and result analysis are presented concerned to Cf-252 fission fragments detection. Measurements of alpha particles detection efficiency using Makrofol E and surface barrier detector are performed. (L.C.J.A.)

  7. Efficiency of liquid flat-plate solar energy collector with solar tracking system

    Directory of Open Access Journals (Sweden)

    Chekerovska Marija

    2015-01-01

    Full Text Available An extensive testing programme is performed on a solar collector experimental set-up, installed on a location in Shtip (Republic of Macedonia, latitude 41º 45’ and longitude 22º 12’, in order to investigate the effect of the sun tracking system implementation on the collector efficiency. The set-up consists of two flat plate solar collectors, one with a fixed surface tilted at 30о towards the South, and the other one equipped with dual-axis rotation system. The study includes development of a 3-D mathematical model of the collectors system and a numerical simulation programme, based on the computational fluid dynamics (CFD approach. The main aim of the mathematical modelling is to provide information on conduction, convection and radiation heat transfer, so as to simulate the heat transfer performances and the energy capture capabilities of the fixed and moving collectors in various operating modes. The feasibility of the proposed method was confirmed by experimental verification, showing significant increase of the daily energy capture by the moving collector, compared to the immobile collector unit. The comparative analysis demonstrates a good agreement between the experimental and numerically predicted results at different running conditions, which is a proof that the presented CFD modelling approach can be used for further investigations of different solar collectors configurations and flow schemes.

  8. Stochastic Synapses Enable Efficient Brain-Inspired Learning Machines

    Science.gov (United States)

    Neftci, Emre O.; Pedroni, Bruno U.; Joshi, Siddharth; Al-Shedivat, Maruan; Cauwenberghs, Gert

    2016-01-01

    Recent studies have shown that synaptic unreliability is a robust and sufficient mechanism for inducing the stochasticity observed in cortex. Here, we introduce Synaptic Sampling Machines (S2Ms), a class of neural network models that uses synaptic stochasticity as a means to Monte Carlo sampling and unsupervised learning. Similar to the original formulation of Boltzmann machines, these models can be viewed as a stochastic counterpart of Hopfield networks, but where stochasticity is induced by a random mask over the connections. Synaptic stochasticity plays the dual role of an efficient mechanism for sampling, and a regularizer during learning akin to DropConnect. A local synaptic plasticity rule implementing an event-driven form of contrastive divergence enables the learning of generative models in an on-line fashion. S2Ms perform equally well using discrete-timed artificial units (as in Hopfield networks) or continuous-timed leaky integrate and fire neurons. The learned representations are remarkably sparse and robust to reductions in bit precision and synapse pruning: removal of more than 75% of the weakest connections followed by cursory re-learning causes a negligible performance loss on benchmark classification tasks. The spiking neuron-based S2Ms outperform existing spike-based unsupervised learners, while potentially offering substantial advantages in terms of power and complexity, and are thus promising models for on-line learning in brain-inspired hardware. PMID:27445650

  9. Methods of Efficient Study Habits and Physics Learning

    Science.gov (United States)

    Zettili, Nouredine

    2010-02-01

    We want to discuss the methods of efficient study habits and how they can be used by students to help them improve learning physics. In particular, we deal with the most efficient techniques needed to help students improve their study skills. We focus on topics such as the skills of how to develop long term memory, how to improve concentration power, how to take class notes, how to prepare for and take exams, how to study scientific subjects such as physics. We argue that the students who conscientiously use the methods of efficient study habits achieve higher results than those students who do not; moreover, a student equipped with the proper study skills will spend much less time to learn a subject than a student who has no good study habits. The underlying issue here is not the quantity of time allocated to the study efforts by the students, but the efficiency and quality of actions so that the student can function at peak efficiency. These ideas were developed as part of Project IMPACTSEED (IMproving Physics And Chemistry Teaching in SEcondary Education), an outreach grant funded by the Alabama Commission on Higher Education. This project is motivated by a major pressing local need: A large number of high school physics teachers teach out of field. )

  10. The Design and Analysis of Efficient Learning Algorithms

    Science.gov (United States)

    1991-01-01

    31] describe in detail how this can be done efficiently; see also Duda and Hart [22]. Let a&,..., &d be the resulting solution, and let h0 = Fd=1 af...Measure. Wiley, second edition, 1986. [13] Anselm Blumer, Andrzej Ehrenfeucht, David Haussler, and Manfred K. Warmuth. Occam’s razor. Information...Processing Letters, 24(6):377-380, April 1987. [14] Anselm Blumer, Andrzej Ehrenfeucht, David Haussler, and Manfred K. Warmuth. Learn- ability and the

  11. Computationally Efficient DOA Tracking Algorithm in Monostatic MIMO Radar with Automatic Association

    Directory of Open Access Journals (Sweden)

    Huaxin Yu

    2014-01-01

    Full Text Available We consider the problem of tracking the direction of arrivals (DOA of multiple moving targets in monostatic multiple-input multiple-output (MIMO radar. A low-complexity DOA tracking algorithm in monostatic MIMO radar is proposed. The proposed algorithm obtains DOA estimation via the difference between previous and current covariance matrix of the reduced-dimension transformation signal, and it reduces the computational complexity and realizes automatic association in DOA tracking. Error analysis and Cramér-Rao lower bound (CRLB of DOA tracking are derived in the paper. The proposed algorithm not only can be regarded as an extension of array-signal-processing DOA tracking algorithm in (Zhang et al. (2008, but also is an improved version of the DOA tracking algorithm in (Zhang et al. (2008. Furthermore, the proposed algorithm has better DOA tracking performance than the DOA tracking algorithm in (Zhang et al. (2008. The simulation results demonstrate effectiveness of the proposed algorithm. Our work provides the technical support for the practical application of MIMO radar.

  12. Efficiency Enhancement of an Envelope Tracking Power Amplifier Combining Supply Shaping and Dynamic Biasing

    DEFF Research Database (Denmark)

    Tafuri, Felice Francesco; Sira, Daniel; Jensen, Ole Kiel

    2013-01-01

    This paper presents a new method to improve the performance of envelope tracking (ET) power amplifiers (PAs). The method consists of combining the supply modulation that characterizes the envelope tracking architecture with supply shaping and dynamic biasing. The inclusion of dynamic biasing allo...

  13. Dynamic Learning from Adaptive Neural Control of Uncertain Robots with Guaranteed Full-State Tracking Precision

    Directory of Open Access Journals (Sweden)

    Min Wang

    2017-01-01

    Full Text Available A dynamic learning method is developed for an uncertain n-link robot with unknown system dynamics, achieving predefined performance attributes on the link angular position and velocity tracking errors. For a known nonsingular initial robotic condition, performance functions and unconstrained transformation errors are employed to prevent the violation of the full-state tracking error constraints. By combining two independent Lyapunov functions and radial basis function (RBF neural network (NN approximator, a novel and simple adaptive neural control scheme is proposed for the dynamics of the unconstrained transformation errors, which guarantees uniformly ultimate boundedness of all the signals in the closed-loop system. In the steady-state control process, RBF NNs are verified to satisfy the partial persistent excitation (PE condition. Subsequently, an appropriate state transformation is adopted to achieve the accurate convergence of neural weight estimates. The corresponding experienced knowledge on unknown robotic dynamics is stored in NNs with constant neural weight values. Using the stored knowledge, a static neural learning controller is developed to improve the full-state tracking performance. A comparative simulation study on a 2-link robot illustrates the effectiveness of the proposed scheme.

  14. Selective visual scaling of time-scale processes facilitates broadband learning of isometric force frequency tracking.

    Science.gov (United States)

    King, Adam C; Newell, Karl M

    2015-10-01

    The experiment investigated the effect of selectively augmenting faster time scales of visual feedback information on the learning and transfer of continuous isometric force tracking tasks to test the generality of the self-organization of 1/f properties of force output. Three experimental groups tracked an irregular target pattern either under a standard fixed gain condition or with selectively enhancement in the visual feedback display of intermediate (4-8 Hz) or high (8-12 Hz) frequency components of the force output. All groups reduced tracking error over practice, with the error lowest in the intermediate scaling condition followed by the high scaling and fixed gain conditions, respectively. Selective visual scaling induced persistent changes across the frequency spectrum, with the strongest effect in the intermediate scaling condition and positive transfer to novel feedback displays. The findings reveal an interdependence of the timescales in the learning and transfer of isometric force output frequency structures consistent with 1/f process models of the time scales of motor output variability.

  15. On Improving the Energy Efficiency and Robustness of Position Tracking for Mobile Devices

    DEFF Research Database (Denmark)

    Kjærgaard, Mikkel Baun

    2010-01-01

    position updates when faced with changing conditions such as delays and changing positioning conditions. Previous work has established dynamic tracking systems, such as our EnTracked system, as a solution to address these issues. In this paper we propose a responsibility division for position tracking...... into sensor management strategies and position update protocols and combine the sensor management strategy of EnTracked with position update protocols, which enables the system to further reduce the power consumption with up to 268 mW extending the battery life with up to 36\\%. As our evaluation identify...... that classical position update protocols have robustness weaknesses we propose a method to improve their robustness. Furthermore, we analyze the dependency of tracking systems on the pedestrian movement patterns and positioning environment, and how the power savings depend on the power characteristics...

  16. Does sound structure affect word learning? An eye-tracking study of Danish learning toddlers

    DEFF Research Database (Denmark)

    Trecca, Fabio; Bleses, Dorthe; Madsen, Thomas O.

    2018-01-01

    closely related languages. In support of this hypothesis, recent work has shown that the phonetic properties of Danish negatively affect online language processing in young Danish children. In this study, we used eye-tracking to investigate whether the challenges associated with processing Danish also...

  17. Deformation data modeling through numerical models: an efficient method for tracking magma transport

    Science.gov (United States)

    Charco, M.; Gonzalez, P. J.; Galán del Sastre, P.

    2017-12-01

    Nowadays, multivariate collected data and robust physical models at volcano observatories are becoming crucial for providing effective volcano monitoring. Nevertheless, the forecast of volcanic eruption is notoriously difficult. Wthin this frame one of the most promising methods to evaluate the volcano hazard is the use of surface ground deformation and in the last decades many developments in the field of deformation modeling has been achieved. In particular, numerical modeling allows realistic media features such as topography and crustal heterogeneities to be included, although it is still very time cosuming to solve the inverse problem for near-real time interpretations. Here, we present a method that can be efficiently used to estimate the location and evolution of magmatic sources base on real-time surface deformation data and Finite Element (FE) models. Generally, the search for the best-fitting magmatic (point) source(s) is conducted for an array of 3-D locations extending below a predefined volume region and the Green functions for all the array components have to be precomputed. We propose a FE model for the pre-computation of Green functions in a mechanically heterogeneous domain which eventually will lead to a better description of the status of the volcanic area. The number of Green functions is reduced here to the number of observational points by using their reciprocity relationship. We present and test this methodology with an optimization method base on a Genetic Algorithm. Following synthetic and sensitivity test to estimate the uncertainty of the model parameters, we apply the tool for magma tracking during 2007 Kilauea volcano intrusion and eruption. We show how data inversion with numerical models can speed up the source parameters estimations for a given volcano showing signs of unrest.

  18. Energy Efficient Sensor Scheduling with a Mobile Sink Node for the Target Tracking Application

    Directory of Open Access Journals (Sweden)

    Malin Premaratne

    2009-01-01

    Full Text Available Measurement losses adversely affect the performance of target tracking. The sensor network’s life span depends on how efficiently the sensor nodes consume energy. In this paper, we focus on minimizing the total energy consumed by the sensor nodes whilst avoiding measurement losses. Since transmitting data over a long distance consumes a significant amount of energy, a mobile sink node collects the measurements and transmits them to the base station. We assume that the default transmission range of the activated sensor node is limited and it can be increased to maximum range only if the mobile sink node is out-side the default transmission range. Moreover, the active sensor node can be changed after a certain time period. The problem is to select an optimal sensor sequence which minimizes the total energy consumed by the sensor nodes. In this paper, we consider two different problems depend on the mobile sink node’s path. First, we assume that the mobile sink node’s position is known for the entire time horizon and use the dynamic programming technique to solve the problem. Second, the position of the sink node is varied over time according to a known Markov chain, and the problem is solved by stochastic dynamic programming. We also present sub-optimal methods to solve our problem. A numerical example is presented in order to discuss the proposed methods’ performance.

  19. Learning-Based Adaptive Optimal Tracking Control of Strict-Feedback Nonlinear Systems.

    Science.gov (United States)

    Gao, Weinan; Jiang, Zhong-Ping; Weinan Gao; Zhong-Ping Jiang; Gao, Weinan; Jiang, Zhong-Ping

    2018-06-01

    This paper proposes a novel data-driven control approach to address the problem of adaptive optimal tracking for a class of nonlinear systems taking the strict-feedback form. Adaptive dynamic programming (ADP) and nonlinear output regulation theories are integrated for the first time to compute an adaptive near-optimal tracker without any a priori knowledge of the system dynamics. Fundamentally different from adaptive optimal stabilization problems, the solution to a Hamilton-Jacobi-Bellman (HJB) equation, not necessarily a positive definite function, cannot be approximated through the existing iterative methods. This paper proposes a novel policy iteration technique for solving positive semidefinite HJB equations with rigorous convergence analysis. A two-phase data-driven learning method is developed and implemented online by ADP. The efficacy of the proposed adaptive optimal tracking control methodology is demonstrated via a Van der Pol oscillator with time-varying exogenous signals.

  20. Drosophila learn efficient paths to a food source.

    Science.gov (United States)

    Navawongse, Rapeechai; Choudhury, Deepak; Raczkowska, Marlena; Stewart, James Charles; Lim, Terrence; Rahman, Mashiur; Toh, Alicia Guek Geok; Wang, Zhiping; Claridge-Chang, Adam

    2016-05-01

    Elucidating the genetic, and neuronal bases for learned behavior is a central problem in neuroscience. A leading system for neurogenetic discovery is the vinegar fly Drosophila melanogaster; fly memory research has identified genes and circuits that mediate aversive and appetitive learning. However, methods to study adaptive food-seeking behavior in this animal have lagged decades behind rodent feeding analysis, largely due to the challenges presented by their small scale. There is currently no method to dynamically control flies' access to food. In rodents, protocols that use dynamic food delivery are a central element of experimental paradigms that date back to the influential work of Skinner. This method is still commonly used in the analysis of learning, memory, addiction, feeding, and many other subjects in experimental psychology. The difficulty of microscale food delivery means this is not a technique used in fly behavior. In the present manuscript we describe a microfluidic chip integrated with machine vision and automation to dynamically control defined liquid food presentations and sensory stimuli. Strikingly, repeated presentations of food at a fixed location produced improvements in path efficiency during food approach. This shows that improved path choice is a learned behavior. Active control of food availability using this microfluidic system is a valuable addition to the methods currently available for the analysis of learned feeding behavior in flies. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

  1. Tracking the maximum efficiency point for the FC system based on extremum seeking scheme to control the air flow

    International Nuclear Information System (INIS)

    Bizon, Nicu

    2014-01-01

    Highlights: • The Maximum Efficiency Point (MEP) is tracked based on air flow rate. • The proposed Extremum Seeking (ES) control assures high performances. • About 10 kW/s search speed and 99.99% stationary accuracy can be obtained. • The energy efficiency increases with 3–12%, according to the power losses. • The control strategy is robust based on self-optimizing ES scheme proposed. - Abstract: An advanced control of the air compressor for the Proton Exchange Membrane Fuel Cell (PEMFC) system is proposed in this paper based on Extremum Seeking (ES) control scheme. The FC net power is mainly depended on the air and hydrogen flow rate and pressure, and heat and water management. This paper proposes to compute the optimal value for the air flow rate based on the advanced ES control scheme in order to maximize the FC net power. In this way, the Maximum Efficiency Point (MEP) will be tracked in real time, with about 10 kW/s search speed and a stationary accuracy of 0.99. Thus, energy efficiency will be close to the maximum value that can be obtained for a given PEMFC stack and compressor group under dynamic load. It is shown that the MEP tracking allows an increasing of the FC net power with 3–12%, depending on the percentage of the FC power supplied to the compressor and the level of the load power. Simulations shows that the performances mentioned above are effective

  2. An efficient cardiac mapping strategy for radiofrequency catheter ablation with active learning.

    Science.gov (United States)

    Feng, Yingjing; Guo, Ziyan; Dong, Ziyang; Zhou, Xiao-Yun; Kwok, Ka-Wai; Ernst, Sabine; Lee, Su-Lin

    2017-07-01

    A major challenge in radiofrequency catheter ablation procedures is the voltage and activation mapping of the endocardium, given a limited mapping time. By learning from expert interventional electrophysiologists (operators), while also making use of an active-learning framework, guidance on performing cardiac voltage mapping can be provided to novice operators or even directly to catheter robots. A learning from demonstration (LfD) framework, based upon previous cardiac mapping procedures performed by an expert operator, in conjunction with Gaussian process (GP) model-based active learning, was developed to efficiently perform voltage mapping over right ventricles (RV). The GP model was used to output the next best mapping point, while getting updated towards the underlying voltage data pattern as more mapping points are taken. A regularized particle filter was used to keep track of the kernel hyperparameter used by GP. The travel cost of the catheter tip was incorporated to produce time-efficient mapping sequences. The proposed strategy was validated on a simulated 2D grid mapping task, with leave-one-out experiments on 25 retrospective datasets, in an RV phantom using the Stereotaxis Niobe ® remote magnetic navigation system, and on a tele-operated catheter robot. In comparison with an existing geometry-based method, regression error was reduced and was minimized at a faster rate over retrospective procedure data. A new method of catheter mapping guidance has been proposed based on LfD and active learning. The proposed method provides real-time guidance for the procedure, as well as a live evaluation of mapping sufficiency.

  3. Automated personnel-assets-consumables-drug tracking in ambulance services for more effective and efficient medical emergency interventions.

    Science.gov (United States)

    Utku, Semih; Özcanhan, Mehmet Hilal; Unluturk, Mehmet Suleyman

    2016-04-01

    Patient delivery time is no longer considered as the only critical factor, in ambulatory services. Presently, five clinical performance indicators are used to decide patient satisfaction. Unfortunately, the emergency ambulance services in rapidly growing metropolitan areas do not meet current satisfaction expectations; because of human errors in the management of the objects onboard the ambulances. But, human involvement in the information management of emergency interventions can be reduced by electronic tracking of personnel, assets, consumables and drugs (PACD) carried in the ambulances. Electronic tracking needs the support of automation software, which should be integrated to the overall hospital information system. Our work presents a complete solution based on a centralized database supported by radio frequency identification (RFID) and bluetooth low energy (BLE) identification and tracking technologies. Each object in an ambulance is identified and tracked by the best suited technology. The automated identification and tracking reduces manual paper documentation and frees the personnel to better focus on medical activities. The presence and amounts of the PACD are automatically monitored, warning about their depletion, non-presence or maintenance dates. The computerized two way hospital-ambulance communication link provides information sharing and instantaneous feedback for better and faster diagnosis decisions. A fully implemented system is presented, with detailed hardware and software descriptions. The benefits and the clinical outcomes of the proposed system are discussed, which lead to improved personnel efficiency and more effective interventions. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  4. Representation learning with deep extreme learning machines for efficient image set classification

    KAUST Repository

    Uzair, Muhammad

    2016-12-09

    Efficient and accurate representation of a collection of images, that belong to the same class, is a major research challenge for practical image set classification. Existing methods either make prior assumptions about the data structure, or perform heavy computations to learn structure from the data itself. In this paper, we propose an efficient image set representation that does not make any prior assumptions about the structure of the underlying data. We learn the nonlinear structure of image sets with deep extreme learning machines that are very efficient and generalize well even on a limited number of training samples. Extensive experiments on a broad range of public datasets for image set classification show that the proposed algorithm consistently outperforms state-of-the-art image set classification methods both in terms of speed and accuracy.

  5. Representation learning with deep extreme learning machines for efficient image set classification

    KAUST Repository

    Uzair, Muhammad; Shafait, Faisal; Ghanem, Bernard; Mian, Ajmal

    2016-01-01

    Efficient and accurate representation of a collection of images, that belong to the same class, is a major research challenge for practical image set classification. Existing methods either make prior assumptions about the data structure, or perform heavy computations to learn structure from the data itself. In this paper, we propose an efficient image set representation that does not make any prior assumptions about the structure of the underlying data. We learn the nonlinear structure of image sets with deep extreme learning machines that are very efficient and generalize well even on a limited number of training samples. Extensive experiments on a broad range of public datasets for image set classification show that the proposed algorithm consistently outperforms state-of-the-art image set classification methods both in terms of speed and accuracy.

  6. The dual-axis solar tracking system efficiency improving via the drive power consumption optimization

    International Nuclear Information System (INIS)

    Rambhowan, Y.; Oree, V.

    2014-01-01

    A major drawback with active dual-axis solar tracking systems is that the power used by the driving mechanism is often drawn from the output power of the solar panel itself. The net energy gain of the photo-voltaic panel is therefore less than its maximum value. This work presents a novel design which uses a three-fold strategy to minimize the power consumed by the tracking mechanism whilst maintaining the power out-put of the photovoltaic panel near its optimal value. The results reveal that the improved tracking system has a significant energy gain of about 43.6% as compared to a fixed photovoltaic panel. Experiments further show that an increase of 1.6% in energy output is achieved over conventional precise dual-axis tracking system. (author)

  7. Decomposing price differentials due to ENERGY STARR labels and energy efficiency features in appliances: proxy for market share tracking?

    International Nuclear Information System (INIS)

    Gardner, John; Skumatz, Lisa A.

    2005-01-01

    This paper summarizes recent work using statistical methods to examine the portions of the apparent price differences for a variety of appliances that are attributable to efficiency labels or components of efficient measures. The work stems from research examining progress in market transformation. The goal was to monitor market progress in the premium associated with efficient equipment compared to standard equipment - and potentially track these changes (hopefully, according to logic, declining) over time. However, the incremental cost metric is always confounded by the fact that the 'feature bundle' on appliances and lighting is not consistent ( i.e. , many efficient products are loaded up with other, high-end features). Based on work conducted by the authors some years ago, we adapted statistical models to decompose the price differentials for efficient and standard refrigerators, clothes washers, and dish washers. The authors used site visits and web searches to gather data on appliance prices and features for a set of efficient and standard models. The authors first examined apparent (raw) price differentials between efficient and standard models. Then, using regression techniques to control for differences in features on the measures, the differences attributable to various features - and in particular to energy efficient features and logos - were estimated. The results showed that while the apparent (gross) price differences for efficient measures are high, the percentage and dollar differences decrease dramatically when the price differences statistically attributable to other features of the measure are accounted for. The work illustrates a promising approach for three important applications in program planning and evaluation: tracking market progress within and between states or service territories, using a proxy variable that is less expensive and complicated to measure than direct indicators of sales or market share, identifying appropriate levels for

  8. Who Deserves My Trust? Cue-Elicited Feedback Negativity Tracks Reputation Learning in Repeated Social Interactions.

    Science.gov (United States)

    Li, Diandian; Meng, Liang; Ma, Qingguo

    2017-01-01

    Trust and trustworthiness contribute to reciprocal behavior and social relationship development. To make better decisions, people need to evaluate others' trustworthiness. They often assess this kind of reputation by learning through repeated social interactions. The present event-related potential (ERP) study explored the reputation learning process in a repeated trust game where subjects made multi-round decisions of investment to different partners. We found that subjects gradually learned to discriminate trustworthy partners from untrustworthy ones based on how often their partners reciprocated the investment, which was indicated by their own investment decisions. Besides, electrophysiological data showed that the faces of the untrustworthy partners induced larger feedback negativity (FN) amplitude than those of the trustworthy partners, but only in the late phase of the game. The ERP results corresponded with the behavioral pattern and revealed that the learned trustworthiness differentiation was coded by the cue-elicited FN component. Consistent with previous research, our findings suggest that the anterior cue-elicited FN reflects the reputation appraisal and tracks the reputation learning process in social interactions.

  9. Who Deserves My Trust? Cue-Elicited Feedback Negativity Tracks Reputation Learning in Repeated Social Interactions

    Directory of Open Access Journals (Sweden)

    Diandian Li

    2017-06-01

    Full Text Available Trust and trustworthiness contribute to reciprocal behavior and social relationship development. To make better decisions, people need to evaluate others’ trustworthiness. They often assess this kind of reputation by learning through repeated social interactions. The present event-related potential (ERP study explored the reputation learning process in a repeated trust game where subjects made multi-round decisions of investment to different partners. We found that subjects gradually learned to discriminate trustworthy partners from untrustworthy ones based on how often their partners reciprocated the investment, which was indicated by their own investment decisions. Besides, electrophysiological data showed that the faces of the untrustworthy partners induced larger feedback negativity (FN amplitude than those of the trustworthy partners, but only in the late phase of the game. The ERP results corresponded with the behavioral pattern and revealed that the learned trustworthiness differentiation was coded by the cue-elicited FN component. Consistent with previous research, our findings suggest that the anterior cue-elicited FN reflects the reputation appraisal and tracks the reputation learning process in social interactions.

  10. Unimodal Learning Enhances Crossmodal Learning in Robotic Audio-Visual Tracking

    DEFF Research Database (Denmark)

    Shaikh, Danish; Bodenhagen, Leon; Manoonpong, Poramate

    2017-01-01

    Crossmodal sensory integration is a fundamental feature of the brain that aids in forming an coherent and unified representation of observed events in the world. Spatiotemporally correlated sensory stimuli brought about by rich sensorimotor experiences drive the development of crossmodal integrat...... a non-holonomic robotic agent towards a moving audio-visual target. Simulation results demonstrate that unimodal learning enhances crossmodal learning and improves both the overall accuracy and precision of multisensory orientation response....

  11. Unimodal Learning Enhances Crossmodal Learning in Robotic Audio-Visual Tracking

    DEFF Research Database (Denmark)

    Shaikh, Danish; Bodenhagen, Leon; Manoonpong, Poramate

    2018-01-01

    Crossmodal sensory integration is a fundamental feature of the brain that aids in forming an coherent and unified representation of observed events in the world. Spatiotemporally correlated sensory stimuli brought about by rich sensorimotor experiences drive the development of crossmodal integrat...... a non-holonomic robotic agent towards a moving audio-visual target. Simulation results demonstrate that unimodal learning enhances crossmodal learning and improves both the overall accuracy and precision of multisensory orientation response....

  12. The effect of action video game playing on sensorimotor learning: Evidence from a movement tracking task.

    Science.gov (United States)

    Gozli, Davood G; Bavelier, Daphne; Pratt, Jay

    2014-10-12

    Research on the impact of action video game playing has revealed performance advantages on a wide range of perceptual and cognitive tasks. It is not known, however, if playing such games confers similar advantages in sensorimotor learning. To address this issue, the present study used a manual motion-tracking task that allowed for a sensitive measure of both accuracy and improvement over time. When the target motion pattern was consistent over trials, gamers improved with a faster rate and eventually outperformed non-gamers. Performance between the two groups, however, did not differ initially. When the target motion was inconsistent, changing on every trial, results revealed no difference between gamers and non-gamers. Together, our findings suggest that video game playing confers no reliable benefit in sensorimotor control, but it does enhance sensorimotor learning, enabling superior performance in tasks with consistent and predictable structure. Copyright © 2014. Published by Elsevier B.V.

  13. An Efficient Inductive Genetic Learning Algorithm for Fuzzy Relational Rules

    Directory of Open Access Journals (Sweden)

    Antonio

    2012-04-01

    Full Text Available Fuzzy modelling research has traditionally focused on certain types of fuzzy rules. However, the use of alternative rule models could improve the ability of fuzzy systems to represent a specific problem. In this proposal, an extended fuzzy rule model, that can include relations between variables in the antecedent of rules is presented. Furthermore, a learning algorithm based on the iterative genetic approach which is able to represent the knowledge using this model is proposed as well. On the other hand, potential relations among initial variables imply an exponential growth in the feasible rule search space. Consequently, two filters for detecting relevant potential relations are added to the learning algorithm. These filters allows to decrease the search space complexity and increase the algorithm efficiency. Finally, we also present an experimental study to demonstrate the benefits of using fuzzy relational rules.

  14. Theranostic Niosomes for Efficient siRNA/microRNA Delivery and Activatable Near-Infrared Fluorescent Tracking of Stem Cells

    DEFF Research Database (Denmark)

    Yang, Chuanxu; Shan, Gao; Song, Ping

    2018-01-01

    RNA interference (RNAi) mediated gene regulation in stem cells offers great potential in regenerative medicine. In this study, we developed a theranostic platform for efficient delivery of small RNAs (siRNA/miRNA) to human mesenchymal stem cells (hMSCs) to promote differentiation, and meanwhile...... OFF/ON activatable fluorescence upon cellular internalization, resulting in efficient NIR labeling and the capability to dynamically monitor stem cells in mice. In addition, iSPN/siRNA achieved simultaneous long-term cell tracking and in vivo gene silencing after implantation in mice. These results...

  15. An efficient central DOA tracking algorithm for multiple incoherently distributed sources

    Science.gov (United States)

    Hassen, Sonia Ben; Samet, Abdelaziz

    2015-12-01

    In this paper, we develop a new tracking method for the direction of arrival (DOA) parameters assuming multiple incoherently distributed (ID) sources. The new approach is based on a simple covariance fitting optimization technique exploiting the central and noncentral moments of the source angular power densities to estimate the central DOAs. The current estimates are treated as measurements provided to the Kalman filter that model the dynamic property of directional changes for the moving sources. Then, the covariance-fitting-based algorithm and the Kalman filtering theory are combined to formulate an adaptive tracking algorithm. Our algorithm is compared to the fast approximated power iteration-total least square-estimation of signal parameters via rotational invariance technique (FAPI-TLS-ESPRIT) algorithm using the TLS-ESPRIT method and the subspace updating via FAPI-algorithm. It will be shown that the proposed algorithm offers an excellent DOA tracking performance and outperforms the FAPI-TLS-ESPRIT method especially at low signal-to-noise ratio (SNR) values. Moreover, the performances of the two methods increase as the SNR values increase. This increase is more prominent with the FAPI-TLS-ESPRIT method. However, their performances degrade when the number of sources increases. It will be also proved that our method depends on the form of the angular distribution function when tracking the central DOAs. Finally, it will be shown that the more the sources are spaced, the more the proposed method can exactly track the DOAs.

  16. Optimal Learning for Efficient Experimentation in Nanotechnology and Biochemistry

    Science.gov (United States)

    2015-12-22

    AFRL-AFOSR-VA-TR-2016-0018 Optimal Learning for Efficient Experimentation in Nanotechnology, Biochemistry Warren Powell TRUSTEES OF PRINCETON... Biochemistry 5a.  CONTRACT NUMBER 5b.  GRANT NUMBER FA9550-12-1-0200 5c.  PROGRAM ELEMENT NUMBER 61102F 6. AUTHOR(S) Warren Powell 5d.  PROJECT NUMBER 5e...scientists. 15. SUBJECT TERMS Biochemistry 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT 18. NUMBER OF 19a.  NAME OF RESPONSIBLE PERSON Warren

  17. Hierarchical Load Tracking Control of a Grid-connected Solid Oxide Fuel Cell for Maximum Electrical Efficiency Operation

    DEFF Research Database (Denmark)

    Li, Yonghui; Wu, Qiuwei; Zhu, Haiyu

    2015-01-01

    efficiency operation obtained at different active power output levels, a hierarchical load tracking control scheme for the grid-connected SOFC was proposed to realize the maximum electrical efficiency operation with the stack temperature bounded. The hierarchical control scheme consists of a fast active...... power control and a slower stack temperature control. The active power control was developed by using a decentralized control method. The efficiency of the proposed hierarchical control scheme was demonstrated by case studies using the benchmark SOFC dynamic model......Based on the benchmark solid oxide fuel cell (SOFC) dynamic model for power system studies and the analysis of the SOFC operating conditions, the nonlinear programming (NLP) optimization method was used to determine the maximum electrical efficiency of the grid-connected SOFC subject...

  18. Efficient Dynamic Adaptation Strategies for Object Tracking Tree in Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    CHEN, M.

    2012-12-01

    Full Text Available Most object tracking trees are established using the predefined mobility profile. However, when the real object's movement behaviors and query rates are different from the predefined mobility profile and query rates, the update cost and query cost of object tracking tree may increase. To upgrade the object tracking tree, the sink needs to send very large messages to collect the real movement information from the network, introducing a very large message overhead, which is referred to as adaptation cost. The Sub Root Message-Tree Adaptive procedure was proposed to dynamically collect the real movement information under the sub-tree and reconstruct the sub-tree to provide good performance based on the collected information. The simulation results indicates that the Sub Root Message-Tree Adaptive procedure is sufficient to achieve good total cost and lower adaptation cost.

  19. Confidence-Based Data Association and Discriminative Deep Appearance Learning for Robust Online Multi-Object Tracking.

    Science.gov (United States)

    Bae, Seung-Hwan; Yoon, Kuk-Jin

    2018-03-01

    Online multi-object tracking aims at estimating the tracks of multiple objects instantly with each incoming frame and the information provided up to the moment. It still remains a difficult problem in complex scenes, because of the large ambiguity in associating multiple objects in consecutive frames and the low discriminability between objects appearances. In this paper, we propose a robust online multi-object tracking method that can handle these difficulties effectively. We first define the tracklet confidence using the detectability and continuity of a tracklet, and decompose a multi-object tracking problem into small subproblems based on the tracklet confidence. We then solve the online multi-object tracking problem by associating tracklets and detections in different ways according to their confidence values. Based on this strategy, tracklets sequentially grow with online-provided detections, and fragmented tracklets are linked up with others without any iterative and expensive association steps. For more reliable association between tracklets and detections, we also propose a deep appearance learning method to learn a discriminative appearance model from large training datasets, since the conventional appearance learning methods do not provide rich representation that can distinguish multiple objects with large appearance variations. In addition, we combine online transfer learning for improving appearance discriminability by adapting the pre-trained deep model during online tracking. Experiments with challenging public datasets show distinct performance improvement over other state-of-the-arts batch and online tracking methods, and prove the effect and usefulness of the proposed methods for online multi-object tracking.

  20. How Do Peers Impact Learning? An Experimental Investigation of Peer-To-Peer Teaching and Ability Tracking

    OpenAIRE

    Kimbrough, Erik O.; McGee, Andrew; Shigeoka, Hitoshi

    2017-01-01

    Classroom peers are believed to influence learning by teaching each other, and the efficacy of this teaching likely depends on classroom composition in terms of peers' ability. Unfortunately, little is known about peer-to-peer teaching because it is never observed in field studies. Furthermore, identifying how peer-to-peer teaching is affected by ability tracking – grouping students of similar ability – is complicated by the fact that tracking is typically accompanied by changes in curriculum...

  1. Efficient nonparametric n -body force fields from machine learning

    Science.gov (United States)

    Glielmo, Aldo; Zeni, Claudio; De Vita, Alessandro

    2018-05-01

    We provide a definition and explicit expressions for n -body Gaussian process (GP) kernels, which can learn any interatomic interaction occurring in a physical system, up to n -body contributions, for any value of n . The series is complete, as it can be shown that the "universal approximator" squared exponential kernel can be written as a sum of n -body kernels. These recipes enable the choice of optimally efficient force models for each target system, as confirmed by extensive testing on various materials. We furthermore describe how the n -body kernels can be "mapped" on equivalent representations that provide database-size-independent predictions and are thus crucially more efficient. We explicitly carry out this mapping procedure for the first nontrivial (three-body) kernel of the series, and we show that this reproduces the GP-predicted forces with meV /Å accuracy while being orders of magnitude faster. These results pave the way to using novel force models (here named "M-FFs") that are computationally as fast as their corresponding standard parametrized n -body force fields, while retaining the nonparametric character, the ease of training and validation, and the accuracy of the best recently proposed machine-learning potentials.

  2. Efficient characterization of labeling uncertainty in closely-spaced targets tracking

    NARCIS (Netherlands)

    Moreno Leon, Carlos; Moreno Leon, Carlos; Driessen, Hans; Mandal, Pranab K.

    2016-01-01

    In this paper we propose a novel solution to the labeled multi-target tracking problem. The method presented is specially effective in scenarios where the targets have once moved in close proximity. When this is the case, disregarding the labeling uncertainty present in a solution (after the targets

  3. Why simulation can be efficient: on the preconditions of efficient learning in complex technology based practices

    Directory of Open Access Journals (Sweden)

    Hofmann Bjørn

    2009-07-01

    Full Text Available Abstract Background It is important to demonstrate learning outcomes of simulation in technology based practices, such as in advanced health care. Although many studies show skills improvement and self-reported change to practice, there are few studies demonstrating patient outcome and societal efficiency. The objective of the study is to investigate if and why simulation can be effective and efficient in a hi-tech health care setting. This is important in order to decide whether and how to design simulation scenarios and outcome studies. Methods Core theoretical insights in Science and Technology Studies (STS are applied to analyze the field of simulation in hi-tech health care education. In particular, a process-oriented framework where technology is characterized by its devices, methods and its organizational setting is applied. Results The analysis shows how advanced simulation can address core characteristics of technology beyond the knowledge of technology's functions. Simulation's ability to address skilful device handling as well as purposive aspects of technology provides a potential for effective and efficient learning. However, as technology is also constituted by organizational aspects, such as technology status, disease status, and resource constraints, the success of simulation depends on whether these aspects can be integrated in the simulation setting as well. This represents a challenge for future development of simulation and for demonstrating its effectiveness and efficiency. Conclusion Assessing the outcome of simulation in education in hi-tech health care settings is worthwhile if core characteristics of medical technology are addressed. This challenges the traditional technical versus non-technical divide in simulation, as organizational aspects appear to be part of technology's core characteristics.

  4. Why simulation can be efficient: on the preconditions of efficient learning in complex technology based practices.

    Science.gov (United States)

    Hofmann, Bjørn

    2009-07-23

    It is important to demonstrate learning outcomes of simulation in technology based practices, such as in advanced health care. Although many studies show skills improvement and self-reported change to practice, there are few studies demonstrating patient outcome and societal efficiency. The objective of the study is to investigate if and why simulation can be effective and efficient in a hi-tech health care setting. This is important in order to decide whether and how to design simulation scenarios and outcome studies. Core theoretical insights in Science and Technology Studies (STS) are applied to analyze the field of simulation in hi-tech health care education. In particular, a process-oriented framework where technology is characterized by its devices, methods and its organizational setting is applied. The analysis shows how advanced simulation can address core characteristics of technology beyond the knowledge of technology's functions. Simulation's ability to address skilful device handling as well as purposive aspects of technology provides a potential for effective and efficient learning. However, as technology is also constituted by organizational aspects, such as technology status, disease status, and resource constraints, the success of simulation depends on whether these aspects can be integrated in the simulation setting as well. This represents a challenge for future development of simulation and for demonstrating its effectiveness and efficiency. Assessing the outcome of simulation in education in hi-tech health care settings is worthwhile if core characteristics of medical technology are addressed. This challenges the traditional technical versus non-technical divide in simulation, as organizational aspects appear to be part of technology's core characteristics.

  5. Manifold Regularized Correlation Object Tracking.

    Science.gov (United States)

    Hu, Hongwei; Ma, Bo; Shen, Jianbing; Shao, Ling

    2018-05-01

    In this paper, we propose a manifold regularized correlation tracking method with augmented samples. To make better use of the unlabeled data and the manifold structure of the sample space, a manifold regularization-based correlation filter is introduced, which aims to assign similar labels to neighbor samples. Meanwhile, the regression model is learned by exploiting the block-circulant structure of matrices resulting from the augmented translated samples over multiple base samples cropped from both target and nontarget regions. Thus, the final classifier in our method is trained with positive, negative, and unlabeled base samples, which is a semisupervised learning framework. A block optimization strategy is further introduced to learn a manifold regularization-based correlation filter for efficient online tracking. Experiments on two public tracking data sets demonstrate the superior performance of our tracker compared with the state-of-the-art tracking approaches.

  6. Efficiency improvements of photo-voltaic panels using a sun - tracking system

    International Nuclear Information System (INIS)

    Al-Mohamad, A.

    2005-01-01

    This paper presents a sun-tracking design, whereby the movement of a photo-voltaic module was controlled to follow the Sun's radiation using a programmable logic-controller unit (PLC). All electronic circuits and the necessary software have been designed and developed to perform the technical tasks. A PLC unit was employed to control and monitor the mechanical movement of the PV module and to collect and store data related to the Sun's radiation. It is found that the daily output the power of the PV was increased by more than 20% in comparison with that of a fixed module. The PV-tracking system can be employed as a standalone device and it could be connected to a personal computer through the RS232 serial port to monitor the whole process on a computer screen. (author)

  7. Efficiency improvements of photo-voltaic panels using a Sun-tracking system

    International Nuclear Information System (INIS)

    Al-Mohamad, Ali

    2004-01-01

    This paper presents a Sun-tracking design, whereby the movement of a photo-voltaic module was controlled to follow the Sun's radiation using a programmable logic-controller (PLC) unit. All electronic circuits and the necessary software have been designed and developed to perform the technical tasks. A PLC unit was employed to control and monitor the mechanical movement of the PV module and to collect and store data related to the Sun's radiation. It is found that the daily output power of the PV was increased by more than 20% in comparison with that of a fixed module. The PV-tracking system can be employed as a stand-alone device and it could be connected to a personal computer through the RS232 serial port to monitor the whole process on a computer screen

  8. Techniques for Efficient Tracking of Road-Network-Based Moving Objects

    DEFF Research Database (Denmark)

    Civilis, Alminas; Jensen, Christian Søndergaard; Saltenis, Simonas

    With the continued advances in wireless communications, geo-positioning, and consumer electronics, an infrastructure is emerging that enables location-based services that rely on the tracking of the continuously changing positions of entire populations of service users, termed moving objects....... The main issue considered is how to represent the location of a moving object in a database so that tracking can be done with as few updates as possible. The paper proposes to use the road network within which the objects are assumed to move for predicting their future positions. The paper presents...... algorithms that modify an initial road-network representation, so that it works better as a basis for predicting an object's position; it proposes to use known movement patterns of the object, in the form of routes; and it proposes to use acceleration profiles together with the routes. Using real GPS...

  9. Techniques for efficient road-network-based tracking of moving objects

    DEFF Research Database (Denmark)

    Civilis, A.; Jensen, Christian Søndergaard; Pakalnis, Stardas

    2005-01-01

    With the continued advances in wireless communications, geo-positioning, and consumer electronics, an infrastructure is emerging that enables location-based services that rely on the tracking of the continuously changing positions of entire populations of service users, termed moving objects....... The main issue considered is how to represent the location of a moving object in a database so that tracking can be done with as few updates as possible. The paper proposes to use the road network within which the objects are assumed to move for predicting their future positions. The paper presents...... algorithms that modify an initial road-network representation, so that it works better as a basis for predicting an object's position; it proposes to use known movement patterns of the object, in the form of routes; and it proposes to use acceleration profiles together with the routes. Using real GPS...

  10. Theoretical and experimental studies of the efficiency of a solid-state track detector utilizing (neutron, alpha) reactions

    International Nuclear Information System (INIS)

    Palfalvi, J.

    1983-04-01

    The neutron sensitivity of Kodak-Pathe LR 115 II type cellulose nitrate track detectors with different (n,α) radiators was investigated by calculations and measurements. The α counting efficiency using an optical microscope is 95% for α particles with maximum energy of 2 MeV. When using an image analyzer the etched through-tracks (holes) with diameters greater than 2 μm are counted. The efficiency then depends only on the original and removed layer thickness but not on the etching temperature within the range of 40 to 60 deg C and the 2.5 to 6 N normality of the NaOH etchant. Efficiency varies from about 3 to 20% for alphas from the 6 Li/n, +a/T reaction if the removed layer lies in the range of 7 to 10 μm, and varies from 2 to 10% for 10 B/n, α/ 7 Li reaction alphas when the layer re--moval is 8 to 10 μm. (author)

  11. The efficiency of multimedia learning into old age.

    Science.gov (United States)

    Van Gerven, Pascal W M; Paas, Fred; Van Merriënboer, Jeroen J G; Hendriks, Maaike; Schmidt, Henk G

    2003-12-01

    On the basis of a multimodal model of working memory, cognitive load theory predicts that a multimedia-based instructional format leads to a better acquisition of complex subject matter than a purely visual instructional format. This study investigated the extent to which age and instructional format had an impact on training efficiency among both young and old adults. It was hypothesised that studying worked examples that are presented as a narrated animation (multimedia condition) is a more efficient means of complex skill training than studying visually presented worked examples (unimodal condition) and solving conventional problems. Furthermore, it was hypothesised that multimedia-based worked examples are especially helpful for elderly learners, who have to deal with a general decline of working-memory resources, because they address both mode-specific working-memory stores. The sample consisted of 60 young (mean age = 15.98 years) and 60 old adults (mean age = 64.48 years). Participants of both age groups were trained in either a conventional, a unimodal, or a multimedia condition. Subsequently, they had to solve a series of test problems. Dependent variables were perceived cognitive load during the training, performance on the test, and efficiency in terms of the ratio between these two variables. Results showed that for both age groups multimedia-based worked examples were more efficient than the other training formats in that less cognitive load led to at least an equal performance level. Although no difference in the beneficial effect of multimedia learning was found between the age groups, multimedia-based instructions seem promising for the elderly.

  12. How are learning strategies reflected in the eyes? Combining results from self-reports and eye-tracking.

    Science.gov (United States)

    Catrysse, Leen; Gijbels, David; Donche, Vincent; De Maeyer, Sven; Lesterhuis, Marije; Van den Bossche, Piet

    2018-03-01

    Up until now, empirical studies in the Student Approaches to Learning field have mainly been focused on the use of self-report instruments, such as interviews and questionnaires, to uncover differences in students' general preferences towards learning strategies, but have focused less on the use of task-specific and online measures. This study aimed at extending current research on students' learning strategies by combining general and task-specific measurements of students' learning strategies using both offline and online measures. We want to clarify how students process learning contents and to what extent this is related to their self-report of learning strategies. Twenty students with different generic learning profiles (according to self-report questionnaires) read an expository text, while their eye movements were registered to answer questions on the content afterwards. Eye-tracking data were analysed with generalized linear mixed-effects models. The results indicate that students with an all-high profile, combining both deep and surface learning strategies, spend more time on rereading the text than students with an all-low profile, scoring low on both learning strategies. This study showed that we can use eye-tracking to distinguish very strategic students, characterized using cognitive processing and regulation strategies, from low strategic students, characterized by a lack of cognitive and regulation strategies. These students processed the expository text according to how they self-reported. © 2017 The British Psychological Society.

  13. Effect of task-related continuous auditory feedback during learning of tracking motion exercises

    Directory of Open Access Journals (Sweden)

    Rosati Giulio

    2012-10-01

    Full Text Available Abstract Background This paper presents the results of a set of experiments in which we used continuous auditory feedback to augment motor training exercises. This feedback modality is mostly underexploited in current robotic rehabilitation systems, which usually implement only very basic auditory interfaces. Our hypothesis is that properly designed continuous auditory feedback could be used to represent temporal and spatial information that could in turn, improve performance and motor learning. Methods We implemented three different experiments on healthy subjects, who were asked to track a target on a screen by moving an input device (controller with their hand. Different visual and auditory feedback modalities were envisaged. The first experiment investigated whether continuous task-related auditory feedback can help improve performance to a greater extent than error-related audio feedback, or visual feedback alone. In the second experiment we used sensory substitution to compare different types of auditory feedback with equivalent visual feedback, in order to find out whether mapping the same information on a different sensory channel (the visual channel yielded comparable effects with those gained in the first experiment. The final experiment applied a continuously changing visuomotor transformation between the controller and the screen and mapped kinematic information, computed in either coordinate system (controller or video, to the audio channel, in order to investigate which information was more relevant to the user. Results Task-related audio feedback significantly improved performance with respect to visual feedback alone, whilst error-related feedback did not. Secondly, performance in audio tasks was significantly better with respect to the equivalent sensory-substituted visual tasks. Finally, with respect to visual feedback alone, video-task-related sound feedback decreased the tracking error during the learning of a novel

  14. An Efficient Ensemble Learning Method for Gene Microarray Classification

    Directory of Open Access Journals (Sweden)

    Alireza Osareh

    2013-01-01

    Full Text Available The gene microarray analysis and classification have demonstrated an effective way for the effective diagnosis of diseases and cancers. However, it has been also revealed that the basic classification techniques have intrinsic drawbacks in achieving accurate gene classification and cancer diagnosis. On the other hand, classifier ensembles have received increasing attention in various applications. Here, we address the gene classification issue using RotBoost ensemble methodology. This method is a combination of Rotation Forest and AdaBoost techniques which in turn preserve both desirable features of an ensemble architecture, that is, accuracy and diversity. To select a concise subset of informative genes, 5 different feature selection algorithms are considered. To assess the efficiency of the RotBoost, other nonensemble/ensemble techniques including Decision Trees, Support Vector Machines, Rotation Forest, AdaBoost, and Bagging are also deployed. Experimental results have revealed that the combination of the fast correlation-based feature selection method with ICA-based RotBoost ensemble is highly effective for gene classification. In fact, the proposed method can create ensemble classifiers which outperform not only the classifiers produced by the conventional machine learning but also the classifiers generated by two widely used conventional ensemble learning methods, that is, Bagging and AdaBoost.

  15. Tracking Student Mistreatment Data to Improve the Emergency Medicine Clerkship Learning Environment

    Directory of Open Access Journals (Sweden)

    Joseph B. House

    2017-12-01

    Full Text Available Introduction Medical student mistreatment is a prevalent and significant challenge for medical schools across the country, associated with negative emotional and professional consequences for students. The Association of American Medical Colleges and Liaison Committee on Medical Education have increasingly emphasized the issue of mistreatment in recent years, and medical schools are tasked with creating a positive learning climate. Methods The authors describe the efforts of an emergency department (ED to improve its clerkship learning environment, using a multifaceted approach for collecting mistreatment data and relaying them to educators and clerkship leadership. Data are gathered through end-of-rotation evaluations, teaching evaluations, and an online reporting system available to medical students. Mistreatment data are then relayed to the ED during semi-annual meetings between clerkship leadership and medical school assistant deans, and through annual mistreatment reports provided to department chairs. Results Over a two-year period, students submitted a total of 56 narrative comments related to mistreatment or unprofessional behavior during their emergency medicine (EM clerkship. Of these comments, 12 were submitted in 2015–16 and 44 were submitted in 2016–17. The most frequently observed themes were students feeling ignored or marginalized by faculty (14 comments; students being prevented from speaking or working with patients and/or attending faculty (11 comments; and students being treated in an unprofessional manner by staff (other than faculty, 8 comments. Conclusion This article details an ED’s efforts to improve its EM clerkship learning environment by tracking mistreatment data and intentionally communicating the results to educators and clerkship leadership. Continued mistreatment data collection and faculty development will be necessary for these efforts to have a measurable effect on the learning environment.

  16. Tracking Reflective Practice-Based Learning by Medical Students during an Ambulatory Clerkship

    Science.gov (United States)

    Goldberg, Harry

    2007-01-01

    Objective To explore the use of web and palm digital assistant (PDA)-based patient logs to facilitate reflective learning in an ambulatory medicine clerkship. Design Thematic analysis of convenience sample of three successive rotations of medical students’ patient log entries. Setting Johns Hopkins University School of Medicine. Participants MS3 and MS4 students rotating through a required block ambulatory medicine clerkship. Interventions Students are required to enter patient encounters into a web-based log system during the clerkship. Patient-linked entries included an open text field entitled, “Learning Need.” Students were encouraged to use this field to enter goals for future study or teaching points related to the encounter. Measurement and Main Results The logs of 59 students were examined. These students entered 3,051 patient encounters, and 51 students entered 1,347 learning need entries (44.1% of encounters). The use of the “Learning Need” field was not correlated with MS year, gender or end-of-clerkship knowledge test performance. There were strong correlations between the use of diagnostic thinking comments and observations of therapeutic relationships (Pearson’s r=.42, p<0.001), and between diagnostic thinking and primary interpretation skills (Pearson’s r=.60, p<0.001), but not between diagnostic thinking and factual knowledge (Pearson’s r =.10, p=.46). CONCLUSIONS We found that when clerkship students were cued to reflect on each patient encounter with the electronic log system, student entries grouped into categories that suggested different levels of reflective thinking. Future efforts should explore the use of such entries to encourage and track habits of reflective practice in the clinical curriculum. PMID:17786523

  17. Energy efficiency learning and practice in housing for youths

    Energy Technology Data Exchange (ETDEWEB)

    Glad, Wiktoria; Thoresson, Josefin (Dept. of Thematic Studies, Linkoeping Univ., Linkoeping (Sweden)), E-mail: wiktoria.glad@liu.se

    2012-01-15

    This paper explores the energy efficiency learning and practices of youths aged 18-25 years. The studied youths are involved in a project, initiated by a municipally owned housing company, to educate residents and change everyday behaviour, making it more sustainable and energy efficient. This project, which forms our case study, covers socio-technical features such as energy systems and the individual metering and billing of heating, electricity, and hot and cold water. How did the youths perceive and use the systems? Have their attitudes and behaviours concerning energy-related practices changed during the project? The results indicate that a combination of technology (e.g. metering and visualized energy use) and social activities (e.g. educational activities and meeting neighbours and housing company staff) changed some practices involving what was perceived as energy wasting behaviour (e.g. using stand-by modes and taking long hot showers), while other practices (e.g. travelling and heating) were harder to change due to socio-technical barriers. The youths displayed knowledge gaps in relation to the energy system and their basic understanding of energy (the difference between heating and electricity)

  18. Efficient collective swimming by harnessing vortices through deep reinforcement learning.

    Science.gov (United States)

    Verma, Siddhartha; Novati, Guido; Koumoutsakos, Petros

    2018-06-05

    Fish in schooling formations navigate complex flow fields replete with mechanical energy in the vortex wakes of their companions. Their schooling behavior has been associated with evolutionary advantages including energy savings, yet the underlying physical mechanisms remain unknown. We show that fish can improve their sustained propulsive efficiency by placing themselves in appropriate locations in the wake of other swimmers and intercepting judiciously their shed vortices. This swimming strategy leads to collective energy savings and is revealed through a combination of high-fidelity flow simulations with a deep reinforcement learning (RL) algorithm. The RL algorithm relies on a policy defined by deep, recurrent neural nets, with long-short-term memory cells, that are essential for capturing the unsteadiness of the two-way interactions between the fish and the vortical flow field. Surprisingly, we find that swimming in-line with a leader is not associated with energetic benefits for the follower. Instead, "smart swimmer(s)" place themselves at off-center positions, with respect to the axis of the leader(s) and deform their body to synchronize with the momentum of the oncoming vortices, thus enhancing their swimming efficiency at no cost to the leader(s). The results confirm that fish may harvest energy deposited in vortices and support the conjecture that swimming in formation is energetically advantageous. Moreover, this study demonstrates that deep RL can produce navigation algorithms for complex unsteady and vortical flow fields, with promising implications for energy savings in autonomous robotic swarms.

  19. Fast Compressive Tracking.

    Science.gov (United States)

    Zhang, Kaihua; Zhang, Lei; Yang, Ming-Hsuan

    2014-10-01

    It is a challenging task to develop effective and efficient appearance models for robust object tracking due to factors such as pose variation, illumination change, occlusion, and motion blur. Existing online tracking algorithms often update models with samples from observations in recent frames. Despite much success has been demonstrated, numerous issues remain to be addressed. First, while these adaptive appearance models are data-dependent, there does not exist sufficient amount of data for online algorithms to learn at the outset. Second, online tracking algorithms often encounter the drift problems. As a result of self-taught learning, misaligned samples are likely to be added and degrade the appearance models. In this paper, we propose a simple yet effective and efficient tracking algorithm with an appearance model based on features extracted from a multiscale image feature space with data-independent basis. The proposed appearance model employs non-adaptive random projections that preserve the structure of the image feature space of objects. A very sparse measurement matrix is constructed to efficiently extract the features for the appearance model. We compress sample images of the foreground target and the background using the same sparse measurement matrix. The tracking task is formulated as a binary classification via a naive Bayes classifier with online update in the compressed domain. A coarse-to-fine search strategy is adopted to further reduce the computational complexity in the detection procedure. The proposed compressive tracking algorithm runs in real-time and performs favorably against state-of-the-art methods on challenging sequences in terms of efficiency, accuracy and robustness.

  20. Theoretical assessment of the maximum power point tracking efficiency of photovoltaic facilities with different converter topologies

    Energy Technology Data Exchange (ETDEWEB)

    Enrique, J.M.; Duran, E.; Andujar, J.M. [Departamento de Ingenieria Electronica, de Sistemas Informaticos y Automatica, Universidad de Huelva (Spain); Sidrach-de-Cardona, M. [Departamento de Fisica Aplicada, II, Universidad de Malaga (Spain)

    2007-01-15

    The operating point of a photovoltaic generator that is connected to a load is determined by the intersection point of its characteristic curves. In general, this point is not the same as the generator's maximum power point. This difference means losses in the system performance. DC/DC converters together with maximum power point tracking systems (MPPT) are used to avoid these losses. Different algorithms have been proposed for maximum power point tracking. Nevertheless, the choice of the configuration of the right converter has not been studied so widely, although this choice, as demonstrated in this work, has an important influence in the optimum performance of the photovoltaic system. In this article, we conduct a study of the three basic topologies of DC/DC converters with resistive load connected to photovoltaic modules. This article demonstrates that there is a limitation in the system's performance according to the type of converter used. Two fundamental conclusions are derived from this study: (1) the buck-boost DC/DC converter topology is the only one which allows the follow-up of the PV module maximum power point regardless of temperature, irradiance and connected load and (2) the connection of a buck-boost DC/DC converter in a photovoltaic facility to the panel output could be a good practice to improve performance. (author)

  1. Automated measurement of mouse social behaviors using depth sensing, video tracking, and machine learning.

    Science.gov (United States)

    Hong, Weizhe; Kennedy, Ann; Burgos-Artizzu, Xavier P; Zelikowsky, Moriel; Navonne, Santiago G; Perona, Pietro; Anderson, David J

    2015-09-22

    A lack of automated, quantitative, and accurate assessment of social behaviors in mammalian animal models has limited progress toward understanding mechanisms underlying social interactions and their disorders such as autism. Here we present a new integrated hardware and software system that combines video tracking, depth sensing, and machine learning for automatic detection and quantification of social behaviors involving close and dynamic interactions between two mice of different coat colors in their home cage. We designed a hardware setup that integrates traditional video cameras with a depth camera, developed computer vision tools to extract the body "pose" of individual animals in a social context, and used a supervised learning algorithm to classify several well-described social behaviors. We validated the robustness of the automated classifiers in various experimental settings and used them to examine how genetic background, such as that of Black and Tan Brachyury (BTBR) mice (a previously reported autism model), influences social behavior. Our integrated approach allows for rapid, automated measurement of social behaviors across diverse experimental designs and also affords the ability to develop new, objective behavioral metrics.

  2. ORGANIZATION OF PROGRESS AND ATTENDANCE TRACKING IN THE MOODLE LEARNING MANAGEMENT SYSTEM

    Directory of Open Access Journals (Sweden)

    A. Scherbyna

    2014-04-01

    Full Text Available The article considers usage of Moodle learning management system for current progress and attendance tracking of full time students. Evaluation systems, which are used in universities of Ukraine, are analyzed. Their basis in most cases is point accumulation system, which is useful for manual calculation of final grades at the end of the semester, but it is not useful for comparison of current students’ achievements at different subjects or achievements at any time during the semester. Also this system is not useful for putting current grades, because teaches often have to use unusual grade scales which are different from 5-point system. Because of that it is proposed to use mathematically equivalent weighted average grade, which allows to avoid mentioned disadvantages. Questions of implementation of proposed system are considered by means of gradebook of Moodle learning management system. Attendance accounting module is considered and method of using subcourse module for attendance and grades shared data import in course gradebook, where student’s rating is calculated for all disciplines is proposed.

  3. Tracking truck flows with programmable mobile devices for drayage efficiency analysis : final report.

    Science.gov (United States)

    2016-05-01

    Inefficient use of drayage trucks results in negative externalities in the form of pollution and congestion. A clear : awareness of the current state of drayage efficiency is especially important in Southern California since the cargo : volume at the...

  4. Lossy compression of TPC data and trajectory tracking efficiency for the ALICE experiment

    International Nuclear Information System (INIS)

    Nicolaucig, A.; Ivanov, M.; Mattavelli, M.

    2003-01-01

    In this paper a quasi-lossless algorithm for the on-line compression of the data generated by the Time Projection Chamber (TPC) detector of the ALICE experiment at CERN is described. The algorithm is based on a lossy source code modeling technique, i.e. it is based on a source model which is lossy if samples of the TPC signal are considered one by one; conversely, the source model is lossless or quasi-lossless if some physical quantities that are of main interest for the experiment are considered. These quantities are the area and the location of the center of mass of each TPC signal pulse, representing the pulse charge and the time localization of the pulse. So as to evaluate the consequences of the error introduced by the lossy compression process, the results of the trajectory tracking algorithms that process data off-line after the experiment are analyzed, in particular, versus their sensibility to the noise introduced by the compression. Two different versions of these off-line algorithms are described, performing cluster finding and particle tracking. The results on how these algorithms are affected by the lossy compression are reported. Entropy coding can be applied to the set of events defined by the source model to reduce the bit rate to the corresponding source entropy. Using TPC simulated data according to the expected ALICE TPC performance, the compression algorithm achieves a data reduction in the range of 34.2% down to 23.7% of the original data rate depending on the desired precision on the pulse center of mass. The number of operations per input symbol required to implement the algorithm is relatively low, so that a real-time implementation of the compression process embedded in the TPC data acquisition chain using low-cost integrated electronics is a realistic option to effectively reduce the data storing cost of ALICE experiment

  5. EFFICIENT SPECTRUM UTILIZATION IN COGNITIVE RADIO THROUGH REINFORCEMENT LEARNING

    Directory of Open Access Journals (Sweden)

    Dhananjay Kumar

    2013-09-01

    Full Text Available Machine learning schemes can be employed in cognitive radio systems to intelligently locate the spectrum holes with some knowledge about the operating environment. In this paper, we formulate a variation of Actor Critic Learning algorithm known as Continuous Actor Critic Learning Automaton (CACLA and compare this scheme with Actor Critic Learning scheme and existing Q–learning scheme. Simulation results show that our CACLA scheme has lesser execution time and achieves higher throughput compared to other two schemes.

  6. Scheduled power tracking control of the wind-storage hybrid system based on the reinforcement learning theory

    Science.gov (United States)

    Li, Ze

    2017-09-01

    In allusion to the intermittency and uncertainty of the wind electricity, energy storage and wind generator are combined into a hybrid system to improve the controllability of the output power. A scheduled power tracking control method is proposed based on the reinforcement learning theory and Q-learning algorithm. In this method, the state space of the environment is formed with two key factors, i.e. the state of charge of the energy storage and the difference value between the actual wind power and scheduled power, the feasible action is the output power of the energy storage, and the corresponding immediate rewarding function is designed to reflect the rationality of the control action. By interacting with the environment and learning from the immediate reward, the optimal control strategy is gradually formed. After that, it could be applied to the scheduled power tracking control of the hybrid system. Finally, the rationality and validity of the method are verified through simulation examples.

  7. Hierarchical Load Tracking Control of a Grid-Connected Solid Oxide Fuel Cell for Maximum Electrical Efficiency Operation

    Directory of Open Access Journals (Sweden)

    Yonghui Li

    2015-03-01

    Full Text Available Based on the benchmark solid oxide fuel cell (SOFC dynamic model for power system studies and the analysis of the SOFC operating conditions, the nonlinear programming (NLP optimization method was used to determine the maximum electrical efficiency of the grid-connected SOFC subject to the constraints of fuel utilization factor, stack temperature and output active power. The optimal operating conditions of the grid-connected SOFC were obtained by solving the NLP problem considering the power consumed by the air compressor. With the optimal operating conditions of the SOFC for the maximum efficiency operation obtained at different active power output levels, a hierarchical load tracking control scheme for the grid-connected SOFC was proposed to realize the maximum electrical efficiency operation with the stack temperature bounded. The hierarchical control scheme consists of a fast active power control and a slower stack temperature control. The active power control was developed by using a decentralized control method. The efficiency of the proposed hierarchical control scheme was demonstrated by case studies using the benchmark SOFC dynamic model.

  8. Global Maximum Power Point Tracking (MPPT of a Photovoltaic Module Array Constructed through Improved Teaching-Learning-Based Optimization

    Directory of Open Access Journals (Sweden)

    Kuei-Hsiang Chao

    2016-11-01

    Full Text Available The present study proposes a maximum power point tracking (MPPT method in which improved teaching-learning-based optimization (I-TLBO is applied to perform global MPPT of photovoltaic (PV module arrays under dissimilar shading situations to ensure the maximum power output of the module arrays. The proposed I-TLBO enables the automatic adjustment of teaching factors according to the self-learning ability of students. Incorporating smart-tracking and self-study strategies can effectively improve the tracking response speed and steady-state tracking performance. To evaluate the feasibility of the proposed I-TLBO, a HIP-2717 PV module array from Sanyo Electric was employed to compose various arrays with different serial and parallel configurations. The arrays were operated under different shading conditions to test the MPPT with double, triple, or quadruple peaks of power-voltage characteristic curves. Boost converters were employed with TMS320F2808 digital signal processors to test the proposed MPPT method. Empirical results confirm that the proposed method exhibits more favorable dynamic and static-state response tracking performance compared with that of conventional TLBO.

  9. Learning energy literacy concepts from energy-efficient homes

    Science.gov (United States)

    Paige, Frederick Eugene

    The purpose of this study is to understand ways that occupants' and visitors' interaction with energy efficient home design affects Energy Literacy. Using a case study approach including interviews, surveys, and observations, I examined the potential for affordable energy efficient homes in the Greenville South Carolina area to "teach" concepts from an Energy Literacy framework developed by dozens of educational partners and federal agencies that comprise the U.S. Global Change Research Program Partners. I paid particular attention to concepts from the framework that are transferable to energy decisions beyond a home's walls. My research reveals ways that interaction with high efficiency homes can effect understanding of the following Energy Literacy concepts: human use of energy is subject to limits and constraints, conservation is one way to manage energy resources, electricity is generated in multiple ways, social and technological innovations effect the amount of energy used by society, and energy use can be calculated and monitored. Examples from my case studies show how the at-home examples can make lessons on energy more personally relevant, easy to understand, and applicable. Specifically, I found that: • Home occupants learn the limits of energy in relation to the concrete and constricting costs associated with their consumption. • Heating and cooling techniques showcase the limits and constraints on different sources of energy. • Relatable systems make it easier to understand energy's limits and constraints. • Indistinct and distant power utilities allow consumers to overlook the root of electricity sources. • Visible examples of electricity generation systems make it clear that electricity is generated in multiple ways. • Small and interactive may mean inefficient electricity generation, but efficient energy education. • Perceptions of expense and complexity create a disconnect between residential energy consumers and renewable electricity

  10. An efficient approach to node localisation and tracking in wireless sensor networks

    CSIR Research Space (South Africa)

    Mwila, MK

    2014-12-01

    Full Text Available and efficient localisation method that makes use of an improved RSSI distance estimation model by including the antenna radiation pattern as well as nodes orientations is presented. Mathematical models for distance estimation, cost function and gradient of cost...

  11. High Efficiency GPS Block III L1 band Envelope Tracking Power Amplifier

    Science.gov (United States)

    2016-03-31

    intermo asymmetric ri nction and is d 30.69MHz w measured with pe Amplifier e CGH40120F Sub-System: F e RFPA and E Fig. 7: Nati The switcher the...Paul T. The Efficiency W ack Power Am Dongsu Ki Bumman, "Hi lator for Enve ess Componen Hassan, M. ing power-sup z LTE Envelop ts Conference

  12. Track in the field : telematics services are improving efficiency and safety by closing the communications gap

    Energy Technology Data Exchange (ETDEWEB)

    Budd, G.

    2010-09-15

    High-tech wireless communications tools and equipment monitoring systems are available to fleet vehicle managers who must deal with a variety of hazards, including inadequate equipment diagnostics, irregular vehicle maintenance, employees with erratic driving habits and service roads in remote locations. This paper described Quadrant, a web-based software developed by WebTech Wireless Inc. The tracking and reporting software integrated a global positioning system with cellular and satellite technology, vehicle monitoring systems, and software applications to provide clients with a range of critical data in an automated real-time basis. The data include information on vehicle location, speeding incidents, idling duration, distances, braking and acceleration patterns, fuel usage and engine diagnostic trouble codes. To accommodate Alberta's oilpatch, WebTech added an overlay of Alberta's legal subdivision grid to its mapping system to include relevant off-highway and rural data. Emergency systems have been modified for sites that ban all radio transmission. 1 ref., 1 fig.

  13. Lossy compression of TPC data and trajectory tracking efficiency for the ALICE experiment

    CERN Document Server

    Nicolaucig, A; Mattavelli, M

    2003-01-01

    In this paper a quasi-lossless algorithm for the on-line compression of the data generated by the Time Projection Chamber (TPC) detector of the ALICE experiment at CERN is described. The algorithm is based on a lossy source code modeling technique, i.e. it is based on a source model which is lossy if samples of the TPC signal are considered one by one; conversely, the source model is lossless or quasi-lossless if some physical quantities that are of main interest for the experiment are considered. These quantities are the area and the location of the center of mass of each TPC signal pulse, representing the pulse charge and the time localization of the pulse. So as to evaluate the consequences of the error introduced by the lossy compression process, the results of the trajectory tracking algorithms that process data off-line after the experiment are analyzed, in particular, versus their sensibility to the noise introduced by the compression. Two different versions of these off- line algorithms are described,...

  14. Reinforcement learning design-based adaptive tracking control with less learning parameters for nonlinear discrete-time MIMO systems.

    Science.gov (United States)

    Liu, Yan-Jun; Tang, Li; Tong, Shaocheng; Chen, C L Philip; Li, Dong-Juan

    2015-01-01

    Based on the neural network (NN) approximator, an online reinforcement learning algorithm is proposed for a class of affine multiple input and multiple output (MIMO) nonlinear discrete-time systems with unknown functions and disturbances. In the design procedure, two networks are provided where one is an action network to generate an optimal control signal and the other is a critic network to approximate the cost function. An optimal control signal and adaptation laws can be generated based on two NNs. In the previous approaches, the weights of critic and action networks are updated based on the gradient descent rule and the estimations of optimal weight vectors are directly adjusted in the design. Consequently, compared with the existing results, the main contributions of this paper are: 1) only two parameters are needed to be adjusted, and thus the number of the adaptation laws is smaller than the previous results and 2) the updating parameters do not depend on the number of the subsystems for MIMO systems and the tuning rules are replaced by adjusting the norms on optimal weight vectors in both action and critic networks. It is proven that the tracking errors, the adaptation laws, and the control inputs are uniformly bounded using Lyapunov analysis method. The simulation examples are employed to illustrate the effectiveness of the proposed algorithm.

  15. More Efficient e-Learning through Design: Color of Text and Background

    Science.gov (United States)

    Zufic, Janko; Kalpic, Damir

    2009-01-01

    Background: The area of research aimed for a more efficient e-learning is slowly widening from purely technical to the areas of psychology, didactics and methodology. The question is whether the text or background color influence the efficiency of memory, i.e. learning. If the answer to that question is positive, then another question arises which…

  16. An efficient approach to the analysis of rail surface irregularities accounting for dynamic train-track interaction and inelastic deformations

    Science.gov (United States)

    Andersson, Robin; Torstensson, Peter T.; Kabo, Elena; Larsson, Fredrik

    2015-11-01

    A two-dimensional computational model for assessment of rolling contact fatigue induced by discrete rail surface irregularities, especially in the context of so-called squats, is presented. Dynamic excitation in a wide frequency range is considered in computationally efficient time-domain simulations of high-frequency dynamic vehicle-track interaction accounting for transient non-Hertzian wheel-rail contact. Results from dynamic simulations are mapped onto a finite element model to resolve the cyclic, elastoplastic stress response in the rail. Ratcheting under multiple wheel passages is quantified. In addition, low cycle fatigue impact is quantified using the Jiang-Sehitoglu fatigue parameter. The functionality of the model is demonstrated by numerical examples.

  17. Using Email to Enable E[superscript 3] (Effective, Efficient, and Engaging) Learning

    Science.gov (United States)

    Kim, ChanMin

    2008-01-01

    This article argues that technology that supports both noncognitive and cognitive aspects can make learning more effective, efficient, and engaging (e[superscript 3]-learning). The technology of interest in this article is email. The investigation focuses on characteristics of email that are likely to enable e[superscript 3]-learning. In addition,…

  18. Adaptive block online learning target tracking based on super pixel segmentation

    Science.gov (United States)

    Cheng, Yue; Li, Jianzeng

    2018-04-01

    Video target tracking technology under the unremitting exploration of predecessors has made big progress, but there are still lots of problems not solved. This paper proposed a new algorithm of target tracking based on image segmentation technology. Firstly we divide the selected region using simple linear iterative clustering (SLIC) algorithm, after that, we block the area with the improved density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm. Each sub-block independently trained classifier and tracked, then the algorithm ignore the failed tracking sub-block while reintegrate the rest of the sub-blocks into tracking box to complete the target tracking. The experimental results show that our algorithm can work effectively under occlusion interference, rotation change, scale change and many other problems in target tracking compared with the current mainstream algorithms.

  19. Tracking influence between naive Bayes models using score-based structure learning

    CSIR Research Space (South Africa)

    Ajoodha, R

    2017-11-01

    Full Text Available Current structure learning practices in Bayesian networks have been developed to learn the structure between observable variables and learning latent parameters independently. One exception establishes a variant of EM for learning the structure...

  20. Efficient generation of image chips for training deep learning algorithms

    Science.gov (United States)

    Han, Sanghui; Fafard, Alex; Kerekes, John; Gartley, Michael; Ientilucci, Emmett; Savakis, Andreas; Law, Charles; Parhan, Jason; Turek, Matt; Fieldhouse, Keith; Rovito, Todd

    2017-05-01

    Training deep convolutional networks for satellite or aerial image analysis often requires a large amount of training data. For a more robust algorithm, training data need to have variations not only in the background and target, but also radiometric variations in the image such as shadowing, illumination changes, atmospheric conditions, and imaging platforms with different collection geometry. Data augmentation is a commonly used approach to generating additional training data. However, this approach is often insufficient in accounting for real world changes in lighting, location or viewpoint outside of the collection geometry. Alternatively, image simulation can be an efficient way to augment training data that incorporates all these variations, such as changing backgrounds, that may be encountered in real data. The Digital Imaging and Remote Sensing Image Image Generation (DIRSIG) model is a tool that produces synthetic imagery using a suite of physics-based radiation propagation modules. DIRSIG can simulate images taken from different sensors with variation in collection geometry, spectral response, solar elevation and angle, atmospheric models, target, and background. Simulation of Urban Mobility (SUMO) is a multi-modal traffic simulation tool that explicitly models vehicles that move through a given road network. The output of the SUMO model was incorporated into DIRSIG to generate scenes with moving vehicles. The same approach was used when using helicopters as targets, but with slight modifications. Using the combination of DIRSIG and SUMO, we quickly generated many small images, with the target at the center with different backgrounds. The simulations generated images with vehicles and helicopters as targets, and corresponding images without targets. Using parallel computing, 120,000 training images were generated in about an hour. Some preliminary results show an improvement in the deep learning algorithm when real image training data are augmented with

  1. Point Climat no. 23 'The new European Energy Efficiency Directive: France is on track'

    International Nuclear Information System (INIS)

    Berghmans, Nicolas; Alberola, Emilie

    2012-01-01

    Among the publications of CDC Climat Research, 'Climate Briefs' presents, in a few pages, hot topics in climate change policy. This issue addresses the following points: On October 4 2012, the European Union adopted a new Directive in order to help reach the common target of a 20% improvement in energy efficiency in 2020. At a time when a major national debate on energy transition is set to take place in France, this new directive will need to be taken into account when defining future energy policy. The measures specified in the European Directive, which focus on buildings and energy suppliers, will enable part of France's goal to be met. The transposition of the Directive into French law will result in the setting of a national target for 2020, and will primarily reinforce an existing requirement that applies to energy suppliers, as well as adding measures aimed at informing energy consumers

  2. Tracking student progress in a game-like physics learning environment with a Monte Carlo Bayesian knowledge tracing model

    Science.gov (United States)

    Gweon, Gey-Hong; Lee, Hee-Sun; Dorsey, Chad; Tinker, Robert; Finzer, William; Damelin, Daniel

    2015-03-01

    In tracking student learning in on-line learning systems, the Bayesian knowledge tracing (BKT) model is a popular model. However, the model has well-known problems such as the identifiability problem or the empirical degeneracy problem. Understanding of these problems remain unclear and solutions to them remain subjective. Here, we analyze the log data from an online physics learning program with our new model, a Monte Carlo BKT model. With our new approach, we are able to perform a completely unbiased analysis, which can then be used for classifying student learning patterns and performances. Furthermore, a theoretical analysis of the BKT model and our computational work shed new light on the nature of the aforementioned problems. This material is based upon work supported by the National Science Foundation under Grant REC-1147621 and REC-1435470.

  3. Learning style preferences and their influence on students' problem solving in kinematics observed by eye-tracking method

    Science.gov (United States)

    Kekule, Martina

    2017-01-01

    The article presents eye-tracking method and its using for observing students when they solve problems from kinematics. Particularly, multiple-choice items in TUG-K test by Robert Beichner. Moreover, student's preference for visual way of learning as a possible influential aspect is proofed and discussed. Learning Style Inventory by Dunn, Dunn&Price was administered to students in order to find out their preferences. More than 20 high school and college students about 20 years old took part in the research. Preferred visual way of learning in contrast to the other ways of learning (audio, tactile, kinesthetic) shows very slight correlation with the total score of the test, none correlation with the average fixation duration and slight correlation with average fixation count on a task and average total visit duration on a task.

  4. Robust and efficient fiducial tracking for augmented reality in HD-laparoscopic video streams

    Science.gov (United States)

    Mueller, M.; Groch, A.; Baumhauer, M.; Maier-Hein, L.; Teber, D.; Rassweiler, J.; Meinzer, H.-P.; Wegner, In.

    2012-02-01

    Augmented Reality (AR) is a convenient way of porting information from medical images into the surgical field of view and can deliver valuable assistance to the surgeon, especially in laparoscopic procedures. In addition, high definition (HD) laparoscopic video devices are a great improvement over the previously used low resolution equipment. However, in AR applications that rely on real-time detection of fiducials from video streams, the demand for efficient image processing has increased due to the introduction of HD devices. We present an algorithm based on the well-known Conditional Density Propagation (CONDENSATION) algorithm which can satisfy these new demands. By incorporating a prediction around an already existing and robust segmentation algorithm, we can speed up the whole procedure while leaving the robustness of the fiducial segmentation untouched. For evaluation purposes we tested the algorithm on recordings from real interventions, allowing for a meaningful interpretation of the results. Our results show that we can accelerate the segmentation by a factor of 3.5 on average. Moreover, the prediction information can be used to compensate for fiducials that are temporarily occluded or out of scope, providing greater stability.

  5. An efficient quasi-3D particle tracking-based approach for transport through fractures with application to dynamic dispersion calculation.

    Science.gov (United States)

    Wang, Lichun; Cardenas, M Bayani

    2015-08-01

    The quantitative study of transport through fractured media has continued for many decades, but has often been constrained by observational and computational challenges. Here, we developed an efficient quasi-3D random walk particle tracking (RWPT) algorithm to simulate solute transport through natural fractures based on a 2D flow field generated from the modified local cubic law (MLCL). As a reference, we also modeled the actual breakthrough curves (BTCs) through direct simulations with the 3D advection-diffusion equation (ADE) and Navier-Stokes equations. The RWPT algorithm along with the MLCL accurately reproduced the actual BTCs calculated with the 3D ADE. The BTCs exhibited non-Fickian behavior, including early arrival and long tails. Using the spatial information of particle trajectories, we further analyzed the dynamic dispersion process through moment analysis. From this, asymptotic time scales were determined for solute dispersion to distinguish non-Fickian from Fickian regimes. This analysis illustrates the advantage and benefit of using an efficient combination of flow modeling and RWPT. Copyright © 2015 Elsevier B.V. All rights reserved.

  6. Design and Analysis of Cost-Efficient Sensor Deployment for Tracking Small UAS with Agent-Based Modeling.

    Science.gov (United States)

    Shin, Sangmi; Park, Seongha; Kim, Yongho; Matson, Eric T

    2016-04-22

    Recently, commercial unmanned aerial systems (UAS) have gained popularity. However, these UAS are potential threats to people in terms of safety in public places, such as public parks or stadiums. To reduce such threats, we consider a design, modeling, and evaluation of a cost-efficient sensor system that detects and tracks small UAS. In this research, we focus on discovering the best sensor deployments by simulating different types and numbers of sensors in a designated area, which provide reasonable detection rates at low costs. Also, the system should cover the crowded areas more thoroughly than vacant areas to reduce direct threats to people underneath. This research study utilized the Agent-Based Modeling (ABM) technique to model a system consisting of independent and heterogeneous agents that interact with each other. Our previous work presented the ability to apply ABM to analyze the sensor configurations with two types of radars in terms of cost-efficiency. The results from the ABM simulation provide a list of candidate configurations and deployments that can be referred to for applications in the real world environment.

  7. Improved efficiency in clinical workflow of reporting measured oncology lesions via PACS-integrated lesion tracking tool.

    Science.gov (United States)

    Sevenster, Merlijn; Travis, Adam R; Ganesh, Rajiv K; Liu, Peng; Kose, Ursula; Peters, Joost; Chang, Paul J

    2015-03-01

    OBJECTIVE. Imaging provides evidence for the response to oncology treatment by the serial measurement of reference lesions. Unfortunately, the identification, comparison, measurement, and documentation of several reference lesions can be an inefficient process. We tested the hypothesis that optimized workflow orchestration and tight integration of a lesion tracking tool into the PACS and speech recognition system can result in improvements in oncologic lesion measurement efficiency. SUBJECTS AND METHODS. A lesion management tool tightly integrated into the PACS workflow was developed. We evaluated the effect of the use of the tool on measurement reporting time by means of a prospective time-motion study on 86 body CT examinations with 241 measureable oncologic lesions with four radiologists. RESULTS. Aggregated measurement reporting time per lesion was 11.64 seconds in standard workflow, 16.67 seconds if readers had to register measurements de novo, and 6.36 seconds for each subsequent follow-up study. Differences were statistically significant (p workflow-integrated lesion management tool, especially for patients with multiple follow-up examinations, reversing the onetime efficiency penalty at baseline registration.

  8. Design and Analysis of Cost-Efficient Sensor Deployment for Tracking Small UAS with Agent-Based Modeling

    Directory of Open Access Journals (Sweden)

    Sangmi Shin

    2016-04-01

    Full Text Available Recently, commercial unmanned aerial systems (UAS have gained popularity. However, these UAS are potential threats to people in terms of safety in public places, such as public parks or stadiums. To reduce such threats, we consider a design, modeling, and evaluation of a cost-efficient sensor system that detects and tracks small UAS. In this research, we focus on discovering the best sensor deployments by simulating different types and numbers of sensors in a designated area, which provide reasonable detection rates at low costs. Also, the system should cover the crowded areas more thoroughly than vacant areas to reduce direct threats to people underneath. This research study utilized the Agent-Based Modeling (ABM technique to model a system consisting of independent and heterogeneous agents that interact with each other. Our previous work presented the ability to apply ABM to analyze the sensor configurations with two types of radars in terms of cost-efficiency. The results from the ABM simulation provide a list of candidate configurations and deployments that can be referred to for applications in the real world environment.

  9. Triangular relationship between sleep spindle activity, general cognitive ability and the efficiency of declarative learning.

    Directory of Open Access Journals (Sweden)

    Caroline Lustenberger

    Full Text Available EEG sleep spindle activity (SpA during non-rapid eye movement (NREM sleep has been reported to be associated with measures of intelligence and overnight performance improvements. The reticular nucleus of the thalamus is generating sleep spindles in interaction with thalamocortical connections. The same system enables efficient encoding and processing during wakefulness. Thus, we examined if the triangular relationship between SpA, measures of intelligence and declarative learning reflect the efficiency of the thalamocortical system. As expected, SpA was associated with general cognitive ability, e.g. information processing speed. SpA was also associated with learning efficiency, however, not with overnight performance improvement in a declarative memory task. SpA might therefore reflect the efficiency of the thalamocortical network and can be seen as a marker for learning during encoding in wakefulness, i.e. learning efficiency.

  10. On the Multi-Modal Object Tracking and Image Fusion Using Unsupervised Deep Learning Methodologies

    Science.gov (United States)

    LaHaye, N.; Ott, J.; Garay, M. J.; El-Askary, H. M.; Linstead, E.

    2017-12-01

    The number of different modalities of remote-sensors has been on the rise, resulting in large datasets with different complexity levels. Such complex datasets can provide valuable information separately, yet there is a bigger value in having a comprehensive view of them combined. As such, hidden information can be deduced through applying data mining techniques on the fused data. The curse of dimensionality of such fused data, due to the potentially vast dimension space, hinders our ability to have deep understanding of them. This is because each dataset requires a user to have instrument-specific and dataset-specific knowledge for optimum and meaningful usage. Once a user decides to use multiple datasets together, deeper understanding of translating and combining these datasets in a correct and effective manner is needed. Although there exists data centric techniques, generic automated methodologies that can potentially solve this problem completely don't exist. Here we are developing a system that aims to gain a detailed understanding of different data modalities. Such system will provide an analysis environment that gives the user useful feedback and can aid in research tasks. In our current work, we show the initial outputs our system implementation that leverages unsupervised deep learning techniques so not to burden the user with the task of labeling input data, while still allowing for a detailed machine understanding of the data. Our goal is to be able to track objects, like cloud systems or aerosols, across different image-like data-modalities. The proposed system is flexible, scalable and robust to understand complex likenesses within multi-modal data in a similar spatio-temporal range, and also to be able to co-register and fuse these images when needed.

  11. Evaluation of an Efficient Approach for Target Tracking from Acoustic Imagery for the Perception System of an Autonomous Underwater Vehicle

    Directory of Open Access Journals (Sweden)

    Sebastián A. Villar

    2014-02-01

    Full Text Available This article describes the core algorithms of the perception system to be included within an autonomous underwater vehicle (AUV. This perception system is based on the acoustic data acquired from side scan sonar (SSS. These data should be processed in an efficient time, so that the perception system is able to detect and recognize a predefined target. This detection and recognition outcome is therefore an important piece of knowledge for the AUVs dynamic mission planner (DMP. Effectively, the DMP should propose different trajectories, navigation depths and other parameters that will change the robot's behaviour according to the perception system output. Hence, the time in which to make a decision is critical in order to assure safe robot operation and to acquire good quality data; consequently, the efficiency of the on-line image processing from acoustic data is a key issue. Current techniques for acoustic data processing are time and computationally intensive. Hence, it was decided to process data coming from a SSS using a technique that is used for radars, due to its efficiency and its amenability to on-line processing. The engineering problem to solve in this case was underwater pipeline tracking for routine inspections in the off-shore industry. Then, an automatic oil pipeline detection system was developed borrowing techniques from the processing of radar measurements. The radar technique is known as Cell Average – Constant False Alarm Rate (CA – CFAR. With a slight variation of the algorithms underlying this radar technique, which consisted of the previous accumulation of partial sums, a great improvement in computing time and effort was achieved. Finally, a comparison with previous approaches over images acquired with a SSS from a vessel in the Salvador de Bahia bay in Brazil showed the feasibility of using this on-board technique for AUV perception.

  12. QUEST: Eliminating Online Supervised Learning for Efficient Classification Algorithms

    Directory of Open Access Journals (Sweden)

    Ardjan Zwartjes

    2016-10-01

    Full Text Available In this work, we introduce QUEST (QUantile Estimation after Supervised Training, an adaptive classification algorithm for Wireless Sensor Networks (WSNs that eliminates the necessity for online supervised learning. Online processing is important for many sensor network applications. Transmitting raw sensor data puts high demands on the battery, reducing network life time. By merely transmitting partial results or classifications based on the sampled data, the amount of traffic on the network can be significantly reduced. Such classifications can be made by learning based algorithms using sampled data. An important issue, however, is the training phase of these learning based algorithms. Training a deployed sensor network requires a lot of communication and an impractical amount of human involvement. QUEST is a hybrid algorithm that combines supervised learning in a controlled environment with unsupervised learning on the location of deployment. Using the SITEX02 dataset, we demonstrate that the presented solution works with a performance penalty of less than 10% in 90% of the tests. Under some circumstances, it even outperforms a network of classifiers completely trained with supervised learning. As a result, the need for on-site supervised learning and communication for training is completely eliminated by our solution.

  13. QUEST: Eliminating Online Supervised Learning for Efficient Classification Algorithms.

    Science.gov (United States)

    Zwartjes, Ardjan; Havinga, Paul J M; Smit, Gerard J M; Hurink, Johann L

    2016-10-01

    In this work, we introduce QUEST (QUantile Estimation after Supervised Training), an adaptive classification algorithm for Wireless Sensor Networks (WSNs) that eliminates the necessity for online supervised learning. Online processing is important for many sensor network applications. Transmitting raw sensor data puts high demands on the battery, reducing network life time. By merely transmitting partial results or classifications based on the sampled data, the amount of traffic on the network can be significantly reduced. Such classifications can be made by learning based algorithms using sampled data. An important issue, however, is the training phase of these learning based algorithms. Training a deployed sensor network requires a lot of communication and an impractical amount of human involvement. QUEST is a hybrid algorithm that combines supervised learning in a controlled environment with unsupervised learning on the location of deployment. Using the SITEX02 dataset, we demonstrate that the presented solution works with a performance penalty of less than 10% in 90% of the tests. Under some circumstances, it even outperforms a network of classifiers completely trained with supervised learning. As a result, the need for on-site supervised learning and communication for training is completely eliminated by our solution.

  14. Community action research track: Community-based participatory research and service-learning experiences for medical students.

    Science.gov (United States)

    Gimpel, Nora; Kindratt, Tiffany; Dawson, Alvin; Pagels, Patti

    2018-04-01

    Community-based participatory research (CBPR) and service-learning are unique experiential approaches designed to train medical students how to provide individualized patient care from a population perspective. Medical schools in the US are required to provide support for service-learning and community projects. Despite this requirement, few medical schools offer structured service-learning. We developed the Community Action Research Track (CART) to integrate population medicine, health promotion/disease prevention and the social determinants of health into the medical school curriculum through CBPR and service-learning experiences. This article provides an overview of CART and reports the program impact based on students' participation, preliminary evaluations and accomplishments. CART is an optional 4‑year service-learning experience for medical students interested in community health. The curriculum includes a coordinated longitudinal program of electives, community service-learning and lecture-based instruction. From 2009-2015, 146 CART students participated. Interests in public health (93%), community service (73%), primary care (73%), CBPR (60%) and community medicine (60%) were the top reasons for enrolment. Significant improvements in mean knowledge were found when measuring the principles of CBPR, levels of prevention, determining health literacy and patient communication strategies (all p's Projects were disseminated by at least 65 posters and four oral presentations at local, national and international professional meetings. Six manuscripts were published in peer-reviewed journals. CART is an innovative curriculum for training future physicians to be community-responsive physicians. CART can be replicated by other medical schools interested in offering a longitudinal CBPR and service-learning track in an urban metropolitan setting.

  15. Efficient generation of pronunciation dictionaries: machine learning factors during bootstrapping

    CSIR Research Space (South Africa)

    Davel, MH

    2004-10-01

    Full Text Available The authors focus on factors related to the underlying rule-extraction algorithms, and demonstrate variants of the Dynamically Expanding Context algorithm, which are beneficial for this application. They show that continuous updating of the learned...

  16. Sample efficient multiagent learning in the presence of Markovian agents

    CERN Document Server

    Chakraborty, Doran

    2014-01-01

    The problem of Multiagent Learning (or MAL) is concerned with the study of how intelligent entities can learn and adapt in the presence of other such entities that are simultaneously adapting. The problem is often studied in the stylized settings provided by repeated matrix games (a.k.a. normal form games). The goal of this book is to develop MAL algorithms for such a setting that achieve a new set of objectives which have not been previously achieved. In particular this book deals with learning in the presence of a new class of agent behavior that has not been studied or modeled before in a MAL context: Markovian agent behavior. Several new challenges arise when interacting with this particular class of agents. The book takes a series of steps towards building completely autonomous learning algorithms that maximize utility while interacting with such agents. Each algorithm is meticulously specified with a thorough formal treatment that elucidates its key theoretical properties.

  17. QUEST : Eliminating online supervised learning for efficient classification algorithms

    NARCIS (Netherlands)

    Zwartjes, Ardjan; Havinga, Paul J.M.; Smit, Gerard J.M.; Hurink, Johann L.

    2016-01-01

    In this work, we introduce QUEST (QUantile Estimation after Supervised Training), an adaptive classification algorithm for Wireless Sensor Networks (WSNs) that eliminates the necessity for online supervised learning. Online processing is important for many sensor network applications. Transmitting

  18. Cascade Error Projection: An Efficient Hardware Learning Algorithm

    Science.gov (United States)

    Duong, T. A.

    1995-01-01

    A new learning algorithm termed cascade error projection (CEP) is presented. CEP is an adaption of a constructive architecture from cascade correlation and the dynamical stepsize of A/D conversion from the cascade back propagation algorithm.

  19. Drosophila learn efficient paths to a food source

    OpenAIRE

    Stewart, James; Lim, Terrence; Claridge-Chang, Adam; Wang, Zhiping; Toh, Alicia; Rahman, Mashiur; Navawongse, Rapeechai; Raczkowska, Marlena; Choudhury, Deepak

    2016-01-01

    Elucidating the genetic, and neuronal bases for learned behavior is a central problem in neuroscience. A leading system for neurogenetic discovery is the vinegar fly Drosophila melanogaster; fly memory research has identified genes and circuits that mediate aversive and appetitive learning. However, methods to study adaptive food-seeking behavior in this animal have lagged decades behind rodent feeding analysis, largely due to the challenges presented by their small scale. There is currently ...

  20. Estimation of track registration efficiency in solution medium and study of gamma irradiation effects on the bulk-etch rate and the activation energy for bulk etching of CR-39 (DOP) Solid State Nuclear Track Detector

    International Nuclear Information System (INIS)

    Kalsi, P.C.

    2010-01-01

    The fission track registration efficiency of diethylene glycol bis allyl carbonate (dioctyl phthalate doped) (CR-39 (DOP)) solid state nuclear track detector (SSNTD) in solution medium (K wet ) has been experimentally determined and is found to be (9.7 ± 0.5).10 -4 cm. This is in good agreement with the values of other SSNTDs. The gamma irradiation effects in the dose range of 50.0-220.0 kGy on the bulk etch rate, V b and the activation energy for bulk etching, E of this solid state nuclear track detector (SSNTD) have also been studied. It is observed that the bulk etch rates increase and the activation energies for bulk etching decrease with the increase in gamma dose. These results have been explained on the basis of scission of the detector due to gamma irradiation

  1. Efficient Photometry In-Frame Calibration (EPIC) Gaussian Corrections for Automated Background Normalization of Rate-Tracked Satellite Imagery

    Science.gov (United States)

    Griesbach, J.; Wetterer, C.; Sydney, P.; Gerber, J.

    Photometric processing of non-resolved Electro-Optical (EO) images has commonly required the use of dark and flat calibration frames that are obtained to correct for charge coupled device (CCD) dark (thermal) noise and CCD quantum efficiency/optical path vignetting effects respectively. It is necessary to account/calibrate for these effects so that the brightness of objects of interest (e.g. stars or resident space objects (RSOs)) may be measured in a consistent manner across the CCD field of view. Detected objects typically require further calibration using aperture photometry to compensate for sky background (shot noise). For this, annuluses are measured around each detected object whose contained pixels are used to estimate an average background level that is subtracted from the detected pixel measurements. In a new photometric calibration software tool developed for AFRL/RD, called Efficient Photometry In-Frame Calibration (EPIC), an automated background normalization technique is proposed that eliminates the requirement to capture dark and flat calibration images. The proposed technique simultaneously corrects for dark noise, shot noise, and CCD quantum efficiency/optical path vignetting effects. With this, a constant detection threshold may be applied for constant false alarm rate (CFAR) object detection without the need for aperture photometry corrections. The detected pixels may be simply summed (without further correction) for an accurate instrumental magnitude estimate. The noise distribution associated with each pixel is assumed to be sampled from a Poisson distribution. Since Poisson distributed data closely resembles Gaussian data for parameterized means greater than 10, the data may be corrected by applying bias subtraction and standard-deviation division. EPIC performs automated background normalization on rate-tracked satellite images using the following technique. A deck of approximately 50-100 images is combined by performing an independent median

  2. Task complexity as a driver for collaborative learning efficiency: The collective working-memory effect

    NARCIS (Netherlands)

    Kirschner, Femke; Paas, Fred; Kirschner, Paul A.

    2010-01-01

    Kirschner, F., Paas, F., & Kirschner, P. A. (2011). Task complexity as a driver for collaborative learning efficiency: The collective working-memory effect. Applied Cognitive Psychology, 25, 615–624. doi: 10.1002/acp.1730.

  3. An efficient fluorescent single-particle position tracking system for long-term pulsed measurements of nitrogen-vacancy centers in diamond

    Science.gov (United States)

    Kim, Kiho; Yun, Jiwon; Lee, Donghyuck; Kim, Dohun

    2018-02-01

    A simple and convenient design enables real-time three-dimensional position tracking of nitrogen-vacancy (NV) centers in diamond. The system consists entirely of commercially available components (a single-photon counter, a high-speed digital-to-analog converter, a phase-sensitive detector-based feedback device, and a piezo stage), eliminating the need for custom programming or rigorous optimization processes. With a large input range of counters and trackers combined with high sensitivity of single-photon counting, high-speed position tracking (upper bound recovery time of 0.9 s upon 250 nm of step-like positional shift) not only of bright ensembles, but also of low-photon-collection-efficiency single to few NV centers (down to 103 s-1) is possible. The tracking requires position modulation of only 10 nm, which allows simultaneous position tracking and pulsed measurements in the long term. Therefore, this tracking system enables measuring a single-spin magnetic resonance and Rabi oscillations at a very high resolution even without photon collection optimization. The system is widely applicable to various fields related to NV center quantum manipulation research such as NV optical trapping, NV tracking in fluid dynamics, and biological sensing using NV centers inside a biological cell.

  4. Making Marble Tracks Can Involve Lots of Fun as Well as STEM Learning

    Science.gov (United States)

    Nagel, Bert

    2015-01-01

    Marble tracks are a very popular toy and big ones can be found in science centres in many countries. If children want to make a marble track themselves it is quite a job. It takes a long time, they can take up a lot of space and most structures are quite fragile, as the materials used can very quickly prove unfit for the task and do not last very…

  5. 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

  6. Efficient Software Assets for Fostering Learning in Applied Games

    NARCIS (Netherlands)

    Maurer, Matthias; Nussbaumer, Alexander; Steiner, Christina; Van der Vegt, Wim; Nadolski, Rob; Nyamsuren, Enkhbold; Albert, Dietrich

    2018-01-01

    Digital game technologies are a promising way to enable training providers to reach other target groups, namely those who are not interested in traditional learning technologies. Theoretically, through using digital game technologies we are able to foster the acquisition of any competence by

  7. Lifelong Learning in Europe: Equity and Efficiency in the Balance

    Science.gov (United States)

    Riddell, Sheila, Ed.; Markowitsch, Jorg, Ed.; Weedon, Elisabet, Ed.

    2012-01-01

    The ongoing economic crisis in Europe raises fundamental questions about the European Union's ability to harmonize educational policy across its member states. With evidence that European unity is clearly faltering, many educational goals, including lifelong learning, are in trouble. In this book, the contributors work toward a greater…

  8. Knowledge-Intensive, Interactive and Efficient Relational Pattern Learning

    Science.gov (United States)

    2006-09-01

    Although the idea of incorporating the ability to learn first order rules from RDBMSs is not new – Stonebraker et. al [6] added this feature to Postgres and...Future Directions. Volume To appear. AAAI Press (2004) 6. Stonebraker, M., Kemnitz, G.: The postgres next-generation database management system

  9. Efficient learning mechanisms hold in the social domain and are implemented in the medial prefrontal cortex.

    Science.gov (United States)

    Seid-Fatemi, Azade; Tobler, Philippe N

    2015-05-01

    When we are learning to associate novel cues with outcomes, learning is more efficient if we take advantage of previously learned associations and thereby avoid redundant learning. The blocking effect represents this sort of efficiency mechanism and refers to the phenomenon in which a novel stimulus is blocked from learning when it is associated with a fully predicted outcome. Although there is sufficient evidence that this effect manifests itself when individuals learn about their own rewards, it remains unclear whether it also does when they learn about others' rewards. We employed behavioral and neuroimaging methods to address this question. We demonstrate that blocking does indeed occur in the social domain and it does so to a similar degree as observed in the individual domain. On the neural level, activations in the medial prefrontal cortex (mPFC) show a specific contribution to blocking and learning-related prediction errors in the social domain. These findings suggest that the efficiency principle that applies to reward learning in the individual domain also applies to that in the social domain, with the mPFC playing a central role in implementing it. © The Author (2014). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  10. Lessons Learned From the Environmental Public Health Tracking Sub-County Data Pilot Project.

    Science.gov (United States)

    Werner, Angela K; Strosnider, Heather; Kassinger, Craig; Shin, Mikyong

    2017-12-07

    Small area data are key to better understanding the complex relationships between environmental health, health outcomes, and risk factors at a local level. In 2014, the Centers for Disease Control and Prevention's National Environmental Public Health Tracking Program (Tracking Program) conducted the Sub-County Data Pilot Project with grantees to consider integration of sub-county data into the National Environmental Public Health Tracking Network (Tracking Network). The Tracking Program and grantees developed sub-county-level data for several data sets during this pilot project, working to standardize processes for submitting data and creating required geographies. Grantees documented challenges they encountered during the pilot project and documented decisions. This article covers the challenges revealed during the project. It includes insights into geocoding, aggregation, population estimates, and data stability and provides recommendations for moving forward. National standards for generating, analyzing, and sharing sub-county data should be established to build a system of sub-county data that allow for comparison of outcomes, geographies, and time. Increasing the availability and accessibility of small area data will not only enhance the Tracking Network's capabilities but also contribute to an improved understanding of environmental health and informed decision making at a local level.

  11. Signal information available for plume source tracking with and without surface waves and learning by undergraduates assisting with the research

    Science.gov (United States)

    Wiley, Megan Beth

    Autonomous vehicles have had limited success in locating point sources of pollutants, chemicals, and other passive scalars. However, animals such as stomatopods, a mantis shrimp, track odor plumes easily for food, mates, and habitat. Laboratory experiments using Planar Laser Induced Fluorescence measured odor concentration downstream of a diffusive source with and without live stomatopods to investigate their source-tracking strategies in unidirectional and "wave-affected" (surface waves with a mean current) flows. Despite the dearth of signal, extreme temporal variation, and meandering plume centerline, the stomatopods were able to locate the source, especially in the wave-affected flow. Differences in the two plumes far from the source (>160 cm) appeared to help the animals in the wave-affected flow position themselves closer to the source (fluid mechanics, and there was little evidence of learning by participation in the RAship. One RA's conceptions of turbulence did change, but a group workshop seemed to support this learning more than the RAship. The documented conceptions could aid in curriculum design, since situating new information within current knowledge seems to deepen learning outcomes. The RAs' conceptions varied widely with some overlap of ideas. The interviews also showed that most RAs did not discuss molecular diffusion as part of the mixing process and some remembered information from course demonstrations, but applied them inappropriately to the interview questions.

  12. The Influence of Emotions and Learning Preferences on Learning Strategy Use before Transition into High-Achiever Track Secondary School

    Science.gov (United States)

    Obergriesser, Stefanie; Stoeger, Heidrun

    2016-01-01

    Research on the relationships between students' achievement emotions and their (self-regulated) learning behavior is growing. However, little is known about the relationships between students' learning preferences and achievement emotions and the extent to which these influence learning strategies. In this study we, first, looked at the…

  13. Efficient Machine Learning Approach for Optimizing Scientific Computing Applications on Emerging HPC Architectures

    Energy Technology Data Exchange (ETDEWEB)

    Arumugam, Kamesh [Old Dominion Univ., Norfolk, VA (United States)

    2017-05-01

    the parallel implementation challenges of such irregular applications on different HPC architectures. In particular, we use supervised learning to predict the computation structure and use it to address the control-ow and memory access irregularities in the parallel implementation of such applications on GPUs, Xeon Phis, and heterogeneous architectures composed of multi-core CPUs with GPUs or Xeon Phis. We use numerical simulation of charged particles beam dynamics simulation as a motivating example throughout the dissertation to present our new approach, though they should be equally applicable to a wide range of irregular applications. The machine learning approach presented here use predictive analytics and forecasting techniques to adaptively model and track the irregular memory access pattern at each time step of the simulation to anticipate the future memory access pattern. Access pattern forecasts can then be used to formulate optimization decisions during application execution which improves the performance of the application at a future time step based on the observations from earlier time steps. In heterogeneous architectures, forecasts can also be used to improve the memory performance and resource utilization of all the processing units to deliver a good aggregate performance. We used these optimization techniques and anticipation strategy to design a cache-aware, memory efficient parallel algorithm to address the irregularities in the parallel implementation of charged particles beam dynamics simulation on different HPC architectures. Experimental result using a diverse mix of HPC architectures shows that our approach in using anticipation strategy is effective in maximizing data reuse, ensuring workload balance, minimizing branch and memory divergence, and in improving resource utilization.

  14. Learning networks as an enabler for informed decisions to target energy-efficiency potentials in companies

    NARCIS (Netherlands)

    Wohlfarth, Katharina; Eichhammer, W.A.; Schlomann, Barbara; Mielicke, Ursula

    2017-01-01

    his paper deals with possibilities of targeting energy efficiency potentials in German companies by delivering information and support within a cooperative management system “Learning Energy Efficiency Networks” (LEEN). Information deficits are pointed out as a relevant barrier to implementing

  15. The Efficiency Challenge: Creating a Transformative Learning Experience in a Principles of Management Course

    Science.gov (United States)

    Durant, Rita A.; Carlon, Donna M.; Downs, Alexis

    2017-01-01

    This article describes the results of the "Efficiency Challenge," a 10-week, Principles of Management course activity that uses reflection and goal setting to help students understand the concept of operational efficiency. With transformative learning theory as a lens, we base our report on 4 years' worth of student reflections regarding…

  16. Cycle Tracks and Parking Environments in China: Learning from College Students at Peking University.

    Science.gov (United States)

    Yuan, Changzheng; Sun, Yangbo; Lv, Jun; Lusk, Anne C

    2017-08-18

    China has a historic system of wide cycle tracks, many of which are now encroached by cars, buses and bus stops. Even with these conditions, college students still bicycle. On campuses, students park their bikes on facilities ranging from kick-stand-plazas to caged sheds with racks, pumps and an attendant. In other countries, including Canada, some of the newer cycle tracks need to be wider to accommodate an increasing number of bicyclists. Other countries will also need to improve their bike parking, which includes garage-basement cages and two-tiered racks. China could provide lessons about cycle tracks and bike parking. This study applied the Maslow Transportation Level of Service (LOS) theory, i.e., for cycle tracks and bike parking, only after the basic needs of safety and security are met for both vehicle occupants and bicyclists can the higher needs of convenience and comfort be met. With random clustering, a self-administered questionnaire was collected from 410 students in six dormitory buildings at Peking University in Beijing and an environmental scan of bicycle parking conducted in school/office and living areas. Cycle tracks (1 = very safe/5 = very unsafe) shared with moving cars were most unsafe (mean = 4.6), followed by sharing with parked cars (4.1) or bus stop users (4.1) ( p racks and bicycle parking services (pumps, etc.). If parking were improved, three quarters indicated they would bicycle more. While caged sheds were preferred, in living areas with 1597 parked bikes, caged sheds were only 74.4% occupied. For the future of China's wide cycle tracks, perhaps a fence-separated bus lane beside a cycle track might be considered or, with China's recent increase in bike riding, shared bikes and E-bikes, perhaps cars/buses could be banned from the wide cycle tracks. In other countries, a widened cycle track entrance should deter cars. Everywhere, bike parking sheds could be built and redesigned with painted lines to offer more space and order, similar

  17. Installing and Commissioning a New Radioactive Waste Tracking System - Lessons Learned

    Energy Technology Data Exchange (ETDEWEB)

    Robert S. Anderson; Miklos Garamszeghy; Fred Rodrigues; Ed Nicholls

    2005-05-01

    Ontario Power Generation (OPG) recognizes the importance of information management particularly with regards to its low and intermediate level waste program. Various computer based waste tracking systems have been used in OPG since the 1980s. These systems tracked the physical receipt, processing, storage, and inventory of the waste. As OPG moved towards long-term management (e.g. disposal), it was recognized that tracking of more detailed waste characterization information was important. This required either substantial modification of the existing system to include a waste characterization module or replacing it entirely with a new system. After a detailed review of available options, it was decided that the existing waste tracking application would be replaced with the Idaho National Laboratory’s (INL) Integrated Waste Tracking System (IWTS). Installing and commissioning a system which must receive historical operational waste management information (data) and provide new features, required much more attention than was originally considered. The operational readiness of IWTS required extensive vetting and preparation of historic data (which itself had been created from multiple databases in varied formats) to ensure a consistent format for import of some 30,000-container records, and merging and linking these container records to a waste stream based characterization database. This paper will discuss some of the strengths and weaknesses contributing to project success or hindrance so that others can understand and minimize the difficulties inherent in a project of this magnitude.

  18. Installing and Commissioning a New Radioactive Waste Tracking System - Lessons Learned

    International Nuclear Information System (INIS)

    Robert S. Anderson; Miklos Garamszeghy; Fred Rodrigues; Ed Nicholls

    2005-01-01

    Ontario Power Generation (OPG) recognizes the importance of information management particularly with regards to its low and intermediate level waste program. Various computer based waste tracking systems have been used in OPG since the 1980s. These systems tracked the physical receipt, processing, storage, and inventory of the waste. As OPG moved towards long-term management (e.g. disposal), it was recognized that tracking of more detailed waste characterization information was important. This required either substantial modification of the existing system to include a waste characterization module or replacing it entirely with a new system. After a detailed review of available options, it was decided that the existing waste tracking application would be replaced with the Idaho National Laboratory's (INL) Integrated Waste Tracking System (IWTS). Installing and commissioning a system which must receive historical operational waste management information (data) and provide new features, required much more attention than was originally considered. The operational readiness of IWTS required extensive vetting and preparation of historic data (which itself had been created from multiple databases in varied formats) to ensure a consistent format for import of some 30,000-container records, and merging and linking these container records to a waste stream based characterization database. This paper will discuss some of the strengths and weaknesses contributing to project success or hindrance so that others can understand and minimize the difficulties inherent in a project of this magnitude

  19. Experiential Learning of the Efficient Market Hypothesis: Two Trading Games

    Science.gov (United States)

    Park, Andreas

    2010-01-01

    In goods markets, an equilibrium price balances demand and supply. In a financial market, an equilibrium price also aggregates people's information to reveal the true value of a financial security. Although the underlying idea of informationally efficient markets is one of the centerpieces of capital market theory, students often have difficulties…

  20. A Method for Using Player Tracking Data in Basketball to Learn Player Skills and Predict Team Performance.

    Science.gov (United States)

    Skinner, Brian; Guy, Stephen J

    2015-01-01

    Player tracking data represents a revolutionary new data source for basketball analysis, in which essentially every aspect of a player's performance is tracked and can be analyzed numerically. We suggest a way by which this data set, when coupled with a network-style model of the offense that relates players' skills to the team's success at running different plays, can be used to automatically learn players' skills and predict the performance of untested 5-man lineups in a way that accounts for the interaction between players' respective skill sets. After developing a general analysis procedure, we present as an example a specific implementation of our method using a simplified network model. While player tracking data is not yet available in the public domain, we evaluate our model using simulated data and show that player skills can be accurately inferred by a simple statistical inference scheme. Finally, we use the model to analyze games from the 2011 playoff series between the Memphis Grizzlies and the Oklahoma City Thunder and we show that, even with a very limited data set, the model can consistently describe a player's interactions with a given lineup based only on his performance with a different lineup.

  1. A Method for Using Player Tracking Data in Basketball to Learn Player Skills and Predict Team Performance.

    Directory of Open Access Journals (Sweden)

    Brian Skinner

    Full Text Available Player tracking data represents a revolutionary new data source for basketball analysis, in which essentially every aspect of a player's performance is tracked and can be analyzed numerically. We suggest a way by which this data set, when coupled with a network-style model of the offense that relates players' skills to the team's success at running different plays, can be used to automatically learn players' skills and predict the performance of untested 5-man lineups in a way that accounts for the interaction between players' respective skill sets. After developing a general analysis procedure, we present as an example a specific implementation of our method using a simplified network model. While player tracking data is not yet available in the public domain, we evaluate our model using simulated data and show that player skills can be accurately inferred by a simple statistical inference scheme. Finally, we use the model to analyze games from the 2011 playoff series between the Memphis Grizzlies and the Oklahoma City Thunder and we show that, even with a very limited data set, the model can consistently describe a player's interactions with a given lineup based only on his performance with a different lineup.

  2. Efficient Exact Inference With Loss Augmented Objective in Structured Learning.

    Science.gov (United States)

    Bauer, Alexander; Nakajima, Shinichi; Muller, Klaus-Robert

    2016-08-19

    Structural support vector machine (SVM) is an elegant approach for building complex and accurate models with structured outputs. However, its applicability relies on the availability of efficient inference algorithms--the state-of-the-art training algorithms repeatedly perform inference to compute a subgradient or to find the most violating configuration. In this paper, we propose an exact inference algorithm for maximizing nondecomposable objectives due to special type of a high-order potential having a decomposable internal structure. As an important application, our method covers the loss augmented inference, which enables the slack and margin scaling formulations of structural SVM with a variety of dissimilarity measures, e.g., Hamming loss, precision and recall, Fβ-loss, intersection over union, and many other functions that can be efficiently computed from the contingency table. We demonstrate the advantages of our approach in natural language parsing and sequence segmentation applications.

  3. Approximate Learning and Inference for Tracking with Non-overlapping Cameras

    NARCIS (Netherlands)

    Zajdel, W.; Kröse, B.; Hamza, M.H.

    2003-01-01

    Tracking with multiple cameras requires partitioning of ob servations from various sensors into trajectories. In this paper we assume that the observations are generated by a hidden, stochastic 'partition' process and propose a hidden Markov model (HMM) as a generative model for the data. The state

  4. A Computer Assisted Method to Track Listening Strategies in Second Language Learning

    Science.gov (United States)

    Roussel, Stephanie

    2011-01-01

    Many studies about listening strategies are based on what learners report while listening to an oral message in the second language (Vandergrift, 2003; Graham, 2006). By recording a video of the computer screen while L2 learners (L1 French) were listening to an MP3-track in German, this study uses a novel approach and recent developments in…

  5. Non-Tenure Track Faculty and Learning Communities: Bridging the Divide to Enhance Teaching Quality

    Science.gov (United States)

    Banasik, MaryJo D.; Dean, Jennifer L.

    2016-01-01

    Institutions of higher education are increasingly hiring non-tenure track faculty members (NTTF) to help meet the demands of the institutional teaching mission. Research suggests NTTF experience inadequate working conditions that hinder performance and negatively impact the quality of undergraduate education. Given the growing number of NTTF…

  6. Number line estimation strategies in children with mathematical learning difficulties measured by eye tracking

    NARCIS (Netherlands)

    van 't Noordende, Jaccoline E|info:eu-repo/dai/nl/369862422; van Hoogmoed, Anne H|info:eu-repo/dai/nl/314839496; Schot, Willemijn D; Kroesbergen, Evelyn H|info:eu-repo/dai/nl/241607949

    INTRODUCTION: Number line estimation is one of the skills related to mathematical performance. Previous research has shown that eye tracking can be used to identify differences in the estimation strategies children with dyscalculia and children with typical mathematical development use on number

  7. Number line estimation strategies in children with mathematical learning difficulties measured by eye tracking

    NARCIS (Netherlands)

    van’t Noordende, Jaccoline E.; van Hoogmoed, Anne H.; Schot, Willemijn D.; Kroesbergen, Evelyn H.

    2016-01-01

    Introduction: Number line estimation is one of the skills related to mathematical performance. Previous research has shown that eye tracking can be used to identify differences in the estimation strategies children with dyscalculia and children with typical mathematical development use on number

  8. SU-G-BRA-02: Development of a Learning Based Block Matching Algorithm for Ultrasound Tracking in Radiotherapy

    Energy Technology Data Exchange (ETDEWEB)

    Shepard, A; Bednarz, B [University of Wisconsin, Madison, WI (United States)

    2016-06-15

    Purpose: To develop an ultrasound learning-based tracking algorithm with the potential to provide real-time motion traces of anatomy-based fiducials that may aid in the effective delivery of external beam radiation. Methods: The algorithm was developed in Matlab R2015a and consists of two main stages: reference frame selection, and localized block matching. Immediately following frame acquisition, a normalized cross-correlation (NCC) similarity metric is used to determine a reference frame most similar to the current frame from a series of training set images that were acquired during a pretreatment scan. Segmented features in the reference frame provide the basis for the localized block matching to determine the feature locations in the current frame. The boundary points of the reference frame segmentation are used as the initial locations for the block matching and NCC is used to find the most similar block in the current frame. The best matched block locations in the current frame comprise the updated feature boundary. The algorithm was tested using five features from two sets of ultrasound patient data obtained from MICCAI 2014 CLUST. Due to the lack of a training set associated with the image sequences, the first 200 frames of the image sets were considered a valid training set for preliminary testing, and tracking was performed over the remaining frames. Results: Tracking of the five vessel features resulted in an average tracking error of 1.21 mm relative to predefined annotations. The average analysis rate was 15.7 FPS with analysis for one of the two patients reaching real-time speeds. Computations were performed on an i5-3230M at 2.60 GHz. Conclusion: Preliminary tests show tracking errors comparable with similar algorithms at close to real-time speeds. Extension of the work onto a GPU platform has the potential to achieve real-time performance, making tracking for therapy applications a feasible option. This work is partially funded by NIH grant R01CA

  9. SU-G-BRA-02: Development of a Learning Based Block Matching Algorithm for Ultrasound Tracking in Radiotherapy

    International Nuclear Information System (INIS)

    Shepard, A; Bednarz, B

    2016-01-01

    Purpose: To develop an ultrasound learning-based tracking algorithm with the potential to provide real-time motion traces of anatomy-based fiducials that may aid in the effective delivery of external beam radiation. Methods: The algorithm was developed in Matlab R2015a and consists of two main stages: reference frame selection, and localized block matching. Immediately following frame acquisition, a normalized cross-correlation (NCC) similarity metric is used to determine a reference frame most similar to the current frame from a series of training set images that were acquired during a pretreatment scan. Segmented features in the reference frame provide the basis for the localized block matching to determine the feature locations in the current frame. The boundary points of the reference frame segmentation are used as the initial locations for the block matching and NCC is used to find the most similar block in the current frame. The best matched block locations in the current frame comprise the updated feature boundary. The algorithm was tested using five features from two sets of ultrasound patient data obtained from MICCAI 2014 CLUST. Due to the lack of a training set associated with the image sequences, the first 200 frames of the image sets were considered a valid training set for preliminary testing, and tracking was performed over the remaining frames. Results: Tracking of the five vessel features resulted in an average tracking error of 1.21 mm relative to predefined annotations. The average analysis rate was 15.7 FPS with analysis for one of the two patients reaching real-time speeds. Computations were performed on an i5-3230M at 2.60 GHz. Conclusion: Preliminary tests show tracking errors comparable with similar algorithms at close to real-time speeds. Extension of the work onto a GPU platform has the potential to achieve real-time performance, making tracking for therapy applications a feasible option. This work is partially funded by NIH grant R01CA

  10. Efficiency of the delta-tracking technique for Monte Carlo calculations applied to neutron-transport simulations of the advanced Candu reactor design

    International Nuclear Information System (INIS)

    Arsenault, Benoit; Le Tellier, Romain; Hebert, Alain

    2008-01-01

    The paper presents the results of a first implementation of a Monte Carlo module in DRAGON Version 4 based on the delta-tracking technique. The Monte Carlo module uses the geometry and the self-shielded multigroup cross-sections calculated with a deterministic model. The module has been tested with three different configurations of an ACR TM -type lattice. The paper also discusses the impact of this approach on the efficiency of the Monte Carlo module. (authors)

  11. Efficient Online Learning Algorithms Based on LSTM Neural Networks.

    Science.gov (United States)

    Ergen, Tolga; Kozat, Suleyman Serdar

    2017-09-13

    We investigate online nonlinear regression and introduce novel regression structures based on the long short term memory (LSTM) networks. For the introduced structures, we also provide highly efficient and effective online training methods. To train these novel LSTM-based structures, we put the underlying architecture in a state space form and introduce highly efficient and effective particle filtering (PF)-based updates. We also provide stochastic gradient descent and extended Kalman filter-based updates. Our PF-based training method guarantees convergence to the optimal parameter estimation in the mean square error sense provided that we have a sufficient number of particles and satisfy certain technical conditions. More importantly, we achieve this performance with a computational complexity in the order of the first-order gradient-based methods by controlling the number of particles. Since our approach is generic, we also introduce a gated recurrent unit (GRU)-based approach by directly replacing the LSTM architecture with the GRU architecture, where we demonstrate the superiority of our LSTM-based approach in the sequential prediction task via different real life data sets. In addition, the experimental results illustrate significant performance improvements achieved by the introduced algorithms with respect to the conventional methods over several different benchmark real life data sets.

  12. Technology utilization and energy efficiency: Lessons learned and future prospects

    International Nuclear Information System (INIS)

    Rosenberg, N.

    1992-01-01

    The concept of energy efficiency within the context of economic and environmental policy making is quite complex. Relatively poor economic performance ratings can weaken the validity of some energy supply systems which tend to reduce energy inputs for specific volumes of output, but don't minimize total cost per unit product; and industry is often slow to adopt new technologies, even those proven to reduce total costs. In this paper, the problems connected with growth in energy requirements in relation to product are first examined within the context of world economic performance history. Three key elements are shown to explain the differences in energy intensity and consumption typology among various countries, i.e., availability of energy sources, prices and government policies. Reference is made to the the role of recent energy prices and policies in the United States whose industrialization has been directly connected with the vast availability of some energy sources. In delineating possible future energy scenarios, the paper cites the strong influence of long term capital investment on the timing of the introduction of energy efficient technologies into industrial process schemes. It illustrates the necessity for flexibility in new energy strategies which are to take advantage the opportunities offered by a wide range of alternative energy sources now being made available through technological innovation

  13. Efficient dynamic graph construction for inductive semi-supervised learning.

    Science.gov (United States)

    Dornaika, F; Dahbi, R; Bosaghzadeh, A; Ruichek, Y

    2017-10-01

    Most of graph construction techniques assume a transductive setting in which the whole data collection is available at construction time. Addressing graph construction for inductive setting, in which data are coming sequentially, has received much less attention. For inductive settings, constructing the graph from scratch can be very time consuming. This paper introduces a generic framework that is able to make any graph construction method incremental. This framework yields an efficient and dynamic graph construction method that adds new samples (labeled or unlabeled) to a previously constructed graph. As a case study, we use the recently proposed Two Phase Weighted Regularized Least Square (TPWRLS) graph construction method. The paper has two main contributions. First, we use the TPWRLS coding scheme to represent new sample(s) with respect to an existing database. The representative coefficients are then used to update the graph affinity matrix. The proposed method not only appends the new samples to the graph but also updates the whole graph structure by discovering which nodes are affected by the introduction of new samples and by updating their edge weights. The second contribution of the article is the application of the proposed framework to the problem of graph-based label propagation using multiple observations for vision-based recognition tasks. Experiments on several image databases show that, without any significant loss in the accuracy of the final classification, the proposed dynamic graph construction is more efficient than the batch graph construction. Copyright © 2017 Elsevier Ltd. All rights reserved.

  14. Neural Control of a Tracking Task via Attention-Gated Reinforcement Learning for Brain-Machine Interfaces.

    Science.gov (United States)

    Wang, Yiwen; Wang, Fang; Xu, Kai; Zhang, Qiaosheng; Zhang, Shaomin; Zheng, Xiaoxiang

    2015-05-01

    Reinforcement learning (RL)-based brain machine interfaces (BMIs) enable the user to learn from the environment through interactions to complete the task without desired signals, which is promising for clinical applications. Previous studies exploited Q-learning techniques to discriminate neural states into simple directional actions providing the trial initial timing. However, the movements in BMI applications can be quite complicated, and the action timing explicitly shows the intention when to move. The rich actions and the corresponding neural states form a large state-action space, imposing generalization difficulty on Q-learning. In this paper, we propose to adopt attention-gated reinforcement learning (AGREL) as a new learning scheme for BMIs to adaptively decode high-dimensional neural activities into seven distinct movements (directional moves, holdings and resting) due to the efficient weight-updating. We apply AGREL on neural data recorded from M1 of a monkey to directly predict a seven-action set in a time sequence to reconstruct the trajectory of a center-out task. Compared to Q-learning techniques, AGREL could improve the target acquisition rate to 90.16% in average with faster convergence and more stability to follow neural activity over multiple days, indicating the potential to achieve better online decoding performance for more complicated BMI tasks.

  15. An Eye-Tracking Study of How Color Coding Affects Multimedia Learning

    Science.gov (United States)

    Ozcelik, Erol; Karakus, Turkan; Kursun, Engin; Cagiltay, Kursat

    2009-01-01

    Color coding has been proposed to promote more effective learning. However, insufficient evidence currently exists to show how color coding leads to better learning. The goal of this study was to investigate the underlying cause of the color coding effect by utilizing eye movement data. Fifty-two participants studied either a color-coded or…

  16. A stochastic frontier analysis of technical efficiency in smallholder maize production in Zimbabwe: The post-fast-track land reform outlook

    Directory of Open Access Journals (Sweden)

    Nelson Mango

    2015-12-01

    Full Text Available This article analyses the technical efficiency of maize production in Zimbabwe’s smallholder farming communities following the fast-track land reform of the year 2000 with a view of highlighting key entry points for policy. Using a randomly selected sample of 522 smallholder maize producers, a stochastic frontier production model was applied, using a linearised Cobb–Douglas production function to determine the production elasticity coefficients of inputs, technical efficiency and the determinants of efficiency. The study finds that maize output responds positively to increases in inorganic fertilisers, seed quantity, the use of labour and the area planted. The technical efficiency analysis suggests that about 90% of farmers in the sample are between 60 and 75% efficient, with an average efficiency in the sample of 65%. The significant determinants of technical efficiency were the gender of the household head, household size, frequency of extension visits, farm size and the farming region. The results imply that the average efficiency of maize production could be improved by 35% through better use of existing resources and technology. The results highlight the need for government and private sector assistance in improving efficiency by promoting access to productive resources and ensuring better and more reliable agricultural extension services.

  17. Dual-track CCS stakeholder engagement: Lessons learned from FutureGen in Illinois

    Science.gov (United States)

    Hund, G.; Greenberg, S.E.

    2011-01-01

    FutureGen, as originally planned, was to be the world's first coal-fueled, near-zero emissions power plant with fully integrated, 90% carbon capture and storage (CCS). From conception through siting and design, it enjoyed strong support from multiple stakeholder groups, which benefited the overall project. Understanding the stakeholder engagement process for this project provides valuable insights into the design of stakeholder programs for future CCS projects. FutureGen is one of few projects worldwide that used open competition for siting both the power plant and storage reservoir. Most site proposals were coordinated by State governments. It was unique in this and other respects relative to the site selection method used on other DOE-supported projects. At the time of site selection, FutureGen was the largest proposed facility designed to combine an integrated gasification combined cycle (IGCC) coal-fueled power plant with a CCS system. Stakeholder engagement by states and the industry consortium responsible for siting, designing, building, and operating the facility took place simultaneously and on parallel tracks. On one track were states spearheading state-wide site assessments to identify candidate sites that they wanted to propose for consideration. On the other track was a public-private partnership between an industry consortium of thirteen coal companies and electric utilities that comprised the FutureGen Alliance (Alliance) and the U.S. Department of Energy (DOE). The partnership was based on a cooperative agreement signed by both parties, which assigned the lead for siting to the Alliance. This paper describes the stakeholder engagement strategies used on both of these tracks and provides examples from the engagement process using the Illinois semi-finalist sites. ?? 2011 Published by Elsevier Ltd.

  18. Underlying Processes of an Inverted Personalization Effect in Multimedia Learning – An Eye-Tracking Study

    Directory of Open Access Journals (Sweden)

    Steffi Zander

    2017-12-01

    Full Text Available One of the frequently examined design principles in multimedia learning is the personalization principle. Based on empirical evidence this principle states that using personalized messages in multimedia learning is more beneficial than using formal language (e.g., using ‘you’ instead of ‘the’. Although there is evidence that these slight changes in regard to the language style affect learning, motivation and the perceived cognitive load, it remains unclear, (1 whether the positive effects of personalized language can be transferred to all kinds of content of learning materials (e.g., specific potentially aversive health issues and (2 which are the underlying processes (e.g., attention allocation of the personalization effect. German university students (N = 37 learned symptoms and causes of cerebral hemorrhages either with a formal or a personalized version of the learning material. Analysis revealed comparable results to the few existing previous studies, indicating an inverted personalization effect for potentially aversive learning material. This effect was specifically revealed in regard to decreased average fixation duration and the number of fixations exclusively on the images in the personalized compared to the formal version. These results can be seen as indicators for an inverted effect of personalization on the level of visual attention.

  19. Efficient Actor-Critic Algorithm with Hierarchical Model Learning and Planning

    Science.gov (United States)

    Fu, QiMing

    2016-01-01

    To improve the convergence rate and the sample efficiency, two efficient learning methods AC-HMLP and RAC-HMLP (AC-HMLP with ℓ 2-regularization) are proposed by combining actor-critic algorithm with hierarchical model learning and planning. The hierarchical models consisting of the local and the global models, which are learned at the same time during learning of the value function and the policy, are approximated by local linear regression (LLR) and linear function approximation (LFA), respectively. Both the local model and the global model are applied to generate samples for planning; the former is used only if the state-prediction error does not surpass the threshold at each time step, while the latter is utilized at the end of each episode. The purpose of taking both models is to improve the sample efficiency and accelerate the convergence rate of the whole algorithm through fully utilizing the local and global information. Experimentally, AC-HMLP and RAC-HMLP are compared with three representative algorithms on two Reinforcement Learning (RL) benchmark problems. The results demonstrate that they perform best in terms of convergence rate and sample efficiency. PMID:27795704

  20. Imitation Learning Based on an Intrinsic Motivation Mechanism for Efficient Coding

    Directory of Open Access Journals (Sweden)

    Jochen eTriesch

    2013-11-01

    Full Text Available A hypothesis regarding the development of imitation learning is presented that is rooted in intrinsic motivations. It is derived from a recently proposed form of intrinsically motivated learning (IML for efficient coding in active perception, wherein an agent learns to perform actions with its sense organs to facilitate efficient encoding of the sensory data. To this end, actions of the sense organs that improve the encoding of the sensory data trigger an internally generated reinforcement signal. Here it is argued that the same IML mechanism might also support the development of imitation when general actions beyond those of the sense organs are considered: The learner first observes a tutor performing a behavior and learns a model of the the behavior's sensory consequences. The learner then acts itself and receives an internally generated reinforcement signal reflecting how well the sensory consequences of its own behavior are encoded by the sensory model. Actions that are more similar to those of the tutor will lead to sensory signals that are easier to encode and produce a higher reinforcement signal. Through this, the learner's behavior is progressively tuned to make the sensory consequences of its actions match the learned sensory model. I discuss this mechanism in the context of human language acquisition and bird song learning where similar ideas have been proposed. The suggested mechanism also offers an account for the development of mirror neurons and makes a number of predictions. Overall, it establishes a connection between principles of efficient coding, intrinsic motivations and imitation.

  1. Selecting Learning Tasks: Effects of Adaptation and Shared Control on Learning Efficiency and Task Involvement

    Science.gov (United States)

    Corbalan, Gemma; Kester, Liesbeth; van Merrienboer, Jeroen J. G.

    2008-01-01

    Complex skill acquisition by performing authentic learning tasks is constrained by limited working memory capacity [Baddeley, A. D. (1992). Working memory. "Science, 255", 556-559]. To prevent cognitive overload, task difficulty and support of each newly selected learning task can be adapted to the learner's competence level and perceived task…

  2. Scalable Track Detection in SAR CCD Images

    Energy Technology Data Exchange (ETDEWEB)

    Chow, James G [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Quach, Tu-Thach [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2017-03-01

    Existing methods to detect vehicle tracks in coherent change detection images, a product of combining two synthetic aperture radar images ta ken at different times of the same scene, rely on simple, fast models to label track pixels. These models, however, are often too simple to capture natural track features such as continuity and parallelism. We present a simple convolutional network architecture consisting of a series of 3-by-3 convolutions to detect tracks. The network is trained end-to-end to learn natural track features entirely from data. The network is computationally efficient and improves the F-score on a standard dataset to 0.988, up fr om 0.907 obtained by the current state-of-the-art method.

  3. Fast-track, ambulatory ultrasound-guided Tru-Cut liver biopsy is feasible and cost-efficient

    DEFF Research Database (Denmark)

    Huang, Chenxi; Lorentzen, Torben; Skjoldbye, Bjørn

    2015-01-01

    INTRODUCTION: Most institutions perform percutaneous liver biopsy with a post-biopsy patient observation period lasting up to eight hours, which is resource-demanding. This study aimed to evaluate the safety of liver biopsy performed in a fast-track set-up with an only one-hour post......-biopsy observation time. METHODS: Patients referred to our institution underwent fast-track ultrasound-guided 18-gauge Tru-Cut liver biopsy procedures. Each single biopsy procedure was followed by a post-procedure observational period of one hour and an additional focused assessment with sonography for trauma before...... safely discharged from our institution. No fatality or long-term complications were found during this study. CONCLUSION: The fast-track approach reported herein is a feasible option when adequate patient information is given. Besides the obvious, positive effect on patient logistics and departmental...

  4. Sample-efficient Strategies for Learning in the Presence of Noise

    DEFF Research Database (Denmark)

    Cesa-Bianchi, N.; Dichterman, E.; Fischer, Paul

    1999-01-01

    In this paper, we prove various results about PAC learning in the presence of malicious noise. Our main interest is the sample size behavior of learning algorithms. We prove the first nontrivial sample complexity lower bound in this model by showing that order of &egr;/&Dgr;2 + d/&Dgr; (up...... to logarithmic factors) examples are necessary for PAC learning any target class of {#123;0,1}#125;-valued functions of VC dimension d, where &egr; is the desired accuracy and &eegr; = &egr;/(1 + &egr;) - &Dgr; the malicious noise rate (it is well known that any nontrivial target class cannot be PAC learned...... with accuracy &egr; and malicious noise rate &eegr; &egr;/(1 + &egr;), this irrespective to sample complexity). We also show that this result cannot be significantly improved in general by presenting efficient learning algorithms for the class of all subsets of d elements and the class of unions of at most d...

  5. Tracking Multiple Statistics: Simultaneous Learning of Object Names and Categories in English and Mandarin Speakers.

    Science.gov (United States)

    Chen, Chi-Hsin; Gershkoff-Stowe, Lisa; Wu, Chih-Yi; Cheung, Hintat; Yu, Chen

    2017-08-01

    Two experiments were conducted to examine adult learners' ability to extract multiple statistics in simultaneously presented visual and auditory input. Experiment 1 used a cross-situational learning paradigm to test whether English speakers were able to use co-occurrences to learn word-to-object mappings and concurrently form object categories based on the commonalities across training stimuli. Experiment 2 replicated the first experiment and further examined whether speakers of Mandarin, a language in which final syllables of object names are more predictive of category membership than English, were able to learn words and form object categories when trained with the same type of structures. The results indicate that both groups of learners successfully extracted multiple levels of co-occurrence and used them to learn words and object categories simultaneously. However, marked individual differences in performance were also found, suggesting possible interference and competition in processing the two concurrent streams of regularities. Copyright © 2016 Cognitive Science Society, Inc.

  6. Personality characteristics and their connection with learning efficiency of deaf and partially deaf pupils in mainstream primary and secondary school

    OpenAIRE

    Kastelic, Helena

    2012-01-01

    This thesis deals with personality characteristics and their connection with learning efficiency of deaf and partially deaf pupils and students in mainstream primary and secondary school. The theoretical part defines learning efficiency and focuses on the most significant factors of learning efficiency, including also personality characteristics of an individual. This thesis represents the idea of inclusion and its advantages and disadvantages and suggests to what extent it is present in our ...

  7. Effective and efficient learning in the operating theater with intraoperative video-enhanced surgical procedure training

    OpenAIRE

    van Det, M.J.; Meijerink, W.J.; Hoff, C.; Middel, B.; Pierie, J.P.

    2013-01-01

    INtraoperative Video Enhanced Surgical procedure Training (INVEST) is a new training method designed to improve the transition from basic skills training in a skills lab to procedural training in the operating theater. Traditionally, the master-apprentice model (MAM) is used for procedural training in the operating theater, but this model lacks uniformity and efficiency at the beginning of the learning curve. This study was designed to investigate the effectiveness and efficiency of INVEST co...

  8. Fast-track, ambulatory ultrasound-guided Tru-Cut liver biopsy is feasible and cost-efficient

    DEFF Research Database (Denmark)

    Huang, Chenxi; Lorentzen, Torben; Skjoldbye, Bjørn

    2015-01-01

    safely discharged from our institution. No fatality or long-term complications were found during this study. CONCLUSION: The fast-track approach reported herein is a feasible option when adequate patient information is given. Besides the obvious, positive effect on patient logistics and departmental...

  9. An Efficient and Robust Method for Lagrangian Magnetic Particle Tracking in Fluid Flow Simulations on Unstructured Grids

    NARCIS (Netherlands)

    Cohen Stuart, D.C.; Kleijn, C.R.; Kenjeres, S.

    2010-01-01

    In this paper we report on a newly developed particle tracking scheme for fluid flow simulations on 3D unstructured grids, aiming to provide detailed insights in the particle behaviour in complex geometries. A possible field of applications is the Magnetic Drug Targeting (MDT) technique, on which

  10. Broad Learning System: An Effective and Efficient Incremental Learning System Without the Need for Deep Architecture.

    Science.gov (United States)

    Chen, C L Philip; Liu, Zhulin

    2018-01-01

    Broad Learning System (BLS) that aims to offer an alternative way of learning in deep structure is proposed in this paper. Deep structure and learning suffer from a time-consuming training process because of a large number of connecting parameters in filters and layers. Moreover, it encounters a complete retraining process if the structure is not sufficient to model the system. The BLS is established in the form of a flat network, where the original inputs are transferred and placed as "mapped features" in feature nodes and the structure is expanded in wide sense in the "enhancement nodes." The incremental learning algorithms are developed for fast remodeling in broad expansion without a retraining process if the network deems to be expanded. Two incremental learning algorithms are given for both the increment of the feature nodes (or filters in deep structure) and the increment of the enhancement nodes. The designed model and algorithms are very versatile for selecting a model rapidly. In addition, another incremental learning is developed for a system that has been modeled encounters a new incoming input. Specifically, the system can be remodeled in an incremental way without the entire retraining from the beginning. Satisfactory result for model reduction using singular value decomposition is conducted to simplify the final structure. Compared with existing deep neural networks, experimental results on the Modified National Institute of Standards and Technology database and NYU NORB object recognition dataset benchmark data demonstrate the effectiveness of the proposed BLS.

  11. Concurrent Unimodal Learning Enhances Multisensory Responses of Bi-Directional Crossmodal Learning in Robotic Audio-Visual Tracking

    DEFF Research Database (Denmark)

    Shaikh, Danish; Bodenhagen, Leon; Manoonpong, Poramate

    2018-01-01

    modalities to independently update modality-specific neural weights on a moment-by-moment basis, in response to dynamic changes in noisy sensory stimuli. The circuit is embodied as a non-holonomic robotic agent that must orient a towards a moving audio-visual target. The circuit continuously learns the best...

  12. Collaborative testing for key-term definitions under representative conditions: Efficiency costs and no learning benefits.

    Science.gov (United States)

    Wissman, Kathryn T; Rawson, Katherine A

    2018-01-01

    Students are expected to learn key-term definitions across many different grade levels and academic disciplines. Thus, investigating ways to promote understanding of key-term definitions is of critical importance for applied purposes. A recent survey showed that learners report engaging in collaborative practice testing when learning key-term definitions, with outcomes also shedding light on the way in which learners report engaging in collaborative testing in real-world contexts (Wissman & Rawson, 2016, Memory, 24, 223-239). However, no research has directly explored the effectiveness of engaging in collaborative testing under representative conditions. Accordingly, the current research evaluates the costs (with respect to efficiency) and the benefits (with respect to learning) of collaborative testing for key-term definitions under representative conditions. In three experiments (ns = 94, 74, 95), learners individually studied key-term definitions and then completed retrieval practice, which occurred either individually or collaboratively (in dyads). Two days later, all learners completed a final individual test. Results from Experiments 1-2 showed a cost (with respect to efficiency) and no benefit (with respect to learning) of engaging in collaborative testing for key-term definitions. Experiment 3 evaluated a theoretical explanation for why collaborative benefits do not emerge under representative conditions. Collectively, outcomes indicate that collaborative testing versus individual testing is less effective and less efficient when learning key-term definitions under representative conditions.

  13. Economic efficiency of e-learning in higher education: An industrial approach

    Directory of Open Access Journals (Sweden)

    Jordi Vilaseca

    2008-07-01

    Full Text Available Little work has been yet done to analyse if e-learning is an efficiency way in economic terms to produce higher education, especially because there are not available data in official statistics. Despite of these important constrains, this paper aims to contribute to the study of economic efficiency of e-learning through the analysis of a sample of e-learning universities during a period of time (1997-2002. We have wanted to obtain some empirical evidence to understand if e-learning is a feasible model of providing education for universities and which are the variables that allow for feasibility attainment. The main findings are: 1 that the rise of the number of students enrolled is consistent with increasing labour productivity rates; 2 that cost labour savings are explained by the improvement of universities’ economic efficiency (or total factor productivity; and 3 that improvement of total factor productivity in e-learning production is due to the attainment of scale economies, but also to two organisational innovations: outsourcing processes that leads to the increase of variable costs consistent with decreasing marginal costs, and the sharing of assets’ control and use that allow for a rise in assets rotation.

  14. Content-based VLE designs improve learning efficiency in constructivist statistics education.

    Science.gov (United States)

    Wessa, Patrick; De Rycker, Antoon; Holliday, Ian Edward

    2011-01-01

    We introduced a series of computer-supported workshops in our undergraduate statistics courses, in the hope that it would help students to gain a deeper understanding of statistical concepts. This raised questions about the appropriate design of the Virtual Learning Environment (VLE) in which such an approach had to be implemented. Therefore, we investigated two competing software design models for VLEs. In the first system, all learning features were a function of the classical VLE. The second system was designed from the perspective that learning features should be a function of the course's core content (statistical analyses), which required us to develop a specific-purpose Statistical Learning Environment (SLE) based on Reproducible Computing and newly developed Peer Review (PR) technology. The main research question is whether the second VLE design improved learning efficiency as compared to the standard type of VLE design that is commonly used in education. As a secondary objective we provide empirical evidence about the usefulness of PR as a constructivist learning activity which supports non-rote learning. Finally, this paper illustrates that it is possible to introduce a constructivist learning approach in large student populations, based on adequately designed educational technology, without subsuming educational content to technological convenience. Both VLE systems were tested within a two-year quasi-experiment based on a Reliable Nonequivalent Group Design. This approach allowed us to draw valid conclusions about the treatment effect of the changed VLE design, even though the systems were implemented in successive years. The methodological aspects about the experiment's internal validity are explained extensively. The effect of the design change is shown to have substantially increased the efficiency of constructivist, computer-assisted learning activities for all cohorts of the student population under investigation. The findings demonstrate that a

  15. Content-based VLE designs improve learning efficiency in constructivist statistics education.

    Directory of Open Access Journals (Sweden)

    Patrick Wessa

    Full Text Available BACKGROUND: We introduced a series of computer-supported workshops in our undergraduate statistics courses, in the hope that it would help students to gain a deeper understanding of statistical concepts. This raised questions about the appropriate design of the Virtual Learning Environment (VLE in which such an approach had to be implemented. Therefore, we investigated two competing software design models for VLEs. In the first system, all learning features were a function of the classical VLE. The second system was designed from the perspective that learning features should be a function of the course's core content (statistical analyses, which required us to develop a specific-purpose Statistical Learning Environment (SLE based on Reproducible Computing and newly developed Peer Review (PR technology. OBJECTIVES: The main research question is whether the second VLE design improved learning efficiency as compared to the standard type of VLE design that is commonly used in education. As a secondary objective we provide empirical evidence about the usefulness of PR as a constructivist learning activity which supports non-rote learning. Finally, this paper illustrates that it is possible to introduce a constructivist learning approach in large student populations, based on adequately designed educational technology, without subsuming educational content to technological convenience. METHODS: Both VLE systems were tested within a two-year quasi-experiment based on a Reliable Nonequivalent Group Design. This approach allowed us to draw valid conclusions about the treatment effect of the changed VLE design, even though the systems were implemented in successive years. The methodological aspects about the experiment's internal validity are explained extensively. RESULTS: The effect of the design change is shown to have substantially increased the efficiency of constructivist, computer-assisted learning activities for all cohorts of the student

  16. Content-Based VLE Designs Improve Learning Efficiency in Constructivist Statistics Education

    Science.gov (United States)

    Wessa, Patrick; De Rycker, Antoon; Holliday, Ian Edward

    2011-01-01

    Background We introduced a series of computer-supported workshops in our undergraduate statistics courses, in the hope that it would help students to gain a deeper understanding of statistical concepts. This raised questions about the appropriate design of the Virtual Learning Environment (VLE) in which such an approach had to be implemented. Therefore, we investigated two competing software design models for VLEs. In the first system, all learning features were a function of the classical VLE. The second system was designed from the perspective that learning features should be a function of the course's core content (statistical analyses), which required us to develop a specific–purpose Statistical Learning Environment (SLE) based on Reproducible Computing and newly developed Peer Review (PR) technology. Objectives The main research question is whether the second VLE design improved learning efficiency as compared to the standard type of VLE design that is commonly used in education. As a secondary objective we provide empirical evidence about the usefulness of PR as a constructivist learning activity which supports non-rote learning. Finally, this paper illustrates that it is possible to introduce a constructivist learning approach in large student populations, based on adequately designed educational technology, without subsuming educational content to technological convenience. Methods Both VLE systems were tested within a two-year quasi-experiment based on a Reliable Nonequivalent Group Design. This approach allowed us to draw valid conclusions about the treatment effect of the changed VLE design, even though the systems were implemented in successive years. The methodological aspects about the experiment's internal validity are explained extensively. Results The effect of the design change is shown to have substantially increased the efficiency of constructivist, computer-assisted learning activities for all cohorts of the student population under

  17. Learning efficient visual search for stimuli containing diagnostic spatial configurations and color-shape conjunctions.

    Science.gov (United States)

    Reavis, Eric A; Frank, Sebastian M; Tse, Peter U

    2018-04-12

    Visual search is often slow and difficult for complex stimuli such as feature conjunctions. Search efficiency, however, can improve with training. Search for stimuli that can be identified by the spatial configuration of two elements (e.g., the relative position of two colored shapes) improves dramatically within a few hundred trials of practice. Several recent imaging studies have identified neural correlates of this learning, but it remains unclear what stimulus properties participants learn to use to search efficiently. Influential models, such as reverse hierarchy theory, propose two major possibilities: learning to use information contained in low-level image statistics (e.g., single features at particular retinotopic locations) or in high-level characteristics (e.g., feature conjunctions) of the task-relevant stimuli. In a series of experiments, we tested these two hypotheses, which make different predictions about the effect of various stimulus manipulations after training. We find relatively small effects of manipulating low-level properties of the stimuli (e.g., changing their retinotopic location) and some conjunctive properties (e.g., color-position), whereas the effects of manipulating other conjunctive properties (e.g., color-shape) are larger. Overall, the findings suggest conjunction learning involving such stimuli might be an emergent phenomenon that reflects multiple different learning processes, each of which capitalizes on different types of information contained in the stimuli. We also show that both targets and distractors are learned, and that reversing learned target and distractor identities impairs performance. This suggests that participants do not merely learn to discriminate target and distractor stimuli, they also learn stimulus identity mappings that contribute to performance improvements.

  18. A Study on the Learning Efficiency of Multimedia-Presented, Computer-Based Science Information

    Science.gov (United States)

    Guan, Ying-Hua

    2009-01-01

    This study investigated the effects of multimedia presentations on the efficiency of learning scientific information (i.e. information on basic anatomy of human brains and their functions, the definition of cognitive psychology, and the structure of human memory). Experiment 1 investigated whether the modality effect could be observed when the…

  19. Learning Efficiency of Two ICT-Based Instructional Strategies in Greek Sheep Farmers

    Science.gov (United States)

    Bellos, Georgios; Mikropoulos, Tassos A.; Deligeorgis, Stylianos; Kominakis, Antonis

    2016-01-01

    Purpose: The objective of the present study was to compare the learning efficiency of two information and communications technology (ICT)-based instructional strategies (multimedia presentation (MP) and concept mapping) in a sample (n = 187) of Greek sheep farmers operating mainly in Western Greece. Design/methodology/approach: In total, 15…

  20. Tensorial dynamic time warping with articulation index representation for efficient audio-template learning.

    Science.gov (United States)

    Le, Long N; Jones, Douglas L

    2018-03-01

    Audio classification techniques often depend on the availability of a large labeled training dataset for successful performance. However, in many application domains of audio classification (e.g., wildlife monitoring), obtaining labeled data is still a costly and laborious process. Motivated by this observation, a technique is proposed to efficiently learn a clean template from a few labeled, but likely corrupted (by noise and interferences), data samples. This learning can be done efficiently via tensorial dynamic time warping on the articulation index-based time-frequency representations of audio data. The learned template can then be used in audio classification following the standard template-based approach. Experimental results show that the proposed approach outperforms both (1) the recurrent neural network approach and (2) the state-of-the-art in the template-based approach on a wildlife detection application with few training samples.

  1. Alpha particle and proton relative thermoluminescence efficiencies in LiF:Mg, Cu, P:is track structure theory up to the task?

    International Nuclear Information System (INIS)

    Horowitz, Y. S.; Siboni, D.; Oster, L.; Livingstone, J.; Guatelli, S.; Rosenfeld, A.; Emfietzoglou, D.; Bilski, P.; Obryk, B.

    2008-01-01

    Low-energy alpha particle and proton heavy charged particle (HCP) relative thermoluminescence (TL) efficiencies are calculated for the major dosimetric glow peak in LiF:Mg, Cu, P (MCP-N) in the framework of track structure theory (TST). The calculations employ previously published TRIPOS-E Monte Carlo track segment values of the radial dose in condensed phase LiF calculated at the Instituto National de Investigaciones Nucleares (Mexico) and experimentally measured normalised 60 Co gamma-induced TL dose-response functions, f(D), carried out at the Inst. of Nuclear Physics (Poland). The motivation for the calculations is to test the validity of TST in a TL system in which f(D) is not supra-linear (f(D) >1) and is not significantly dependent on photon energy contrary to the behaviour of the dose-response of composite peak 5 in the glow curve of LiF:Mg, Ti (TLD-100). The calculated HCP relative efficiencies in LiF:MCP-N are 23-87 % lower than the experimentally measured values, indicating a weakness in the major premise of TST which exclusively relates HCP effects to the radiation action of the secondary electrons liberated by the HCP slowing down. However, an analysis of the uncertainties involved in the TST calculations and experiments (i.e. experimental measurement of f(D) at high levels of dose, sample light self-absorption and accuracy in the estimation of D R, especially towards the end of the HCP track) indicate that these may be too large to enable a definite conclusion. More accurate estimation of sample light self-absorption, improved measurements of f(D) and full-track Monte Carlo calculations of D R incorporating improvements of the low-energy electron transport are indicated in order to reduce uncertainties and enable a final conclusion. (authors)

  2. Alpha particle and proton relative thermoluminescence efficiencies in LiF:Mg,Cu,P:is track structure theory up to the task?

    Science.gov (United States)

    Horowitz, Y S; Siboni, D; Oster, L; Livingstone, J; Guatelli, S; Rosenfeld, A; Emfietzoglou, D; Bilski, P; Obryk, B

    2012-07-01

    Low-energy alpha particle and proton heavy charged particle (HCP) relative thermoluminescence (TL) efficiencies are calculated for the major dosimetric glow peak in LiF:Mg,Cu,P (MCP-N) in the framework of track structure theory (TST). The calculations employ previously published TRIPOS-E Monte Carlo track segment values of the radial dose in condensed phase LiF calculated at the Instituto National de Investigaciones Nucleares (Mexico) and experimentally measured normalised (60)Co gamma-induced TL dose-response functions, f(D), carried out at the Institute of Nuclear Physics (Poland). The motivation for the calculations is to test the validity of TST in a TL system in which f(D) is not supralinear (f(D) >1) and is not significantly dependent on photon energy contrary to the behaviour of the dose-response of composite peak 5 in the glow curve of LiF:Mg,Ti (TLD-100). The calculated HCP relative efficiencies in LiF:MCP-N are 23-87% lower than the experimentally measured values, indicating a weakness in the major premise of TST which exclusively relates HCP effects to the radiation action of the secondary electrons liberated by the HCP slowing down. However, an analysis of the uncertainties involved in the TST calculations and experiments (i.e. experimental measurement of f(D) at high levels of dose, sample light self-absorption and accuracy in the estimation of D(r), especially towards the end of the HCP track) indicate that these may be too large to enable a definite conclusion. More accurate estimation of sample light self-absorption, improved measurements of f(D) and full-track Monte Carlo calculations of D(r) incorporating improvements of the low-energy electron transport are indicated in order to reduce uncertainties and enable a final conclusion.

  3. Efficiency studies for a tracking detector based on square 1.5 m long scintillating fibers read out by SiPM

    International Nuclear Information System (INIS)

    Sanchez Majos, S.; Achenbach, P.; Pochodzalla, J.

    2009-01-01

    A tracking detector based on 1.5 m long scintillating fibers is being developed for the electron arm of the KAOS spectrometer at the Mainz Microtron MAMI. Measurements on light attenuation, particle detection efficiencies and accidental coincidence rates with a prototype set-up using 2x2mm 2 fibers read out by silicon photomultipliers (SiPM) are presented. The highest efficiency at the lowest accidental coincidence rate was reached for high trigger thresholds at the largest SiPM bias voltages. The influence of signal attenuation and dispersion on detection efficiencies is discussed. The results are in good agreement with a Monte Carlo model that was used to predict detector characteristics for different fiber geometries.

  4. Methodologies for tracking learning paths in the Making a filmmaker research study of young Nordic filmmakers

    DEFF Research Database (Denmark)

    Frølunde, Lisbeth; Lindstrand, Fredrik; Öhman-Gullberg, Lisa

    The aim of this paper is twofold. On the one hand, to present an outline of the ongoing research study Making a filmmaker, which examines how young Nordic filmmakers create their own learning paths along formal and/or informal contexts. Our focus in this paper is especially directed towards issues...

  5. The Smart Gut: Tracking Affective Associative Learning with Measures of "Liking", Facial Electromyography, and Preferential Looking

    Science.gov (United States)

    Armel, K. Carrie; Pulido, Carmen; Wixted, John T.; Chiba, Andrea A.

    2009-01-01

    We demonstrate here that initially neutral items can acquire "specific" value based on their associated outcomes, and that responses of physiological systems to such previously meaningless stimuli can rapidly reflect this associative history. Each participant participated in an associative learning task in which four neutral abstract pictures were…

  6. Advanced Tracking of Vehicles

    DEFF Research Database (Denmark)

    Jensen, Christian Søndergaard; Li, K.-J.; Pakalnis, Stardas

    2005-01-01

    efficient tracking techniques. More specifically, while almost all commercially available tracking solutions simply offer time-based sampling of positions, this paper's techniques aim to offer a guaranteed tracking accuracy for each vehicle at the lowest possible costs, in terms of network traffic...

  7. Determination of detection efficiency for radon and radon daughters with CR 39 track detector - a Monte Carlo study

    International Nuclear Information System (INIS)

    Nikezic, D.

    1994-01-01

    The detection effciency, ρ, (or a calibration coefficient k) for radon measurements with a solid state nuclear track detector CR 39 was determined by many authors. There is a considerable discrepancy among reported values for ρ. This situation was a challenge to develop a software program to calculation ρ. This software is based on Bethe-Bloch's expression for the stopping power for heavy charged particles in a medium, as wll as on the Monte Carlo Method. Track parameters were calculated by using an iterative procedure as given in G. Somogyi et al., Nucl. Instr. and Meth. 109 (1973) 211. Results for an open detector and for the detector in a diffusion chamber were presented in this article. (orig.)

  8. On increasing the spectral efficiency and transmissivity in the data transmission channel on the spacecraft-ground tracking station line

    Science.gov (United States)

    Andrianov, M. N.; Kostenko, V. I.; Likhachev, S. F.

    2018-01-01

    The algorithms for achieving a practical increase in the rate of data transmission on the space-craft-ground tracking station line has been considered. This increase is achieved by applying spectral-effective modulation techniques, the technology of orthogonal frequency compression of signals using millimeterrange radio waves. The advantages and disadvantages of each of three algorithms have been revealed. A significant advantage of data transmission in the millimeter range has been indicated.

  9. Quantifying the statistical importance of utilizing regression over classic energy intensity calculations for tracking efficiency improvements in industry

    Energy Technology Data Exchange (ETDEWEB)

    Nimbalkar, Sachin U. [ORNL; Wenning, Thomas J. [ORNL; Guo, Wei [ORNL

    2017-08-01

    In the United States, manufacturing facilities account for about 32% of total domestic energy consumption in 2014. Robust energy tracking methodologies are critical to understanding energy performance in manufacturing facilities. Due to its simplicity and intuitiveness, the classic energy intensity method (i.e. the ratio of total energy use over total production) is the most widely adopted. However, the classic energy intensity method does not take into account the variation of other relevant parameters (i.e. product type, feed stock type, weather, etc.). Furthermore, the energy intensity method assumes that the facilities’ base energy consumption (energy use at zero production) is zero, which rarely holds true. Therefore, it is commonly recommended to utilize regression models rather than the energy intensity approach for tracking improvements at the facility level. Unfortunately, many energy managers have difficulties understanding why regression models are statistically better than utilizing the classic energy intensity method. While anecdotes and qualitative information may convince some, many have major reservations about the accuracy of regression models and whether it is worth the time and effort to gather data and build quality regression models. This paper will explain why regression models are theoretically and quantitatively more accurate for tracking energy performance improvements. Based on the analysis of data from 114 manufacturing plants over 12 years, this paper will present quantitative results on the importance of utilizing regression models over the energy intensity methodology. This paper will also document scenarios where regression models do not have significant relevance over the energy intensity method.

  10. Learning the trajectory of a moving visual target and evolution of its tracking in the monkey

    Science.gov (United States)

    Bourrelly, Clara; Quinet, Julie; Cavanagh, Patrick

    2016-01-01

    An object moving in the visual field triggers a saccade that brings its image onto the fovea. It is followed by a combination of slow eye movements and catch-up saccades that try to keep the target image on the fovea as long as possible. The accuracy of this ability to track the “here-and-now” location of a visual target contrasts with the spatiotemporally distributed nature of its encoding in the brain. We show in six experimentally naive monkeys how this performance is acquired and gradually evolves during successive daily sessions. During the early exposure, the tracking is mostly saltatory, made of relatively large saccades separated by low eye velocity episodes, demonstrating that accurate (here and now) pursuit is not spontaneous and that gaze direction lags behind its location most of the time. Over the sessions, while the pursuit velocity is enhanced, the gaze is more frequently directed toward the current target location as a consequence of a 25% reduction in the number of catch-up saccades and a 37% reduction in size (for the first saccade). This smoothing is observed at several scales: during the course of single trials, across the set of trials within a session, and over successive sessions. We explain the neurophysiological processes responsible for this combined evolution of saccades and pursuit in the absence of stringent training constraints. More generally, our study shows that the oculomotor system can be used to discover the neural mechanisms underlying the ability to synchronize a motor effector with a dynamic external event. PMID:27683886

  11. "Sickle cell anemia: tracking down a mutation": an interactive learning laboratory that communicates basic principles of genetics and cellular biology.

    Science.gov (United States)

    Jarrett, Kevin; Williams, Mary; Horn, Spencer; Radford, David; Wyss, J Michael

    2016-03-01

    "Sickle cell anemia: tracking down a mutation" is a full-day, inquiry-based, biology experience for high school students enrolled in genetics or advanced biology courses. In the experience, students use restriction endonuclease digestion, cellulose acetate gel electrophoresis, and microscopy to discover which of three putative patients have the sickle cell genotype/phenotype using DNA and blood samples from wild-type and transgenic mice that carry a sickle cell mutation. The inquiry-based, problem-solving approach facilitates the students' understanding of the basic concepts of genetics and cellular and molecular biology and provides experience with contemporary tools of biotechnology. It also leads to students' appreciation of the causes and consequences of this genetic disease, which is relatively common in individuals of African descent, and increases their understanding of the first principles of genetics. This protocol provides optimal learning when led by well-trained facilitators (including the classroom teacher) and carried out in small groups (6:1 student-to-teacher ratio). This high-quality experience can be offered to a large number of students at a relatively low cost, and it is especially effective in collaboration with a local science museum and/or university. Over the past 15 yr, >12,000 students have completed this inquiry-based learning experience and demonstrated a consistent, substantial increase in their understanding of the disease and genetics in general. Copyright © 2016 The American Physiological Society.

  12. Design of Open Content Social Learning That Increases Learning Efficiency and Engagement Based on Open Pedagogy

    Science.gov (United States)

    John, Benneaser; Thavavel, V.; Jayaraj, Jayakumar; Muthukumar, A.; Jeevanandam, Poornaselvan Kittu

    2016-01-01

    Academic writing skills are crucial when students, e.g., in teacher education programs, write their undergraduate theses. A multi-modal web-based and self-regulated learning resource on academic writing was developed, using texts, hypertext, moving images, podcasts and templates. A study, using surveys and a focus group, showed that students used…

  13. The Influence of Learning Strategies in the Acquisition, Retention, and Transfer of a Visual Tracking Task

    Science.gov (United States)

    1979-08-01

    Psychology, Psychoanalysis and Neurology X. N. Y.: Van Nostrand Reinhold, 1977. Craik , F. I. M., & Lockhart , R. S. Levels of processing : A framework for...Morris, C. D., & Stein, B. S. Some general constraints oil learning and memory research. In F. I. M. Craik & L. S. Cermak (Eds.), Levels of processing ... Craik & Lockhart , 1972; Craik & Tulving, 1975). Although the dependent measures differ, the conclusions drawn remain similar. Strategy usage has a

  14. LIPS TRACKING IDENTIFICATION OF A CORRECT QURANIC LETTERS PRONUNCIATION FOR TAJWEED TEACHING AND LEARNING

    Directory of Open Access Journals (Sweden)

    Tareq Altalmas

    2017-05-01

    Full Text Available Mastering the recitation of the holy Quran is an obligation among Muslims. It is an important task to fulfill other Ibadat like prayer, pilgrimage and zikr. However, the traditional way of teaching Quran recitation is a hard task due to the extensive training time and effort required from both teacher and learner. In fact, learning the correct pronunciation of the Quranic letters is the first step in mastering Tajweed (Rules and Guidance in Quranic recitation. The pronunciation of Arabic letters is based on its points of articulation and the characteristics of a particular letter. In this paper we implement the lip identification technique from video signal acquired from expert to extract the movement data of the lips while pronouncing the correct Quranic letters. The extracted lip movement data from expert helps in categorizing the letters into 5 groups and in deciding the final shape of the lips. Later the technique was then tested among a public reciter and then compared for similarity verification between the public and the professional reciter. The system is able to extract the lips movement of the random user and draw the displacement graph and compared with the pronunciation of the expert. The error will be shown if the user has mistakenly pronounced the letter and suggested for improvement. More subjects with different background will be tested in very near future with feedback instructions. Machine learning techniques will be implemented at later stage for the real time application for learning process.

  15. The formation and development of corporate culture of learning organization: efficiency assessment

    Directory of Open Access Journals (Sweden)

    T. O. Tolstykh

    2017-01-01

    Full Text Available In modern conditions of digitalization of the economy, its integration with the policy society questions of formation and development of corporate culture of the learning organisation are of particular relevance. Digital transformation of business dictates the need for the emergence and development of learning organizations, creating and preserving knowledge. In this situation, the openness of issues of assessment of efficiency of processes of formation and development defines the importance of the proposed research. Corporate culture is regarded by most scholars as the most important internal resource of the organization, able to provide her with stability in a crisis and give impetus to the development and transition to qualitatively different levels of the life cycle. This position assumes that a strong corporate culture should be aimed at building a learning organization, able to quickly adapt to changes in the external and internal environment. This article examines the issue of assessment of efficiency of corporate culture; it is shown that in addition to the empirical, sociological methods and qualitative approach to evaluation, is acceptable investment approach. This option appears when you use the aggregate target-oriented and project management methods, which allows in a systematic manner to carry out the formation and development of corporate culture. The assessment should be subject to software development activities and (or development of the corporate culture of a learning organization. In evidence to draw conclusions on the example of agricultural companies, a calculation of the economic efficiency of the program of formation of corporate culture of a learning organization. Calculation of net discounted income, the net present value of the project, profitability index, project profitability, payback period. This confirms the social and economic effects of the proposed program on the formation of corporate culture of independent

  16. A bayesian approach for learning and tracking switching, non-stationary opponents

    CSIR Research Space (South Africa)

    Hernandez-Leal, P

    2016-02-01

    Full Text Available of interactions. We propose using a Bayesian framework to address this problem. Bayesian policy reuse (BPR) has been empirically shown to be efficient at correctly detecting the best policy to use from a library in sequential decision tasks. In this paper we...

  17. A Novel Energy-Efficient Multi-Sensor Fusion Wake-Up Control Strategy Based on a Biomimetic Infectious-Immune Mechanism for Target Tracking.

    Science.gov (United States)

    Zhou, Jie; Liang, Yan; Shen, Qiang; Feng, Xiaoxue; Pan, Quan

    2018-04-18

    A biomimetic distributed infection-immunity model (BDIIM), inspired by the immune mechanism of an infected organism, is proposed in order to achieve a high-efficiency wake-up control strategy based on multi-sensor fusion for target tracking. The resultant BDIIM consists of six sub-processes reflecting the infection-immunity mechanism: occurrence probabilities of direct-infection (DI) and cross-infection (CI), immunity/immune-deficiency of DI and CI, pathogen amount of DI and CI, immune cell production, immune memory, and pathogen accumulation under immunity state. Furthermore, a corresponding relationship between the BDIIM and sensor wake-up control is established to form the collaborative wake-up method. Finally, joint surveillance and target tracking are formulated in the simulation, in which we show that the energy cost and position tracking error are reduced to 50.8% and 78.9%, respectively. Effectiveness of the proposed BDIIM algorithm is shown, and this model is expected to have a significant role in guiding the performance improvement of multi-sensor networks.

  18. Mit Blended Learning zur effizienten Literatursuche / Blended Learning: a way to efficient literature search

    Directory of Open Access Journals (Sweden)

    Schubnell, Brigitte

    2007-12-01

    Full Text Available Since 2004 Information Literacy has been part of medical studies at the University of Zurich. The practical course “transfer of knowledge” takes place in the 2nd semester and is mandatory. In 2004 and 2005 the Main Library University of Zurich introduced the medical students, roughly 300, into the topic using a classic approach, i.e. by presentations and exercises. 32 double lessons were given by five employees of the Main Library. Evaluations showed that the students had only little interest in the topic and rated the course poorly. Among others, the following reasons were considered to explain the students’ weak motivation:lack of stimulation such as relevance for exams or other controls of performance limited possibilities to apply the gained knowledge in the 1st year of medical studies; thus students considered some contents irrelevant some exercises were not designed optimally, thereby challenging the students too little In the summer semester 2006 the course was given for the first time using blended learning. The goal was to increase the activity of the students and to make the contents available in the long term. The new form of teaching and learning has been a success and in 2007 the second round took place in the new mode. In the new course, students acquire the theory by self-study using an e-learning module. The period of self-study, which is finished by an online-test, is followed by a double lesson. This double lesson has the character of an exercise and is used to discuss problems and to consolidate the learnt contents using a given question. The e-learning module is freely accessible on the Virtual Education Platform Medicine (VAM.

  19. Distraction during learning with hypermedia: Difficult tasks help to keep task goals on track

    Directory of Open Access Journals (Sweden)

    Katharina eScheiter

    2014-03-01

    Full Text Available In educational hypermedia environments, students are often confronted with potential sources of distraction arising from additional information that, albeit interesting, is unrelated to their current task goal. The paper investigates the conditions under which distraction occurs and hampers performance. Based on theories of volitional action control it was hypothesized that interesting information, especially if related to a pending goal, would interfere with task performance only when working on easy, but not on difficult tasks. In Experiment 1, 66 students learned about probability theory using worked examples and solved corresponding test problems, whose task difficulty was manipulated. As a second factor, the presence of interesting information unrelated to the primary task was varied. Results showed that students solved more easy than difficult probability problems correctly. However, the presence of interesting, but task-irrelevant information did not interfere with performance. In Experiment 2, 68 students again engaged in example-based learning and problem solving in the presence of task-irrelevant information. Problem-solving difficulty was varied as a first factor. Additionally, the presence of a pending goal related to the task-irrelevant information was manipulated. As expected, problem-solving performance declined when a pending goal was present during working on easy problems, whereas no interference was observed for difficult problems. Moreover, the presence of a pending goal reduced the time on task-relevant information and increased the time on task-irrelevant information while working on easy tasks. However, as revealed by mediation analyses these changes in overt information processing behavior did not explain the decline in problem-solving performance. As an alternative explanation it is suggested that goal conflicts resulting from pending goals claim cognitive resources, which are then no longer available for learning and

  20. Strategies to enhance price and quality competition in health care: lessons learned from tracking local markets.

    Science.gov (United States)

    Lesser, Cara S; Ginsburg, Paul B

    2006-06-01

    Drawing on observations from tracking changes in local health care markets over the past ten years, this article critiques two Federal Trade Commission and Department of Justice recommendations to enhance price and quality competition. First, we take issue with the notion that consumers, acting independently, will drive greater competition in health care markets. Rather we suggest an important role remains for trusted agents who can analyze inherently complex price and quality information and negotiate on consumers' behalf. With aggregated information identifying providers who deliver cost-effective care, consumers would be better positioned to respond to financial incentives about where to seek care and thereby drive more meaningful competition among providers to reduce costs and improve quality. Second, we take issue with the FTC/DOJ recommendation to provide more direct subsidies to prevent distortions in competition. In the current political environment, it is not practical to provide direct subsidies for all of the unfunded care that exists in health care markets today; instead, some interference with competition may be necessary to protect cross subsidies. Barriers can be reduced, though, by revising pricing policies that have resulted in marked disparities in the relative profitability of different services.

  1. Progressive sampling-based Bayesian optimization for efficient and automatic machine learning model selection.

    Science.gov (United States)

    Zeng, Xueqiang; Luo, Gang

    2017-12-01

    Machine learning is broadly used for clinical data analysis. Before training a model, a machine learning algorithm must be selected. Also, the values of one or more model parameters termed hyper-parameters must be set. Selecting algorithms and hyper-parameter values requires advanced machine learning knowledge and many labor-intensive manual iterations. To lower the bar to machine learning, miscellaneous automatic selection methods for algorithms and/or hyper-parameter values have been proposed. Existing automatic selection methods are inefficient on large data sets. This poses a challenge for using machine learning in the clinical big data era. To address the challenge, this paper presents progressive sampling-based Bayesian optimization, an efficient and automatic selection method for both algorithms and hyper-parameter values. We report an implementation of the method. We show that compared to a state of the art automatic selection method, our method can significantly reduce search time, classification error rate, and standard deviation of error rate due to randomization. This is major progress towards enabling fast turnaround in identifying high-quality solutions required by many machine learning-based clinical data analysis tasks.

  2. Managing magnetic nanoparticle aggregation and cellular uptake: a precondition for efficient stem-cell differentiation and MRI tracking.

    Science.gov (United States)

    Fayol, Delphine; Luciani, Nathalie; Lartigue, Lenaic; Gazeau, Florence; Wilhelm, Claire

    2013-02-01

    The labeling of stem cells with iron oxide nanoparticles is increasingly used to enable MRI cell tracking and magnetic cell manipulation, stimulating the fields of tissue engineering and cell therapy. However, the impact of magnetic labeling on stem-cell differentiation is still controversial. One compromising factor for successful differentiation may arise from early interactions of nanoparticles with cells during the labeling procedure. It is hypothesized that the lack of control over nanoparticle colloidal stability in biological media may lead to undesirable nanoparticle localization, overestimation of cellular uptake, misleading MRI cell tracking, and further impairment of differentiation. Herein a method is described for labeling mesenchymal stem cells (MSC), in which the physical state of citrate-coated nanoparticles (dispersed versus aggregated) can be kinetically tuned through electrostatic and magnetic triggers, as monitored by diffusion light scattering in the extracellular medium and by optical and electronic microscopy in cells. A set of statistical cell-by-cell measurements (flow cytometry, single-cell magnetophoresis, and high-resolution MRI cellular detection) is used to independently quantify the nanoparticle cell uptake and the effects of nanoparticle aggregation. Such aggregation confounds MRI cell detection as well as global iron quantification and has adverse effects on chondrogenetic differentiation. Magnetic labeling conditions with perfectly stable nanoparticles-suitable for obtaining differentiation-capable magnetic stem cells for use in cell therapy-are subsequently identified. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  3. Highly efficient maximum power point tracking using DC-DC coupled inductor single-ended primary inductance converter for photovoltaic power systems

    Science.gov (United States)

    Quamruzzaman, M.; Mohammad, Nur; Matin, M. A.; Alam, M. R.

    2016-10-01

    Solar photovoltaics (PVs) have nonlinear voltage-current characteristics, with a distinct maximum power point (MPP) depending on factors such as solar irradiance and operating temperature. To extract maximum power from the PV array at any environmental condition, DC-DC converters are usually used as MPP trackers. This paper presents the performance analysis of a coupled inductor single-ended primary inductance converter for maximum power point tracking (MPPT) in a PV system. A detailed model of the system has been designed and developed in MATLAB/Simulink. The performance evaluation has been conducted on the basis of stability, current ripple reduction and efficiency at different operating conditions. Simulation results show considerable ripple reduction in the input and output currents of the converter. Both the MPPT and converter efficiencies are significantly improved. The obtained simulation results validate the effectiveness and suitability of the converter model in MPPT and show reasonable agreement with the theoretical analysis.

  4. Efficient DoA Tracking of Variable Number of Moving Stochastic EM Sources in Far-Field Using PNN-MLP Model

    Directory of Open Access Journals (Sweden)

    Zoran Stanković

    2015-01-01

    Full Text Available An efficient neural network-based approach for tracking of variable number of moving electromagnetic (EM sources in far-field is proposed in the paper. Electromagnetic sources considered here are of stochastic radiation nature, mutually uncorrelated, and at arbitrary angular distance. The neural network model is based on combination of probabilistic neural network (PNN and the Multilayer Perceptron (MLP networks and it performs real-time calculations in two stages, determining at first the number of moving sources present in an observed space sector in specific moments in time and then calculating their angular positions in azimuth plane. Once successfully trained, the neural network model is capable of performing an accurate and efficient direction of arrival (DoA estimation within the training boundaries which is illustrated on the appropriate example.

  5. Energy efficiency and renewable energies: first lessons learned from AFD and FFEM funding

    International Nuclear Information System (INIS)

    Ries, Alain; Dubus, Koulm; Naudet, Jean David

    2008-04-01

    The French Agency for Development (AFD) has been always more involved in projects dealing with issues related to global warming, and more particularly in projects aiming at developing energy efficiency and renewable energies, these projects involved different expertises (energy, urban planning, transports, agriculture, and so on). In order to highlight lessons learned from these diversity of projects and interventions, this report first proposes an analysis of these projects related to energy efficiency and renewable energies in terms of concerned sectors, of intervener, of funding type, and of evolution in time. Then, the authors highlight lessons learned in terms of project starting conditions (national framework, funding, technical abilities, social and environmental factors), in terms of funding conditions for these projects (concessional financing, specialised credit lines), and in terms of climatic impact assessment and of criteria of project selection (practices and reductions of greenhouse gas emissions, improvement of the climatic impact for project financed by the AFD)

  6. Adaptive and Energy Efficient Walking in a Hexapod Robot under Neuromechanical Control and Sensorimotor Learning

    DEFF Research Database (Denmark)

    Xiong, Xiaofeng; Wörgötter, Florentin; Manoonpong, Poramate

    2016-01-01

    The control of multilegged animal walking is a neuromechanical process, and to achieve this in an adaptive and energy efficient way is a difficult and challenging problem. This is due to the fact that this process needs in real time: 1) to coordinate very many degrees of freedom of jointed legs; 2......) to generate the proper leg stiffness (i.e., compliance); and 3) to determine joint angles that give rise to particular positions at the endpoints of the legs. To tackle this problem for a robotic application, here we present a neuromechanical controller coupled with sensorimotor learning. The controller...... energy efficient walking, compared to other small legged robots. In addition, this paper also shows that the tight combination of neural control with tunable muscle-like functions, guided by sensory feedback and coupled with sensorimotor learning, is a way forward to better understand and solve adaptive...

  7. Novel Machine Learning-Based Techniques for Efficient Resource Allocation in Next Generation Wireless Networks

    KAUST Repository

    AlQuerm, Ismail A.

    2018-02-21

    There is a large demand for applications of high data rates in wireless networks. These networks are becoming more complex and challenging to manage due to the heterogeneity of users and applications specifically in sophisticated networks such as the upcoming 5G. Energy efficiency in the future 5G network is one of the essential problems that needs consideration due to the interference and heterogeneity of the network topology. Smart resource allocation, environmental adaptivity, user-awareness and energy efficiency are essential features in the future networks. It is important to support these features at different networks topologies with various applications. Cognitive radio has been found to be the paradigm that is able to satisfy the above requirements. It is a very interdisciplinary topic that incorporates flexible system architectures, machine learning, context awareness and cooperative networking. Mitola’s vision about cognitive radio intended to build context-sensitive smart radios that are able to adapt to the wireless environment conditions while maintaining quality of service support for different applications. Artificial intelligence techniques including heuristics algorithms and machine learning are the shining tools that are employed to serve the new vision of cognitive radio. In addition, these techniques show a potential to be utilized in an efficient resource allocation for the upcoming 5G networks’ structures such as heterogeneous multi-tier 5G networks and heterogeneous cloud radio access networks due to their capability to allocate resources according to real-time data analytics. In this thesis, we study cognitive radio from a system point of view focusing closely on architectures, artificial intelligence techniques that can enable intelligent radio resource allocation and efficient radio parameters reconfiguration. We propose a modular cognitive resource management architecture, which facilitates a development of flexible control for

  8. A perceptual advantage for onomatopoeia in early word learning: Evidence from eye-tracking.

    Science.gov (United States)

    Laing, Catherine E

    2017-09-01

    A perceptual advantage for iconic forms in infant language learning has been widely reported in the literature, termed the "sound symbolism bootstrapping hypothesis" by Imai and Kita (2014). However, empirical research in this area is limited mainly to sound symbolic forms, which are very common in languages such as Japanese but less so in Indo-European languages such as English. In this study, we extended this body of research to onomatopoeia-words that are thought to be present across most of the world's languages and that are known to be dominant in infants' early lexicons. In a picture-mapping task, 10- and 11-month-old infants showed a processing advantage for onomatopoeia (e.g., woof woof) over their conventional counterparts (e.g., doggie). However, further analysis suggests that the input may play a key role in infants' experience and processing of these forms. Copyright © 2017 Elsevier Inc. All rights reserved.

  9. Marginal space learning for medical image analysis efficient detection and segmentation of anatomical structures

    CERN Document Server

    Zheng, Yefeng

    2014-01-01

    Presents an award winning image analysis technology (Thomas Edison Patent Award, MICCAI Young Investigator Award) that achieves object detection and segmentation with state-of-the-art accuracy and efficiency Flexible, machine learning-based framework, applicable across multiple anatomical structures and imaging modalities Thirty five clinical applications on detecting and segmenting anatomical structures such as heart chambers and valves, blood vessels, liver, kidney, prostate, lymph nodes, and sub-cortical brain structures, in CT, MRI, X-Ray and Ultrasound.

  10. TensorFlow Agents: Efficient Batched Reinforcement Learning in TensorFlow

    OpenAIRE

    Hafner, Danijar; Davidson, James; Vanhoucke, Vincent

    2017-01-01

    We introduce TensorFlow Agents, an efficient infrastructure paradigm for building parallel reinforcement learning algorithms in TensorFlow. We simulate multiple environments in parallel, and group them to perform the neural network computation on a batch rather than individual observations. This allows the TensorFlow execution engine to parallelize computation, without the need for manual synchronization. Environments are stepped in separate Python processes to progress them in parallel witho...

  11. Efficient Sum of Outer Products Dictionary Learning (SOUP-DIL) and Its Application to Inverse Problems.

    Science.gov (United States)

    Ravishankar, Saiprasad; Nadakuditi, Raj Rao; Fessler, Jeffrey A

    2017-12-01

    The sparsity of signals in a transform domain or dictionary has been exploited in applications such as compression, denoising and inverse problems. More recently, data-driven adaptation of synthesis dictionaries has shown promise compared to analytical dictionary models. However, dictionary learning problems are typically non-convex and NP-hard, and the usual alternating minimization approaches for these problems are often computationally expensive, with the computations dominated by the NP-hard synthesis sparse coding step. This paper exploits the ideas that drive algorithms such as K-SVD, and investigates in detail efficient methods for aggregate sparsity penalized dictionary learning by first approximating the data with a sum of sparse rank-one matrices (outer products) and then using a block coordinate descent approach to estimate the unknowns. The resulting block coordinate descent algorithms involve efficient closed-form solutions. Furthermore, we consider the problem of dictionary-blind image reconstruction, and propose novel and efficient algorithms for adaptive image reconstruction using block coordinate descent and sum of outer products methodologies. We provide a convergence study of the algorithms for dictionary learning and dictionary-blind image reconstruction. Our numerical experiments show the promising performance and speedups provided by the proposed methods over previous schemes in sparse data representation and compressed sensing-based image reconstruction.

  12. Energy Efficient Power Allocation in Multi-tier 5G Networks Using Enhanced Online Learning

    KAUST Repository

    Alqerm, Ismail

    2017-07-25

    The multi-tier heterogeneous structure of 5G with dense small cells deployment, relays, and device-to-device (D2D) communications operating in an underlay fashion is envisioned as a potential solution to satisfy the future demand for cellular services. However, efficient power allocation among dense secondary transmitters that maintains quality of service (QoS) for macro (primary) cell users and secondary cell users is a critical challenge for operating such radio. In this paper, we focus on the power allocation problem in the multi-tier 5G network structure using a non-cooperative methodology with energy efficiency consideration. Therefore, we propose a distributive intuition-based online learning scheme for power allocation in the downlink of the 5G systems, where each transmitter surmises other transmitters power allocation strategies without information exchange. The proposed learning model exploits a brief state representation to account for the problem of dimensionality in online learning and expedite the convergence. The convergence of the proposed scheme is proved and numerical results demonstrate its capability to achieve fast convergence with QoS guarantee and significant improvement in system energy efficiency.

  13. An efficient dictionary learning algorithm and its application to 3-D medical image denoising.

    Science.gov (United States)

    Li, Shutao; Fang, Leyuan; Yin, Haitao

    2012-02-01

    In this paper, we propose an efficient dictionary learning algorithm for sparse representation of given data and suggest a way to apply this algorithm to 3-D medical image denoising. Our learning approach is composed of two main parts: sparse coding and dictionary updating. On the sparse coding stage, an efficient algorithm named multiple clusters pursuit (MCP) is proposed. The MCP first applies a dictionary structuring strategy to cluster the atoms with high coherence together, and then employs a multiple-selection strategy to select several competitive atoms at each iteration. These two strategies can greatly reduce the computation complexity of the MCP and assist it to obtain better sparse solution. On the dictionary updating stage, the alternating optimization that efficiently approximates the singular value decomposition is introduced. Furthermore, in the 3-D medical image denoising application, a joint 3-D operation is proposed for taking the learning capabilities of the presented algorithm to simultaneously capture the correlations within each slice and correlations across the nearby slices, thereby obtaining better denoising results. The experiments on both synthetically generated data and real 3-D medical images demonstrate that the proposed approach has superior performance compared to some well-known methods. © 2011 IEEE

  14. A Flexible Maximum Power Point Tracking Control Strategy Considering Both Conversion Efficiency and Power Fluctuation for Large-inertia Wind Turbines

    Directory of Open Access Journals (Sweden)

    Hongmin Meng

    2017-07-01

    Full Text Available In wind turbine control, maximum power point tracking (MPPT control is the main control mode for partial-load regimes. Efficiency potentiation of energy conversion and power smoothing are both two important control objectives in partial-load regime. However, on the one hand, low power fluctuation signifies inefficiency of energy conversion. On the other hand, enhancing efficiency may increase output power fluctuation as well. Thus the two objectives are contradictory and difficult to balance. This paper proposes a flexible MPPT control framework to improve the performance of both conversion efficiency and power smoothing, by adaptively compensating the torque reference value. The compensation was determined by a proposed model predictive control (MPC method with dynamic weights in the cost function, which improved control performance. The computational burden of the MPC solver was reduced by transforming the cost function representation. Theoretical analysis proved the good stability and robustness. Simulation results showed that the proposed method not only kept efficiency at a high level, but also reduced power fluctuations as much as possible. Therefore, the proposed method could improve wind farm profits and power grid reliability.

  15. Synergetic motor control paradigm for optimizing energy efficiency of multijoint reaching via tacit learning

    Science.gov (United States)

    Hayashibe, Mitsuhiro; Shimoda, Shingo

    2014-01-01

    A human motor system can improve its behavior toward optimal movement. The skeletal system has more degrees of freedom than the task dimensions, which incurs an ill-posed problem. The multijoint system involves complex interaction torques between joints. To produce optimal motion in terms of energy consumption, the so-called cost function based optimization has been commonly used in previous works.Even if it is a fact that an optimal motor pattern is employed phenomenologically, there is no evidence that shows the existence of a physiological process that is similar to such a mathematical optimization in our central nervous system.In this study, we aim to find a more primitive computational mechanism with a modular configuration to realize adaptability and optimality without prior knowledge of system dynamics.We propose a novel motor control paradigm based on tacit learning with task space feedback. The motor command accumulation during repetitive environmental interactions, play a major role in the learning process. It is applied to a vertical cyclic reaching which involves complex interaction torques.We evaluated whether the proposed paradigm can learn how to optimize solutions with a 3-joint, planar biomechanical model. The results demonstrate that the proposed method was valid for acquiring motor synergy and resulted in energy efficient solutions for different load conditions. The case in feedback control is largely affected by the interaction torques. In contrast, the trajectory is corrected over time with tacit learning toward optimal solutions.Energy efficient solutions were obtained by the emergence of motor synergy. During learning, the contribution from feedforward controller is augmented and the one from the feedback controller is significantly minimized down to 12% for no load at hand, 16% for a 0.5 kg load condition.The proposed paradigm could provide an optimization process in redundant system with dynamic-model-free and cost-function-free approach

  16. Synergetic motor control paradigm for optimizing energy efficiency of multijoint reaching via tacit learning.

    Science.gov (United States)

    Hayashibe, Mitsuhiro; Shimoda, Shingo

    2014-01-01

    A human motor system can improve its behavior toward optimal movement. The skeletal system has more degrees of freedom than the task dimensions, which incurs an ill-posed problem. The multijoint system involves complex interaction torques between joints. To produce optimal motion in terms of energy consumption, the so-called cost function based optimization has been commonly used in previous works.Even if it is a fact that an optimal motor pattern is employed phenomenologically, there is no evidence that shows the existence of a physiological process that is similar to such a mathematical optimization in our central nervous system.In this study, we aim to find a more primitive computational mechanism with a modular configuration to realize adaptability and optimality without prior knowledge of system dynamics.We propose a novel motor control paradigm based on tacit learning with task space feedback. The motor command accumulation during repetitive environmental interactions, play a major role in the learning process. It is applied to a vertical cyclic reaching which involves complex interaction torques.We evaluated whether the proposed paradigm can learn how to optimize solutions with a 3-joint, planar biomechanical model. The results demonstrate that the proposed method was valid for acquiring motor synergy and resulted in energy efficient solutions for different load conditions. The case in feedback control is largely affected by the interaction torques. In contrast, the trajectory is corrected over time with tacit learning toward optimal solutions.Energy efficient solutions were obtained by the emergence of motor synergy. During learning, the contribution from feedforward controller is augmented and the one from the feedback controller is significantly minimized down to 12% for no load at hand, 16% for a 0.5 kg load condition.The proposed paradigm could provide an optimization process in redundant system with dynamic-model-free and cost-function-free approach.

  17. Using Eye Tracking to Investigate First Year Students' Digital Proficiency and Their Use of a Learning Management System in an Open Distance Environment

    Science.gov (United States)

    Mabila, Jabulisiwe; Gelderblom, Helene; Ssemugabi, Samuel

    2014-01-01

    The internet gives individuals access to learning through online technologies. The prolific use of Learning Management Systems (LMSs) in higher education institutions makes Information and Communication Technology (ICT) skills or e-skills very important. ICT skill levels have been positively related to students' effectiveness and efficiency in…

  18. Validation of mobile eye tracking as novel and efficient means for differentiating progressive supranuclear palsy from Parkinson’s disease

    Directory of Open Access Journals (Sweden)

    Svenja eMarx

    2012-12-01

    Full Text Available Background: The decreased ability to carry out vertical saccades is a key symptom of Progressive Supranuclear Palsy (PSP. Objective measurement devices can help to reliably detect subtle eye-movement disturbances to improve sensitivity and specificity of the clinical diagnosis. The present study aims at transferring findings from restricted stationary video-oculography to a wearable head-mounted device, which can be readily applied in clinical practice.Methods: We investigated the eye movements in 10 possible or probable PSP patients, 11 Parkinson’s disease (PD patients and 10 age-matched healthy controls (HC using a mobile, gaze-driven video camera setup (EyeSeeCam. Ocular movements were analyzed during a standardized fixation protocol and in an unrestricted real-life scenario while walking along a corridor.Results: The EyeSeeCam detected prominent impairment of both saccade velocity and amplitude in PSP patients, differentiating them from PD and HCs. Differences were particularly evident for saccades in the vertical plane, and stronger for saccades than for other eye movements. Differences were more pronounced during the standardized protocol than in the real-life scenario. Conclusions: Combined analysis of saccade velocity and saccade amplitude during the fixation protocol with the EyeSeeCam provides a simple, rapid (< 20s and reliable tool to differentiate clinically established PSP patients from PD and HCs. As such, our findings prepare the ground for using wearable eye-tracking in patients with uncertain diagnoses.

  19. Extracellular NGFR Spacers Allow Efficient Tracking and Enrichment of Fully Functional CAR-T Cells Co-Expressing a Suicide Gene.

    Science.gov (United States)

    Casucci, Monica; Falcone, Laura; Camisa, Barbara; Norelli, Margherita; Porcellini, Simona; Stornaiuolo, Anna; Ciceri, Fabio; Traversari, Catia; Bordignon, Claudio; Bonini, Chiara; Bondanza, Attilio

    2018-01-01

    Chimeric antigen receptor (CAR)-T cell immunotherapy is at the forefront of innovative cancer therapeutics. However, lack of standardization of cellular products within the same clinical trial and lack of harmonization between different trials have hindered the clear identification of efficacy and safety determinants that should be unveiled in order to advance the field. With the aim of facilitating the isolation and in vivo tracking of CAR-T cells, we here propose the inclusion within the CAR molecule of a novel extracellular spacer based on the low-affinity nerve-growth-factor receptor (NGFR). We screened four different spacer designs using as target antigen the CD44 isoform variant 6 (CD44v6). We successfully generated NGFR-spaced CD44v6 CAR-T cells that could be efficiently enriched with clinical-grade immuno-magnetic beads without negative consequences on subsequent expansion, immuno-phenotype, in vitro antitumor reactivity, and conditional ablation when co-expressing a suicide gene. Most importantly, these cells could be tracked with anti-NGFR monoclonal antibodies in NSG mice, where they expanded, persisted, and exerted potent antitumor effects against both high leukemia and myeloma burdens. Similar results were obtained with NGFR-enriched CAR-T cells specific for CD19 or CEA, suggesting the universality of this strategy. In conclusion, we have demonstrated that the incorporation of the NGFR marker gene within the CAR sequence allows for a single molecule to simultaneously work as a therapeutic and selection/tracking gene. Looking ahead, NGFR spacer enrichment might allow good manufacturing procedures-manufacturing of standardized CAR-T cell products with high therapeutic potential, which could be harmonized in different clinical trials and used in combination with a suicide gene for future application in the allogeneic setting.

  20. Extracellular NGFR Spacers Allow Efficient Tracking and Enrichment of Fully Functional CAR-T Cells Co-Expressing a Suicide Gene

    Directory of Open Access Journals (Sweden)

    Monica Casucci

    2018-03-01

    Full Text Available Chimeric antigen receptor (CAR-T cell immunotherapy is at the forefront of innovative cancer therapeutics. However, lack of standardization of cellular products within the same clinical trial and lack of harmonization between different trials have hindered the clear identification of efficacy and safety determinants that should be unveiled in order to advance the field. With the aim of facilitating the isolation and in vivo tracking of CAR-T cells, we here propose the inclusion within the CAR molecule of a novel extracellular spacer based on the low-affinity nerve-growth-factor receptor (NGFR. We screened four different spacer designs using as target antigen the CD44 isoform variant 6 (CD44v6. We successfully generated NGFR-spaced CD44v6 CAR-T cells that could be efficiently enriched with clinical-grade immuno-magnetic beads without negative consequences on subsequent expansion, immuno-phenotype, in vitro antitumor reactivity, and conditional ablation when co-expressing a suicide gene. Most importantly, these cells could be tracked with anti-NGFR monoclonal antibodies in NSG mice, where they expanded, persisted, and exerted potent antitumor effects against both high leukemia and myeloma burdens. Similar results were obtained with NGFR-enriched CAR-T cells specific for CD19 or CEA, suggesting the universality of this strategy. In conclusion, we have demonstrated that the incorporation of the NGFR marker gene within the CAR sequence allows for a single molecule to simultaneously work as a therapeutic and selection/tracking gene. Looking ahead, NGFR spacer enrichment might allow good manufacturing procedures-manufacturing of standardized CAR-T cell products with high therapeutic potential, which could be harmonized in different clinical trials and used in combination with a suicide gene for future application in the allogeneic setting.

  1. Extracellular NGFR Spacers Allow Efficient Tracking and Enrichment of Fully Functional CAR-T Cells Co-Expressing a Suicide Gene

    Science.gov (United States)

    Casucci, Monica; Falcone, Laura; Camisa, Barbara; Norelli, Margherita; Porcellini, Simona; Stornaiuolo, Anna; Ciceri, Fabio; Traversari, Catia; Bordignon, Claudio; Bonini, Chiara; Bondanza, Attilio

    2018-01-01

    Chimeric antigen receptor (CAR)-T cell immunotherapy is at the forefront of innovative cancer therapeutics. However, lack of standardization of cellular products within the same clinical trial and lack of harmonization between different trials have hindered the clear identification of efficacy and safety determinants that should be unveiled in order to advance the field. With the aim of facilitating the isolation and in vivo tracking of CAR-T cells, we here propose the inclusion within the CAR molecule of a novel extracellular spacer based on the low-affinity nerve-growth-factor receptor (NGFR). We screened four different spacer designs using as target antigen the CD44 isoform variant 6 (CD44v6). We successfully generated NGFR-spaced CD44v6 CAR-T cells that could be efficiently enriched with clinical-grade immuno-magnetic beads without negative consequences on subsequent expansion, immuno-phenotype, in vitro antitumor reactivity, and conditional ablation when co-expressing a suicide gene. Most importantly, these cells could be tracked with anti-NGFR monoclonal antibodies in NSG mice, where they expanded, persisted, and exerted potent antitumor effects against both high leukemia and myeloma burdens. Similar results were obtained with NGFR-enriched CAR-T cells specific for CD19 or CEA, suggesting the universality of this strategy. In conclusion, we have demonstrated that the incorporation of the NGFR marker gene within the CAR sequence allows for a single molecule to simultaneously work as a therapeutic and selection/tracking gene. Looking ahead, NGFR spacer enrichment might allow good manufacturing procedures-manufacturing of standardized CAR-T cell products with high therapeutic potential, which could be harmonized in different clinical trials and used in combination with a suicide gene for future application in the allogeneic setting. PMID:29619024

  2. Tracking-by-detection of surgical instruments in minimally invasive surgery via the convolutional neural network deep learning-based method.

    Science.gov (United States)

    Zhao, Zijian; Voros, Sandrine; Weng, Ying; Chang, Faliang; Li, Ruijian

    2017-12-01

    Worldwide propagation of minimally invasive surgeries (MIS) is hindered by their drawback of indirect observation and manipulation, while monitoring of surgical instruments moving in the operated body required by surgeons is a challenging problem. Tracking of surgical instruments by vision-based methods is quite lucrative, due to its flexible implementation via software-based control with no need to modify instruments or surgical workflow. A MIS instrument is conventionally split into a shaft and end-effector portions, while a 2D/3D tracking-by-detection framework is proposed, which performs the shaft tracking followed by the end-effector one. The former portion is described by line features via the RANSAC scheme, while the latter is depicted by special image features based on deep learning through a well-trained convolutional neural network. The method verification in 2D and 3D formulation is performed through the experiments on ex-vivo video sequences, while qualitative validation on in-vivo video sequences is obtained. The proposed method provides robust and accurate tracking, which is confirmed by the experimental results: its 3D performance in ex-vivo video sequences exceeds those of the available state-of -the-art methods. Moreover, the experiments on in-vivo sequences demonstrate that the proposed method can tackle the difficult condition of tracking with unknown camera parameters. Further refinements of the method will refer to the occlusion and multi-instrumental MIS applications.

  3. COGNITIVE LOAD MEASUREMENT WITHIN THE RESEARCH OF EFFICIENT USAGE OF LEARNING SOFTWARE

    Directory of Open Access Journals (Sweden)

    Tetiana M. Derkach

    2011-05-01

    Full Text Available The methods of cognitive load measurement are described within the research of efficient usage of learning Software. Their classification is given, main advantages and disadvantages are analyzed, as well as area of use of these methods is defined. The article presents an overview of modern Software and Hardware that can be used for cognitive load measurement while studying with information technologies and practical examples of such methods. The use of the secondary task method is reasoned to be the most optimal for cognitive load measurement as well as for detection of optimal conditions for student work with different learning materials. This method allows to receive objective quantification of cognitive load and to investigate its dynamics accurately.

  4. The importance of learning when supporting emergent technologies for energy efficiency-A case study on policy intervention for learning for the development of energy efficient windows in Sweden

    International Nuclear Information System (INIS)

    Kiss, Bernadett; Neij, Lena

    2011-01-01

    The role of policy instruments to promote the development and diffusion of energy efficient technologies has been repeatedly accentuated in the context of climate change and sustainable development. To better understand the impact of policy instruments and to provide insights into technology change, assessments of various kinds are needed. This study analyzes the introduction and development of energy efficient windows in Sweden and the policy incentives applied to support this process. The study focuses on the assessment of technology and market development of energy efficient windows in Sweden; and by applying the concept of learning, it assesses how conditions for learning-by-searching, learning-by-doing, learning-by-using and learning-by-interacting have been supported by different policies. The results show successful progress in technology development and an improvement in best available technology of Swedish windows from 1.8 W/m 2 K in the 1970s to 0.7 W/m 2 K in 2010; in the same time period the market share of energy efficient windows increased from 20% in 1970 (average U-value of 2.0 W/m 2 K) to 80-85% in 2010 (average U-value of 1.3-1.2 W/m 2 K). The assessment shows that various policy instruments have facilitated all four learning processes resulting in the acknowledged slow but successful development of energy efficient windows. - Highlights: → Policy instruments for learning and technology change are assessed. → The development and diffusion of energy efficient windows (EEWs) in Sweden is taken as showcase. → Learning has been supported by various policies resulting in successful development of EEWs. → The thermal performance of EEWs improved with 2/3 and their market share increased by 3/5 in 40 years. → Main policies for learning are RD and D, technology procurement, testing and voluntary initiatives.

  5. Recognition of decays of charged tracks with neural network techniques

    International Nuclear Information System (INIS)

    Stimpfl-Abele, G.

    1991-01-01

    We developed neural-network learning techniques for the recognition of decays of charged tracks using a feed-forward network with error back-propagation. Two completely different methods are described in detail and their efficiencies for several NN architectures are compared with conventional methods. Excellent results are obtained. (orig.)

  6. Energy-efficient STDP-based learning circuits with memristor synapses

    Science.gov (United States)

    Wu, Xinyu; Saxena, Vishal; Campbell, Kristy A.

    2014-05-01

    It is now accepted that the traditional von Neumann architecture, with processor and memory separation, is ill suited to process parallel data streams which a mammalian brain can efficiently handle. Moreover, researchers now envision computing architectures which enable cognitive processing of massive amounts of data by identifying spatio-temporal relationships in real-time and solving complex pattern recognition problems. Memristor cross-point arrays, integrated with standard CMOS technology, are expected to result in massively parallel and low-power Neuromorphic computing architectures. Recently, significant progress has been made in spiking neural networks (SNN) which emulate data processing in the cortical brain. These architectures comprise of a dense network of neurons and the synapses formed between the axons and dendrites. Further, unsupervised or supervised competitive learning schemes are being investigated for global training of the network. In contrast to a software implementation, hardware realization of these networks requires massive circuit overhead for addressing and individually updating network weights. Instead, we employ bio-inspired learning rules such as the spike-timing-dependent plasticity (STDP) to efficiently update the network weights locally. To realize SNNs on a chip, we propose to use densely integrating mixed-signal integrate-andfire neurons (IFNs) and cross-point arrays of memristors in back-end-of-the-line (BEOL) of CMOS chips. Novel IFN circuits have been designed to drive memristive synapses in parallel while maintaining overall power efficiency (<1 pJ/spike/synapse), even at spike rate greater than 10 MHz. We present circuit design details and simulation results of the IFN with memristor synapses, its response to incoming spike trains and STDP learning characterization.

  7. The Goal Specificity Effect on Strategy Use and Instructional Efficiency during Computer-Based Scientific Discovery Learning

    Science.gov (United States)

    Kunsting, Josef; Wirth, Joachim; Paas, Fred

    2011-01-01

    Using a computer-based scientific discovery learning environment on buoyancy in fluids we investigated the "effects of goal specificity" (nonspecific goals vs. specific goals) for two goal types (problem solving goals vs. learning goals) on "strategy use" and "instructional efficiency". Our empirical findings close an important research gap,…

  8. Statistical characteristics of climbing fiber spikes necessary for efficient cerebellar learning.

    Science.gov (United States)

    Kuroda, S; Yamamoto, K; Miyamoto, H; Doya, K; Kawat, M

    2001-03-01

    Mean firing rates (MFRs), with analogue values, have thus far been used as information carriers of neurons in most brain theories of learning. However, the neurons transmit the signal by spikes, which are discrete events. The climbing fibers (CFs), which are known to be essential for cerebellar motor learning, fire at the ultra-low firing rates (around 1 Hz), and it is not yet understood theoretically how high-frequency information can be conveyed and how learning of smooth and fast movements can be achieved. Here we address whether cerebellar learning can be achieved by CF spikes instead of conventional MFR in an eye movement task, such as the ocular following response (OFR), and an arm movement task. There are two major afferents into cerebellar Purkinje cells: parallel fiber (PF) and CF, and the synaptic weights between PFs and Purkinje cells have been shown to be modulated by the stimulation of both types of fiber. The modulation of the synaptic weights is regulated by the cerebellar synaptic plasticity. In this study we simulated cerebellar learning using CF signals as spikes instead of conventional MFR. To generate the spikes we used the following four spike generation models: (1) a Poisson model in which the spike interval probability follows a Poisson distribution, (2) a gamma model in which the spike interval probability follows the gamma distribution, (3) a max model in which a spike is generated when a synaptic input reaches maximum, and (4) a threshold model in which a spike is generated when the input crosses a certain small threshold. We found that, in an OFR task with a constant visual velocity, learning was successful with stochastic models, such as Poisson and gamma models, but not in the deterministic models, such as max and threshold models. In an OFR with a stepwise velocity change and an arm movement task, learning could be achieved only in the Poisson model. In addition, for efficient cerebellar learning, the distribution of CF spike

  9. Energy-efficient algorithm for classification of states of wireless sensor network using machine learning methods

    Science.gov (United States)

    Yuldashev, M. N.; Vlasov, A. I.; Novikov, A. N.

    2018-05-01

    This paper focuses on the development of an energy-efficient algorithm for classification of states of a wireless sensor network using machine learning methods. The proposed algorithm reduces energy consumption by: 1) elimination of monitoring of parameters that do not affect the state of the sensor network, 2) reduction of communication sessions over the network (the data are transmitted only if their values can affect the state of the sensor network). The studies of the proposed algorithm have shown that at classification accuracy close to 100%, the number of communication sessions can be reduced by 80%.

  10. Large angle tracking and high discriminating tracking in nuclear emulsion

    International Nuclear Information System (INIS)

    Matsuo, Tomokazu; Shibuya, Hiroshi; Ogawa, Satoru; Fukuda, Tsutomu; Mikado, Shoji

    2015-01-01

    Nuclear emulsion is a high resolution and re-analyzable detector. Conventional “Track Selector” which have angle acceptance |tan θ|<0.6 are widely used to find tracks in emulsion. We made a new track selector “Fine Track Selector” (FTS) which has large angle acceptance and high discriminating ability. The FTS reduces fake tracks using new algorithms, navigation etc. FTS also keeps finding efficiency of tracks around 90% in an angle range of |tan θ| < 3.5. FTS was applied to the τ candidate in OPERA and no additional tracks found. FTS will be useful to our new J-PARC emulsion experiment.

  11. Adaptive Moving Object Tracking Integrating Neural Networks And Intelligent Processing

    Science.gov (United States)

    Lee, James S. J.; Nguyen, Dziem D.; Lin, C.

    1989-03-01

    A real-time adaptive scheme is introduced to detect and track moving objects under noisy, dynamic conditions including moving sensors. This approach integrates the adaptiveness and incremental learning characteristics of neural networks with intelligent reasoning and process control. Spatiotemporal filtering is used to detect and analyze motion, exploiting the speed and accuracy of multiresolution processing. A neural network algorithm constitutes the basic computational structure for classification. A recognition and learning controller guides the on-line training of the network, and invokes pattern recognition to determine processing parameters dynamically and to verify detection results. A tracking controller acts as the central control unit, so that tracking goals direct the over-all system. Performance is benchmarked against the Widrow-Hoff algorithm, for target detection scenarios presented in diverse FLIR image sequences. Efficient algorithm design ensures that this recognition and control scheme, implemented in software and commercially available image processing hardware, meets the real-time requirements of tracking applications.

  12. Local learning-networks on energy efficiency in industry - Successful initiative in Germany

    International Nuclear Information System (INIS)

    Jochem, Eberhard; Gruber, Edelgard

    2007-01-01

    Profitable energy-efficiency potentials are often not exploited in industry since management tends not to focus on energy issues. Sharing experiences between companies reveals possibilities for reducing the transaction costs involved. For this purpose, regionally or locally-organised learning networks of companies have been established. Social mechanisms are used to motivate management to pay more attention to energy efficiency in Switzerland and Germany. The main elements of the activities include initial consultation for each company with an experienced engineer, agreement on a common target for energy-efficiency improvement, regular meetings with technical presentations and an exchange of experiences, yearly control of energy consumption and CO 2 emissions as well as scientific monitoring and evaluation of the process. The results of some evaluations show that substantial progress has been made in implementing organisational measures and investments in energy efficiency in the participating companies. The reasons for these achievements are discussed and conclusions drawn about the opportunities and limits of this instrument. Finally, a recommendation is made to implement this instrument on a broader level

  13. UBIQUITOUS, FREE, AND EFFICIENT ONLINE COLLABORATION TOOLS FOR TEACHING AND LEARNING

    Directory of Open Access Journals (Sweden)

    Jace HARGIS

    2008-10-01

    Full Text Available This article provides an overview of free, online tools that make collaboration effective, efficient, and engaging. Each tool is available world-wide wherever there is access to the internet. These tools help create a more collaborative environment because they allow for voice, video, text, simultaneous editing, and immediate feedback. The tools presented are easy to use, user friendly, and have online support available if needed. Methods for using the tools are suggested, and examples of how they have been used by the authors are discussed. Professional presentations, instructional activities, meetings, and preparing manuscripts or other collaborative documents can all be developed in collaborative online meetings using Skype, Google tools including Talk, Chat, Calendar, Docs, and Notebooks, and Second Life. These may also be used to enhance education in distance learning or on campus classes. The features, functionality, and intuitive ease of use promote collaborative efforts, increasing the effective and efficient use of time while decreasing costs. Hyperlinks are provided for tools so users can determine technology specifications, download necessary files, learn more about their capabilities, and locate help or support information.

  14. Learning Management Systems on Blended Learning Courses

    DEFF Research Database (Denmark)

    Kuran, Mehmet Şükrü; Pedersen, Jens Myrup; Elsner, Raphael

    2017-01-01

    LMSes, Moodle, Blackboard Learn, Canvas, and Stud.IP with respect to these. We explain how these features were utilized to increase the efficiency, tractability, and quality of experience of the course. We found that an LMS with advanced features such as progress tracking, modular course support...

  15. Staged Inference using Conditional Deep Learning for energy efficient real-time smart diagnosis.

    Science.gov (United States)

    Parsa, Maryam; Panda, Priyadarshini; Sen, Shreyas; Roy, Kaushik

    2017-07-01

    Recent progress in biosensor technology and wearable devices has created a formidable opportunity for remote healthcare monitoring systems as well as real-time diagnosis and disease prevention. The use of data mining techniques is indispensable for analysis of the large pool of data generated by the wearable devices. Deep learning is among the promising methods for analyzing such data for healthcare applications and disease diagnosis. However, the conventional deep neural networks are computationally intensive and it is impractical to use them in real-time diagnosis with low-powered on-body devices. We propose Staged Inference using Conditional Deep Learning (SICDL), as an energy efficient approach for creating healthcare monitoring systems. For smart diagnostics, we observe that all diagnoses are not equally challenging. The proposed approach thus decomposes the diagnoses into preliminary analysis (such as healthy vs unhealthy) and detailed analysis (such as identifying the specific type of cardio disease). The preliminary diagnosis is conducted real-time with a low complexity neural network realized on the resource-constrained on-body device. The detailed diagnosis requires a larger network that is implemented remotely in cloud and is conditionally activated only for detailed diagnosis (unhealthy individuals). We evaluated the proposed approach using available physiological sensor data from Physionet databases, and achieved 38% energy reduction in comparison to the conventional deep learning approach.

  16. How Good Is Good: Improved Tracking and Managing of Safety Goals, Performance Indicators, Production Targets and Significant Events Using Learning Curves

    International Nuclear Information System (INIS)

    Duffey, Rommey B.; Saull, John W.

    2002-01-01

    We show a new way to track and measure safety and performance using learning curves derived on a mathematical basis. When unusual or abnormal events occur in plants and equipment, the regulator and good management practice requires they be reported, investigated, understood and rectified. In addition to reporting so-called 'significant events', both management and the regulator often set targets for individual and collective performance, which are used for both reward and criticism. For almost completely safe systems, like nuclear power plants, commercial aircraft and chemical facilities, many parameters are tracked and measured. Continuous improvement has to be demonstrated, as well as meeting reduced occurrence rates, which are set as management goals or targets. This process usually takes the form of statistics for availability of plant and equipment, forced or unplanned maintenance outage, loss of safety function, safety or procedural violations, etc. These are often rolled up into a set of so-called 'Performance Indicators' as measures of how well safety and operation is being managed at a given facility. The overall operating standards of an industry are also measured. A whole discipline is formed of tracking, measuring, reporting, managing and understanding the plethora of indicators and data. Decreasing occurrence rates and meeting or exceeding goals are seen and rewarded as virtues. Managers and operators need to know how good is their safety management system that has been adopted and used (and paid for), and whether it can itself be improved. We show the importance of accumulated experience in correctly measuring and tracking the decreasing event and error rates speculating a finite minimum rate. We show that the rate of improvement constitutes a measurable 'learning curve', and the attainment of the goals and targets can be affected by the adopted measures. We examine some of the available data on significant events, reportable occurrences, and loss of

  17. Tracking Porters

    DEFF Research Database (Denmark)

    Bruun, Maja Hojer; Krause-Jensen, Jakob; Saltofte, Margit

    2015-01-01

    . In this chapter, we argue that although anthropology has its specific methodology – including a myriad of ethnographic data-gathering tools, techniques, analytical approaches and theories – it must first and foremost be understood as a craft. Anthropology as craft requires a specific ‘anthropological sensibility......’ that differs from the standardized procedures of normal science. To establish our points we use an example of problem-based project work conducted by a group of Techno-Anthropology students at Aalborg University, we focus on key aspects of this craft and how the students began to learn it: For two weeks...... the students followed the work of a group of porters. Drawing on anthropological concepts and research strategies the students gained crucial insights about the potential effects of using tracking technologies in the hospital....

  18. Effective and efficient learning in the operating theater with intraoperative video-enhanced surgical procedure training.

    Science.gov (United States)

    van Det, M J; Meijerink, W J H J; Hoff, C; Middel, B; Pierie, J P E N

    2013-08-01

    INtraoperative Video Enhanced Surgical procedure Training (INVEST) is a new training method designed to improve the transition from basic skills training in a skills lab to procedural training in the operating theater. Traditionally, the master-apprentice model (MAM) is used for procedural training in the operating theater, but this model lacks uniformity and efficiency at the beginning of the learning curve. This study was designed to investigate the effectiveness and efficiency of INVEST compared to MAM. Ten surgical residents with no laparoscopic experience were recruited for a laparoscopic cholecystectomy training curriculum either by the MAM or with INVEST. After a uniform course in basic laparoscopic skills, each trainee performed six cholecystectomies that were digitally recorded. For 14 steps of the procedure, an observer who was blinded for the type of training determined whether the step was performed entirely by the trainee (2 points), partially by the trainee (1 point), or by the supervisor (0 points). Time measurements revealed the total procedure time and the amount of effective procedure time during which the trainee acted as the operating surgeon. Results were compared between both groups. Trainees in the INVEST group were awarded statistically significant more points (115.8 vs. 70.2; p < 0.001) and performed more steps without the interference of the supervisor (46.6 vs. 18.8; p < 0.001). Total procedure time was not lengthened by INVEST, and the part performed by trainees was significantly larger (69.9 vs. 54.1 %; p = 0.004). INVEST enhances effectiveness and training efficiency for procedural training inside the operating theater without compromising operating theater time efficiency.

  19. Factor Analysis on Criteria Affecting Lean Retrofit for Energy Efficient Initiatives in Higher Learning Institution Buildings

    Directory of Open Access Journals (Sweden)

    Abidin Nur IzieAdiana

    2017-01-01

    Full Text Available The expansion of Higher Learning Institution (HLI is a global concerns on energy demand due to campus act like a small city. Intensive mode of operation of a building is correlated to the energy utilization. Improvement in the current energy efficiency is crucial effort to minimize the environmental effect through minimisation of energy in operation by retrofitting and upgrade the existing building system or components to be more efficient. Basically, there are three recommended steps for the improvement known as lean initiatives, green technology and clean energy in response to becoming zero energy solutions for building. The deliberation of this paper is aimed to highlight the criteria affecting in retrofitting of existing building in HLI with lean initiatives in order to achieve energy efficiency and reduction of energy comsumption. Attention is devoted to reviewing the lean energy retrofitting initiatives criteria for daylighting (side lighting, daylighting (skylight and glazing. The questionnaire survey was employed and distributed to the architects who has an expertise in green building design. Factor analysis was adopted as a method of analysis by using Principal Component with Varimax Rotation. The result is presented through summarizing the sub-criteria according to its importance with a factor loading 0.50 and above. The result found that majority of the criteria developed achieved the significant factor loading value and in accordance with the protocal of analysis. In conclusion the results from analysis of this paper assists the stakeholders in assessing the significant criteria based on the desired lean energy retrofitting initiatives and also provides a huge contribution for future planning improvement in existing buildings to become an energy efficient building.

  20. Learning High-Order Filters for Efficient Blind Deconvolution of Document Photographs

    KAUST Repository

    Xiao, Lei

    2016-09-16

    Photographs of text documents taken by hand-held cameras can be easily degraded by camera motion during exposure. In this paper, we propose a new method for blind deconvolution of document images. Observing that document images are usually dominated by small-scale high-order structures, we propose to learn a multi-scale, interleaved cascade of shrinkage fields model, which contains a series of high-order filters to facilitate joint recovery of blur kernel and latent image. With extensive experiments, we show that our method produces high quality results and is highly efficient at the same time, making it a practical choice for deblurring high resolution text images captured by modern mobile devices. © Springer International Publishing AG 2016.

  1. Towards more efficient e-learning, intelligence and adapted teaching material

    Directory of Open Access Journals (Sweden)

    Damir Kalpić

    2010-12-01

    Full Text Available This article presents results of a research project in which we attempted to determine the relationship between efficient E-learning and teaching materials adapted based on students’ structure of intelligence. The project was conducted on approximately 500 students, 23 classes, nine elementary schools, with ten teachers of history, informatics and several licensed psychologists. E-teaching material was prepared for the subject of History for eight-grade students of elementary school. Students were tested for the structure of intelligence, and based on their most prominent component, they were divided into groups, using teaching materials adapted to their most prominent intelligence component. The results have shown that use of the adapted teaching materials achieved 6-12% better results than E-materials not adapted to students’ structure of intelligence.

  2. Improving efficiency of two-type maximum power point tracking methods of tip-speed ratio and optimum torque in wind turbine system using a quantum neural network

    International Nuclear Information System (INIS)

    Ganjefar, Soheil; Ghassemi, Ali Akbar; Ahmadi, Mohamad Mehdi

    2014-01-01

    In this paper, a quantum neural network (QNN) is used as controller in the adaptive control structures to improve efficiency of the maximum power point tracking (MPPT) methods in the wind turbine system. For this purpose, direct and indirect adaptive control structures equipped with QNN are used in tip-speed ratio (TSR) and optimum torque (OT) MPPT methods. The proposed control schemes are evaluated through a battery-charging windmill system equipped with PMSG (permanent magnet synchronous generator) at a random wind speed to demonstrate transcendence of their effectiveness as compared to PID controller and conventional neural network controller (CNNC). - Highlights: • Using a new control method to harvest the maximum power from wind energy system. • Using an adaptive control scheme based on quantum neural network (QNN). • Improving of MPPT-TSR method by direct adaptive control scheme based on QNN. • Improving of MPPT-OT method by indirect adaptive control scheme based on QNN. • Using a windmill system based on PMSG to evaluate proposed control schemes

  3. Tracking efficiency and charge sharing of 3D silicon sensors at different angles in a 1.4T magnetic field

    CERN Document Server

    Gjersdal, H; Slaviec, T; Sandaker, H; Tsung, J; Bolle, E; Da Via, C; Wermes, N; Borri, M; Grinstein, S; Nordahl, P; Hugging, F; Dorholt, O; Rohne, O; La Rosa, A; Sjobaek, K; Tsybychev, D; Mastroberardino, A; Fazio, S; Su, D; Young, C; Hasi, J; Grenier, P; Hansson, P; Jackson, P; Kenney, C; Kocian, M

    2011-01-01

    A 3D silicon sensor fabricated at Stanford with electrodes penetrating throughout the entire silicon wafer and with active edges was tested in a 1.4 T magnetic field with a 180 GeV/c pion beam at the CERN SPS in May 2009. The device under test was bump-bonded to the ATLAS pixel FE-I3 readout electronics chip. Three readout electrodes were used to cover the 400 pm long pixel side, this resulting in a p-n inter-electrode distance of similar to 71 mu m. Its behavior was confronted with a planar sensor of the type presently installed in the ATLAS inner tracker. Time over threshold, charge sharing and tracking efficiency data were collected at zero and 15 angles with and without magnetic field. The latest is the angular configuration expected for the modules of the Insertable B-Layer (IBL) currently under study for the LHC phase 1 upgrade expected in 2014. (C) 2010 Elsevier B.V. All rights reserved.

  4. Building Energy-Efficient Schools in New Orleans: Lessons Learned (Brochure)

    Energy Technology Data Exchange (ETDEWEB)

    2011-12-01

    This case study presents the lessons learned from incorporating energy efficiency in the rebuilding and renovating of New Orleans K-12 schools after Hurricanes Katrina and Rita. Hurricane Katrina was the largest natural disaster in the United States, striking the Gulf Coast on August 29, 2005, and flooding 80% of New Orleans; to make matters worse, the city was flooded again only three weeks later by the effects of Hurricane Rita. Many of the buildings, including schools, were heavily damaged. The devastation of schools in New Orleans from the hurricanes was exacerbated by many years of deferred school maintenance. This case study presents the lessons learned from incorporating energy efficiency in the rebuilding and renovating of New Orleans K-12 schools after Hurricanes Katrina and Rita. The experiences of four new schools-Langston Hughes Elementary School, Andrew H. Wilson Elementary School (which was 50% new construction and 50% major renovation), L.B. Landry High School, and Lake Area High School-and one major renovation, Joseph A. Craig Elementary School-are described to help other school districts and design teams with their in-progress and future school building projects in hot-humid climates. Before Hurricane Katrina, New Orleans had 128 public schools. As part of the recovery planning, New Orleans Public Schools underwent an assessment and planning process to determine how many schools were needed and in what locations. Following a series of public town hall meetings and a district-wide comprehensive facility assessment, a Master Plan was developed, which outlined the renovation or construction of 85 schools throughout the city, which are expected to be completed by 2017. New Orleans Public Schools expects to build or renovate approximately eight schools each year over a 10-year period to achieve 21st century schools district-wide. Reconstruction costs are estimated at nearly $2 billion.

  5. Joint Machine Learning and Game Theory for Rate Control in High Efficiency Video Coding.

    Science.gov (United States)

    Gao, Wei; Kwong, Sam; Jia, Yuheng

    2017-08-25

    In this paper, a joint machine learning and game theory modeling (MLGT) framework is proposed for inter frame coding tree unit (CTU) level bit allocation and rate control (RC) optimization in High Efficiency Video Coding (HEVC). First, a support vector machine (SVM) based multi-classification scheme is proposed to improve the prediction accuracy of CTU-level Rate-Distortion (R-D) model. The legacy "chicken-and-egg" dilemma in video coding is proposed to be overcome by the learning-based R-D model. Second, a mixed R-D model based cooperative bargaining game theory is proposed for bit allocation optimization, where the convexity of the mixed R-D model based utility function is proved, and Nash bargaining solution (NBS) is achieved by the proposed iterative solution search method. The minimum utility is adjusted by the reference coding distortion and frame-level Quantization parameter (QP) change. Lastly, intra frame QP and inter frame adaptive bit ratios are adjusted to make inter frames have more bit resources to maintain smooth quality and bit consumption in the bargaining game optimization. Experimental results demonstrate that the proposed MLGT based RC method can achieve much better R-D performances, quality smoothness, bit rate accuracy, buffer control results and subjective visual quality than the other state-of-the-art one-pass RC methods, and the achieved R-D performances are very close to the performance limits from the FixedQP method.

  6. Efficient Learning for the Poor: New Insights into Literacy Acquisition for Children

    Science.gov (United States)

    Abadzi, Helen

    2008-11-01

    Reading depends on the speed of visual recognition and capacity of short-term memory. To understand a sentence, the mind must read it fast enough to capture it within the limits of the short-term memory. This means that children must attain a minimum speed of fairly accurate reading to understand a passage. Learning to read involves "tricking" the brain into perceiving groups of letters as coherent words. This is achieved most efficiently by pairing small units consistently with sounds rather than learning entire words. To link the letters with sounds, explicit and extensive practice is needed; the more complex the spelling of a language, the more practice is necessary. However, schools of low-income students often waste instructional time and lack reading resources, so students cannot get sufficient practice to automatize reading and may remain illiterate for years. Lack of reading fluency in the early grades creates inefficiencies that affect the entire educational system. Neurocognitive research on reading points to benchmarks and monitoring indicators. All students should attain reading speeds of 45-60 words per minute by the end of grade 2 and 120-150 words per minute for grades 6-8.

  7. An Efficient Semi-supervised Learning Approach to Predict SH2 Domain Mediated Interactions.

    Science.gov (United States)

    Kundu, Kousik; Backofen, Rolf

    2017-01-01

    Src homology 2 (SH2) domain is an important subclass of modular protein domains that plays an indispensable role in several biological processes in eukaryotes. SH2 domains specifically bind to the phosphotyrosine residue of their binding peptides to facilitate various molecular functions. For determining the subtle binding specificities of SH2 domains, it is very important to understand the intriguing mechanisms by which these domains recognize their target peptides in a complex cellular environment. There are several attempts have been made to predict SH2-peptide interactions using high-throughput data. However, these high-throughput data are often affected by a low signal to noise ratio. Furthermore, the prediction methods have several additional shortcomings, such as linearity problem, high computational complexity, etc. Thus, computational identification of SH2-peptide interactions using high-throughput data remains challenging. Here, we propose a machine learning approach based on an efficient semi-supervised learning technique for the prediction of 51 SH2 domain mediated interactions in the human proteome. In our study, we have successfully employed several strategies to tackle the major problems in computational identification of SH2-peptide interactions.

  8. Learning Frameworks for Cooperative Spectrum Sensing and Energy-Efficient Data Protection in Cognitive Radio Networks

    Directory of Open Access Journals (Sweden)

    Vinh Quang Do

    2018-05-01

    Full Text Available This paper studies learning frameworks for energy-efficient data communications in an energy-harvesting cognitive radio network in which secondary users (SUs harvest energy from solar power while opportunistically accessing a licensed channel for data transmission. The SUs perform spectrum sensing individually, and send local decisions about the presence of the primary user (PU on the channel to a fusion center (FC. We first design a new cooperative spectrum-sensing technique based on a convolutional neural network in which the FC uses historical sensing data to train the network for classification problem. The system is assumed to operate in a time-slotted manner. At the beginning of each time slot, the FC uses the current local decisions as input for the trained network to decide whether the PU is active or not in that time slot. In addition, legitimate transmissions can be vulnerable to a hidden eavesdropper, which always passively listens to the communication. Therefore, we further propose a transfer learning actor–critic algorithm for an SU to decide its operation mode to increase the security level under the constraint of limited energy. In this approach, the SU directly interacts with the environment to learn its dynamics (i.e., an arrival of harvested energy; then, the SU can either stay idle to save energy or transmit to the FC secured data that are encrypted using a suitable private-key encryption method to maximize the long-term effective security level of the network. We finally present numerical simulation results under various configurations to evaluate our proposed schemes.

  9. A TLBO based gradient descent learning-functional link higher order ANN: An efficient model for learning from non-linear data

    Directory of Open Access Journals (Sweden)

    Bighnaraj Naik

    2018-01-01

    Full Text Available All the higher order ANNs (HONNs including functional link ANN (FLANN are sensitive to random initialization of weight and rely on the learning algorithms adopted. Although a selection of efficient learning algorithms for HONNs helps to improve the performance, on the other hand, initialization of weights with optimized weights rather than random weights also play important roles on its efficiency. In this paper, the problem solving approach of the teaching learning based optimization (TLBO along with learning ability of the gradient descent learning (GDL is used to obtain the optimal set of weight of FLANN learning model. TLBO does not require any specific parameters rather it requires only some of the common independent parameters like number of populations, number of iterations and stopping criteria, thereby eliminating the intricacy in selection of algorithmic parameters for adjusting the set of weights of FLANN model. The proposed TLBO-FLANN is implemented in MATLAB and compared with GA-FLANN, PSO-FLANN and HS-FLANN. The TLBO-FLANN is tested on various 5-fold cross validated benchmark data sets from UCI machine learning repository and analyzed under the null-hypothesis by using Friedman test, Holm’s procedure and post hoc ANOVA statistical analysis (Tukey test & Dunnett test.

  10. The role of affordances in children's learning performance and efficiency when using virtual manipulative mathematics touch-screen apps

    Science.gov (United States)

    Moyer-Packenham, Patricia S.; Bullock, Emma K.; Shumway, Jessica F.; Tucker, Stephen I.; Watts, Christina M.; Westenskow, Arla; Anderson-Pence, Katie L.; Maahs-Fladung, Cathy; Boyer-Thurgood, Jennifer; Gulkilik, Hilal; Jordan, Kerry

    2016-03-01

    This paper focuses on understanding the role that affordances played in children's learning performance and efficiency during clinical interviews of their interactions with mathematics apps on touch-screen devices. One hundred children, ages 3 to 8, each used six different virtual manipulative mathematics apps during 30-40-min interviews. The study used a convergent mixed methods design, in which quantitative and qualitative data were collected concurrently to answer the research questions (Creswell and Plano Clark 2011). Videos were used to capture each child's interactions with the virtual manipulative mathematics apps, document learning performance and efficiency, and record children's interactions with the affordances within the apps. Quantitized video data answered the research question on differences in children's learning performance and efficiency between pre- and post-assessments. A Wilcoxon matched pairs signed-rank test was used to explore these data. Qualitative video data was used to identify affordance access by children when using each app, identifying 95 potential helping and hindering affordances among the 18 apps. The results showed that there were changes in children's learning performance and efficiency when children accessed a helping or a hindering affordance. Helping affordances were more likely to be accessed by children who progressed between the pre- and post-assessments, and the same affordances had helping and hindering effects for different children. These results have important implications for the design of virtual manipulative mathematics learning apps.

  11. Radio-tracking manatees from land and space: tag design, implementation, and lessons learned from long-term study

    Science.gov (United States)

    Deutsch, C.J.; Bonde, R.K.; Reid, J.P.

    1998-01-01

    West Indian manatees (Trichechus manatus) were tracked along the Atlantic coast of Florida and Georgia (N = 83 manatees, n = 439 tag deployments, 1986-1996) and in eastern Puerto Rico (N = 8, n = 43, 1992-1996) using conventional and satellite-based radio-telemetry systems. A floating radio-tag, attached by a flexible tether to a padded belt around the base of the tail, enabled us to track manatees in saltwater environments. The tag incorporated VHF (very high frequency) and ultrasonic transmitters for field tracking and tag recovery, and an Argos satellite-monitored transmitter for remote tracking. We located each animal in the field about twice per week, received more than 60 000 good-quality Argos locations, and recovered tags in over 90% of deployments. The tag was designed to detach from the belt when entangled to prevent injury or drowning, and this often led to premature termination of tracking bouts. We had considerable success, however, in retagging belted manatees without recapture (97% of 392 retagging events). Most individuals were radio-tagged more than once (median = 3.0, maximum = 43) for a median total duration of 7.5 months (maximum = 6.8 yr). Data obtained through Argos have been valuable in addressing questions relating to long-distance movements, site fidelity, and identification of high-use areas. Fine-scale analyses of manatee habitat use and movements may require restricting the data set to the highest location quality or developing new analytical techniques to incorporate locational error. Field tracking provided useful ancillary data on life-history parameters, but sample sizes were small and survival estimates imprecise. Modification of the existing tag design to include Global Positioning System (GPS) functionality, with its finer spatial and temporal resolution, will offer new opportunities to address critical research and management problems facing this endangered species.

  12. "Sickle Cell Anemia: Tracking down a Mutation": An Interactive Learning Laboratory That Communicates Basic Principles of Genetics and Cellular Biology

    Science.gov (United States)

    Jarrett, Kevin; Williams, Mary; Horn, Spencer; Radford, David; Wyss, J. Michael

    2016-01-01

    "Sickle cell anemia: tracking down a mutation" is a full-day, inquiry-based, biology experience for high school students enrolled in genetics or advanced biology courses. In the experience, students use restriction endonuclease digestion, cellulose acetate gel electrophoresis, and microscopy to discover which of three putative patients…

  13. Procedural Learning and Associative Memory Mechanisms Contribute to Contextual Cueing: Evidence from fMRI and Eye-Tracking

    Science.gov (United States)

    Manelis, Anna; Reder, Lynne M.

    2012-01-01

    Using a combination of eye tracking and fMRI in a contextual cueing task, we explored the mechanisms underlying the facilitation of visual search for repeated spatial configurations. When configurations of distractors were repeated, greater activation in the right hippocampus corresponded to greater reductions in the number of saccades to locate…

  14. An Eye Tracking Comparison of External Pointing Cues and Internal Continuous Cues in Learning with Complex Animations

    Science.gov (United States)

    Boucheix, Jean-Michel; Lowe, Richard K.

    2010-01-01

    Two experiments used eye tracking to investigate a novel cueing approach for directing learner attention to low salience, high relevance aspects of a complex animation. In the first experiment, comprehension of a piano mechanism animation containing spreading-colour cues was compared with comprehension obtained with arrow cues or no cues. Eye…

  15. Community Tracking in a cMOOC and Nomadic Learner Behavior Identification on a Connectivist Rhizomatic Learning Network

    Science.gov (United States)

    Bozkurt, Aras; Honeychurch, Sarah; Caines, Autumm; Bali, Maha; Koutropoulos, Apostolos; Cormier, Dave

    2016-01-01

    This article contributes to the literature on connectivism, connectivist MOOCs (cMOOCs) and rhizomatic learning by examining participant interactions, community formation and nomadic learner behavior in a particular cMOOC, #rhizo15, facilitated for 6 weeks by Dave Cormier. It further focuses on what we can learn by observing Twitter interactions…

  16. How Are Learning Strategies Reflected in the Eyes? Combining Results from Self-Reports and Eye-Tracking

    Science.gov (United States)

    Catrysse, Leen; Gijbels, David; Donche, Vincent; De Maeyer, Sven; Lesterhuis, Marije; Van den Bossche, Piet

    2018-01-01

    Background: Up until now, empirical studies in the Student Approaches to Learning field have mainly been focused on the use of self-report instruments, such as interviews and questionnaires, to uncover differences in students' general preferences towards learning strategies, but have focused less on the use of task-specific and online measures.…

  17. Route-Learning Strategies in Typical and Atypical Development; Eye Tracking Reveals Atypical Landmark Selection in Williams Syndrome

    Science.gov (United States)

    Farran, E. K.; Formby, S.; Daniyal, F.; Holmes, T.; Van Herwegen, J.

    2016-01-01

    Background: Successful navigation is crucial to everyday life. Individuals with Williams syndrome (WS) have impaired spatial abilities. This includes a deficit in spatial navigation abilities such as learning the route from A to B. To-date, to determine whether participants attend to landmarks when learning a route, landmark recall tasks have been…

  18. The Role of Affordances in Children's Learning Performance and Efficiency When Using Virtual Manipulative Mathematics Touch-Screen Apps

    Science.gov (United States)

    Moyer-Packenham, Patricia S.; Bullock, Emma K.; Shumway, Jessica F.; Tucker, Stephen I.; Watts, Christina M.; Westenskow, Arla; Anderson-Pence, Katie L.; Maahs-Fladung, Cathy; Boyer-Thurgood, Jennifer; Gulkilik, Hilal; Jordan, Kerry

    2016-01-01

    This paper focuses on understanding the role that affordances played in children's learning performance and efficiency during clinical interviews of their interactions with mathematics apps on touch-screen devices. One hundred children, ages 3 to 8, each used six different virtual manipulative mathematics apps during 30-40-min interviews. The…

  19. Effects of an Instructional Gaming Characteristic on Learning Effectiveness, Efficiency, and Engagement: Using a Storyline for Teaching Basic Statistical Skills

    Science.gov (United States)

    Novak, Elena; Johnson, Tristan E.; Tenenbaum, Gershon; Shute, Valerie J.

    2016-01-01

    The study explored instructional benefits of a storyline gaming characteristic (GC) on learning effectiveness, efficiency, and engagement with the use of an online instructional simulation for graduate students in an introductory statistics course. A storyline is a game-design element that connects scenes with the educational content. In order to…

  20. SU-E-J-150: Impact of Intrafractional Prostate Motion On the Accuracy and Efficiency of Prostate SBRT Delivery: A Retrospective Analysis of Prostate Tracking Log Files

    Energy Technology Data Exchange (ETDEWEB)

    Xiang, H; Hirsch, A; Willins, J; Kachnic, J [Massachusetts General Hospital and Harvard Medical School, Boston, MA (United States); Boston Medical Center and Boston University School of Medicine, Boston, MA (United States); Qureshi, M; Katz, M; Nicholas, B; Keohan, S [Boston Medical Center and Boston University School of Medicine, Boston, MA (United States); De Armas, R [Massachusetts Institute of Technology, Cambridge, MA (United States); Lu, H; Efstathiou, J; Zietman, A [Massachusetts General Hospital and Harvard Medical School, Boston, MA (United States)

    2014-06-01

    Purpose: To measure intrafractional prostate motion by time-based stereotactic x-ray imaging and investigate the impact on the accuracy and efficiency of prostate SBRT delivery. Methods: Prostate tracking log files with 1,892 x-ray image registrations from 18 SBRT fractions for 6 patients were retrospectively analyzed. Patient setup and beam delivery sessions were reviewed to identify extended periods of large prostate motion that caused delays in setup or interruptions in beam delivery. The 6D prostate motions were compared to the clinically used PTV margin of 3–5 mm (3 mm posterior, 5 mm all other directions), a hypothetical PTV margin of 2–3 mm (2 mm posterior, 3 mm all other directions), and the rotation correction limits (roll ±2°, pitch ±5° and yaw ±3°) of CyberKnife to quantify beam delivery accuracy. Results: Significant incidents of treatment start delay and beam delivery interruption were observed, mostly related to large pitch rotations of ≥±5°. Optimal setup time of 5–15 minutes was recorded in 61% of the fractions, and optimal beam delivery time of 30–40 minutes in 67% of the fractions. At a default imaging interval of 15 seconds, the percentage of prostate motion beyond PTV margin of 3–5 mm varied among patients, with a mean at 12.8% (range 0.0%–31.1%); and the percentage beyond PTV margin of 2–3 mm was at a mean of 36.0% (range 3.3%–83.1%). These timely detected offsets were all corrected real-time by the robotic manipulator or by operator intervention at the time of treatment interruptions. Conclusion: The durations of patient setup and beam delivery were directly affected by the occurrence of large prostate motion. Frequent imaging of down to 15 second interval is necessary for certain patients. Techniques for reducing prostate motion, such as using endorectal balloon, can be considered to assure consistently higher accuracy and efficiency of prostate SBRT delivery.

  1. SU-E-J-150: Impact of Intrafractional Prostate Motion On the Accuracy and Efficiency of Prostate SBRT Delivery: A Retrospective Analysis of Prostate Tracking Log Files

    International Nuclear Information System (INIS)

    Xiang, H; Hirsch, A; Willins, J; Kachnic, J; Qureshi, M; Katz, M; Nicholas, B; Keohan, S; De Armas, R; Lu, H; Efstathiou, J; Zietman, A

    2014-01-01

    Purpose: To measure intrafractional prostate motion by time-based stereotactic x-ray imaging and investigate the impact on the accuracy and efficiency of prostate SBRT delivery. Methods: Prostate tracking log files with 1,892 x-ray image registrations from 18 SBRT fractions for 6 patients were retrospectively analyzed. Patient setup and beam delivery sessions were reviewed to identify extended periods of large prostate motion that caused delays in setup or interruptions in beam delivery. The 6D prostate motions were compared to the clinically used PTV margin of 3–5 mm (3 mm posterior, 5 mm all other directions), a hypothetical PTV margin of 2–3 mm (2 mm posterior, 3 mm all other directions), and the rotation correction limits (roll ±2°, pitch ±5° and yaw ±3°) of CyberKnife to quantify beam delivery accuracy. Results: Significant incidents of treatment start delay and beam delivery interruption were observed, mostly related to large pitch rotations of ≥±5°. Optimal setup time of 5–15 minutes was recorded in 61% of the fractions, and optimal beam delivery time of 30–40 minutes in 67% of the fractions. At a default imaging interval of 15 seconds, the percentage of prostate motion beyond PTV margin of 3–5 mm varied among patients, with a mean at 12.8% (range 0.0%–31.1%); and the percentage beyond PTV margin of 2–3 mm was at a mean of 36.0% (range 3.3%–83.1%). These timely detected offsets were all corrected real-time by the robotic manipulator or by operator intervention at the time of treatment interruptions. Conclusion: The durations of patient setup and beam delivery were directly affected by the occurrence of large prostate motion. Frequent imaging of down to 15 second interval is necessary for certain patients. Techniques for reducing prostate motion, such as using endorectal balloon, can be considered to assure consistently higher accuracy and efficiency of prostate SBRT delivery

  2. Energy Tracking Software Platform

    Energy Technology Data Exchange (ETDEWEB)

    Ryan Davis; Nathan Bird; Rebecca Birx; Hal Knowles

    2011-04-04

    Acceleration has created an interactive energy tracking and visualization platform that supports decreasing electric, water, and gas usage. Homeowners have access to tools that allow them to gauge their use and track progress toward a smaller energy footprint. Real estate agents have access to consumption data, allowing for sharing a comparison with potential home buyers. Home builders have the opportunity to compare their neighborhood's energy efficiency with competitors. Home energy raters have a tool for gauging the progress of their clients after efficiency changes. And, social groups are able to help encourage members to reduce their energy bills and help their environment. EnergyIT.com is the business umbrella for all energy tracking solutions and is designed to provide information about our energy tracking software and promote sales. CompareAndConserve.com (Gainesville-Green.com) helps homeowners conserve energy through education and competition. ToolsForTenants.com helps renters factor energy usage into their housing decisions.

  3. Automated Proton Track Identification in MicroBooNE Using Gradient Boosted Decision Trees

    Energy Technology Data Exchange (ETDEWEB)

    Woodruff, Katherine [New Mexico State U.

    2017-10-02

    MicroBooNE is a liquid argon time projection chamber (LArTPC) neutrino experiment that is currently running in the Booster Neutrino Beam at Fermilab. LArTPC technology allows for high-resolution, three-dimensional representations of neutrino interactions. A wide variety of software tools for automated reconstruction and selection of particle tracks in LArTPCs are actively being developed. Short, isolated proton tracks, the signal for low- momentum-transfer neutral current (NC) elastic events, are easily hidden in a large cosmic background. Detecting these low-energy tracks will allow us to probe interesting regions of the proton's spin structure. An effective method for selecting NC elastic events is to combine a highly efficient track reconstruction algorithm to find all candidate tracks with highly accurate particle identification using a machine learning algorithm. We present our work on particle track classification using gradient tree boosting software (XGBoost) and the performance on simulated neutrino data.

  4. Learning Achievement and the Efficiency of Learning the Concept of Vector Addition at Three Different Grade Levels

    Science.gov (United States)

    Gubrud, Allan R.; Novak, Joseph D.

    1973-01-01

    Empirical data relate to Bruner's and Ausubel's theories of learning concepts at different age levels. The concept of vector addition was taught to eighth, ninth, and tenth grade students. The concept was learned and retained by high ability ninth and all tenth grade students. (PS)

  5. Improving Learning in a Traditional, Large-Scale Science Module with a Simple and Efficient Learning Design

    DEFF Research Database (Denmark)

    Godsk, Mikkel

    2014-01-01

    the impact on teaching and learning in terms of how the teacher and the students used the materials and the impact on the students’ performance and satisfaction. The article concludes that replacing face-to-face lectures with webcasts and online activities has the potential to improve learning in terms...... of a better student performance, higher student satisfaction, and a higher degree of flexibility for the students. In addition, the article discusses implications of using learning design for educational development, how learning design may help breaking with the perception that facilitating blended learning...... is a daunting process, and, ultimately, its potential for addressing some of the grand challenges in science education and the political agenda of today....

  6. Clinical efficiency of the Auditory Verbal Learning Test for patients with internal carotid artery stenosis

    International Nuclear Information System (INIS)

    Seki, Yasuko; Maeshima, Shinichiro; Osawa, Aiko; Imura, Junko; Kohyama, Shinya; Yamane, Fumitaka; Ishihara, Shoichiro; Tanahashi, Norio

    2010-01-01

    Most patients who have an internal carotid artery (ICA) stenosis with cerebral lesion have some cognitive dysfunction. To clarify the clinical efficiency of the Auditory Verbal Learning Test (AVLT) and to assess the relationship between AVLT and cerebral damage, we examined AVLT in patients with ICA stenosis. 44 patients (35 males and 9 females) with ICA stenosis aged 56 to 83 (69.6±6.5) years old were evaluated. The educational periods were from 9 to 16 (12.3±2.8) years. Their activities of daily living (ADL) were independent. We assessed cognitive function with neuropsychological tests including AVLT, Mini-mental State Examination (MMSE), Raven's coloured progressive matrices (RCPM) and Frontal Assessment Battery (FAB), etc. We assessed cerebral damage (periventricular high intensity; PVH and white matter hyperintensity; WMH) with MRI. Then, we investigated the relationship between AVLT and other neuropsychological tests, and the relationship between AVLT and carotid/cerebral lesion. There was no association with lesion side of ICA stenosis and the scores of AVLT. In patients with ICA stenosis and cerebral damage (PVH and/or WMH), there was a significant relationship between the severity of cerebral damage and the scores in AVLT. AVLT had a significant relationship to other neuropsychological tests. AVLT might be a good cognitive assessment for patients who have cerebral damage due to ICA stenosis. (author)

  7. DCDM1: Lessons Learned from the World's Most Energy Efficient Data Center

    Energy Technology Data Exchange (ETDEWEB)

    Sickinger, David E [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Van Geet, Otto D [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Carter, Thomas [Johnson Controls

    2018-05-03

    This presentation discusses the holistic approach to design the world's most energy-efficient data center, which is located at the U.S. Department of Energy National Renewable Energy Laboratory (NREL). This high-performance computing (HPC) data center has achieved a trailing twelve-month average power usage effectiveness (PUE) of 1.04 and features a chiller-less design, component-level warm-water liquid cooling, and waste heat capture and reuse. We provide details of the demonstrated PUE and energy reuse effectiveness (ERE) and lessons learned during four years of production operation. Recent efforts to dramatically reduce the water footprint will also be discussed. Johnson Controls partnered with NREL and Sandia National Laboratories to deploy a thermosyphon cooler (TSC) as a test bed at NREL's HPC data center that resulted in a 50% reduction in water usage during the first year of operation. The Thermosyphon Cooler Hybrid System (TCHS) integrates the control of a dry heat rejection device with an open cooling tower.

  8. 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.

  9. Motor sequence learning-induced neural efficiency in functional brain connectivity.

    Science.gov (United States)

    Karim, Helmet T; Huppert, Theodore J; Erickson, Kirk I; Wollam, Mariegold E; Sparto, Patrick J; Sejdić, Ervin; VanSwearingen, Jessie M

    2017-02-15

    Previous studies have shown the functional neural circuitry differences before and after an explicitly learned motor sequence task, but have not assessed these changes during the process of motor skill learning. Functional magnetic resonance imaging activity was measured while participants (n=13) were asked to tap their fingers to visually presented sequences in blocks that were either the same sequence repeated (learning block) or random sequences (control block). Motor learning was associated with a decrease in brain activity during learning compared to control. Lower brain activation was noted in the posterior parietal association area and bilateral thalamus during the later periods of learning (not during the control). Compared to the control condition, we found the task-related motor learning was associated with decreased connectivity between the putamen and left inferior frontal gyrus and left middle cingulate brain regions. Motor learning was associated with changes in network activity, spatial extent, and connectivity. Copyright © 2016 Elsevier B.V. All rights reserved.

  10. Energy Efficient Power Allocation in Multi-tier 5G Networks Using Enhanced Online Learning

    KAUST Repository

    Alqerm, Ismail; Shihada, Basem

    2017-01-01

    of the 5G systems, where each transmitter surmises other transmitters power allocation strategies without information exchange. The proposed learning model exploits a brief state representation to account for the problem of dimensionality in online learning

  11. Enhanced machine learning scheme for energy efficient resource allocation in 5G heterogeneous cloud radio access networks

    KAUST Repository

    Alqerm, Ismail

    2018-02-15

    Heterogeneous cloud radio access networks (H-CRAN) is a new trend of 5G that aims to leverage the heterogeneous and cloud radio access networks advantages. Low power remote radio heads (RRHs) are exploited to provide high data rates for users with high quality of service requirements (QoS), while high power macro base stations (BSs) are deployed for coverage maintenance and low QoS users support. However, the inter-tier interference between the macro BS and RRHs and energy efficiency are critical challenges that accompany resource allocation in H-CRAN. Therefore, we propose a centralized resource allocation scheme using online learning, which guarantees interference mitigation and maximizes energy efficiency while maintaining QoS requirements for all users. To foster the performance of such scheme with a model-free learning, we consider users\\' priority in resource blocks (RBs) allocation and compact state representation based learning methodology to enhance the learning process. Simulation results confirm that the proposed resource allocation solution can mitigate interference, increase energy and spectral efficiencies significantly, and maintain users\\' QoS requirements.

  12. Energy Tracking Diagrams

    Science.gov (United States)

    Scherr, Rachel E.; Harrer, Benedikt W.; Close, Hunter G.; Daane, Abigail R.; DeWater, Lezlie S.; Robertson, Amy D.; Seeley, Lane; Vokos, Stamatis

    2016-01-01

    Energy is a crosscutting concept in science and features prominently in national science education documents. In the "Next Generation Science Standards," the primary conceptual learning goal is for learners to conserve energy as they "track" the transfers and transformations of energy within, into, or out of the system of…

  13. Hardware-Efficient On-line Learning through Pipelined Truncated-Error Backpropagation in Binary-State Networks

    Directory of Open Access Journals (Sweden)

    Hesham Mostafa

    2017-09-01

    Full Text Available Artificial neural networks (ANNs trained using backpropagation are powerful learning architectures that have achieved state-of-the-art performance in various benchmarks. Significant effort has been devoted to developing custom silicon devices to accelerate inference in ANNs. Accelerating the training phase, however, has attracted relatively little attention. In this paper, we describe a hardware-efficient on-line learning technique for feedforward multi-layer ANNs that is based on pipelined backpropagation. Learning is performed in parallel with inference in the forward pass, removing the need for an explicit backward pass and requiring no extra weight lookup. By using binary state variables in the feedforward network and ternary errors in truncated-error backpropagation, the need for any multiplications in the forward and backward passes is removed, and memory requirements for the pipelining are drastically reduced. Further reduction in addition operations owing to the sparsity in the forward neural and backpropagating error signal paths contributes to highly efficient hardware implementation. For proof-of-concept validation, we demonstrate on-line learning of MNIST handwritten digit classification on a Spartan 6 FPGA interfacing with an external 1Gb DDR2 DRAM, that shows small degradation in test error performance compared to an equivalently sized binary ANN trained off-line using standard back-propagation and exact errors. Our results highlight an attractive synergy between pipelined backpropagation and binary-state networks in substantially reducing computation and memory requirements, making pipelined on-line learning practical in deep networks.

  14. Hardware-Efficient On-line Learning through Pipelined Truncated-Error Backpropagation in Binary-State Networks.

    Science.gov (United States)

    Mostafa, Hesham; Pedroni, Bruno; Sheik, Sadique; Cauwenberghs, Gert

    2017-01-01

    Artificial neural networks (ANNs) trained using backpropagation are powerful learning architectures that have achieved state-of-the-art performance in various benchmarks. Significant effort has been devoted to developing custom silicon devices to accelerate inference in ANNs. Accelerating the training phase, however, has attracted relatively little attention. In this paper, we describe a hardware-efficient on-line learning technique for feedforward multi-layer ANNs that is based on pipelined backpropagation. Learning is performed in parallel with inference in the forward pass, removing the need for an explicit backward pass and requiring no extra weight lookup. By using binary state variables in the feedforward network and ternary errors in truncated-error backpropagation, the need for any multiplications in the forward and backward passes is removed, and memory requirements for the pipelining are drastically reduced. Further reduction in addition operations owing to the sparsity in the forward neural and backpropagating error signal paths contributes to highly efficient hardware implementation. For proof-of-concept validation, we demonstrate on-line learning of MNIST handwritten digit classification on a Spartan 6 FPGA interfacing with an external 1Gb DDR2 DRAM, that shows small degradation in test error performance compared to an equivalently sized binary ANN trained off-line using standard back-propagation and exact errors. Our results highlight an attractive synergy between pipelined backpropagation and binary-state networks in substantially reducing computation and memory requirements, making pipelined on-line learning practical in deep networks.

  15. Gaussian Processes for Data-Efficient Learning in Robotics and Control.

    Science.gov (United States)

    Deisenroth, Marc Peter; Fox, Dieter; Rasmussen, Carl Edward

    2015-02-01

    Autonomous learning has been a promising direction in control and robotics for more than a decade since data-driven learning allows to reduce the amount of engineering knowledge, which is otherwise required. However, autonomous reinforcement learning (RL) approaches typically require many interactions with the system to learn controllers, which is a practical limitation in real systems, such as robots, where many interactions can be impractical and time consuming. To address this problem, current learning approaches typically require task-specific knowledge in form of expert demonstrations, realistic simulators, pre-shaped policies, or specific knowledge about the underlying dynamics. In this paper, we follow a different approach and speed up learning by extracting more information from data. In particular, we learn a probabilistic, non-parametric Gaussian process transition model of the system. By explicitly incorporating model uncertainty into long-term planning and controller learning our approach reduces the effects of model errors, a key problem in model-based learning. Compared to state-of-the art RL our model-based policy search method achieves an unprecedented speed of learning. We demonstrate its applicability to autonomous learning in real robot and control tasks.

  16. Upgrade tracking with the UT Hits

    CERN Document Server

    Gandini, P; Wang, J

    2014-01-01

    The performance of the LHCb tracking system for the upgrade on long tracks is evaluated in terms of efficiency and ghost rate reduction for several different sets of requirements. We find that the efficiency is quite high and that the ghost rate reduction is substantial. We also describe the current algorithm for adding UT hits to the tracks.

  17. Learning by Doing Approach in the Internet Environment to Improve the Teaching Efficiency of Information Technology

    Science.gov (United States)

    Zhang, X.-S.; Xie, Hua

    This paper presents a learning-by-doing method in the Internet environment to enhance the results of information technology education by experimental work in the classroom of colleges. In this research, an practical approach to apply the "learning by doing" paradigm in Internet-based learning, both for higher educational environments and life-long training systems, taking into account available computer and network resources, such as blogging, podcasting, social networks, wiki etc. We first introduce the different phases in the learning process, which aimed at showing to the readers that the importance of the learning by doing paradigm, which is not implemented in many Internet-based educational environments. Secondly, we give the concept of learning by doing in the different perfective. Then, we identify the most important trends in this field, and give a real practical case for the application of this approach. The results show that the attempt methods are much better than traditional teaching methods.

  18. The efficiency of the RULES-4 classification learning algorithm in predicting the density of agents

    Directory of Open Access Journals (Sweden)

    Ziad Salem

    2014-12-01

    Full Text Available Learning is the act of obtaining new or modifying existing knowledge, behaviours, skills or preferences. The ability to learn is found in humans, other organisms and some machines. Learning is always based on some sort of observations or data such as examples, direct experience or instruction. This paper presents a classification algorithm to learn the density of agents in an arena based on the measurements of six proximity sensors of a combined actuator sensor units (CASUs. Rules are presented that were induced by the learning algorithm that was trained with data-sets based on the CASU’s sensor data streams collected during a number of experiments with “Bristlebots (agents in the arena (environment”. It was found that a set of rules generated by the learning algorithm is able to predict the number of bristlebots in the arena based on the CASU’s sensor readings with satisfying accuracy.

  19. SciNet: Lessons Learned from Building a Power-efficient Top-20 System and Data Centre

    International Nuclear Information System (INIS)

    Loken, Chris; Gruner, Daniel; Groer, Leslie; Peltier, Richard; Bunn, Neil; Craig, Michael; Henriques, Teresa; Dempsey, Jillian; Yu, Ching-Hsing; Chen, Joseph; Dursi, L Jonathan; Chong, Jason; Northrup, Scott; Pinto, Jaime; Knecht, Neil; Van Zon, Ramses

    2010-01-01

    SciNet, one of seven regional HPC consortia operating under the Compute Canada umbrella, runs Canada's first and third fastest computers (as of June 2010) in a state-of-the-art, highly energy-efficient datacentre with a Power Usage Effectiveness (PUE) design-point of 1.16. Power efficiency, computational 'bang for the buck' and system capability for a handful of flagship science projects were important criteria in choosing the nature of the computers and the data centre itself. Here we outline some of the lessons learned in putting together the systems and the data centre that hosts Canada's fastest computer to date.

  20. 眼球追蹤技術在學習與教育上的應用 Eye Tracking Technology for Learning and Education

    Directory of Open Access Journals (Sweden)

    陳學志 Hsueh-Chih Chen

    2010-12-01

    Full Text Available 本文目的為對眼球追蹤技術在學習與教育上的應用進行論述。人類的認知訊息處理歷程中有80%以上的訊息是由視覺獲得,而眼球運動也是認知過程中最為重要的感官訊息來源,近來發展的眼球追蹤技術提供了自然且即時的測量來探討認知、情緒、動機等議題,因此,眼球追蹤技術已經被廣泛地使用在各個領域中。本文針對眼球追蹤與眼球追蹤儀的基本概念、眼動指標、操作方法、資料分析方法等進行介紹。並針對眼動與閱讀、眼動與教學、眼動與問題解決及眼動與情意特質的運用等議題進行論述。藉由本文的介紹將使讀者對眼動研究在學習與教育上的應用有基本的認識。 The purpose of this article is to canvass the advantages of using eye-tracking technologies for applications in learning and education. More than 80% of the course of human cognitive processing is based on information acquired from visual modals. Eye movement plays an unrivaled role in inferring a given individual internal state. Recent developments in eye tracking technology have shown a powerful natural and real-time measuring for cognition, emotion, and motivation. This is evidenced from a wide range of reported studies and researches. We reviewed the representative research of reading, problem-solving, teaching, affect disposition, and other issues by displaying how eye-tracking technology can support advanced studies on mental processes. In addition, basic mechanical concepts, indicators, operation methods, and data analysis for the use of eye-tracking technology were introduced and discussed.

  1. A hybrid fuzzy logic and extreme learning machine for improving efficiency of circulating water systems in power generation plant

    Science.gov (United States)

    Aziz, Nur Liyana Afiqah Abdul; Siah Yap, Keem; Afif Bunyamin, Muhammad

    2013-06-01

    This paper presents a new approach of the fault detection for improving efficiency of circulating water system (CWS) in a power generation plant using a hybrid Fuzzy Logic System (FLS) and Extreme Learning Machine (ELM) neural network. The FLS is a mathematical tool for calculating the uncertainties where precision and significance are applied in the real world. It is based on natural language which has the ability of "computing the word". The ELM is an extremely fast learning algorithm for neural network that can completed the training cycle in a very short time. By combining the FLS and ELM, new hybrid model, i.e., FLS-ELM is developed. The applicability of this proposed hybrid model is validated in fault detection in CWS which may help to improve overall efficiency of power generation plant, hence, consuming less natural recourses and producing less pollutions.

  2. A hybrid fuzzy logic and extreme learning machine for improving efficiency of circulating water systems in power generation plant

    International Nuclear Information System (INIS)

    Aziz, Nur Liyana Afiqah Abdul; Yap, Keem Siah; Bunyamin, Muhammad Afif

    2013-01-01

    This paper presents a new approach of the fault detection for improving efficiency of circulating water system (CWS) in a power generation plant using a hybrid Fuzzy Logic System (FLS) and Extreme Learning Machine (ELM) neural network. The FLS is a mathematical tool for calculating the uncertainties where precision and significance are applied in the real world. It is based on natural language which has the ability of c omputing the word . The ELM is an extremely fast learning algorithm for neural network that can completed the training cycle in a very short time. By combining the FLS and ELM, new hybrid model, i.e., FLS-ELM is developed. The applicability of this proposed hybrid model is validated in fault detection in CWS which may help to improve overall efficiency of power generation plant, hence, consuming less natural recourses and producing less pollutions.

  3. Least Square Fast Learning Network for modeling the combustion efficiency of a 300WM coal-fired boiler.

    Science.gov (United States)

    Li, Guoqiang; Niu, Peifeng; Wang, Huaibao; Liu, Yongchao

    2014-03-01

    This paper presents a novel artificial neural network with a very fast learning speed, all of whose weights and biases are determined by the twice Least Square method, so it is called Least Square Fast Learning Network (LSFLN). In addition, there is another difference from conventional neural networks, which is that the output neurons of LSFLN not only receive the information from the hidden layer neurons, but also receive the external information itself directly from the input neurons. In order to test the validity of LSFLN, it is applied to 6 classical regression applications, and also employed to build the functional relation between the combustion efficiency and operating parameters of a 300WM coal-fired boiler. Experimental results show that, compared with other methods, LSFLN with very less hidden neurons could achieve much better regression precision and generalization ability at a much faster learning speed. Copyright © 2013 Elsevier Ltd. All rights reserved.

  4. Computational Properties of the Hippocampus Increase the Efficiency of Goal-Directed Foraging through Hierarchical Reinforcement Learning

    Directory of Open Access Journals (Sweden)

    Eric Chalmers

    2016-12-01

    Full Text Available The mammalian brain is thought to use a version of Model-based Reinforcement Learning (MBRL to guide goal-directed behavior, wherein animals consider goals and make plans to acquire desired outcomes. However, conventional MBRL algorithms do not fully explain animals’ ability to rapidly adapt to environmental changes, or learn multiple complex tasks. They also require extensive computation, suggesting that goal-directed behavior is cognitively expensive. We propose here that key features of processing in the hippocampus support a flexible MBRL mechanism for spatial navigation that is computationally efficient and can adapt quickly to change. We investigate this idea by implementing a computational MBRL framework that incorporates features inspired by computational properties of the hippocampus: a hierarchical representation of space, forward sweeps through future spatial trajectories, and context-driven remapping of place cells. We find that a hierarchical abstraction of space greatly reduces the computational load (mental effort required for adaptation to changing environmental conditions, and allows efficient scaling to large problems. It also allows abstract knowledge gained at high levels to guide adaptation to new obstacles. Moreover, a context-driven remapping mechanism allows learning and memory of multiple tasks. Simulating dorsal or ventral hippocampal lesions in our computational framework qualitatively reproduces behavioral deficits observed in rodents with analogous lesions. The framework may thus embody key features of how the brain organizes model-based RL to efficiently solve navigation and other difficult tasks.

  5. The market value of energy efficiency: What have we learned? What do we still need to learn?

    International Nuclear Information System (INIS)

    Lee, D.; Nevin, R.; Farhar, B.C.

    1998-01-01

    The Environmental Protection Agency (EPA) and the Department of Energy (DOE) at the National Energy Renewable Laboratory (NREL) are investigating the market value of energy efficiency in residential homes. The demonstrated market value for energy efficiency is crucial to the success of EPA's ENERGY STAR Homes Program, providing market information to builders who are deciding whether to construct ENERGY STAR Homes, to lenders who may want to understand the performance of mortgages for energy-efficient homes, and to home owners seeking returns on additional investments in energy-efficient homes. The paper discusses the current dilemma facing the ENERGY STAR Homes Program and the need to demonstrate the value of energy efficiency. The paper presents a brief literature review of past studies on the market value of energy efficiency, as well as a recent analysis on the American Housing Survey. this study suggests that property values increase by $20 to $24 for every $1 reduction in annual fuel bill. Finally, the paper concludes with a summary of a joint research project between EPA and NREL on the market value of ENERGY STAR Homes, and the potential implications of this research

  6. Worked examples are more efficient for learning than high-assistance instructional software

    NARCIS (Netherlands)

    McLaren, Bruce M.; van Gog, Tamara; Ganoe, Craig; Yaron, David; Karabinos, Michael

    2015-01-01

    The ‘assistance dilemma’, an important issue in the Learning Sciences, is concerned with how much guidance or assistance should be provided to help students learn. A recent study comparing three high-assistance approaches (worked examples, tutored problems, and erroneous examples) and one

  7. Particle tracking

    International Nuclear Information System (INIS)

    Mais, H.; Ripken, G.; Wrulich, A.; Schmidt, F.

    1986-02-01

    After a brief description of typical applications of particle tracking in storage rings and after a short discussion of some limitations and problems related with tracking we summarize some concepts and methods developed in the qualitative theory of dynamical systems. We show how these concepts can be applied to the proton ring HERA. (orig.)

  8. Timber tracking

    DEFF Research Database (Denmark)

    Düdder, Boris; Ross, Omry

    2017-01-01

    Managing and verifying forest products in a value chain is often reliant on easily manipulated document or digital tracking methods - Chain of Custody Systems. We aim to create a new means of tracking timber by developing a tamper proof digital system based on Blockchain technology. Blockchain...

  9. From Creatures of Habit to Goal-Directed Learners: Tracking the Developmental Emergence of Model-Based Reinforcement Learning.

    Science.gov (United States)

    Decker, Johannes H; Otto, A Ross; Daw, Nathaniel D; Hartley, Catherine A

    2016-06-01

    Theoretical models distinguish two decision-making strategies that have been formalized in reinforcement-learning theory. A model-based strategy leverages a cognitive model of potential actions and their consequences to make goal-directed choices, whereas a model-free strategy evaluates actions based solely on their reward history. Research in adults has begun to elucidate the psychological mechanisms and neural substrates underlying these learning processes and factors that influence their relative recruitment. However, the developmental trajectory of these evaluative strategies has not been well characterized. In this study, children, adolescents, and adults performed a sequential reinforcement-learning task that enabled estimation of model-based and model-free contributions to choice. Whereas a model-free strategy was apparent in choice behavior across all age groups, a model-based strategy was absent in children, became evident in adolescents, and strengthened in adults. These results suggest that recruitment of model-based valuation systems represents a critical cognitive component underlying the gradual maturation of goal-directed behavior. © The Author(s) 2016.

  10. An efficient flow-based botnet detection using supervised machine learning

    DEFF Research Database (Denmark)

    Stevanovic, Matija; Pedersen, Jens Myrup

    2014-01-01

    Botnet detection represents one of the most crucial prerequisites of successful botnet neutralization. This paper explores how accurate and timely detection can be achieved by using supervised machine learning as the tool of inferring about malicious botnet traffic. In order to do so, the paper...... introduces a novel flow-based detection system that relies on supervised machine learning for identifying botnet network traffic. For use in the system we consider eight highly regarded machine learning algorithms, indicating the best performing one. Furthermore, the paper evaluates how much traffic needs...... to accurately and timely detect botnet traffic using purely flow-based traffic analysis and supervised machine learning. Additionally, the results show that in order to achieve accurate detection traffic flows need to be monitored for only a limited time period and number of packets per flow. This indicates...

  11. Reinforcement function design and bias for efficient learning in mobile robots

    International Nuclear Information System (INIS)

    Touzet, C.; Santos, J.M.

    1998-01-01

    The main paradigm in sub-symbolic learning robot domain is the reinforcement learning method. Various techniques have been developed to deal with the memorization/generalization problem, demonstrating the superior ability of artificial neural network implementations. In this paper, the authors address the issue of designing the reinforcement so as to optimize the exploration part of the learning. They also present and summarize works relative to the use of bias intended to achieve the effective synthesis of the desired behavior. Demonstrative experiments involving a self-organizing map implementation of the Q-learning and real mobile robots (Nomad 200 and Khepera) in a task of obstacle avoidance behavior synthesis are described. 3 figs., 5 tabs

  12. Individual Differences and Learning Efficiency: A Re-examination and A Re-emphasis

    Science.gov (United States)

    Burck, Harman D.; Reardon, Robert C.

    1970-01-01

    Develops thesis that teacher differences are the most important variables in student learning--that if a student is not doing successful work, it is because of poor instruction and ineffective methods. Several teacher characteristics are examined as illustrations. (JES)

  13. The oxidation of PET track-etched membranes by hydrogen peroxide as an effective method to increase efficiency of UV-induced graft polymerization

    OpenAIRE

    Il'ya Korolkov; Abzal Taltenov; Anastassiya Mashentseva; Olgun Guven

    2015-01-01

    In this article, we report on functionalization of track-etched membrane based on poly(ethylene terephthalate) (PET TeMs) oxidized by advanced oxidation systems and by grafting of acrylic acid using photochemical initiation technique for the purpose of increasing functionality thus expanding its practical application. Among advanced oxidation processes (H2O2/UV) system had been chosen to introduce maximum concentration of carboxylic acid groups. Benzophenone (BP) photo-initiator was first im...

  14. Predicting performance on the Raven’s Matrices: The roles of associative learning and retrieval efficiency

    OpenAIRE

    Lilienthal, Lindsey; Tamez, Elaine; Myerson, Joel; Hale, Sandra

    2013-01-01

    Previous studies have shown that performance on Williams and Pearlberg’s (2006) complex associative learning task is a good predictor of fluid intelligence. This task is similar in structure to that used in studying the fan effect (Anderson, 1974), as both tasks involve forming multiple associations and require retrieval in the face of interference. The purpose of the present study was to investigate the relations among complex associative learning, working memory, and fluid in...

  15. A Matter of Time: Faster Percolator Analysis via Efficient SVM Learning for Large-Scale Proteomics.

    Science.gov (United States)

    Halloran, John T; Rocke, David M

    2018-05-04

    Percolator is an important tool for greatly improving the results of a database search and subsequent downstream analysis. Using support vector machines (SVMs), Percolator recalibrates peptide-spectrum matches based on the learned decision boundary between targets and decoys. To improve analysis time for large-scale data sets, we update Percolator's SVM learning engine through software and algorithmic optimizations rather than heuristic approaches that necessitate the careful study of their impact on learned parameters across different search settings and data sets. We show that by optimizing Percolator's original learning algorithm, l 2 -SVM-MFN, large-scale SVM learning requires nearly only a third of the original runtime. Furthermore, we show that by employing the widely used Trust Region Newton (TRON) algorithm instead of l 2 -SVM-MFN, large-scale Percolator SVM learning is reduced to nearly only a fifth of the original runtime. Importantly, these speedups only affect the speed at which Percolator converges to a global solution and do not alter recalibration performance. The upgraded versions of both l 2 -SVM-MFN and TRON are optimized within the Percolator codebase for multithreaded and single-thread use and are available under Apache license at bitbucket.org/jthalloran/percolator_upgrade .

  16. Making tracks

    Energy Technology Data Exchange (ETDEWEB)

    Anon.

    1986-10-15

    In many modern tracking chambers, the sense wires, rather than being lined up uniformly, are grouped into clusters to facilitate the pattern recognition process. However, with higher energy machines providing collisions richer in secondary particles, event reconstruction becomes more complicated. A Caltech / Illinois / SLAC / Washington group developed an ingenious track finding and fitting approach for the Mark III detector used at the SPEAR electron-positron ring at SLAC (Stanford). This capitalizes on the detector's triggering, which uses programmable logic circuits operating in parallel, each 'knowing' the cell patterns for all tracks passing through a specific portion of the tracker (drift chamber)

  17. Large Radius Tracking at the ATLAS Experiment

    CERN Document Server

    Lutz, Margaret Susan; The ATLAS collaboration

    2017-01-01

    Many exotics and SUSY models include particles which are long lived resulting in decays which are highly displaced from the proton-proton interaction point (IP). The standard track reconstruction algorithm used by the ATLAS collaboration is optimized for tracks from “primary” particles, which originate close to the IP. Thus, tight restrictions on the transverse and longitudinal impact parameters, as well as on several other tracking variables, are applied to improve the track reconstruction performance and to reduce the fake rate. This track reconstruction is very efficient for primary particles, but not for the non-prompt particles mentioned above.  In order to reconstruct tracks with large impact parameters due to displaced decays, a tracking algorithm has been optimized to re-run with loosened requirements over the hits left over after standard track reconstruction has finished. Enabling this “retracking” has significantly increased the efficiency of reconstructing tracks from displaced decays, wh...

  18. Myocardium tracking via matching distributions.

    Science.gov (United States)

    Ben Ayed, Ismail; Li, Shuo; Ross, Ian; Islam, Ali

    2009-01-01

    The goal of this study is to investigate automatic myocardium tracking in cardiac Magnetic Resonance (MR) sequences using global distribution matching via level-set curve evolution. Rather than relying on the pixelwise information as in existing approaches, distribution matching compares intensity distributions, and consequently, is well-suited to the myocardium tracking problem. Starting from a manual segmentation of the first frame, two curves are evolved in order to recover the endocardium (inner myocardium boundary) and the epicardium (outer myocardium boundary) in all the frames. For each curve, the evolution equation is sought following the maximization of a functional containing two terms: (1) a distribution matching term measuring the similarity between the non-parametric intensity distributions sampled from inside and outside the curve to the model distributions of the corresponding regions estimated from the previous frame; (2) a gradient term for smoothing the curve and biasing it toward high gradient of intensity. The Bhattacharyya coefficient is used as a similarity measure between distributions. The functional maximization is obtained by the Euler-Lagrange ascent equation of curve evolution, and efficiently implemented via level-set. The performance of the proposed distribution matching was quantitatively evaluated by comparisons with independent manual segmentations approved by an experienced cardiologist. The method was applied to ten 2D mid-cavity MR sequences corresponding to ten different subjects. Although neither shape prior knowledge nor curve coupling were used, quantitative evaluation demonstrated that the results were consistent with manual segmentations. The proposed method compares well with existing methods. The algorithm also yields a satisfying reproducibility. Distribution matching leads to a myocardium tracking which is more flexible and applicable than existing methods because the algorithm uses only the current data, i.e., does not

  19. Career Performance Trajectories in Track and Field Jumping Events from Youth to Senior Success: The Importance of Learning and Development.

    Science.gov (United States)

    Boccia, Gennaro; Moisè, Paolo; Franceschi, Alberto; Trova, Francesco; Panero, Davide; La Torre, Antonio; Rainoldi, Alberto; Schena, Federico; Cardinale, Marco

    2017-01-01

    The idea that early sport success can be detrimental for long-term sport performance is still under debate. Therefore, the aims of this study were to examine the career trajectories of Italian high and long jumpers to provide a better understanding of performance development in jumping events. The official long-jump and high-jump rankings of the Italian Track and Field Federation were collected from the age of 12 to career termination, for both genders from the year 1994 to 2014. Top-level athletes were identified as those with a percentile of their personal best performance between 97 and 100. The age of entering competitions of top-level athletes was not different than the rest of the athletic population, whereas top-level athletes performed their personal best later than the rest of the athletes. Top-level athletes showed an overall higher rate of improvement in performance from the age of 13 to the age of 18 years when compared to all other individuals. Only 10-25% of the top-level adult athletes were top-level at the age of 16. Around 60% of the top-level young at the age of 16 did not maintain the same level of performance in adulthood. Female high-jump represented an exception from this trend since in this group most top-level young become top-level adult athletes. These findings suggest that performance before the age of 16 is not a good predictor of adult performance in long and high jump. The annual rate of improvements from 13 to 18 years should be included as a predictor of success rather than performance per se. Coaches should be careful about predicting future success based on performances obtained during youth in jumping events.

  20. Career Performance Trajectories in Track and Field Jumping Events from Youth to Senior Success: The Importance of Learning and Development.

    Directory of Open Access Journals (Sweden)

    Gennaro Boccia

    Full Text Available The idea that early sport success can be detrimental for long-term sport performance is still under debate. Therefore, the aims of this study were to examine the career trajectories of Italian high and long jumpers to provide a better understanding of performance development in jumping events.The official long-jump and high-jump rankings of the Italian Track and Field Federation were collected from the age of 12 to career termination, for both genders from the year 1994 to 2014. Top-level athletes were identified as those with a percentile of their personal best performance between 97 and 100.The age of entering competitions of top-level athletes was not different than the rest of the athletic population, whereas top-level athletes performed their personal best later than the rest of the athletes. Top-level athletes showed an overall higher rate of improvement in performance from the age of 13 to the age of 18 years when compared to all other individuals. Only 10-25% of the top-level adult athletes were top-level at the age of 16. Around 60% of the top-level young at the age of 16 did not maintain the same level of performance in adulthood. Female high-jump represented an exception from this trend since in this group most top-level young become top-level adult athletes.These findings suggest that performance before the age of 16 is not a good predictor of adult performance in long and high jump. The annual rate of improvements from 13 to 18 years should be included as a predictor of success rather than performance per se. Coaches should be careful about predicting future success based on performances obtained during youth in jumping events.

  1. An Energy-Efficient Spectrum-Aware Reinforcement Learning-Based Clustering Algorithm for Cognitive Radio Sensor Networks.

    Science.gov (United States)

    Mustapha, Ibrahim; Mohd Ali, Borhanuddin; Rasid, Mohd Fadlee A; Sali, Aduwati; Mohamad, Hafizal

    2015-08-13

    It is well-known that clustering partitions network into logical groups of nodes in order to achieve energy efficiency and to enhance dynamic channel access in cognitive radio through cooperative sensing. While the topic of energy efficiency has been well investigated in conventional wireless sensor networks, the latter has not been extensively explored. In this paper, we propose a reinforcement learning-based spectrum-aware clustering algorithm that allows a member node to learn the energy and cooperative sensing costs for neighboring clusters to achieve an optimal solution. Each member node selects an optimal cluster that satisfies pairwise constraints, minimizes network energy consumption and enhances channel sensing performance through an exploration technique. We first model the network energy consumption and then determine the optimal number of clusters for the network. The problem of selecting an optimal cluster is formulated as a Markov Decision Process (MDP) in the algorithm and the obtained simulation results show convergence, learning and adaptability of the algorithm to dynamic environment towards achieving an optimal solution. Performance comparisons of our algorithm with the Groupwise Spectrum Aware (GWSA)-based algorithm in terms of Sum of Square Error (SSE), complexity, network energy consumption and probability of detection indicate improved performance from the proposed approach. The results further reveal that an energy savings of 9% and a significant Primary User (PU) detection improvement can be achieved with the proposed approach.

  2. Individualized tracking of self-directed motor learning in group-housed mice performing a skilled lever positioning task in the home cage.

    Science.gov (United States)

    Silasi, Gergely; Boyd, Jamie D; Bolanos, Federico; LeDue, Jeff M; Scott, Stephen H; Murphy, Timothy H

    2018-01-01

    Skilled forelimb function in mice is traditionally studied through behavioral paradigms that require extensive training by investigators and are limited by the number of trials individual animals are able to perform within a supervised session. We developed a skilled lever positioning task that mice can perform within their home cage. The task requires mice to use their forelimb to precisely hold a lever mounted on a rotary encoder within a rewarded position to dispense a water reward. A Raspberry Pi microcomputer is used to record lever position during trials and to control task parameters, thus making this low-footprint apparatus ideal for use within animal housing facilities. Custom Python software automatically increments task difficulty by requiring a longer hold duration, or a more accurate hold position, to dispense a reward. The performance of individual animals within group-housed mice is tracked through radio-frequency identification implants, and data stored on the microcomputer may be accessed remotely through an active internet connection. Mice continuously engage in the task for over 2.5 mo and perform ~500 trials/24 h. Mice required ~15,000 trials to learn to hold the lever within a 10° range for 1.5 s and were able to further refine movement accuracy by limiting their error to a 5° range within each trial. These results demonstrate the feasibility of autonomously training group-housed mice on a forelimb motor task. This paradigm may be used in the future to assess functional recovery after injury or cortical reorganization induced by self-directed motor learning. NEW & NOTEWORTHY We developed a low-cost system for fully autonomous training of group-housed mice on a forelimb motor task. We demonstrate the feasibility of tracking both end-point, as well as kinematic performance of individual mice, with each performing thousands of trials over 2.5 mo. The task is run and controlled by a Raspberry Pi microcomputer, which allows for cages to be

  3. Learning energy efficiency: experience curves for household appliances and space heating, cooling, and lighting technologies

    NARCIS (Netherlands)

    Weiss, M.; Junginger, H.M.; Patel, M.K.

    2008-01-01

    Improving demand side energy efficiency is an important strategy for establishing a sustainable energy system. Large potentials for energy efficiency improvements exist in the residential and commercial buildings sector. This sector currently accounts for almost 40% of the European Union’s (EU)

  4. Identifying Tracks Duplicates via Neural Network

    CERN Document Server

    Sunjerga, Antonio; CERN. Geneva. EP Department

    2017-01-01

    The goal of the project is to study feasibility of state of the art machine learning techniques in track reconstruction. Machine learning techniques provide promising ways to speed up the pattern recognition of tracks by adding more intelligence in the algorithms. Implementation of neural network to process of track duplicates identifying will be discussed. Different approaches are shown and results are compared to method that is currently in use.

  5. Community Tracking in a cMooc and Nomadic Learner Behavior Identification on a Connectivist Rhizomatic Learning Network

    Directory of Open Access Journals (Sweden)

    Aras BOZKURT

    2016-10-01

    Full Text Available This article contributes to the literature on connectivism, connectivist MOOCs (cMOOCs and rhizomatic learning by examining participant interactions, community formation and nomadic learner behavior in a particular cMOOC, #rhizo15, facilitated for 6 weeks by Dave Cormier. It further focuses on what we can learn by observing Twitter interactions particularly. As an explanatory mixed research design, Social Network Analysis and content analysis were employed for the purposes of the research. SNA is used at the macro, meso and micro levels, and content analysis of one week of the MOOC was conducted using the Community of Inquiry framework. The macro level analysis demonstrates that communities in a rhizomatic connectivist networks have chaotic relationships with other communities in different dimensions (clarified by use of hashtags of concurrent, past and future events. A key finding at the meso level was that as #rhizo15 progressed and number of active participants decreased, interaction increased in overall network. The micro level analysis further reveals that, though completely online, the nature of open online ecosystems are very convenient to facilitate the formation of community. The content analysis of week 3 tweets demonstrated that cognitive presence was the most frequently observed, while teaching presence (teaching behaviors of both facilitator and participants was the lowest. This research recognizes the limitations of looking only at Twitter when #rhizo15 conversations occurred over multiple platforms frequented by overlapping but not identical groups of people. However, it provides a valuable partial perspective at the macro meso and micro levels that contribute to our understanding of community-building in cMOOCs.

  6. Why tracks

    International Nuclear Information System (INIS)

    Burchart, J.; Kral, J.

    1979-01-01

    A comparison is made of two methods of determining the age of rocks, ie., the krypton-argon method and the fission tracks method. The former method is more accurate but is dependent on the temperature and on the grain size of the investigated rocks (apatites, biotites, muscovites). As for the method of fission tracks, the determination is not dependent on grain size. This method allows dating and the determination of uranium concentration and distribution in rocks. (H.S.)

  7. Tracking Your Development

    CERN Document Server

    Hennum, Kelly M

    2011-01-01

    This book provides you with the means to set development goals and to track your progress on achieving them. It can help you efficiently gather and make sense of information about your progress and avoid common pitfalls that can block your development. Tracking your development can be captures in a few steps: articulating your goal, creating an action plan, gathering information about your behavior, indentifying barriers and support, and revising your action plan. Taking these steps will greatly increase the likelihood of achieving your goals.

  8. Influence of tracks densities in solid state nuclear track detectors

    International Nuclear Information System (INIS)

    Guedes O, S.; Hadler N.; Lunes, P.; Saenz T, C.

    1996-01-01

    When Solid State Nuclear Track Detectors (SSNTD) is employed to measure nuclear tracks produced mainly by fission fragments and alpha particles, it is considered that the tracks observation work is performed under an efficiency, ε 0 , which is independent of the track density (number of tracks/area unit). There are not published results or experimental data supporting such an assumption. In this work the dependence of ε 0 with track density is studied basing on experimental data. To perform this, pieces of CR-39 cut from a sole 'mother sheet' were coupled to thin uranium films for different exposition times and the resulting ratios between track density and exposition time were compared. Our results indicate that ε 0 is constant for track densities between 10 3 and 10 5 cm -2 . At our etching conditions track overlapping makes impossible the counting for densities around 1.7 x 10 5 cm -2 . For track densities less than 10 3 cm -2 , ε 0 , was not observed to be constant. (authors). 4 refs., 2 figs

  9. Hyperspectral Vehicle BRDF Learning: An Exploration of Vehicle Reflectance Variation and Optimal Measures of Spectral Similarity for Vehicle Reacquisition and Tracking Algorithms

    Science.gov (United States)

    Svejkosky, Joseph

    The spectral signatures of vehicles in hyperspectral imagery exhibit temporal variations due to the preponderance of surfaces with material properties that display non-Lambertian bi-directional reflectance distribution functions (BRDFs). These temporal variations are caused by changing illumination conditions, changing sun-target-sensor geometry, changing road surface properties, and changing vehicle orientations. To quantify these variations and determine their relative importance in a sub-pixel vehicle reacquisition and tracking scenario, a hyperspectral vehicle BRDF sampling experiment was conducted in which four vehicles were rotated at different orientations and imaged over a six-hour period. The hyperspectral imagery was calibrated using novel in-scene methods and converted to reflectance imagery. The resulting BRDF sampled time-series imagery showed a strong vehicle level BRDF dependence on vehicle shape in off-nadir imaging scenarios and a strong dependence on vehicle color in simulated nadir imaging scenarios. The imagery also exhibited spectral features characteristic of sampling the BRDF of non-Lambertian targets, which were subsequently verified with simulations. In addition, the imagery demonstrated that the illumination contribution from vehicle adjacent horizontal surfaces significantly altered the shape and magnitude of the vehicle reflectance spectrum. The results of the BRDF sampling experiment illustrate the need for a target vehicle BRDF model and detection scheme that incorporates non-Lambertian BRDFs. A new detection algorithm called Eigenvector Loading Regression (ELR) is proposed that learns a hyperspectral vehicle BRDF from a series of BRDF measurements using regression in a lower dimensional space and then applies the learned BRDF to make test spectrum predictions. In cases of non-Lambertian vehicle BRDF, this detection methodology performs favorably when compared to subspace detections algorithms and graph-based detection algorithms that

  10. Relationship of Teaching Efficiency with Academic Self-Efficacy and Self-Directed Learning among English Language Students: University Students’ Perspectives

    Directory of Open Access Journals (Sweden)

    Maryam Shohoudi

    2015-09-01

    Full Text Available Introduction: Self-directed learning is originated from adult education which has currently gained a special place in educational systems and is influenced by many variables such as teaching self-efficacy and self-directed learning. This research investigated the relationship of teachers’ teaching with academic self-efficacy and self-directed learning from English language students' perspectives. Methods: The study population comprised of all bachelor, master and Ph.D. English language students of Allameh Tabataba’i University (2014-2015 who had passed at least one semester. A total of 159 students were selected as study sample using Cochran formula and proportional stratified sampling. The data were collected through three standard questionnaires with confirmed validity and reliability. Data were analyzed by one-sample t-test, Pearson correlation and multiple regression. Results: With regard to teaching efficiency, content presentation, learning evaluation and class management skills were higher than average and lesson planning and control over content skills were at an average level. Also, all dimensions of academic self-efficacy and self-directed learning were significantly higher than average. The correlation between teaching efficiency and self-efficacy (r=0.367 and self-directed learning (r=0.571, and between self-efficacy and self-directed learning (r=0.523 was statistically significant (P<0.01. Moreover, a combination of teaching efficiency dimensions could predict different dimensions of self-efficacy and all components of self-directed learning. Furthermore, self-efficacy dimensions were good predictors of self-directed learning. Conclusion: Success in the realm of academia and organizational learning depends on the learners’ updated knowledge and skills and self-directed learning. Also, it seems teachers’ efficient teaching affects students’ academic self-efficacy, orienting them toward self-directed learning.

  11. Integrating Heuristic and Machine-Learning Methods for Efficient Virtual Machine Allocation in Data Centers

    OpenAIRE

    Pahlevan, Ali; Qu, Xiaoyu; Zapater Sancho, Marina; Atienza Alonso, David

    2017-01-01

    Modern cloud data centers (DCs) need to tackle efficiently the increasing demand for computing resources and address the energy efficiency challenge. Therefore, it is essential to develop resource provisioning policies that are aware of virtual machine (VM) characteristics, such as CPU utilization and data communication, and applicable in dynamic scenarios. Traditional approaches fall short in terms of flexibility and applicability for large-scale DC scenarios. In this paper we propose a heur...

  12. Inquiring the Most Critical Teacher's Technology Education Competences in the Highest Efficient Technology Education Learning Organization

    Science.gov (United States)

    Yung-Kuan, Chan; Hsieh, Ming-Yuan; Lee, Chin-Feng; Huang, Chih-Cheng; Ho, Li-Chih

    2017-01-01

    Under the hyper-dynamic education situation, this research, in order to comprehensively explore the interplays between Teacher Competence Demands (TCD) and Learning Organization Requests (LOR), cross-employs the data refined method of Descriptive Statistics (DS) method and Analysis of Variance (ANOVA) and Principal Components Analysis (PCA)…

  13. Efficient Learning for the Poor : Insights from the Frontier of Cognitive Neuroscience

    OpenAIRE

    Abadzi, Helen

    2006-01-01

    This book integrates research into applications that extend from preschool brain development to the memory of adult educators. In layman's terms, it provides explanations and answers to questions such as: Why do children have to read fast before they can understand what they read? How do health, nutrition, and stimulation influence brain development? Why should students learn basic skills...

  14. Impact of Augmented Reality on Programming Language Learning: Efficiency and Perception

    Science.gov (United States)

    Teng, Chin-Hung; Chen, Jr-Yi; Chen, Zhi-Hong

    2018-01-01

    Although the learning of programming language is critical in science and technology education, it might be difficult for some students, especially novices. One possible reason might be the fact that programming language, especially for three-dimensional (3D) applications, is too complex and abstract for these students to understand. Programming…

  15. Artificial Intelligence and Second Language Learning: An Efficient Approach to Error Remediation

    Science.gov (United States)

    Dodigovic, Marina

    2007-01-01

    While theoretical approaches to error correction vary in the second language acquisition (SLA) literature, most sources agree that such correction is useful and leads to learning. While some point out the relevance of the communicative context in which the correction takes place, others stress the value of consciousness-raising. Trying to…

  16. Learning High-Order Filters for Efficient Blind Deconvolution of Document Photographs

    KAUST Repository

    Xiao, Lei; Wang, Jue; Heidrich, Wolfgang; Hirsch, Michael

    2016-01-01

    by small-scale high-order structures, we propose to learn a multi-scale, interleaved cascade of shrinkage fields model, which contains a series of high-order filters to facilitate joint recovery of blur kernel and latent image. With extensive experiments

  17. Efficient Learning for the Poor: New Insights into Literacy Acquisition for Children

    Science.gov (United States)

    Abadzi, Helen

    2008-01-01

    Reading depends on the speed of visual recognition and capacity of short-term memory. To understand a sentence, the mind must read it fast enough to capture it within the limits of the short-term memory. This means that children must attain a minimum speed of fairly accurate reading to understand a passage. Learning to read involves "tricking" the…

  18. The Efficiency of Different Online Learning Media--An Empirical Study

    Science.gov (United States)

    Köbler, Franziska J.; Nitzschner, Marco M.

    2014-01-01

    In the current study, it was examined whether successful learning is related to using different types of media. We compared the comprehension of an economic concept in novices (N = 82) under three conditions: a Wikipedia article, a funny, and a serious YouTube video. The media were presented in English which is a foreign language to most of the…

  19. Low Working Memory Capacity Impedes both Efficiency and Learning of Number Transcoding in Children

    Science.gov (United States)

    Camos, Valerie

    2008-01-01

    This study aimed to evaluate the impact of individual differences in working memory capacity on number transcoding. A recently proposed model, ADAPT (a developmental asemantic procedural transcoding model), accounts for the development of number transcoding from verbal form to Arabic form by two mechanisms: the learning of new production rules…

  20. Instructional Efficiency of the Integration of Graphing Calculators in Teaching and Learning Mathematics

    Science.gov (United States)

    Tajuddin, Nor'ain Mohd; Tarmizi, Rohani Ahmad; Konting, Mohd Majid; Ali, Wan Zah Wan

    2009-01-01

    This quasi-experimental study with non-equivalent control group post-test only design was conducted to investigate the effects of using graphing calculators in mathematics teaching and learning on Form Four Malaysian secondary school students' performance and their meta-cognitive awareness level. Graphing calculator strategy refers to the use of…

  1. Efficient Prediction of Low-Visibility Events at Airports Using Machine-Learning Regression

    Science.gov (United States)

    Cornejo-Bueno, L.; Casanova-Mateo, C.; Sanz-Justo, J.; Cerro-Prada, E.; Salcedo-Sanz, S.

    2017-11-01

    We address the prediction of low-visibility events at airports using machine-learning regression. The proposed model successfully forecasts low-visibility events in terms of the runway visual range at the airport, with the use of support-vector regression, neural networks (multi-layer perceptrons and extreme-learning machines) and Gaussian-process algorithms. We assess the performance of these algorithms based on real data collected at the Valladolid airport, Spain. We also propose a study of the atmospheric variables measured at a nearby tower related to low-visibility atmospheric conditions, since they are considered as the inputs of the different regressors. A pre-processing procedure of these input variables with wavelet transforms is also described. The results show that the proposed machine-learning algorithms are able to predict low-visibility events well. The Gaussian process is the best algorithm among those analyzed, obtaining over 98% of the correct classification rate in low-visibility events when the runway visual range is {>}1000 m, and about 80% under this threshold. The performance of all the machine-learning algorithms tested is clearly affected in extreme low-visibility conditions ({algorithm performance in daytime and nighttime conditions, and for different prediction time horizons.

  2. Behavioral Objectives, the Cult of Efficiency, and Foreign Language Learning: Are They Compatible?

    Science.gov (United States)

    Tumposky, Nancy Rennau

    1984-01-01

    Surveys the literature regarding the use of behavioral objectives in education and in foreign language instruction and examines the roots of the behavioral objectives movement in behaviorist psychology and the scientific management movement of the 1920s. Discusses implications for foreign and second language learning and provides suggestions for…

  3. Success for energy efficient renovation of dwellings—Learning from private homeowners

    International Nuclear Information System (INIS)

    Risholt, Birgit; Berker, Thomas

    2013-01-01

    Large scale energy efficient renovation of buildings is one of the most important tools to realize the society's need of a more sustainable building stock. Most Norwegians own their own homes. Therefore private homeowners are a focus group for the government urging to accelerate the dwelling energy efficiency rates. Success factors were identified in the in-depth study of the decision process of eleven homeowners. Large differences in energy use due to the building's condition and the occupants' behavior was encountered in the sample. Only homeowners who were conscious consumers and did not trust expert advice or that had special knowledge due to their professions succeeded in realizing energy efficiency by renovation. Lack of knowledge, bad advice from craftsmen or priority to work that they can do themselves stopped other homeowners from implementing energy efficiency. Increased knowledge on all the gains from energy efficiency, the availability of attractive products and services as well as easy access to reliable advice on the better renovation solutions have a large potential to get more homeowners to make energy efficient choices in the process of renovation. Coordination of more of policy strategies including specific information and incentives are needed to facilitate this. - Highlights: • Private homeowners are a key group to increase the dwelling energy efficiency rates. • The annual energy use varies from 103 kW h/m 2 to 240 kW h/m 2 in similar dwellings. • Homeowners that are conscious consumers or have knowledge succeed in saving energy. • Access to relevant and reliable advice can get homeowners to realize energy savings. • Craftsmen could be mediators between available products and the specific building

  4. Resource-efficient ILC for LTI/LTV systems through LQ tracking and stable inversion: enabling large feedforward tasks on a position-dependent printer

    NARCIS (Netherlands)

    van Zundert, J.; Bolder, J.J.; Koekebakker, S.H.; Oomen, T.A.E.

    Iterative learning control (ILC) enables high performance for systems that execute repeating tasks. Norm-optimal ILC based on lifted system representations provides an analytic expression for the optimal feedforward signal. However, for large tasks the computational load increases rapidly for

  5. Financing energy efficiency in developing countries-lessons learned and remaining challenges

    International Nuclear Information System (INIS)

    Sarkar, Ashok; Singh, Jas

    2010-01-01

    Although energy efficiency implementation is increasingly being recognized by policymakers worldwide as one of the most effective means to mitigating rising energy prices, tackling potential environmental risks, and enhancing energy security, mainstreaming its financing in developing country markets continues to be a challenge. Experience shows that converting cost-effective energy savings potential, particularly the demand-side improvement opportunities across sectors, into investments face many barriers and unforeseen transaction costs. This paper draws upon selected experiences with financing energy efficiency in developing countries to explore the key factors of various programmatic approaches and financing instruments that have been applied successfully for delivering energy efficiency solutions. Through case studies, a diverse range of institutional issues are examined related to the identification, packaging, designing, and monitoring approaches that have been used to catalyze traditional and innovative financing of energy efficiency projects. With adequate liquidity in major developing country markets and availability of modern energy savings technologies, it is often the institutional issues that become a key challenge to address in order to finance and implement robust programs. As further operational experience is gained, increased knowledge sharing can lead to scaling-up of such energy efficiency investments. The paper concludes with some ideas for accelerating implementation.

  6. Financing energy efficiency in developing countries. Lessons learned and remaining challenges

    Energy Technology Data Exchange (ETDEWEB)

    Sarkar, Ashok [Energy Unit, Energy, Transport and Water Department, World Bank (United States); Singh, Jas [Energy Sector Management Assistance Program (ESMAP), Energy, Transport and Water Department, World Bank (United States)

    2010-10-15

    Although energy efficiency implementation is increasingly being recognized by policymakers worldwide as one of the most effective means to mitigating rising energy prices, tackling potential environmental risks, and enhancing energy security, mainstreaming its financing in developing country markets continues to be a challenge. Experience shows that converting cost-effective energy savings potential, particularly the demand-side improvement opportunities across sectors, into investments face many barriers and unforeseen transaction costs. This paper draws upon selected experiences with financing energy efficiency in developing countries to explore the key factors of various programmatic approaches and financing instruments that have been applied successfully for delivering energy efficiency solutions. Through case studies, a diverse range of institutional issues are examined related to the identification, packaging, designing, and monitoring approaches that have been used to catalyze traditional and innovative financing of energy efficiency projects. With adequate liquidity in major developing country markets and availability of modern energy savings technologies, it is often the institutional issues that become a key challenge to address in order to finance and implement robust programs. As further operational experience is gained, increased knowledge sharing can lead to scaling-up of such energy efficiency investments. The paper concludes with some ideas for accelerating implementation. (author)

  7. Financing energy efficiency in developing countries-lessons learned and remaining challenges

    Energy Technology Data Exchange (ETDEWEB)

    Sarkar, Ashok, E-mail: asarkar@worldbank.or [Energy Unit, Energy, Transport and Water Department, World Bank (United States); Singh, Jas, E-mail: jsingh3@worldbank.or [Energy Sector Management Assistance Program (ESMAP), Energy, Transport and Water Department, World Bank (United States)

    2010-10-15

    Although energy efficiency implementation is increasingly being recognized by policymakers worldwide as one of the most effective means to mitigating rising energy prices, tackling potential environmental risks, and enhancing energy security, mainstreaming its financing in developing country markets continues to be a challenge. Experience shows that converting cost-effective energy savings potential, particularly the demand-side improvement opportunities across sectors, into investments face many barriers and unforeseen transaction costs. This paper draws upon selected experiences with financing energy efficiency in developing countries to explore the key factors of various programmatic approaches and financing instruments that have been applied successfully for delivering energy efficiency solutions. Through case studies, a diverse range of institutional issues are examined related to the identification, packaging, designing, and monitoring approaches that have been used to catalyze traditional and innovative financing of energy efficiency projects. With adequate liquidity in major developing country markets and availability of modern energy savings technologies, it is often the institutional issues that become a key challenge to address in order to finance and implement robust programs. As further operational experience is gained, increased knowledge sharing can lead to scaling-up of such energy efficiency investments. The paper concludes with some ideas for accelerating implementation.

  8. A machine learning approach for efficient uncertainty quantification using multiscale methods

    Science.gov (United States)

    Chan, Shing; Elsheikh, Ahmed H.

    2018-02-01

    Several multiscale methods account for sub-grid scale features using coarse scale basis functions. For example, in the Multiscale Finite Volume method the coarse scale basis functions are obtained by solving a set of local problems over dual-grid cells. We introduce a data-driven approach for the estimation of these coarse scale basis functions. Specifically, we employ a neural network predictor fitted using a set of solution samples from which it learns to generate subsequent basis functions at a lower computational cost than solving the local problems. The computational advantage of this approach is realized for uncertainty quantification tasks where a large number of realizations has to be evaluated. We attribute the ability to learn these basis functions to the modularity of the local problems and the redundancy of the permeability patches between samples. The proposed method is evaluated on elliptic problems yielding very promising results.

  9. D0 central tracking chamber performance studies

    International Nuclear Information System (INIS)

    Pizzuto, D.

    1991-12-01

    The performance of the completed DO central tracking chamber was studied using cosmic rays at the State University of New York at Stony Brook. Also studied was a prototype tracking chamber identical in design to the completed DO tracking chamber. The prototype chamber was exposed to a collimated beam of 150 GeV pions at the Fermilab NWA test facility. Results indicate an RΦ tracking resolution compatible with the limitations imposed by physical considerations, excellent 2 track resolution, and a high track reconstruction efficiency along with a good rejection power against γ → e + e - events

  10. Multimedia Scenario Based Learning Programme for Enhancing the English Language Efficiency among Primary School Students

    Directory of Open Access Journals (Sweden)

    Navnath Tupe

    2015-07-01

    Full Text Available This research was undertaken with a view to assess the deficiencies in English language among Primary School Children and to develop Multimedia Scenario Based Learning Programme (MSBLP for mastery of English language which required special attention and effective treatment. The experimental study with pretest, post-test control group design was employed to carry out the experiment of MSBLP in a sample school and to determine its efficacy for enhancing English Language skills among Primary School Students. In India, the Central and State Government has made great efforts to Education for All (EFA and initiated several programs to provide universal access to education, to reduce the drop-out rates and ensure achievement of minimum levels of learning. To our surprise the scenario had not much changed inside the classroom even implementing several programmes. However, it was still unclear how effective was the delivery of the course content in the classroom. An intensive training for teachers on a regular basis on a state-wide scale may not be feasible again and again. Hence, multimedia offers pragmatic solutions So that this research paper devoted to explore the issues of learning English and describes the creation of MSBLP as a solution in scientific manner.

  11. The use of multimedia tools for improving movement notion and increasing the efficiency of motor learning in skiing

    Directory of Open Access Journals (Sweden)

    Ruzicka Ivan

    2016-01-01

    Full Text Available The aim of this paper is focused on the problem of improving movement notion and increasing the efficiency of motor learning in skiing using multimedia tools. The text approaches the system providing a targeted feedback in the process of the acquisition of skiing skills. The platform influencing the movement notion introduces innovative means of the acquisition of essential skiing skills in ski courses organized by the Department of PE and Sport of the Faculty of Education, University of Hradec Králové. The paper presents the selected results of the survey realized by an enquiring method, which was aimed to find out opinions on a monitored platform among students specializing in physical education and sport, who took part in this form of education. The research results indicate that the use of multimedia tools in providing visual feedback can effectively influence the process and the final effect of the acquisition of skiing skills. Positive opinions of the overwhelming majority of respondents illustrate that the use of video analysis in combination with verbal mistake correction is an effective support in skiing practice and it is an efficient platform that accelerates results in learning skiing technique, especially in the context of educational courses. Conclusions also point to some of the negative aspects related to the use of multimedia tools within the platform.

  12. RLAM: A Dynamic and Efficient Reinforcement Learning-Based Adaptive Mapping Scheme in Mobile WiMAX Networks

    Directory of Open Access Journals (Sweden)

    M. Louta

    2014-01-01

    Full Text Available WiMAX (Worldwide Interoperability for Microwave Access constitutes a candidate networking technology towards the 4G vision realization. By adopting the Orthogonal Frequency Division Multiple Access (OFDMA technique, the latest IEEE 802.16x amendments manage to provide QoS-aware access services with full mobility support. A number of interesting scheduling and mapping schemes have been proposed in research literature. However, they neglect a considerable asset of the OFDMA-based wireless systems: the dynamic adjustment of the downlink-to-uplink width ratio. In order to fully exploit the supported mobile WiMAX features, we design, develop, and evaluate a rigorous adaptive model, which inherits its main aspects from the reinforcement learning field. The model proposed endeavours to efficiently determine the downlink-to-uplinkwidth ratio, on a frame-by-frame basis, taking into account both the downlink and uplink traffic in the Base Station (BS. Extensive evaluation results indicate that the model proposed succeeds in providing quite accurate estimations, keeping the average error rate below 15% with respect to the optimal sub-frame configurations. Additionally, it presents improved performance compared to other learning methods (e.g., learning automata and notable improvements compared to static schemes that maintain a fixed predefined ratio in terms of service ratio and resource utilization.

  13. Learning Methods for Efficient Adoption of Contemporary Technologies in Architectural Design

    Science.gov (United States)

    Mahdavinejad, Mohammadjavad; Dehghani, Sohaib; Shahsavari, Fatemeh

    2013-01-01

    The interaction between technology and history is one of the most significant issues in achieving an efficient and progressive architecture in any era. This is a concept which stems from lesson of traditional architecture of Iran. Architecture as a part of art, has permanently been transforming just like a living organism. In fact, it has been…

  14. Efficiency gains in Danish district heating. Is there anything to learn from benchmarking?

    DEFF Research Database (Denmark)

    Munksgaard, Jesper; Pade, Lise-Lotte; Fristrup, P.

    2005-01-01

    Facing a market structure of independent heating systems and cost-of-service regulation the regulator considers ways to create incentives for increasing efficiency in heat production.One way is to implement benchmark regulation. The aim of this paper is twofold: (1) To investigate the potential f...

  15. Promoting high efficiency residential HVAC equipment: Lessons learned from leading utility programs

    Energy Technology Data Exchange (ETDEWEB)

    Neme, C.; Peters, J.; Rouleau, D.

    1998-07-01

    The Consortium for Energy Efficiency recently sponsored a study of leading electric utility efforts to promote high efficiency residential HVAC equipment. Given growing concerns from some utilities about the level of expenditures associated with rebate programs, special emphasis was placed on assessing the success of financing and other non-rebate options for promoting efficiency. Emphasis was also placed on review of efforts--rebate or otherwise--to push the market to very high levels of efficiency (i.e., SEER 13). This paper presents the results of the study. It includes discussion of key lessons from the utility programs analyzed. It also examines program participation rates and other potential indicators of market impacts. One notable conclusion is that several utility programs have pushed market shares for SEER 12 equipment to about 50% (the national average is less than 20%). At least one utility program has achieved a 50% market share for SEER 13 equipment (the national average is less than 3%). In general, financing does not appear to have as broad an appeal as consumer rebates. However, one unique utility program which combines the other of customer financing with modest incentives to contractors--in the form of frequent seller points that can be redeemed for advertising, technician training, travel and other merchandise--offers some promise that high participation rates can be achieved without customer rebates.

  16. Using learning curves on energy-efficient technologies to estimate future energy savings and emission reduction potentials in the U.S. iron and steel industry

    Energy Technology Data Exchange (ETDEWEB)

    Karali, Nihan [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Park, Won Young [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); McNeil, Michael A. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

    2015-06-18

    Increasing concerns on non-sustainable energy use and climate change spur a growing research interest in energy efficiency potentials in various critical areas such as industrial production. This paper focuses on learning curve aspects of energy efficiency measures in the U.S iron and steel sector. A number of early-stage efficient technologies (i.e., emerging or demonstration technologies) are technically feasible and have the potential to make a significant contribution to energy saving and CO2 emissions reduction, but fall short economically to be included. However, they may also have the cost effective potential for significant cost reduction and/or performance improvement in the future under learning effects such as ‘learning-by-doing’. The investigation is carried out using ISEEM, a technology oriented, linear optimization model. We investigated how steel demand is balanced with/without the availability learning curve, compared to a Reference scenario. The retrofit (or investment in some cases) costs of energy efficient technologies decline in the scenario where learning curve is applied. The analysis also addresses market penetration of energy efficient technologies, energy saving, and CO2 emissions in the U.S. iron and steel sector with/without learning impact. Accordingly, the study helps those who use energy models better manage the price barriers preventing unrealistic diffusion of energy-efficiency technologies, better understand the market and learning system involved, predict future achievable learning rates more accurately, and project future savings via energy-efficiency technologies with presence of learning. We conclude from our analysis that, most of the existing energy efficiency technologies that are currently used in the U.S. iron and steel sector are cost effective. Penetration levels increases through the years, even though there is no price reduction. However, demonstration technologies are not economically

  17. The role of fluid intelligence and learning in analogical reasoning: How to become neurally efficient?

    Science.gov (United States)

    Dix, Annika; Wartenburger, Isabell; van der Meer, Elke

    2016-10-01

    This study on analogical reasoning evaluates the impact of fluid intelligence on adaptive changes in neural efficiency over the course of an experiment and specifies the underlying cognitive processes. Grade 10 students (N=80) solved unfamiliar geometric analogy tasks of varying difficulty. Neural efficiency was measured by the event-related desynchronization (ERD) in the alpha band, an indicator of cortical activity. Neural efficiency was defined as a low amount of cortical activity accompanying high performance during problem-solving. Students solved the tasks faster and more accurately the higher their FI was. Moreover, while high FI led to greater cortical activity in the first half of the experiment, high FI was associated with a neurally more efficient processing (i.e., better performance but same amount of cortical activity) in the second half of the experiment. Performance in difficult tasks improved over the course of the experiment for all students while neural efficiency increased for students with higher but decreased for students with lower fluid intelligence. Based on analyses of the alpha sub-bands, we argue that high fluid intelligence was associated with a stronger investment of attentional resource in the integration of information and the encoding of relations in this unfamiliar task in the first half of the experiment (lower-2 alpha band). Students with lower fluid intelligence seem to adapt their applied strategies over the course of the experiment (i.e., focusing on task-relevant information; lower-1 alpha band). Thus, the initially lower cortical activity and its increase in students with lower fluid intelligence might reflect the overcoming of mental overload that was present in the first half of the experiment. Copyright © 2016 Elsevier Inc. All rights reserved.

  18. Online Tracking

    Science.gov (United States)

    ... can disable blocking on those sites. Tagged with: computer security , cookies , Do Not Track , personal information , privacy June ... email Looking for business guidance on privacy and ... The Federal Trade Commission (FTC) is the nation’s consumer protection agency. The FTC works to prevent fraudulent, deceptive ...

  19. DLTAP: A Network-efficient Scheduling Method for Distributed Deep Learning Workload in Containerized Cluster Environment

    OpenAIRE

    Qiao Wei; Li Ying; Wu Zhong-Hai

    2017-01-01

    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 ...

  20. An Efficient Hardware Circuit for Spike Sorting Based on Competitive Learning Networks

    Directory of Open Access Journals (Sweden)

    Huan-Yuan Chen

    2017-09-01

    Full Text Available This study aims to present an effective VLSI circuit for multi-channel spike sorting. The circuit supports the spike detection, feature extraction and classification operations. The detection circuit is implemented in accordance with the nonlinear energy operator algorithm. Both the peak detection and area computation operations are adopted for the realization of the hardware architecture for feature extraction. The resulting feature vectors are classified by a circuit for competitive learning (CL neural networks. The CL circuit supports both online training and classification. In the proposed architecture, all the channels share the same detection, feature extraction, learning and classification circuits for a low area cost hardware implementation. The clock-gating technique is also employed for reducing the power dissipation. To evaluate the performance of the architecture, an application-specific integrated circuit (ASIC implementation is presented. Experimental results demonstrate that the proposed circuit exhibits the advantages of a low chip area, a low power dissipation and a high classification success rate for spike sorting.

  1. Efficient discovery of responses of proteins to compounds using active learning

    Science.gov (United States)

    2014-01-01

    Background Drug discovery and development has been aided by high throughput screening methods that detect compound effects on a single target. However, when using focused initial screening, undesirable secondary effects are often detected late in the development process after significant investment has been made. An alternative approach would be to screen against undesired effects early in the process, but the number of possible secondary targets makes this prohibitively expensive. Results This paper describes methods for making this global approach practical by constructing predictive models for many target responses to many compounds and using them to guide experimentation. We demonstrate for the first time that by jointly modeling targets and compounds using descriptive features and using active machine learning methods, accurate models can be built by doing only a small fraction of possible experiments. The methods were evaluated by computational experiments using a dataset of 177 assays and 20,000 compounds constructed from the PubChem database. Conclusions An average of nearly 60% of all hits in the dataset were found after exploring only 3% of the experimental space which suggests that active learning can be used to enable more complete characterization of compound effects than otherwise affordable. The methods described are also likely to find widespread application outside drug discovery, such as for characterizing the effects of a large number of compounds or inhibitory RNAs on a large number of cell or tissue phenotypes. PMID:24884564

  2. Development of self-learning Monte Carlo technique for more efficient modeling of nuclear logging measurements

    International Nuclear Information System (INIS)

    Zazula, J.M.

    1988-01-01

    The self-learning Monte Carlo technique has been implemented to the commonly used general purpose neutron transport code MORSE, in order to enhance sampling of the particle histories that contribute to a detector response. The parameters of all the biasing techniques available in MORSE, i.e. of splitting, Russian roulette, source and collision outgoing energy importance sampling, path length transformation and additional biasing of the source angular distribution are optimized. The learning process is iteratively performed after each batch of particles, by retrieving the data concerning the subset of histories that passed the detector region and energy range in the previous batches. This procedure has been tested on two sample problems in nuclear geophysics, where an unoptimized Monte Carlo calculation is particularly inefficient. The results are encouraging, although the presented method does not directly minimize the variance and the convergence of our algorithm is restricted by the statistics of successful histories from previous random walk. Further applications for modeling of the nuclear logging measurements seem to be promising. 11 refs., 2 figs., 3 tabs. (author)

  3. An Efficient Hardware Circuit for Spike Sorting Based on Competitive Learning Networks

    Science.gov (United States)

    Chen, Huan-Yuan; Chen, Chih-Chang

    2017-01-01

    This study aims to present an effective VLSI circuit for multi-channel spike sorting. The circuit supports the spike detection, feature extraction and classification operations. The detection circuit is implemented in accordance with the nonlinear energy operator algorithm. Both the peak detection and area computation operations are adopted for the realization of the hardware architecture for feature extraction. The resulting feature vectors are classified by a circuit for competitive learning (CL) neural networks. The CL circuit supports both online training and classification. In the proposed architecture, all the channels share the same detection, feature extraction, learning and classification circuits for a low area cost hardware implementation. The clock-gating technique is also employed for reducing the power dissipation. To evaluate the performance of the architecture, an application-specific integrated circuit (ASIC) implementation is presented. Experimental results demonstrate that the proposed circuit exhibits the advantages of a low chip area, a low power dissipation and a high classification success rate for spike sorting. PMID:28956859

  4. Fast-Spiking Interneurons Supply Feedforward Control of Bursting, Calcium, and Plasticity for Efficient Learning.

    Science.gov (United States)

    Owen, Scott F; Berke, Joshua D; Kreitzer, Anatol C

    2018-02-08

    Fast-spiking interneurons (FSIs) are a prominent class of forebrain GABAergic cells implicated in two seemingly independent network functions: gain control and network plasticity. Little is known, however, about how these roles interact. Here, we use a combination of cell-type-specific ablation, optogenetics, electrophysiology, imaging, and behavior to describe a unified mechanism by which striatal FSIs control burst firing, calcium influx, and synaptic plasticity in neighboring medium spiny projection neurons (MSNs). In vivo silencing of FSIs increased bursting, calcium transients, and AMPA/NMDA ratios in MSNs. In a motor sequence task, FSI silencing increased the frequency of calcium transients but reduced the specificity with which transients aligned to individual task events. Consistent with this, ablation of FSIs disrupted the acquisition of striatum-dependent egocentric learning strategies. Together, our data support a model in which feedforward inhibition from FSIs temporally restricts MSN bursting and calcium-dependent synaptic plasticity to facilitate striatum-dependent sequence learning. Copyright © 2018 Elsevier Inc. All rights reserved.

  5. Harnessing wake vortices for efficient collective swimming via deep reinfrcement learning

    Science.gov (United States)

    Verma, Siddartha; Novati, Guido; Koumoutsakos, Petros; ChairComputing Science Team

    2017-11-01

    Collective motion may bestow evolutionary advantages to a number of animal species. Soaring flocks of birds, teeming swarms of insects, and swirling masses of schooling fish, all to some extent enjoy anti-predator benefits, increased foraging success, and enhanced problem-solving abilities. Coordinated activity may also provide energetic benefits, as in the case of large groups of fish where swimmers exploit unsteady flow-patterns generated in the wake. Both experimental and computational investigations of such scenarios are hampered by difficulties associated with studying multiple swimmers. Consequentially, the precise energy-saving mechanisms at play remain largely unknown. We combine high-fidelity numerical simulations of multiple, self propelled swimmers with novel deep reinforcement learning algorithms to discover optimal ways for swimmers to interact with unsteady wakes, in a fully unsupervised manner. We identify optimal flow-interaction strategies devised by the resulting autonomous swimmers, and use it to formulate an effective control-logic. We demonstrate, via 3D simulations of controlled groups that swimmers exploiting the learned strategy exhibit a significant reduction in energy-expenditure. ERC Advanced Investigator Award 341117.

  6. Laser-Based Slam with Efficient Occupancy Likelihood Map Learning for Dynamic Indoor Scenes

    Science.gov (United States)

    Li, Li; Yao, Jian; Xie, Renping; Tu, Jinge; Feng, Chen

    2016-06-01

    Location-Based Services (LBS) have attracted growing attention in recent years, especially in indoor environments. The fundamental technique of LBS is the map building for unknown environments, this technique also named as simultaneous localization and mapping (SLAM) in robotic society. In this paper, we propose a novel approach for SLAMin dynamic indoor scenes based on a 2D laser scanner mounted on a mobile Unmanned Ground Vehicle (UGV) with the help of the grid-based occupancy likelihood map. Instead of applying scan matching in two adjacent scans, we propose to match current scan with the occupancy likelihood map learned from all previous scans in multiple scales to avoid the accumulation of matching errors. Due to that the acquisition of the points in a scan is sequential but not simultaneous, there unavoidably exists the scan distortion at different extents. To compensate the scan distortion caused by the motion of the UGV, we propose to integrate a velocity of a laser range finder (LRF) into the scan matching optimization framework. Besides, to reduce the effect of dynamic objects such as walking pedestrians often existed in indoor scenes as much as possible, we propose a new occupancy likelihood map learning strategy by increasing or decreasing the probability of each occupancy grid after each scan matching. Experimental results in several challenged indoor scenes demonstrate that our proposed approach is capable of providing high-precision SLAM results.

  7. The oxidation of PET track-etched membranes by hydrogen peroxide as an effective method to increase efficiency of UV-induced graft polymerization

    Directory of Open Access Journals (Sweden)

    Il'ya Korolkov

    2015-12-01

    Full Text Available In this article, we report on functionalization of track-etched membrane based on poly(ethylene terephthalate (PET TeMs oxidized by advanced oxidation systems and by grafting of acrylic acid using photochemical initiation technique for the purpose of increasing functionality thus expanding its practical application. Among advanced oxidation processes (H2O2/UV system had been chosen to introduce maximum concentration of carboxylic acid groups. Benzophenone (BP photo-initiator was first immobilized on the surfaces of cylindrical pores which were later filled with aq. acrylic acid solution. UV-irradiation from both sides of PET TeMs has led to the formation of grafted poly(acrylic acid (PAA chains inside the membrane nanochannels. Effect of oxygen-rich surface of PET TeMs on BP adsorption and subsequent process of photo-induced graft polymerization of acrylic acid (AA were studied by ESR. The surface of oxidized and AA grafted PET TeMs was characterized by UV-vis, ATR-FTIR, XPS spectroscopies and by SEM.

  8. Symplectic Tracking of Multi-Isotopic Heavy-Ion Beams in SixTrack

    CERN Document Server

    Hermes, Pascal; De Maria, Riccardo

    2016-01-01

    The software SixTrack provides symplectic proton tracking over a large number of turns. The code is used for the tracking of beam halo particles and the simulation of their interaction with the collimators to study the efficiency of the LHC collimation system. Tracking simulations for heavy-ion beams require taking into account the mass to charge ratio of each particle because heavy ions can be subject to fragmentation at their passage through the collimators. In this paper we present the derivation of a Hamiltonian for multi-isotopic heavy-ion beams and symplectic tracking maps derived from it. The resulting tracking maps were implemented in the tracking software SixTrack. With this modification, SixTrack can be used to natively track heavy-ion beams of multiple isotopes through a magnetic accelerator lattice.

  9. High-Bandwidth, High-Efficiency Envelope Tracking Power Supply for 40W RF Power Amplifier Using Paralleled Bandpass Current Sources

    DEFF Research Database (Denmark)

    Høyerby, Mikkel Christian Wendelboe; Andersen, Michael Andreas E.

    2005-01-01

    This paper presents a high-performance power conversion scheme for power supply applications that require very high output voltage slew rates (dV/dt). The concept is to parallel 2 switching bandpass current sources, each optimized for its passband frequency space and the expected load current....... The principle is demonstrated with a power supply, designed for supplying a 40 W linear RF power amplifier for efficient amplification of a 16-QAM modulated data stream...

  10. Better learning in energy-efficient school buildings. A guideline; Besseres Lernen in energieeffizienten Schulen. Leitfaden

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2010-07-01

    Modernization of school buildings involves more than a reduction of energy cost, due to the fact that the room climate of a school affects the learning success of its pupils. Users' demands on architecture and technical systems are highly complex: Rooms are used at different times and for different lengths of time, there are vacation times when the building is not used, and the rooms must meet high demands in terms of air change, lighting, acoustics and blinding as well as in terms of safety, hygiene and pollutant-free air. All this necessitates an integral sanitation concept that takes account of all aspects. Further, the modernization of school buildings should also serve to enhance the pupils' environmental and energy awareness. This guide takes account of all these aspects. (orig./AKB)

  11. Tracking change over time

    Science.gov (United States)

    ,

    2011-01-01

    Landsat satellites capture images of Earth from space-and have since 1972! These images provide a long-term record of natural and human-induced changes on the global landscape. Comparing images from multiple years reveals slow and subtle changes as well as rapid and devastating ones. Landsat images are available over the Internet at no charge. Using the free software MultiSpec, students can track changes to the landscape over time-just like remote sensing scientists do! The objective of the Tracking Change Over Time lesson plan is to get students excited about studying the changing Earth. Intended for students in grades 5-8, the lesson plan is flexible and may be used as a student self-guided tutorial or as a teacher-led class lesson. Enhance students' learning of geography, map reading, earth science, and problem solving by seeing landscape changes from space.

  12. Structural Sparse Tracking

    KAUST Repository

    Zhang, Tianzhu

    2015-06-01

    Sparse representation has been applied to visual tracking by finding the best target candidate with minimal reconstruction error by use of target templates. However, most sparse representation based trackers only consider holistic or local representations and do not make full use of the intrinsic structure among and inside target candidates, thereby making the representation less effective when similar objects appear or under occlusion. In this paper, we propose a novel Structural Sparse Tracking (SST) algorithm, which not only exploits the intrinsic relationship among target candidates and their local patches to learn their sparse representations jointly, but also preserves the spatial layout structure among the local patches inside each target candidate. We show that our SST algorithm accommodates most existing sparse trackers with the respective merits. Both qualitative and quantitative evaluations on challenging benchmark image sequences demonstrate that the proposed SST algorithm performs favorably against several state-of-the-art methods.

  13. Robust visual tracking via multiscale deep sparse networks

    Science.gov (United States)

    Wang, Xin; Hou, Zhiqiang; Yu, Wangsheng; Xue, Yang; Jin, Zefenfen; Dai, Bo

    2017-04-01

    In visual tracking, deep learning with offline pretraining can extract more intrinsic and robust features. It has significant success solving the tracking drift in a complicated environment. However, offline pretraining requires numerous auxiliary training datasets and is considerably time-consuming for tracking tasks. To solve these problems, a multiscale sparse networks-based tracker (MSNT) under the particle filter framework is proposed. Based on the stacked sparse autoencoders and rectifier linear unit, the tracker has a flexible and adjustable architecture without the offline pretraining process and exploits the robust and powerful features effectively only through online training of limited labeled data. Meanwhile, the tracker builds four deep sparse networks of different scales, according to the target's profile type. During tracking, the tracker selects the matched tracking network adaptively in accordance with the initial target's profile type. It preserves the inherent structural information more efficiently than the single-scale networks. Additionally, a corresponding update strategy is proposed to improve the robustness of the tracker. Extensive experimental results on a large scale benchmark dataset show that the proposed method performs favorably against state-of-the-art methods in challenging environments.

  14. Robust Visual Tracking via Exclusive Context Modeling

    KAUST Repository

    Zhang, Tianzhu

    2015-02-09

    In this paper, we formulate particle filter-based object tracking as an exclusive sparse learning problem that exploits contextual information. To achieve this goal, we propose the context-aware exclusive sparse tracker (CEST) to model particle appearances as linear combinations of dictionary templates that are updated dynamically. Learning the representation of each particle is formulated as an exclusive sparse representation problem, where the overall dictionary is composed of multiple {group} dictionaries that can contain contextual information. With context, CEST is less prone to tracker drift. Interestingly, we show that the popular L₁ tracker [1] is a special case of our CEST formulation. The proposed learning problem is efficiently solved using an accelerated proximal gradient method that yields a sequence of closed form updates. To make the tracker much faster, we reduce the number of learning problems to be solved by using the dual problem to quickly and systematically rank and prune particles in each frame. We test our CEST tracker on challenging benchmark sequences that involve heavy occlusion, drastic illumination changes, and large pose variations. Experimental results show that CEST consistently outperforms state-of-the-art trackers.

  15. Asymmetric learning by doing and dynamically efficient policy: implications for domestic and international emissions permit trading of allocating permits usefully

    International Nuclear Information System (INIS)

    Read, Peter

    2000-01-01

    Learning by doing leads to cost reductions as suppliers move down the 'experience curve'. This results in a beneficial supply side inter-temporal externality that, for dynamic efficiency, requires a higher incentive for abatement innovations than the penalty on emissions. This effect can be achieved by a dedicated emissions tax or by a proportionate abatement obligation or by allocating permits usefully. The latter arrangement is compatible with the effective cap on emissions that is secured by an emissions trading scheme. Each of the three possibilities results in a reduced loss of international competitivity in policy-committed regions, in less 'leakage, and in more technology transfer. Implications for trading in emissions permits and in project-related credits are discussed. (Author)

  16. An Entropy-Based Kernel Learning Scheme toward Efficient Data Prediction in Cloud-Assisted Network Environments

    Directory of Open Access Journals (Sweden)

    Xiong Luo

    2016-07-01

    Full Text Available With the recent emergence of wireless sensor networks (WSNs in the cloud computing environment, it is now possible to monitor and gather physical information via lots of sensor nodes to meet the requirements of cloud services. Generally, those sensor nodes collect data and send data to sink node where end-users can query all the information and achieve cloud applications. Currently, one of the main disadvantages in the sensor nodes is that they are with limited physical performance relating to less memory for storage and less source of power. Therefore, in order to avoid such limitation, it is necessary to develop an efficient data prediction method in WSN. To serve this purpose, by reducing the redundant data transmission between sensor nodes and sink node while maintaining the required acceptable errors, this article proposes an entropy-based learning scheme for data prediction through the use of kernel least mean square (KLMS algorithm. The proposed scheme called E-KLMS develops a mechanism to maintain the predicted data synchronous at both sides. Specifically, the kernel-based method is able to adjust the coefficients adaptively in accordance with every input, which will achieve a better performance with smaller prediction errors, while employing information entropy to remove these data which may cause relatively large errors. E-KLMS can effectively solve the tradeoff problem between prediction accuracy and computational efforts while greatly simplifying the training structure compared with some other data prediction approaches. What’s more, the kernel-based method and entropy technique could ensure the prediction effect by both improving the accuracy and reducing errors. Experiments with some real data sets have been carried out to validate the efficiency and effectiveness of E-KLMS learning scheme, and the experiment results show advantages of the our method in prediction accuracy and computational time.

  17. A machine learning approach for predicting CRISPR-Cas9 cleavage efficiencies and patterns underlying its mechanism of action.

    Science.gov (United States)

    Abadi, Shiran; Yan, Winston X; Amar, David; Mayrose, Itay

    2017-10-01

    The adaptation of the CRISPR-Cas9 system as a genome editing technique has generated much excitement in recent years owing to its ability to manipulate targeted genes and genomic regions that are complementary to a programmed single guide RNA (sgRNA). However, the efficacy of a specific sgRNA is not uniquely defined by exact sequence homology to the target site, thus unintended off-targets might additionally be cleaved. Current methods for sgRNA design are mainly concerned with predicting off-targets for a given sgRNA using basic sequence features and employ elementary rules for ranking possible sgRNAs. Here, we introduce CRISTA (CRISPR Target Assessment), a novel algorithm within the machine learning framework that determines the propensity of a genomic site to be cleaved by a given sgRNA. We show that the predictions made with CRISTA are more accurate than other available methodologies. We further demonstrate that the occurrence of bulges is not a rare phenomenon and should be accounted for in the prediction process. Beyond predicting cleavage efficiencies, the learning process provides inferences regarding patterns that underlie the mechanism of action of the CRISPR-Cas9 system. We discover that attributes that describe the spatial structure and rigidity of the entire genomic site as well as those surrounding the PAM region are a major component of the prediction capabilities.

  18. A machine learning approach for predicting CRISPR-Cas9 cleavage efficiencies and patterns underlying its mechanism of action.

    Directory of Open Access Journals (Sweden)

    Shiran Abadi

    2017-10-01

    Full Text Available The adaptation of the CRISPR-Cas9 system as a genome editing technique has generated much excitement in recent years owing to its ability to manipulate targeted genes and genomic regions that are complementary to a programmed single guide RNA (sgRNA. However, the efficacy of a specific sgRNA is not uniquely defined by exact sequence homology to the target site, thus unintended off-targets might additionally be cleaved. Current methods for sgRNA design are mainly concerned with predicting off-targets for a given sgRNA using basic sequence features and employ elementary rules for ranking possible sgRNAs. Here, we introduce CRISTA (CRISPR Target Assessment, a novel algorithm within the machine learning framework that determines the propensity of a genomic site to be cleaved by a given sgRNA. We show that the predictions made with CRISTA are more accurate than other available methodologies. We further demonstrate that the occurrence of bulges is not a rare phenomenon and should be accounted for in the prediction process. Beyond predicting cleavage efficiencies, the learning process provides inferences regarding patterns that underlie the mechanism of action of the CRISPR-Cas9 system. We discover that attributes that describe the spatial structure and rigidity of the entire genomic site as well as those surrounding the PAM region are a major component of the prediction capabilities.

  19. Learning Automata Based Caching for Efficient Data Access in Delay Tolerant Networks

    Directory of Open Access Journals (Sweden)

    Zhenjie Ma

    2018-01-01

    Full Text Available Effective data access is one of the major challenges in Delay Tolerant Networks (DTNs that are characterized by intermittent network connectivity and unpredictable node mobility. Currently, different data caching schemes have been proposed to improve the performance of data access in DTNs. However, most existing data caching schemes perform poorly due to the lack of global network state information and the changing network topology in DTNs. In this paper, we propose a novel data caching scheme based on cooperative caching in DTNs, aiming at improving the successful rate of data access and reducing the data access delay. In the proposed scheme, learning automata are utilized to select a set of caching nodes as Caching Node Set (CNS in DTNs. Unlike the existing caching schemes failing to address the challenging characteristics of DTNs, our scheme is designed to automatically self-adjust to the changing network topology through the well-designed voting and updating processes. The proposed scheme improves the overall performance of data access in DTNs compared with the former caching schemes. The simulations verify the feasibility of our scheme and the improvements in performance.

  20. Unsupervised Learning for Efficient Texture Estimation From Limited Discrete Orientation Data

    Science.gov (United States)

    Niezgoda, Stephen R.; Glover, Jared

    2013-11-01

    The estimation of orientation distribution functions (ODFs) from discrete orientation data, as produced by electron backscatter diffraction or crystal plasticity micromechanical simulations, is typically achieved via techniques such as the Williams-Imhof-Matthies-Vinel (WIMV) algorithm or generalized spherical harmonic expansions, which were originally developed for computing an ODF from pole figures measured by X-ray or neutron diffraction. These techniques rely on ad-hoc methods for choosing parameters, such as smoothing half-width and bandwidth, and for enforcing positivity constraints and appropriate normalization. In general, such approaches provide little or no information-theoretic guarantees as to their optimality in describing the given dataset. In the current study, an unsupervised learning algorithm is proposed which uses a finite mixture of Bingham distributions for the estimation of ODFs from discrete orientation data. The Bingham distribution is an antipodally-symmetric, max-entropy distribution on the unit quaternion hypersphere. The proposed algorithm also introduces a minimum message length criterion, a common tool in information theory for balancing data likelihood with model complexity, to determine the number of components in the Bingham mixture. This criterion leads to ODFs which are less likely to overfit (or underfit) the data, eliminating the need for a priori parameter choices.

  1. Precise object tracking under deformation

    International Nuclear Information System (INIS)

    Saad, M.H

    2010-01-01

    The precise object tracking is an essential issue in several serious applications such as; robot vision, automated surveillance (civil and military), inspection, biomedical image analysis, video coding, motion segmentation, human-machine interface, visualization, medical imaging, traffic systems, satellite imaging etc. This frame-work focuses on the precise object tracking under deformation such as scaling , rotation, noise, blurring and change of illumination. This research is a trail to solve these serious problems in visual object tracking by which the quality of the overall system will be improved. Developing a three dimensional (3D) geometrical model to determine the current pose of an object and predict its future location based on FIR model learned by the OLS. This framework presents a robust ranging technique to track a visual target instead of the traditional expensive ranging sensors. The presented research work is applied to real video stream and achieved high precession results.

  2. Extremely Efficient Design of Organic Thin Film Solar Cells via Learning-Based Optimization

    Directory of Open Access Journals (Sweden)

    Mine Kaya

    2017-11-01

    Full Text Available Design of efficient thin film photovoltaic (PV cells require optical power absorption to be computed inside a nano-scale structure of photovoltaics, dielectric and plasmonic materials. Calculating power absorption requires Maxwell’s electromagnetic equations which are solved using numerical methods, such as finite difference time domain (FDTD. The computational cost of thin film PV cell design and optimization is therefore cumbersome, due to successive FDTD simulations. This cost can be reduced using a surrogate-based optimization procedure. In this study, we deploy neural networks (NNs to model optical absorption in organic PV structures. We use the corresponding surrogate-based optimization procedure to maximize light trapping inside thin film organic cells infused with metallic particles. Metallic particles are known to induce plasmonic effects at the metal–semiconductor interface, thus increasing absorption. However, a rigorous design procedure is required to achieve the best performance within known design guidelines. As a result of using NNs to model thin film solar absorption, the required time to complete optimization is decreased by more than five times. The obtained NN model is found to be very reliable. The optimization procedure results in absorption enhancement greater than 200%. Furthermore, we demonstrate that once a reliable surrogate model such as the developed NN is available, it can be used for alternative analyses on the proposed design, such as uncertainty analysis (e.g., fabrication error.

  3. Extending Correlation Filter-Based Visual Tracking by Tree-Structured Ensemble and Spatial Windowing.

    Science.gov (United States)

    Gundogdu, Erhan; Ozkan, Huseyin; Alatan, A Aydin

    2017-11-01

    Correlation filters have been successfully used in visual tracking due to their modeling power and computational efficiency. However, the state-of-the-art correlation filter-based (CFB) tracking algorithms tend to quickly discard the previous poses of the target, since they consider only a single filter in their models. On the contrary, our approach is to register multiple CFB trackers for previous poses and exploit the registered knowledge when an appearance change occurs. To this end, we propose a novel tracking algorithm [of complexity O(D) ] based on a large ensemble of CFB trackers. The ensemble [of size O(2 D ) ] is organized over a binary tree (depth D ), and learns the target appearance subspaces such that each constituent tracker becomes an expert of a certain appearance. During tracking, the proposed algorithm combines only the appearance-aware relevant experts to produce boosted tracking decisions. Additionally, we propose a versatile spatial windowing technique to enhance the individual expert trackers. For this purpose, spatial windows are learned for target objects as well as the correlation filters and then the windowed regions are processed for more robust correlations. In our extensive experiments on benchmark datasets, we achieve a substantial performance increase by using the proposed tracking algorithm together with the spatial windowing.

  4. GENFIT - a generic track-fitting toolkit

    Energy Technology Data Exchange (ETDEWEB)

    Rauch, Johannes [Technische Universitaet Muenchen (Germany); Schlueter, Tobias [Ludwig-Maximilians-Universitaet Muenchen (Germany)

    2014-07-01

    GENFIT is an experiment-independent track-fitting toolkit, which combines fitting algorithms, track representations, and measurement geometries into a modular framework. We report on a significantly improved version of GENFIT, based on experience gained in the Belle II, PANDA, and FOPI experiments. Improvements concern the implementation of additional track-fitting algorithms, enhanced implementations of Kalman fitters, enhanced visualization capabilities, and additional implementations of measurement types suited for various kinds of tracking detectors. The data model has been revised, allowing for efficient track merging, smoothing, residual calculation and alignment.

  5. High-Efficiency Housing at the Fort Peck Indian Reservation: Opportunities and Lessons Learned

    Energy Technology Data Exchange (ETDEWEB)

    Lisell, Lars J [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Desai, Jal D [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Dean, Jesse D [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Rehder, Tim [U.S. Environmental Protection Agency (EPA), Region 8

    2018-03-13

    This project was initiated to provide design assistance in an effort to maximize energy performance for affordable housing at the Fort Peck Indian Reservation near Poplar, Montana. The Make It Right Foundation (MIRF) built 20 high performing homes (LEED Platinum) in 2015 and 2016 with three (3) different design options. NREL and EPA set out to provide energy analysis along with measurement and verification (M and V) of the homes to characterize energy use and provide clarity for future decision making with regard to tribal housing options. The results included herein summarize the energy end uses and documents projected energy impacts from various aspects of the MIRF home designs and construction. This report includes an analysis of energy use in 5 MIRF homes, comparing energy use across the different styles and configurations. Energy models were created for the 2 styles of MIRF homes, including renewable energy assessment for photovoltaic (PV) systems. Existing tribal housing has also been analyzed, with 5 housing units being analyzed for energy use and an energy model being created for 1 housing unit. The findings of this study highlight many of the challenges that arise when attempting to construct high performance housing in a region where such construction practices are still relatively rare. Homes in Poplar are well designed and, for the most part, and include climate specific design considerations appropriate for northeastern Montana. The most significant issues identified in MIRF homes were related to the work done to put the homes on the foundation, insulate the crawlspaces, and do final connection with the utilities. The Taxed II Credit homes are well designed and well suited to northeastern Montana, and with slight modifications to the design and construction could be very efficient. All occupant comfort and energy usage issues that were identified during the site visits can be remedied through retrofit measures that are relatively inexpensive. Energy

  6. Precise Object Tracking under Deformation

    International Nuclear Information System (INIS)

    Saad, M.H.

    2010-01-01

    The precise object tracking is an essential issue in several serious applications such as; robot vision, automated surveillance (civil and military), inspection, biomedical image analysis, video coding, motion segmentation, human-machine interface, visualization, medical imaging, traffic systems, satellite imaging etc. This framework focuses on the precise object tracking under deformation such as scaling, rotation, noise, blurring and change of illumination. This research is a trail to solve these serious problems in visual object tracking by which the quality of the overall system will be improved. Developing a three dimensional (3D) geometrical model to determine the current pose of an object and predict its future location based on FIR model learned by the OLS. This framework presents a robust ranging technique to track a visual target instead of the traditional expensive ranging sensors. The presented research work is applied to real video stream and achieved high precession results. xiiiThe precise object tracking is an essential issue in several serious applications such as; robot vision, automated surveillance (civil and military), inspection, biomedical image analysis, video coding, motion segmentation, human-machine interface, visualization, medical imaging, traffic systems, satellite imaging etc. This framework focuses on the precise object tracking under deformation such as scaling, rotation, noise, blurring and change of illumination. This research is a trail to solve these serious problems in visual object tracking by which the quality of the overall system will be improved. Developing a three dimensional (3D) geometrical model to determine the current pose of an object and predict its future location based on FIR model learned by the OLS. This framework presents a robust ranging technique to track a visual target instead of the traditional expensive ranging sensors. The presented research work is applied to real video stream and achieved high

  7. Eye tracking in user experience design

    CERN Document Server

    Romano Bergstorm, Jennifer

    2014-01-01

    Eye Tracking for User Experience Design explores the many applications of eye tracking to better understand how users view and interact with technology. Ten leading experts in eye tracking discuss how they have taken advantage of this new technology to understand, design, and evaluate user experience. Real-world stories are included from these experts who have used eye tracking during the design and development of products ranging from information websites to immersive games. They also explore recent advances in the technology which tracks how users interact with mobile devices, large-screen displays and video game consoles. Methods for combining eye tracking with other research techniques for a more holistic understanding of the user experience are discussed. This is an invaluable resource to those who want to learn how eye tracking can be used to better understand and design for their users. * Includes highly relevant examples and information for those who perform user research and design interactive experi...

  8. Tracking errors in a prototype real-time tumour tracking system

    International Nuclear Information System (INIS)

    Sharp, Gregory C; Jiang, Steve B; Shimizu, Shinichi; Shirato, Hiroki

    2004-01-01

    In motion-compensated radiation therapy, radio-opaque markers can be implanted in or near a tumour and tracked in real-time using fluoroscopic imaging. Tracking these implanted markers gives highly accurate position information, except when tracking fails due to poor or ambiguous imaging conditions. This study investigates methods for automatic detection of tracking errors, and assesses the frequency and impact of tracking errors on treatments using the prototype real-time tumour tracking system. We investigated four indicators for automatic detection of tracking errors, and found that the distance between corresponding rays was most effective. We also found that tracking errors cause a loss of gating efficiency of between 7.6 and 10.2%. The incidence of treatment beam delivery during tracking errors was estimated at between 0.8% and 1.25%

  9. High-resolution 3D coronary vessel wall imaging with near 100% respiratory efficiency using epicardial fat tracking: reproducibility and comparison with standard methods.

    Science.gov (United States)

    Scott, Andrew D; Keegan, Jennifer; Firmin, David N

    2011-01-01

    To quantitatively assess the performance and reproducibility of 3D spiral coronary artery wall imaging with beat-to-beat respiratory-motion-correction (B2B-RMC) compared to navigator gated 2D spiral and turbo-spin-echo (TSE) acquisitions. High-resolution (0.7 × 0.7 mm) cross-sectional right coronary wall acquisitions were performed in 10 subjects using four techniques (B2B-RMC 3D spiral with alternate (2RR) and single (1RR) R-wave gating, navigator-gated 2D spiral (2RR) and navigator-gated 2D TSE (2RR)) on two occasions. Wall thickness measurements were compared with repeated measures analysis of variance (ANOVA). Reproducibility was assessed with the intraclass correlation coefficient (ICC). In all, 91% (73/80) of acquisitions were successful (failures: four TSE, two 3D spiral (1RR) and one 3D spiral (2RR)). Respiratory efficiency of the B2B-RMC was less variable and substantially higher than for navigator gating (99.6 ± 1.2% vs. 39.0 ± 7.5%, P B2B-RMC permits coronary vessel wall assessment over multiple thin contiguous slices in a clinically feasible duration. Excellent reproducibility of the technique potentially enables studies of disease progression/regression. Copyright © 2010 Wiley-Liss, Inc.

  10. Latent tracks in polymeric etched track detectors

    International Nuclear Information System (INIS)

    Yamauchi, Tomoya

    2013-01-01

    Track registration properties in polymeric track detectors, including Poly(allyl diglycol carbonate), Bispenol A polycarbonate, Poly(ethylen terephtarate), and Polyimide, have been investigated by means of Fourie transform Infararede FT-IR spectrometry. Chemical criterion on the track formation threshold has been proposes, in stead of the conventional physical track registration models. (author)

  11. Tracking telecommuting

    Energy Technology Data Exchange (ETDEWEB)

    Stastny, P.

    2007-03-15

    Many employees are now choosing to work from home using laptops and telephones. Employers in the oil and gas industry are now reaping a number of benefits from their telecommuting employees, including increased productivity; higher levels of employee satisfaction, and less absenteeism. Providing a telecommunication option can prove to be advantageous for employers wishing to hire or retain employees. Telecommuting may also help to reduce greenhouse gas (GHG) emissions. This article provided details of Teletrips Inc., a company that aids in the production of corporate social responsibility reports. Teletrips provides reports that document employee savings in time, vehicle depreciation maintenance, and gasoline costs. Teletrips currently tracks 12 companies in Calgary, and plans to grow through the development of key technology partnerships. The company is also working with the federal government to provide their clients with emission trading credits, and has forged a memorandum of understanding with the British Columbia government for tracking emissions. Calgary now openly supports telecommuting and is encouraging businesses in the city to adopt telecommuting on a larger scale. It was concluded that the expanding needs for road infrastructure and the energy used by cars to move workers in and out of the city are a massive burden to the city's tax base. 1 fig.

  12. INNER TRACKING

    CERN Document Server

    P. Sharp

    The CMS Inner Tracking Detector continues to make good progress. The Objective for 2006 was to complete all of the CMS Tracker sub-detectors and to start the integration of the sub-detectors into the Tracker Support Tube (TST). The Objective for 2007 is to deliver to CMS a completed, installed, commissioned and calibrated Tracking System (Silicon Strip and Pixels) aligned to < 100µ in April 2008 ready for the first physics collisions at LHC. In November 2006 all of the sub-detectors had been delivered to the Tracker Integration facility (TIF) at CERN and the tests and QA procedures to be carried out on each sub-detector before integration had been established. In December 2006, TIB/TID+ was integrated into TOB+, TIB/TID- was being prepared for integration, and TEC+ was undergoing tests at the final tracker operating temperature (-100 C) in the Lyon cold room. In February 2007, TIB/TID- has been integrated into TOB-, and the installation of the pixel support tube and the services for TI...

  13. Excellent areas. Learning from energy efficient newly built houses. Halfway the knowledge and learning phase; Excellente gebieden. Leerschool voor energiezuinige nieuwbouw. Halverwege het kennis- en leertraject

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2013-07-15

    The so-called Excellent Areas concern 19 innovative newly built houses projects in the residential and utility sector. Houses and office buildings are constructed with at least an energy performance coefficient (EPC), 25% stricter than as described in the Dutch Building Code. This project is aimed at preparing for the Spring Agreement in which it is agreed to tighten the EPC in 2015 and to reach the nearly zero-energy ambition for 2020. Municipalities, project developers and other parties in the energy-efficient building business thus gain experience with innovative construction methods and techniques. And in particular in the search for innovation in the construction process and new forms of cooperation and financing. The knowledge and experiences are supported, shared and disseminated in the knowledge and learning process of Excellent Areas [Dutch] De Excellente Gebieden zijn 19 innovatieve nieuwbouwprojecten in de woning- en utiliteitsbouw. Hier worden woningen en kantoren gebouwd met tenminste een 25% scherpere energieprestatiecoefficient (EPC) dan het Bouwbesluit voorschrijft. Dit ter voorbereiding op de in het Lente-akkoord afgesproken aanscherping van de EPC in 2015 en de bijna energieneutrale ambitie voor 2020. Gemeenten, projectontwikkelaars en andere partijen die energiezuinig bouwen, doen uitgebreide praktijkervaring op met innovatieve bouwmethoden en technieken. En vooral ook met het zoeken naar innovatie in het bouwproces en nieuwe vormen van samenwerking en financiering. Deze kennis en ervaring wordt ondersteund, gedeeld en verspreid in het kennis- en leertraject Excellente Gebieden.

  14. An Energy-Efficient and Scalable Deep Learning/Inference Processor With Tetra-Parallel MIMD Architecture for Big Data Applications.

    Science.gov (United States)

    Park, Seong-Wook; Park, Junyoung; Bong, Kyeongryeol; Shin, Dongjoo; Lee, Jinmook; Choi, Sungpill; Yoo, Hoi-Jun

    2015-12-01

    Deep Learning algorithm is widely used for various pattern recognition applications such as text recognition, object recognition and action recognition because of its best-in-class recognition accuracy compared to hand-crafted algorithm and shallow learning based algorithms. Long learning time caused by its complex structure, however, limits its usage only in high-cost servers or many-core GPU platforms so far. On the other hand, the demand on customized pattern recognition within personal devices will grow gradually as more deep learning applications will be developed. This paper presents a SoC implementation to enable deep learning applications to run with low cost platforms such as mobile or portable devices. Different from conventional works which have adopted massively-parallel architecture, this work adopts task-flexible architecture and exploits multiple parallelism to cover complex functions of convolutional deep belief network which is one of popular deep learning/inference algorithms. In this paper, we implement the most energy-efficient deep learning and inference processor for wearable system. The implemented 2.5 mm × 4.0 mm deep learning/inference processor is fabricated using 65 nm 8-metal CMOS technology for a battery-powered platform with real-time deep inference and deep learning operation. It consumes 185 mW average power, and 213.1 mW peak power at 200 MHz operating frequency and 1.2 V supply voltage. It achieves 411.3 GOPS peak performance and 1.93 TOPS/W energy efficiency, which is 2.07× higher than the state-of-the-art.

  15. Effects of an Instructional Gaming Characteristic on Learning Effectiveness, Efficiency, and Engagement: Using a Storyline to Teach Basic Statistical Analytical Skills

    Science.gov (United States)

    Novak, Elena

    2012-01-01

    The study explored instructional benefits of a storyline gaming characteristic (GC) on learning effectiveness, efficiency, and engagement with the use of an online instructional simulation for graduate students in an introductory statistics course. In addition, the study focused on examining the effects of a storyline GC on specific learning…

  16. Fibre tracking

    International Nuclear Information System (INIS)

    Gaillard, J.M.

    1994-03-01

    A large-size scintillating plastic fibre tracking detector was built as part of the upgrade of the UA2 central detector at the SPS proton-antiproton collider. The cylindrical fibre detector of average radius of 40 cm consisted of 60000 plastic fibres with an active length of 2.1 m. One of the main motivations was to improve the electron identification. The fibre ends were bunched to be coupled to read-out systems of image intensifier plus CCD, 32 in total. The quality and the reliability of the UA2 fibre detector performance exceeded expectations throughout its years of operation. A few examples of the use of image intensifiers and of scintillating fibres in biological instrumentation are described. (R.P.) 11 refs., 15 figs., 2 tabs

  17. Screening for Dyslexia Using Eye Tracking during Reading.

    Science.gov (United States)

    Nilsson Benfatto, Mattias; Öqvist Seimyr, Gustaf; Ygge, Jan; Pansell, Tony; Rydberg, Agneta; Jacobson, Christer

    2016-01-01

    Dyslexia is a neurodevelopmental reading disability estimated to affect 5-10% of the population. While there is yet no full understanding of the cause of dyslexia, or agreement on its precise definition, it is certain that many individuals suffer persistent problems in learning to read for no apparent reason. Although it is generally agreed that early intervention is the best form of support for children with dyslexia, there is still a lack of efficient and objective means to help identify those at risk during the early years of school. Here we show that it is possible to identify 9-10 year old individuals at risk of persistent reading difficulties by using eye tracking during reading to probe the processes that underlie reading ability. In contrast to current screening methods, which rely on oral or written tests, eye tracking does not depend on the subject to produce some overt verbal response and thus provides a natural means to objectively assess the reading process as it unfolds in real-time. Our study is based on a sample of 97 high-risk subjects with early identified word decoding difficulties and a control group of 88 low-risk subjects. These subjects were selected from a larger population of 2165 school children attending second grade. Using predictive modeling and statistical resampling techniques, we develop classification models from eye tracking records less than one minute in duration and show that the models are able to differentiate high-risk subjects from low-risk subjects with high accuracy. Although dyslexia is fundamentally a language-based learning disability, our results suggest that eye movements in reading can be highly predictive of individual reading ability and that eye tracking can be an efficient means to identify children at risk of long-term reading difficulties.

  18. Screening for Dyslexia Using Eye Tracking during Reading.

    Directory of Open Access Journals (Sweden)

    Mattias Nilsson Benfatto

    Full Text Available Dyslexia is a neurodevelopmental reading disability estimated to affect 5-10% of the population. While there is yet no full understanding of the cause of dyslexia, or agreement on its precise definition, it is certain that many individuals suffer persistent problems in learning to read for no apparent reason. Although it is generally agreed that early intervention is the best form of support for children with dyslexia, there is still a lack of efficient and objective means to help identify those at risk during the early years of school. Here we show that it is possible to identify 9-10 year old individuals at risk of persistent reading difficulties by using eye tracking during reading to probe the processes that underlie reading ability. In contrast to current screening methods, which rely on oral or written tests, eye tracking does not depend on the subject to produce some overt verbal response and thus provides a natural means to objectively assess the reading process as it unfolds in real-time. Our study is based on a sample of 97 high-risk subjects with early identified word decoding difficulties and a control group of 88 low-risk subjects. These subjects were selected from a larger population of 2165 school children attending second grade. Using predictive modeling and statistical resampling techniques, we develop classification models from eye tracking records less than one minute in duration and show that the models are able to differentiate high-risk subjects from low-risk subjects with high accuracy. Although dyslexia is fundamentally a language-based learning disability, our results suggest that eye movements in reading can be highly predictive of individual reading ability and that eye tracking can be an efficient means to identify children at risk of long-term reading difficulties.

  19. Efficiency of e-learning in an information literacy course for medical students at the Masaryk University

    OpenAIRE

    Kratochvíl Jiří

    2014-01-01

    Purpose: The main goal of this paper is to argue E-learning can be a viable alternative teaching method for Information Literacy according to a comparation of librarian’s time spent face-to-face teaching with tutoring the E-learning course, average time spent a week on learning by the students, time flexibility of E-learning, students’ satisfaction with E-learning and students’ ability to gain practical skills and theoretical knowledge through E-learning. Design/methodology/approach: Sati...

  20. Absolute versus Relative Environmental Sustainability: What can the Cradle-to-Cradle and Eco-efficiency Concepts Learn from Each Other?

    DEFF Research Database (Denmark)

    Bjørn, Anders; Hauschild, Michael Zwicky

    2013-01-01

    The cradle-to-cradle (C2C) concept has emerged as an alternative to the more established eco-efficiency concept based on life cycle assessment (LCA). The two concepts differ fundamentally in that eco-efficiency aims to reduce the negative environmental footprint of human activities while C2C...... attempts to increase the positive footprint. This article discusses the strengths and weaknesses of each concept and suggests how they may learn from each other. The eco-efficiency concept involves no long-term vision or strategy, the links between resource consumption and waste emissions are not well...... related to the sustainability state, and increases in eco-efficiency may lead to increases in consumption levels and hence overall impact. The C2C concept's disregard for energy efficiency means that many current C2C products will likely not perform well in an LCA. Inherent drawbacks are restrictions...