WorldWideScience

Sample records for learning times volume

  1. Pragmatics & Language Learning. Volume 14

    Science.gov (United States)

    Bardovi-Harlig, Kathleen, Ed.; Félix-Brasdefer, J. César, Ed.

    2016-01-01

    This volume contains a selection of papers presented at the 2014 International Conference of Pragmatics and Language Learning at Indiana University. It includes fourteen papers on a variety of topics, with a diversity of first and second languages, and a wide range of methods used to collect pragmatic data in L2 and FL settings. This volume is…

  2. Learning During Stressful Times

    Science.gov (United States)

    Shors, Tracey J.

    2012-01-01

    Stressful life events can have profound effects on our cognitive and motor abilities, from those that could be construed as adaptive to those not so. In this review, I discuss the general notion that acute stressful experience necessarily impairs our abilities to learn and remember. The effects of stress on operant conditioning, that is, learned helplessness, as well as those on classical conditioning procedures are discussed in the context of performance and adaptation. Studies indicating sex differences in learning during stressful times are discussed, as are those attributing different responses to the existence of multiple memory systems and nonlinear relationships. The intent of this review is to highlight the apparent plasticity of the stress response, how it might have evolved to affect both performance and learning processes, and the potential problems with interpreting stress effects on learning as either good or bad. An appreciation for its plasticity may provide new avenues for investigating its underlying neuronal mechanisms. PMID:15054128

  3. Constellation Program Lessons Learned. Volume 2; Detailed Lessons Learned

    Science.gov (United States)

    Rhatigan, Jennifer; Neubek, Deborah J.; Thomas, L. Dale

    2011-01-01

    These lessons learned are part of a suite of hardware, software, test results, designs, knowledge base, and documentation that comprises the legacy of the Constellation Program. The context, summary information, and lessons learned are presented in a factual format, as known and described at the time. While our opinions might be discernable in the context, we have avoided all but factually sustainable statements. Statements should not be viewed as being either positive or negative; their value lies in what we did and what we learned that is worthy of passing on. The lessons include both "dos" and "don ts." In many cases, one person s "do" can be viewed as another person s "don t"; therefore, we have attempted to capture both perspectives when applicable and useful. While Volume I summarizes the views of those who managed the program, this Volume II encompasses the views at the working level, describing how the program challenges manifested in day-to-day activities. Here we see themes that were perhaps hinted at, but not completely addressed, in Volume I: unintended consequences of policies that worked well at higher levels but lacked proper implementation at the working level; long-term effects of the "generation gap" in human space flight development, the need to demonstrate early successes at the expense of thorough planning, and the consequences of problems and challenges not yet addressed because other problems and challenges were more immediate or manifest. Not all lessons learned have the benefit of being operationally vetted, since the program was cancelled shortly after Preliminary Design Review. We avoid making statements about operational consequences (with the exception of testing and test flights that did occur), but we do attempt to provide insight into how operational thinking influenced design and testing. The lessons have been formatted with a description, along with supporting information, a succinct statement of the lesson learned, and

  4. Learning Time and Educational Effectiveness.

    Science.gov (United States)

    Anderson, Lorin W.

    1980-01-01

    To explore the relationship between time and school learning, this paper defines the three kinds of learning time identified by researchers--allocated time, time-on-task, and academic learning time--and relates them to curriculum development. The author cites evidence that time-on-task is related to student achievement and describes two…

  5. Prime Time for Learning.

    Science.gov (United States)

    Leidy, Vivian; And Others

    1981-01-01

    Five elementary teachers explain how they orient pupils and get learning started on the first day of school--whether or not their supplies or textbooks have arrived--by building learning activities around a common interest like dogs, earthworms, football, or the Statue of Liberty. (Editor/SJL)

  6. Time and Associative Learning.

    Science.gov (United States)

    Balsam, Peter D; Drew, Michael R; Gallistel, C R

    2010-01-01

    In a basic associative learning paradigm, learning is said to have occurred when the conditioned stimulus evokes an anticipatory response. This learning is widely believed to depend on the contiguous presentation of conditioned and unconditioned stimulus. However, what it means to be contiguous has not been rigorously defined. Here we examine the empirical bases for these beliefs and suggest an alternative view based on the hypothesis that learning about the temporal relationships between events determines the speed of emergence, vigor and form of conditioned behavior. This temporal learning occurs very rapidly and prior to the appearance of the anticipatory response. The temporal relations are learned even when no anticipatory response is evoked. The speed with which an anticipatory response emerges is proportional to the informativeness of the predictive cue (CS) regarding the rate of occurrence of the predicted event (US). This analysis gives an account of what we mean by "temporal pairing" and is in accord with the data on speed of acquisition and basic findings in the cue competition literature. In this account, learning depends on perceiving and encoding temporal regularities rather than stimulus contiguities.

  7. Establishing Time for Professional Learning

    Science.gov (United States)

    Journal of Staff Development, 2013

    2013-01-01

    Time for collaborative learning is an essential resource for educators working to implement college- and career-ready standards. The pages in this article include tools from the workbook "Establishing Time for Professional Learning." The tools support a complete process to help educators effectively find and use time. The following…

  8. Implementing US Department of Energy lessons learned programs. Volume 2

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1995-08-01

    The DOE Lessons Learned Handbook is a two-volume publication developed to supplement the DOE Lessons Learned Standard (DOE-STD-7501-95) with information that will organizations in developing or improving their lessons learned programs. Volume 1 includes greater detail than the Standard in areas such as identification and documentation of lessons learned; it also contains sections on specific processes such as training and performance measurement. Volume 2 (this document) contains examples of program documents developed by existing lessons learned programs as well as communications material, functional categories, transmittal documents, sources of professional and industry lessons learned, and frequently asked questions about the Lessons Learned List Service.

  9. Time varying, multivariate volume data reduction

    Energy Technology Data Exchange (ETDEWEB)

    Ahrens, James P [Los Alamos National Laboratory; Fout, Nathaniel [UC DAVIS; Ma, Kwan - Liu [UC DAVIS

    2010-01-01

    Large-scale supercomputing is revolutionizing the way science is conducted. A growing challenge, however, is understanding the massive quantities of data produced by large-scale simulations. The data, typically time-varying, multivariate, and volumetric, can occupy from hundreds of gigabytes to several terabytes of storage space. Transferring and processing volume data of such sizes is prohibitively expensive and resource intensive. Although it may not be possible to entirely alleviate these problems, data compression should be considered as part of a viable solution, especially when the primary means of data analysis is volume rendering. In this paper we present our study of multivariate compression, which exploits correlations among related variables, for volume rendering. Two configurations for multidimensional compression based on vector quantization are examined. We emphasize quality reconstruction and interactive rendering, which leads us to a solution using graphics hardware to perform on-the-fly decompression during rendering. In this paper we present a solution which addresses the need for data reduction in large supercomputing environments where data resulting from simulations occupies tremendous amounts of storage. Our solution employs a lossy encoding scheme to acrueve data reduction with several options in terms of rate-distortion behavior. We focus on encoding of multiple variables together, with optional compression in space and time. The compressed volumes can be rendered directly with commodity graphics cards at interactive frame rates and rendering quality similar to that of static volume renderers. Compression results using a multivariate time-varying data set indicate that encoding multiple variables results in acceptable performance in the case of spatial and temporal encoding as compared to independent compression of variables. The relative performance of spatial vs. temporal compression is data dependent, although temporal compression has the

  10. Regional hippocampal volumes and development predict learning and memory.

    Science.gov (United States)

    Tamnes, Christian K; Walhovd, Kristine B; Engvig, Andreas; Grydeland, Håkon; Krogsrud, Stine K; Østby, Ylva; Holland, Dominic; Dale, Anders M; Fjell, Anders M

    2014-01-01

    The hippocampus is an anatomically and functionally heterogeneous structure, but longitudinal studies of its regional development are scarce and it is not known whether protracted maturation of the hippocampus in adolescence is related to memory development. First, we investigated hippocampal subfield development using 170 longitudinally acquired brain magnetic resonance imaging scans from 85 participants aged 8-21 years. Hippocampal subfield volumes were estimated by the use of automated segmentation of 7 subfields, including the cornu ammonis (CA) sectors and the dentate gyrus (DG), while longitudinal subfield volumetric change was quantified using a nonlinear registration procedure. Second, associations between subfield volumes and change and verbal learning/memory across multiple retention intervals (5 min, 30 min and 1 week) were tested. It was hypothesized that short and intermediate memory would be more closely related to CA2-3/CA4-DG and extended, remote memory to CA1. Change rates were significantly different across hippocampal subfields, but nearly all subfields showed significant volume decreases over time throughout adolescence. Several subfield volumes were larger in the right hemisphere and in males, while for change rates there were no hemisphere or sex differences. Partly in support of the hypotheses, greater volume of CA1 and CA2-3 was related to recall and retention after an extended delay, while longitudinal reduction of CA2-3 and CA4-DG was related to learning. This suggests continued regional development of the hippocampus across adolescence and that volume and volume change in specific subfields differentially predict verbal learning and memory over different retention intervals, but future high-resolution studies are called for. © 2014 S. Karger AG, Basel.

  11. Constellation Program: Lessons Learned. Volume 1; Executive Summary

    Science.gov (United States)

    Rhatigan, Jennifer L. (Editor)

    2011-01-01

    This document (Volume I) provides an executive summary of the lessons learned from the Constellation Program. A companion Volume II provides more detailed analyses for those seeking further insight and information. In this volume, Section 1.0 introduces the approach in preparing and organizing the content to enable rapid assimilation of the lessons. Section 2.0 describes the contextual framework in which the Constellation Program was formulated and functioned that is necessary to understand most of the lessons. Context of a former program may seem irrelevant in the heady days of new program formulation. However, readers should take some time to understand the context. Many of the lessons would be different in a different context, so the reader should reflect on the similarities and differences in his or her current circumstances. Section 3.0 summarizes key findings developed from the significant lessons learned at the program level that appear in Section 4.0. Readers can use the key findings in Section 3.0 to peruse for particular topics, and will find more supporting detail and analyses in Section 4.0 in a topical format. Appendix A contains a white paper describing the Constellation Program formulation that may be of use to readers wanting more context or background information. The reader will no doubt recognize some very similar themes from previous lessons learned, blue-ribbon committee reviews, National Academy reviews, and advisory panel reviews for this and other large-scale human spaceflight programs; including Apollo, Space Shuttle, Shuttle/Mir, and the ISS. This could represent an inability to learn lessons from previous generations; however, it is more likely that similar challenges persist in the Agency structure and approach to program formulation, budget advocacy, and management. Perhaps the greatest value of these Constellation lessons learned can be found in viewing them in context with these previous efforts to guide and advise the Agency and its

  12. Timepiece: Extending and Enhancing Learning Time.

    Science.gov (United States)

    Anderson, Lorin W., Ed.; Walberg, Herbert J., Ed.

    This publication offers suggestions for making more productive use of time, a scarce and valued educational resource. The chapter authors, authorities on the use of educational time, write about how to extend and enhance learning time within and outside schools. In "Productive Use of Time," Herbert Walberg describes how learning time can be…

  13. Inspiratory time and tidal volume during intermittent positive pressure ventilation.

    OpenAIRE

    Field, D; Milner, A D; Hopkin, I E

    1985-01-01

    We measured the tidal volume achieved during intermittent positive pressure ventilation using various inspiratory times with a minimum of 0.2 seconds. Results indicate that tidal volume shows no reduction with inspiratory times down to 0.4 seconds. An inspiratory time of 0.3 seconds, however, is likely to reduce tidal volume by 8%, and at 0.2 seconds a 22% fall may be anticipated.

  14. E-Learning, Time and Unconscious Thinking

    Science.gov (United States)

    Mathew, David

    2014-01-01

    This article views the temporal dimensions of e-learning through a psychoanalytic lens, and asks the reader to consider links between online learning and psychoanalysis. It argues that time and its associated philosophical puzzles impinge on both psychoanalytic theory and on e-learning at two specific points. The first is in the distinction…

  15. Communication, timing, and common learning

    Czech Academy of Sciences Publication Activity Database

    Steiner, Jakub; Stewart, C.

    2011-01-01

    Roč. 146, č. 1 (2011), s. 230-247 ISSN 0022-0531 Institutional research plan: CEZ:AV0Z70850503 Keywords : common knowledge * learning * communication Subject RIV: AH - Economics Impact factor: 1.235, year: 2011

  16. Using Technology to Improve Student Learning. NCREL Viewpoints, Volume 12

    Science.gov (United States)

    Gahala, Jan, Ed.

    2004-01-01

    "Viewpoints" is a multimedia package containing two audio CDs and a short, informative booklet. This volume of "Viewpoints" focuses on how technology can help improve student learning. The audio CDs provide the voices, or viewpoints, of various leaders from the education field who work closely with technology issues. Their…

  17. Leading Learning in Our Times

    Science.gov (United States)

    Trilling, Bernie

    2010-01-01

    Important tools that schools need to support a 21st century approach to teaching and learning include the usual suspects: the Internet, pen and paper, cell phones, educational games, tests and quizzes, good teachers, caring communities, educational funding, and loving parents. All of these items and more contribute to a 21st century education, but…

  18. Learned Interval Time Facilitates Associate Memory Retrieval

    Science.gov (United States)

    van de Ven, Vincent; Kochs, Sarah; Smulders, Fren; De Weerd, Peter

    2017-01-01

    The extent to which time is represented in memory remains underinvestigated. We designed a time paired associate task (TPAT) in which participants implicitly learned cue-time-target associations between cue-target pairs and specific cue-target intervals. During subsequent memory testing, participants showed increased accuracy of identifying…

  19. Learning about Learning: Action Learning in Times of Organisational Change

    Science.gov (United States)

    Hill, Robyn

    2009-01-01

    This paper explores the conduct and outcomes of an action learning activity during a period of intense organisational change in a medium-sized vocational education and training organisation in Victoria, Australia. This organisation was the subject of significant change due to government-driven and statewide amalgamation, downsizing and sector…

  20. Hard Times for HRD, Lean Times for Learning?: Workplace Participatory Practices as Enablers of Learning

    Science.gov (United States)

    Warhurst, Russell

    2013-01-01

    Purpose: This article aims to show how in times of austerity when formal HRD activity is curtailed and yet the need for learning is greatest, non-formal learning methods such as workplace involvement and participation initiated by line managers can compensate by enabling the required learning and change. Design/methodology/approach: A qualitative…

  1. An experimental result of estimating an application volume by machine learning techniques.

    Science.gov (United States)

    Hasegawa, Tatsuhito; Koshino, Makoto; Kimura, Haruhiko

    2015-01-01

    In this study, we improved the usability of smartphones by automating a user's operations. We developed an intelligent system using machine learning techniques that periodically detects a user's context on a smartphone. We selected the Android operating system because it has the largest market share and highest flexibility of its development environment. In this paper, we describe an application that automatically adjusts application volume. Adjusting the volume can be easily forgotten because users need to push the volume buttons to alter the volume depending on the given situation. Therefore, we developed an application that automatically adjusts the volume based on learned user settings. Application volume can be set differently from ringtone volume on Android devices, and these volume settings are associated with each specific application including games. Our application records a user's location, the volume setting, the foreground application name and other such attributes as learning data, thereby estimating whether the volume should be adjusted using machine learning techniques via Weka.

  2. Time series analysis of brain regional volume by MR image

    International Nuclear Information System (INIS)

    Tanaka, Mika; Tarusawa, Ayaka; Nihei, Mitsuyo; Fukami, Tadanori; Yuasa, Tetsuya; Wu, Jin; Ishiwata, Kiichi; Ishii, Kenji

    2010-01-01

    The present study proposed a methodology of time series analysis of volumes of frontal, parietal, temporal and occipital lobes and cerebellum because such volumetric reports along the process of individual's aging have been scarcely presented. Subjects analyzed were brain images of 2 healthy males and 18 females of av. age of 69.0 y, of which T1-weighted 3D SPGR (spoiled gradient recalled in the steady state) acquisitions with a GE SIGNA EXCITE HD 1.5T machine were conducted for 4 times in the time series of 42-50 months. The image size was 256 x 256 x (86-124) voxels with digitization level 16 bits. As the template for the regions, the standard gray matter atlas (icbn452 a tlas p robability g ray) and its labeled one (icbn.Labels), provided by UCLA Laboratory of Neuro Imaging, were used for individual's standardization. Segmentation, normalization and coregistration were performed with the MR imaging software SPM8 (Statistic Parametric Mapping 8). Volumes of regions were calculated as their voxel ratio to the whole brain voxel in percent. It was found that the regional volumes decreased with aging in all above lobes examined and cerebellum in average percent per year of -0.11, -0.07, -0.04, -0.02, and -0.03, respectively. The procedure for calculation of the regional volumes, which has been manually operated hitherto, can be automatically conducted for the individual brain using the standard atlases above. (T.T.)

  3. Students' Pressure, Time Management and Effective Learning

    Science.gov (United States)

    Sun, Hechuan; Yang, Xiaolin

    2009-01-01

    Purpose: This paper aims to survey the status quo of the student pressure and the relationship between their daily time management and their learning outcomes in three different types of higher secondary schools at Shenyang, the capital city of Liaoning Province in mainland China. Design/methodology/approach: An investigation was carried out in 14…

  4. Modeling Time Series Data for Supervised Learning

    Science.gov (United States)

    Baydogan, Mustafa Gokce

    2012-01-01

    Temporal data are increasingly prevalent and important in analytics. Time series (TS) data are chronological sequences of observations and an important class of temporal data. Fields such as medicine, finance, learning science and multimedia naturally generate TS data. Each series provide a high-dimensional data vector that challenges the learning…

  5. Iterative volume morphing and learning for mobile tumor based on 4DCT.

    Science.gov (United States)

    Mao, Songan; Wu, Huanmei; Sandison, George; Fang, Shiaofen

    2017-02-21

    During image-guided cancer radiation treatment, three-dimensional (3D) tumor volumetric information is important for treatment success. However, it is typically not feasible to image a patient's 3D tumor continuously in real time during treatment due to concern over excessive patient radiation dose. We present a new iterative morphing algorithm to predict the real-time 3D tumor volume based on time-resolved computed tomography (4DCT) acquired before treatment. An offline iterative learning process has been designed to derive a target volumetric deformation function from one breathing phase to another. Real-time volumetric prediction is performed to derive the target 3D volume during treatment delivery. The proposed iterative deformable approach for tumor volume morphing and prediction based on 4DCT is innovative because it makes three major contributions: (1) a novel approach to landmark selection on 3D tumor surfaces using a minimum bounding box; (2) an iterative morphing algorithm to generate the 3D tumor volume using mapped landmarks; and (3) an online tumor volume prediction strategy based on previously trained deformation functions utilizing 4DCT. The experimental performance showed that the maximum morphing deviations are 0.27% and 1.25% for original patient data and artificially generated data, which is promising. This newly developed algorithm and implementation will have important applications for treatment planning, dose calculation and treatment validation in cancer radiation treatment.

  6. Linear time relational prototype based learning.

    Science.gov (United States)

    Gisbrecht, Andrej; Mokbel, Bassam; Schleif, Frank-Michael; Zhu, Xibin; Hammer, Barbara

    2012-10-01

    Prototype based learning offers an intuitive interface to inspect large quantities of electronic data in supervised or unsupervised settings. Recently, many techniques have been extended to data described by general dissimilarities rather than Euclidean vectors, so-called relational data settings. Unlike the Euclidean counterparts, the techniques have quadratic time complexity due to the underlying quadratic dissimilarity matrix. Thus, they are infeasible already for medium sized data sets. The contribution of this article is twofold: On the one hand we propose a novel supervised prototype based classification technique for dissimilarity data based on popular learning vector quantization (LVQ), on the other hand we transfer a linear time approximation technique, the Nyström approximation, to this algorithm and an unsupervised counterpart, the relational generative topographic mapping (GTM). This way, linear time and space methods result. We evaluate the techniques on three examples from the biomedical domain.

  7. Overcoming Learning Time And Space Constraints Through Technological Tool

    Directory of Open Access Journals (Sweden)

    Nafiseh Zarei

    2015-08-01

    Full Text Available Today the use of technological tools has become an evolution in language learning and language acquisition. Many instructors and lecturers believe that integrating Web-based learning tools into language courses allows pupils to become active learners during learning process. This study investigate how the Learning Management Blog (LMB overcomes the learning time and space constraints that contribute to students’ language learning and language acquisition processes. The participants were 30 ESL students at National University of Malaysia. A qualitative approach comprising an open-ended questionnaire and a semi-structured interview was used to collect data. The results of the study revealed that the students’ language learning and acquisition processes were enhanced. The students did not face any learning time and space limitations while being engaged in the learning process via the LMB. They learned and acquired knowledge using the language learning materials and forum at anytime and anywhere. Keywords: learning time, learning space, learning management blog

  8. Real-time display of flow-pressure-volume loops.

    Science.gov (United States)

    Morozoff, P E; Evans, R W

    1992-01-01

    Graphic display of respiratory waveforms can be valuable for monitoring the progress of ventilated patients. A system has been developed that can display flow-pressure-volume loops as derived from a patient's respiratory circuit in real time. It can also display, store, print, and retrieve ventilatory waveforms. Five loops can be displayed at once: current, previous, reference, "ideal," and previously saved. Two components, the data-display device (DDD) and the data-collection device (DCD), comprise the system. An IBM 286/386 computer with a graphics card (VGA) and bidirectional parallel port is used for the DDD; an eight-bit microprocessor card and an A/D convertor card make up the DCD. A real-time multitasking operating system was written to control the DDD, while the DCD operates from in-line assembly code. The DCD samples the pressure and flow sensors at 100 Hz and looks for a complete flow waveform pattern based on flow slope. These waveforms are then passed to the DDD via the mutual parallel port. Within the DDD a process integrates the flow to create a volume signal and performs a multilinear regression on the pressure, flow, and volume data to calculate the elastance, resistance, pressure offset, and coefficient of determination. Elastance, resistance, and offset are used to calculate Pr and Pc where: Pr[k] = P[k]-offset-(elastance.V[k]) and Pc[k] = P[k]-offset-(resistance.F[k]). Volume vs. Pc and flow vs. Pr can be displayed in real time. Patient data from previous clinical tests were loaded into the device to verify the software calculations. An analog waveform generator was used to simulate flow and pressure waveforms that validated the system.(ABSTRACT TRUNCATED AT 250 WORDS)

  9. An Empirical Investigation of Individual Differences in Time to Learn

    Science.gov (United States)

    Anderson, Lorin W.

    1976-01-01

    Results show that student differences in time-on-task to learn to criterion are alterable and can be minimized over a sequence of learning units given appropriate adaptive learning strategies. (Author/DEP)

  10. Time factor in e-learning and assessment

    OpenAIRE

    Romero Velasco, Margarida

    2010-01-01

    Peer-reviewed Peer reviewed Time is probably one of the most polysemous words in education. In e-learning, characterization of the time factor is particularly relevant because of the high level of flexibility in the teaching and learning times, and the resulting responsibility of the e-learners in regulating their learning times.

  11. Volume correction factor in time dose relationships in brachytherapy

    International Nuclear Information System (INIS)

    Supe, S.J.; Sasane, J.B.

    1987-01-01

    Paterson's clinical data about the maximum tolerance doses for various volumes of interstitial implants with Ra-226 delivered in seven days was made use of in deriving volume correction factors for TDF and CRE concepts respectively for brachytherapy. The derived volume correction factors for TDF and for CRE differ fromthe one assumed for CRE by Kirk et al. and implied for TDF by Goitein. A normalising volume of 70 cc has been suggested for both CRE and TDF concepts for brachytherapy. A table showing the volume corrected TDF is presented for various volumes and dose rates for continuous irradiation. The use of this table is illustrated with examples. (orig.) [de

  12. Blended learning pedagogy: the time is now!

    Science.gov (United States)

    Pizzi, Michael A

    2014-07-01

    Pedagogy is rapidly changing. To develop best practice in academia, it is important that we change with the changing needs of students. This article suggests that blended learning is one of the most important pedagogical formats that can enhance student learning, optimize the use of active learning strategies, and potentially improve student learning outcomes.

  13. Learning to stay ahead of time

    DEFF Research Database (Denmark)

    Staunæs, Dorthe; Raffnsøe, Sverre

    2014-01-01

    In the context of an ongoing change, management is required to take the form of a leadership that must be reignited over and over again. The article examines a new art of leadership that may be viewed as an attempt to keep up with these challenges and stay ahead of time. It emerges from...... a pilgrimage leadership learning laboratory on the road to Santiago de la Compostela. This moving lab creates situations of extraordinary intensity that border on hyperreality and force the leader to find him/herself anew on the verge of him/herself. Conceived as pilgrimage, leadership moves ahead of time...... as it reaches into and anticipates a future still unknown. In this setting, anticipatory affects and the virtual take up a predominant role. As it emerges here, leadership distinguishes itself not only from leadership in the traditional sense, but also from management and governmentality....

  14. Precarious Learning and Labour in Financialized Times

    Directory of Open Access Journals (Sweden)

    Jamie Magnusson

    2013-07-01

    Full Text Available Our current globalized economic regimes of financialized capital have systematically altered relations of learning and labour through the dynamics of precarity, debt, and the political economy of new wars. The risks of these regimes are absorbed unevenly across transnational landscapes, creating cartographies of violence and dispossession, particularly among youth, indigenous, working class, and racialized women. Presently there is surprisingly little discussion on the relevance of financialization for adult educators. Transnational resistances organizing against neoliberal restructuring, austerity policies, and debt crises are emerging at the same time that massive investments are being made into homeland security and the carceral state. This paper opens up discussion on the implications of financialized times for educators, and develops an analytic framework for examining how these global realities are best addressed at local sites of adult and higher education.

  15. The Implementation of Cumulative Learning Theory in Calculating Triangular Prism and Tube Volumes

    Science.gov (United States)

    Muklis, M.; Abidin, C.; Pamungkas, M. D.; Masriyah

    2018-01-01

    This study aims at describing the application of cumulative learning theory in calculating the volume of a triangular prism and a tube as well as revealing the students’ responses toward the learning. The research method used was descriptive qualitative with elementary school students as the subjects of the research. Data obtained through observation, field notes, questionnaire, tests, and interviews. The results from the application of cumulative learning theory obtained positive students’ responses in following the learning and students’ learning outcomes was dominantly above the average. This showed that cumulative learning could be used as a reference to be implemented in learning, so as to improve the students’ achievement.

  16. In-Time On-Place Learning

    Science.gov (United States)

    Bauters, Merja; Purma, Jukka; Leinonen, Teemu

    2014-01-01

    The aim of this short paper is to look at how mobile video recording devices could support learning related to physical practices or places and situations at work. This paper discusses particular kind of workplace learning, namely learning using short video clips that are related to physical environment and tasks preformed in situ. The paper…

  17. Learning Potential Among the Moderately and Severely Retarded. Studies in Learning Potential, Volume 3, Number 52.

    Science.gov (United States)

    Hamilton, James L.; Budoff, Milton

    The study investigated the feasibility of M. Budoff and M. Friedman's (1964) learning potential paradigm as an assessment approach with 40 moderately and severely mentally retarded persons (aged 12 to 22 years). Ss were tested three times: initially, after one week, and after one month with a match-to-sample block design test. Twenty of the Ss…

  18. An algorithm for learning real-time automata

    NARCIS (Netherlands)

    Verwer, S.E.; De Weerdt, M.M.; Witteveen, C.

    2007-01-01

    We describe an algorithm for learning simple timed automata, known as real-time automata. The transitions of real-time automata can have a temporal constraint on the time of occurrence of the current symbol relative to the previous symbol. The learning algorithm is similar to the redblue fringe

  19. Theories of Matter, Space and Time, Volume 2; Quantum theories

    Science.gov (United States)

    Evans, N.; King, S. F.

    2018-06-01

    This book and its prequel Theories of Matter Space and Time: Classical Theories grew out of courses that we have both taught as part of the undergraduate degree program in Physics at Southampton University, UK. Our goal was to guide the full MPhys undergraduate cohort through some of the trickier areas of theoretical physics that we expect our undergraduates to master. Here we teach the student to understand first quantized relativistic quantum theories. We first quickly review the basics of quantum mechanics which should be familiar to the reader from a prior course. Then we will link the Schrödinger equation to the principle of least action introducing Feynman's path integral methods. Next, we present the relativistic wave equations of Klein, Gordon and Dirac. Finally, we convert Maxwell's equations of electromagnetism to a wave equation for photons and make contact with quantum electrodynamics (QED) at a first quantized level. Between the two volumes we hope to move a student's understanding from their prior courses to a place where they are ready, beyond, to embark on graduate level courses on quantum field theory.

  20. Quality of E-Learners’ Time and Learning Performance Beyond Quantitative Time-on-Task

    Directory of Open Access Journals (Sweden)

    Margarida Romero

    2011-06-01

    Full Text Available AbstractAlong with the amount of time spent learning (or time-on-task, the quality of learning time has a real influence on learning performance. Quality of time in online learning depends on students’ time availability and their willingness to devote quality cognitive time to learning activities. However, the quantity and quality of the time spent by adult e-learners on learning activities can be reduced by professional, family, and social commitments. Considering that the main time pattern followed by most adult e-learners is a professional one, it may be beneficial for online education programs to offer a certain degree of flexibility in instructional time that might allow adult learners to adjust their learning times to their professional constraints. However, using the time left over once professional and family requirements have been fulfilled could lead to a reduction in quality time for learning. This paper starts by introducing the concept of quality of learning time from an online student-centred perspective. The impact of students’ time-related variables (working hours, time-on-task engagement, time flexibility, time of day, day of week is then analyzed according to individual and collaborative grades achieved during an online master’s degree program. The data show that both students’ time flexibility (r = .98 and especially their availability to learn in the morning are related to better grades in individual (r = .93 and collaborative activities (r = .46.

  1. Human choice and climate change. Volume 4: What have we learned?

    International Nuclear Information System (INIS)

    Raynor, S.; Malone, E.

    1998-01-01

    This book is Volume 4 of a four-volume set which assesses social science research that is relevant to global climate change from a wide-ranging interdisciplinary perspective. Attention is focused on lessons learned as related to climate change. This series is indispensable reading for scientists and engineers wishing to make an effective contribution to the climate change policy debate

  2. Working and Learning in Times of Uncertainty

    DEFF Research Database (Denmark)

    This book analyses the challenges of globalisation and uncertainty impacting on working and learning at individual, organisational and societal levels. Each of the contributions addresses two overall questions: How is working and learning affected by uncertainty and globalisation? And, in what ways...... do individuals, organisations, political actors and education systems respond to these challenges? Part 1 focuses on the micro level of working and learning for understanding the learning processes from an individual point of view by reflecting on learners’ needs and situations at work and in school......). Finally, Part 3 addresses the macro level of working and learning by analysing how to govern, structure and organise vocational, professional and adult education at the boundaries of work, education and policy making....

  3. Change of the human taste bud volume over time.

    Science.gov (United States)

    Srur, Ehab; Stachs, Oliver; Guthoff, Rudolf; Witt, Martin; Pau, Hans Wilhelm; Just, Tino

    2010-08-01

    The specific aim of this study is to measure the taste volume in healthy human subjects over a 2.5-month period and to demonstrate morphological changes of the peripheral taste organs. Eighteen human taste buds in four fungiform papillae (fPap) were examined over a 10-week period. The fungiform papillae investigated were selected based on the form of the papillae or the arrangement of surface taste pores. Measurements were performed over 10 consecutive weeks, with five scans in a day once a week. The following parameters were measured: height and diameter of the taste bud, diameter of the fungiform papilla and diameter of the taste pore. The findings of this exploratory study indicated that (1) taste bud volumes changed over a 10-week period, (2) the interval between two volume maxima within the 10-week period was 3-5 weeks, and (3) the diameter of the fPap did not correlate with the volume of a single taste bud or with the volume of all taste buds in the fPap within the 10-week period. This exploratory in vivo study revealed changes in taste bud volumes in healthy humans with age-related gustatory sensitivity. These findings need to be considered when studying the effect of denervation of fungiform papillae in vivo using confocal microscopy. Crown Copyright 2009. Published by Elsevier Ireland Ltd. All rights reserved.

  4. Learning Styles of Medical Students Change in Relation to Time

    Science.gov (United States)

    Gurpinar, Erol; Bati, Hilal; Tetik, Cihat

    2011-01-01

    The aim of the present study was to investigate if any changes exist in the learning styles of medical students over time and in relation to different curriculum models with these learning styles. This prospective cohort study was conducted in three different medical faculties, which implement problem-based learning (PBL), hybrid, and integrated…

  5. Real-time Color Codes for Assessing Learning Process

    OpenAIRE

    Dzelzkalēja, L; Kapenieks, J

    2016-01-01

    Effective assessment is an important way for improving the learning process. There are existing guidelines for assessing the learning process, but they lack holistic digital knowledge society considerations. In this paper the authors propose a method for real-time evaluation of students’ learning process and, consequently, for quality evaluation of teaching materials both in the classroom and in the distance learning environment. The main idea of the proposed Color code method (CCM) is to use...

  6. Regional differences in brain volume predict the acquisition of skill in a complex real-time strategy videogame.

    Science.gov (United States)

    Basak, Chandramallika; Voss, Michelle W; Erickson, Kirk I; Boot, Walter R; Kramer, Arthur F

    2011-08-01

    Previous studies have found that differences in brain volume among older adults predict performance in laboratory tasks of executive control, memory, and motor learning. In the present study we asked whether regional differences in brain volume as assessed by the application of a voxel-based morphometry technique on high resolution MRI would also be useful in predicting the acquisition of skill in complex tasks, such as strategy-based video games. Twenty older adults were trained for over 20 h to play Rise of Nations, a complex real-time strategy game. These adults showed substantial improvements over the training period in game performance. MRI scans obtained prior to training revealed that the volume of a number of brain regions, which have been previously associated with subsets of the trained skills, predicted a substantial amount of variance in learning on the complex game. Thus, regional differences in brain volume can predict learning in complex tasks that entail the use of a variety of perceptual, cognitive and motor processes. Copyright © 2011 Elsevier Inc. All rights reserved.

  7. Characteristics and Consequences of Adult Learning Methods and Strategies. Practical Evaluation Reports, Volume 2, Number 1

    Science.gov (United States)

    Trivette, Carol M.; Dunst, Carl J.; Hamby, Deborah W.; O'Herin, Chainey E.

    2009-01-01

    The effectiveness of four adult learning methods (accelerated learning, coaching, guided design, and just-in-time training) constituted the focus of this research synthesis. Findings reported in "How People Learn" (Bransford et al., 2000) were used to operationally define six adult learning method characteristics, and to code and analyze…

  8. Time will tell: The role of mobile learning analytics in self-regulated learning

    NARCIS (Netherlands)

    Tabuenca, Bernardo; Kalz, Marco; Drachsler, Hendrik; Specht, Marcus

    2015-01-01

    This longitudinal study explores the effects of tracking and monitoring time devoted to learn with a mobile tool, on self-regulated learning. Graduate students (n = 36) from three different online courses used their own mobile devices to track how much time they devoted to learn over a period of

  9. Deepwater Horizon - Estimating surface oil volume distribution in real time

    Science.gov (United States)

    Lehr, B.; Simecek-Beatty, D.; Leifer, I.

    2011-12-01

    Spill responders to the Deepwater Horizon (DWH) oil spill required both the relative spatial distribution and total oil volume of the surface oil. The former was needed on a daily basis to plan and direct local surface recovery and treatment operations. The latter was needed less frequently to provide information for strategic response planning. Unfortunately, the standard spill observation methods were inadequate for an oil spill this size, and new, experimental, methods, were not ready to meet the operational demands of near real-time results. Traditional surface oil estimation tools for large spills include satellite-based sensors to define the spatial extent (but not thickness) of the oil, complemented with trained observers in small aircraft, sometimes supplemented by active or passive remote sensing equipment, to determine surface percent coverage of the 'thick' part of the slick, where the vast majority of the surface oil exists. These tools were also applied to DWH in the early days of the spill but the shear size of the spill prevented synoptic information of the surface slick through the use small aircraft. Also, satellite images of the spill, while large in number, varied considerably in image quality, requiring skilled interpretation of them to identify oil and eliminate false positives. Qualified staff to perform this task were soon in short supply. However, large spills are often events that overcome organizational inertia to the use of new technology. Two prime examples in DWH were the application of hyper-spectral scans from a high-altitude aircraft and more traditional fixed-wing aircraft using multi-spectral scans processed by use of a neural network to determine, respectively, absolute or relative oil thickness. But, with new technology, come new challenges. The hyper-spectral instrument required special viewing conditions that were not present on a daily basis and analysis infrastructure to process the data that was not available at the command

  10. Time for the Global Rollout of Endoscopic Lung Volume Reduction

    NARCIS (Netherlands)

    Koegelenberg, Coenraad F. N.; Slebos, Dirk-Jan; Shah, Pallav L.; Theron, Johan; Dheda, Keertan; Allwood, Brian W.; Herth, Felix J. F.

    2015-01-01

    Chronic obstructive pulmonary disease remains one of the most common causes of morbidity and mortality globally. The disease is generally managed with pharmacotherapy, as well as guidance about smoking cessation and pulmonary rehabilitation. Endoscopic lung volume reduction (ELVR) has been proposed

  11. Intraoperative stroke volume optimization using stroke volume, arterial pressure, and heart rate: closed-loop (learning intravenous resuscitator) versus anesthesiologists.

    Science.gov (United States)

    Rinehart, Joseph; Chung, Elena; Canales, Cecilia; Cannesson, Maxime

    2012-10-01

    The authors compared the performance of a group of anesthesia providers to closed-loop (Learning Intravenous Resuscitator [LIR]) management in a simulated hemorrhage scenario using cardiac output monitoring. A prospective cohort study. In silico simulation. University hospital anesthesiologists and the LIR closed-loop fluid administration system. Using a patient simulator, a 90-minute simulated hemorrhage protocol was run, which included a 1,200-mL blood loss over 30 minutes. Twenty practicing anesthesiology providers were asked to manage this scenario by providing fluids and vasopressor medication at their discretion. The simulation program was also run 20 times with the LIR closed-loop algorithm managing fluids and an additional 20 times with no intervention. Simulated patient weight, height, heart rate, mean arterial pressure, and cardiac output (CO) were similar at baseline. The mean stroke volume, the mean arterial pressure, CO, and the final CO were higher in the closed-loop group than in the practitioners group, and the coefficient of variance was lower. The closed-loop group received slightly more fluid (2.1 v 1.9 L, p closed-loop maintained more stable hemodynamics than the practitioners primarily because the fluid was given earlier in the protocol and CO optimized before the hemorrhage began, whereas practitioners tended to resuscitate well but only after significant hemodynamic change indicated the need. Overall, these data support the potential usefulness of this closed-loop algorithm in clinical settings in which dynamic predictors are not available or applicable. Published by Elsevier Inc.

  12. A new electric method for non-invasive continuous monitoring of stroke volume and ventricular volume-time curves

    Directory of Open Access Journals (Sweden)

    Konings Maurits K

    2012-08-01

    Full Text Available Abstract Background In this paper a new non-invasive, operator-free, continuous ventricular stroke volume monitoring device (Hemodynamic Cardiac Profiler, HCP is presented, that measures the average stroke volume (SV for each period of 20 seconds, as well as ventricular volume-time curves for each cardiac cycle, using a new electric method (Ventricular Field Recognition with six independent electrode pairs distributed over the frontal thoracic skin. In contrast to existing non-invasive electric methods, our method does not use the algorithms of impedance or bioreactance cardiography. Instead, our method is based on specific 2D spatial patterns on the thoracic skin, representing the distribution, over the thorax, of changes in the applied current field caused by cardiac volume changes during the cardiac cycle. Since total heart volume variation during the cardiac cycle is a poor indicator for ventricular stroke volume, our HCP separates atrial filling effects from ventricular filling effects, and retrieves the volume changes of only the ventricles. Methods ex-vivo experiments on a post-mortem human heart have been performed to measure the effects of increasing the blood volume inside the ventricles in isolation, leaving the atrial volume invariant (which can not be done in-vivo. These effects have been measured as a specific 2D pattern of voltage changes on the thoracic skin. Furthermore, a working prototype of the HCP has been developed that uses these ex-vivo results in an algorithm to decompose voltage changes, that were measured in-vivo by the HCP on the thoracic skin of a human volunteer, into an atrial component and a ventricular component, in almost real-time (with a delay of maximally 39 seconds. The HCP prototype has been tested in-vivo on 7 human volunteers, using G-suit inflation and deflation to provoke stroke volume changes, and LVot Doppler as a reference technique. Results The ex-vivo measurements showed that ventricular filling

  13. Precarious Learning and Labour in Financialized Times

    Science.gov (United States)

    Magnusson, Jamie

    2013-01-01

    Our current globalized economic regimes of financialized capital have systematically altered relations of learning and labour through the dynamics of precarity, debt, and the political economy of new wars. The risks of these regimes are absorbed unevenly across transnational landscapes, creating cartographies of violence and dispossession,…

  14. Learning to trust : network effects through time.

    NARCIS (Netherlands)

    Barrera, D.; Bunt, G. van de

    2009-01-01

    This article investigates the effects of information originating from social networks on the development of interpersonal trust relations in the context of a dialysis department of a Dutch medium-sized hospital. Hypotheses on learning effects are developed from existing theories and tested using

  15. Learning to trust: network effects through time

    NARCIS (Netherlands)

    Barrera, D.; van de Bunt, G

    2009-01-01

    This article investigates the effects of information originating from social networks on the development of interpersonal trust relations in the context of a dialysis department of a Dutch medium-sized hospital. Hypotheses on learning effects are developed from existing theories and tested using

  16. Recent advances in marching-on-in-time schemes for solving time domain volume integral equations

    KAUST Repository

    Sayed, Sadeed Bin; Ulku, Huseyin Arda; Bagci, Hakan

    2015-01-01

    Transient electromagnetic field interactions on inhomogeneous penetrable scatterers can be analyzed by solving time domain volume integral equations (TDVIEs). TDVIEs are constructed by setting the summation of the incident and scattered field intensities to the total field intensity on the volumetric support of the scatterer. The unknown can be the field intensity or flux/current density. Representing the total field intensity in terms of the unknown using the relevant constitutive relation and the scattered field intensity in terms of the spatiotemporal convolution of the unknown with the Green function yield the final form of the TDVIE. The unknown is expanded in terms of local spatial and temporal basis functions. Inserting this expansion into the TDVIE and testing the resulting equation at discrete times yield a system of equations that is solved by the marching on-in-time (MOT) scheme. At each time step, a smaller system of equations, termed MOT system is solved for the coefficients of the expansion. The right-hand side of this system consists of the tested incident field and discretized spatio-temporal convolution of the unknown samples computed at the previous time steps with the Green function.

  17. Recent advances in marching-on-in-time schemes for solving time domain volume integral equations

    KAUST Repository

    Sayed, Sadeed Bin

    2015-05-16

    Transient electromagnetic field interactions on inhomogeneous penetrable scatterers can be analyzed by solving time domain volume integral equations (TDVIEs). TDVIEs are constructed by setting the summation of the incident and scattered field intensities to the total field intensity on the volumetric support of the scatterer. The unknown can be the field intensity or flux/current density. Representing the total field intensity in terms of the unknown using the relevant constitutive relation and the scattered field intensity in terms of the spatiotemporal convolution of the unknown with the Green function yield the final form of the TDVIE. The unknown is expanded in terms of local spatial and temporal basis functions. Inserting this expansion into the TDVIE and testing the resulting equation at discrete times yield a system of equations that is solved by the marching on-in-time (MOT) scheme. At each time step, a smaller system of equations, termed MOT system is solved for the coefficients of the expansion. The right-hand side of this system consists of the tested incident field and discretized spatio-temporal convolution of the unknown samples computed at the previous time steps with the Green function.

  18. Images in Language: Metaphors and Metamorphoses. Visual Learning. Volume 1

    Science.gov (United States)

    Benedek, Andras, Ed.; Nyiri, Kristof, Ed.

    2011-01-01

    Learning and teaching are faced with radically new challenges in today's rapidly changing world and its deeply transformed communicational environment. We are living in an era of images. Contemporary visual technology--film, video, interactive digital media--is promoting but also demanding a new approach to education: the age of visual learning…

  19. Active learning reduces annotation time for clinical concept extraction.

    Science.gov (United States)

    Kholghi, Mahnoosh; Sitbon, Laurianne; Zuccon, Guido; Nguyen, Anthony

    2017-10-01

    To investigate: (1) the annotation time savings by various active learning query strategies compared to supervised learning and a random sampling baseline, and (2) the benefits of active learning-assisted pre-annotations in accelerating the manual annotation process compared to de novo annotation. There are 73 and 120 discharge summary reports provided by Beth Israel institute in the train and test sets of the concept extraction task in the i2b2/VA 2010 challenge, respectively. The 73 reports were used in user study experiments for manual annotation. First, all sequences within the 73 reports were manually annotated from scratch. Next, active learning models were built to generate pre-annotations for the sequences selected by a query strategy. The annotation/reviewing time per sequence was recorded. The 120 test reports were used to measure the effectiveness of the active learning models. When annotating from scratch, active learning reduced the annotation time up to 35% and 28% compared to a fully supervised approach and a random sampling baseline, respectively. Reviewing active learning-assisted pre-annotations resulted in 20% further reduction of the annotation time when compared to de novo annotation. The number of concepts that require manual annotation is a good indicator of the annotation time for various active learning approaches as demonstrated by high correlation between time rate and concept annotation rate. Active learning has a key role in reducing the time required to manually annotate domain concepts from clinical free text, either when annotating from scratch or reviewing active learning-assisted pre-annotations. Copyright © 2017 Elsevier B.V. All rights reserved.

  20. Treatment Time or Convection Volume in HDF : What Drives the Reduced Mortality Risk?

    NARCIS (Netherlands)

    de Roij van Zuijdewijn, Camiel L M; Nubé, Menso J.; ter Wee, Piet M.; Blankestijn, Peter J.; Lévesque, Renée; van den Dorpel, Marinus A.; Bots, Michiel L.; Grooteman, Muriel P C

    Background/Aims: Treatment time is associated with survival in hemodialysis (HD) patients and with convection volume in hemodiafiltration (HDF) patients. High-volume HDF is associated with improved survival. Therefore, we investigated whether this survival benefit is explained by treatment time.

  1. Time to rethink the neural mechanisms of learning and memory.

    Science.gov (United States)

    Gallistel, Charles R; Balsam, Peter D

    2014-02-01

    Most studies in the neurobiology of learning assume that the underlying learning process is a pairing - dependent change in synaptic strength that requires repeated experience of events presented in close temporal contiguity. However, much learning is rapid and does not depend on temporal contiguity, which has never been precisely defined. These points are well illustrated by studies showing that the temporal relations between events are rapidly learned- even over long delays- and that this knowledge governs the form and timing of behavior. The speed with which anticipatory responses emerge in conditioning paradigms is determined by the information that cues provide about the timing of rewards. The challenge for understanding the neurobiology of learning is to understand the mechanisms in the nervous system that encode information from even a single experience, the nature of the memory mechanisms that can encode quantities such as time, and how the brain can flexibly perform computations based on this information. Copyright © 2013 Elsevier Inc. All rights reserved.

  2. Professional Learning in Part-time University Study

    DEFF Research Database (Denmark)

    Rasmussen, Palle

    2007-01-01

    The theme of this article is adult students' learning in part-time studies at university level in Denmark. One issue discussed is the interplay of research and teaching in this kind of study programme. Examples are presented from the Master of Learning Processes study programme at Aalborg...

  3. Radiologists' preferences for just-in-time learning.

    Science.gov (United States)

    Kahn, Charles E; Ehlers, Kevin C; Wood, Beverly P

    2006-09-01

    Effective learning can occur at the point of care, when opportunities arise to acquire information and apply it to a clinical problem. To assess interest in point-of-care learning, we conducted a survey to explore radiologists' attitudes and preferences regarding the use of just-in-time learning (JITL) in radiology. Following Institutional Review Board approval, we invited 104 current radiology residents and 86 radiologists in practice to participate in a 12-item Internet-based survey to assess their attitudes toward just-in-time learning. Voluntary participation in the survey was solicited by e-mail; respondents completed the survey on a web-based form. Seventy-nine physicians completed the questionnaire, including 47 radiology residents and 32 radiologists in practice; the overall response rate was 42%. Respondents generally expressed a strong interest for JITL: 96% indicated a willingness to try such a system, and 38% indicated that they definitely would use a JITL system. They expressed a preference for learning interventions of 5-10 min in length. Current and recent radiology trainees have expressed a strong interest in just-in-time learning. The information from this survey should be useful in pursuing the design of learning interventions and systems for delivering just-in-time learning to radiologists.

  4. Dose, time and volume effects in interstitial radiation therapy

    International Nuclear Information System (INIS)

    Burgers, J.M.V.

    1982-01-01

    This study presents the main features and uncertainties of interstitial therapy and was undertaken to examine whether differences could be found in different clinical situations treated by interstitial implants with removable sources, that were not simply related to dose. In chapter 2, dating from 1978, continuous low dose rate irradiation is discussed from the radiobiological point of view together with some points related to variation in dose rate. A benefit of continuous low dose rate irradiation could be surmised in a few situations with special cell-kinetic properties. The problem of dose specification, the sharp dose gradient and other volume characteristics are discussed in chapter 3. Possible adjustments to variations in dose rate are discussed in chapter 4. The clinical material is reviewed in chapter 5, including aspects of dose specification, dose fall-off and variation in dose rate. The general discussion and conclusions are given in chapter 6. (Auth.)

  5. Increasing instruction time in school does increase learning

    DEFF Research Database (Denmark)

    Andersen, Simon Calmar; Humlum, Maria; Nandrup, Anne Brink

    2016-01-01

    Increasing instruction time in school is a central element in the attempts of many governments to improve student learning, but prior research—mainly based on observational data—disputes the effect of this approach and points out the potential negative effects on student behavior. Based on a large......-scale, cluster-randomized trial, we find that increasing instruction time increases student learning and that a general increase in instruction time is at least as efficient as an expert-developed, detailed teaching program that increases instruction with the same amount of time. These findings support the value...... of increased instruction time....

  6. Lung lesion doubling times: values and variability based on method of volume determination

    International Nuclear Information System (INIS)

    Eisenbud Quint, Leslie; Cheng, Joan; Schipper, Matthew; Chang, Andrew C.; Kalemkerian, Gregory

    2008-01-01

    Purpose: To determine doubling times (DTs) of lung lesions based on volumetric measurements from thin-section CT imaging. Methods: Previously untreated patients with ≥ two thin-section CT scans showing a focal lung lesion were identified. Lesion volumes were derived using direct volume measurements and volume calculations based on lesion area and diameter. Growth rates (GRs) were compared by tissue diagnosis and measurement technique. Results: 54 lesions were evaluated including 8 benign lesions, 10 metastases, 3 lymphomas, 15 adenocarcinomas, 11 squamous carcinomas, and 7 miscellaneous lung cancers. Using direct volume measurements, median DTs were 453, 111, 15, 181, 139 and 137 days, respectively. Lung cancer DTs ranged from 23-2239 days. There were no significant differences in GRs among the different lesion types. There was considerable variability among GRs using different volume determination methods. Conclusions: Lung cancer doubling times showed a substantial range, and different volume determination methods gave considerably different DTs

  7. Online Quiz Time Limits and Learning Outcomes in Economics

    Science.gov (United States)

    Evans, Brent; Culp, Robert

    2015-01-01

    In an effort to better understand the impact of timing limits, the authors compare the learning outcomes of students who completed timed quizzes with students who took untimed quizzes in economics principles courses. Students were assigned two online quizzes--one timed and one untimed--and re-tested on the material the following class day. Our…

  8. Time for Learning: An Exploratory Analysis of NAEP Data

    Science.gov (United States)

    Ginsburg, Alan; Chudowsky, Naomi

    2012-01-01

    This report uses NAEP background data to track time and learning since the mid-1990s in three areas: student absenteeism; classroom instructional time in mathematics, reading, music and the visual arts; and homework time expected by teachers. Key report findings are: (1) Students with higher rates of "monthly absenteeism" score…

  9. A Model for Learning Over Time: The Big Picture

    Science.gov (United States)

    Amato, Herbert K.; Konin, Jeff G.; Brader, Holly

    2002-01-01

    Objective: To present a method of describing the concept of “learning over time” with respect to its implementation into an athletic training education program curriculum. Background: The formal process of learning over time has recently been introduced as a required way for athletic training educational competencies and clinical proficiencies to be delivered and mastered. Learning over time incorporates the documented cognitive, psychomotor, and affective skills associated with the acquisition, progression, and reflection of information. This method of academic preparation represents a move away from a quantitative-based learning module toward a proficiency-based mastery of learning. Little research or documentation can be found demonstrating either the specificity of this concept or suggestions for its application. Description: We present a model for learning over time that encompasses multiple indicators for assessment in a successive format. Based on a continuum approach, cognitive, psychomotor, and affective characteristics are assessed at different levels in classroom and clinical environments. Clinical proficiencies are a common set of entry-level skills that need to be integrated into the athletic training educational domains. Objective documentation is presented, including the skill breakdown of a task and a matrix to identify a timeline of competency and proficiency delivery. Clinical Advantages: The advantages of learning over time pertain to the integration of cognitive knowledge into clinical skill acquisition. Given the fact that learning over time has been implemented as a required concept for athletic training education programs, this model may serve to assist those program faculty who have not yet developed, or are in the process of developing, a method of administering this approach to learning. PMID:12937551

  10. Impaired Verbal Learning Is Associated with Larger Caudate Volumes in Early Onset Schizophrenia Spectrum Disorders.

    Directory of Open Access Journals (Sweden)

    Monica Juuhl-Langseth

    Full Text Available Both brain structural abnormalities and neurocognitive impairments are core features of schizophrenia. We have previously reported enlargements in subcortical brain structure volumes and impairment of neurocognitive functioning as measured by the MATRICS Cognitive Consensus Battery (MCCB in early onset schizophrenia spectrum disorders (EOS. To our knowledge, no previous study has investigated whether neurocognitive performance and volumetric abnormalities in subcortical brain structures are related in EOS.Twenty-four patients with EOS and 33 healthy controls (HC were included in the study. Relationships between the caudate nucleus, the lateral and fourth ventricles volumes and neurocognitive performance were investigated with multivariate linear regression analyses. Intracranial volume, age, antipsychotic medication and IQ were included as independent predictor-variables.The caudate volume was negatively correlated with verbal learning performance uniquely in the EOS group (r=-.454, p=.034. There were comparable positive correlations between the lateral ventricular volume and the processing speed, attention and reasoning and problem solving domains for both the EOS patients and the healthy controls. Antipsychotic medication was related to ventricular enlargements, but did not affect the brain structure-function relationship.Enlargement of the caudate volume was related to poorer verbal learning performance in patients with EOS. Despite a 32% enlargement of the lateral ventricles in the EOS group, associations to processing speed, attention and reasoning and problem solving were similar for both the EOS and the HC groups.

  11. Timing of quizzes during learning: Effects on motivation and retention.

    Science.gov (United States)

    Healy, Alice F; Jones, Matt; Lalchandani, Lakshmi A; Tack, Lindsay Anderson

    2017-06-01

    This article investigates how the timing of quizzes given during learning impacts retention of studied material. We investigated the hypothesis that interspersing quizzes among study blocks increases student engagement, thus improving learning. Participants learned 8 artificial facts about each of 8 plant categories, with the categories blocked during learning. Quizzes about 4 of the 8 facts from each category occurred either immediately after studying the facts for that category (standard) or after studying the facts from all 8 categories (postponed). In Experiment 1, participants were given tests shortly after learning and several days later, including both the initially quizzed and unquizzed facts. Test performance was better in the standard than in the postponed condition, especially for categories learned later in the sequence. This result held even for the facts not quizzed during learning, suggesting that the advantage cannot be due to any direct testing effects. Instead the results support the hypothesis that interrupting learning with quiz questions is beneficial because it can enhance learner engagement. Experiment 2 provided further support for this hypothesis, based on participants' retrospective ratings of their task engagement during the learning phase. These findings have practical implications for when to introduce quizzes in the classroom. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  12. Human learning: Power laws or multiple characteristic time scales?

    Directory of Open Access Journals (Sweden)

    Gottfried Mayer-Kress

    2006-09-01

    Full Text Available The central proposal of A. Newell and Rosenbloom (1981 was that the power law is the ubiquitous law of learning. This proposition is discussed in the context of the key factors that led to the acceptance of the power law as the function of learning. We then outline the principles of an epigenetic landscape framework for considering the role of the characteristic time scales of learning and an approach to system identification of the processes of performance dynamics. In this view, the change of performance over time is the product of a superposition of characteristic exponential time scales that reflect the influence of different processes. This theoretical approach can reproduce the traditional power law of practice – within the experimental resolution of performance data sets - but we hypothesize that this function may prove to be a special and perhaps idealized case of learning.

  13. Real-time particle volume fraction measurement in centrifuges by wireless electrical resistance detector

    International Nuclear Information System (INIS)

    Nagae, Fumiya; Okawa, Kazuya; Matsuno, Shinsuke; Takei, Masahiro; Zhao Tong; Ichijo, Noriaki

    2015-01-01

    In this study, wireless electrical resistance detector is developed as first step in order to develop electrical resistance tomography (ERT) that are attached wireless communication, and miniaturized. And the particle volume fraction measurement results appropriateness is qualitatively examined. The real-time particle volume fraction measurement is essential for centrifuges, because rotational velocity and supply should be controlled based on the results in order to obtain the effective separation, shorten process time and save energy. However, a technique for the particle volume fraction measurement in centrifuges has not existed yet. In other words, the real-time particle volume fraction measurement in centrifuges becomes innovative technologies. The experiment device reproduces centrifugation in two-phase using particle and salt solution as measuring object. The particle concentration is measured changing rotational velocity, supply and measurement section position. The measured concentration changes coincide with anticipated tendency of concentration changes. Therefore the particle volume fraction measurement results appropriateness are qualitatively indicated. (author)

  14. Delayed High School Starting Times. Information Capsule. Volume 0908

    Science.gov (United States)

    Blazer, Christie

    2009-01-01

    Educators around the nation are considering pushing high school starting times back until later in the morning, based on evidence suggesting that amount of sleep and circadian rhythms play a part in adolescents' academic performance. While research confirms that adolescents do not get enough sleep and that insufficient sleep can negatively…

  15. Applying ARIMA model for annual volume time series of the Magdalena River

    Directory of Open Access Journals (Sweden)

    Gloria Amaris

    2017-04-01

    Conclusions: The simulated results obtained with the ARIMA model compared to the observed data showed a fairly good adjustment of the minimum and maximum magnitudes. This allows concluding that it is a good tool for estimating minimum and maximum volumes, even though this model is not capable of simulating the exact behaviour of an annual volume time series.

  16. OIT Times Newsletter: Volume 3, Number 1, Winter 2000

    Energy Technology Data Exchange (ETDEWEB)

    Sousa, L.

    1999-12-16

    The Winter 2000 edition of the OIT Times newsletter, a quarterly publication produced by the Office of Industrial Technologies, highlights the 1999 start-up projects, announces the OIT solicitation schedule for FY2000, and features the success of the Ohio diecasting showcase. One of the quarterly highlights was Secretary Richardson's presentation of a Certificate of Partnership to Malden Mills CEO Aaron Feuerstein at the dedication of the plant's new, advanced cogeneration system.

  17. Time representation in reinforcement learning models of the basal ganglia

    Directory of Open Access Journals (Sweden)

    Samuel Joseph Gershman

    2014-01-01

    Full Text Available Reinforcement learning models have been influential in understanding many aspects of basal ganglia function, from reward prediction to action selection. Time plays an important role in these models, but there is still no theoretical consensus about what kind of time representation is used by the basal ganglia. We review several theoretical accounts and their supporting evidence. We then discuss the relationship between reinforcement learning models and the timing mechanisms that have been attributed to the basal ganglia. We hypothesize that a single computational system may underlie both reinforcement learning and interval timing—the perception of duration in the range of seconds to hours. This hypothesis, which extends earlier models by incorporating a time-sensitive action selection mechanism, may have important implications for understanding disorders like Parkinson's disease in which both decision making and timing are impaired.

  18. E-learning for Part-Time Medical Studies

    Directory of Open Access Journals (Sweden)

    Półjanowicz Wiesław

    2016-12-01

    Full Text Available Distance education undoubtedly has many advantages, such as individualization of the learning process, unified transmission of teaching materials, the opportunity to study at any place and any time, reduction of financial costs for commuting to classes or accommodation of participants, etc. Adequate working conditions on the e-learning portal must also be present, eg. well-prepared, substantive courses and good communication between the participants. Therefore, an important element in the process of conducting e-learning courses is to measure the increase of knowledge and satisfaction of participants with distance learning. It allows for fine-tuning the content of the course and for classes to be properly organized. This paper presents the results of teaching and assessment of satisfaction with e-learning courses in “Problems of multiculturalism in medicine”, “Selected issues of visual rehabilitation” and “Ophthalmology and Ophthalmic Nursing”, which were carried out experimentally at the Faculty of Health Sciences at the Medical University of Bialystok for nursing students for the 2010/2011 academic year. The study group consisted of 72 part-time students who learnt in e-learning mode and the control group of 87 students who learnt in the traditional way. The students’ opinions about the teaching process and final exam scores were analyzed based on a specially prepared survey questionnaire. Organization of e-learning classes was rated positively by 90% of students. The average result on the final exams for all distance learning subjects was at the level of 82%, while for classes taught in the traditional form it was 81%. Based on these results, we conclude that distance learning is as effective as learning according to the traditional form in medical education studies.

  19. A novel time series link prediction method: Learning automata approach

    Science.gov (United States)

    Moradabadi, Behnaz; Meybodi, Mohammad Reza

    2017-09-01

    Link prediction is a main social network challenge that uses the network structure to predict future links. The common link prediction approaches to predict hidden links use a static graph representation where a snapshot of the network is analyzed to find hidden or future links. For example, similarity metric based link predictions are a common traditional approach that calculates the similarity metric for each non-connected link and sort the links based on their similarity metrics and label the links with higher similarity scores as the future links. Because people activities in social networks are dynamic and uncertainty, and the structure of the networks changes over time, using deterministic graphs for modeling and analysis of the social network may not be appropriate. In the time-series link prediction problem, the time series link occurrences are used to predict the future links In this paper, we propose a new time series link prediction based on learning automata. In the proposed algorithm for each link that must be predicted there is one learning automaton and each learning automaton tries to predict the existence or non-existence of the corresponding link. To predict the link occurrence in time T, there is a chain consists of stages 1 through T - 1 and the learning automaton passes from these stages to learn the existence or non-existence of the corresponding link. Our preliminary link prediction experiments with co-authorship and email networks have provided satisfactory results when time series link occurrences are considered.

  20. Preventive maintenance basis: Volume 31 -- Relays -- timing. Final report

    International Nuclear Information System (INIS)

    Worledge, D.; Hinchcliffe, G.

    1998-07-01

    US nuclear power plants are implementing preventive maintenance (PM) tasks with little documented basis beyond fundamental vendor information to support the tasks or their intervals. The Preventive Maintenance Basis project provides utilities with the technical basis for PM tasks and task intervals associated with 40 specific components such as valves, electric motors, pumps, and HVAC equipment. This document provides a program of preventive maintenance tasks suitable for application to timing relays. The PM tasks that are recommended provide a cost-effective way to intercept the causes and mechanisms that lead to degradation and failure. They can be used in conjunction with material from other sources, to develop a complete PM program or to improve an existing program

  1. Enrichment in Massachusetts Expanded Learning Time (ELT) Schools. Issue Brief

    Science.gov (United States)

    Caven, Meghan; Checkoway, Amy; Gamse, Beth; Luck, Rachel; Wu, Sally

    2012-01-01

    This brief highlights key information about enrichment activities, which represent one of the main components of the Massachusetts Expanded Learning Time (ELT) initiative. Over time, the ELT initiative has supported over two dozen schools across the Commonwealth. A comprehensive evaluation of the ELT initiative found that implementation of the…

  2. Learning and Teaching Problems in Part-Time Higher Education.

    Science.gov (United States)

    Trotman-Dickenson, D. I.

    1988-01-01

    Results of a British survey of the administrations of six universities and six public colleges, employers, and employees who were part-time students are reported and discussed. The survey assessed the perceptions of those groups concerning problems in the instruction and learning of part-time students. (MSE)

  3. Pulmonary blood volume and transit time in cirrhosis: relation to lung function

    DEFF Research Database (Denmark)

    Møller, Søren; Burchardt, H; Øgard, CG

    2006-01-01

    BACKGROUND/AIMS: In cirrhosis a systemic vasodilatation leads to an abnormal distribution of the blood volume with a contracted central blood volume. In addition, the patients have a ventilation/perfusion imbalance with a low diffusing capacity. As the size of the pulmonary blood volume (PBV) has...... not been determined separately we assessed PBV and pulmonary transit time (PTT) in relation to lung function in patients with cirrhosis and in controls. METHODS: Pulmonary and cardiac haemodynamics and transit times were determined by radionuclide techniques in 22 patients with alcoholic cirrhosis......, in the controls, Pvolume...

  4. Real-time interactive three-dimensional display of CT and MR imaging volume data

    International Nuclear Information System (INIS)

    Yla-Jaaski, J.; Kubler, O.; Kikinis, R.

    1987-01-01

    Real-time reconstruction of surfaces from CT and MR imaging volume data is demonstrated using a new algorithm and implementation in a parallel computer system. The display algorithm accepts noncubic 16-bit voxels directly as input. Operations such as interpolation, classification by thresholding, depth coding, simple lighting effects, and removal of parts of the volume by clipping planes are all supported on-line. An eight-processor implementation of the algorithm renders surfaces from typical CT data sets in real time to allow interactive rotation of the volume

  5. Unsupervised Learning Through Randomized Algorithms for High-Volume High-Velocity Data (ULTRA-HV).

    Energy Technology Data Exchange (ETDEWEB)

    Pinar, Ali [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Kolda, Tamara G. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Carlberg, Kevin Thomas [Wake Forest Univ., Winston-Salem, MA (United States); Ballard, Grey [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Mahoney, Michael [Univ. of California, Berkeley, CA (United States)

    2018-01-01

    Through long-term investments in computing, algorithms, facilities, and instrumentation, DOE is an established leader in massive-scale, high-fidelity simulations, as well as science-leading experimentation. In both cases, DOE is generating more data than it can analyze and the problem is intensifying quickly. The need for advanced algorithms that can automatically convert the abundance of data into a wealth of useful information by discovering hidden structures is well recognized. Such efforts however, are hindered by the massive volume of the data and its high velocity. Here, the challenge is developing unsupervised learning methods to discover hidden structure in high-volume, high-velocity data.

  6. Menstrual variation of breast volume and T2 relaxation times in cyclical mastalgia

    International Nuclear Information System (INIS)

    Hussain, Zainab; Brooks, Jonathan; Percy, Dave

    2008-01-01

    Purpose: Hormonal activity causes breast volume to change during the menstrual cycle. One possible cause of this volume change is thought to be due to water retention or oedema within the tissues. We used magnetic resonance imaging (MRI) to study the variation in breast volume and 1 H Magnetic Resonance Spectroscopy (MRS) to measure T 2 relaxation times which are known to increase with increasing tissue water content. We hypothesised that an increase in breast volume will elevate T 2 relaxation due to the presence of an increased water content within the breast. T 2 Relaxation time and volume were studied in fifteen control subjects and in a cohort of eight patients with cyclical mastalgia in order to determine whether changes in breast volume and T 2 relaxation times differed in controls and patients during menses, ovulation and premenses. Method: Breast volume was determined by the Cavalieri method in combination with point counting techniques on MR images and T 2 relaxation times of the water and fat in a voxel of breast tissue were obtained using 1 H Magnetic Resonance Spectroscopy (MRS). Results: Statistical analysis (ANOVA) demonstrated highly significant differences in breast volume between the three stages of the cycle (p 2 of fat or water did not depend on stage of cycle. T-tests demonstrated no significant differences in T 2 of water or fat between patient and control groups. The average T 2 relaxation time of water was lowest in the patient and control groups during ovulation and highest in the patient group during premenses. Conclusion: We have performed the first combined volumetric and spectroscopic study of women with cyclical mastalgia and demonstrated that the global changes in volumes and T 2 were not significantly different from normal menstrual variations

  7. Aerobic fitness relates to learning on a virtual morris water task and hippocampal volume in adolescents

    Science.gov (United States)

    Herting, Megan M.; Nagel, Bonnie J.

    2012-01-01

    In rodents, exercise increases hippocampal neurogenesis and allows for better learning and memory performance on water maze tasks. While exercise has also been shown to be beneficial for the brain and behavior in humans, no study has examined how exercise impacts spatial learning using a directly translational water maze task, or if these relationships exist during adolescence – a developmental period which the animal literature has shown to be especially vulnerable to exercise effects. In this study, we investigated the influence of aerobic fitness on hippocampal size and subsequent learning and memory, including visuospatial memory using a human analogue of the Morris Water Task, in 34 adolescents. Results showed that higher aerobic fitness predicted better learning on the virtual Morris Water Task and larger hippocampal volumes. No relationship between virtual Morris Water Task memory recall and aerobic fitness was detected. Aerobic fitness, however, did not relate to global brain volume, or verbal learning, which might suggest some specificity of the influence of aerobic fitness on the adolescent brain. This study provides a direct translational approach to the existing animal literature on exercise, as well as adds to the sparse research that exists on how aerobic exercise impacts the developing human brain and memory. PMID:22610054

  8. Aerobic fitness relates to learning on a virtual Morris Water Task and hippocampal volume in adolescents.

    Science.gov (United States)

    Herting, Megan M; Nagel, Bonnie J

    2012-08-01

    In rodents, exercise increases hippocampal neurogenesis and allows for better learning and memory performance on water maze tasks. While exercise has also been shown to be beneficial for the brain and behavior in humans, no study has examined how exercise impacts spatial learning using a directly translational water maze task, or if these relationships exist during adolescence--a developmental period which the animal literature has shown to be especially vulnerable to exercise effects. In this study, we investigated the influence of aerobic fitness on hippocampal size and subsequent learning and memory, including visuospatial memory using a human analogue of the Morris Water Task, in 34 adolescents. Results showed that higher aerobic fitness predicted better learning on the virtual Morris Water Task and larger hippocampal volumes. No relationship between virtual Morris Water Task memory recall and aerobic fitness was detected. Aerobic fitness, however, did not relate to global brain volume or verbal learning, which might suggest some specificity of the influence of aerobic fitness on the adolescent brain. This study provides a direct translational approach to the existing animal literature on exercise, as well as adds to the sparse research that exists on how aerobic exercise impacts the developing human brain and memory. Published by Elsevier B.V.

  9. Active controllers and the time duration to learn a task

    Science.gov (United States)

    Repperger, D. W.; Goodyear, C.

    1986-01-01

    An active controller was used to help train naive subjects involved in a compensatory tracking task. The controller is called active in this context because it moves the subject's hand in a direction to improve tracking. It is of interest here to question whether the active controller helps the subject to learn a task more rapidly than the passive controller. Six subjects, inexperienced to compensatory tracking, were run to asymptote root mean square error tracking levels with an active controller or a passive controller. The time required to learn the task was defined several different ways. The results of the different measures of learning were examined across pools of subjects and across controllers using statistical tests. The comparison between the active controller and the passive controller as to their ability to accelerate the learning process as well as reduce levels of asymptotic tracking error is reported here.

  10. Cluster analysis of activity-time series in motor learning

    DEFF Research Database (Denmark)

    Balslev, Daniela; Nielsen, Finn Årup; Frutiger, Sally A.

    2002-01-01

    Neuroimaging studies of learning focus on brain areas where the activity changes as a function of time. To circumvent the difficult problem of model selection, we used a data-driven analytic tool, cluster analysis, which extracts representative temporal and spatial patterns from the voxel...... practice-related activity in a fronto-parieto-cerebellar network, in agreement with previous studies of motor learning. These voxels were separated from a group of voxels showing an unspecific time-effect and another group of voxels, whose activation was an artifact from smoothing. Hum. Brain Mapping 15...

  11. Menstrual variation of breast volume and T{sub 2} relaxation times in cyclical mastalgia

    Energy Technology Data Exchange (ETDEWEB)

    Hussain, Zainab [Department of Medical Imaging, University of Liverpool, Johnstone Building, Brownlow Hill, P.O. Box 147, Liverpool, Merseyside L69 3GB (United Kingdom); Magnetic Resonance and Image Analysis Research Centre, University of Liverpool, Johnstone Building, Brownlow Hill, P.O. Box 147, Liverpool, Merseyside L69 3GB (United Kingdom)], E-mail: zay@liverpool.ac.uk; Brooks, Jonathan [Magnetic Resonance and Image Analysis Research Centre, University of Liverpool, Johnstone Building, Brownlow Hill, P.O. Box 147, Liverpool, Merseyside L69 3GB (United Kingdom); Department of Human Anatomy and Genetics, University of Oxford, Oxford (United Kingdom); Percy, Dave [Centre for Operational Research and Applied Statistics, University of Salford, Salford, Greater Manchester M5 4WT (United Kingdom)

    2008-02-15

    Purpose: Hormonal activity causes breast volume to change during the menstrual cycle. One possible cause of this volume change is thought to be due to water retention or oedema within the tissues. We used magnetic resonance imaging (MRI) to study the variation in breast volume and {sup 1}H Magnetic Resonance Spectroscopy (MRS) to measure T{sub 2} relaxation times which are known to increase with increasing tissue water content. We hypothesised that an increase in breast volume will elevate T{sub 2} relaxation due to the presence of an increased water content within the breast. T{sub 2} Relaxation time and volume were studied in fifteen control subjects and in a cohort of eight patients with cyclical mastalgia in order to determine whether changes in breast volume and T{sub 2} relaxation times differed in controls and patients during menses, ovulation and premenses. Method: Breast volume was determined by the Cavalieri method in combination with point counting techniques on MR images and T{sub 2} relaxation times of the water and fat in a voxel of breast tissue were obtained using {sup 1}H Magnetic Resonance Spectroscopy (MRS). Results: Statistical analysis (ANOVA) demonstrated highly significant differences in breast volume between the three stages of the cycle (p < 0.0005) with breast volume being greatest premenstrually. Patients did not exhibit an increase in volume premenstrually, significantly above controls. T{sub 2} of fat or water did not depend on stage of cycle. T-tests demonstrated no significant differences in T{sub 2} of water or fat between patient and control groups. The average T{sub 2} relaxation time of water was lowest in the patient and control groups during ovulation and highest in the patient group during premenses. Conclusion: We have performed the first combined volumetric and spectroscopic study of women with cyclical mastalgia and demonstrated that the global changes in volumes and T{sub 2} were not significantly different from normal

  12. Polysynchronous: Dialogic Construction of Time in Online Learning

    Science.gov (United States)

    Oztok, Murat; Wilton, Lesley; Zingaro, Daniel; Mackinnon, Kim; Makos, Alexandra; Phirangee, Krystle; Brett, Clare; Hewitt, Jim

    2014-01-01

    Online learning has been conceptualized for decades as being delivered in one of two modes: synchronous or asynchronous. Technological determinism falls short in describing the role that the individuals' psychological, social and pedagogical factors play in their perception, experience and understanding of time online. This article explores…

  13. Probability Learning: Changes in Behavior across Time and Development

    Science.gov (United States)

    Plate, Rista C.; Fulvio, Jacqueline M.; Shutts, Kristin; Green, C. Shawn; Pollak, Seth D.

    2018-01-01

    Individuals track probabilities, such as associations between events in their environments, but less is known about the degree to which experience--within a learning session and over development--influences people's use of incoming probabilistic information to guide behavior in real time. In two experiments, children (4-11 years) and adults…

  14. Instructional Advice, Time Advice and Learning Questions in Computer Simulations

    Science.gov (United States)

    Rey, Gunter Daniel

    2010-01-01

    Undergraduate students (N = 97) used an introductory text and a computer simulation to learn fundamental concepts about statistical analyses (e.g., analysis of variance, regression analysis and General Linear Model). Each learner was randomly assigned to one cell of a 2 (with or without instructional advice) x 2 (with or without time advice) x 2…

  15. Adaptation and learning: characteristic time scales of performance dynamics.

    Science.gov (United States)

    Newell, Karl M; Mayer-Kress, Gottfried; Hong, S Lee; Liu, Yeou-Teh

    2009-12-01

    A multiple time scales landscape model is presented that reveals structures of performance dynamics that were not resolved in the traditional power law analysis of motor learning. It shows the co-existence of separate processes during and between practice sessions that evolve in two independent dimensions characterized by time scales that differ by about an order of magnitude. Performance along the slow persistent dimension of learning improves often as much and sometimes more during rest (memory consolidation and/or insight generation processes) than during a practice session itself. In contrast, the process characterized by the fast, transient dimension of adaptation reverses direction between practice sessions, thereby significantly degrading performance at the beginning of the next practice session (warm-up decrement). The theoretical model fits qualitatively and quantitatively the data from Snoddy's [Snoddy, G. S. (1926). Learning and stability. Journal of Applied Psychology, 10, 1-36] classic learning study of mirror tracing and other averaged and individual data sets, and provides a new account of the processes of change in adaptation and learning. 2009 Elsevier B.V. All rights reserved.

  16. Online gaming for learning optimal team strategies in real time

    Science.gov (United States)

    Hudas, Gregory; Lewis, F. L.; Vamvoudakis, K. G.

    2010-04-01

    This paper first presents an overall view for dynamical decision-making in teams, both cooperative and competitive. Strategies for team decision problems, including optimal control, zero-sum 2-player games (H-infinity control) and so on are normally solved for off-line by solving associated matrix equations such as the Riccati equation. However, using that approach, players cannot change their objectives online in real time without calling for a completely new off-line solution for the new strategies. Therefore, in this paper we give a method for learning optimal team strategies online in real time as team dynamical play unfolds. In the linear quadratic regulator case, for instance, the method learns the Riccati equation solution online without ever solving the Riccati equation. This allows for truly dynamical team decisions where objective functions can change in real time and the system dynamics can be time-varying.

  17. Patterns of digital volume pulse waveform and pulse transit time in ...

    African Journals Online (AJOL)

    In this study the digital volume pulse wave and the pulse transit time of the thumb and big toe were analyzed in young and older subjects some of whom were hypertensive. We aimed to study the components and patterns of the pulse waveform and the pulse transit time and how they might change. Material and Methods: ...

  18. An efficient explicit marching on in time solver for magnetic field volume integral equation

    KAUST Repository

    Sayed, Sadeed Bin; Ulku, H. Arda; Bagci, Hakan

    2015-01-01

    An efficient explicit marching on in time (MOT) scheme for solving the magnetic field volume integral equation is proposed. The MOT system is cast in the form of an ordinary differential equation and is integrated in time using a PE(CE)m multistep

  19. Time-sensitive Customer Churn Prediction based on PU Learning

    OpenAIRE

    Wang, Li; Chen, Chaochao; Zhou, Jun; Li, Xiaolong

    2018-01-01

    With the fast development of Internet companies throughout the world, customer churn has become a serious concern. To better help the companies retain their customers, it is important to build a customer churn prediction model to identify the customers who are most likely to churn ahead of time. In this paper, we propose a Time-sensitive Customer Churn Prediction (TCCP) framework based on Positive and Unlabeled (PU) learning technique. Specifically, we obtain the recent data by shortening the...

  20. Regional Differences in Brain Volume Predict the Acquisition of Skill in a Complex Real-Time Strategy Videogame

    Science.gov (United States)

    Basak, Chandramallika; Voss, Michelle W.; Erickson, Kirk I.; Boot, Walter R.; Kramer, Arthur F.

    2011-01-01

    Previous studies have found that differences in brain volume among older adults predict performance in laboratory tasks of executive control, memory, and motor learning. In the present study we asked whether regional differences in brain volume as assessed by the application of a voxel-based morphometry technique on high resolution MRI would also…

  1. Numerical simulation of homogenization time measurement by probes with different volume size

    International Nuclear Information System (INIS)

    Thyn, J.; Novy, M.; Zitny, R.; Mostek, M.; Jahoda, M.

    2004-01-01

    Results of continuous homogenization time measurement of liquid in a stirred tank depend on the scale of scrutiny. Experimental techniques use a probe, which is situated inside as a conductivity method, or outside of the tank as in the case of gamma-radiotracer methods. Expected value of homogenization time evaluated for a given degree of homogenization is higher when using the conductivity method because the conductivity probe measures relatively small volume in contrast to application of radiotracer, when the volume is much greater. Measurement through the wall of tank is a great advantage of radiotracer application but a comparison of the results with another method supposes a determination of measured volume, which is not easy. Simulation of measurement by CFD code can help to solve the problem. Methodology for CFD simulation of radiotracer experiments was suggested. Commercial software was used for simulation of liquid homogenization in mixed vessel with Rushton turbine. Numerical simulation of liquid homogenization time by CFD for different values of detected volume was confronted with measurement of homogenization time with conductivity probe and with different radioisotopes 198 Au, 82 Br and 24 Na. Detected size of the tank volume was affected by different energy of radioisotope used. (author)

  2. Spaced learning and innovative teaching: school time, pedagogy of attention and learning awareness

    Directory of Open Access Journals (Sweden)

    Garzia Maeca

    2016-06-01

    Full Text Available Currently, the ‘time’ variable has taken on the function of instructional and pedagogical innovation catalyst, after representing-over the years-a symbol of democratisation, learning opportunity and instruction quality, able to incorporate themes such as school dropout, personalisation and vocation into learning. Spaced Learning is a teaching methodology useful to quickly seize information in long-term memory based on a particular arrangement of the lesson time that comprises three input sessions and two intervals. Herein we refer to a teachers’ training initiative on Spaced Learning within the programme ‘DocentiInFormAzione’ in the EDOC@WORK3.0 Project in Apulia region in 2015. The training experience aimed at increasing teachers’ competencies in the Spaced Learning method implemented in a context of collaborative reflection and reciprocal enrichment. The intent of the article is to show how a process of rooting of the same culture of innovation, which opens to the discovery (or rediscovery of effective teaching practices sustained by scientific evidences, can be successfully implemented and to understand how or whether this innovation- based on the particular organisation of instructional time-links learning awareness to learning outcomes.

  3. A Parametric Learning and Identification Based Robust Iterative Learning Control for Time Varying Delay Systems

    Directory of Open Access Journals (Sweden)

    Lun Zhai

    2014-01-01

    Full Text Available A parametric learning based robust iterative learning control (ILC scheme is applied to the time varying delay multiple-input and multiple-output (MIMO linear systems. The convergence conditions are derived by using the H∞ and linear matrix inequality (LMI approaches, and the convergence speed is analyzed as well. A practical identification strategy is applied to optimize the learning laws and to improve the robustness and performance of the control system. Numerical simulations are illustrated to validate the above concepts.

  4. Revisiting the returns-volume relationship: Time variation, alternative measures and the financial crisis

    Science.gov (United States)

    Cook, Steve; Watson, Duncan

    2017-03-01

    Following its introduction in the seminal study of Osborne (1959), a voluminous literature has emerged examining the returns-volume relationship for financial assets. The present paper revisits this relationship in an examination of the FTSE100 which extends the existing literature in two ways. First, alternative daily measures of the FTSE100 index are used to create differing returns and absolute returns series to employ in an examination of returns-volume causality. Second, rolling regression analysis is utilised to explore potential time variation in the returns-volume relationship. The findings obtained depict a hitherto unconsidered complexity in this relationship with the type of returns series considered and financial crisis found to be significant underlying factors. The implications of the newly derived results for both the understanding of the nature of the returns-volume relationship and the development of theories in connection to it are discussed.

  5. Learning of time series through neuron-to-neuron instruction

    Energy Technology Data Exchange (ETDEWEB)

    Miyazaki, Y [Department of Physics, Kyoto University, Kyoto 606-8502, (Japan); Kinzel, W [Institut fuer Theoretische Physik, Universitaet Wurzburg, 97074 Wurzburg (Germany); Shinomoto, S [Department of Physics, Kyoto University, Kyoto (Japan)

    2003-02-07

    A model neuron with delayline feedback connections can learn a time series generated by another model neuron. It has been known that some student neurons that have completed such learning under the instruction of a teacher's quasi-periodic sequence mimic the teacher's time series over a long interval, even after instruction has ceased. We found that in addition to such faithful students, there are unfaithful students whose time series eventually diverge exponentially from that of the teacher. In order to understand the circumstances that allow for such a variety of students, the orbit dimension was estimated numerically. The quasi-periodic orbits in question were found to be confined in spaces with dimensions significantly smaller than that of the full phase space.

  6. Learning of time series through neuron-to-neuron instruction

    International Nuclear Information System (INIS)

    Miyazaki, Y; Kinzel, W; Shinomoto, S

    2003-01-01

    A model neuron with delayline feedback connections can learn a time series generated by another model neuron. It has been known that some student neurons that have completed such learning under the instruction of a teacher's quasi-periodic sequence mimic the teacher's time series over a long interval, even after instruction has ceased. We found that in addition to such faithful students, there are unfaithful students whose time series eventually diverge exponentially from that of the teacher. In order to understand the circumstances that allow for such a variety of students, the orbit dimension was estimated numerically. The quasi-periodic orbits in question were found to be confined in spaces with dimensions significantly smaller than that of the full phase space

  7. Finite time convergent learning law for continuous neural networks.

    Science.gov (United States)

    Chairez, Isaac

    2014-02-01

    This paper addresses the design of a discontinuous finite time convergent learning law for neural networks with continuous dynamics. The neural network was used here to obtain a non-parametric model for uncertain systems described by a set of ordinary differential equations. The source of uncertainties was the presence of some external perturbations and poor knowledge of the nonlinear function describing the system dynamics. A new adaptive algorithm based on discontinuous algorithms was used to adjust the weights of the neural network. The adaptive algorithm was derived by means of a non-standard Lyapunov function that is lower semi-continuous and differentiable in almost the whole space. A compensator term was included in the identifier to reject some specific perturbations using a nonlinear robust algorithm. Two numerical examples demonstrated the improvements achieved by the learning algorithm introduced in this paper compared to classical schemes with continuous learning methods. The first one dealt with a benchmark problem used in the paper to explain how the discontinuous learning law works. The second one used the methane production model to show the benefits in engineering applications of the learning law proposed in this paper. Copyright © 2013 Elsevier Ltd. All rights reserved.

  8. Influence of learning strategy on response time during complex value-based learning and choice.

    Directory of Open Access Journals (Sweden)

    Shiva Farashahi

    Full Text Available Measurements of response time (RT have long been used to infer neural processes underlying various cognitive functions such as working memory, attention, and decision making. However, it is currently unknown if RT is also informative about various stages of value-based choice, particularly how reward values are constructed. To investigate these questions, we analyzed the pattern of RT during a set of multi-dimensional learning and decision-making tasks that can prompt subjects to adopt different learning strategies. In our experiments, subjects could use reward feedback to directly learn reward values associated with possible choice options (object-based learning. Alternatively, they could learn reward values of options' features (e.g. color, shape and combine these values to estimate reward values for individual options (feature-based learning. We found that RT was slower when the difference between subjects' estimates of reward probabilities for the two alternative objects on a given trial was smaller. Moreover, RT was overall faster when the preceding trial was rewarded or when the previously selected object was present. These effects, however, were mediated by an interaction between these factors such that subjects were faster when the previously selected object was present rather than absent but only after unrewarded trials. Finally, RT reflected the learning strategy (i.e. object-based or feature-based approach adopted by the subject on a trial-by-trial basis, indicating an overall faster construction of reward value and/or value comparison during object-based learning. Altogether, these results demonstrate that the pattern of RT can be informative about how reward values are learned and constructed during complex value-based learning and decision making.

  9. Real-time individualized training vectors for experiential learning.

    Energy Technology Data Exchange (ETDEWEB)

    Willis, Matt; Tucker, Eilish Marie; Raybourn, Elaine Marie; Glickman, Matthew R.; Fabian, Nathan

    2011-01-01

    Military training utilizing serious games or virtual worlds potentially generate data that can be mined to better understand how trainees learn in experiential exercises. Few data mining approaches for deployed military training games exist. Opportunities exist to collect and analyze these data, as well as to construct a full-history learner model. Outcomes discussed in the present document include results from a quasi-experimental research study on military game-based experiential learning, the deployment of an online game for training evidence collection, and results from a proof-of-concept pilot study on the development of individualized training vectors. This Lab Directed Research & Development (LDRD) project leveraged products within projects, such as Titan (Network Grand Challenge), Real-Time Feedback and Evaluation System, (America's Army Adaptive Thinking and Leadership, DARWARS Ambush! NK), and Dynamic Bayesian Networks to investigate whether machine learning capabilities could perform real-time, in-game similarity vectors of learner performance, toward adaptation of content delivery, and quantitative measurement of experiential learning.

  10. Cluster analysis of activity-time series in motor learning

    DEFF Research Database (Denmark)

    Balslev, Daniela; Nielsen, Finn Å; Futiger, Sally A

    2002-01-01

    Neuroimaging studies of learning focus on brain areas where the activity changes as a function of time. To circumvent the difficult problem of model selection, we used a data-driven analytic tool, cluster analysis, which extracts representative temporal and spatial patterns from the voxel......-time series. The optimal number of clusters was chosen using a cross-validated likelihood method, which highlights the clustering pattern that generalizes best over the subjects. Data were acquired with PET at different time points during practice of a visuomotor task. The results from cluster analysis show...

  11. Representation Learning from Time Labelled Heterogeneous Data for Mobile Crowdsensing

    Directory of Open Access Journals (Sweden)

    Chunmei Ma

    2016-01-01

    Full Text Available Mobile crowdsensing is a new paradigm that can utilize pervasive smartphones to collect and analyze data to benefit users. However, sensory data gathered by smartphone usually involves different data types because of different granularity and multiple sensor sources. Besides, the data are also time labelled. The heterogeneous and time sequential data raise new challenges for data analyzing. Some existing solutions try to learn each type of data one by one and analyze them separately without considering time information. In addition, the traditional methods also have to determine phone orientation because some sensors equipped in smartphone are orientation related. In this paper, we think that a combination of multiple sensors can represent an invariant feature for a crowdsensing context. Therefore, we propose a new representation learning method of heterogeneous data with time labels to extract typical features using deep learning. We evaluate that our proposed method can adapt data generated by different orientations effectively. Furthermore, we test the performance of the proposed method by recognizing two group mobile activities, walking/cycling and driving/bus with smartphone sensors. It achieves precisions of 98.6% and 93.7% in distinguishing cycling from walking and bus from driving, respectively.

  12. Pulmonary blood volume and transit time in cirrhosis: relation to lung function

    DEFF Research Database (Denmark)

    Møller, Søren; Burchardt, H; Øgard, CG

    2006-01-01

    BACKGROUND/AIMS: In cirrhosis a systemic vasodilatation leads to an abnormal distribution of the blood volume with a contracted central blood volume. In addition, the patients have a ventilation/perfusion imbalance with a low diffusing capacity. As the size of the pulmonary blood volume (PBV) has...... in cirrhosis. The relation between PBV and PTT and the low diffusing capacity suggests the pulmonary vascular compartment as an important element in the pathophysiology of the lung dysfunction in cirrhosis....... not been determined separately we assessed PBV and pulmonary transit time (PTT) in relation to lung function in patients with cirrhosis and in controls. METHODS: Pulmonary and cardiac haemodynamics and transit times were determined by radionuclide techniques in 22 patients with alcoholic cirrhosis...

  13. Unsupervised learning by spike timing dependent plasticity in phase change memory (PCM synapses

    Directory of Open Access Journals (Sweden)

    Stefano eAmbrogio

    2016-03-01

    Full Text Available We present a novel one-transistor/one-resistor (1T1R synapse for neuromorphic networks, based on phase change memory (PCM technology. The synapse is capable of spike-timing dependent plasticity (STDP, where gradual potentiation relies on set transition, namely crystallization, in the PCM, while depression is achieved via reset or amorphization of a chalcogenide active volume. STDP characteristics are demonstrated by experiments under variable initial conditions and number of pulses. Finally, we support the applicability of the 1T1R synapse for learning and recognition of visual patterns by simulations of fully connected neuromorphic networks with 2 or 3 layers with high recognition efficiency. The proposed scheme provides a feasible low-power solution for on-line unsupervised machine learning in smart reconfigurable sensors.

  14. Effect of chronotype and student learning time on mathematical ability based on self-regulated learning

    Science.gov (United States)

    Ratnaningsih, N.; El Akbar, R. R.; Hidayat, E.

    2018-05-01

    One of ways to improve students' learning ability is conduct a research, with purpose to obtain a method to improve students' ability. Research often carried out on the modification of teaching methods, uses of teaching media, motivation, interests and talents of students. Research related to the internal condition of students becomes very interesting to studied, including research on circadian rhythms. Every person in circadian rhythms has its own Chronotype, which divided into two types namely early type and night late type. Chronotype affects the comfort in activity, for example a person with Chronotype category of early type tends to be more comfort in daytime activities. The purpose of this study is to examine the conditions of students, related Chronotype suitable or appropriate for student learning time. This suitability then studied in relation to the ability of learning mathematics with self- regulated learning approach. This study consists of three stages; (i) student Chronotype measurement, (ii) data retrieval, and (iii) analysis of research results. The results show the relationship between the students' learning ability in mathematics to learning time corresponding to Chronotype.

  15. Fixation and escape times in stochastic game learning

    International Nuclear Information System (INIS)

    Realpe-Gomez, John; Szczesny, Bartosz; Galla, Tobias; Dall’Asta, Luca

    2012-01-01

    Evolutionary dynamics in finite populations is known to fixate eventually in the absence of mutation. We here show that a similar phenomenon can be found in stochastic game dynamical batch learning, and investigate fixation in learning processes in a simple 2×2 game, for two-player games with cyclic interaction, and in the context of the best-shot network game. The analogues of finite populations in evolution are here finite batches of observations between strategy updates. We study when and how such fixation can occur, and present results on the average time-to-fixation from numerical simulations. Simple cases are also amenable to analytical approaches and we provide estimates of the behaviour of so-called escape times as a function of the batch size. The differences and similarities with escape and fixation in evolutionary dynamics are discussed. (paper)

  16. Interactive dual-volume rendering visualization with real-time fusion and transfer function enhancement

    Science.gov (United States)

    Macready, Hugh; Kim, Jinman; Feng, David; Cai, Weidong

    2006-03-01

    Dual-modality imaging scanners combining functional PET and anatomical CT constitute a challenge in volumetric visualization that can be limited by the high computational demand and expense. This study aims at providing physicians with multi-dimensional visualization tools, in order to navigate and manipulate the data running on a consumer PC. We have maximized the utilization of pixel-shader architecture of the low-cost graphic hardware and the texture-based volume rendering to provide visualization tools with high degree of interactivity. All the software was developed using OpenGL and Silicon Graphics Inc. Volumizer, tested on a Pentium mobile CPU on a PC notebook with 64M graphic memory. We render the individual modalities separately, and performing real-time per-voxel fusion. We designed a novel "alpha-spike" transfer function to interactively identify structure of interest from volume rendering of PET/CT. This works by assigning a non-linear opacity to the voxels, thus, allowing the physician to selectively eliminate or reveal information from the PET/CT volumes. As the PET and CT are rendered independently, manipulations can be applied to individual volumes, for instance, the application of transfer function to CT to reveal the lung boundary while adjusting the fusion ration between the CT and PET to enhance the contrast of a tumour region, with the resultant manipulated data sets fused together in real-time as the adjustments are made. In addition to conventional navigation and manipulation tools, such as scaling, LUT, volume slicing, and others, our strategy permits efficient visualization of PET/CT volume rendering which can potentially aid in interpretation and diagnosis.

  17. Impact of the time window on plasma volume measurement with indocyanine green

    International Nuclear Information System (INIS)

    Jacob, M; Chappell, D; Conzen, P; Finsterer, U; Rehm, M; Krafft, A; Becker, B F

    2008-01-01

    Recent reports have questioned the accuracy of the indocyanine green dilution technique for measuring plasma volume. Our objective was to evaluate the impact of different time windows for monoexponential extrapolation. We retrospectively analysed 31 indocyanine green decay curves to investigate the problem in principle (group 1) and prospectively performed another 21 plasma volume measurements to estimate its practical impact (group 2). To monoexponentially extrapolate back to the specific extinction at the time of dye injection, two different time windows were applied to each decay curve, comparing the plasma volumes resulting from sampling within a short (≤5 min) versus a longer (>5 min) period of time. Extrapolating back from the longer period led to a higher apparent plasma volume relative to the shorter period in both groups, the difference being 348 ± 171 ml (group 1) and 384 ± 131 ml (group 2; mean ± SD; p < 0.05 each). This result was due to a reliable monoexponentiality of decay only up to the 5th min after dye injection. Thus, to estimate the initial distribution space of indocyanine green via monoexponential extrapolation, the first linear kinetic of indocyanine green decay should be taken

  18. On the design of a real-time volume rendering engine

    NARCIS (Netherlands)

    Smit, Jaap; Wessels, H.L.F.; van der Horst, A.; Bentum, Marinus Jan

    1992-01-01

    An architecture for a Real-Time Volume Rendering Engine (RT-VRE) is given, capable of computing 750 × 750 × 512 samples from a 3D dataset at a rate of 25 images per second. The RT-VRE uses for this purpose 64 dedicated rendering chips, cooperating with 16 RISC-processors. A plane interpolator

  19. On the design of a real-time volume rendering engine

    NARCIS (Netherlands)

    Smit, Jaap; Wessels, H.J.; van der Horst, A.; Bentum, Marinus Jan

    1995-01-01

    An architecture for a Real-Time Volume Rendering Engine (RT-VRE) is given, capable of computing 750 × 750 × 512 samples from a 3D dataset at a rate of 25 images per second. The RT-VRE uses for this purpose 64 dedicated rendering chips, cooperating with 16 RISC-processors. A plane interpolator

  20. The 3 R's of Learning Time: Rethink, Reshape, Reclaim

    Science.gov (United States)

    Sackey, Shera Carter

    2012-01-01

    The Learning School Alliance is a network of schools collaborating about professional practice. The network embodies Learning Forward's purpose to advance effective job-embedded professional learning that leads to student outcomes. A key component of Learning Forward's Standards for Professional Learning is a focus on collaborative learning,…

  1. The race to learn: spike timing and STDP can coordinate learning and recall in CA3.

    Science.gov (United States)

    Nolan, Christopher R; Wyeth, Gordon; Milford, Michael; Wiles, Janet

    2011-06-01

    The CA3 region of the hippocampus has long been proposed as an autoassociative network performing pattern completion on known inputs. The dentate gyrus (DG) region is often proposed as a network performing the complementary function of pattern separation. Neural models of pattern completion and separation generally designate explicit learning phases to encode new information and assume an ideal fixed threshold at which to stop learning new patterns and begin recalling known patterns. Memory systems are significantly more complex in practice, with the degree of memory recall depending on context-specific goals. Here, we present our spike-timing separation and completion (STSC) model of the entorhinal cortex (EC), DG, and CA3 network, ascribing to each region a role similar to that in existing models but adding a temporal dimension by using a spiking neural network. Simulation results demonstrate that (a) spike-timing dependent plasticity in the EC-CA3 synapses provides a pattern completion ability without recurrent CA3 connections, (b) the race between activation of CA3 cells via EC-CA3 synapses and activation of the same cells via DG-CA3 synapses distinguishes novel from known inputs, and (c) modulation of the EC-CA3 synapses adjusts the learned versus test input similarity required to evoke a direct CA3 response prior to any DG activity, thereby adjusting the pattern completion threshold. These mechanisms suggest that spike timing can arbitrate between learning and recall based on the novelty of each individual input, ensuring control of the learn-recall decision resides in the same subsystem as the learned memories themselves. The proposed modulatory signal does not override this decision but biases the system toward either learning or recall. The model provides an explanation for empirical observations that a reduction in novelty produces a corresponding reduction in the latency of responses in CA3 and CA1. Copyright © 2010 Wiley-Liss, Inc.

  2. Oscillations, Timing, Plasticity, and Learning in the Cerebellum.

    Science.gov (United States)

    Cheron, G; Márquez-Ruiz, J; Dan, B

    2016-04-01

    The highly stereotyped, crystal-like architecture of the cerebellum has long served as a basis for hypotheses with regard to the function(s) that it subserves. Historically, most clinical observations and experimental work have focused on the involvement of the cerebellum in motor control, with particular emphasis on coordination and learning. Two main models have been suggested to account for cerebellar functioning. According to Llinás's theory, the cerebellum acts as a control machine that uses the rhythmic activity of the inferior olive to synchronize Purkinje cell populations for fine-tuning of coordination. In contrast, the Ito-Marr-Albus theory views the cerebellum as a motor learning machine that heuristically refines synaptic weights of the Purkinje cell based on error signals coming from the inferior olive. Here, we review the role of timing of neuronal events, oscillatory behavior, and synaptic and non-synaptic influences in functional plasticity that can be recorded in awake animals in various physiological and pathological models in a perspective that also includes non-motor aspects of cerebellar function. We discuss organizational levels from genes through intracellular signaling, synaptic network to system and behavior, as well as processes from signal production and processing to memory, delegation, and actual learning. We suggest an integrative concept for control and learning based on articulated oscillation templates.

  3. A test of alternative estimators for volume at time 1 from remeasured point samples

    Science.gov (United States)

    Francis A. Roesch; Edwin J. Green; Charles T. Scott

    1993-01-01

    Two estimators for volume at time 1 for use with permanent horizontal point samples are evaluated. One estimator, used traditionally, uses only the trees sampled at time 1, while the second estimator, originally presented by Roesch and coauthors (F.A. Roesch, Jr., E.J. Green, and C.T. Scott. 1989. For. Sci. 35(2):281-293). takes advantage of additional sample...

  4. Applying ARIMA model for annual volume time series of the Magdalena River

    OpenAIRE

    Gloria Amaris; Humberto Ávila; Thomas Guerrero

    2017-01-01

    Context: Climate change effects, human interventions, and river characteristics are factors that increase the risk on the population and the water resources. However, negative impacts such as flooding, and river droughts may be previously identified using appropriate numerical tools. Objectives: The annual volume (Millions of m3/year) time series of the Magdalena River was analyzed by an ARIMA model, using the historical time series of the Calamar station (Instituto de Hidrología, Meteoro...

  5. T3 glottic cancer: an analysis of dose time-volume factors

    International Nuclear Information System (INIS)

    Harwood, A.R.; Beale, F.A.; Cummings, B.J.; Hawkins, N.V.; Keane, T.J.; Rider, W.D.

    1980-01-01

    This report analyzes dose-time-volume factors in 112 patients with T3N0M0 glottic cancer who were treated with radical radiotherapy with surgery for salvage between 1963 and 1977. 55% of the patients are alive and well 5 years following treatment; 26% died of glottic cancer and 19% died of intercurrent disease. In the 1965 to 1969 time period, 31% died of tumor as compared to 16% in the 1975 to 1977 time period. Overall local control by radiotherapy was 51%; 2/3 of the failures were surgically salvaged. 44% were locally controlled by radiotherapy in the 1965 to 1969 time period and 57% in the 1975 to 1977 time period. Analysis of dose-time-volume factors reveals that the optimal dose is greater than 1700 ret and a minimal volume of 6 x 8 cm should be used. A dose-cure curve for T3 glottic cancer is constructed and compared with the dose complication curve for the larynx and the dose-cure curve for T1N0M0 glottic cancer. A comparison of cure rates between 112 patients treated with radical radiotherapy and surgery for salvage versus 28 patients treated with combined pre-operative irradiation and surgery reveals no difference in the proportion of patients who died of glottic cancer or in the number of patients alive at 5 years following treatment

  6. Optimal models of extreme volume-prices are time-dependent

    International Nuclear Information System (INIS)

    Rocha, Paulo; Boto, João Pedro; Raischel, Frank; Lind, Pedro G

    2015-01-01

    We present evidence that the best model for empirical volume-price distributions is not always the same and it strongly depends in (i) the region of the volume-price spectrum that one wants to model and (ii) the period in time that is being modelled. To show these two features we analyze stocks of the New York stock market with four different models: Γ, Γ-inverse, log-normal, and Weibull distributions. To evaluate the accuracy of each model we use standard relative deviations as well as the Kullback-Leibler distance and introduce an additional distance particularly suited to evaluate how accurate are the models for the distribution tails (large volume-price). Finally we put our findings in perspective and discuss how they can be extended to other situations in finance engineering

  7. Implementation of machine learning for high-volume manufacturing metrology challenges (Conference Presentation)

    Science.gov (United States)

    Timoney, Padraig; Kagalwala, Taher; Reis, Edward; Lazkani, Houssam; Hurley, Jonathan; Liu, Haibo; Kang, Charles; Isbester, Paul; Yellai, Naren; Shifrin, Michael; Etzioni, Yoav

    2018-03-01

    In recent years, the combination of device scaling, complex 3D device architecture and tightening process tolerances have strained the capabilities of optical metrology tools to meet process needs. Two main categories of approaches have been taken to address the evolving process needs. In the first category, new hardware configurations are developed to provide more spectral sensitivity. Most of this category of work will enable next generation optical metrology tools to try to maintain pace with next generation process needs. In the second category, new innovative algorithms have been pursued to increase the value of the existing measurement signal. These algorithms aim to boost sensitivity to the measurement parameter of interest, while reducing the impact of other factors that contribute to signal variability but are not influenced by the process of interest. This paper will evaluate the suitability of machine learning to address high volume manufacturing metrology requirements in both front end of line (FEOL) and back end of line (BEOL) sectors from advanced technology nodes. In the FEOL sector, initial feasibility has been demonstrated to predict the fin CD values from an inline measurement using machine learning. In this study, OCD spectra were acquired after an etch process that occurs earlier in the process flow than where the inline CD is measured. The fin hard mask etch process is known to impact the downstream inline CD value. Figure 1 shows the correlation of predicted CD vs downstream inline CD measurement obtained after the training of the machine learning algorithm. For BEOL, machine learning is shown to provide an additional source of information in prediction of electrical resistance from structures that are not compatible for direct copper height measurement. Figure 2 compares the trench height correlation to electrical resistance (Rs) and the correlation of predicted Rs to the e-test Rs value for a far back end of line (FBEOL) metallization level

  8. A Distance Learning Review--The Communicational Module "Learning on Demand--Anywhere at Any Time"

    Science.gov (United States)

    Tatkovic, Nevenka; Ruzic, Maja

    2004-01-01

    The society of knowledge refers to the society marked with the principle which requires that knowledge, information and life-time learning hold a key to success in the world of IT technology. Internet, World Wide Web, Web Based Education and ever so growing speed of IT and communicational technologies have enabled the application of new modes,…

  9. Real-time interpolation for true 3-dimensional ultrasound image volumes.

    Science.gov (United States)

    Ji, Songbai; Roberts, David W; Hartov, Alex; Paulsen, Keith D

    2011-02-01

    We compared trilinear interpolation to voxel nearest neighbor and distance-weighted algorithms for fast and accurate processing of true 3-dimensional ultrasound (3DUS) image volumes. In this study, the computational efficiency and interpolation accuracy of the 3 methods were compared on the basis of a simulated 3DUS image volume, 34 clinical 3DUS image volumes from 5 patients, and 2 experimental phantom image volumes. We show that trilinear interpolation improves interpolation accuracy over both the voxel nearest neighbor and distance-weighted algorithms yet achieves real-time computational performance that is comparable to the voxel nearest neighbor algrorithm (1-2 orders of magnitude faster than the distance-weighted algorithm) as well as the fastest pixel-based algorithms for processing tracked 2-dimensional ultrasound images (0.035 seconds per 2-dimesional cross-sectional image [76,800 pixels interpolated, or 0.46 ms/1000 pixels] and 1.05 seconds per full volume with a 1-mm(3) voxel size [4.6 million voxels interpolated, or 0.23 ms/1000 voxels]). On the basis of these results, trilinear interpolation is recommended as a fast and accurate interpolation method for rectilinear sampling of 3DUS image acquisitions, which is required to facilitate subsequent processing and display during operating room procedures such as image-guided neurosurgery.

  10. Effectiveness of Cooperative Learning Instructional Tools With Predict-Observe-Explain Strategy on the Topic of Cuboid and Cube Volume

    Science.gov (United States)

    Nurhuda; Lukito, A.; Masriyah

    2018-01-01

    This study aims to develop instructional tools and implement it to see the effectiveness. The method used in this research referred to Designing Effective Instruction. Experimental research with two-group pretest-posttest design method was conducted. The instructional tools have been developed is cooperative learning model with predict-observe-explain strategy on the topic of cuboid and cube volume which consist of lesson plans, POE tasks, and Tests. Instructional tools were of good quality by criteria of validity, practicality, and effectiveness. These instructional tools was very effective for teaching the volume of cuboid and cube. Cooperative instructional tool with predict-observe-explain (POE) strategy was good of quality because the teacher was easy to implement the steps of learning, students easy to understand the material and students’ learning outcomes completed classically. Learning by using this instructional tool was effective because learning activities were appropriate and students were very active. Students’ learning outcomes were completed classically and better than conventional learning. This study produced a good instructional tool and effectively used in learning. Therefore, these instructional tools can be used as an alternative to teach volume of cuboid and cube topics.

  11. Spatial distribution of residence time, microbe and storage volume of groundwater in headwater catchments

    Science.gov (United States)

    Tsujimura, Maki; Ogawa, Mahiro; Yamamoto, Chisato; Sakakibara, Koichi; Sugiyama, Ayumi; Kato, Kenji; Nagaosa, Kazuyo; Yano, Shinjiro

    2017-04-01

    Headwater catchments in mountainous region are the most important recharge area for surface and subsurface waters, and time and stock information of the water is principal to understand hydrological processes in the catchments. Also, a variety of microbes are included in the groundwater and spring water, and those varies in time and space, suggesting that information of microbe could be used as tracer for groundwater flow system. However, there have been few researches to evaluate the relationship among the residence time, microbe and storage volume of the groundwater in headwater catchments. We performed an investigation on age dating using SF6 and CFCs, microbe counting in the spring water, and evaluation of groundwater storage volume based on water budget analysis in 8 regions underlain by different lithology, those are granite, dacite, sedimentary rocks, serpentinite, basalt and volcanic lava all over Japan. We conducted hydrometric measurements and sampling of spring water in base flow conditions during the rainless periods 2015 and 2016 in those regions, and SF6, CFCs, stable isotopic ratios of oxygen-18 and deuterium, inorganic solute concentrations and total number of prokaryotes were determined on all water samples. Residence time of spring water ranged from 0 to 16 years in all regions, and storage volume of the groundwater within topographical watershed was estimated to be 0.1 m to 222 m in water height. The spring with the longer residence time tends to have larger storage volume in the watershed, and the spring underlain by dacite tends to have larger storage volume as compared with that underlain by sand stone and chert. Also, total number of prokaryotes in the spring water ranged from 103 to 105 cells/mL, and the spring tends to show clear increasing of total number of prokaryotes with decreasing of residence time. Thus, we observed a certain relationship among residence time, storage volume and total number of prokaryotes in the spring water, and

  12. Usage Volume and Trends Indicate Academic Library Online Learning Objects and Tutorials Are Being Used

    Directory of Open Access Journals (Sweden)

    Ruby Muriel Lavallee Warren

    2017-03-01

    Full Text Available A Review of: Hess, A. N., & Hristova, M. (2016. To search or to browse: How users navigate a new interface for online library tutorials. College & Undergraduate Libraries, 23(2, 168-183. http://dx.doi.org/10.1080/10691316.2014.963274 Objective – To discover how users interact with a new online interface for learning objects, user preferences for types of access when given both browsing and searching options, and user needs for tutorial subject matter. Design – Mixed methods, with quantitative analysis of web traffic and qualitative analysis of recorded search terms through grounded textual theory. Setting – An academic library in the Western United States of America. Subjects – Users of the Libraries’ online tutorials and learning objects. Methods – The researchers collected web traffic statistics and organically occurring searches from the Libraries’ tutorial access interface. They defined the collection period as the 2013/2014 academic year, with collection beginning in September 2013 and ending in April 2014. Web traffic for organic searches, facilitated searches (search results accessed through clicking on particular words in a tag cloud, and categorical browsing was collected via Google Analytics. They categorized other interaction types (accessing featured content, leaving the page, etc. under an umbrella term of “other.” Their analysis of web traffic was limited to unique page views, with unique page views defined as views registered to different browser sessions. Unique page views were analyzed to determine which types of interface interaction occurred most frequently, both on-campus and off-campus, and whether there were differences in types of interaction preferred over time or by users with different points of origin. Individual organic search keywords and phrases, and the dates and times of those searches, were separately collected and recorded. One of the researchers coded the recorded organic search terms using

  13. Overlay improvements using a real time machine learning algorithm

    Science.gov (United States)

    Schmitt-Weaver, Emil; Kubis, Michael; Henke, Wolfgang; Slotboom, Daan; Hoogenboom, Tom; Mulkens, Jan; Coogans, Martyn; ten Berge, Peter; Verkleij, Dick; van de Mast, Frank

    2014-04-01

    While semiconductor manufacturing is moving towards the 14nm node using immersion lithography, the overlay requirements are tightened to below 5nm. Next to improvements in the immersion scanner platform, enhancements in the overlay optimization and process control are needed to enable these low overlay numbers. Whereas conventional overlay control methods address wafer and lot variation autonomously with wafer pre exposure alignment metrology and post exposure overlay metrology, we see a need to reduce these variations by correlating more of the TWINSCAN system's sensor data directly to the post exposure YieldStar metrology in time. In this paper we will present the results of a study on applying a real time control algorithm based on machine learning technology. Machine learning methods use context and TWINSCAN system sensor data paired with post exposure YieldStar metrology to recognize generic behavior and train the control system to anticipate on this generic behavior. Specific for this study, the data concerns immersion scanner context, sensor data and on-wafer measured overlay data. By making the link between the scanner data and the wafer data we are able to establish a real time relationship. The result is an inline controller that accounts for small changes in scanner hardware performance in time while picking up subtle lot to lot and wafer to wafer deviations introduced by wafer processing.

  14. Effects of Time, Heat, and Oxygen on K Basin Sludge Agglomeration, Strength, and Solids Volume

    Energy Technology Data Exchange (ETDEWEB)

    Delegard, Calvin H.; Sinkov, Sergey I.; Schmidt, Andrew J.; Daniel, Richard C.; Burns, Carolyn A.

    2011-01-04

    Sludge disposition will be managed in two phases under the K Basin Sludge Treatment Project. The first phase is to retrieve the sludge that currently resides in engineered containers in the K West (KW) Basin pool at ~10 to 18°C. The second phase is to retrieve the sludge from interim storage in the sludge transport and storage containers (STSCs) and treat and package it in preparation for eventual shipment to the Waste Isolation Pilot Plant. The work described in this report was conducted to gain insight into how sludge may change during long-term containerized storage in the STSCs. To accelerate potential physical and chemical changes, the tests were performed at temperatures and oxygen partial pressures significantly greater than those expected in the T Plant canyon cells where the STSCs will be stored. Tests were conducted to determine the effects of 50°C oxygenated water exposure on settled quiescent uraninite (UO2) slurry and a full simulant of KW containerized sludge to determine the effects of oxygen and heat on the composition and mechanical properties of sludge. Shear-strength measurements by vane rheometry also were conducted for UO2 slurry, mixtures of UO2 and metaschoepite (UO3•2H2O), and for simulated KW containerized sludge. The results from these tests and related previous tests are compared to determine whether the settled solids in the K Basin sludge materials change in volume because of oxidation of UO2 by dissolved atmospheric oxygen to form metaschoepite. The test results also are compared to determine if heating or other factors alter sludge volumes and to determine the effects of sludge composition and settling times on sludge shear strength. It has been estimated that the sludge volume will increase with time because of a uranium metal → uraninite → metaschoepite oxidation sequence. This increase could increase the number of containers required for storage and increase overall costs of sludge management activities. However, the volume

  15. Effects of Time, Heat, and Oxygen on K Basin Sludge Agglomeration, Strength, and Solids Volume

    International Nuclear Information System (INIS)

    Delegard, Calvin H.; Sinkov, Sergey I.; Schmidt, Andrew J.; Daniel, Richard C.; Burns, Carolyn A.

    2011-01-01

    Sludge disposition will be managed in two phases under the K Basin Sludge Treatment Project. The first phase is to retrieve the sludge that currently resides in engineered containers in the K West (KW) Basin pool at ∼10 to 18 C. The second phase is to retrieve the sludge from interim storage in the sludge transport and storage containers (STSCs) and treat and package it in preparation for eventual shipment to the Waste Isolation Pilot Plant. The work described in this report was conducted to gain insight into how sludge may change during long-term containerized storage in the STSCs. To accelerate potential physical and chemical changes, the tests were performed at temperatures and oxygen partial pressures significantly greater than those expected in the T Plant canyon cells where the STSCs will be stored. Tests were conducted to determine the effects of 50 C oxygenated water exposure on settled quiescent uraninite (UO 2 ) slurry and a full simulant of KW containerized sludge to determine the effects of oxygen and heat on the composition and mechanical properties of sludge. Shear-strength measurements by vane rheometry also were conducted for UO 2 slurry, mixtures of UO2 and metaschoepite (UO 3 · 2H 2 O), and for simulated KW containerized sludge. The results from these tests and related previous tests are compared to determine whether the settled solids in the K Basin sludge materials change in volume because of oxidation of UO2 by dissolved atmospheric oxygen to form metaschoepite. The test results also are compared to determine if heating or other factors alter sludge volumes and to determine the effects of sludge composition and settling times on sludge shear strength. It has been estimated that the sludge volume will increase with time because of a uranium metal → uraninite → metaschoepite oxidation sequence. This increase could increase the number of containers required for storage and increase overall costs of sludge management activities. However, the

  16. JiTTree: A Just-in-Time Compiled Sparse GPU Volume Data Structure

    KAUST Repository

    Labschutz, Matthias

    2015-08-12

    Sparse volume data structures enable the efficient representation of large but sparse volumes in GPU memory for computation and visualization. However, the choice of a specific data structure for a given data set depends on several factors, such as the memory budget, the sparsity of the data, and data access patterns. In general, there is no single optimal sparse data structure, but a set of several candidates with individual strengths and drawbacks. One solution to this problem are hybrid data structures which locally adapt themselves to the sparsity. However, they typically suffer from increased traversal overhead which limits their utility in many applications. This paper presents JiTTree, a novel sparse hybrid volume data structure that uses just-in-time compilation to overcome these problems. By combining multiple sparse data structures and reducing traversal overhead we leverage their individual advantages. We demonstrate that hybrid data structures adapt well to a large range of data sets. They are especially superior to other sparse data structures for data sets that locally vary in sparsity. Possible optimization criteria are memory, performance and a combination thereof. Through just-in-time (JIT) compilation, JiTTree reduces the traversal overhead of the resulting optimal data structure. As a result, our hybrid volume data structure enables efficient computations on the GPU, while being superior in terms of memory usage when compared to non-hybrid data structures.

  17. JiTTree: A Just-in-Time Compiled Sparse GPU Volume Data Structure

    KAUST Repository

    Labschutz, Matthias; Bruckner, Stefan; Groller, M. Eduard; Hadwiger, Markus; Rautek, Peter

    2015-01-01

    Sparse volume data structures enable the efficient representation of large but sparse volumes in GPU memory for computation and visualization. However, the choice of a specific data structure for a given data set depends on several factors, such as the memory budget, the sparsity of the data, and data access patterns. In general, there is no single optimal sparse data structure, but a set of several candidates with individual strengths and drawbacks. One solution to this problem are hybrid data structures which locally adapt themselves to the sparsity. However, they typically suffer from increased traversal overhead which limits their utility in many applications. This paper presents JiTTree, a novel sparse hybrid volume data structure that uses just-in-time compilation to overcome these problems. By combining multiple sparse data structures and reducing traversal overhead we leverage their individual advantages. We demonstrate that hybrid data structures adapt well to a large range of data sets. They are especially superior to other sparse data structures for data sets that locally vary in sparsity. Possible optimization criteria are memory, performance and a combination thereof. Through just-in-time (JIT) compilation, JiTTree reduces the traversal overhead of the resulting optimal data structure. As a result, our hybrid volume data structure enables efficient computations on the GPU, while being superior in terms of memory usage when compared to non-hybrid data structures.

  18. JiTTree: A Just-in-Time Compiled Sparse GPU Volume Data Structure.

    Science.gov (United States)

    Labschütz, Matthias; Bruckner, Stefan; Gröller, M Eduard; Hadwiger, Markus; Rautek, Peter

    2016-01-01

    Sparse volume data structures enable the efficient representation of large but sparse volumes in GPU memory for computation and visualization. However, the choice of a specific data structure for a given data set depends on several factors, such as the memory budget, the sparsity of the data, and data access patterns. In general, there is no single optimal sparse data structure, but a set of several candidates with individual strengths and drawbacks. One solution to this problem are hybrid data structures which locally adapt themselves to the sparsity. However, they typically suffer from increased traversal overhead which limits their utility in many applications. This paper presents JiTTree, a novel sparse hybrid volume data structure that uses just-in-time compilation to overcome these problems. By combining multiple sparse data structures and reducing traversal overhead we leverage their individual advantages. We demonstrate that hybrid data structures adapt well to a large range of data sets. They are especially superior to other sparse data structures for data sets that locally vary in sparsity. Possible optimization criteria are memory, performance and a combination thereof. Through just-in-time (JIT) compilation, JiTTree reduces the traversal overhead of the resulting optimal data structure. As a result, our hybrid volume data structure enables efficient computations on the GPU, while being superior in terms of memory usage when compared to non-hybrid data structures.

  19. The Negative Impact of Community Stressors on Learning Time: Examining Inequalities between California High Schools

    Science.gov (United States)

    Mirra, Nicole; Rogers, John

    2015-01-01

    Allocated classroom time is not the same as time available for learning--a host of economic and social stressors undermine learning time in schools serving low-income students. When time is limited, it is hard to meet rigorous learning standards. The challenge is compounded in high-poverty schools where community stressors place additional demands…

  20. Explicit solution of the time domain volume integral equation using a stable predictor-corrector scheme

    KAUST Repository

    Al Jarro, Ahmed; Salem, Mohamed; Bagci, Hakan; Benson, Trevor; Sewell, Phillip D.; Vuković, Ana

    2012-01-01

    An explicit marching-on-in-time (MOT) scheme for solving the time domain volume integral equation is presented. The proposed method achieves its stability by employing, at each time step, a corrector scheme, which updates/corrects fields computed by the explicit predictor scheme. The proposedmethod is computationally more efficient when compared to the existing filtering techniques used for the stabilization of explicit MOT schemes. Numerical results presented in this paper demonstrate that the proposed method maintains its stability even when applied to the analysis of electromagnetic wave interactions with electrically large structures meshed using approximately half a million discretization elements.

  1. Explicit solution of the time domain volume integral equation using a stable predictor-corrector scheme

    KAUST Repository

    Al Jarro, Ahmed

    2012-11-01

    An explicit marching-on-in-time (MOT) scheme for solving the time domain volume integral equation is presented. The proposed method achieves its stability by employing, at each time step, a corrector scheme, which updates/corrects fields computed by the explicit predictor scheme. The proposedmethod is computationally more efficient when compared to the existing filtering techniques used for the stabilization of explicit MOT schemes. Numerical results presented in this paper demonstrate that the proposed method maintains its stability even when applied to the analysis of electromagnetic wave interactions with electrically large structures meshed using approximately half a million discretization elements.

  2. Registration of clinical volumes to beams-eye-view images for real-time tracking

    Energy Technology Data Exchange (ETDEWEB)

    Bryant, Jonathan H.; Rottmann, Joerg; Lewis, John H.; Mishra, Pankaj; Berbeco, Ross I., E-mail: rberbeco@lroc.harvard.edu [Department of Radiation Oncology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute and Harvard Medical School, Boston, Massachusetts 02115 (United States); Keall, Paul J. [Radiation Physics Laboratory, Sydney Medical School, University of Sydney, Sydney, New South Wales 2006 (Australia)

    2014-12-15

    Purpose: The authors combine the registration of 2D beam’s eye view (BEV) images and 3D planning computed tomography (CT) images, with relative, markerless tumor tracking to provide automatic absolute tracking of physician defined volumes such as the gross tumor volume (GTV). Methods: During treatment of lung SBRT cases, BEV images were continuously acquired with an electronic portal imaging device (EPID) operating in cine mode. For absolute registration of physician-defined volumes, an intensity based 2D/3D registration to the planning CT was performed using the end-of-exhale (EoE) phase of the four dimensional computed tomography (4DCT). The volume was converted from Hounsfield units into electron density by a calibration curve and digitally reconstructed radiographs (DRRs) were generated for each beam geometry. Using normalized cross correlation between the DRR and an EoE BEV image, the best in-plane rigid transformation was found. The transformation was applied to physician-defined contours in the planning CT, mapping them into the EPID image domain. A robust multiregion method of relative markerless lung tumor tracking quantified deviations from the EoE position. Results: The success of 2D/3D registration was demonstrated at the EoE breathing phase. By registering at this phase and then employing a separate technique for relative tracking, the authors are able to successfully track target volumes in the BEV images throughout the entire treatment delivery. Conclusions: Through the combination of EPID/4DCT registration and relative tracking, a necessary step toward the clinical implementation of BEV tracking has been completed. The knowledge of tumor volumes relative to the treatment field is important for future applications like real-time motion management, adaptive radiotherapy, and delivered dose calculations.

  3. What time is it? Deep learning approaches for circadian rhythms.

    Science.gov (United States)

    Agostinelli, Forest; Ceglia, Nicholas; Shahbaba, Babak; Sassone-Corsi, Paolo; Baldi, Pierre

    2016-06-15

    Circadian rhythms date back to the origins of life, are found in virtually every species and every cell, and play fundamental roles in functions ranging from metabolism to cognition. Modern high-throughput technologies allow the measurement of concentrations of transcripts, metabolites and other species along the circadian cycle creating novel computational challenges and opportunities, including the problems of inferring whether a given species oscillate in circadian fashion or not, and inferring the time at which a set of measurements was taken. We first curate several large synthetic and biological time series datasets containing labels for both periodic and aperiodic signals. We then use deep learning methods to develop and train BIO_CYCLE, a system to robustly estimate which signals are periodic in high-throughput circadian experiments, producing estimates of amplitudes, periods, phases, as well as several statistical significance measures. Using the curated data, BIO_CYCLE is compared to other approaches and shown to achieve state-of-the-art performance across multiple metrics. We then use deep learning methods to develop and train BIO_CLOCK to robustly estimate the time at which a particular single-time-point transcriptomic experiment was carried. In most cases, BIO_CLOCK can reliably predict time, within approximately 1 h, using the expression levels of only a small number of core clock genes. BIO_CLOCK is shown to work reasonably well across tissue types, and often with only small degradation across conditions. BIO_CLOCK is used to annotate most mouse experiments found in the GEO database with an inferred time stamp. All data and software are publicly available on the CircadiOmics web portal: circadiomics.igb.uci.edu/ fagostin@uci.edu or pfbaldi@uci.edu Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

  4. Forecasting air quality time series using deep learning.

    Science.gov (United States)

    Freeman, Brian S; Taylor, Graham; Gharabaghi, Bahram; Thé, Jesse

    2018-04-13

    This paper presents one of the first applications of deep learning (DL) techniques to predict air pollution time series. Air quality management relies extensively on time series data captured at air monitoring stations as the basis of identifying population exposure to airborne pollutants and determining compliance with local ambient air standards. In this paper, 8 hr averaged surface ozone (O 3 ) concentrations were predicted using deep learning consisting of a recurrent neural network (RNN) with long short-term memory (LSTM). Hourly air quality and meteorological data were used to train and forecast values up to 72 hours with low error rates. The LSTM was able to forecast the duration of continuous O 3 exceedances as well. Prior to training the network, the dataset was reviewed for missing data and outliers. Missing data were imputed using a novel technique that averaged gaps less than eight time steps with incremental steps based on first-order differences of neighboring time periods. Data were then used to train decision trees to evaluate input feature importance over different time prediction horizons. The number of features used to train the LSTM model was reduced from 25 features to 5 features, resulting in improved accuracy as measured by Mean Absolute Error (MAE). Parameter sensitivity analysis identified look-back nodes associated with the RNN proved to be a significant source of error if not aligned with the prediction horizon. Overall, MAE's less than 2 were calculated for predictions out to 72 hours. Novel deep learning techniques were used to train an 8-hour averaged ozone forecast model. Missing data and outliers within the captured data set were replaced using a new imputation method that generated calculated values closer to the expected value based on the time and season. Decision trees were used to identify input variables with the greatest importance. The methods presented in this paper allow air managers to forecast long range air pollution

  5. Ensemble Deep Learning for Biomedical Time Series Classification

    Directory of Open Access Journals (Sweden)

    Lin-peng Jin

    2016-01-01

    Full Text Available Ensemble learning has been proved to improve the generalization ability effectively in both theory and practice. In this paper, we briefly outline the current status of research on it first. Then, a new deep neural network-based ensemble method that integrates filtering views, local views, distorted views, explicit training, implicit training, subview prediction, and Simple Average is proposed for biomedical time series classification. Finally, we validate its effectiveness on the Chinese Cardiovascular Disease Database containing a large number of electrocardiogram recordings. The experimental results show that the proposed method has certain advantages compared to some well-known ensemble methods, such as Bagging and AdaBoost.

  6. Real-time bladder volume monitoring by the application of a new implantable bladder volume sensor for a small animal model

    Directory of Open Access Journals (Sweden)

    Dong Sup Lee

    2011-04-01

    Full Text Available Although real-time monitoring of bladder volume together with intravesical pressure can provide more information for understanding the functional changes of the urinary bladder, it still entails difficulties in the accurate prediction of real-time bladder volume in urodynamic studies with small animal models. We studied a new implantable bladder volume monitoring device with eight rats. During cystometry, microelectrodes prepared by the microelectromechanical systems process were placed symmetrically on both lateral walls of the bladder, and the expanded bladder volume was calculated. Immunohistological study was done after 1 week and after 4 weeks to evaluate the biocompatibility of the microelectrode. From the point that infused saline volume into the bladder was higher than 0.6 mL, estimated bladder volume was statistically correlated with the volume of saline injected (p<0.01. Additionally, the microelectromechanical system microelectrodes used in this study showed reliable biocompatibility. Therefore, the device can be used to evaluate changes in bladder volume in studies with small animals, and it may help to provide more information about functional changes in the bladder in laboratory studies. Furthermore, owing to its biocompatibility, the device could be chronically implanted in conscious ambulating animals, thus allowing a novel longitudinal study to be performed for a specific purpose.

  7. Evidence for an alternation strategy in time-place learning.

    Science.gov (United States)

    Pizzo, Matthew J; Crystal, Jonathon D

    2004-11-30

    Many different conclusions concerning what type of mechanism rats use to solve a daily time-place task have emerged in the literature. The purpose of this study was to test three competing explanations of time-place discrimination. Rats (n = 10) were tested twice daily in a T-maze, separated by approximately 7 h. Food was available at one location in the morning and another location in the afternoon. After the rats learned to visit each location at the appropriate time, tests were omitted to evaluate whether the rats were utilizing time-of-day (i.e., a circadian oscillator) or an alternation strategy (i.e., visiting a correct location is a cue to visit the next location). Performance on this test was significantly lower than chance, ruling out the use of time-of-day. A phase advance of the light cycle was conducted to test the alternation strategy and timing with respect to the light cycle (i.e., an interval timer). There was no difference between probe and baseline performance. These results suggest that the rats used an alternation strategy to meet the temporal and spatial contingencies in the time-place task.

  8. A topological approach to migration and visualization of time-varying volume data

    International Nuclear Information System (INIS)

    Fujishiro, Issei; Otsuka, Rieko; Hamaoka, Aya; Takeshima, Yuriko; Takahashi, Shigeo

    2004-01-01

    Rapid advance in high performance computing and measurement technologies has recently made it possible to produce a stupendous amount of time-varying volume datasets in various disciplines. However, there exist a few known visual exploration tools which allow us to investigate the core of their complex behavior effectively. In this article, our previous approach to topological volume skeletonization is extended to capture the topological skeleton of a 4D volumetric field in terms of critical timing. A cyclic information drilldown scheme, termed T-map, is presented, where a wide choice of information visualization techniques are deployed so that the users are allowed to repeatedly squeeze partial spatiotemporal domains of interest until the size gets fitted into an available computing storage space, prior to topologically-accentuated visualization of the pinpointed volumetric domains. A case study with datasets from atomic collision research is performed to illustrate the feasibility of the present method. (author)

  9. An explicit marching on-in-time solver for the time domain volume magnetic field integral equation

    KAUST Repository

    Sayed, Sadeed Bin

    2014-07-01

    Transient scattering from inhomogeneous dielectric objects can be modeled using time domain volume integral equations (TDVIEs). TDVIEs are oftentimes solved using marching on-in-time (MOT) techniques. Classical MOT-TDVIE solvers expand the field induced on the scatterer using local spatio-temporal basis functions. Inserting this expansion into the TDVIE and testing the resulting equation in space and time yields a system of equations that is solved by time marching. Depending on the type of the basis and testing functions and the time step, the time marching scheme can be implicit (N. T. Gres, et al., Radio Sci., 36(3), 379-386, 2001) or explicit (A. Al-Jarro, et al., IEEE Trans. Antennas Propag., 60(11), 5203-5214, 2012). Implicit MOT schemes are known to be more stable and accurate. However, under low-frequency excitation, i.e., when the time step size is large, they call for inversion of a full matrix system at very time step.

  10. An explicit marching on-in-time solver for the time domain volume magnetic field integral equation

    KAUST Repository

    Sayed, Sadeed Bin; Ulku, Huseyin Arda; Bagci, Hakan

    2014-01-01

    Transient scattering from inhomogeneous dielectric objects can be modeled using time domain volume integral equations (TDVIEs). TDVIEs are oftentimes solved using marching on-in-time (MOT) techniques. Classical MOT-TDVIE solvers expand the field induced on the scatterer using local spatio-temporal basis functions. Inserting this expansion into the TDVIE and testing the resulting equation in space and time yields a system of equations that is solved by time marching. Depending on the type of the basis and testing functions and the time step, the time marching scheme can be implicit (N. T. Gres, et al., Radio Sci., 36(3), 379-386, 2001) or explicit (A. Al-Jarro, et al., IEEE Trans. Antennas Propag., 60(11), 5203-5214, 2012). Implicit MOT schemes are known to be more stable and accurate. However, under low-frequency excitation, i.e., when the time step size is large, they call for inversion of a full matrix system at very time step.

  11. Time series classification using k-Nearest neighbours, Multilayer Perceptron and Learning Vector Quantization algorithms

    Directory of Open Access Journals (Sweden)

    Jiří Fejfar

    2012-01-01

    Full Text Available We are presenting results comparison of three artificial intelligence algorithms in a classification of time series derived from musical excerpts in this paper. Algorithms were chosen to represent different principles of classification – statistic approach, neural networks and competitive learning. The first algorithm is a classical k-Nearest neighbours algorithm, the second algorithm is Multilayer Perceptron (MPL, an example of artificial neural network and the third one is a Learning Vector Quantization (LVQ algorithm representing supervised counterpart to unsupervised Self Organizing Map (SOM.After our own former experiments with unlabelled data we moved forward to the data labels utilization, which generally led to a better accuracy of classification results. As we need huge data set of labelled time series (a priori knowledge of correct class which each time series instance belongs to, we used, with a good experience in former studies, musical excerpts as a source of real-world time series. We are using standard deviation of the sound signal as a descriptor of a musical excerpts volume level.We are describing principle of each algorithm as well as its implementation briefly, giving links for further research. Classification results of each algorithm are presented in a confusion matrix showing numbers of misclassifications and allowing to evaluate overall accuracy of the algorithm. Results are compared and particular misclassifications are discussed for each algorithm. Finally the best solution is chosen and further research goals are given.

  12. Use of a theoretical equation of state to interpret time-dependent free volume in polymer glasses

    International Nuclear Information System (INIS)

    Curro, J.G.; Lagasse, R.R.; Simha, R.

    1981-01-01

    Many physical properties of polymer glasses change spontaneously during isothermal aging by a process commonly modeled as collapse of free volume. The model has not been verified rigorously because free volume cannot be unambiguously measured. In the present investigation we tentatively identify the free-volume fraction with the fraction of empty sites in the equation of state of Simha and Somcynsky. With this theory, volume recovery measurements can be analyzed to yield directly the time-dependent, free-volume fraction. Using this approach, recent volume measurements on poly(methyl methacrylate) are analyzed. The resulting free-volume fractions are then used in the Doolittle equation to predict the shift in stress relaxation curves at 23 0 C. These predicted shift factors agree with the experimental measurements of Cizmecioglu et al. In addition, it is shown that previous assumptions concerning temperature dependence of free volume are inconsistent with the theory

  13. Identification of flow paths and quantification of return flow volumes and timing at field scale

    Science.gov (United States)

    Claes, N.; Paige, G. B.; Parsekian, A.

    2017-12-01

    Flood irrigation, which constitutes a large part of agricultural water use, accounts for a significant amount of the water that is diverted from western streams. Return flow, the portion of the water applied to irrigated areas that returns to the stream, is important for maintaining base flows in streams and ecological function of riparian zones and wetlands hydrologically linked with streams. Prediction of timing and volumes of return flow during and after flood irrigation pose a challenge due to the heterogeneity of pedogenic and soil physical factors that influence vadose zone processes. In this study, we quantify volumes of return flow and potential pathways in the subsurface through a vadose zone flow model that is informed by both hydrological and geophysical observations in a Bayesian setting. We couple a two-dimensional vadose zone flow model through a Bayesian Markov Chain Monte Carlo approach with time lapse ERT, borehole NMR datasets that are collected during and after flood irrigation experiments, and soil physical lab analysis. The combination of both synthetic models and field observations leads to flow path identification and allows for quantification of volumes and timing and associated uncertainties of subsurface return that stems from flood irrigation. The quantification of the impact of soil heterogeneity enables us to translate these results to other sites and predict return flow under different soil physical settings. This is key when managing irrigation water resources and predictions of outcomes of different scenarios have to be evaluated.

  14. Real-time volume rendering of digital medical images on an iOS device

    Science.gov (United States)

    Noon, Christian; Holub, Joseph; Winer, Eliot

    2013-03-01

    Performing high quality 3D visualizations on mobile devices, while tantalizingly close in many areas, is still a quite difficult task. This is especially true for 3D volume rendering of digital medical images. Allowing this would empower medical personnel a powerful tool to diagnose and treat patients and train the next generation of physicians. This research focuses on performing real time volume rendering of digital medical images on iOS devices using custom developed GPU shaders for orthogonal texture slicing. An interactive volume renderer was designed and developed with several new features including dynamic modification of render resolutions, an incremental render loop, a shader-based clipping algorithm to support OpenGL ES 2.0, and an internal backface culling algorithm for properly sorting rendered geometry with alpha blending. The application was developed using several application programming interfaces (APIs) such as OpenSceneGraph (OSG) as the primary graphics renderer coupled with iOS Cocoa Touch for user interaction, and DCMTK for DICOM I/O. The developed application rendered volume datasets over 450 slices up to 50-60 frames per second, depending on the specific model of the iOS device. All rendering is done locally on the device so no Internet connection is required.

  15. The Impact of Students' Temporal Perspectives on Time-on-Task and Learning Performance in Game Based Learning

    Science.gov (United States)

    Romero, Margarida; Usart, Mireia

    2013-01-01

    The use of games for educational purposes has been considered as a learning methodology that attracts the students' attention and may allow focusing individuals on the learning activity through the [serious games] SG game dynamic. Based on the hypothesis that students' Temporal Perspective has an impact on learning performance and time-on-task,…

  16. Timing of low tidal volume ventilation and intensive care unit mortality in acute respiratory distress syndrome. A prospective cohort study.

    Science.gov (United States)

    Needham, Dale M; Yang, Ting; Dinglas, Victor D; Mendez-Tellez, Pedro A; Shanholtz, Carl; Sevransky, Jonathan E; Brower, Roy G; Pronovost, Peter J; Colantuoni, Elizabeth

    2015-01-15

    Reducing tidal volume decreases mortality in acute respiratory distress syndrome (ARDS). However, the effect of the timing of low tidal volume ventilation is not well understood. To evaluate the association of intensive care unit (ICU) mortality with initial tidal volume and with tidal volume change over time. Multivariable, time-varying Cox regression analysis of a multisite, prospective study of 482 patients with ARDS with 11,558 twice-daily tidal volume assessments (evaluated in milliliter per kilogram of predicted body weight [PBW]) and daily assessment of other mortality predictors. An increase of 1 ml/kg PBW in initial tidal volume was associated with a 23% increase in ICU mortality risk (adjusted hazard ratio, 1.23; 95% confidence interval [CI], 1.06-1.44; P = 0.008). Moreover, a 1 ml/kg PBW increase in subsequent tidal volumes compared with the initial tidal volume was associated with a 15% increase in mortality risk (adjusted hazard ratio, 1.15; 95% CI, 1.02-1.29; P = 0.019). Compared with a prototypical patient receiving 8 days with a tidal volume of 6 ml/kg PBW, the absolute increase in ICU mortality (95% CI) of receiving 10 and 8 ml/kg PBW, respectively, across all 8 days was 7.2% (3.0-13.0%) and 2.7% (1.2-4.6%). In scenarios with variation in tidal volume over the 8-day period, mortality was higher when a larger volume was used earlier. Higher tidal volumes shortly after ARDS onset were associated with a greater risk of ICU mortality compared with subsequent tidal volumes. Timely recognition of ARDS and adherence to low tidal volume ventilation is important for reducing mortality. Clinical trial registered with www.clinicaltrials.gov (NCT 00300248).

  17. Learning the language of time: Children's acquisition of duration words.

    Science.gov (United States)

    Tillman, Katharine A; Barner, David

    2015-05-01

    Children use time words like minute and hour early in development, but take years to acquire their precise meanings. Here we investigate whether children assign meaning to these early usages, and if so, how. To do this, we test their interpretation of seven time words: second, minute, hour, day, week, month, and year. We find that preschoolers infer the orderings of time words (e.g., hour>minute), but have little to no knowledge of the absolute durations they encode. Knowledge of absolute duration is learned much later in development - many years after children first start using time words in speech - and in many children does not emerge until they have acquired formal definitions for the words. We conclude that associating words with the perception of duration does not come naturally to children, and that early intuitive meanings of time words are instead rooted in relative orderings, which children may infer from their use in speech. Copyright © 2015 Elsevier Inc. All rights reserved.

  18. Intracranial MRA: single volume vs. multiple thin slab 3D time-of-flight acquisition.

    Science.gov (United States)

    Davis, W L; Warnock, S H; Harnsberger, H R; Parker, D L; Chen, C X

    1993-01-01

    Single volume three-dimensional (3D) time-of-flight (TOF) MR angiography is the most commonly used noninvasive method for evaluating the intracranial vasculature. The sensitivity of this technique to signal loss from flow saturation limits its utility. A recently developed multislab 3D TOF technique, MOTSA, is less affected by flow saturation and would therefore be expected to yield improved vessel visualization. To study this hypothesis, intracranial MR angiograms were obtained on 10 volunteers using three techniques: MOTSA, single volume 3D TOF using a standard 4.9 ms TE (3D TOFA), and single volume 3D TOF using a 6.8 ms TE (3D TOFB). All three sets of axial source images and maximum intensity projection (MIP) images were reviewed. Each exam was evaluated for the number of intracranial vessels visualized. A total of 502 vessel segments were studied with each technique. With use of the MIP images, 86% of selected vessels were visualized with MOTSA, 64% with 3D TOFA (TE = 4.9 ms), and 67% with TOFB (TE = 6.8 ms). Similarly, with the axial source images, 91% of selected vessels were visualized with MOTSA, 77% with 3D TOFA (TE = 4.9 ms), and 82% with 3D TOFB (TE = 6.8 ms). There is improved visualization of selected intracranial vessels in normal volunteers with MOTSA as compared with single volume 3D TOF. These improvements are believed to be primarily a result of decreased sensitivity to flow saturation seen with the MOTSA technique. No difference in overall vessel visualization was noted for the two single volume 3D TOF techniques.

  19. Hierarchical Meta-Learning in Time Series Forecasting for Improved Interference-Less Machine Learning

    Directory of Open Access Journals (Sweden)

    David Afolabi

    2017-11-01

    Full Text Available The importance of an interference-less machine learning scheme in time series prediction is crucial, as an oversight can have a negative cumulative effect, especially when predicting many steps ahead of the currently available data. The on-going research on noise elimination in time series forecasting has led to a successful approach of decomposing the data sequence into component trends to identify noise-inducing information. The empirical mode decomposition method separates the time series/signal into a set of intrinsic mode functions ranging from high to low frequencies, which can be summed up to reconstruct the original data. The usual assumption that random noises are only contained in the high-frequency component has been shown not to be the case, as observed in our previous findings. The results from that experiment reveal that noise can be present in a low frequency component, and this motivates the newly-proposed algorithm. Additionally, to prevent the erosion of periodic trends and patterns within the series, we perform the learning of local and global trends separately in a hierarchical manner which succeeds in detecting and eliminating short/long term noise. The algorithm is tested on four datasets from financial market data and physical science data. The simulation results are compared with the conventional and state-of-the-art approaches for time series machine learning, such as the non-linear autoregressive neural network and the long short-term memory recurrent neural network, respectively. Statistically significant performance gains are recorded when the meta-learning algorithm for noise reduction is used in combination with these artificial neural networks. For time series data which cannot be decomposed into meaningful trends, applying the moving average method to create meta-information for guiding the learning process is still better than the traditional approach. Therefore, this new approach is applicable to the forecasting

  20. Timing of ectocranial suture activity in Gorilla gorilla as related to cranial volume and dental eruption.

    Science.gov (United States)

    Cray, James; Cooper, Gregory M; Mooney, Mark P; Siegel, Michael I

    2011-05-01

    Research has shown that Pan and Homo have similar ectocranial suture synostosis patterns and a similar suture ontogeny (relative timing of suture fusion during the species ontogeny). This ontogeny includes patency during and after neurocranial expansion with a delayed bony response associated with adaptation to biomechanical forces generated by mastication. Here we investigate these relationships for Gorilla by examining the association among ectocranial suture morphology, cranial volume (as a proxy for neurocranial expansion) and dental development (as a proxy for the length of time that it has been masticating hard foods and exerting such strains on the cranial vault) in a large sample of Gorilla gorilla skulls. Two-hundred and fifty-five Gorilla gorilla skulls were examined for ectocranial suture closure status, cranial volume and dental eruption. Regression models were calculated for cranial volumes by suture activity, and Kendall's tau (a non-parametric measure of association) was calculated for dental eruption status by suture activity. Results suggest that, as reported for Pan and Homo, neurocranial expansion precedes suture synostosis activity. Here, Gorilla was shown to have a strong relationship between dental development and suture activity (synostosis). These data are suggestive of suture fusion extending further into ontogeny than brain expansion, similar to Homo and Pan. This finding allows for the possibility that masticatory forces influence ectocranial suture morphology. © 2011 The Authors. Journal of Anatomy © 2011 Anatomical Society of Great Britain and Ireland.

  1. Using Online Lectures to Make Time for Active Learning

    Science.gov (United States)

    Prunuske, Amy J.; Batzli, Janet; Howell, Evelyn; Miller, Sarah

    2012-01-01

    To make time in class for group activities devoted to critical thinking, we integrated a series of short online lectures into the homework assignments of a large, introductory biology course at a research university. The majority of students viewed the online lectures before coming to class and reported that the online lectures helped them to complete the in-class activity and did not increase the amount of time they devoted to the course. In addition, students who viewed the online lecture performed better on clicker questions designed to test lower-order cognitive skills. The in-class activities then gave the students practice analyzing the information in groups and provided the instructor with feedback about the students’ understanding of the material. On the basis of the results of this study, we support creating hybrid course models that allow students to learn the fundamental information outside of class time, thereby creating time during the class period to be dedicated toward the conceptual understanding of the material. PMID:22714412

  2. Taxi Time Prediction at Charlotte Airport Using Fast-Time Simulation and Machine Learning Techniques

    Science.gov (United States)

    Lee, Hanbong

    2016-01-01

    Accurate taxi time prediction is required for enabling efficient runway scheduling that can increase runway throughput and reduce taxi times and fuel consumptions on the airport surface. Currently NASA and American Airlines are jointly developing a decision-support tool called Spot and Runway Departure Advisor (SARDA) that assists airport ramp controllers to make gate pushback decisions and improve the overall efficiency of airport surface traffic. In this presentation, we propose to use Linear Optimized Sequencing (LINOS), a discrete-event fast-time simulation tool, to predict taxi times and provide the estimates to the runway scheduler in real-time airport operations. To assess its prediction accuracy, we also introduce a data-driven analytical method using machine learning techniques. These two taxi time prediction methods are evaluated with actual taxi time data obtained from the SARDA human-in-the-loop (HITL) simulation for Charlotte Douglas International Airport (CLT) using various performance measurement metrics. Based on the taxi time prediction results, we also discuss how the prediction accuracy can be affected by the operational complexity at this airport and how we can improve the fast time simulation model before implementing it with an airport scheduling algorithm in a real-time environment.

  3. An efficient explicit marching on in time solver for magnetic field volume integral equation

    KAUST Repository

    Sayed, Sadeed Bin

    2015-07-25

    An efficient explicit marching on in time (MOT) scheme for solving the magnetic field volume integral equation is proposed. The MOT system is cast in the form of an ordinary differential equation and is integrated in time using a PE(CE)m multistep scheme. At each time step, a system with a Gram matrix is solved for the predicted/corrected field expansion coefficients. Depending on the type of spatial testing scheme Gram matrix is sparse or consists of blocks with only diagonal entries regardless of the time step size. Consequently, the resulting MOT scheme is more efficient than its implicit counterparts, which call for inversion of fuller matrix system at lower frequencies. Numerical results, which demonstrate the efficiency, accuracy, and stability of the proposed MOT scheme, are presented.

  4. EDUCATIONAL LEAPFROGGING IN THE mLEARNING TIME

    Directory of Open Access Journals (Sweden)

    Abdel Rahman IBRAHIM SULEIMAN

    2014-07-01

    Full Text Available In this theoretical study, researcher tries to shed light on the modern strategy of education, Mobile learning is this strategy, which has become a reality exists in the educational institutions and aims researcher of this study. Trying to figure out the reality of Mobil Determining if the mobile learning part of the E-Learning. Trying for identify future of mobile learning. And the researcher collect the information and the data from previous research in addition to what has been published on websites and blogs and has reached the researcher to achieve the successes of Mobile learning at the level of the educational process now , and that this strategy of mobile learning is not part of the e-learning, and generation of generations , but a new way for the development of the educational process educational , researcher is expected to evolve Mobile learning expands even at the all levels of educational.

  5. AIED 2009 Workshops Proceeedings Volume 10: Natural Language Processing in Support of Learning: Metrics, Feedback and Connectivity

    NARCIS (Netherlands)

    Dessus, Philippe; Trausan-Matu, Stefan; Van Rosmalen, Peter; Wild, Fridolin

    2009-01-01

    Dessus, P., Trausan-Matu, S., Van Rosmalen, P., & Wild, F. (Eds.) (2009). AIED 2009 Workshops Proceedings Volume 10 Natural Language Processing in Support of Learning: Metrics, Feedback and Connectivity. In S. D. Craig & D. Dicheva (Eds.), AIED 2009: 14th International Conference in Artificial

  6. Car-following Behavior Model Learning Using Timed Automata

    NARCIS (Netherlands)

    Zhang, Yihuan; Lin, Q.; Wang, Jun; Verwer, S.E.; Dochain, D.; Henrion, D.; Peaucelle, D.

    Learning driving behavior is fundamental for autonomous vehicles to “understand” traffic situations. This paper proposes a novel method for learning a behavioral model of car-following using automata learning algorithms. The model is interpretable for car-following behavior analysis. Frequent common

  7. Mental Time Travel, Memory and the Social Learning Strategies Tournament

    Science.gov (United States)

    Fogarty, L.; Rendell, L.; Laland, K. N.

    2012-01-01

    The social learning strategies tournament was an open computer-based tournament investigating the best way to learn in a changing environment. Here we present an analysis of the impact of memory on the ability of strategies entered into the social learning strategies tournament (Rendell, Boyd, et al., 2010) to modify their own behavior to suit a…

  8. Parallel, explicit, and PWTD-enhanced time domain volume integral equation solver

    KAUST Repository

    Liu, Yang

    2013-07-01

    Time domain volume integral equations (TDVIEs) are useful for analyzing transient scattering from inhomogeneous dielectric objects in applications as varied as photonics, optoelectronics, and bioelectromagnetics. TDVIEs typically are solved by implicit marching-on-in-time (MOT) schemes [N. T. Gres et al., Radio Sci., 36, 379-386, 2001], requiring the solution of a system of equations at each and every time step. To reduce the computational cost associated with such schemes, [A. Al-Jarro et al., IEEE Trans. Antennas Propagat., 60, 5203-5215, 2012] introduced an explicit MOT-TDVIE method that uses a predictor-corrector technique to stably update field values throughout the scatterer. By leveraging memory-efficient nodal spatial discretization and scalable parallelization schemes [A. Al-Jarro et al., in 28th Int. Rev. Progress Appl. Computat. Electromagn., 2012], this solver has been successfully applied to the analysis of scattering phenomena involving 0.5 million spatial unknowns. © 2013 IEEE.

  9. Understanding the Effects of Time on Collaborative Learning Processes in Problem Based Learning: A Mixed Methods Study

    Science.gov (United States)

    Hommes, J.; Van den Bossche, P.; de Grave, W.; Bos, G.; Schuwirth, L.; Scherpbier, A.

    2014-01-01

    Little is known how time influences collaborative learning groups in medical education. Therefore a thorough exploration of the development of learning processes over time was undertaken in an undergraduate PBL curriculum over 18 months. A mixed-methods triangulation design was used. First, the quantitative study measured how various learning…

  10. Glacier Volume Change Estimation Using Time Series of Improved Aster Dems

    Science.gov (United States)

    Girod, Luc; Nuth, Christopher; Kääb, Andreas

    2016-06-01

    Volume change data is critical to the understanding of glacier response to climate change. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) system embarked on the Terra (EOS AM-1) satellite has been a unique source of systematic stereoscopic images covering the whole globe at 15m resolution and at a consistent quality for over 15 years. While satellite stereo sensors with significantly improved radiometric and spatial resolution are available to date, the potential of ASTER data lies in its long consistent time series that is unrivaled, though not fully exploited for change analysis due to lack of data accuracy and precision. Here, we developed an improved method for ASTER DEM generation and implemented it in the open source photogrammetric library and software suite MicMac. The method relies on the computation of a rational polynomial coefficients (RPC) model and the detection and correction of cross-track sensor jitter in order to compute DEMs. ASTER data are strongly affected by attitude jitter, mainly of approximately 4 km and 30 km wavelength, and improving the generation of ASTER DEMs requires removal of this effect. Our sensor modeling does not require ground control points and allows thus potentially for the automatic processing of large data volumes. As a proof of concept, we chose a set of glaciers with reference DEMs available to assess the quality of our measurements. We use time series of ASTER scenes from which we extracted DEMs with a ground sampling distance of 15m. Our method directly measures and accounts for the cross-track component of jitter so that the resulting DEMs are not contaminated by this process. Since the along-track component of jitter has the same direction as the stereo parallaxes, the two cannot be separated and the elevations extracted are thus contaminated by along-track jitter. Initial tests reveal no clear relation between the cross-track and along-track components so that the latter seems not to be

  11. Whole-Volume Clustering of Time Series Data from Zebrafish Brain Calcium Images via Mixture Modeling.

    Science.gov (United States)

    Nguyen, Hien D; Ullmann, Jeremy F P; McLachlan, Geoffrey J; Voleti, Venkatakaushik; Li, Wenze; Hillman, Elizabeth M C; Reutens, David C; Janke, Andrew L

    2018-02-01

    Calcium is a ubiquitous messenger in neural signaling events. An increasing number of techniques are enabling visualization of neurological activity in animal models via luminescent proteins that bind to calcium ions. These techniques generate large volumes of spatially correlated time series. A model-based functional data analysis methodology via Gaussian mixtures is suggested for the clustering of data from such visualizations is proposed. The methodology is theoretically justified and a computationally efficient approach to estimation is suggested. An example analysis of a zebrafish imaging experiment is presented.

  12. GLACIER VOLUME CHANGE ESTIMATION USING TIME SERIES OF IMPROVED ASTER DEMS

    Directory of Open Access Journals (Sweden)

    L. Girod

    2016-06-01

    Full Text Available Volume change data is critical to the understanding of glacier response to climate change. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER system embarked on the Terra (EOS AM-1 satellite has been a unique source of systematic stereoscopic images covering the whole globe at 15m resolution and at a consistent quality for over 15 years. While satellite stereo sensors with significantly improved radiometric and spatial resolution are available to date, the potential of ASTER data lies in its long consistent time series that is unrivaled, though not fully exploited for change analysis due to lack of data accuracy and precision. Here, we developed an improved method for ASTER DEM generation and implemented it in the open source photogrammetric library and software suite MicMac. The method relies on the computation of a rational polynomial coefficients (RPC model and the detection and correction of cross-track sensor jitter in order to compute DEMs. ASTER data are strongly affected by attitude jitter, mainly of approximately 4 km and 30 km wavelength, and improving the generation of ASTER DEMs requires removal of this effect. Our sensor modeling does not require ground control points and allows thus potentially for the automatic processing of large data volumes. As a proof of concept, we chose a set of glaciers with reference DEMs available to assess the quality of our measurements. We use time series of ASTER scenes from which we extracted DEMs with a ground sampling distance of 15m. Our method directly measures and accounts for the cross-track component of jitter so that the resulting DEMs are not contaminated by this process. Since the along-track component of jitter has the same direction as the stereo parallaxes, the two cannot be separated and the elevations extracted are thus contaminated by along-track jitter. Initial tests reveal no clear relation between the cross-track and along-track components so that the latter

  13. A Lecture Supporting System Based on Real-Time Learning Analytics

    Science.gov (United States)

    Shimada, Atsushi; Konomi, Shin'ichi

    2017-01-01

    A new lecture supporting system based on real-time learning analytics is proposed. Our target is on-site classrooms where teachers give their lectures, and a lot of students listen to teachers' explanation, conduct exercises etc. We utilize not only an e-Learning system, but also an e-Book system to collect real-time learning activities during the…

  14. Information extraction from dynamic PS-InSAR time series using machine learning

    Science.gov (United States)

    van de Kerkhof, B.; Pankratius, V.; Chang, L.; van Swol, R.; Hanssen, R. F.

    2017-12-01

    Due to the increasing number of SAR satellites, with shorter repeat intervals and higher resolutions, SAR data volumes are exploding. Time series analyses of SAR data, i.e. Persistent Scatterer (PS) InSAR, enable the deformation monitoring of the built environment at an unprecedented scale, with hundreds of scatterers per km2, updated weekly. Potential hazards, e.g. due to failure of aging infrastructure, can be detected at an early stage. Yet, this requires the operational data processing of billions of measurement points, over hundreds of epochs, updating this data set dynamically as new data come in, and testing whether points (start to) behave in an anomalous way. Moreover, the quality of PS-InSAR measurements is ambiguous and heterogeneous, which will yield false positives and false negatives. Such analyses are numerically challenging. Here we extract relevant information from PS-InSAR time series using machine learning algorithms. We cluster (group together) time series with similar behaviour, even though they may not be spatially close, such that the results can be used for further analysis. First we reduce the dimensionality of the dataset in order to be able to cluster the data, since applying clustering techniques on high dimensional datasets often result in unsatisfying results. Our approach is to apply t-distributed Stochastic Neighbor Embedding (t-SNE), a machine learning algorithm for dimensionality reduction of high-dimensional data to a 2D or 3D map, and cluster this result using Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The results show that we are able to detect and cluster time series with similar behaviour, which is the starting point for more extensive analysis into the underlying driving mechanisms. The results of the methods are compared to conventional hypothesis testing as well as a Self-Organising Map (SOM) approach. Hypothesis testing is robust and takes the stochastic nature of the observations into account

  15. Effect of water volume based on water absorption and mixing time on physical properties of tapioca starch – wheat composite bread

    Science.gov (United States)

    Prameswari, I. K.; Manuhara, G. J.; Amanto, B. S.; Atmaka, W.

    2018-05-01

    Tapioca starch application in bread processing change water absorption level by the dough, while sufficient mixing time makes the optimal water absorption. This research aims to determine the effect of variations in water volume and mixing time on physical properties of tapioca starch – wheat composite bread and the best method for the composite bread processing. This research used Complete Randomized Factorial Design (CRFD) with two factors: variations of water volume (111,8 ml, 117,4 ml, 123 ml) and mixing time (16 minutes, 17 minutes 36 seconds, 19 minutes 12 seconds). The result showed that water volume significantly affected on dough volume, bread volume and specific volume, baking expansion, and crust thickness. Mixing time significantly affected on dough volume and specific volume, bread volume and specific volume, baking expansion, bread height, and crust thickness. While the combination of water volume and mixing time significantly affected for all physical properties parameters except crust thickness.

  16. Development of volume rendering module for real-time visualization system

    International Nuclear Information System (INIS)

    Otani, Takayuki; Muramatsu, Kazuhiro

    2000-03-01

    Volume rendering is a method to visualize the distribution of physical quantities in the three dimensional space from any viewpoint by tracing the ray direction on the ordinary two dimensional monitoring display. It enables to provide the interior information as well as the surfacial one by producing the translucent images. Therefore, it is regarded as a very useful means as well as an important one in the analysis of the computational results of the scientific calculations, although it has, unfortunately, disadvantage to need a large amount of computing time. This report describes algorithm and its performance of the volume rendering soft-ware which was developed as an important functional module in the real-time visualization system PATRAS. This module can directly visualize the computed results on BFC grid. Moreover, it has already realized the speed-up in some parts of the software by the use of a newly developed heuristic technique. This report includes the investigation on the speed-up of the software by parallel processing. (author)

  17. Hippocampal dose volume histogram predicts Hopkins Verbal Learning Test scores after brain irradiation

    Directory of Open Access Journals (Sweden)

    Catherine Okoukoni, PhD

    2017-10-01

    Full Text Available Purpose: Radiation-induced cognitive decline is relatively common after treatment for primary and metastatic brain tumors; however, identifying dosimetric parameters that are predictive of radiation-induced cognitive decline is difficult due to the heterogeneity of patient characteristics. The memory function is especially susceptible to radiation effects after treatment. The objective of this study is to correlate volumetric radiation doses received by critical neuroanatomic structures to post–radiation therapy (RT memory impairment. Methods and materials: Between 2008 and 2011, 53 patients with primary brain malignancies were treated with conventionally fractionated RT in prospectively accrued clinical trials performed at our institution. Dose-volume histogram analysis was performed for the hippocampus, parahippocampus, amygdala, and fusiform gyrus. Hopkins Verbal Learning Test-Revised scores were obtained at least 6 months after RT. Impairment was defined as an immediate recall score ≤15. For each anatomic region, serial regression was performed to correlate volume receiving a given dose (VD(Gy with memory impairment. Results: Hippocampal V53.4Gy to V60.9Gy significantly predicted post-RT memory impairment (P < .05. Within this range, the hippocampal V55Gy was the most significant predictor (P = .004. Hippocampal V55Gy of 0%, 25%, and 50% was associated with tumor-induced impairment rates of 14.9% (95% confidence interval [CI], 7.2%-28.7%, 45.9% (95% CI, 24.7%-68.6%, and 80.6% (95% CI, 39.2%-96.4%, respectively. Conclusions: The hippocampal V55Gy is a significant predictor for impairment, and a limiting dose below 55 Gy may minimize radiation-induced cognitive impairment.

  18. BOX MEDIA MODEL THROUGH THE USE OF CONTEXTUAL UNDERSTANDING TO IMPROVE STUDENT LEARNING CONCEPTS IN VOLUME BEAM

    Directory of Open Access Journals (Sweden)

    Dede Rohaeni

    2016-05-01

    Full Text Available Abstract. This research is motivated Cilengkrang Elementary School fifth grade students in the learning of the beam volume is still experiencing difficulties. This happens because the learning process that takes place is conventional. Learning by applying a contextual model chosen researchers by reason students will know if the learning is associated with the real world of students. The method used in this research is a classroom action research methods to the design of the research procedure refers to the spiral model Kemmis and MC. Tujuanpenelitianini is to obtain an overview of the planning, implementation and improvement of students' understanding of the results of the application of the concept model of contextual learning in the classroom beam volume V Elementary School Cilengkrang. The method used in this research is a classroom action research methods to the design of the research procedure refers to the spiral model Kemmis and MC. Taggart. Based on the implementation of the actions performed by three cycles, as a whole has shown an increase from the initial data, both process and outcomes of learning. So that the application of contextual models can enhance students' understanding of class V SDN Cilengkrang Northern District of Sumedang Sumedang district of the concept of the beam volume.   Keywords: Contextual Model, Mathematics, Mathematics Learning Objectives     Abstrak. Penelitian ini dilatarbelakangi siswa kelas V SDN Cilengkrang dalam pembelajaran volume balok masih mengalami kesulitan. Ini terjadi karena proses pembelajaran yang berlangsung bersifat konvensional. Pembelajaran dengan menerapkan model kontekstual dipilih peneliti dengan alasan siswa akan paham jika pembelajaran dikaitkan dengan dunia nyata siswa. Metode penelitian yang digunakan dalam penelitian ini adalah metode penelitian tindakan kelas dengan rancangan prosedur penelitiannya mengacu pada model spiral Kemmis dan MC. Tujuanpenelitianini yaitu untuk memperoleh

  19. Machine learning in heart failure: ready for prime time.

    Science.gov (United States)

    Awan, Saqib Ejaz; Sohel, Ferdous; Sanfilippo, Frank Mario; Bennamoun, Mohammed; Dwivedi, Girish

    2018-03-01

    The aim of this review is to present an up-to-date overview of the application of machine learning methods in heart failure including diagnosis, classification, readmissions and medication adherence. Recent studies have shown that the application of machine learning techniques may have the potential to improve heart failure outcomes and management, including cost savings by improving existing diagnostic and treatment support systems. Recently developed deep learning methods are expected to yield even better performance than traditional machine learning techniques in performing complex tasks by learning the intricate patterns hidden in big medical data. The review summarizes the recent developments in the application of machine and deep learning methods in heart failure management.

  20. Volume doubling time and growth rate of renal cell carcinoma determined by helical CT: a single-institution experience

    International Nuclear Information System (INIS)

    Lee, Ji Young; Kim, Chan Kyo; Choi, Dongil; Park, Byung Kwan

    2008-01-01

    The purpose of this study was to retrospectively evaluate the volume doubling time (VDT) and growth rate of renal cell carcinomas (RCC) on a serial computed tomography (CT) scan. Thirty pathologically proven RCCs were reviewed with helical CT. Each tumor underwent at least two CT scans. Tumor volume was determined using an area measuring tool and the summation-of-areas technique. Growth rate was evaluated in terms of diameter and volume changes. VDT and volume growth rate were compared in relation to several factors (initial diameter, initial volume, diameter growth rate, volume growth rate, tumor grade, tumor subtype, sex or age). Mean VDT of RCCs was 505 days. Mean diameter and volume growth rate were 0.59 cm/year and 19.1 cm 3 /year, respectively. For volume and diameter growth rate, tumors ≤4 cm showed lower rates than those >4 cm (P 0.05). Volume growth rate was moderately to strongly positively correlated with initial diameter, initial volume and diameter growth rate (P < 0.05). In conclusion, small RCCs grew at a slow rate both diametrically and volumetrically. More accurate assessment of tumor growth rate and VDT may be helpful to understand the natural history of RCC. (orig.)

  1. In real time: exploring nursing students' learning during an international experience.

    Science.gov (United States)

    Afriyie Asenso, Barbara; Reimer-Kirkham, Sheryl; Astle, Barbara

    2013-10-11

    Abstract Nursing education has increasingly turned to international learning experiences to educate students who are globally minded and aware of social injustices in local and global communities. To date, research with international learning experiences has focused on the benefits for the students participating, after they have completed the international experience. The purpose of this qualitative study was to explore how nursing students learn during the international experience. The sample consisted of eight nursing students who enrolled in an international learning experience, and data were collected in "real time" in Zambia. The students were observed during learning activities and were interviewed three times. Three major themes emerged from the thematic analysis: expectations shaped students' learning, engagement facilitated learning, and critical reflection enhanced learning. Implications are discussed, related to disrupting media representations of Africa that shape students' expectations, and educational strategies for transformative learning and global citizenship.

  2. Effects of gauge volume on pseudo-strain induced in strain measurement using time-of-flight neutron diffraction

    International Nuclear Information System (INIS)

    Suzuki, Hiroshi; Harjo, Stefanus; Abe, Jun; Xu, Pingguang; Aizawa, Kazuya; Akita, Koichi

    2013-01-01

    Spurious or pseudo-strains observed in time-of-flight (TOF) neutron diffraction due to neutron attenuation, surface-effects and a strain distribution within the gauge volume were investigated. Experiments were carried out on annealed and bent ferritic steel bars to test these effects. The most representative position in the gauge volume corresponds to the neutron-weighted center of gravity (ncog), which takes into account variations in intensity within the gauge volume due to neutron attenuation and/or absence of material in the gauge volume. The average strain in the gauge volume was observed to be weighted towards the ncog position but following an increase in the size of the gauge volume the weighted average strain was changed because of the change in the ncog position when a strain gradient appeared within the gauge volume. On the other hand, typical pseudo-strains, which are well known, did appear in through-surface strain measurements when the gauge volume was incompletely filled by the sample. Tensile pseudo-strains due to the surface-effect increased near the sample surface and exhibited a similar trend regardless of the size of the gauge volume, while the pseudo-strains increased faster for the smaller gauge volume. Furthermore, a pseudo-strain due to a change in the ncog position was observed even when the gauge volume was perfectly filled in the sample, and it increased with an increase in the size of the gauge volume. These pseudo-strains measured were much larger than those simulated by the conventional modeling, whereas they were simulated by taking into account an incident neutron beam divergence additionally in the model. Therefore, the incident divergence of the incident neutron beam must be carefully designed to avoid pseudo-strains in time-of-flight neutron diffractometry

  3. Nonlinear isochrones in murine left ventricular pressure-volume loops: how well does the time-varying elastance concept hold?

    Science.gov (United States)

    Claessens, T E; Georgakopoulos, D; Afanasyeva, M; Vermeersch, S J; Millar, H D; Stergiopulos, N; Westerhof, N; Verdonck, P R; Segers, P

    2006-04-01

    The linear time-varying elastance theory is frequently used to describe the change in ventricular stiffness during the cardiac cycle. The concept assumes that all isochrones (i.e., curves that connect pressure-volume data occurring at the same time) are linear and have a common volume intercept. Of specific interest is the steepest isochrone, the end-systolic pressure-volume relationship (ESPVR), of which the slope serves as an index for cardiac contractile function. Pressure-volume measurements, achieved with a combined pressure-conductance catheter in the left ventricle of 13 open-chest anesthetized mice, showed a marked curvilinearity of the isochrones. We therefore analyzed the shape of the isochrones by using six regression algorithms (two linear, two quadratic, and two logarithmic, each with a fixed or time-varying intercept) and discussed the consequences for the elastance concept. Our main observations were 1) the volume intercept varies considerably with time; 2) isochrones are equally well described by using quadratic or logarithmic regression; 3) linear regression with a fixed intercept shows poor correlation (R(2) volume intercept of the ESPVR. In conclusion, the linear time-varying elastance fails to provide a sufficiently robust model to account for changes in pressure and volume during the cardiac cycle in the mouse ventricle. A new framework accounting for the nonlinear shape of the isochrones needs to be developed.

  4. Timing of antibiotics, volume, and vasoactive infusions in children with sepsis admitted to intensive care.

    Science.gov (United States)

    van Paridon, Bregje M; Sheppard, Cathy; G, Garcia Guerra; Joffe, Ari R

    2015-08-17

    Early administration of antibiotics for sepsis, and of fluid boluses and vasoactive agents for septic shock, is recommended. Evidence for this in children is limited. The Alberta Sepsis Network prospectively enrolled eligible children admitted to the Pediatric Intensive Care Unit (PICU) with sepsis from 04/2012-10/2014. Demographics, severity of illness, and outcomes variables were prospectively entered into the ASN database after deferred consent. Timing of interventions were determined by retrospective chart review using a study manual and case-report-form. We aimed to determine the association of intervention timing and outcome in children with sepsis. Univariate (t-test and Fisher's Exact) and multiple linear regression statistics evaluated predictors of outcomes of PICU length of stay (LOS) and ventilation days. Seventy-nine children, age median 60 (IQR 22-133) months, 40 (51%) female, 39 (49%) with severe underlying co-morbidity, 44 (56%) with septic shock, and median PRISM-III 10.5 [IQR 6.0-17.0] were enrolled. Most patients presented in an ED: 36 (46%) at an outlying hospital ED, and 21 (27%) at the Children's Hospital ED. Most infections were pneumonia with/without empyema (42, 53%), meningitis (11, 14%), or bacteremia (10, 13%). The time from presentation to acceptable antibiotic administration was a median of 115.0 [IQR 59.0-323.0] minutes; 20 (25%) of patients received their antibiotics in the first hour from presentation. Independent predictors of PICU LOS were PRISM-III, and severe underlying co-morbidity, but not time to antibiotics. In the septic shock subgroup, the volume of fluid boluses given in the first 2 hours was independently associated with longer PICU LOS (effect size 0.22 days; 95% CI 0.5, 0.38; per ml/kg). Independent predictors of ventilator days were PRISM-III score and severe underlying co-morbidity. In the septic shock subgroup, volume of fluid boluses in the first 2 hours was independently associated with more ventilator days

  5. Parallel PWTD-Accelerated Explicit Solution of the Time Domain Electric Field Volume Integral Equation

    KAUST Repository

    Liu, Yang

    2016-03-25

    A parallel plane-wave time-domain (PWTD)-accelerated explicit marching-on-in-time (MOT) scheme for solving the time domain electric field volume integral equation (TD-EFVIE) is presented. The proposed scheme leverages pulse functions and Lagrange polynomials to spatially and temporally discretize the electric flux density induced throughout the scatterers, and a finite difference scheme to compute the electric fields from the Hertz electric vector potentials radiated by the flux density. The flux density is explicitly updated during time marching by a predictor-corrector (PC) scheme and the vector potentials are efficiently computed by a scalar PWTD scheme. The memory requirement and computational complexity of the resulting explicit PWTD-PC-EFVIE solver scale as ( log ) s s O N N and ( ) s t O N N , respectively. Here, s N is the number of spatial basis functions and t N is the number of time steps. A scalable parallelization of the proposed MOT scheme on distributed- memory CPU clusters is described. The efficiency, accuracy, and applicability of the resulting (parallelized) PWTD-PC-EFVIE solver are demonstrated via its application to the analysis of transient electromagnetic wave interactions on canonical and real-life scatterers represented with up to 25 million spatial discretization elements.

  6. Parallel PWTD-Accelerated Explicit Solution of the Time Domain Electric Field Volume Integral Equation

    KAUST Repository

    Liu, Yang; Al-Jarro, Ahmed; Bagci, Hakan; Michielssen, Eric

    2016-01-01

    A parallel plane-wave time-domain (PWTD)-accelerated explicit marching-on-in-time (MOT) scheme for solving the time domain electric field volume integral equation (TD-EFVIE) is presented. The proposed scheme leverages pulse functions and Lagrange polynomials to spatially and temporally discretize the electric flux density induced throughout the scatterers, and a finite difference scheme to compute the electric fields from the Hertz electric vector potentials radiated by the flux density. The flux density is explicitly updated during time marching by a predictor-corrector (PC) scheme and the vector potentials are efficiently computed by a scalar PWTD scheme. The memory requirement and computational complexity of the resulting explicit PWTD-PC-EFVIE solver scale as ( log ) s s O N N and ( ) s t O N N , respectively. Here, s N is the number of spatial basis functions and t N is the number of time steps. A scalable parallelization of the proposed MOT scheme on distributed- memory CPU clusters is described. The efficiency, accuracy, and applicability of the resulting (parallelized) PWTD-PC-EFVIE solver are demonstrated via its application to the analysis of transient electromagnetic wave interactions on canonical and real-life scatterers represented with up to 25 million spatial discretization elements.

  7. Learning Probabilistic Inference through Spike-Timing-Dependent Plasticity.

    Science.gov (United States)

    Pecevski, Dejan; Maass, Wolfgang

    2016-01-01

    Numerous experimental data show that the brain is able to extract information from complex, uncertain, and often ambiguous experiences. Furthermore, it can use such learnt information for decision making through probabilistic inference. Several models have been proposed that aim at explaining how probabilistic inference could be performed by networks of neurons in the brain. We propose here a model that can also explain how such neural network could acquire the necessary information for that from examples. We show that spike-timing-dependent plasticity in combination with intrinsic plasticity generates in ensembles of pyramidal cells with lateral inhibition a fundamental building block for that: probabilistic associations between neurons that represent through their firing current values of random variables. Furthermore, by combining such adaptive network motifs in a recursive manner the resulting network is enabled to extract statistical information from complex input streams, and to build an internal model for the distribution p (*) that generates the examples it receives. This holds even if p (*) contains higher-order moments. The analysis of this learning process is supported by a rigorous theoretical foundation. Furthermore, we show that the network can use the learnt internal model immediately for prediction, decision making, and other types of probabilistic inference.

  8. Learning Probabilistic Inference through Spike-Timing-Dependent Plasticity123

    Science.gov (United States)

    Pecevski, Dejan

    2016-01-01

    Abstract Numerous experimental data show that the brain is able to extract information from complex, uncertain, and often ambiguous experiences. Furthermore, it can use such learnt information for decision making through probabilistic inference. Several models have been proposed that aim at explaining how probabilistic inference could be performed by networks of neurons in the brain. We propose here a model that can also explain how such neural network could acquire the necessary information for that from examples. We show that spike-timing-dependent plasticity in combination with intrinsic plasticity generates in ensembles of pyramidal cells with lateral inhibition a fundamental building block for that: probabilistic associations between neurons that represent through their firing current values of random variables. Furthermore, by combining such adaptive network motifs in a recursive manner the resulting network is enabled to extract statistical information from complex input streams, and to build an internal model for the distribution p* that generates the examples it receives. This holds even if p* contains higher-order moments. The analysis of this learning process is supported by a rigorous theoretical foundation. Furthermore, we show that the network can use the learnt internal model immediately for prediction, decision making, and other types of probabilistic inference. PMID:27419214

  9. Museums as spaces and times for learning and social participation.

    Directory of Open Access Journals (Sweden)

    César M.

    2014-12-01

    Full Text Available A museum is valued according to its collections, communication and knowledge exchange with visitors (Primo, 1999. Museums should be in dialogue with the public, contributing to their development (Skramstad, 2004 and collective memory (Wertsch, 2004. Social interactions and working in participants’ zone of proximal development (Vygotsky, 1934/1962 play an important role in non-formal learning opportunities that take place at museums. The National Museum of Natural History and Science (Lisbon University offers weekly holiday programmes for children and teenagers, aiming at developing scientific literacy in intercultural and inclusive spaces and times, facilitating knowledge appropriation and social participation. We studied these programmes, assuming an interpretive approach (Denzin, 2002 and developing an intrinsic case study (Stake, 1995. The main participants were these children and teenagers, their parents, and museum educational agents. Data collecting instruments included observation, interviews, questionnaires, children and teenagers’ protocols and tasks inspired in projective techniques. Data treatment and analysis was based on a narrative content analysis (Clandinin & Connelly, 1998 from which inductive categories emerged (Hamido & César, 2009. Some examples illuminate participants’ expectancies, their engagement in activities, and the contributions of social interactions and non-formal education to the development of scientific literacy.

  10. Project Management in Real Time: A Service-Learning Project

    Science.gov (United States)

    Larson, Erik; Drexler, John A., Jr.

    2010-01-01

    This article describes a service-learning assignment for a project management course. It is designed to facilitate hands-on student learning of both the technical and the interpersonal aspects of project management, and it involves student engagement with real customers and real stakeholders in the creation of real events with real outcomes. As…

  11. Age and time effects on implicit and explicit learning

    NARCIS (Netherlands)

    Verneau, M.; Kamp, J. van der; Savelsbergh, G.J.P.; Looze, M.P. de

    2014-01-01

    Study Context: It has been proposed that effects of aging are more pronounced for explicit than for implicit motor learning. The authors evaluated this claim by comparing the efficacy of explicit and implicit learning of a movement sequence in young and older adults, and by testing the resilience

  12. Age and Time Effects on Implicit and Explicit Learning

    NARCIS (Netherlands)

    Verneau, M.M.N.; van der Kamp, J.; Savelsbergh, G.J.P.; de Looze, M.P.

    2014-01-01

    Study Context: It has been proposed that effects of aging are more pronounced for explicit than for implicit motor learning. The authors evaluated this claim by comparing the efficacy of explicit and implicit learning of a movement sequence in young and older adults, and by testing the resilience

  13. Language Learning Attitudes: Ingrained Or Shaped In Time?

    Directory of Open Access Journals (Sweden)

    Gökçe DİŞLEN DAĞGÖL

    2017-09-01

    Full Text Available Language learning has become an essential need in today’s world. From academic to social settings, humans need to communicate in a different language to survive in their community. However, despite this increasing importance of language, it is difficult to say we have attained successful language learning on a large scale since there are a lot of factors in language learning process. Language attitudes, one of these factors, influence this process both positively and negatively, depending on how we view learning a foreign language. Therefore, this study deals with the issue of language attitudes to uncover learners’ language conceptions and probable effects on their learning. Moreover, this study aims to reveal the potential role of past learning experiences on the development of language beliefs positively or negatively. Thus, 35 university students in their 1st, 2nd, 3rd and 4th years constitute the participants of the study. Based on mixed research design, the study is comprised of both quantitative and qualitative data. Quantitative data were gathered through Attitude Scale towards English Course, and the analyses were performed with Statistical Packages for Social Sciences (SPSS 17.0 version for Windows. The qualitative data were collected from students’ reports of their own autobiographies regarding their previous language learning experiences in elementary, secondary, high school and university years, and were subjected to the content analysis. The study showed language attitudes from behavioural, cognitive and affective perspectives and found out different factors in shaping their learning conceptions.

  14. Combined use of positron emission tomography and volume doubling time in lung cancer screening with low-dose CT scanning

    DEFF Research Database (Denmark)

    Ashraf, H; Dirksen, A; Jakobsen, Annika Loft

    2011-01-01

    In lung cancer screening the ability to distinguish malignant from benign nodules is a key issue. This study evaluates the ability of positron emission tomography (PET) and volume doubling time (VDT) to discriminate between benign and malignant nodules.......In lung cancer screening the ability to distinguish malignant from benign nodules is a key issue. This study evaluates the ability of positron emission tomography (PET) and volume doubling time (VDT) to discriminate between benign and malignant nodules....

  15. Forecasting of exported volume for brazilian fruits by time series analysis: an arima/garch approach

    Directory of Open Access Journals (Sweden)

    Abdinardo Moreira Barreto de Oliveira

    2015-06-01

    Full Text Available The aim of this paper was to offer econometric forecasting models to the Brazilian exported volume fruits, with a view to assisting the planning and production control, also motivated by the existence of a few published papers dealing with this issue. In this sense, it was used the ARIMA/GARCH models, considering, likewise, the occurrence of a multiplicative stochastic seasonality in these series. They were collected 300 observations of exported net weight (kg between Jan/1989 and Dec/2013 of the following fruits: pineapple, banana, orange, lemon, apple, papaya, mango, watermelon, melon and grape, which selection criteria was its importance in the exported basket fruit, because they represented 97% of total received dollars, and 99% of total volume sold in 2010, of a population about 28 kinds of exported fruits. The results showed that it was not only observed the existence of a 12 month multiplicative seasonality in banana and mango. On the other hand, they were identified two fruits groups: (1 those which are continuously exported, and (2 those which have export peaks. On the quality of the models, they were considered satisfactory for six of the ten fruits analyzed. On the volatility, it was seen a high persistence in banana and papaya series, pointing to the existence of a structural break in time series, which could be linked to the economic crises happened in the last 17 years.

  16. A Time Marching Scheme for Solving Volume Integral Equations on Nonlinear Scatterers

    KAUST Repository

    Bagci, Hakan

    2015-01-01

    Transient electromagnetic field interactions on inhomogeneous penetrable scatterers can be analyzed by solving time domain volume integral equations (TDVIEs). TDVIEs are oftentimes solved using marchingon-in-time (MOT) schemes. Unlike finite difference and finite element schemes, MOT-TDVIE solvers require discretization of only the scatterers, do not call for artificial absorbing boundary conditions, and are more robust to numerical phase dispersion. On the other hand, their computational cost is high, they suffer from late-time instabilities, and their implicit nature makes incorporation of nonlinear constitutive relations more difficult. Development of plane-wave time-domain (PWTD) and FFT-based schemes has significantly reduced the computational cost of the MOT-TDVIE solvers. Additionally, latetime instability problem has been alleviated for all practical purposes with the development of accurate integration schemes and specially designed temporal basis functions. Addressing the third challenge is the topic of this presentation. I will talk about an explicit MOT scheme developed for solving the TDVIE on scatterers with nonlinear material properties. The proposed scheme separately discretizes the TDVIE and the nonlinear constitutive relation between electric field intensity and flux density. The unknown field intensity and flux density are expanded using half and full Schaubert-Wilton-Glisson (SWG) basis functions in space and polynomial temporal interpolators in time. The resulting coupled system of the discretized TDVIE and constitutive relation is integrated in time using an explicit P E(CE) m scheme to yield the unknown expansion coefficients. Explicitness of time marching allows for straightforward incorporation of the nonlinearity as a function evaluation on the right hand side of the coupled system of equations. Consequently, the resulting MOT scheme does not call for a Newton-like nonlinear solver. Numerical examples, which demonstrate the applicability

  17. A Time Marching Scheme for Solving Volume Integral Equations on Nonlinear Scatterers

    KAUST Repository

    Bagci, Hakan

    2015-01-07

    Transient electromagnetic field interactions on inhomogeneous penetrable scatterers can be analyzed by solving time domain volume integral equations (TDVIEs). TDVIEs are oftentimes solved using marchingon-in-time (MOT) schemes. Unlike finite difference and finite element schemes, MOT-TDVIE solvers require discretization of only the scatterers, do not call for artificial absorbing boundary conditions, and are more robust to numerical phase dispersion. On the other hand, their computational cost is high, they suffer from late-time instabilities, and their implicit nature makes incorporation of nonlinear constitutive relations more difficult. Development of plane-wave time-domain (PWTD) and FFT-based schemes has significantly reduced the computational cost of the MOT-TDVIE solvers. Additionally, latetime instability problem has been alleviated for all practical purposes with the development of accurate integration schemes and specially designed temporal basis functions. Addressing the third challenge is the topic of this presentation. I will talk about an explicit MOT scheme developed for solving the TDVIE on scatterers with nonlinear material properties. The proposed scheme separately discretizes the TDVIE and the nonlinear constitutive relation between electric field intensity and flux density. The unknown field intensity and flux density are expanded using half and full Schaubert-Wilton-Glisson (SWG) basis functions in space and polynomial temporal interpolators in time. The resulting coupled system of the discretized TDVIE and constitutive relation is integrated in time using an explicit P E(CE) m scheme to yield the unknown expansion coefficients. Explicitness of time marching allows for straightforward incorporation of the nonlinearity as a function evaluation on the right hand side of the coupled system of equations. Consequently, the resulting MOT scheme does not call for a Newton-like nonlinear solver. Numerical examples, which demonstrate the applicability

  18. The Relationship between Motivation, Learning Approaches, Academic Performance and Time Spent

    Science.gov (United States)

    Everaert, Patricia; Opdecam, Evelien; Maussen, Sophie

    2017-01-01

    Previous literature calls for further investigation in terms of precedents and consequences of learning approaches (deep learning and surface learning). Motivation as precedent and time spent and academic performance as consequences are addressed in this paper. The study is administered in a first-year undergraduate course. Results show that the…

  19. Incremental Impact of Time on Students' Use of E-Learning via Facebook

    Science.gov (United States)

    Moghavvemi, Sedigheh; Salarzadeh Janatabadi, Hashem

    2018-01-01

    The majority of studies utilised the cross-sectional method to measure students' intention to learn and investigate their corresponding learning behaviours. Only a few studies have measured the process of change in students' learning behaviour in the context of time. The main purpose of this study is to determine the effects of using a Facebook…

  20. Crumpled Molecules and Edible Plastic: Science Learning Activation in Out-of-School Time

    Science.gov (United States)

    Dorph, Rena; Schunn, Christian D.; Crowley, Kevin

    2017-01-01

    The Coalition for Science After School highlights the dual nature of outcomes for science learning during out-of- school time (OST): Learning experiences should not only be positive in the moment, but also position youth for future success. Several frameworks speak to the first set of immediate outcomes--what youth learn, think, and feel as the…

  1. Editorial volume 3 - issue 3: The future of the Learning Management System

    Directory of Open Access Journals (Sweden)

    Yngve Nordkvelle

    2007-12-01

    Full Text Available Time Magazine argued in 2006 that the person of the year truly was “You”. This was in deed a significant gesture to the fact that digital technologies change the way people interact and live their lives. What made “You” a candidate for “Person of the year”, was that the development of the Internet had made it possible for anyone to publish and express your personality on the Web; or rather of “Web 2.0”. In 2007, the notion of “Web 2.0” has been on headlines for many conferences and conventions, articles and in the news. While some enthusiasts already prepare for the developments of “Web 3.0”, most people face the challenge of trying to grapple with how new technological changes affect their everyday life in the present tense. So, if “You” was the person of the year in 2006, Web 2.0 was the technology of the year in 2007. And then again, the notion of what consequences Web 2.0 might have for teaching and learning in the area of higher education, lifelong learning and adult education will be raised in numerous contexts. Some years ago, the Australian professor of teaching in Higher Education, Craig McInnis, described how most teachers in higher education felt that technological changes were among the most important factors affecting academic life. A report on the status of how Norwegian institutions have adopted ICT in teaching and learning, found that all institutions now use Learning Management Systems for their average teaching and administration tasks. Hence, the report concluded: the LMS has successfully brought the Norwegian academic into the digital age. The LMS is more or less synonymous with ICT. What worried the authors of the said report was that the use of the LMS was not considered sophisticated or innovative. Likewise, the influential report written by Zemsky and Massy (2004, found that much of the use was trivial and not primarily for the benefit of teaching and learning. We can predict that many

  2. Blood Volume, Plasma Volume and Circulation Time in a High-Energy-Demand Teleost, the Yellowfin Tuna (Thunnus Albacares)

    DEFF Research Database (Denmark)

    Brill, R.W.; Cousins, K.L.; Jones, D.R.

    1998-01-01

    We measured red cell space with 51Cr-labeled red blood cells, and dextran space with 500 kDa fluorescein-isothiocyanate-labeled dextran (FITC-dextran), in two groups of yellowfin tuna (Thunnus albacares). Red cell space was 13.8+/-0.7 ml kg-1 (mean +/- s.e.m.) Assuming a whole- body hematocrit...... for albacore (Thunnus alalunga, 82-197 ml kg-1). Plasma volume within the primary circulatory system (calculated from the 51Cr-labeled red blood cell data) was 32.9+/-2.3 ml kg-1. Dextran space was 37.0+/-3.7 ml kg-1. Because 500 kDa FITC-dextran appeared to remain within the vascular space, these data imply...

  3. EFFECTS OF COOPERATIVE LEARNING MODEL TYPE STAD JUST-IN TIME BASED ON THE RESULTS OF LEARNING TEACHING PHYSICS COURSE IN PHYSICS SCHOOL IN PHYSICS PROGRAM FACULTY UNIMED

    Directory of Open Access Journals (Sweden)

    Teguh Febri Sudarma

    2013-06-01

    Full Text Available Research was aimed to determine: (1 Students’ learning outcomes that was taught with just in time teaching based STAD cooperative learning method and STAD cooperative learning method (2 Students’ outcomes on Physics subject that had high learning activity compared with low learning activity. The research sample was random by raffling four classes to get two classes. The first class taught with just in time teaching based STAD cooperative learning method, while the second class was taught with STAD cooperative learning method. The instrument used was conceptual understanding that had been validated with 7 essay questions. The average gain values of students learning results with just in time teaching based STAD cooperative learning method 0,47 higher than average gain values of students learning results with STAD cooperative learning method. The high learning activity and low learning activity gave different learning results. In this case the average gain values of students learning results with just in time teaching based STAD cooperative learning method 0,48 higher than average gain values of students learning results with STAD cooperative learning method. There was interaction between learning model and learning activity to the physics learning result test in students

  4. Time-delayed contrast-enhanced MRI improves detection of brain metastases and apparent treatment volumes.

    Science.gov (United States)

    Kushnirsky, Marina; Nguyen, Vinh; Katz, Joel S; Steinklein, Jared; Rosen, Lisa; Warshall, Craig; Schulder, Michael; Knisely, Jonathan P S

    2016-02-01

    Contrast-enhanced MRI is the preeminent diagnostic test for brain metastasis (BM). Detection of BMs for stereotactic radiosurgery (SRS) planning may improve with a time delay following administration of a high-relaxivity agent for 1.5-T and 3-T imaging systems. Metastasis detection with time-delayed MRI was evaluated in this study. Fifty-three volumetric MRI studies from 38 patients undergoing SRS for BMs were evaluated. All studies used 0.1-mmol/kg gadobenate dimeglumine (MultiHance; Bracco Diagnostics) immediately after injection, followed by 2 more axial T1-weighted sequences after 5-minute intervals (final image acquisition commenced 15 minutes after contrast injection). Two studies were motion limited and excluded. Two hundred eighty-seven BMs were identified. The studies were randomized and examined separately by 3 radiologists, who were blinded to the temporal sequence. Each radiologist recorded the number of BMs detected per scan. A Wilcoxon signed-rank test compared BM numbers between scans. One radiologist determined the scan on which BMs were best defined. All confirmed, visible tumors were contoured using iPlan RT treatment planning software on each of the 3 MRI data sets. A linear mixed model was used to analyze volume changes. The interclass correlations for Scans 1, 2, and 3 were 0.7392, 0.7951, and 0.7290, respectively, demonstrating excellent interrater reliability. At least 1 new lesion was detected in the second scan as compared with the first in 35.3% of subjects (95% CI 22.4%-49.9%). The increase in BM numbers between Scans 1 and 2 ranged from 1 to 10. At least 1 new lesion was detected in the third scan as compared with the second in 21.6% of subjects (95% CI 11.3%-35.3%). The increase in BM numbers between Scans 2 and 3 ranged from 1 to 9. Between Scans 1 and 3, additional tumors were seen on 43.1% of scans (increase ranged from 1 to 14). The median increase in tumor number for all comparisons was 1. There was a significant increase in number

  5. Machine-learning-based Brokers for Real-time Classification of the LSST Alert Stream

    Science.gov (United States)

    Narayan, Gautham; Zaidi, Tayeb; Soraisam, Monika D.; Wang, Zhe; Lochner, Michelle; Matheson, Thomas; Saha, Abhijit; Yang, Shuo; Zhao, Zhenge; Kececioglu, John; Scheidegger, Carlos; Snodgrass, Richard T.; Axelrod, Tim; Jenness, Tim; Maier, Robert S.; Ridgway, Stephen T.; Seaman, Robert L.; Evans, Eric Michael; Singh, Navdeep; Taylor, Clark; Toeniskoetter, Jackson; Welch, Eric; Zhu, Songzhe; The ANTARES Collaboration

    2018-05-01

    The unprecedented volume and rate of transient events that will be discovered by the Large Synoptic Survey Telescope (LSST) demand that the astronomical community update its follow-up paradigm. Alert-brokers—automated software system to sift through, characterize, annotate, and prioritize events for follow-up—will be critical tools for managing alert streams in the LSST era. The Arizona-NOAO Temporal Analysis and Response to Events System (ANTARES) is one such broker. In this work, we develop a machine learning pipeline to characterize and classify variable and transient sources only using the available multiband optical photometry. We describe three illustrative stages of the pipeline, serving the three goals of early, intermediate, and retrospective classification of alerts. The first takes the form of variable versus transient categorization, the second a multiclass typing of the combined variable and transient data set, and the third a purity-driven subtyping of a transient class. Although several similar algorithms have proven themselves in simulations, we validate their performance on real observations for the first time. We quantitatively evaluate our pipeline on sparse, unevenly sampled, heteroskedastic data from various existing observational campaigns, and demonstrate very competitive classification performance. We describe our progress toward adapting the pipeline developed in this work into a real-time broker working on live alert streams from time-domain surveys.

  6. Single machine total completion time minimization scheduling with a time-dependent learning effect and deteriorating jobs

    Science.gov (United States)

    Wang, Ji-Bo; Wang, Ming-Zheng; Ji, Ping

    2012-05-01

    In this article, we consider a single machine scheduling problem with a time-dependent learning effect and deteriorating jobs. By the effects of time-dependent learning and deterioration, we mean that the job processing time is defined by a function of its starting time and total normal processing time of jobs in front of it in the sequence. The objective is to determine an optimal schedule so as to minimize the total completion time. This problem remains open for the case of -1 < a < 0, where a denotes the learning index; we show that an optimal schedule of the problem is V-shaped with respect to job normal processing times. Three heuristic algorithms utilising the V-shaped property are proposed, and computational experiments show that the last heuristic algorithm performs effectively and efficiently in obtaining near-optimal solutions.

  7. The effect of water volume and mixing time on physical properties of bread made from modified cassava starch-wheat composite flour

    Science.gov (United States)

    Srirejeki, S.; Manuhara, G. J.; Amanto, B. S.; Atmaka, W.; Laksono, P. W.

    2018-03-01

    Modification of cassava starch with soaking in the whey (by product on cheese production) resulted in changes of the flour characteristics. Adjustments of processing condition are important to be studied in the making of bread from modified cassava starch and wheat composite flour (30:70). This research aims to determine the effect of water volume and mixing time on the physical properties of the bread. The experimental design of this research was Completely Randomized Factorial Design (CRFD) with two factors which were water volume and mixing time. The variation of water volume significantly affected on bread height, dough volume, dough specific volume, and crust thickness. The variation of mixing time had a significant effect on the increase of dough volume and dough specific volume. The combination of water volume and mixing time had a significant effect on dough height, bread volume, bread specific volume, baking expansion, and weight loss.

  8. Machine learning application in the life time of materials

    OpenAIRE

    Yu, Xiaojiao

    2017-01-01

    Materials design and development typically takes several decades from the initial discovery to commercialization with the traditional trial and error development approach. With the accumulation of data from both experimental and computational results, data based machine learning becomes an emerging field in materials discovery, design and property prediction. This manuscript reviews the history of materials science as a disciplinary the most common machine learning method used in materials sc...

  9. Analysis of electromagnetic wave interactions on nonlinear scatterers using time domain volume integral equations

    KAUST Repository

    Ulku, Huseyin Arda

    2014-07-06

    Effects of material nonlinearities on electromagnetic field interactions become dominant as field amplitudes increase. A typical example is observed in plasmonics, where highly localized fields “activate” Kerr nonlinearities. Naturally, time domain solvers are the method of choice when it comes simulating these nonlinear effects. Oftentimes, finite difference time domain (FDTD) method is used for this purpose. This is simply due to the fact that explicitness of the FDTD renders the implementation easier and the material nonlinearity can be easily accounted for using an auxiliary differential equation (J.H. Green and A. Taflove, Opt. Express, 14(18), 8305-8310, 2006). On the other hand, explicit marching on-in-time (MOT)-based time domain integral equation (TDIE) solvers have never been used for the same purpose even though they offer several advantages over FDTD (E. Michielssen, et al., ECCOMAS CFD, The Netherlands, Sep. 5-8, 2006). This is because explicit MOT solvers have never been stabilized until not so long ago. Recently an explicit but stable MOT scheme has been proposed for solving the time domain surface magnetic field integral equation (H.A. Ulku, et al., IEEE Trans. Antennas Propag., 61(8), 4120-4131, 2013) and later it has been extended for the time domain volume electric field integral equation (TDVEFIE) (S. B. Sayed, et al., Pr. Electromagn. Res. S., 378, Stockholm, 2013). This explicit MOT scheme uses predictor-corrector updates together with successive over relaxation during time marching to stabilize the solution even when time step is as large as in the implicit counterpart. In this work, an explicit MOT-TDVEFIE solver is proposed for analyzing electromagnetic wave interactions on scatterers exhibiting Kerr nonlinearity. Nonlinearity is accounted for using the constitutive relation between the electric field intensity and flux density. Then, this relation and the TDVEFIE are discretized together by expanding the intensity and flux - sing half

  10. Breast cancer screening in Italy: evaluating key performance indicators for time trends and activity volumes.

    Science.gov (United States)

    Giordano, Livia; Castagno, Roberta; Giorgi, Daniela; Piccinelli, Cristiano; Ventura, Leonardo; Segnan, Nereo; Zappa, Marco

    2015-01-01

    Together with the National centre for screening monitoring (ONS), GISMa supports annual collection of data on national breast screening activities. Aggregated data on implementation and performance are gathered through a standardized form to calculate process and impact indicators. Analyzed data belong to 153 local programmes in the period 2006-2011 (2006-2012 for participation rate only). During the whole period, Italian crude participation rate exceeded GISMa's acceptable standard (50%), even though a higher participation in northern and central Italy compared to southern Italy and Islands was observed. Time trend analysis of diagnostic indicators confirmed in 2011 an adequate quality of breast screening performance, especially at subsequent screening. Recall rate at initial screening did not reach the acceptable standard (performance was achieved at subsequent screening. The same trend was followed by the overall detection rate and positive predictive value. They both showed a progressive reduction (from 6.2‰ in 2006 to 4.5‰ in 2011 for DR and from 8.0% in 2006 to 5.2% in 2011 for PPV, respectively) at initial screening and a good, stable trend at subsequent screening. Activity volume analysis shows that in programmes with greater activity (test/year ≥10,000) RR at both initial and subsequent screening has a better performance. This is also true for DR and PPV where programmes with high volumes of activity do better, especially when compared with those that interpret fewer than 5,000 mammograms per year. In spite of a few limits, these results are reassuring, and they reward the efforts made by screening professionals. It is therefore important to continue to monitor screening indicators and suggest, test, and evaluate new strategies for continuous improvement.

  11. Open-source Software for Demand Forecasting of Clinical Laboratory Test Volumes Using Time-series Analysis.

    Science.gov (United States)

    Mohammed, Emad A; Naugler, Christopher

    2017-01-01

    Demand forecasting is the area of predictive analytics devoted to predicting future volumes of services or consumables. Fair understanding and estimation of how demand will vary facilitates the optimal utilization of resources. In a medical laboratory, accurate forecasting of future demand, that is, test volumes, can increase efficiency and facilitate long-term laboratory planning. Importantly, in an era of utilization management initiatives, accurately predicted volumes compared to the realized test volumes can form a precise way to evaluate utilization management initiatives. Laboratory test volumes are often highly amenable to forecasting by time-series models; however, the statistical software needed to do this is generally either expensive or highly technical. In this paper, we describe an open-source web-based software tool for time-series forecasting and explain how to use it as a demand forecasting tool in clinical laboratories to estimate test volumes. This tool has three different models, that is, Holt-Winters multiplicative, Holt-Winters additive, and simple linear regression. Moreover, these models are ranked and the best one is highlighted. This tool will allow anyone with historic test volume data to model future demand.

  12. Real-time 3-dimensional fetal echocardiography with an instantaneous volume-rendered display: early description and pictorial essay.

    Science.gov (United States)

    Sklansky, Mark S; DeVore, Greggory R; Wong, Pierre C

    2004-02-01

    Random fetal motion, rapid fetal heart rates, and cumbersome processing algorithms have limited reconstructive approaches to 3-dimensional fetal cardiac imaging. Given the recent development of real-time, instantaneous volume-rendered sonographic displays of volume data, we sought to apply this technology to fetal cardiac imaging. We obtained 1 to 6 volume data sets on each of 30 fetal hearts referred for formal fetal echocardiography. Each volume data set was acquired over 2 to 8 seconds and stored on the system's hard drive. Rendered images were subsequently processed to optimize translucency, smoothing, and orientation and cropped to reveal "surgeon's eye views" of clinically relevant anatomic structures. Qualitative comparison was made with conventional fetal echocardiography for each subject. Volume-rendered displays identified all major abnormalities but failed to identify small ventricular septal defects in 2 patients. Important planes and views not visualized during the actual scans were generated with minimal processing of rendered image displays. Volume-rendered displays tended to have slightly inferior image quality compared with conventional 2-dimensional images. Real-time 3-dimensional echocardiography with instantaneous volume-rendered displays of the fetal heart represents a new approach to fetal cardiac imaging with tremendous clinical potential.

  13. Open-source software for demand forecasting of clinical laboratory test volumes using time-series analysis

    Directory of Open Access Journals (Sweden)

    Emad A Mohammed

    2017-01-01

    Full Text Available Background: Demand forecasting is the area of predictive analytics devoted to predicting future volumes of services or consumables. Fair understanding and estimation of how demand will vary facilitates the optimal utilization of resources. In a medical laboratory, accurate forecasting of future demand, that is, test volumes, can increase efficiency and facilitate long-term laboratory planning. Importantly, in an era of utilization management initiatives, accurately predicted volumes compared to the realized test volumes can form a precise way to evaluate utilization management initiatives. Laboratory test volumes are often highly amenable to forecasting by time-series models; however, the statistical software needed to do this is generally either expensive or highly technical. Method: In this paper, we describe an open-source web-based software tool for time-series forecasting and explain how to use it as a demand forecasting tool in clinical laboratories to estimate test volumes. Results: This tool has three different models, that is, Holt-Winters multiplicative, Holt-Winters additive, and simple linear regression. Moreover, these models are ranked and the best one is highlighted. Conclusion: This tool will allow anyone with historic test volume data to model future demand.

  14. A matter of timing: harm reduction in learned helplessness.

    Science.gov (United States)

    Richter, Sophie Helene; Sartorius, Alexander; Gass, Peter; Vollmayr, Barbara

    2014-11-03

    Learned helplessness has excellent validity as an animal model for depression, but problems in reproducibility limit its use and the high degree of stress involved in the paradigm raises ethical concerns. We therefore aimed to identify which and how many trials of the learned helplessness paradigm are necessary to distinguish between helpless and non-helpless rats. A trial-by-trial reanalysis of tests from 163 rats with congenital learned helplessness or congenital non-learned helplessness and comparison of 82 rats exposed to inescapable shock with 38 shock-controls revealed that neither the first test trials, when rats showed unspecific hyperlocomotion, nor trials of the last third of the test, when almost all animals responded quickly to the stressor, contributed to sensitivity and specificity of the test. Considering only trials 3-10 improved the classification of helpless and non-helpless rats. The refined analysis allows abbreviation of the test for learned helplessness from 15 trials to 10 trials thereby reducing pain and stress of the experimental animals without losing statistical power.

  15. Deciding between compensated volume balance and real time transient models for pipeline leak detection system

    Energy Technology Data Exchange (ETDEWEB)

    Baptista, Renan Martins [PETROBRAS, Rio de Janeiro, RJ (Brazil). Centro de Pesquisas. Div. de Explotacao]. E-mail: renan@cenpes.petrobras.com.br

    2000-07-01

    This paper describes a technical procedure to assess a software based leak detection system (LDS), by deciding between a simpler low cost, less effective product, having a fast installation and tuning, and a complex one with high cost and efficiency, which however takes a long time to be properly installed. This is a common decision among the pipeline operating companies, considering that the majority of the lines are short, with single phase liquid flow (which may include batches), basic communication system and instrumentation. Service companies offer realistic solutions for liquid flow, but usually designed to big pipeline networks, flowing multiple batches and allowing multiple fluid entrances and deliveries. Those solutions are sometimes impractical to short pipelines, due to its high cost, as well as long tuning procedures, complex instrumentation, communication and computer requirements. It is intended to approach here the best solution according to its cost. In a practical sense, it means to differentiate the various LDS techniques. Those techniques are available in a considerable number, and they are still spreading, according to the different scenarios. However, two most known and worldwide implemented techniques hold the majority of the market: the Compensated Volume Balance (CVB), which is less accurate, reliable and robust, but cheaper, simpler and faster to install, and the Real Time Transient Model (RTTM), which is very reliable, accurate and robust, but expensive and complex. This work will describe a way to define whether one can use or not a CVB in a pipeline. (author)

  16. DeepRT: deep learning for peptide retention time prediction in proteomics

    OpenAIRE

    Ma, Chunwei; Zhu, Zhiyong; Ye, Jun; Yang, Jiarui; Pei, Jianguo; Xu, Shaohang; Zhou, Ruo; Yu, Chang; Mo, Fan; Wen, Bo; Liu, Siqi

    2017-01-01

    Accurate predictions of peptide retention times (RT) in liquid chromatography have many applications in mass spectrometry-based proteomics. Herein, we present DeepRT, a deep learning based software for peptide retention time prediction. DeepRT automatically learns features directly from the peptide sequences using the deep convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) model, which eliminates the need to use hand-crafted features or rules. After the feature learning, pr...

  17. Cases on Challenges Facing E-Learning and National Development: Institutional Studies and Practices. e-Learning Practices. Volume II

    Science.gov (United States)

    Demiray, Ugur, Ed.

    2010-01-01

    E-Learning offers many opportunities for individuals and institutions all over the world. Individuals can access to education they need almost anytime and anywhere they are ready to. Institutions are able to provide more cost-effective training to their employees. E-learning context is very important. It is common to find educators who perceive…

  18. Data Mining Foundations and Intelligent Paradigms Volume 2 Statistical, Bayesian, Time Series and other Theoretical Aspects

    CERN Document Server

    Jain, Lakhmi

    2012-01-01

    Data mining is one of the most rapidly growing research areas in computer science and statistics. In Volume 2 of this three volume series, we have brought together contributions from some of the most prestigious researchers in theoretical data mining. Each of the chapters is self contained. Statisticians and applied scientists/ engineers will find this volume valuable. Additionally, it provides a sourcebook for graduate students interested in the current direction of research in data mining.

  19. Change of tumor target volume during waiting time for intensity-modulated radiotherapy (IMRT) in nasopharyngeal carcinoma

    International Nuclear Information System (INIS)

    Chen Bo; Yi Junlin; Gao Li; Xu Guozhen; Huang Xiaodong; Zhang Zhong; Luo Jingwei; Li Suyan

    2007-01-01

    Objective: To determine the influence of change in tumor target volume of nasopharyngeal carcinoma (NPC) while waiting for intensity modulated radiation therapy (IMRT). Methods: From March 2005 to December 2005, 31 patients with nasopharyngeal carcinoma received IMRT as the initial treatment at the Cancer Hospital of Chinese Academic of Medical Sciences. The original simulation CT scan was acquired before IMRT planning. A second CT scan was acquired before the start of radiotherapy. Wait- ing time was defined as the duration between CT simulation and start of radiotherapy. CT-CT fusion was used to minimize the error of delineation between the first tumor target volume (GTV) and the second tumor target volume (sGTV). Tumor target volume was calculated by treatment planning system. T test was carried out to analyse the difference between GTV and sGTV. Pearson correlation and multivariate linear regression was used to analyse the influence factor of the change betweent GTV and sGTV. Results: Median waiting time was 18 days (range, 9-27 days). There were significant differences between GTV and sGTV of both primary tumor (P=0.009) and metastatic lymphoma (P=0.005 ). Both Pearson correlation and multivariate linear regression showed that the change of primary tumor target volume had significant correlation with the first tumor target volume but had no significant correlation with the waiting time, sex, age, T stage and N stage (1992 Chinese Fuzhou Staging Classification). Conclusions: Within the range of the waiting time ob- served in our study, large volume primary tumor would have had a significant increase in volume, but whether the therapeutic effect would be influenced or not would need to be proved by study of large number of cases. Patients with large volume tumor should be considered to reduce the influence of waiting time by enlarging gross target volume and clinical targe volume and by neoadjuveant chemotherapy. For avoiding the unnecessary high-dose to normal

  20. Towards Real-Time Speech Emotion Recognition for Affective E-Learning

    Science.gov (United States)

    Bahreini, Kiavash; Nadolski, Rob; Westera, Wim

    2016-01-01

    This paper presents the voice emotion recognition part of the FILTWAM framework for real-time emotion recognition in affective e-learning settings. FILTWAM (Framework for Improving Learning Through Webcams And Microphones) intends to offer timely and appropriate online feedback based upon learner's vocal intonations and facial expressions in order…

  1. Michigan Journal of Community Service Learning. Volume 13, Number 1, Fall 2006

    Science.gov (United States)

    Howard, Jeffrey, Ed.

    2006-01-01

    The "Michigan Journal of Community Service Learning" ("MJCSL") is a national, peer-reviewed journal consisting of articles written by faculty and service-learning educators on research, theory, pedagogy, and issues pertinent to the service-learning community. The "MJCSL" aims to: (1) widen the community of…

  2. Food Gardening and Intergenerational Learning in Times of ...

    African Journals Online (AJOL)

    The focus of discussion is the intergenerational interactions and learning ... pastoralism and, to a lesser degree, cultivation (Mayer, 1971; Mostert, 1992). ... discouraged about the hard physical work and rather limited economic ... in the Amanzi for Food project, a middle-aged female participant, Mrs Peters, has involved a.

  3. Real-Time Barcode Detection and Classification Using Deep Learning

    DEFF Research Database (Denmark)

    Hansen, Daniel Kold; Nasrollahi, Kamal; Rasmussen, Christoffer Bøgelund

    2017-01-01

    Barcodes, in their different forms, can be found on almost any packages available in the market. Detecting and then decoding of barcodes have therefore great applications. We describe how to adapt the state-of-the- art deep learning-based detector of You Only Look Once (YOLO) for the purpose...

  4. Food Gardening and Intergenerational Learning in Times of ...

    African Journals Online (AJOL)

    Uncertainty is a universal phenomenon, a lived experience, an unease about acting ... uncertainty through mediations of knowledge, the formation of new social relations and ... Environmental Affairs and Tourism, 53% of young people in the country are ... Bubomi learning network connected to the Amanzi for Food project.

  5. A Computational Model of the Temporal Dynamics of Plasticity in Procedural Learning: Sensitivity to Feedback Timing

    Directory of Open Access Journals (Sweden)

    Vivian V. Valentin

    2014-07-01

    Full Text Available The evidence is now good that different memory systems mediate the learning of different types of category structures. In particular, declarative memory dominates rule-based (RB category learning and procedural memory dominates information-integration (II category learning. For example, several studies have reported that feedback timing is critical for II category learning, but not for RB category learning – results that have broad support within the memory systems literature. Specifically, II category learning has been shown to be best with feedback delays of 500ms compared to delays of 0 and 1000ms, and highly impaired with delays of 2.5 seconds or longer. In contrast, RB learning is unaffected by any feedback delay up to 10 seconds. We propose a neurobiologically detailed theory of procedural learning that is sensitive to different feedback delays. The theory assumes that procedural learning is mediated by plasticity at cortical-striatal synapses that are modified by dopamine-mediated reinforcement learning. The model captures the time-course of the biochemical events in the striatum that cause synaptic plasticity, and thereby accounts for the empirical effects of various feedback delays on II category learning.

  6. The diagnostic utility of sonographic carotid flow time in determining volume responsiveness.

    Science.gov (United States)

    Shokoohi, Hamid; Berry, Grant W; Shahkolahi, Murteza; King, Jackson; King, Jordan; Salimian, Mohammad; Poshtmashad, Ameneh; Pourmand, Ali

    2017-04-01

    We aimed to predict volume responsiveness and to assess the diagnostic accuracy of carotid flow time (FTc) with the change in hydration status before and after a passive leg raise (PLR) maneuver. Participants who presented at a community health fair in a dehydrated state following a prolonged fast while observing the month of Ramadan were recruited. Sonographic FTc measurements were obtained in the semi-Fowler position and after a PLR maneuver while participants were in a fasting state and repeated approximately 3 hours after breaking their fast. In total, 123 participants with mean age of 47±14 years, 55% male, were enrolled. Participants had fasted for an average of 16.9 hours and consumed an average of 933 mL between the 2 ultrasound measurements. Mean FTc values were significantly lower in the fasting state compared with the nonfasting state (312±22 vs 345±25milliseconds, P value change in FTc of ≥5% provides a reliable diagnostic accuracy for predicting fluid status. Copyright © 2016 Elsevier Inc. All rights reserved.

  7. Monitoring the snowpack volume in a sinkhole on Mount Lebanon using time lapse Photogrammetry

    Science.gov (United States)

    Abou Chakra, C.; Gascoin, S.; Somma, J.; Drapeau, L.; Fanise, P.

    2017-12-01

    Lebanon is one of the richest country in the Middle East for water resources, thanks to its mountain ranges that trigger precipitation from the moist air masses coming from the Mediterranean Sea. Snowpack acts as natural water storage in winter and supply fresh water during spring and summer. Yet, Lebanon is facing a serious water scarcity problem due to: i) decreasing amount of precipitation and climate change; ii) major growth of population of original residence and large number of refugees during regional wars. Therefore, continuous and systematic monitoring of the Lebanese water resources is becoming crucial. The Mount Lebanon is made of karstic depressions named "sinkholes". It is important to monitor the snowmelt process inside these sinkholes because of their key role as "containers" of seasonal snow. By isolating the snowpack from sun radiation and wind, they slow down the natural melting process and sublimation, thus delaying as well the low water flow period. An observatory is set up to monitor the snowpack evolution in a pilot sinkhole located in Mount Lebanon. The system uses three time-lapse cameras and structure-from-motion principles to reconstruct the snow volume within the sinkhole. The approach is validated by standard topographic surveys. The results indicate that snow depth can be retrieved with an accuracy between 20 and 60 cm (residuals standard deviation) and a low bias of 50 cm after coregistration of the digital elevation models.

  8. Left ventricular time volume curve analysis in the detection of limited ischaemic heart disease

    International Nuclear Information System (INIS)

    Liechtenstein, M.; Blanchett, W.; Andrews, J.; Hunt, D.

    1982-01-01

    The aim of the study was to determine whether limited coronary artery disease (CAD) could be accurately detected using the Cardiac Gated Blood Pool (CGBP) scan with exercise. Regional left ventricular time volume curves (RLTVD) were generated from 52 studies (46 patients: 22 normals, 24 abnormals). The parameters assessed both globally and regionally and at rest (R) and exercise (Ex) were: (1) the ejection fraction (EF) (2) the change in ejection fraction from R to Ex (δEF) (3) an early contraction index (ECI) (4) a maximal emptying index (DR) and (5) a maximal refilling index (AR). After careful analysis of these parameters it was decided that our diagnostic criteria would rely on the following: (1) the EF at R and Ex (2) the δ EF (3) the ECI at Ex (4) the AR at Ex This study showed that both the sensitivity and the specificity of the CGBP scan can be improved considerably with the inclusion of RLTVC from the levels obtained when the EF parameters alone are considered. It is possible with this technique to accurately diagnose limited CAD. (Author)

  9. An investigation of the relation between the 30 meter running time and the femoral volume fraction in the thigh

    Directory of Open Access Journals (Sweden)

    MY Tasmektepligil

    2009-12-01

    Full Text Available Leg components are thought to be a related to speed. Only a limited number of studies have, however, examined the interaction between speed and bone size. In this study, we examined the relationship between the time taken by football players to run thirty meters and the fraction which the femur forms compared to the entire thigh region. Data collected from thirty male football players of average age 17.3 (between 16-19 years old were analyzed. First we detected the thirty meter running times and then we estimated the volume fraction of the femur to the entire thigh region using stereological methods on magnetic resonance images. Our data showed that there was a highly negative relationship between the 30 meter running times and the volume fraction of the bone to the thigh region. Thus, 30 meter running time decreases as the fraction of the bone to the thigh region increases. In other words, speed increases as the fraction of bone volume increases. Our data indicate that selecting sportsman whose femoral volume fractions are high will provide a significant benefit to enhancing performance in those branches of sports which require speed. Moreover, we concluded that training which can increase the bone volume fraction should be practiced when an increase in speed is desired and that the changes in the fraction of thigh region components should be monitored during these trainings.

  10. Using deep learning to segment breast and fibroglandular tissue in MRI volumes.

    Science.gov (United States)

    Dalmış, Mehmet Ufuk; Litjens, Geert; Holland, Katharina; Setio, Arnaud; Mann, Ritse; Karssemeijer, Nico; Gubern-Mérida, Albert

    2017-02-01

    Automated segmentation of breast and fibroglandular tissue (FGT) is required for various computer-aided applications of breast MRI. Traditional image analysis and computer vision techniques, such atlas, template matching, or, edge and surface detection, have been applied to solve this task. However, applicability of these methods is usually limited by the characteristics of the images used in the study datasets, while breast MRI varies with respect to the different MRI protocols used, in addition to the variability in breast shapes. All this variability, in addition to various MRI artifacts, makes it a challenging task to develop a robust breast and FGT segmentation method using traditional approaches. Therefore, in this study, we investigated the use of a deep-learning approach known as "U-net." We used a dataset of 66 breast MRI's randomly selected from our scientific archive, which includes five different MRI acquisition protocols and breasts from four breast density categories in a balanced distribution. To prepare reference segmentations, we manually segmented breast and FGT for all images using an in-house developed workstation. We experimented with the application of U-net in two different ways for breast and FGT segmentation. In the first method, following the same pipeline used in traditional approaches, we trained two consecutive (2C) U-nets: first for segmenting the breast in the whole MRI volume and the second for segmenting FGT inside the segmented breast. In the second method, we used a single 3-class (3C) U-net, which performs both tasks simultaneously by segmenting the volume into three regions: nonbreast, fat inside the breast, and FGT inside the breast. For comparison, we applied two existing and published methods to our dataset: an atlas-based method and a sheetness-based method. We used Dice Similarity Coefficient (DSC) to measure the performances of the automated methods, with respect to the manual segmentations. Additionally, we computed

  11. Atlas-Based Segmentation Improves Consistency and Decreases Time Required for Contouring Postoperative Endometrial Cancer Nodal Volumes

    International Nuclear Information System (INIS)

    Young, Amy V.; Wortham, Angela; Wernick, Iddo; Evans, Andrew; Ennis, Ronald D.

    2011-01-01

    Purpose: Accurate target delineation of the nodal volumes is essential for three-dimensional conformal and intensity-modulated radiotherapy planning for endometrial cancer adjuvant therapy. We hypothesized that atlas-based segmentation ('autocontouring') would lead to time savings and more consistent contours among physicians. Methods and Materials: A reference anatomy atlas was constructed using the data from 15 postoperative endometrial cancer patients by contouring the pelvic nodal clinical target volume on the simulation computed tomography scan according to the Radiation Therapy Oncology Group 0418 trial using commercially available software. On the simulation computed tomography scans from 10 additional endometrial cancer patients, the nodal clinical target volume autocontours were generated. Three radiation oncologists corrected the autocontours and delineated the manual nodal contours under timed conditions while unaware of the other contours. The time difference was determined, and the overlap of the contours was calculated using Dice's coefficient. Results: For all physicians, manual contouring of the pelvic nodal target volumes and editing the autocontours required a mean ± standard deviation of 32 ± 9 vs. 23 ± 7 minutes, respectively (p = .000001), a 26% time savings. For each physician, the time required to delineate the manual contours vs. correcting the autocontours was 30 ± 3 vs. 21 ± 5 min (p = .003), 39 ± 12 vs. 30 ± 5 min (p = .055), and 29 ± 5 vs. 20 ± 5 min (p = .0002). The mean overlap increased from manual contouring (0.77) to correcting the autocontours (0.79; p = .038). Conclusion: The results of our study have shown that autocontouring leads to increased consistency and time savings when contouring the nodal target volumes for adjuvant treatment of endometrial cancer, although the autocontours still required careful editing to ensure that the lymph nodes at risk of recurrence are properly included in the target volume.

  12. A method to combine target volume data from 3D and 4D planned thoracic radiotherapy patient cohorts for machine learning applications

    NARCIS (Netherlands)

    Johnson, Corinne; Price, Gareth; Khalifa, Jonathan; Faivre-Finn, Corinne; Dekker, Andre; Moore, Christopher; van Herk, Marcel

    2017-01-01

    The gross tumour volume (GTV) is predictive of clinical outcome and consequently features in many machine-learned models. 4D-planning, however, has prompted substitution of the GTV with the internal gross target volume (iGTV). We present and validate a method to synthesise GTV data from the iGTV,

  13. Exploring Complex Engineering Learning Over Time with Epistemic Network Analysis

    OpenAIRE

    Svarovsky, Gina Navoa

    2011-01-01

    Recently, K-12 engineering education has received increased attention as a pathway to building stronger foundations in math andscience and introducing young people to the profession. However, the National Academy of Engineering found that many K-12engineering programs focus heavily on engineering design and science and math learning while minimizing the development ofengineering habits of mind. This narrowly-focused engineering activity can leave young people – and in particular, girls – with...

  14. Once upon a time.... Storytelling to enhance teaching and learning.

    Science.gov (United States)

    Lordly, Daphne

    2007-01-01

    The impact of storytelling in the classroom was examined, as was what motivates individuals to engage in storytelling. A storytelling methodology was introduced in an undergraduate nutrition course as an opportunity to enhance the teaching and learning environment. A 28-item, multi-part, self-administered survey was then distributed to the class (n=17). Survey responses (n=15, 88% response) indicate that educators' and students' storytelling can positively influence the learning environment. This occurs through the creation of a greater focus on personalized information, glimpses of real-life experience, a connection with a topic as participants recognize similarities in their own personal experience and knowledge, and connections between different topics and through the emphasis on key concepts. Stories initiate useful conversations about unexplored struggles within practice, such as the emotional dimension(s) of an issue or what it means to be professional. Students are motivated to participate in storytelling through an external focus on others (i.e., helping others to learn) and an internal focus on self (i.e., seeking a connection with others to promote social dialogue). Several challenges related to the use of storytelling in the classroom emerged. Storytelling develops ways of knowing and dialoguing about issues, which has the potential to influence how students will approach their professional practice.

  15. Mechanistic Fluid Transport Model to Estimate Gastrointestinal Fluid Volume and Its Dynamic Change Over Time.

    Science.gov (United States)

    Yu, Alex; Jackson, Trachette; Tsume, Yasuhiro; Koenigsknecht, Mark; Wysocki, Jeffrey; Marciani, Luca; Amidon, Gordon L; Frances, Ann; Baker, Jason R; Hasler, William; Wen, Bo; Pai, Amit; Sun, Duxin

    2017-11-01

    Gastrointestinal (GI) fluid volume and its dynamic change are integral to study drug disintegration, dissolution, transit, and absorption. However, key questions regarding the local volume and its absorption, secretion, and transit remain unanswered. The dynamic fluid compartment absorption and transit (DFCAT) model is proposed to estimate in vivo GI volume and GI fluid transport based on magnetic resonance imaging (MRI) quantified fluid volume. The model was validated using GI local concentration of phenol red in human GI tract, which was directly measured by human GI intubation study after oral dosing of non-absorbable phenol red. The measured local GI concentration of phenol red ranged from 0.05 to 168 μg/mL (stomach), to 563 μg/mL (duodenum), to 202 μg/mL (proximal jejunum), and to 478 μg/mL (distal jejunum). The DFCAT model characterized observed MRI fluid volume and its dynamic changes from 275 to 46.5 mL in stomach (from 0 to 30 min) with mucus layer volume of 40 mL. The volumes of the 30 small intestine compartments were characterized by a max of 14.98 mL to a min of 0.26 mL (0-120 min) and a mucus layer volume of 5 mL per compartment. Regional fluid volumes over 0 to 120 min ranged from 5.6 to 20.38 mL in the proximal small intestine, 36.4 to 44.08 mL in distal small intestine, and from 42 to 64.46 mL in total small intestine. The DFCAT model can be applied to predict drug dissolution and absorption in the human GI tract with future improvements.

  16. Understanding the effects of time on collaborative learning processes in problem based learning: a mixed methods study.

    Science.gov (United States)

    Hommes, J; Van den Bossche, P; de Grave, W; Bos, G; Schuwirth, L; Scherpbier, A

    2014-10-01

    Little is known how time influences collaborative learning groups in medical education. Therefore a thorough exploration of the development of learning processes over time was undertaken in an undergraduate PBL curriculum over 18 months. A mixed-methods triangulation design was used. First, the quantitative study measured how various learning processes developed within and over three periods in the first 1,5 study years of an undergraduate curriculum. Next, a qualitative study using semi-structured individual interviews focused on detailed development of group processes driving collaborative learning during one period in seven tutorial groups. The hierarchic multilevel analyses of the quantitative data showed that a varying combination of group processes developed within and over the three observed periods. The qualitative study illustrated development in psychological safety, interdependence, potency, group learning behaviour, social and task cohesion. Two new processes emerged: 'transactive memory' and 'convergence in mental models'. The results indicate that groups are dynamic social systems with numerous contextual influences. Future research should thus include time as an important influence on collaborative learning. Practical implications are discussed.

  17. Time Spent, Workload, and Student and Faculty Perceptions in a Blended Learning Environment

    Science.gov (United States)

    Schumacher, Christie; Arif, Sally

    2016-01-01

    Objective. To evaluate student perception and time spent on asynchronous online lectures in a blended learning environment (BLE) and to assess faculty workload and perception. Methods. Students (n=427) time spent viewing online lectures was measured in three courses. Students and faculty members completed a survey to assess perceptions of a BLE. Faculty members recorded time spent creating BLEs. Results. Total time spent in the BLE was less than the allocated time for two of the three courses by 3-15%. Students preferred online lectures for their flexibility, students’ ability to apply information learned, and congruence with their learning styles. Faculty members reported the BLE facilitated higher levels of learning during class sessions but noted an increase in workload. Conclusion. A BLE increased faculty workload but was well received by students. Time spent viewing online lectures was less than what was allocated in two of the three courses. PMID:27667839

  18. Dynamic time warping and machine learning for signal quality assessment of pulsatile signals

    International Nuclear Information System (INIS)

    Li, Q; Clifford, G D

    2012-01-01

    In this work, we describe a beat-by-beat method for assessing the clinical utility of pulsatile waveforms, primarily recorded from cardiovascular blood volume or pressure changes, concentrating on the photoplethysmogram (PPG). Physiological blood flow is nonstationary, with pulses changing in height, width and morphology due to changes in heart rate, cardiac output, sensor type and hardware or software pre-processing requirements. Moreover, considerable inter-individual and sensor-location variability exists. Simple template matching methods are therefore inappropriate, and a patient-specific adaptive initialization is therefore required. We introduce dynamic time warping to stretch each beat to match a running template and combine it with several other features related to signal quality, including correlation and the percentage of the beat that appeared to be clipped. The features were then presented to a multi-layer perceptron neural network to learn the relationships between the parameters in the presence of good- and bad-quality pulses. An expert-labeled database of 1055 segments of PPG, each 6 s long, recorded from 104 separate critical care admissions during both normal and verified arrhythmic events, was used to train and test our algorithms. An accuracy of 97.5% on the training set and 95.2% on test set was found. The algorithm could be deployed as a stand-alone signal quality assessment algorithm for vetting the clinical utility of PPG traces or any similar quasi-periodic signal. (paper)

  19. Dynamic time warping and machine learning for signal quality assessment of pulsatile signals.

    Science.gov (United States)

    Li, Q; Clifford, G D

    2012-09-01

    In this work, we describe a beat-by-beat method for assessing the clinical utility of pulsatile waveforms, primarily recorded from cardiovascular blood volume or pressure changes, concentrating on the photoplethysmogram (PPG). Physiological blood flow is nonstationary, with pulses changing in height, width and morphology due to changes in heart rate, cardiac output, sensor type and hardware or software pre-processing requirements. Moreover, considerable inter-individual and sensor-location variability exists. Simple template matching methods are therefore inappropriate, and a patient-specific adaptive initialization is therefore required. We introduce dynamic time warping to stretch each beat to match a running template and combine it with several other features related to signal quality, including correlation and the percentage of the beat that appeared to be clipped. The features were then presented to a multi-layer perceptron neural network to learn the relationships between the parameters in the presence of good- and bad-quality pulses. An expert-labeled database of 1055 segments of PPG, each 6 s long, recorded from 104 separate critical care admissions during both normal and verified arrhythmic events, was used to train and test our algorithms. An accuracy of 97.5% on the training set and 95.2% on test set was found. The algorithm could be deployed as a stand-alone signal quality assessment algorithm for vetting the clinical utility of PPG traces or any similar quasi-periodic signal.

  20. Adult Education and Lifelong Learning in Europe and Beyond: Comparative Perspectives from the 2015 Würzburg Winter School. Studies in Pedagogy, Andragogy, and Gerontagogy. Volume 67

    Science.gov (United States)

    Egetenmeyer, Regina, Ed.

    2016-01-01

    This volume presents comparisons of adult education and lifelong learning with a focus on educational policies, professionalization in adult education, participation in adult learning and education, quality in adult education, and educational guidance and counselling. The essays are based on comparisons discussed at the international Winter School…

  1. A study on the pulmonary mean transit time and the pulmonary blood volume by RI-cardiogram

    International Nuclear Information System (INIS)

    Ushio, Norio

    1987-01-01

    The pulmonary mean transit time and the pulmonary blood volume in cases of cardio-pulmonary disease were measured using Giuntini's method which is considered the most appropriate among radiocardiographic methods. The errors in this method were confirmed to be almost negligible. The results obtained were as follows: 1) The pulmonary mean transit time was related to the systemic mean transit time and markedly prolonged in left heart failure. On the other hand, it was markedly shortened in some cases of chronic pulmonary disease, particularly pulmonary emphysema. 2) The pulmonary blood volume tended to increase in left heart disorders and mitral valve disease and tended to decrease in the chronic pulmonary disease. The decrease was conspicuous particularly in some cases of pulmonary emphysema. 3) A structural change of the pulmonary vascular system in the chronic pulmonary disease appeared to bring about shortening of the pulmonary mean transit time and a decrease in the pulmonary blood volume. The pathophysiology of cardio-pulmonary disease can be more clarified by the RI-cardiogram used in this study, in which the pulmonary mean transit time and the pulmonary blood volume are used as the indicator. (author)

  2. The right time to learn: mechanisms and optimization of spaced learning

    Science.gov (United States)

    Smolen, Paul; Zhang, Yili; Byrne, John H.

    2016-01-01

    For many types of learning, spaced training, which involves repeated long inter-trial intervals, leads to more robust memory formation than does massed training, which involves short or no intervals. Several cognitive theories have been proposed to explain this superiority, but only recently have data begun to delineate the underlying cellular and molecular mechanisms of spaced training, and we review these theories and data here. Computational models of the implicated signalling cascades have predicted that spaced training with irregular inter-trial intervals can enhance learning. This strategy of using models to predict optimal spaced training protocols, combined with pharmacotherapy, suggests novel ways to rescue impaired synaptic plasticity and learning. PMID:26806627

  3. Volumization of the Brow at the Time of Blepharoplasty: Treating the Eyebrow Fat Pad as an Independent Unit.

    Science.gov (United States)

    Vrcek, Ivan; Chou, Eva; Somogyi, Marie; Shore, John W

    Loss of volume in the sub-brow fat pad with associated descent of the eyebrow is a common anatomical finding resulting in both functional and aesthetic consequences. A variety of techniques have been described to address brow position at the time of blepharoplasty. To our knowledge, none of these techniques treat the sub-brow fat pad as an isolated unit. Doing so enables the surgeon to stabilize and volumize the brow without resultant tension on the blepharoplasty wound. The authors describe a technique for addressing volume loss in the eyebrow with associated brow descent that treats the sub-brow fat pad as an isolated unit. A retrospective review of all patients undergoing brow ptosis repair by a single surgeon (J.W.S.) over an 11-month period was performed. Eighteen patients and 33 brows underwent the technique described. Patients were followed for an average of 11 weeks (range: 4 weeks to 20 weeks). All patients preoperatively displayed both visually significant dermatochalasis and brow descent below the orbital rim. Evaluation of pre- and postoperative photos demonstrates successful volumization of the brow with skin redraping without focal dimpling or undue tension on the eyelid wound. Performing a dissection that allows the sub-brow fat pad to be elevated in isolation from the overlying orbicularis and underlying periosteum allows for volumization and of the brow without compromising closure. This technique is a safe and effective means of volumizing the brow and treating secondary brow descent.

  4. Recognition of Time Stamps on Full-Disk Hα Images Using Machine Learning Methods

    Science.gov (United States)

    Xu, Y.; Huang, N.; Jing, J.; Liu, C.; Wang, H.; Fu, G.

    2016-12-01

    Observation and understanding of the physics of the 11-year solar activity cycle and 22-year magnetic cycle are among the most important research topics in solar physics. The solar cycle is responsible for magnetic field and particle fluctuation in the near-earth environment that have been found increasingly important in affecting the living of human beings in the modern era. A systematic study of large-scale solar activities, as made possible by our rich data archive, will further help us to understand the global-scale magnetic fields that are closely related to solar cycles. The long-time-span data archive includes both full-disk and high-resolution Hα images. Prior to the widely use of CCD cameras in 1990s, 35-mm films were the major media to store images. The research group at NJIT recently finished the digitization of film data obtained by the National Solar Observatory (NSO) and Big Bear Solar Observatory (BBSO) covering the period of 1953 to 2000. The total volume of data exceeds 60 TB. To make this huge database scientific valuable, some processing and calibration are required. One of the most important steps is to read the time stamps on all of the 14 million images, which is almost impossible to be done manually. We implemented three different methods to recognize the time stamps automatically, including Optical Character Recognition (OCR), Classification Tree and TensorFlow. The latter two are known as machine learning algorithms which are very popular now a day in pattern recognition area. We will present some sample images and the results of clock recognition from all three methods.

  5. Generalization bounds of ERM-based learning processes for continuous-time Markov chains.

    Science.gov (United States)

    Zhang, Chao; Tao, Dacheng

    2012-12-01

    Many existing results on statistical learning theory are based on the assumption that samples are independently and identically distributed (i.i.d.). However, the assumption of i.i.d. samples is not suitable for practical application to problems in which samples are time dependent. In this paper, we are mainly concerned with the empirical risk minimization (ERM) based learning process for time-dependent samples drawn from a continuous-time Markov chain. This learning process covers many kinds of practical applications, e.g., the prediction for a time series and the estimation of channel state information. Thus, it is significant to study its theoretical properties including the generalization bound, the asymptotic convergence, and the rate of convergence. It is noteworthy that, since samples are time dependent in this learning process, the concerns of this paper cannot (at least straightforwardly) be addressed by existing methods developed under the sample i.i.d. assumption. We first develop a deviation inequality for a sequence of time-dependent samples drawn from a continuous-time Markov chain and present a symmetrization inequality for such a sequence. By using the resultant deviation inequality and symmetrization inequality, we then obtain the generalization bounds of the ERM-based learning process for time-dependent samples drawn from a continuous-time Markov chain. Finally, based on the resultant generalization bounds, we analyze the asymptotic convergence and the rate of convergence of the learning process.

  6. Learning with Literature in the EFL Classroom. Anglo-American Studies. Volume 49

    Science.gov (United States)

    Delanoy, Werner, Ed.; Eisenmann, Maria, Ed.; Matz, Frauke, Ed.

    2015-01-01

    "Learning with Literature in the EFL Classroom" provides a comprehensive, in-depth and state-of-the-art introduction to literature learning in EFL contexts. Paying attention to both theoretical and practical concerns, the study focuses on a wide range of literary genres, different age and ability groups and new topics for literature…

  7. Online Learning Solutions for Freeway Travel Time Prediction

    NARCIS (Netherlands)

    Van Lint, J.W.C.

    2008-01-01

    Providing travel time information to travelers on available route alternatives in traffic networks is widely believed to yield positive effects on individual drive behavior and (route/departure time) choice behavior, as well as on collective traffic operations in terms of, for example, overall time

  8. Business Faculty Time Management: Lessons Learned from the Trenches

    Science.gov (United States)

    Cummings, Richard G.; Holmes, Linda E.

    2009-01-01

    Teaching, research, and service expectations of the academic profession may sometimes seem overwhelming. Although much has been written about time management in general, there has not been much written about time management in the academic professions and even less written about time management for academics in the business disciplines. This paper…

  9. Trip Travel Time Forecasting Based on Selective Forgetting Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Zhiming Gui

    2014-01-01

    Full Text Available Travel time estimation on road networks is a valuable traffic metric. In this paper, we propose a machine learning based method for trip travel time estimation in road networks. The method uses the historical trip information extracted from taxis trace data as the training data. An optimized online sequential extreme machine, selective forgetting extreme learning machine, is adopted to make the prediction. Its selective forgetting learning ability enables the prediction algorithm to adapt to trip conditions changes well. Experimental results using real-life taxis trace data show that the forecasting model provides an effective and practical way for the travel time forecasting.

  10. Learning of pitch and time structures in an artificial grammar setting.

    Science.gov (United States)

    Prince, Jon B; Stevens, Catherine J; Jones, Mari Riess; Tillmann, Barbara

    2018-04-12

    Despite the empirical evidence for the power of the cognitive capacity of implicit learning of structures and regularities in several modalities and materials, it remains controversial whether implicit learning extends to the learning of temporal structures and regularities. We investigated whether (a) an artificial grammar can be learned equally well when expressed in duration sequences as when expressed in pitch sequences, (b) learning of the artificial grammar in either duration or pitch (as the primary dimension) sequences can be influenced by the properties of the secondary dimension (invariant vs. randomized), and (c) learning can be boosted when the artificial grammar is expressed in both pitch and duration. After an exposure phase with grammatical sequences, learning in a subsequent test phase was assessed in a grammaticality judgment task. Participants in both the pitch and duration conditions showed incidental (not fully implicit) learning of the artificial grammar when the secondary dimension was invariant, but randomizing the pitch sequence prevented learning of the artificial grammar in duration sequences. Expressing the artificial grammar in both pitch and duration resulted in disproportionately better performance, suggesting an interaction between the learning of pitch and temporal structure. The findings are relevant to research investigating the learning of temporal structures and the learning of structures presented simultaneously in 2 dimensions (e.g., space and time, space and objects). By investigating learning, the findings provide further insight into the potential specificity of pitch and time processing, and their integrated versus independent processing, as previously debated in music cognition research. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

  11. Identification of critical time-consuming student support activities in e-learning

    NARCIS (Netherlands)

    De Vries, Fred; Kester, Liesbeth; Sloep, Peter; Van Rosmalen, Peter; Pannekeet, Kees; Koper, Rob

    2005-01-01

    Please cite the original publication: De Vries, F., Kester, L., Sloep, P., Van Rosmalen, P., Pannekeet, K., & Koper, R. (2005). Identification of critical time-consuming student support activities in e-learning. Research in Learning Technology (ALT-J), 13(3), 219-229.

  12. The Role of Age and Occupational Future Time Perspective in Workers' Motivation to Learn

    Science.gov (United States)

    Kochoian, Nané; Raemdonck, Isabel; Frenay, Mariane; Zacher, Hannes

    2017-01-01

    The purpose of this paper is to better understand the relationship between employees' chronological age and their motivation to learn, by adopting a lifespan perspective. Based on socioemotional selectivity theory, we suggest that occupational future time perspective mediates the relationship between age and motivation to learn. In accordance with…

  13. Online discussion compensates for suboptimal timing of supportive information presentation in a digitally supported learning environment

    NARCIS (Netherlands)

    Noroozi, O.; Busstra, M.C.; Mulder, M.; Biemans, H.J.A.; Tobi, H.; Geelen, A.; Veer, van 't P.; Chizari, M.

    2012-01-01

    This study used a sequential set-up to investigate the consecutive effects of timing of supportive information presentation (information before vs. information during the learning task clusters) in interactive digital learning materials (IDLMs) and type of collaboration (personal discussion vs.

  14. Effects of Business School Student's Study Time on the Learning Process

    Science.gov (United States)

    Tetteh, Godson Ayertei

    2016-01-01

    Purpose: This paper aims to clarify the relationship between the student's study time and the learning process in the higher education system by adapting the total quality management (TQM) principles-process approach. Contrary to Deming's (1982) constancy of purpose to improve the learning process, some students in higher education postpone their…

  15. Flexible Learning and Teaching: Looking Beyond the Binary of Full-time/Part-time Provision in South African Higher Education

    Directory of Open Access Journals (Sweden)

    Barbara M Jones

    2015-06-01

    Full Text Available This paper engages with literature on flexible learning and teaching in order to explore whether it may be possible, within the South African context, to have flexible learning and teaching provide a third way which goes beyond the current practice of full-time/part-time provision. This binary classification of students is a proxy for day-time/after-hours delivery.  The argument is made that effective, flexible learning and teaching requires a fundamental shift in thinking about learning and teaching in higher education that moves us beyond such binaries. The paper proposes that in order to ensure access and success for students, ‘common knowledge’ (Edwards, 2010 will need to be co-constructed which understands flexible learning and teaching in ways which will meet needs of a diversity of students, including working students. It will require ‘resourceful leadership’ (Edwards, 2014 within the university that recognises, enhances and gives purpose to the capability of colleagues at every level of the systems they lead. Also, it will require the building of ‘common knowledge’ between certain sectors of universities and particular workplaces.

  16. Benefits for Voice Learning Caused by Concurrent Faces Develop over Time.

    Science.gov (United States)

    Zäske, Romi; Mühl, Constanze; Schweinberger, Stefan R

    2015-01-01

    Recognition of personally familiar voices benefits from the concurrent presentation of the corresponding speakers' faces. This effect of audiovisual integration is most pronounced for voices combined with dynamic articulating faces. However, it is unclear if learning unfamiliar voices also benefits from audiovisual face-voice integration or, alternatively, is hampered by attentional capture of faces, i.e., "face-overshadowing". In six study-test cycles we compared the recognition of newly-learned voices following unimodal voice learning vs. bimodal face-voice learning with either static (Exp. 1) or dynamic articulating faces (Exp. 2). Voice recognition accuracies significantly increased for bimodal learning across study-test cycles while remaining stable for unimodal learning, as reflected in numerical costs of bimodal relative to unimodal voice learning in the first two study-test cycles and benefits in the last two cycles. This was independent of whether faces were static images (Exp. 1) or dynamic videos (Exp. 2). In both experiments, slower reaction times to voices previously studied with faces compared to voices only may result from visual search for faces during memory retrieval. A general decrease of reaction times across study-test cycles suggests facilitated recognition with more speaker repetitions. Overall, our data suggest two simultaneous and opposing mechanisms during bimodal face-voice learning: while attentional capture of faces may initially impede voice learning, audiovisual integration may facilitate it thereafter.

  17. English made easy volume one a new ESL approach learning English through pictures

    CERN Document Server

    Crichton, Jonathan

    2015-01-01

    This is a fun and user–friendly way to learn English English Made Easy is a breakthrough in English language learning—imaginatively exploiting how pictures and text can work together to create understanding and help learners learn more productively. It gives beginner English learners easy access to the vocabulary, grammar and functions of English as it is actually used in a comprehensive range of social situations. Self–guided students and classroom learners alike will be delighted by the way they are helped to progress easily from one unit to the next, using a combination of pictures and text

  18. Impact of an electronic health record operating room management system in ophthalmology on documentation time, surgical volume, and staffing.

    Science.gov (United States)

    Sanders, David S; Read-Brown, Sarah; Tu, Daniel C; Lambert, William E; Choi, Dongseok; Almario, Bella M; Yackel, Thomas R; Brown, Anna S; Chiang, Michael F

    2014-05-01

    Although electronic health record (EHR) systems have potential benefits, such as improved safety and quality of care, most ophthalmology practices in the United States have not adopted these systems. Concerns persist regarding potential negative impacts on clinical workflow. In particular, the impact of EHR operating room (OR) management systems on clinical efficiency in the ophthalmic surgery setting is unknown. To determine the impact of an EHR OR management system on intraoperative nursing documentation time, surgical volume, and staffing requirements. For documentation time and circulating nurses per procedure, a prospective cohort design was used between January 10, 2012, and January 10, 2013. For surgical volume and overall staffing requirements, a case series design was used between January 29, 2011, and January 28, 2013. This study involved ophthalmic OR nurses (n = 13) and surgeons (n = 25) at an academic medical center. Electronic health record OR management system implementation. (1) Documentation time (percentage of operating time documenting [POTD], absolute documentation time in minutes), (2) surgical volume (procedures/time), and (3) staffing requirements (full-time equivalents, circulating nurses/procedure). Outcomes were measured during a baseline period when paper documentation was used and during the early (first 3 months) and late (4-12 months) periods after EHR implementation. There was a worsening in total POTD in the early EHR period (83%) vs paper baseline (41%) (P system implementation was associated with worsening of intraoperative nursing documentation time especially in shorter procedures. However, it is possible to implement an EHR OR management system without serious negative impacts on surgical volume and staffing requirements.

  19. Just-in-Time Training for High-Risk Low-Volume Therapies: An Approach to Ensure Patient Safety.

    Science.gov (United States)

    Helman, Stephanie; Lisanti, Amy Jo; Adams, Ann; Field, Cynthia; Davis, Katherine Finn

    2016-01-01

    High-risk low-volume therapies are those therapies that are practiced infrequently and yet carry an increased risk to patients because of their complexity. Staff nurses are required to competently manage these therapies to treat patients' unique needs and optimize outcomes; however, maintaining competence is challenging. This article describes implementation of Just-in-Time Training, which requires validation of minimum competency of bedside nurses managing high-risk low-volume therapies through direct observation of a return-demonstration competency checklist.

  20. Geological Time, Biological Events and the Learning Transfer Problem

    Science.gov (United States)

    Johnson, Claudia C.; Middendorf, Joan; Rehrey, George; Dalkilic, Mehmet M.; Cassidy, Keely

    2014-01-01

    Comprehension of geologic time does not come easily, especially for students who are studying the earth sciences for the first time. This project investigated the potential success of two teaching interventions that were designed to help non-science majors enrolled in an introductory geology class gain a richer conceptual understanding of the…

  1. Learning for sustainability in times of accelerating change

    NARCIS (Netherlands)

    Wals, A.E.J.; Corcoran, P.B.

    2012-01-01

    We live in turbulent times, our world is changing at accelerating speed. Information is everywhere, but wisdom appears in short supply when trying to address key inter-related challenges of our time such as; runaway climate change, the loss of biodiversity, the depletion of natural resources, the

  2. Time series forecasting based on deep extreme learning machine

    NARCIS (Netherlands)

    Guo, Xuqi; Pang, Y.; Yan, Gaowei; Qiao, Tiezhu; Yang, Guang-Hong; Yang, Dan

    2017-01-01

    Multi-layer Artificial Neural Networks (ANN) has caught widespread attention as a new method for time series forecasting due to the ability of approximating any nonlinear function. In this paper, a new local time series prediction model is established with the nearest neighbor domain theory, in

  3. Impact of learning adaptability and time management disposition on study engagement among Chinese baccalaureate nursing students.

    Science.gov (United States)

    Liu, Jing-Ying; Liu, Yan-Hui; Yang, Ji-Peng

    2014-01-01

    The aim of this study was to explore the relationships among study engagement, learning adaptability, and time management disposition in a sample of Chinese baccalaureate nursing students. A convenient sample of 467 baccalaureate nursing students was surveyed in two universities in Tianjin, China. Students completed a questionnaire that included their demographic information, Chinese Utrecht Work Engagement Scale-Student Questionnaire, Learning Adaptability Scale, and Adolescence Time Management Disposition Scale. One-way analysis of variance tests were used to assess the relationship between certain characteristics of baccalaureate nursing students. Pearson correlation was performed to test the correlation among study engagement, learning adaptability, and time management disposition. Hierarchical linear regression analyses were performed to explore the mediating role of time management disposition. The results revealed that study engagement (F = 7.20, P < .01) and learning adaptability (F = 4.41, P < .01) differed across grade groups. Learning adaptability (r = 0.382, P < .01) and time management disposition (r = 0.741, P < .01) were positively related with study engagement. Time management disposition had a partially mediating effect on the relationship between study engagement and learning adaptability. The findings implicate that educators should not only promote interventions to increase engagement of baccalaureate nursing students but also focus on development, investment in adaptability, and time management. Copyright © 2014 Elsevier Inc. All rights reserved.

  4. Changes in forced expiratory volume in 1 second over time in COPD

    DEFF Research Database (Denmark)

    Vestbo, Jørgen; Edwards, Lisa D; Scanlon, Paul D

    2011-01-01

    A key feature of chronic obstructive pulmonary disease (COPD) is an accelerated rate of decline in forced expiratory volume in 1 second (FEV(1)), but data on the variability and determinants of this change in patients who have established disease are scarce.......A key feature of chronic obstructive pulmonary disease (COPD) is an accelerated rate of decline in forced expiratory volume in 1 second (FEV(1)), but data on the variability and determinants of this change in patients who have established disease are scarce....

  5. Distributing learning over time: the spacing effect in children's acquisition and generalization of science concepts.

    Science.gov (United States)

    Vlach, Haley A; Sandhofer, Catherine M

    2012-01-01

    The spacing effect describes the robust finding that long-term learning is promoted when learning events are spaced out in time rather than presented in immediate succession. Studies of the spacing effect have focused on memory processes rather than for other types of learning, such as the acquisition and generalization of new concepts. In this study, early elementary school children (5- to 7-year-olds; N = 36) were presented with science lessons on 1 of 3 schedules: massed, clumped, and spaced. The results revealed that spacing lessons out in time resulted in higher generalization performance for both simple and complex concepts. Spaced learning schedules promote several types of learning, strengthening the implications of the spacing effect for educational practices and curriculum. © 2012 The Authors. Child Development © 2012 Society for Research in Child Development, Inc.

  6. Giving English Language Learners the Time They Need to Succeed: Profiles of Three Expanded Learning Time Schools

    Science.gov (United States)

    Farbman, David A.

    2015-01-01

    With the number of students who are English language learners (ELLs) likely to double in coming years, it is more important than ever for schools across the U.S. to design and implement educational practices and strategies that best meet ELLs' learning needs, says the report, "Giving English Language Learners the Time They Need to…

  7. Dissociable effects of practice variability on learning motor and timing skills.

    Science.gov (United States)

    Caramiaux, Baptiste; Bevilacqua, Frédéric; Wanderley, Marcelo M; Palmer, Caroline

    2018-01-01

    Motor skill acquisition inherently depends on the way one practices the motor task. The amount of motor task variability during practice has been shown to foster transfer of the learned skill to other similar motor tasks. In addition, variability in a learning schedule, in which a task and its variations are interweaved during practice, has been shown to help the transfer of learning in motor skill acquisition. However, there is little evidence on how motor task variations and variability schedules during practice act on the acquisition of complex motor skills such as music performance, in which a performer learns both the right movements (motor skill) and the right time to perform them (timing skill). This study investigated the impact of rate (tempo) variability and the schedule of tempo change during practice on timing and motor skill acquisition. Complete novices, with no musical training, practiced a simple musical sequence on a piano keyboard at different rates. Each novice was assigned to one of four learning conditions designed to manipulate the amount of tempo variability across trials (large or small tempo set) and the schedule of tempo change (randomized or non-randomized order) during practice. At test, the novices performed the same musical sequence at a familiar tempo and at novel tempi (testing tempo transfer), as well as two novel (but related) sequences at a familiar tempo (testing spatial transfer). We found that practice conditions had little effect on learning and transfer performance of timing skill. Interestingly, practice conditions influenced motor skill learning (reduction of movement variability): lower temporal variability during practice facilitated transfer to new tempi and new sequences; non-randomized learning schedule improved transfer to new tempi and new sequences. Tempo (rate) and the sequence difficulty (spatial manipulation) affected performance variability in both timing and movement. These findings suggest that there is a

  8. Making time for learning-oriented leadership in multidisciplinary hospital management groups.

    Science.gov (United States)

    Singer, Sara J; Hayes, Jennifer E; Gray, Garry C; Kiang, Mathew V

    2015-01-01

    Although the clinical requirements of health care delivery imply the need for interdisciplinary management teams to work together to promote frontline learning, such interdisciplinary, learning-oriented leadership is atypical. We designed this study to identify behaviors enabling groups of diverse managers to perform as learning-oriented leadership teams on behalf of quality and safety. We randomly selected 12 of 24 intact groups of hospital managers from one hospital to participate in a Safety Leadership Team Training program. We collected primary data from March 2008 to February 2010 including pre- and post-staff surveys, multiple interviews, observations, and archival data from management groups. We examined the level and trend in frontline perceptions of managers' learning-oriented leadership following the intervention and ability of management groups to achieve objectives on targeted improvement projects. Among the 12 intervention groups, we identified higher- and lower-performing intervention groups and behaviors that enabled higher performers to work together more successfully. Management groups that achieved more of their performance goals and whose staff perceived more and greater improvement in their learning-oriented leadership after participation in Safety Leadership Team Training invested in structures that created learning capacity and conscientiously practiced prescribed learning-oriented management and problem-solving behaviors. They made the time to do these things because they envisioned the benefits of learning, valued the opportunity to learn, and maintained an environment of mutual respect and psychological safety within their group. Learning in management groups requires vision of what learning can accomplish; will to explore, practice, and build learning capacity; and mutual respect that sustains a learning environment.

  9. Estimation of free volumes of polystyrene by positron annihilation life-time technique

    International Nuclear Information System (INIS)

    Li, Hong-Ling; Ujihira, Yusuke; Nanasawa, Atsushi.

    1996-01-01

    Differences of size, content, and size distribution of free volumes in linear and three-armed polystyrenes, synthesized by radical, and anionic processes, were observed by positron annihilation lifetime measurements. For the polystyrene samples of different architectures and molecular weight distributions, the temperature dependence of an average free volume radius was quite similar to each other. The radius increased with increasing temperature (T), from 0.27 nm (60 K) to 0.30 nm (glass transition temperature: T g = 363 K), then to 0.35 nm (423 K), showing αβ transition temperature about 300 K. With increasing T, the free volume content decreased from 35% (60 K) to 25% (260 K) for radically polymerized linear polystyrene and to 22% (320 K) for anionically polymerized three-armed polystyrene, and then turned to increase to 35% at 350 K and 400 K, respectively. In contrast, the content for anionically polymerized linear polystyrene decreased from 45% (60 K) to 33% (300 K) and turned to increase to 35% at 350-400 K. The free volume content decreased reciprocally with an increase in the molecular weight at 333 K, suggesting differences in molecular motion between the edge and middle portions of the chain molecule. (author)

  10. Changes in forced expiratory volume in 1 second over time in COPD

    DEFF Research Database (Denmark)

    Vestbo, Jørgen; Edwards, Lisa D; Scanlon, Paul D

    2011-01-01

    A key feature of chronic obstructive pulmonary disease (COPD) is an accelerated rate of decline in forced expiratory volume in 1 second (FEV(1)), but data on the variability and determinants of this change in patients who have established disease are scarce....

  11. Using Response Times to Assess Learning Progress: A Joint Model for Responses and Response Times

    Science.gov (United States)

    Wang, Shiyu; Zhang, Susu; Douglas, Jeff; Culpepper, Steven

    2018-01-01

    Analyzing students' growth remains an important topic in educational research. Most recently, Diagnostic Classification Models (DCMs) have been used to track skill acquisition in a longitudinal fashion, with the purpose to provide an estimate of students' learning trajectories in terms of the change of fine-grained skills overtime. Response time…

  12. Drusen volume development over time and its relevance to the course of age-related macular degeneration.

    Science.gov (United States)

    Schlanitz, Ferdinand G; Baumann, Bernhard; Kundi, Michael; Sacu, Stefan; Baratsits, Magdalena; Scheschy, Ulrike; Shahlaee, Abtin; Mittermüller, Tamara J; Montuoro, Alessio; Roberts, Philipp; Pircher, Michael; Hitzenberger, Christoph K; Schmidt-Erfurth, Ursula

    2017-02-01

    To quantify the change in drusen volume over time and identify its prognostic value for individual risk assessment. A prospective observational study over a minimum of 3 years and maximum of 5 years and follow-up examination every 3 months was conducted at the ophthalmology department of the Medical University of Vienna. 109 patients presenting early and intermediate age-related macular degeneration (AMD) were included, of which 30 patients concluded a regular follow-up for at least 3 years. 50 eyes of 30 patients were imaged every 3 months using spectral-domain and polarisation-sensitive optical coherence tomography (OCT). Drusen volume was measured using an automated algorithm. Data of a 6-month follow-up were segmented manually by expert graders. Gradings from 24 000 individual B-scans showed solid correlation between manual and automated segmentation with an initial mean drusen volume of 0.17 mm 3 . The increase in drusen volume was shown to be comparable among all eyes, and a model for long-term drusen volume development could be fitted as a cubic polynomial function and an R 2 =0.955. Spontaneous drusen regression was observed in 22 of 50 eyes. In this group, four eyes developed choroidal neovascularisation and three geographic atrophy. Drusen volume increase over time can be described by a cubic function. Spontaneous regression appears to precede conversion to advanced AMD. OCT might be a promising tool for predicting the individual risk of progression of AMD. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

  13. A Time to Define: Making the Specific Learning Disability Definition Prescribe Specific Learning Disability

    Science.gov (United States)

    Kavale, Kenneth A.; Spaulding, Lucinda S.; Beam, Andrea P.

    2009-01-01

    Unlike other special education categories defined in U.S. law (Individuals with Disabilities Education Act), the definition of specific learning disability (SLD) has not changed since first proposed in 1968. Thus, although the operational definition of SLD has responded to new knowledge and understanding about the construct, the formal definition…

  14. A presentation system for just-in-time learning in radiology.

    Science.gov (United States)

    Kahn, Charles E; Santos, Amadeu; Thao, Cheng; Rock, Jayson J; Nagy, Paul G; Ehlers, Kevin C

    2007-03-01

    There is growing interest in bringing medical educational materials to the point of care. We sought to develop a system for just-in-time learning in radiology. A database of 34 learning modules was derived from previously published journal articles. Learning objectives were specified for each module, and multiple-choice test items were created. A web-based system-called TEMPO-was developed to allow radiologists to select and view the learning modules. Web services were used to exchange clinical context information between TEMPO and the simulated radiology work station. Preliminary evaluation was conducted using the System Usability Scale (SUS) questionnaire. TEMPO identified learning modules that were relevant to the age, sex, imaging modality, and body part or organ system of the patient being viewed by the radiologist on the simulated clinical work station. Users expressed a high degree of satisfaction with the system's design and user interface. TEMPO enables just-in-time learning in radiology, and can be extended to create a fully functional learning management system for point-of-care learning in radiology.

  15. Carotid wall volume quantification from magnetic resonance images using deformable model fitting and learning-based correction of systematic errors

    International Nuclear Information System (INIS)

    Hameeteman, K; Niessen, W J; Klein, S; Van 't Klooster, R; Selwaness, M; Van der Lugt, A; Witteman, J C M

    2013-01-01

    We present a method for carotid vessel wall volume quantification from magnetic resonance imaging (MRI). The method combines lumen and outer wall segmentation based on deformable model fitting with a learning-based segmentation correction step. After selecting two initialization points, the vessel wall volume in a region around the bifurcation is automatically determined. The method was trained on eight datasets (16 carotids) from a population-based study in the elderly for which one observer manually annotated both the lumen and outer wall. An evaluation was carried out on a separate set of 19 datasets (38 carotids) from the same study for which two observers made annotations. Wall volume and normalized wall index measurements resulting from the manual annotations were compared to the automatic measurements. Our experiments show that the automatic method performs comparably to the manual measurements. All image data and annotations used in this study together with the measurements are made available through the website http://ergocar.bigr.nl. (paper)

  16. [A technological device for optimizing the time taken for blind people to learn Braille].

    Science.gov (United States)

    Hernández, Cesar; Pedraza, Luis F; López, Danilo

    2011-10-01

    This project was aimed at designing and putting an electronic prototype into practice for improving the initial time taken by visually handicapped people for learning Braille, especially children. This project was mainly based on a prototype digital electronic device which identifies and translates material written by a user in Braille by a voice synthesis system, producing artificial words to determine whether a handicapped person's writing in Braille has been correct. A global system for mobile communications (GSM) module was also incorporated into the device which allowed it to send text messages, thereby involving innovation in the field of articles for aiding visually handicapped people. This project's main result was an easily accessed and understandable prototype device which improved visually handicapped people's initial learning of Braille. The time taken for visually handicapped people to learn Braille became significantly reduced whilst their interest increased, as did their concentration time regarding such learning.

  17. Peer-assisted learning: time for nomenclature clarification

    Directory of Open Access Journals (Sweden)

    Alexander Olaussen

    2016-07-01

    Full Text Available Background: Peer-assisted learning (PAL is used throughout all levels of healthcare education. Lack of formalised agreement on different PAL programmes may confuse the literature. Given the increasing interest in PAL as an education philosophy, the terms need clarification. The aim of this review is to 1 describe different PAL programmes, 2 clarify the terminology surrounding PAL, and 3 propose a simple pragmatic way of defining PAL programmes based on their design. Methods: A review of current PAL programmes within the healthcare setting was conducted. Each programme was scrutinised based on two aspects: the relationship between student and teacher, and the student to teacher ratio. The studies were then shown to fit exclusively into the novel proposed classification. Results: The 34 programmes found, demonstrate a wide variety in terms used. We established six terms, which exclusively applied to the programmes. The relationship between student and teacher was categorised as peer-to-peer or near-peer. The student to teacher ratio suited three groupings, named intuitively ‘Mentoring’ (1:1 or 1:2, ‘Tutoring’ (1:3–10, and ‘Didactic’ (1:>10. From this, six novel terms – all under the heading of PAL – are suggested: ‘Peer Mentoring’, ‘Peer Tutoring’, ‘Peer Didactic’, ‘Near-Peer Mentoring’, ‘Near-Peer Tutoring’, and ‘Near-Peer Didactic’. Conclusions: We suggest herein a simple pragmatic terminology to overcome ambiguous terminology. Academically, clear terms will allow effective and efficient research, ensuring furthering of the educational philosophy.

  18. Peer-assisted learning: time for nomenclature clarification

    Science.gov (United States)

    Olaussen, Alexander; Reddy, Priya; Irvine, Susan; Williams, Brett

    2016-01-01

    Background Peer-assisted learning (PAL) is used throughout all levels of healthcare education. Lack of formalised agreement on different PAL programmes may confuse the literature. Given the increasing interest in PAL as an education philosophy, the terms need clarification. The aim of this review is to 1) describe different PAL programmes, 2) clarify the terminology surrounding PAL, and 3) propose a simple pragmatic way of defining PAL programmes based on their design. Methods A review of current PAL programmes within the healthcare setting was conducted. Each programme was scrutinised based on two aspects: the relationship between student and teacher, and the student to teacher ratio. The studies were then shown to fit exclusively into the novel proposed classification. Results The 34 programmes found, demonstrate a wide variety in terms used. We established six terms, which exclusively applied to the programmes. The relationship between student and teacher was categorised as peer-to-peer or near-peer. The student to teacher ratio suited three groupings, named intuitively ‘Mentoring’ (1:1 or 1:2), ‘Tutoring’ (1:3–10), and ‘Didactic’ (1:>10). From this, six novel terms – all under the heading of PAL – are suggested: ‘Peer Mentoring’, ‘Peer Tutoring’, ‘Peer Didactic’, ‘Near-Peer Mentoring’, ‘Near-Peer Tutoring’, and ‘Near-Peer Didactic’. Conclusions We suggest herein a simple pragmatic terminology to overcome ambiguous terminology. Academically, clear terms will allow effective and efficient research, ensuring furthering of the educational philosophy. PMID:27415590

  19. Early Life Stress-Related Elevations in Reaction Time Variability Are Associated with Brain Volume Reductions in HIV+ Adults

    Directory of Open Access Journals (Sweden)

    Uraina S. Clark

    2018-01-01

    Full Text Available There is burgeoning evidence that, among HIV+ adults, exposure to high levels of early life stress (ELS is associated with increased cognitive impairment as well as brain volume abnormalities and elevated neuropsychiatric symptoms. Currently, we have a limited understanding of the degree to which cognitive difficulties observed in HIV+ High-ELS samples reflect underlying neural abnormalities rather than increases in neuropsychiatric symptoms. Here, we utilized a behavioral marker of cognitive function, reaction time intra-individual variability (RT-IIV, which is sensitive to both brain volume reductions and neuropsychiatric symptoms, to elucidate the unique contributions of brain volume abnormalities and neuropsychiatric symptoms to cognitive difficulties in HIV+ High-ELS adults. We assessed the relation of RT-IIV to neuropsychiatric symptom levels and total gray and white matter volumes in 44 HIV+ adults (26 with high ELS. RT-IIV was examined during a working memory task. Self-report measures assessed current neuropsychiatric symptoms (depression, stress, post-traumatic stress disorder. Magnetic resonance imaging was used to quantify total gray and white matter volumes. Compared to Low-ELS participants, High-ELS participants exhibited elevated RT-IIV, elevated neuropsychiatric symptoms, and reduced gray and white matter volumes. Across the entire sample, RT-IIV was significantly associated with gray and white matter volumes, whereas significant associations with neuropsychiatric symptoms were not observed. In the High-ELS group, despite the presence of elevated neuropsychiatric symptom levels, brain volume reductions explained more than 13% of the variance in RT-IIV, whereas neuropsychiatric symptoms explained less than 1%. Collectively, these data provide evidence that, in HIV+ High-ELS adults, ELS-related cognitive difficulties (as indexed by RT-IIV exhibit strong associations with global brain volumes, whereas ELS-related elevations in

  20. An algorithm for learning real-time automata (extended abstract)

    NARCIS (Netherlands)

    Verwer, S.E.; De Weerdt, M.M.; Witteveen, C.

    2007-01-01

    A common model for discrete event systems is a deterministic finite automaton (DFA). An advantage of this model is that it can be interpreted by domain experts. When observing a real-world system, however, there often is more information than just the sequence of discrete events: the time at which

  1. No Time to Think: Policy, Pedagogy and Professional Learning

    Science.gov (United States)

    Leonard, Simon N.; Roberts, Philip

    2016-01-01

    In this study, we seek to illuminate the effects of the global policy convergence in education through a close study of its enactment within an Australian Teacher Education course. Building on an examination of the changing priorities of a cohort of pre-service teachers over a short space of time, we argue that the enactment of New Public…

  2. Time and Practice: Learning to Become a Geographer

    Science.gov (United States)

    Downs, Roger M.

    2014-01-01

    A goal of geography education is fostering geographic literacy for all and building significant expertise for some. How much time and practice do students need to become literate or expert in geography? There is not an answer to this question. Using two concepts from cognitive psychology--the ideas of ten thousand hours and deliberate…

  3. Knowledge collaboration & learning for sustainable innovation: an introduction to this special volume (Editorial)

    NARCIS (Netherlands)

    Huisingh, D.; Tukker, A.; Lozano, R.; Quist, J.

    2013-01-01

    This Special Volume is divided into two parts: the first part with seventeen articles (compiled by Rodrigo Lozano, Francisco J. Lozano, Karel Mulder, Donald Huisingh, and Tom Waas) focuses on the EMSU conference stream; whilst the second part with eleven papers (compiled by Jaco Quist and Arnold

  4. Problem based learning: the effect of real time data on the website to student independence

    Science.gov (United States)

    Setyowidodo, I.; Pramesti, Y. S.; Handayani, A. D.

    2018-05-01

    Learning science developed as an integrative science rather than disciplinary education, the reality of the nation character development has not been able to form a more creative and independent Indonesian man. Problem Based Learning based on real time data in the website is a learning method focuses on developing high-level thinking skills in problem-oriented situations by integrating technology in learning. The essence of this study is the presentation of authentic problems in the real time data situation in the website. The purpose of this research is to develop student independence through Problem Based Learning based on real time data in website. The type of this research is development research with implementation using purposive sampling technique. Based on the study there is an increase in student self-reliance, where the students in very high category is 47% and in the high category is 53%. This learning method can be said to be effective in improving students learning independence in problem-oriented situations.

  5. The Effect of Inquiry Training Learning Model Based on Just in Time Teaching for Problem Solving Skill

    Science.gov (United States)

    Turnip, Betty; Wahyuni, Ida; Tanjung, Yul Ifda

    2016-01-01

    One of the factors that can support successful learning activity is the use of learning models according to the objectives to be achieved. This study aimed to analyze the differences in problem-solving ability Physics student learning model Inquiry Training based on Just In Time Teaching [JITT] and conventional learning taught by cooperative model…

  6. The effect of amyloid pathology and glucose metabolism on cortical volume loss over time in Alzheimer's disease

    Energy Technology Data Exchange (ETDEWEB)

    Adriaanse, Sofie M. [VU University Medical Center, Department of Radiology and Nuclear Medicine, Amsterdam (Netherlands); VU University Medical Center, Department of Radiology and Nuclear Medicine, Alzheimer Center, Neuroscience Campus Amsterdam, P.O. Box 7057, Amsterdam (Netherlands); Van Dijk, Koene R.A. [Harvard University, Department of Psychology, Center for Brain Science, Cambridge, MA (United States); Massachusetts General Hospital, Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA (United States); Ossenkoppele, Rik; Tolboom, Nelleke; Zwan, Marissa D.; Barkhof, Frederik; Berckel, Bart N.M. van [VU University Medical Center, Department of Radiology and Nuclear Medicine, Alzheimer Center, Neuroscience Campus Amsterdam, Amsterdam (Netherlands); Reuter, Martin [Massachusetts General Hospital, Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA (United States); Massachusetts Institute of Technology, Computer Science and Artificial Intelligence Laboratory, Division of Health Sciences and Technology, Cambridge, MA (United States); Yaqub, Maqsood; Boellaard, Ronald; Windhorst, Albert D.; Lammertsma, Adriaan A. [VU University Medical Center, Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, Amsterdam (Netherlands); Flier, Wiesje M. van der; Scheltens, Philip [VU University Medical Center, Department of Neurology, Alzheimer Center, Neuroscience Campus Amsterdam, Amsterdam (Netherlands)

    2014-06-15

    The present multimodal neuroimaging study examined whether amyloid pathology and glucose metabolism are related to cortical volume loss over time in Alzheimer's disease (AD) patients and healthy elderly controls. Structural MRI scans of eleven AD patients and ten controls were available at baseline and follow-up (mean interval 2.5 years). Change in brain structure over time was defined as percent change of cortical volume within seven a-priori defined regions that typically show the strongest structural loss in AD. In addition, two PET scans were performed at baseline: [{sup 11}C]PIB to assess amyloid-β plaque load and [{sup 18}F]FDG to assess glucose metabolism. [{sup 11}C]PIB binding and [{sup 18}F]FDG uptake were measured in the precuneus, a region in which both amyloid deposition and glucose hypometabolism occur early in the course of AD. While amyloid-β plaque load at baseline was not related to cortical volume loss over time in either group, glucose metabolism within the group of AD patients was significantly related to volume loss over time (rho = 0.56, p < 0.05). The present study shows that in a group of AD patients amyloid-β plaque load as measured by [{sup 11}C]PIB behaves as a trait marker (i.e., all AD patients showed elevated levels of amyloid, not related to subsequent disease course), whilst hypometabolism as measured by [{sup 18}F]FDG changed over time indicating that it could serve as a state marker that is predictive of neurodegeneration. (orig.)

  7. Thermosphere-ionosphere-mesosphere energetics and dynamics (TIMED). The TIMED mission and science program report of the science definition team. Volume 1: Executive summary

    Science.gov (United States)

    1991-01-01

    A Science Definition Team was established in December 1990 by the Space Physics Division, NASA, to develop a satellite program to conduct research on the energetics, dynamics, and chemistry of the mesosphere and lower thermosphere/ionosphere. This two-volume publication describes the TIMED (Thermosphere-Ionosphere-Mesosphere, Energetics and Dynamics) mission and associated science program. The report outlines the scientific objectives of the mission, the program requirements, and the approach towards meeting these requirements.

  8. Higher Volume at Time of Breast Conserving Surgery Reduces Re-Excision in DCIS

    Directory of Open Access Journals (Sweden)

    J. H. Wolf

    2011-01-01

    Full Text Available Purpose. The purpose of this study was to compare the surgical and pathological variables which impact rate of re-excision following breast conserving therapy (BCS with or without concurrent additional margin excision (AM. Methods. The pathology database was queried for all patients with DCIS from January 2004 to September 2008. Pathologic assessment included volume of excision, subtype, size, distance from margin, grade, necrosis, multifocality, calcifications, and ER/PR status. Results. 405 cases were identified and 201 underwent BCS, 151-BCS-AM, and 53-mastectomy. Among the 201 BCS patients, 190 underwent re-excision for close or involved margins. 129 of these were treated with BCS and 61 with BCS-AM (P<.0001. The incidence of residual DCIS in the re-excision specimens was 32% (n=65 for BCS and 22% (n=33 for BCS-AM (P<.05. For both the BCS and the BCS-AM cohorts, volume of tissue excised is inversely correlated to the rate of re-excision (P=.0284. Multifocality (P=.0002 and ER status (P=.0382 were also significant predictors for rate of re-excision and variation in surgical technique was insignificant. Conclusions. The rate of positive margins, re-excision, and residual disease was significantly higher in patients with lower volume of excision. The performance of concurrent additional margin excision increases the efficacy of BCS for DCIS.

  9. Rhythm and timing in autism: Learning to dance

    Directory of Open Access Journals (Sweden)

    Pat eAmos

    2013-04-01

    Full Text Available In recent years, a significant body of research has focused on challenges to neural connectivity as a key to understanding autism. In contrast to attempts to identify a single static, primarily brain-based deficit, children and adults diagnosed with autism are increasingly perceived as out of sync with their internal and external environments in dynamic ways that must also involve operations of the peripheral nervous systems. The noisiness that seems to occur in both directions of neural flow may help explain challenges to movement and sensing, and ultimately to entrainment with circadian rhythms and social interactions. across the autism spectrum. Profound differences in the rhythm and timing of movement have been tracked to infancy. Difficulties with self-synchrony inhibit praxis, and can disrupt the dance of relationships through which caregiver and child build meaning. Different sensory aspects of a situation may fail to match up; ultimately, intentions and actions themselves may be uncoupled. This uncoupling may help explain the expressions of alienation from the actions of one’s body which recur in the autobiographical autism literature. Multi-modal/cross-modal coordination of different types of sensory information into coherent events may be difficult to achieve because amodal properties (e.g. rhythm and tempo that help unite perceptions are unreliable. One question posed to the connectivity research concerns the role of rhythm and timing in this operation, and whether these can be mobilized to reduce overload and enhance performance. A case is made for developmental research addressing how people with autism actively explore and make sense of their environments. The parent/author recommends investigating approaches such as scaffolding interactions via rhythm, following the person’s lead, slowing the pace, discriminating between intentional communication and stray motor patterns, and organizing information through one sensory mode at

  10. Aptitude for Destruction. Volume 1: Organizational Learning in Terrorist Groups and Its Implications for Combating Terrorism

    Science.gov (United States)

    2005-01-01

    jihad in Af- ghanistan. Over its history, the group has had active cells in several different coun- tries, including Indonesia, Malaysia , the...and South Armagh, London, UK: Coronet Books, LIR, 2000. Hedberg, Bo, "How Organizations Learn and Unlearn?" in Paul C. Nystrim and William H. Starbuck

  11. Special nuclear materials cutoff exercise: Issues and lessons learned. Volume 3

    Energy Technology Data Exchange (ETDEWEB)

    Libby, R.A.; Segal, J.E.; Stanbro, W.D.; Davis, C.

    1995-08-01

    This document is appendices D-J for the Special Nuclear Materials Cutoff Exercise: Issues and Lessons Learned. Included are discussions of the US IAEA Treaty, safeguard regulations for nuclear materials, issue sheets for the PUREX process, and the LANL follow up activity for reprocessing nuclear materials.

  12. Special nuclear materials cutoff exercise: Issues and lessons learned. Volume 3

    International Nuclear Information System (INIS)

    Libby, R.A.; Segal, J.E.; Stanbro, W.D.; Davis, C.

    1995-08-01

    This document is appendices D-J for the Special Nuclear Materials Cutoff Exercise: Issues and Lessons Learned. Included are discussions of the US IAEA Treaty, safeguard regulations for nuclear materials, issue sheets for the PUREX process, and the LANL follow up activity for reprocessing nuclear materials

  13. Service-Learning. National Dropout Prevention Center/Network Newsletter. Volume 22, Number 4

    Science.gov (United States)

    Duckenfield, Marty, Ed.

    2011-01-01

    The "National Dropout Prevention Newsletter" is published quarterly by the National Dropout Prevention Center/Network. This issue contains the following articles: (1) Dropouts and Democracy (Robert Shumer); (2) 2011 NDPN Crystal Star Winners; (3) Service-Learning as Dropout Intervention and More (Michael VanKeulen); and (4) Teacher…

  14. Learning in Video Game Affinity Spaces. New Literacies and Digital Epistemologies. Volume 51

    Science.gov (United States)

    Hayes, Elisabeth R., Ed.; Duncan, Sean C., Ed.

    2012-01-01

    As video games have become an important economic and cultural force, scholars are increasingly trying to better understand the ways that engagement with games may drive learning, literacy, and social participation in the twenty-first century. In this book, the authors consider games and just as importantly, the social interactions around games,…

  15. SU-C-BRA-05: Delineating High-Dose Clinical Target Volumes for Head and Neck Tumors Using Machine Learning Algorithms

    Energy Technology Data Exchange (ETDEWEB)

    Cardenas, C [Department of Radiation Physics, The University of Texas M.D. Anderson Cancer Center, Houston, TX (United States); The University of Texas Graduate School of Biomedical Sciences, Houston, TX (United States); Wong, A [Department of Radiation Oncology, The University of Texas M.D. Anderson Cancer Center, Houston, TX (United States); School of Medicine, The University of Texas Health Sciences Center at San Antonio, San Antonio, TX (United States); Mohamed, A; Fuller, C [Department of Radiation Oncology, The University of Texas M.D. Anderson Cancer Center, Houston, TX (United States); Yang, J; Court, L; Aristophanous, M [Department of Radiation Physics, The University of Texas M.D. Anderson Cancer Center, Houston, TX (United States); Rao, A [Department of Bioinformatics and Computational Biology, The University of Texas M.D. Anderson Cancer Center, Houston, TX (United States)

    2016-06-15

    Purpose: To develop and test population-based machine learning algorithms for delineating high-dose clinical target volumes (CTVs) in H&N tumors. Automating and standardizing the contouring of CTVs can reduce both physician contouring time and inter-physician variability, which is one of the largest sources of uncertainty in H&N radiotherapy. Methods: Twenty-five node-negative patients treated with definitive radiotherapy were selected (6 right base of tongue, 11 left and 9 right tonsil). All patients had GTV and CTVs manually contoured by an experienced radiation oncologist prior to treatment. This contouring process, which is driven by anatomical, pathological, and patient specific information, typically results in non-uniform margin expansions about the GTV. Therefore, we tested two methods to delineate high-dose CTV given a manually-contoured GTV: (1) regression-support vector machines(SVM) and (2) classification-SVM. These models were trained and tested on each patient group using leave-one-out cross-validation. The volume difference(VD) and Dice similarity coefficient(DSC) between the manual and auto-contoured CTV were calculated to evaluate the results. Distances from GTV-to-CTV were computed about each patient’s GTV and these distances, in addition to distances from GTV to surrounding anatomy in the expansion direction, were utilized in the regression-SVM method. The classification-SVM method used categorical voxel-information (GTV, selected anatomical structures, else) from a 3×3×3cm3 ROI centered about the voxel to classify voxels as CTV. Results: Volumes for the auto-contoured CTVs ranged from 17.1 to 149.1cc and 17.4 to 151.9cc; the average(range) VD between manual and auto-contoured CTV were 0.93 (0.48–1.59) and 1.16(0.48–1.97); while average(range) DSC values were 0.75(0.59–0.88) and 0.74(0.59–0.81) for the regression-SVM and classification-SVM methods, respectively. Conclusion: We developed two novel machine learning methods to delineate

  16. SU-C-BRA-05: Delineating High-Dose Clinical Target Volumes for Head and Neck Tumors Using Machine Learning Algorithms

    International Nuclear Information System (INIS)

    Cardenas, C; Wong, A; Mohamed, A; Fuller, C; Yang, J; Court, L; Aristophanous, M; Rao, A

    2016-01-01

    Purpose: To develop and test population-based machine learning algorithms for delineating high-dose clinical target volumes (CTVs) in H&N tumors. Automating and standardizing the contouring of CTVs can reduce both physician contouring time and inter-physician variability, which is one of the largest sources of uncertainty in H&N radiotherapy. Methods: Twenty-five node-negative patients treated with definitive radiotherapy were selected (6 right base of tongue, 11 left and 9 right tonsil). All patients had GTV and CTVs manually contoured by an experienced radiation oncologist prior to treatment. This contouring process, which is driven by anatomical, pathological, and patient specific information, typically results in non-uniform margin expansions about the GTV. Therefore, we tested two methods to delineate high-dose CTV given a manually-contoured GTV: (1) regression-support vector machines(SVM) and (2) classification-SVM. These models were trained and tested on each patient group using leave-one-out cross-validation. The volume difference(VD) and Dice similarity coefficient(DSC) between the manual and auto-contoured CTV were calculated to evaluate the results. Distances from GTV-to-CTV were computed about each patient’s GTV and these distances, in addition to distances from GTV to surrounding anatomy in the expansion direction, were utilized in the regression-SVM method. The classification-SVM method used categorical voxel-information (GTV, selected anatomical structures, else) from a 3×3×3cm3 ROI centered about the voxel to classify voxels as CTV. Results: Volumes for the auto-contoured CTVs ranged from 17.1 to 149.1cc and 17.4 to 151.9cc; the average(range) VD between manual and auto-contoured CTV were 0.93 (0.48–1.59) and 1.16(0.48–1.97); while average(range) DSC values were 0.75(0.59–0.88) and 0.74(0.59–0.81) for the regression-SVM and classification-SVM methods, respectively. Conclusion: We developed two novel machine learning methods to delineate

  17. The time course of ethanol tolerance: associative learning

    Directory of Open Access Journals (Sweden)

    J.L.O. Bueno

    2007-11-01

    Full Text Available The effect of different contextual stimuli on different ethanol-induced internal states was investigated during the time course of both the hypothermic effect of the drug and of drug tolerance. Minimitters were surgically implanted in 16 Wistar rats to assess changes in their body temperature under the effect of ethanol. Rat groups were submitted to ethanol or saline trials every other day. The animals were divided into two groups, one receiving a constant dose (CD of ethanol injected intraperitoneally, and the other receiving increasing doses (ID during the 10 training sessions. During the ethanol training sessions, conditioned stimuli A (tone and B (buzzer were presented at "state +" (35 min after drug injection and "state -" (170 min after drug injection, respectively. Conditioned stimuli C (bip and D (white noise were presented at moments equivalent to stimuli A and B, respectively, but during the saline training sessions. All stimuli lasted 15 min. The CD group, but not the ID group, developed tolerance to the hypothermic effect of ethanol. Stimulus A (associated with drug "state +" induced hyperthermia with saline injection in the ID group. Stimulus B (associated with drug "state -" reduced ethanol tolerance in the CD group and modulated the hypothermic effect of the drug in the ID group. These results indicate that contextual stimuli acquire modulatory conditioned properties that are associated with the time course of both the action of the drug and the development of drug tolerance.

  18. TEXPLORE temporal difference reinforcement learning for robots and time-constrained domains

    CERN Document Server

    Hester, Todd

    2013-01-01

    This book presents and develops new reinforcement learning methods that enable fast and robust learning on robots in real-time. Robots have the potential to solve many problems in society, because of their ability to work in dangerous places doing necessary jobs that no one wants or is able to do. One barrier to their widespread deployment is that they are mainly limited to tasks where it is possible to hand-program behaviors for every situation that may be encountered. For robots to meet their potential, they need methods that enable them to learn and adapt to novel situations that they were not programmed for. Reinforcement learning (RL) is a paradigm for learning sequential decision making processes and could solve the problems of learning and adaptation on robots. This book identifies four key challenges that must be addressed for an RL algorithm to be practical for robotic control tasks. These RL for Robotics Challenges are: 1) it must learn in very few samples; 2) it must learn in domains with continuou...

  19. The time course of location-avoidance learning in fear of spiders.

    Science.gov (United States)

    Rinck, Mike; Koene, Marieke; Telli, Sibel; Moerman-van den Brink, Wiltine; Verhoeven, Barbara; Becker, Eni S

    2016-01-01

    Two experiments were designed to study the time course of avoidance learning in spider fearfuls (SFs) under controlled experimental conditions. To achieve this, we employed an immersive virtual environment (IVE): While walking freely through a virtual art museum to search for specific paintings, the participants were exposed to virtual spiders. Unbeknown to the participants, only two of four museum rooms contained spiders, allowing for avoidance learning. Indeed, the more SF the participants were, the faster they learned to avoid the rooms that contained spiders (Experiment. 1), and within the first six trials, high fearfuls already developed a preference for starting their search task in rooms without spiders (Experiment 2). These results illustrate the time course of avoidance learning in SFs, and they speak to the usefulness of IVEs in fundamental anxiety research.

  20. Time course influences transfer of visual perceptual learning across spatial location.

    Science.gov (United States)

    Larcombe, S J; Kennard, C; Bridge, H

    2017-06-01

    Visual perceptual learning describes the improvement of visual perception with repeated practice. Previous research has established that the learning effects of perceptual training may be transferable to untrained stimulus attributes such as spatial location under certain circumstances. However, the mechanisms involved in transfer have not yet been fully elucidated. Here, we investigated the effect of altering training time course on the transferability of learning effects. Participants were trained on a motion direction discrimination task or a sinusoidal grating orientation discrimination task in a single visual hemifield. The 4000 training trials were either condensed into one day, or spread evenly across five training days. When participants were trained over a five-day period, there was transfer of learning to both the untrained visual hemifield and the untrained task. In contrast, when the same amount of training was condensed into a single day, participants did not show any transfer of learning. Thus, learning time course may influence the transferability of perceptual learning effects. Copyright © 2017 Elsevier Ltd. All rights reserved.

  1. Enhancement of Online Robotics Learning Using Real-Time 3D Visualization Technology

    OpenAIRE

    Richard Chiou; Yongjin (james) Kwon; Tzu-Liang (bill) Tseng; Robin Kizirian; Yueh-Ting Yang

    2010-01-01

    This paper discusses a real-time e-Lab Learning system based on the integration of 3D visualization technology with a remote robotic laboratory. With the emergence and development of the Internet field, online learning is proving to play a significant role in the upcoming era. In an effort to enhance Internet-based learning of robotics and keep up with the rapid progression of technology, a 3- Dimensional scheme of viewing the robotic laboratory has been introduced in addition to the remote c...

  2. A real-time articulatory visual feedback approach with target presentation for second language pronunciation learning.

    Science.gov (United States)

    Suemitsu, Atsuo; Dang, Jianwu; Ito, Takayuki; Tiede, Mark

    2015-10-01

    Articulatory information can support learning or remediating pronunciation of a second language (L2). This paper describes an electromagnetic articulometer-based visual-feedback approach using an articulatory target presented in real-time to facilitate L2 pronunciation learning. This approach trains learners to adjust articulatory positions to match targets for a L2 vowel estimated from productions of vowels that overlap in both L1 and L2. Training of Japanese learners for the American English vowel /æ/ that included visual training improved its pronunciation regardless of whether audio training was also included. Articulatory visual feedback is shown to be an effective method for facilitating L2 pronunciation learning.

  3. Learning characteristics of a space-time neural network as a tether skiprope observer

    Science.gov (United States)

    Lea, Robert N.; Villarreal, James A.; Jani, Yashvant; Copeland, Charles

    1993-01-01

    The Software Technology Laboratory at the Johnson Space Center is testing a Space Time Neural Network (STNN) for observing tether oscillations present during retrieval of a tethered satellite. Proper identification of tether oscillations, known as 'skiprope' motion, is vital to safe retrieval of the tethered satellite. Our studies indicate that STNN has certain learning characteristics that must be understood properly to utilize this type of neural network for the tethered satellite problem. We present our findings on the learning characteristics including a learning rate versus momentum performance table.

  4. Learning Constructive Primitives for Real-time Dynamic Difficulty Adjustment in Super Mario Bros

    OpenAIRE

    Shi, Peizhi; Chen, Ke

    2017-01-01

    Among the main challenges in procedural content generation (PCG), content quality assurance and dynamic difficulty adjustment (DDA) of game content in real time are two major issues concerned in adaptive content generation. Motivated by the recent learning-based PCG framework, we propose a novel approach to seamlessly address two issues in Super Mario Bros (SMB). To address the quality assurance issue, we exploit the synergy between rule-based and learning-based methods to produce quality gam...

  5. Why Hong Kong students favour more face-to-face classroom time in blended learning

    OpenAIRE

    Henri,James; Lee,Sandra

    2007-01-01

    A three year study in student characteristics, needs and learning styles guided instructors at the University of Hong Kong Faculty of Education to improve teaching and learning in a core module: Information Literacy. A mixed-method approach analyzed data collected from undergraduate, in-service teachers in a BEd program, and helped instructors in the program to gain insight into the Hong Kong teacher working, post-service towards a BEd in Library and Information Science. Part-time students in...

  6. A Study of Time Spent Working at Learning Centers. Technical Report #17.

    Science.gov (United States)

    Omori, Sharon; And Others

    This study examined the proportion of time children in the Kamehameha Early Education Program schools spend at actual school work in learning centers. Systematic time-sampled observations using multiple observers were conducted in December-January and again in March-April. The subjects, 12 children (6 kindergarteners and 6 first graders) were…

  7. An Integrated Theory of Prospective Time Interval Estimation: The Role of Cognition, Attention, and Learning

    Science.gov (United States)

    Taatgen, Niels A.; van Rijn, Hedderik; Anderson, John

    2007-01-01

    A theory of prospective time perception is introduced and incorporated as a module in an integrated theory of cognition, thereby extending existing theories and allowing predictions about attention and learning. First, a time perception module is established by fitting existing datasets (interval estimation and bisection and impact of secondary…

  8. Learning Management System Calendar Reminders and Effects on Time Management and Academic Performance

    Science.gov (United States)

    Mei, Jianyang

    2016-01-01

    This research project uses a large research university in the Midwest as a research site to explore the time management skills of international students and analyzes how using the Course Hack, an online Learning Management System (LMS) calendar tool, improves participants' time management skills and positively impacts their academic performance,…

  9. Evaluation of Online Log Variables That Estimate Learners' Time Management in a Korean Online Learning Context

    Science.gov (United States)

    Jo, Il-Hyun; Park, Yeonjeong; Yoon, Meehyun; Sung, Hanall

    2016-01-01

    The purpose of this study was to identify the relationship between the psychological variables and online behavioral patterns of students, collected through a learning management system (LMS). As the psychological variable, time and study environment management (TSEM), one of the sub-constructs of MSLQ, was chosen to verify a set of time-related…

  10. Cerebellar motor learning versus cerebellar motor timing: the climbing fibre story

    Science.gov (United States)

    Llinás, Rodolfo R

    2011-01-01

    Abstract Theories concerning the role of the climbing fibre system in motor learning, as opposed to those addressing the olivocerebellar system in the organization of motor timing, are briefly contrasted. The electrophysiological basis for the motor timing hypothesis in relation to the olivocerebellar system is treated in detail. PMID:21486816

  11. Time-place learning over a lifetime : Absence of memory loss in trained old mice

    NARCIS (Netherlands)

    Mulder, Cornelis K; Reckman, Gerlof A R; Gerkema, Menno P; van der Zee, Eddy A

    Time-place learning (TPL) offers the possibility to study the functional interaction between cognition and the circadian system with aging. With TPL, animals link biological significant events with the location and the time of day. This what-where-when type of memory provides animals with an

  12. A longitudinal study on time perspectives: relations with academic delay of gratification and learning environment

    NARCIS (Netherlands)

    Peetsma, T.; Schuitema, J.; van der Veen, I.

    2012-01-01

    After they start secondary school (at age 12 in the Netherlands), students' time perspectives on school and professional career and self-regulated learning decrease, while their perspectives on leisure increase. We aimed to investigate relations in the developments in time perspectives and delay of

  13. An integrated theory of prospective time interval estimation : The role of cognition, attention, and learning

    NARCIS (Netherlands)

    Taatgen, Niels A.; van Rijn, Hedderik; Anderson, John

    A theory of prospective time perception is introduced and incorporated as a module in an integrated theory of cognition, thereby extending existing theories and allowing predictions about attention and learning. First, a time perception module is established by fitting existing datasets (interval

  14. Time-Place Learning over a Lifetime: Absence of Memory Loss in Trained Old Mice

    Science.gov (United States)

    Mulder, Cornelis K.; Reckman, Gerlof A. R.; Gerkema, Menno P.; Van der Zee, Eddy A.

    2015-01-01

    Time-place learning (TPL) offers the possibility to study the functional interaction between cognition and the circadian system with aging. With TPL, animals link biological significant events with the location and the time of day. This what-where-when type of memory provides animals with an experience-based daily schedule. Mice were tested for…

  15. Real-time yield estimation based on deep learning

    Science.gov (United States)

    Rahnemoonfar, Maryam; Sheppard, Clay

    2017-05-01

    Crop yield estimation is an important task in product management and marketing. Accurate yield prediction helps farmers to make better decision on cultivation practices, plant disease prevention, and the size of harvest labor force. The current practice of yield estimation based on the manual counting of fruits is very time consuming and expensive process and it is not practical for big fields. Robotic systems including Unmanned Aerial Vehicles (UAV) and Unmanned Ground Vehicles (UGV), provide an efficient, cost-effective, flexible, and scalable solution for product management and yield prediction. Recently huge data has been gathered from agricultural field, however efficient analysis of those data is still a challenging task. Computer vision approaches currently face diffident challenges in automatic counting of fruits or flowers including occlusion caused by leaves, branches or other fruits, variance in natural illumination, and scale. In this paper a novel deep convolutional network algorithm was developed to facilitate the accurate yield prediction and automatic counting of fruits and vegetables on the images. Our method is robust to occlusion, shadow, uneven illumination and scale. Experimental results in comparison to the state-of-the art show the effectiveness of our algorithm.

  16. Examining the Effect of Time Constraint on the Online Mastery Learning Approach towards Improving Postgraduate Students' Achievement

    Science.gov (United States)

    Ee, Mong Shan; Yeoh, William; Boo, Yee Ling; Boulter, Terry

    2018-01-01

    Time control plays a critical role within the online mastery learning (OML) approach. This paper examines the two commonly implemented mastery learning strategies--personalised system of instructions and learning for mastery (LFM)--by focusing on what occurs when there is an instructional time constraint. Using a large data set from a postgraduate…

  17. Reinforcement learning using a continuous time actor-critic framework with spiking neurons.

    Directory of Open Access Journals (Sweden)

    Nicolas Frémaux

    2013-04-01

    Full Text Available Animals repeat rewarded behaviors, but the physiological basis of reward-based learning has only been partially elucidated. On one hand, experimental evidence shows that the neuromodulator dopamine carries information about rewards and affects synaptic plasticity. On the other hand, the theory of reinforcement learning provides a framework for reward-based learning. Recent models of reward-modulated spike-timing-dependent plasticity have made first steps towards bridging the gap between the two approaches, but faced two problems. First, reinforcement learning is typically formulated in a discrete framework, ill-adapted to the description of natural situations. Second, biologically plausible models of reward-modulated spike-timing-dependent plasticity require precise calculation of the reward prediction error, yet it remains to be shown how this can be computed by neurons. Here we propose a solution to these problems by extending the continuous temporal difference (TD learning of Doya (2000 to the case of spiking neurons in an actor-critic network operating in continuous time, and with continuous state and action representations. In our model, the critic learns to predict expected future rewards in real time. Its activity, together with actual rewards, conditions the delivery of a neuromodulatory TD signal to itself and to the actor, which is responsible for action choice. In simulations, we show that such an architecture can solve a Morris water-maze-like navigation task, in a number of trials consistent with reported animal performance. We also use our model to solve the acrobot and the cartpole problems, two complex motor control tasks. Our model provides a plausible way of computing reward prediction error in the brain. Moreover, the analytically derived learning rule is consistent with experimental evidence for dopamine-modulated spike-timing-dependent plasticity.

  18. On learning science and pseudoscience from prime-time television programming

    Science.gov (United States)

    Whittle, Christopher Henry

    The purpose of the present dissertation is to determine whether the viewing of two particular prime-time television programs, ER and The X-Files, increases viewer knowledge of science and to identify factors that may influence learning from entertainment television programming. Viewer knowledge of scientific dialogue from two science-based prime-time television programs, ER, a serial drama in a hospital emergency room and The X-Files, a drama about two Federal Bureau of Investigation agents who pursue alleged extraterrestrial life and paranormal activity, is studied. Level of viewing, education level, science education level, experiential factors, level of parasocial interaction, and demographic characteristics are assessed as independent variables affecting learning from entertainment television viewing. The present research involved a nine-month long content analysis of target television program dialogue and data collection from an Internet-based survey questionnaire posted to target program-specific on-line "chat" groups. The present study demonstrated that entertainment television program viewers incidentally learn science from entertainment television program dialogue. The more they watch, the more they learn. Viewing a pseudoscientific fictional television program does necessarily influence viewer beliefs in pseudoscience. Higher levels of formal science study are reflected in more science learning and less learning of pseudoscience from entertainment television program viewing. Pseudoscience learning from entertainment television programming is significantly related to experience with paranormal phenomena, higher levels of viewer parasocial interaction, and specifically, higher levels of cognitive parasocial interaction. In summary, the greater a viewer's understanding of science the more they learn when they watch their favorite science-based prime-time television programs. Viewers of pseudoscience-based prime-time television programming with higher levels

  19. The effect of future time perspective on delay discounting is mediated by the gray matter volume of vmPFC.

    Science.gov (United States)

    Guo, Yiqun; Chen, Zhiyi; Feng, Tingyong

    2017-07-28

    Although several previous studies have shown that individuals' attitude towards time could affect their intertemporal preference, little is known about the neural basis of the relation between time perspective (TP) and delay discounting. In the present study, we quantified the gray matter (GM) cortical volume using voxel-based morphometry (VBM) methods to investigate the effect of TP on delay discounting (DD) across two independent samples. For group 1 (102 healthy college students; 46 male; 20.40 ± 1.87 years), behavioral results showed that only Future TP was a significant predictor of DD, and higher scores on Future TP were related to lower discounting rates. Whole-brain analysis revealed that steeper discounting correlated with greater GM volume in the ventromedial prefrontal cortex (vmPFC) and ventral part of posterior cingulate cortex (vPCC). Also, GM volume of a cluster in the vmPFC was correlated with Future TP. Interestingly, there was an overlapping region in vmPFC that was correlated with both DD and Future TP. Region-of-interest analysis further indicated that the overlapping region of vmPFC played a partially mediating role in the relation between Future TP and DD in the other independent dataset (Group 2, 36 healthy college students; 14 male; 20.18±1.80 years). Taken together, our results provide a new perspective from neural basis for explaining the relation between DD and future TP. Copyright © 2017 Elsevier Ltd. All rights reserved.

  20. Why Hong Kong students favour more face-to-face classroom time in blended learning

    Directory of Open Access Journals (Sweden)

    James Henri

    Full Text Available A three year study in student characteristics, needs and learning styles guided instructors at the University of Hong Kong Faculty of Education to improve teaching and learning in a core module: Information Literacy. A mixed-method approach analyzed data collected from undergraduate, in-service teachers in a BEd program, and helped instructors in the program to gain insight into the Hong Kong teacher working, post-service towards a BEd in Library and Information Science. Part-time students indicated a preference for a combination of online and face-to-face teaching, with more face-to-face class time in that mix. These findings would also be informative for other part-time programs using blended teaching and learning models.

  1. Estimating the implicit component of visuomotor rotation learning by constraining movement preparation time.

    Science.gov (United States)

    Leow, Li-Ann; Gunn, Reece; Marinovic, Welber; Carroll, Timothy J

    2017-08-01

    When sensory feedback is perturbed, accurate movement is restored by a combination of implicit processes and deliberate reaiming to strategically compensate for errors. Here, we directly compare two methods used previously to dissociate implicit from explicit learning on a trial-by-trial basis: 1 ) asking participants to report the direction that they aim their movements, and contrasting this with the directions of the target and the movement that they actually produce, and 2 ) manipulating movement preparation time. By instructing participants to reaim without a sensory perturbation, we show that reaiming is possible even with the shortest possible preparation times, particularly when targets are narrowly distributed. Nonetheless, reaiming is effortful and comes at the cost of increased variability, so we tested whether constraining preparation time is sufficient to suppress strategic reaiming during adaptation to visuomotor rotation with a broad target distribution. The rate and extent of error reduction under preparation time constraints were similar to estimates of implicit learning obtained from self-report without time pressure, suggesting that participants chose not to apply a reaiming strategy to correct visual errors under time pressure. Surprisingly, participants who reported aiming directions showed less implicit learning according to an alternative measure, obtained during trials performed without visual feedback. This suggests that the process of reporting can affect the extent or persistence of implicit learning. The data extend existing evidence that restricting preparation time can suppress explicit reaiming and provide an estimate of implicit visuomotor rotation learning that does not require participants to report their aiming directions. NEW & NOTEWORTHY During sensorimotor adaptation, implicit error-driven learning can be isolated from explicit strategy-driven reaiming by subtracting self-reported aiming directions from movement directions, or

  2. Gastric pH and residual volume after 1 and 2 h fasting time for clear fluids in children†.

    Science.gov (United States)

    Schmidt, A R; Buehler, P; Seglias, L; Stark, T; Brotschi, B; Renner, T; Sabandal, C; Klaghofer, R; Weiss, M; Schmitz, A

    2015-03-01

    Current guidelines suggest a fasting time of 2 h for clear fluids, which is often exceeded in clinical practice, leading to discomfort, dehydration and stressful anaesthesia induction to patients, especially in the paediatric population. Shorter fluid fasting might be a strategy to improve patient comfort but has not been investigated yet. This prospective clinical trial compares gastric pH and residual volume after 1 vs 2 h of preoperative clear fluid fasting. Children (1-16 yr, ASA I or II) undergoing elective procedures in general anaesthesia requiring tracheal intubation were randomized into group A with 60 min or B with 120 min preoperative clear fluid fasting. To determine gastric pH and residual volume, the gastric content was sampled in supine, left and right lateral patient position using an oro-gastric tube after intubation. Data are median (interquartile range) for group A or B (PPatient characteristic data were similar between the two groups, except for gender (46/33 males in group A/B; P=0.02). Despite significantly shorter fasting times for clear fluids in group A compared with group B (76/136 min; P<0.001), no significant difference was observed regarding gastric pH [1.43 (1.30-1.56)/1.44 (1.29-1.68), P=0.66] or residual volume [0.43 (0.21-0.84)/0.46 (0.19-0.78) ml kg(-1), P=0.47]. One hour clear fluid fasting does not alter gastric pH or residual volume significantly compared with 2 h fasting. The study was approved by the local ethics committee (KEK-ZH-Nr. 2011-0034) and registered with ClinicalTrials.gov (NCT01516775). © The Author 2014. Published by Oxford University Press on behalf of the British Journal of Anaesthesia. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  3. Supervised Learning Using Spike-Timing-Dependent Plasticity of Memristive Synapses.

    Science.gov (United States)

    Nishitani, Yu; Kaneko, Yukihiro; Ueda, Michihito

    2015-12-01

    We propose a supervised learning model that enables error backpropagation for spiking neural network hardware. The method is modeled by modifying an existing model to suit the hardware implementation. An example of a network circuit for the model is also presented. In this circuit, a three-terminal ferroelectric memristor (3T-FeMEM), which is a field-effect transistor with a gate insulator composed of ferroelectric materials, is used as an electric synapse device to store the analog synaptic weight. Our model can be implemented by reflecting the network error to the write voltage of the 3T-FeMEMs and introducing a spike-timing-dependent learning function to the device. An XOR problem was successfully demonstrated as a benchmark learning by numerical simulations using the circuit properties to estimate the learning performance. In principle, the learning time per step of this supervised learning model and the circuit is independent of the number of neurons in each layer, promising a high-speed and low-power calculation in large-scale neural networks.

  4. Comparative Effectiveness of Low-Volume Time-Efficient Resistance Training Versus Endurance Training in Patients With Heart Failure

    DEFF Research Database (Denmark)

    Munch, Gregers Winding; Birgitte Rosenmeier, Jaya; Petersen, Morten

    2018-01-01

    -related quality of life in lower New York Heart Association-stage HF patients, despite less time required as well as lower energy expenditure during TRE than during AMC. Therefore, TRE might represent a time-efficient exercise modality for improving adherence to exercise in patients with class I-II HF.......PURPOSE: Cardiorespiratory fitness is positively related to heart failure (HF) prognosis, but lack of time and low energy are barriers for adherence to exercise. We, therefore, compared the effect of low-volume time-based resistance exercise training (TRE) with aerobic moderate-intensity cycling...... (AMC) on maximal and submaximal exercise capacity, health-related quality of life, and vascular function. METHODS: Twenty-eight HF patients (New York Heart Association class I-II) performed AMC (n = 14) or TRE (n = 14). Maximal and submaximal exercise capacity, health-related quality of life...

  5. Incoherent dictionary learning for reducing crosstalk noise in least-squares reverse time migration

    Science.gov (United States)

    Wu, Juan; Bai, Min

    2018-05-01

    We propose to apply a novel incoherent dictionary learning (IDL) algorithm for regularizing the least-squares inversion in seismic imaging. The IDL is proposed to overcome the drawback of traditional dictionary learning algorithm in losing partial texture information. Firstly, the noisy image is divided into overlapped image patches, and some random patches are extracted for dictionary learning. Then, we apply the IDL technology to minimize the coherency between atoms during dictionary learning. Finally, the sparse representation problem is solved by a sparse coding algorithm, and image is restored by those sparse coefficients. By reducing the correlation among atoms, it is possible to preserve most of the small-scale features in the image while removing much of the long-wavelength noise. The application of the IDL method to regularization of seismic images from least-squares reverse time migration shows successful performance.

  6. Learning Bounds of ERM Principle for Sequences of Time-Dependent Samples

    Directory of Open Access Journals (Sweden)

    Mingchen Yao

    2015-01-01

    Full Text Available Many generalization results in learning theory are established under the assumption that samples are independent and identically distributed (i.i.d.. However, numerous learning tasks in practical applications involve the time-dependent data. In this paper, we propose a theoretical framework to analyze the generalization performance of the empirical risk minimization (ERM principle for sequences of time-dependent samples (TDS. In particular, we first present the generalization bound of ERM principle for TDS. By introducing some auxiliary quantities, we also give a further analysis of the generalization properties and the asymptotical behaviors of ERM principle for TDS.

  7. Optimal critic learning for robot control in time-varying environments.

    Science.gov (United States)

    Wang, Chen; Li, Yanan; Ge, Shuzhi Sam; Lee, Tong Heng

    2015-10-01

    In this paper, optimal critic learning is developed for robot control in a time-varying environment. The unknown environment is described as a linear system with time-varying parameters, and impedance control is employed for the interaction control. Desired impedance parameters are obtained in the sense of an optimal realization of the composite of trajectory tracking and force regulation. Q -function-based critic learning is developed to determine the optimal impedance parameters without the knowledge of the system dynamics. The simulation results are presented and compared with existing methods, and the efficacy of the proposed method is verified.

  8. Supervised spike-timing-dependent plasticity: a spatiotemporal neuronal learning rule for function approximation and decisions.

    Science.gov (United States)

    Franosch, Jan-Moritz P; Urban, Sebastian; van Hemmen, J Leo

    2013-12-01

    How can an animal learn from experience? How can it train sensors, such as the auditory or tactile system, based on other sensory input such as the visual system? Supervised spike-timing-dependent plasticity (supervised STDP) is a possible answer. Supervised STDP trains one modality using input from another one as "supervisor." Quite complex time-dependent relationships between the senses can be learned. Here we prove that under very general conditions, supervised STDP converges to a stable configuration of synaptic weights leading to a reconstruction of primary sensory input.

  9. Do learning collaboratives strengthen communication? A comparison of organizational team communication networks over time.

    Science.gov (United States)

    Bunger, Alicia C; Lengnick-Hall, Rebecca

    Collaborative learning models were designed to support quality improvements, such as innovation implementation by promoting communication within organizational teams. Yet the effect of collaborative learning approaches on organizational team communication during implementation is untested. The aim of this study was to explore change in communication patterns within teams from children's mental health organizations during a year-long learning collaborative focused on implementing a new treatment. We adopt a social network perspective to examine intraorganizational communication within each team and assess change in (a) the frequency of communication among team members, (b) communication across organizational hierarchies, and (c) the overall structure of team communication networks. A pretest-posttest design compared communication among 135 participants from 21 organizational teams at the start and end of a learning collaborative. At both time points, participants were asked to list the members of their team and rate the frequency of communication with each along a 7-point Likert scale. Several individual, pair-wise, and team level communication network metrics were calculated and compared over time. At the individual level, participants reported communicating with more team members by the end of the learning collaborative. Cross-hierarchical communication did not change. At the team level, these changes manifested differently depending on team size. In large teams, communication frequency increased, and networks grew denser and slightly less centralized. In small teams, communication frequency declined, growing more sparse and centralized. Results suggest that team communication patterns change minimally but evolve differently depending on size. Learning collaboratives may be more helpful for enhancing communication among larger teams; thus, managers might consider selecting and sending larger staff teams to learning collaboratives. This study highlights key future

  10. SU-F-P-20: Predicting Waiting Times in Radiation Oncology Using Machine Learning

    International Nuclear Information System (INIS)

    Joseph, A; Herrera, D; Hijal, T; Kildea, J; Hendren, L; Leung, A; Wainberg, J; Sawaf, M; Gorshkov, M; Maglieri, R; Keshavarz, M

    2016-01-01

    Purpose: Waiting times remain one of the most vexing patient satisfaction challenges facing healthcare. Waiting time uncertainty can cause patients, who are already sick or in pain, to worry about when they will receive the care they need. These waiting periods are often difficult for staff to predict and only rough estimates are typically provided based on personal experience. This level of uncertainty leaves most patients unable to plan their calendar, making the waiting experience uncomfortable, even painful. In the present era of electronic health records (EHRs), waiting times need not be so uncertain. Extensive EHRs provide unprecedented amounts of data that can statistically cluster towards representative values when appropriate patient cohorts are selected. Predictive modelling, such as machine learning, is a powerful approach that benefits from large, potentially complex, datasets. The essence of machine learning is to predict future outcomes by learning from previous experience. The application of a machine learning algorithm to waiting time data has the potential to produce personalized waiting time predictions such that the uncertainty may be removed from the patient’s waiting experience. Methods: In radiation oncology, patients typically experience several types of waiting (eg waiting at home for treatment planning, waiting in the waiting room for oncologist appointments and daily waiting in the waiting room for radiotherapy treatments). A daily treatment wait time model is discussed in this report. To develop a prediction model using our large dataset (with more than 100k sample points) a variety of machine learning algorithms from the Python package sklearn were tested. Results: We found that the Random Forest Regressor model provides the best predictions for daily radiotherapy treatment waiting times. Using this model, we achieved a median residual (actual value minus predicted value) of 0.25 minutes and a standard deviation residual of 6.5 minutes

  11. SU-F-P-20: Predicting Waiting Times in Radiation Oncology Using Machine Learning

    Energy Technology Data Exchange (ETDEWEB)

    Joseph, A; Herrera, D; Hijal, T; Kildea, J [McGill University Health Centre, Montreal, Quebec (Canada); Hendren, L; Leung, A; Wainberg, J; Sawaf, M; Gorshkov, M; Maglieri, R; Keshavarz, M [McGill University, Montreal, Quebec (Canada)

    2016-06-15

    Purpose: Waiting times remain one of the most vexing patient satisfaction challenges facing healthcare. Waiting time uncertainty can cause patients, who are already sick or in pain, to worry about when they will receive the care they need. These waiting periods are often difficult for staff to predict and only rough estimates are typically provided based on personal experience. This level of uncertainty leaves most patients unable to plan their calendar, making the waiting experience uncomfortable, even painful. In the present era of electronic health records (EHRs), waiting times need not be so uncertain. Extensive EHRs provide unprecedented amounts of data that can statistically cluster towards representative values when appropriate patient cohorts are selected. Predictive modelling, such as machine learning, is a powerful approach that benefits from large, potentially complex, datasets. The essence of machine learning is to predict future outcomes by learning from previous experience. The application of a machine learning algorithm to waiting time data has the potential to produce personalized waiting time predictions such that the uncertainty may be removed from the patient’s waiting experience. Methods: In radiation oncology, patients typically experience several types of waiting (eg waiting at home for treatment planning, waiting in the waiting room for oncologist appointments and daily waiting in the waiting room for radiotherapy treatments). A daily treatment wait time model is discussed in this report. To develop a prediction model using our large dataset (with more than 100k sample points) a variety of machine learning algorithms from the Python package sklearn were tested. Results: We found that the Random Forest Regressor model provides the best predictions for daily radiotherapy treatment waiting times. Using this model, we achieved a median residual (actual value minus predicted value) of 0.25 minutes and a standard deviation residual of 6.5 minutes

  12. American Academic: A National Survey of Part-time/Adjunct Faculty. Volume 2

    Science.gov (United States)

    American Federation of Teachers (NJ), 2010

    2010-01-01

    Plainly, part-time/adjunct faculty members now play a vital role in educating the nation's college students. Even so, the data and research on part-time/adjunct faculty members have tended to be pretty spotty. This survey, conducted by Hart Research Associates on behalf of the American Federation of Teachers, is one of the first nationwide…

  13. Learning dictionaries of sparse codes of 3D movements of body joints for real-time human activity understanding.

    Directory of Open Access Journals (Sweden)

    Jin Qi

    Full Text Available Real-time human activity recognition is essential for human-robot interactions for assisted healthy independent living. Most previous work in this area is performed on traditional two-dimensional (2D videos and both global and local methods have been used. Since 2D videos are sensitive to changes of lighting condition, view angle, and scale, researchers begun to explore applications of 3D information in human activity understanding in recently years. Unfortunately, features that work well on 2D videos usually don't perform well on 3D videos and there is no consensus on what 3D features should be used. Here we propose a model of human activity recognition based on 3D movements of body joints. Our method has three steps, learning dictionaries of sparse codes of 3D movements of joints, sparse coding, and classification. In the first step, space-time volumes of 3D movements of body joints are obtained via dense sampling and independent component analysis is then performed to construct a dictionary of sparse codes for each activity. In the second step, the space-time volumes are projected to the dictionaries and a set of sparse histograms of the projection coefficients are constructed as feature representations of the activities. Finally, the sparse histograms are used as inputs to a support vector machine to recognize human activities. We tested this model on three databases of human activities and found that it outperforms the state-of-the-art algorithms. Thus, this model can be used for real-time human activity recognition in many applications.

  14. Learning dictionaries of sparse codes of 3D movements of body joints for real-time human activity understanding.

    Science.gov (United States)

    Qi, Jin; Yang, Zhiyong

    2014-01-01

    Real-time human activity recognition is essential for human-robot interactions for assisted healthy independent living. Most previous work in this area is performed on traditional two-dimensional (2D) videos and both global and local methods have been used. Since 2D videos are sensitive to changes of lighting condition, view angle, and scale, researchers begun to explore applications of 3D information in human activity understanding in recently years. Unfortunately, features that work well on 2D videos usually don't perform well on 3D videos and there is no consensus on what 3D features should be used. Here we propose a model of human activity recognition based on 3D movements of body joints. Our method has three steps, learning dictionaries of sparse codes of 3D movements of joints, sparse coding, and classification. In the first step, space-time volumes of 3D movements of body joints are obtained via dense sampling and independent component analysis is then performed to construct a dictionary of sparse codes for each activity. In the second step, the space-time volumes are projected to the dictionaries and a set of sparse histograms of the projection coefficients are constructed as feature representations of the activities. Finally, the sparse histograms are used as inputs to a support vector machine to recognize human activities. We tested this model on three databases of human activities and found that it outperforms the state-of-the-art algorithms. Thus, this model can be used for real-time human activity recognition in many applications.

  15. Time to learn: the outlook for renewal of patient-centred education in the digital age.

    Science.gov (United States)

    Glick, T H; Moore, G T

    2001-05-01

    Major forces in society and within health systems are fragmenting patient care and clinical learning. The distancing of physician and trainee from the patient undermines learning about the patient-doctor relationship. The disconnection of care and learning from one successive venue to another impedes the ability of trainees to learn about illness longitudinally. As a conceptual piece, our methods have been those of witnessing the experiences of patients, practitioners, and students over time and observing the impact of fragmented systems and changing expectations on care and learning. We have reflected on the opportunities created by digital information systems and interactive telemedicine to help renew essential relationships. Although there is, as yet, little in the literature on educational or health outcomes of this kind of technological enablement, we anticipate opportunities for a renewed focus on the patient in that patient's own space and time. Multimedia applications can achieve not only real-time connections, but can help construct a "virtual patient" as a platform for supervision and assessment, permitting preceptors to evaluate trainee-patient interactions, utilization of Web-based data and human resources, and on-line professionalism. Just as diverse elements in society are capitalizing upon digital technology to create advantageous relationships, all of the elements in the complex systems of health care and medical training can be better connected, so as to put the patient back in the centre of care and the trainee's ongoing relationship to the patient back in the centre of education.

  16. Mechanical energy yields and pressure volume and pressure time curves for whole core fuel-coolant interactions

    Energy Technology Data Exchange (ETDEWEB)

    Coddington, P [United Kingdom Atomic Energy Authority, Atomic Energy Establishment, Winfrith, Dorchester, Dorset (United Kingdom)

    1979-10-15

    In determining the damage consequences of a whole core Fuel-Coolant Interaction (FCI), one measure of the strength of a FCI that can be used and is independent of the system geometry is the constant volume mixing mechanical yield (often referred to as the Hicks-Menzies yield), which represents a near upper limit to the mechanical work of a FCI. This paper presents a recalculation of the Hicks-Menzies yields for UO{sub 2} and sodium for a range of initial fuel temperatures and fuel to coolant mass ratios, using recently published UO{sub 2} and sodium equation of state data. The work presented here takes a small number of postulated FCIs with as wide range as possible of thermal interaction parameters and determines their pressure-volume P(V) and pressure-time P(t) relations, using geometrical constraints representative of the reactor. Then by examining these P(V) and P(t) curves a representative pressure-relative volume curve or range of possible curves, for use in containment analysis, is recommended

  17. Pointillist, Cyclical, and Overlapping: Multidimensional Facets of Time in Online Learning

    Directory of Open Access Journals (Sweden)

    Pekka Ihanainen

    2011-11-01

    Full Text Available A linear, sequential time conception based on in-person meetings and pedagogical activities is not enough for those who practice and hope to enhance contemporary education, particularly where online interactions are concerned. In this article, we propose a new model for understanding time in pedagogical contexts. Conceptual parts of the model will be employed as a “cultural technology” to help us relate to evolving phenomena, both physical and virtual. We label these constructs as pointillist, cyclical, and overlapping times.Pointillist time and learning takes place in “dots” of actions that consist of small, discrete moments (e.g., tweeting. Producing, receiving, and sharing ideas in this context are separate points in each actor’s timeline. Cyclical time and learning emerges from intensive periods, which are highly visible in online forums. This construct reveals itself through interactions that often exist in multiple online environments. Overlapping time and learning involves various configurations of linear, pointillist, and cyclical layers, which are mainly evident through the simultaneous uses of social communication technologies.Pointillist, cyclical, and overlapping time constructs enable new orientations for conceptualizing time in pedagogy. In this article we also introduce de-, re-, and en- modes of these pedagogies that connect with approaches to meet the needs of learners for individualization, personalization, and cyborgization.

  18. Marine Corps Center for Lessons Learned. Volume 8, Issue 11, November 2012

    Science.gov (United States)

    2012-11-01

    MARINE CORPS CENTER FOR LESSONS LEARNED M C C L L R E P O R T: F E AT U R E D A R T I C L E S A N D L E S S O N S : R E G U L A R F E AT U R E S...L O A D S F R O M T H E M C C L L W E B S I T E , OCTOBER 2012 R E G U L A R F E AT U R E S : Photo credit: Sgt Rachael Moore A Joint Terminal...Follower by Ira Chaleff, and ▪ Fahim Speaks by Fahim Fazli and Michael Moffett . 19 MCCLL Products "in the Pipeline" Several recent, ongoing and

  19. Neuromodulated Spike-Timing-Dependent Plasticity and Theory of Three-Factor Learning Rules

    Directory of Open Access Journals (Sweden)

    Wulfram eGerstner

    2016-01-01

    Full Text Available Classical Hebbian learning puts the emphasis on joint pre- and postsynaptic activity, but neglects the potential role of neuromodulators. Since neuromodulators convey information about novelty or reward, the influence of neuromodulatorson synaptic plasticity is useful not just for action learning in classical conditioning, but also to decide 'when' to create new memories in response to a flow of sensory stimuli.In this review, we focus on timing requirements for pre- and postsynaptic activity in conjunction with one or several phasic neuromodulatory signals. While the emphasis of the text is on conceptual models and mathematical theories, we also discusssome experimental evidence for neuromodulation of Spike-Timing-Dependent Plasticity.We highlight the importance of synaptic mechanisms in bridging the temporal gap between sensory stimulation and neuromodulatory signals, and develop a framework for a class of neo-Hebbian three-factor learning rules that depend on presynaptic activity, postsynaptic variables as well as the influence of neuromodulators.

  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. Analysis of electromagnetic wave interactions on nonlinear scatterers using time domain volume integral equations

    KAUST Repository

    Ulku, Huseyin Arda; Sayed, Sadeed Bin; Bagci, Hakan

    2014-01-01

    solvers are the method of choice when it comes simulating these nonlinear effects. Oftentimes, finite difference time domain (FDTD) method is used for this purpose. This is simply due to the fact that explicitness of the FDTD renders the implementation

  2. Technical reference manual for TIME4. Version 1.0, Volume 1

    International Nuclear Information System (INIS)

    Wilmot, R.D.; Ringrose, P.S.; Larkin, J.P.A.; Kleissen, F.A.T.

    1991-07-01

    This document is the Technical Reference Manual for the TIME4 model. TIME4 is the environmental change model developed for use in the probabilistic risk analysis of deep disposal of radioactive waste. The Technical Reference Manual describes the theoretical background to the model. The modelling method is described, followed by a review of related work and a detailed description for each sub-model. (author)

  3. The effect of pre-anaesthetic fasting time and type of food on gastric content volume and acidity in dogs.

    Science.gov (United States)

    Savvas, Ioannis; Rallis, Timoleon; Raptopoulos, Dimitris

    2009-11-01

    To investigate the effect of pre-anaesthetic fasting time and variety of food on gastric content (GC) volume and pH in dogs. Randomized, cross-over, prospective experimental study. Fifteen mongrel dogs (nine females and six males 1-4 years old, weighing 10-24.5 kg). Each dog received the same seven treatments in random order: dry food 3 hours before anaesthesia (BA) (treatment 3D), canned food (half daily rate) 3 hours BA (treatment 3C), 0% fat cow milk 3 hours BA (treatment 3M), dry food 10 hours BA (treatment 10D), canned food 10 hours BA (treatment 10C), low fat canned food 10 hours BA (treatment 10F) and low protein canned food 10 hours BA (treatment 10P). All animals were pre-medicated with propionyl promazine and anaesthesia was induced with thiopental sodium and maintained with halothane. GC was aspirated using an orogastric catheter and its volume and pH were measured. Treatment 10F had significantly lower GC pH than all the 3-hour treatments. Treatments 10D and 10P had significantly lower pH than treatments 3D and 3C. Treatment 3M had significantly lower pH than the other 3-hour treatments. Treatment 3D had significantly greater gastric volume than treatments 3M, 10C, 10F and 10P. Canned food at half the daily rate administered 3 hours before anaesthesia did not increase significantly the GC volume compared to the other types of food used. The GC pH was also high. This type of food fed 3 hours before induction of anaesthesia may be of benefit in reduction of the incidence of gastro-oesophageal reflux during anaesthesia in dogs.

  4. Estimating changes in lichen mat volume through time and related effects on barren ground caribou (Rangifer tarandus groenlandicus) movement.

    Science.gov (United States)

    Rickbeil, Gregory J M; Hermosilla, Txomin; Coops, Nicholas C; White, Joanne C; Wulder, Michael A

    2017-01-01

    Lichens form a critical portion of barren ground caribou (Rangifer tarandus groenlandicus) diets, especially during winter months. Here, we assess lichen mat volume across five herd ranges in the Northwest Territories and Nunavut, Canada, using newly developed composite Landsat imagery. The lichen volume estimator (LVE) was adapted for use across 700 000 km2 of barren ground caribou habitat annually from 1984-2012. We subsequently assessed how LVE changed temporally throughout the time series for each pixel using Theil-Sen's slopes, and spatially by assessing whether slope values were centered in local clusters of similar values. Additionally, we assessed how LVE estimates resulted in changes in barren ground caribou movement rates using an extensive telemetry data set from 2006-2011. The Ahiak/Beverly herd had the largest overall increase in LVE (median = 0.033), while the more western herds had the least (median slopes below zero in all cases). LVE slope pixels were arranged in significant clusters across the study area, with the Cape Bathurst, Bathurst, and Bluenose East herds having the most significant clusters of negative slopes (more than 20% of vegetated land in each case). The Ahiak/Beverly and Bluenose West had the most significant positive clusters (16.3% and 18.5% of vegetated land respectively). Barren ground caribou displayed complex reactions to changing lichen conditions depending on season; the majority of detected associations with movement data agreed with current understanding of barren ground caribou foraging behavior (the exception was an increase in movement velocity at high lichen volume estimates in Fall). The temporal assessment of LVE identified areas where shifts in ecological conditions may have resulted in changing lichen mat conditions, while assessing the slope estimates for clustering identified zones beyond the pixel scale where forage conditions may be changing. Lichen volume estimates associated with barren ground caribou

  5. Estimating changes in lichen mat volume through time and related effects on barren ground caribou (Rangifer tarandus groenlandicus) movement

    Science.gov (United States)

    Hermosilla, Txomin; Coops, Nicholas C.; White, Joanne C.; Wulder, Michael A.

    2017-01-01

    Lichens form a critical portion of barren ground caribou (Rangifer tarandus groenlandicus) diets, especially during winter months. Here, we assess lichen mat volume across five herd ranges in the Northwest Territories and Nunavut, Canada, using newly developed composite Landsat imagery. The lichen volume estimator (LVE) was adapted for use across 700 000 km2 of barren ground caribou habitat annually from 1984–2012. We subsequently assessed how LVE changed temporally throughout the time series for each pixel using Theil-Sen’s slopes, and spatially by assessing whether slope values were centered in local clusters of similar values. Additionally, we assessed how LVE estimates resulted in changes in barren ground caribou movement rates using an extensive telemetry data set from 2006–2011. The Ahiak/Beverly herd had the largest overall increase in LVE (median = 0.033), while the more western herds had the least (median slopes below zero in all cases). LVE slope pixels were arranged in significant clusters across the study area, with the Cape Bathurst, Bathurst, and Bluenose East herds having the most significant clusters of negative slopes (more than 20% of vegetated land in each case). The Ahiak/Beverly and Bluenose West had the most significant positive clusters (16.3% and 18.5% of vegetated land respectively). Barren ground caribou displayed complex reactions to changing lichen conditions depending on season; the majority of detected associations with movement data agreed with current understanding of barren ground caribou foraging behavior (the exception was an increase in movement velocity at high lichen volume estimates in Fall). The temporal assessment of LVE identified areas where shifts in ecological conditions may have resulted in changing lichen mat conditions, while assessing the slope estimates for clustering identified zones beyond the pixel scale where forage conditions may be changing. Lichen volume estimates associated with barren ground caribou

  6. A clip-based protocol for breast boost radiotherapy provides clear target visualisation and demonstrates significant volume reduction over time

    Energy Technology Data Exchange (ETDEWEB)

    Lewis, Lorraine [Department of Radiation Oncology, Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, New South Wales (Australia); Cox, Jennifer [Department of Radiation Oncology, Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, New South Wales (Australia); Faculty of Health Sciences, University of Sydney, Sydney, New South Wales (Australia); Morgia, Marita [Department of Radiation Oncology, Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, New South Wales (Australia); Atyeo, John [Faculty of Health Sciences, University of Sydney, Sydney, New South Wales (Australia); Lamoury, Gillian [Department of Radiation Oncology, Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, New South Wales (Australia)

    2015-09-15

    The clinical target volume (CTV) for early stage breast cancer is difficult to clearly identify on planning computed tomography (CT) scans. Surgical clips inserted around the tumour bed should help to identify the CTV, particularly if the seroma has been reabsorbed, and enable tracking of CTV changes over time. A surgical clip-based CTV delineation protocol was introduced. CTV visibility and its post-operative shrinkage pattern were assessed. The subjects were 27 early stage breast cancer patients receiving post-operative radiotherapy alone and 15 receiving post-operative chemotherapy followed by radiotherapy. The radiotherapy alone (RT/alone) group received a CT scan at median 25 days post-operatively (CT1rt) and another at 40 Gy, median 68 days (CT2rt). The chemotherapy/RT group (chemo/RT) received a CT scan at median 18 days post-operatively (CT1ch), a planning CT scan at median 126 days (CT2ch), and another at 40 Gy (CT3ch). There was no significant difference (P = 0.08) between the initial mean CTV for each cohort. The RT/alone cohort showed significant CTV volume reduction of 38.4% (P = 0.01) at 40 Gy. The Chemo/RT cohort had significantly reduced volumes between CT1ch: median 54 cm{sup 3} (4–118) and CT2ch: median 16 cm{sup 3}, (2–99), (P = 0.01), but no significant volume reduction thereafter. Surgical clips enable localisation of the post-surgical seroma for radiotherapy targeting. Most seroma shrinkage occurs early, enabling CT treatment planning to take place at 7 weeks, which is within the 9 weeks recommended to limit disease recurrence.

  7. A clip-based protocol for breast boost radiotherapy provides clear target visualisation and demonstrates significant volume reduction over time

    International Nuclear Information System (INIS)

    Lewis, Lorraine; Cox, Jennifer; Morgia, Marita; Atyeo, John; Lamoury, Gillian

    2015-01-01

    The clinical target volume (CTV) for early stage breast cancer is difficult to clearly identify on planning computed tomography (CT) scans. Surgical clips inserted around the tumour bed should help to identify the CTV, particularly if the seroma has been reabsorbed, and enable tracking of CTV changes over time. A surgical clip-based CTV delineation protocol was introduced. CTV visibility and its post-operative shrinkage pattern were assessed. The subjects were 27 early stage breast cancer patients receiving post-operative radiotherapy alone and 15 receiving post-operative chemotherapy followed by radiotherapy. The radiotherapy alone (RT/alone) group received a CT scan at median 25 days post-operatively (CT1rt) and another at 40 Gy, median 68 days (CT2rt). The chemotherapy/RT group (chemo/RT) received a CT scan at median 18 days post-operatively (CT1ch), a planning CT scan at median 126 days (CT2ch), and another at 40 Gy (CT3ch). There was no significant difference (P = 0.08) between the initial mean CTV for each cohort. The RT/alone cohort showed significant CTV volume reduction of 38.4% (P = 0.01) at 40 Gy. The Chemo/RT cohort had significantly reduced volumes between CT1ch: median 54 cm 3 (4–118) and CT2ch: median 16 cm 3 , (2–99), (P = 0.01), but no significant volume reduction thereafter. Surgical clips enable localisation of the post-surgical seroma for radiotherapy targeting. Most seroma shrinkage occurs early, enabling CT treatment planning to take place at 7 weeks, which is within the 9 weeks recommended to limit disease recurrence

  8. Learning of temporal motor patterns: An analysis of continuous vs. reset timing

    Directory of Open Access Journals (Sweden)

    Rodrigo eLaje

    2011-10-01

    Full Text Available Our ability to generate well-timed sequences of movements is critical to an array of behaviors, including the ability to play a musical instrument or a video game. Here we address two questions relating to timing with the goal of better understanding the neural mechanisms underlying temporal processing. First, how does accuracy and variance change over the course of learning of complex spatiotemporal patterns? Second, is the timing of sequential responses most consistent with starting and stopping an internal timer at each interval or with continuous timing?To address these questions we used a psychophysical task in which subjects learned to reproduce a sequence of finger taps in the correct order and at the correct times—much like playing a melody at the piano. This task allowed us to calculate the variance of the responses at different time points using data from the same trials. Our results show that while standard Weber’s law is clearly violated, variance does increase as a function of time squared, as expected according to the generalized form of Weber’s law—which separates the source of variance into time-dependent and time-independent components. Over the course of learning, both the time-independent variance and the coefficient of the time-dependent term decrease. Our analyses also suggest that timing of sequential events does not rely on the resetting of an internal timer at each event.We describe and interpret our results in the context of computer simulations that capture some of our psychophysical findings. Specifically, we show that continuous timing, as opposed to reset timing, is expected from population clock models in which timing emerges from the internal dynamics of recurrent neural networks.

  9. Sporadic insulinomas on volume perfusion CT: dynamic enhancement patterns and timing of optimal tumour-parenchyma contrast

    Energy Technology Data Exchange (ETDEWEB)

    Zhu, Liang; Xue, Hua-dan; Liu, Wei; Wang, Xuan; Sun, Hao; Li, Ping; Jin, Zheng-yu [Peking Union Medical College Hospital, Department of Radiology, Beijing (China); Wu, Wen-ming; Zhao, Yu-pei [Peking Union Medical College Hospital, Department of General Surgery, Beijing (China)

    2017-08-15

    To assess enhancement patterns of sporadic insulinomas on volume perfusion CT (VPCT), and to identify timing of optimal tumour-parenchyma contrast. Consecutive patients who underwent VPCT for clinically suspected insulinomas were retrospectively identified. Patients with insulinomas confirmed by surgery were included, and patients with familial syndromes were excluded. Two radiologists evaluated VPCT images in consensus. Tumour-parenchyma contrast at each time point was measured, and timing of optimal contrast was determined. Time duration of hyperenhancement (tumour-parenchyma contrast >20 Hounsfield units, HU) was recorded. Perfusion parameters were evaluated. Three dynamic enhancement patterns were observed in 63 tumours: persistent hyperenhancement (hyperenhancement time window ≥10 s) in 39 (61.9%), transient hyperenhancement (hyperenhancement <10 s) in 19 (30.2%) and non-hyperenhancement in 5 (7.9%). Timing of optimal contrast was 9 s after abdominal aorta threshold (AAT) of 200 HU, with tumour-parenchyma contrast of 77.6 ± 57.2 HU. At 9 s after AAT, 14 (22.2%) tumours were non-hyperenhancing, nine of which had missed transient hyperenhancement. Insulinomas with transient and persistent hyperenhancement patterns had significantly increased perfusion. Insulinomas have variable enhancement patterns. Tumour-parenchyma contrast is time-dependent. Optimal timing of enhancement is 9 s after AAT. VPCT enables tumour detection even if the hyperenhancement is transient. (orig.)

  10. 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 .

  11. Changes in Search Path Complexity and Length During Learning of a Virtual Water Maze: Age Differences and Differential Associations with Hippocampal Subfield Volumes.

    Science.gov (United States)

    Daugherty, Ana M; Bender, Andrew R; Yuan, Peng; Raz, Naftali

    2016-06-01

    Impairment of hippocampus-dependent cognitive processes has been proposed to underlie age-related deficits in navigation. Animal studies suggest a differential role of hippocampal subfields in various aspects of navigation, but that hypothesis has not been tested in humans. In this study, we examined the association between volume of hippocampal subfields and age differences in virtual spatial navigation. In a sample of 65 healthy adults (age 19-75 years), advanced age was associated with a slower rate of improvement operationalized as shortening of the search path over 25 learning trials on a virtual Morris water maze task. The deficits were partially explained by greater complexity of older adults' search paths. Larger subiculum and entorhinal cortex volumes were associated with a faster decrease in search path complexity, which in turn explained faster shortening of search distance. Larger Cornu Ammonis (CA)1-2 volume was associated with faster distance shortening, but not in path complexity reduction. Age differences in regional volumes collectively accounted for 23% of the age-related variance in navigation learning. Independent of subfield volumes, advanced age was associated with poorer performance across all trials, even after reaching the asymptote. Thus, subiculum and CA1-2 volumes were associated with speed of acquisition, but not magnitude of gains in virtual maze navigation. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  12. A Social Practice Theory of Learning and Becoming across Contexts and Time

    Science.gov (United States)

    Penuel, William R.; DiGiacomo, Daniela K.; Van Horne, Katie; Kirshner, Ben

    2016-01-01

    This paper presents a social practice theory of learning and becoming across contexts and time. Our perspective is rooted in the Danish tradition of critical psychology (Dreier, 1997; Mørck & Huniche, 2006; Nissen, 2005), and we use social practice theory to interpret the pathway of one adolescent whom we followed as part of a longitudinal…

  13. Active Learning and Just-in-Time Teaching in a Material and Energy Balances Course

    Science.gov (United States)

    Liberatore, Matthew W.

    2013-01-01

    The delivery of a material and energy balances course is enhanced through a series of in-class and out-of-class exercises. An active learning classroom is achieved, even at class sizes over 150 students, using multiple instructors in a single classroom, problem solving in teams, problems based on YouTube videos, and just-in-time teaching. To avoid…

  14. Assessment of Stand-Alone Displays for Time Management in a Creativity-Driven Learning Environment

    DEFF Research Database (Denmark)

    Frimodt-Møller, Søren

    2017-01-01

    This paper considers the pros and cons of stand-alone displays, analog (e.g. billboards, blackboards, whiteboards, large pieces of paper etc.) as well as digital (e.g. large shared screens, digital whiteboards or similar), as tools for time management processes in a creativity-driven learning...

  15. Part-Time Community College Instructors Teaching in Learning Communities: An Exploratory Multiple Case Study

    Science.gov (United States)

    Paterson, John W.

    2017-01-01

    Community colleges have a greater portion of students at-risk for college completion than four-year schools and faculty at these institutions are overwhelmingly and increasingly part-time. Learning communities have been identified as a high-impact practice with numerous benefits documented for community college instructors and students: a primary…

  16. Nuclear power plant monitoring using real-time learning neural network

    International Nuclear Information System (INIS)

    Nabeshima, Kunihiko; Tuerkcan, E.; Ciftcioglu, O.

    1994-01-01

    In the present research, artificial neural network (ANN) with real-time adaptive learning is developed for the plant wide monitoring of Borssele Nuclear Power Plant (NPP). Adaptive ANN learning capability is integrated to the monitoring system so that robust and sensitive on-line monitoring is achieved in real-time environment. The major advantages provided by ANN are that system modelling is formed by means of measurement information obtained from a multi-output process system, explicit modelling is not required and the modelling is not restricted to linear systems. Also ANN can respond very fast to anomalous operational conditions. The real-time ANN learning methodology with adaptive real-time monitoring capability is described below for the wide-range and plant-wide data from an operating nuclear power plant. The layered neural network with error backpropagation algorithm for learning has three layers. The network type is auto-associative, inputs and outputs are exactly the same, using 12 plant signals. (author)

  17. Focusing on Student Learning to Guide the Use of Staff Time

    Science.gov (United States)

    Bates, Imelda; Baume, David; Assinder, Susan

    2010-01-01

    The paper develops and illustrates a model for designing courses. The model gives explicit attention to educational considerations, principally to the importance of active, goal-directed student learning. It also explores economic considerations, principally how to make the best possible use of the time of the teacher in planning and running the…

  18. Pre-Service Post Graduate Teachers' First Time Experience with Constructivist Learning Environment (CLE) Using MOODLE

    Science.gov (United States)

    Boopathiraj, C.; Chellamani, K.

    2015-01-01

    The aim of this study is to enlighten and discuss Post Graduate student teachers' first time experiences and their level of satisfaction with the use of Moodle Learning Management System (LMS) during their "Research Methods in Education" course offered online. This study investigated 30 pre-service Post Graduate student teachers' to…

  19. Perceived Learning and Timely Graduation for Business Undergraduates Taking an Online or Hybrid Course

    Science.gov (United States)

    Blau, Gary; Drennan, Rob B.; Hochner, Arthur; Kapanjie, Darin

    2016-01-01

    An online survey tested the impact of background, technological, and course-related variables on perceived learning and timely graduation for a complete data sample of 263 business undergraduates taking at least one online or hybrid course in the fall of 2015. Hierarchical regression results showed that course-related variables (instructor…

  20. Examining the Relations of Time Management and Procrastination within a Model of Self-Regulated Learning

    Science.gov (United States)

    Wolters, Christopher A.; Won, Sungjun; Hussain, Maryam

    2017-01-01

    The primary goal of this study was to investigate whether college students' academic time management could be used to understand their engagement in traditional and active forms of procrastination within a model of self-regulated learning. College students (N = 446) completed a self-report survey that assessed motivational and strategic aspects of…

  1. Cerebral activation related to implicit sequence learning in a Double Serial Reaction Time task

    NARCIS (Netherlands)

    van der Graaf, FHCE; Maguire, RP; Leenders, KL; de Jong, BM

    2006-01-01

    Using functional magnetic resonance imaging (fMRI), we examined the distribution of cerebral activations related to implicitly learning a series of fixed stimulus-response combinations. In a novel - bimanual - variant of the Serial Reaction Time task (SRT), simultaneous finger movements of the two

  2. Feasibility of a real-time hand hygiene notification machine learning system in outpatient clinics.

    Science.gov (United States)

    Geilleit, R; Hen, Z Q; Chong, C Y; Loh, A P; Pang, N L; Peterson, G M; Ng, K C; Huis, A; de Korne, D F

    2018-04-09

    Various technologies have been developed to improve hand hygiene (HH) compliance in inpatient settings; however, little is known about the feasibility of machine learning technology for this purpose in outpatient clinics. To assess the effectiveness, user experiences, and costs of implementing a real-time HH notification machine learning system in outpatient clinics. In our mixed methods study, a multi-disciplinary team co-created an infrared guided sensor system to automatically notify clinicians to perform HH just before first patient contact. Notification technology effects were measured by comparing HH compliance at baseline (without notifications) with real-time auditory notifications that continued till HH was performed (intervention I) or notifications lasting 15 s (intervention II). User experiences were collected during daily briefings and semi-structured interviews. Costs of implementation of the system were calculated and compared to the current observational auditing programme. Average baseline HH performance before first patient contact was 53.8%. With real-time auditory notifications that continued till HH was performed, overall HH performance increased to 100% (P machine learning system were estimated to be 46% lower than the observational auditing programme. Machine learning technology that enables real-time HH notification provides a promising cost-effective approach to both improving and monitoring HH, and deserves further development in outpatient settings. Copyright © 2018 The Healthcare Infection Society. Published by Elsevier Ltd. All rights reserved.

  3. Dynamic Hebbian Cross-Correlation Learning Resolves the Spike Timing Dependent Plasticity Conundrum

    Directory of Open Access Journals (Sweden)

    Tjeerd V. olde Scheper

    2018-01-01

    Full Text Available Spike Timing-Dependent Plasticity has been found to assume many different forms. The classic STDP curve, with one potentiating and one depressing window, is only one of many possible curves that describe synaptic learning using the STDP mechanism. It has been shown experimentally that STDP curves may contain multiple LTP and LTD windows of variable width, and even inverted windows. The underlying STDP mechanism that is capable of producing such an extensive, and apparently incompatible, range of learning curves is still under investigation. In this paper, it is shown that STDP originates from a combination of two dynamic Hebbian cross-correlations of local activity at the synapse. The correlation of the presynaptic activity with the local postsynaptic activity is a robust and reliable indicator of the discrepancy between the presynaptic neuron and the postsynaptic neuron's activity. The second correlation is between the local postsynaptic activity with dendritic activity which is a good indicator of matching local synaptic and dendritic activity. We show that this simple time-independent learning rule can give rise to many forms of the STDP learning curve. The rule regulates synaptic strength without the need for spike matching or other supervisory learning mechanisms. Local differences in dendritic activity at the synapse greatly affect the cross-correlation difference which determines the relative contributions of different neural activity sources. Dendritic activity due to nearby synapses, action potentials, both forward and back-propagating, as well as inhibitory synapses will dynamically modify the local activity at the synapse, and the resulting STDP learning rule. The dynamic Hebbian learning rule ensures furthermore, that the resulting synaptic strength is dynamically stable, and that interactions between synapses do not result in local instabilities. The rule clearly demonstrates that synapses function as independent localized

  4. Autonomous learning by simple dynamical systems with a discrete-time formulation

    Science.gov (United States)

    Bilen, Agustín M.; Kaluza, Pablo

    2017-05-01

    We present a discrete-time formulation for the autonomous learning conjecture. The main feature of this formulation is the possibility to apply the autonomous learning scheme to systems in which the errors with respect to target functions are not well-defined for all times. This restriction for the evaluation of functionality is a typical feature in systems that need a finite time interval to process a unit piece of information. We illustrate its application on an artificial neural network with feed-forward architecture for classification and a phase oscillator system with synchronization properties. The main characteristics of the discrete-time formulation are shown by constructing these systems with predefined functions.

  5. How To Dance through Time. Volume V: Victorian Era Couple Dances. [Videotape].

    Science.gov (United States)

    Teten, Carol

    This 55-minute VHS videotape is the fifth in a series of "How To Dance Through Time" videos. It continues the tradition of the romance of the mid-19th century couple dances, focusing on Victorian era couple dances. The videotape offers 35 variations of the renowned 19th century couple dances, including the waltz, the polka, the galop,…

  6. Integrated payload and mission planning, phase 3. Volume 3: Ground real-time mission operations

    Science.gov (United States)

    White, W. J.

    1977-01-01

    The payloads tentatively planned to fly on the first two Spacelab missions were analyzed to examine the cost relationships of providing mission operations support from onboard vs the ground-based Payload Operations Control Center (POCC). The quantitative results indicate that use of a POCC, with data processing capability, to support real-time mission operations is the most cost effective case.

  7. How To Dance through Time. Volume III: The Majesty of Renaissance Dance. [Videotape].

    Science.gov (United States)

    Teten, Carol

    This 42-minute VHS videotape is the third in a series of "How To Dance Through Time" videos. It highlights the intricacies of an Italian court dance suite, which mirrors the episodic changes of courtship. Nido D'Amore" (The Nest of Love) exposes the technique for all the dance suites of the era, and features The Opening (which…

  8. Method and timing of tumor volume measurement for outcome prediction in cervical cancer using magnetic resonance imaging

    International Nuclear Information System (INIS)

    Mayr, Nina A.; Taoka, Toshiaki; Yuh, William T.C.; Denning, Leah M.; Zhen, Weining K.; Paulino, Arnold C.; Gaston, Robert C.; Sorosky, Joel I.; Meeks, Sanford L.; Walker, Joan L.; Mannel, Robert S.; Buatti, John M.

    2002-01-01

    Purpose: Recently, imaging-based tumor volume before, during, and after radiation therapy (RT) has been shown to predict tumor response in cervical cancer. However, the effectiveness of different methods and timing of imaging-based tumor size assessment have not been investigated. The purpose of this study was to compare the predictive value for treatment outcome derived from simple diameter-based ellipsoid tumor volume measurement using orthogonal diameters (with ellipsoid computation) with that derived from more complex contour tracing/region-of-interest (ROI) analysis 3D tumor volumetry. Methods and Materials: Serial magnetic resonance imaging (MRI) examinations were prospectively performed in 60 patients with advanced cervical cancer (Stages IB 2 -IVB/recurrent) at the start of RT, during early RT (20-25 Gy), mid-RT (45-50 Gy), and at follow-up (1-2 months after RT completion). ROI-based volumetry was derived by tracing the entire tumor region in each MR slice on the computer work station. For the diameter-based surrogate ''ellipsoid volume,'' the three orthogonal diameters (d 1 , d 2 , d 3 ) were measured on film hard copies to calculate volume as an ellipsoid (d 1 x d 2 x d 3 x π/6). Serial tumor volumes and regression rates determined by each method were correlated with local control, disease-free and overall survival, and the results were compared between the two measuring methods. Median post-therapy follow-up was 4.9 years (range, 2.0-8.2 years). Results: The best method and time point of tumor size measurement for the prediction of outcome was the tumor regression rate in the mid-therapy MRI examination (at 45-50 Gy) using 3D ROI volumetry. For the pre-RT measurement both the diameter-based method and ROI volumetry provided similar predictive accuracy, particularly for patients with small ( 3 ) and large (≥100 cm 3 ) pre-RT tumor size. However, the pre-RT tumor size measured by either method had much less predictive value for the intermediate-size (40

  9. CAT-PUMA: CME Arrival Time Prediction Using Machine learning Algorithms

    Science.gov (United States)

    Liu, Jiajia; Ye, Yudong; Shen, Chenglong; Wang, Yuming; Erdélyi, Robert

    2018-04-01

    CAT-PUMA (CME Arrival Time Prediction Using Machine learning Algorithms) quickly and accurately predicts the arrival of Coronal Mass Ejections (CMEs) of CME arrival time. The software was trained via detailed analysis of CME features and solar wind parameters using 182 previously observed geo-effective partial-/full-halo CMEs and uses algorithms of the Support Vector Machine (SVM) to make its predictions, which can be made within minutes of providing the necessary input parameters of a CME.

  10. Real-time modeling of primitive environments through wavelet sensors and Hebbian learning

    Science.gov (United States)

    Vaccaro, James M.; Yaworsky, Paul S.

    1999-06-01

    Modeling the world through sensory input necessarily provides a unique perspective for the observer. Given a limited perspective, objects and events cannot always be encoded precisely but must involve crude, quick approximations to deal with sensory information in a real- time manner. As an example, when avoiding an oncoming car, a pedestrian needs to identify the fact that a car is approaching before ascertaining the model or color of the vehicle. In our methodology, we use wavelet-based sensors with self-organized learning to encode basic sensory information in real-time. The wavelet-based sensors provide necessary transformations while a rank-based Hebbian learning scheme encodes a self-organized environment through translation, scale and orientation invariant sensors. Such a self-organized environment is made possible by combining wavelet sets which are orthonormal, log-scale with linear orientation and have automatically generated membership functions. In earlier work we used Gabor wavelet filters, rank-based Hebbian learning and an exponential modulation function to encode textural information from images. Many different types of modulation are possible, but based on biological findings the exponential modulation function provided a good approximation of first spike coding of `integrate and fire' neurons. These types of Hebbian encoding schemes (e.g., exponential modulation, etc.) are useful for quick response and learning, provide several advantages over contemporary neural network learning approaches, and have been found to quantize data nonlinearly. By combining wavelets with Hebbian learning we can provide a real-time front-end for modeling an intelligent process, such as the autonomous control of agents in a simulated environment.

  11. Children benefit differently from night- and day-time sleep in motor learning.

    Science.gov (United States)

    Yan, Jin H

    2017-08-01

    Motor skill acquisition occurs while practicing (on-line) and when asleep or awake (off-line). However, developmental questions still remain about whether children of various ages benefit similarly or differentially from night- and day-time sleeping. The likely circadian effects (time-of-day) and the possible between-test-interference (order effects) associated with children's off-line motor learning are currently unknown. Therefore, this study examines the contributions of over-night sleeping and mid-day napping to procedural skill learning. One hundred and eight children were instructed to practice a finger sequence task using computer keyboards. After an equivalent 11-h interval in one of the three states (sleep, nap, wakefulness), children performed the same sequence in retention tests and a novel sequence in transfer tests. Changes in the movement time and sequence accuracy were evaluated between ages (6-7, 8-9, 10-11years) during practice, and from skill training to retrievals across three states. Results suggest that night-time sleeping and day-time napping improved the tapping speed, especially for the 6-year-olds. The circadian factor did not affect off-line motor learning in children. The interference between the two counter-balanced retrieval tests was not found for the off-line motor learning. This research offers possible evidence about the age-related motor learning characteristics in children and a potential means for enhancing developmental motor skills. The dynamics between age, experience, memory formation, and the theoretical implications of motor skill acquisition are discussed. Copyright © 2017 Elsevier B.V. All rights reserved.

  12. An investigation of fMRI time series stationarity during motor sequence learning foot tapping tasks.

    Science.gov (United States)

    Muhei-aldin, Othman; VanSwearingen, Jessie; Karim, Helmet; Huppert, Theodore; Sparto, Patrick J; Erickson, Kirk I; Sejdić, Ervin

    2014-04-30

    Understanding complex brain networks using functional magnetic resonance imaging (fMRI) is of great interest to clinical and scientific communities. To utilize advanced analysis methods such as graph theory for these investigations, the stationarity of fMRI time series needs to be understood as it has important implications on the choice of appropriate approaches for the analysis of complex brain networks. In this paper, we investigated the stationarity of fMRI time series acquired from twelve healthy participants while they performed a motor (foot tapping sequence) learning task. Since prior studies have documented that learning is associated with systematic changes in brain activation, a sequence learning task is an optimal paradigm to assess the degree of non-stationarity in fMRI time-series in clinically relevant brain areas. We predicted that brain regions involved in a "learning network" would demonstrate non-stationarity and may violate assumptions associated with some advanced analysis approaches. Six blocks of learning, and six control blocks of a foot tapping sequence were performed in a fixed order. The reverse arrangement test was utilized to investigate the time series stationarity. Our analysis showed some non-stationary signals with a time varying first moment as a major source of non-stationarity. We also demonstrated a decreased number of non-stationarities in the third block as a result of priming and repetition. Most of the current literature does not examine stationarity prior to processing. The implication of our findings is that future investigations analyzing complex brain networks should utilize approaches robust to non-stationarities, as graph-theoretical approaches can be sensitive to non-stationarities present in data. Copyright © 2014 Elsevier B.V. All rights reserved.

  13. Autism: Too eager to learn? Event related potential findings of increased dependency on intentional learning in a serial reaction time task.

    Science.gov (United States)

    Zwart, Fenny S; Vissers, Constance Th W M; van der Meij, Roemer; Kessels, Roy P C; Maes, Joseph H R

    2017-09-01

    It has been suggested that people with autism spectrum disorder (ASD) have an increased tendency to use explicit (or intentional) learning strategies. This altered learning may play a role in the development of the social communication difficulties characterizing ASD. In the current study, we investigated incidental and intentional sequence learning using a Serial Reaction Time (SRT) task in an adult ASD population. Response times and event related potentials (ERP) components (N2b and P3) were assessed as indicators of learning and knowledge. Findings showed that behaviorally, sequence learning and ensuing explicit knowledge were similar in ASD and typically developing (TD) controls. However, ERP findings showed that learning in the TD group was characterized by an enhanced N2b, while learning in the ASD group was characterized by an enhanced P3. These findings suggest that learning in the TD group might be more incidental in nature, whereas learning in the ASD group is more intentional or effortful. Increased intentional learning might serve as a strategy for individuals with ASD to control an overwhelming environment. Although this led to similar behavioral performances on the SRT task, it is very plausible that this intentional learning has adverse effects in more complex social situations, and hence contributes to the social impairments found in ASD. Autism Res 2017, 10: 1533-1543. © 2017 International Society for Autism Research, Wiley Periodicals, Inc. © 2017 International Society for Autism Research, Wiley Periodicals, Inc.

  14. Experiments with Online Reinforcement Learning in Real-Time Strategy Games

    DEFF Research Database (Denmark)

    Toftgaard Andersen, Kresten; Zeng, Yifeng; Dahl Christensen, Dennis

    2009-01-01

    Real-time strategy (RTS) games provide a challenging platform to implement online reinforcement learning (RL) techniques in a real application. Computer, as one game player, monitors opponents' (human or other computers) strategies and then updates its own policy using RL methods. In this article......, we first examine the suitability of applying the online RL in various computer games. Reinforcement learning application depends on both RL complexity and the game features. We then propose a multi-layer framework for implementing online RL in an RTS game. The framework significantly reduces RL...... the effectiveness of our proposed framework and shed light on relevant issues in using online RL in RTS games....

  15. Time-specific measurements of energy deposition from radiation fields in simulated sub-micron tissue volumes

    International Nuclear Information System (INIS)

    Famiano, M.A.

    1997-01-01

    A tissue-equivalent spherical proportional counter is used with a modified amplifier system to measure specific energy deposited from a uniform radiation field for short periods of time (∼1 micros to seconds) in order to extrapolate to dose in sub-micron tissue volumes. The energy deposited during these time intervals is compared to biological repair processes occurring within the same intervals after the initial energy deposition. The signal is integrated over a variable collection time which is adjusted with a square-wave pulse. Charge from particle passages is collected on the anode during the period in which the integrator is triggered, and the signal decays quickly to zero after the integrator feedback switch resets; the process repeats for every triggering pulse. Measurements of energy deposited from x rays, 137 Cs gamma rays, and electrons from a 90 Sr/ 90 Y source for various time intervals are taken. Spectral characteristics as a function of charge collection time are observed and frequency plots of specific energy and collection time-interval are presented. In addition, a threshold energy flux is selected for each radiation type at which the formation of radicals (based on current measurements) in mammalian cells equals the rate at which radicals are repaired

  16. Event timing in associative learning: from biochemical reaction dynamics to behavioural observations.

    Directory of Open Access Journals (Sweden)

    Ayse Yarali

    Full Text Available Associative learning relies on event timing. Fruit flies for example, once trained with an odour that precedes electric shock, subsequently avoid this odour (punishment learning; if, on the other hand the odour follows the shock during training, it is approached later on (relief learning. During training, an odour-induced Ca(++ signal and a shock-induced dopaminergic signal converge in the Kenyon cells, synergistically activating a Ca(++-calmodulin-sensitive adenylate cyclase, which likely leads to the synaptic plasticity underlying the conditioned avoidance of the odour. In Aplysia, the effect of serotonin on the corresponding adenylate cyclase is bi-directionally modulated by Ca(++, depending on the relative timing of the two inputs. Using a computational approach, we quantitatively explore this biochemical property of the adenylate cyclase and show that it can generate the effect of event timing on associative learning. We overcome the shortage of behavioural data in Aplysia and biochemical data in Drosophila by combining findings from both systems.

  17. Learning of Precise Spike Times with Homeostatic Membrane Potential Dependent Synaptic Plasticity.

    Directory of Open Access Journals (Sweden)

    Christian Albers

    Full Text Available Precise spatio-temporal patterns of neuronal action potentials underly e.g. sensory representations and control of muscle activities. However, it is not known how the synaptic efficacies in the neuronal networks of the brain adapt such that they can reliably generate spikes at specific points in time. Existing activity-dependent plasticity rules like Spike-Timing-Dependent Plasticity are agnostic to the goal of learning spike times. On the other hand, the existing formal and supervised learning algorithms perform a temporally precise comparison of projected activity with the target, but there is no known biologically plausible implementation of this comparison. Here, we propose a simple and local unsupervised synaptic plasticity mechanism that is derived from the requirement of a balanced membrane potential. Since the relevant signal for synaptic change is the postsynaptic voltage rather than spike times, we call the plasticity rule Membrane Potential Dependent Plasticity (MPDP. Combining our plasticity mechanism with spike after-hyperpolarization causes a sensitivity of synaptic change to pre- and postsynaptic spike times which can reproduce Hebbian spike timing dependent plasticity for inhibitory synapses as was found in experiments. In addition, the sensitivity of MPDP to the time course of the voltage when generating a spike allows MPDP to distinguish between weak (spurious and strong (teacher spikes, which therefore provides a neuronal basis for the comparison of actual and target activity. For spatio-temporal input spike patterns our conceptually simple plasticity rule achieves a surprisingly high storage capacity for spike associations. The sensitivity of the MPDP to the subthreshold membrane potential during training allows robust memory retrieval after learning even in the presence of activity corrupted by noise. We propose that MPDP represents a biophysically plausible mechanism to learn temporal target activity patterns.

  18. Learning of Precise Spike Times with Homeostatic Membrane Potential Dependent Synaptic Plasticity.

    Science.gov (United States)

    Albers, Christian; Westkott, Maren; Pawelzik, Klaus

    2016-01-01

    Precise spatio-temporal patterns of neuronal action potentials underly e.g. sensory representations and control of muscle activities. However, it is not known how the synaptic efficacies in the neuronal networks of the brain adapt such that they can reliably generate spikes at specific points in time. Existing activity-dependent plasticity rules like Spike-Timing-Dependent Plasticity are agnostic to the goal of learning spike times. On the other hand, the existing formal and supervised learning algorithms perform a temporally precise comparison of projected activity with the target, but there is no known biologically plausible implementation of this comparison. Here, we propose a simple and local unsupervised synaptic plasticity mechanism that is derived from the requirement of a balanced membrane potential. Since the relevant signal for synaptic change is the postsynaptic voltage rather than spike times, we call the plasticity rule Membrane Potential Dependent Plasticity (MPDP). Combining our plasticity mechanism with spike after-hyperpolarization causes a sensitivity of synaptic change to pre- and postsynaptic spike times which can reproduce Hebbian spike timing dependent plasticity for inhibitory synapses as was found in experiments. In addition, the sensitivity of MPDP to the time course of the voltage when generating a spike allows MPDP to distinguish between weak (spurious) and strong (teacher) spikes, which therefore provides a neuronal basis for the comparison of actual and target activity. For spatio-temporal input spike patterns our conceptually simple plasticity rule achieves a surprisingly high storage capacity for spike associations. The sensitivity of the MPDP to the subthreshold membrane potential during training allows robust memory retrieval after learning even in the presence of activity corrupted by noise. We propose that MPDP represents a biophysically plausible mechanism to learn temporal target activity patterns.

  19. A Stable Marching on-in-time Scheme for Solving the Time Domain Electric Field Volume Integral Equation on High-contrast Scatterers

    KAUST Repository

    Sayed, Sadeed Bin

    2015-05-05

    A time domain electric field volume integral equation (TD-EFVIE) solver is proposed for characterizing transient electromagnetic wave interactions on high-contrast dielectric scatterers. The TD-EFVIE is discretized using the Schaubert- Wilton-Glisson (SWG) and approximate prolate spherical wave (APSW) functions in space and time, respectively. The resulting system of equations can not be solved by a straightforward application of the marching on-in-time (MOT) scheme since the two-sided APSW interpolation functions require the knowledge of unknown “future” field samples during time marching. Causality of the MOT scheme is restored using an extrapolation technique that predicts the future samples from known “past” ones. Unlike the extrapolation techniques developed for MOT schemes that are used in solving time domain surface integral equations, this scheme trains the extrapolation coefficients using samples of exponentials with exponents on the complex frequency plane. This increases the stability of the MOT-TD-EFVIE solver significantly, since the temporal behavior of decaying and oscillating electromagnetic modes induced inside the scatterers is very accurately taken into account by this new extrapolation scheme. Numerical results demonstrate that the proposed MOT solver maintains its stability even when applied to analyzing wave interactions on high-contrast scatterers.

  20. A Stable Marching on-in-time Scheme for Solving the Time Domain Electric Field Volume Integral Equation on High-contrast Scatterers

    KAUST Repository

    Sayed, Sadeed Bin; Ulku, Huseyin; Bagci, Hakan

    2015-01-01

    A time domain electric field volume integral equation (TD-EFVIE) solver is proposed for characterizing transient electromagnetic wave interactions on high-contrast dielectric scatterers. The TD-EFVIE is discretized using the Schaubert- Wilton-Glisson (SWG) and approximate prolate spherical wave (APSW) functions in space and time, respectively. The resulting system of equations can not be solved by a straightforward application of the marching on-in-time (MOT) scheme since the two-sided APSW interpolation functions require the knowledge of unknown “future” field samples during time marching. Causality of the MOT scheme is restored using an extrapolation technique that predicts the future samples from known “past” ones. Unlike the extrapolation techniques developed for MOT schemes that are used in solving time domain surface integral equations, this scheme trains the extrapolation coefficients using samples of exponentials with exponents on the complex frequency plane. This increases the stability of the MOT-TD-EFVIE solver significantly, since the temporal behavior of decaying and oscillating electromagnetic modes induced inside the scatterers is very accurately taken into account by this new extrapolation scheme. Numerical results demonstrate that the proposed MOT solver maintains its stability even when applied to analyzing wave interactions on high-contrast scatterers.

  1. Occupy Education: Living and Learning Sustainability. Global Studies in Education. Volume 22

    Science.gov (United States)

    Evans, Tina Lynn

    2012-01-01

    "Occupy Education" is motivated by the sustainability crisis and energized by the drive for social justice that inspired the Occupy movement. Situated within the struggle for sustainability taking place amid looming resource shortages, climate change, economic instability, and ecological breakdown, the book is a timely contribution to community…

  2. Dedicated Shift Wrap-up Time Does Not Improve Resident Sign-out Volume or Efficiency.

    Science.gov (United States)

    Jeanmonod, Rebecca K; Brook, Christopher; Winther, Mark; Pathak, Soma; Boyd, Molly

    2010-02-01

    Sign-out (SO) is a challenge to the emergency physician. Some training programs have instituted overlapping 9-hour shifts. The residents see patients for eight hours, and have one hour of wrap-up time. This hour helps them complete patient care, leaving fewer patients to sign-out. We examined whether this strategy impacts SO burden. This is a retrospective review of patients evaluated by emergency medicine (EM) residents working 9-hour (eight hours of patient care, one hour wrap-up time) and 12-hour shifts (12 hours patient care, no reserved time for wrap-up). Data were collected by reviewing the clinical tracker. A patient was assigned to the resident who initiated care and dictated the chart. SO was defined as any patient in the ED without disposition at change of shift. Patient turn-around-time (TAT) was also recorded. One-hundred sixty-one postgraduate-year-one resident (PGY1), 264 postgraduate-year-two resident (PGY2), and 193 postgraduate-year-three resident (PGY3) shifts were included. PGY1s signed out 1.9 patients per 12-hour shift. PGY2s signed out 2.3 patients on 12-hour shifts and 1.8 patients on 9-hour shifts. PGY3s signed out 2.1 patients on 12-hour shifts and 2.0 patients on 9-hour shifts. When we controlled for patients seen per hour, SO burden was constant by class regardless of shift length, with PGY2s signing out 18% of patients seen compared to 15% for PGY3s. PGY1s signed out 18% of patients seen. TAT for patients seen by PGY1s and PGY2s was similar, at 189 and 187 minutes, respectively. TAT for patients seen by PGY3s was significantly less at 175 minutes. The additional hour devoted to wrapping up patients in the ED had no affect on SO burden. The SO burden represented a fixed percentage of the total number of patients seen by the residents. PGY3s sign-out a smaller percentage of patients seen compared to other classes, and have faster TATs.

  3. Dedicated Shift Wrap-up Time Does Not Improve Resident Sign-out Volume or Efficiency

    Directory of Open Access Journals (Sweden)

    Jeanmonod, Rebecca K

    2010-02-01

    Full Text Available Objectives: Sign-out (SO is a challenge to the emergency physician. Some training programs have instituted overlapping 9-hour shifts. The residents see patients for eight hours, and have one hour of wrap-up time. This hour helps them complete patient care, leaving fewer patients to sign-out. We examined whether this strategy impacts SO burden.Methods: This is a retrospective review of patients evaluated by emergency medicine (EM residents working 9-hour (eight hours of patient care, one hour wrap-up time and 12-hour shifts (12 hours patient care, no reserved time for wrap-up. Data were collected by reviewing the clinical tracker. A patient was assigned to the resident who initiated care and dictated the chart. SO was defined as any patient in the ED without disposition at change of shift. Patient turn-around-time (TAT was also recorded.Results: One-hundred sixty-one postgraduate-year-one resident (PGY1, 264 postgraduate-year-two resident (PGY2, and 193 postgraduate-year-three resident (PGY3 shifts were included. PGY1s signed out 1.9 patients per 12-hour shift. PGY2s signed out 2.3 patients on 12-hour shifts and 1.8 patients on 9-hour shifts. PGY3s signed out 2.1 patients on 12-hour shifts and 2.0 patients on 9-hour shifts. When we controlled for patients seen per hour, SO burden was constant by class regardless of shift length, with PGY2s signing out 18% of patients seen compared to 15% for PGY3s. PGY1s signed out 18% of patients seen. TAT for patients seen by PGY1s and PGY2s was similar, at 189 and 187 minutes, respectively. TAT for patients seen by PGY3s was significantly less at 175 minutes.Conclusion: The additional hour devoted to wrapping up patients in the ED had no affect on SO burden. The SO burden represented a fixed percentage of the total number of patients seen by the residents. PGY3s sign-out a smaller percentage of patients seen compared to other classes, and have faster TATs. [West J Emerg Med. 2010; 11(1:35-39].

  4. Developing Mentors: Adult participation, practices, and learning in an out-of-school time STEM program

    Science.gov (United States)

    Scipio, Deana Aeolani

    This dissertation examines learning within an out-of-school time (OST) Science, Technology, Engineering, and Mathematics (STEM) broadening participation program. The dissertation includes an introduction, three empirical chapters (written as individual articles), and a conclusion. The dissertation context is a chemical oceanography OST program for middle school students called Project COOL---Chemical Oceanography Outside the Lab. The program was a collaboration between middle school OST programming, a learning sciences research laboratory, and a chemical oceanography laboratory. Both labs were located at a research-based university in the Pacific Northwest of the United States. Participants include 34 youth, 12 undergraduates, and five professional scientists. The dissertation data corpus includes six years of ethnographic field notes across three field sites, 400 hours of video and audio recordings, 40 hours of semi-structured interviews, and more than 100 participant generated artifacts. Analysis methods include comparative case analysis, cognitive mapping, semiotic cluster analysis, video interaction analysis, and discourse analysis. The first empirical article focuses on synthesizing productive programmatic features from four years of design-based research.. The second article is a comparative case study of three STEM mentors from non-dominant communities in the 2011 COOL OST Program. The third article is a comparative case study of undergraduates learning to be mentors in the 2014 COOL OST Program. Findings introduce Deep Hanging as a theory of learning in practice. Deep Hanging entails authentic tasks in rich contexts, providing access, capitalizing on opportunity, and building interpersonal relationships. Taken together, these three chapters illuminate the process of designing a rich OST learning environment and the kinds of learning in practice that occurred for adult learners learning to be mentors through their participation in the COOL OST program. In

  5. 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.

  6. Predicting the time of conversion to MCI in the elderly: role of verbal expression and learning.

    Science.gov (United States)

    Oulhaj, Abderrahim; Wilcock, Gordon K; Smith, A David; de Jager, Celeste A

    2009-11-03

    Increasing awareness that minimal or mild cognitive impairment (MCI) in the elderly may be a precursor of dementia has led to an increase in the number of people attending memory clinics. We aimed to develop a way of predicting the period of time before cognitive impairment occurs in community-dwelling elderly. The method is illustrated by the use of simple tests of different cognitive domains. A cohort of 241 normal elderly volunteers was followed for up to 20 years with regular assessments of cognitive abilities using the Cambridge Cognitive Examination (CAMCOG); 91 participants developed MCI. We used interval-censored survival analysis statistical methods to model which baseline cognitive tests best predicted the time to convert to MCI. Out of several baseline variables, only age and CAMCOG subscores for expression and learning/memory were predictors of the time to conversion. The time to conversion was 14% shorter for each 5 years of age, 17% shorter for each point lower in the expression score, and 15% shorter for each point lower in the learning score. We present in tabular form the probability of converting to MCI over intervals between 2 and 10 years for different combinations of expression and learning scores. In apparently normal elderly people, subtle measurable cognitive deficits that occur within the normal range on standard testing protocols reliably predict the time to clinically relevant cognitive impairment long before clinical symptoms are reported.

  7. New digital measurement methods for left ventricular volume using real-time three-dimensional echocardiography: comparison with electromagnetic flow method and magnetic resonance imaging

    Science.gov (United States)

    Qin, J. J.; Jones, M.; Shiota, T.; Greenberg, N. L.; Firstenberg, M. S.; Tsujino, H.; Zetts, A. D.; Sun, J. P.; Cardon, L. A.; Odabashian, J. A.; hide

    2000-01-01

    AIM: The aim of this study was to investigate the feasibility and accuracy of using symmetrically rotated apical long axis planes for the determination of left ventricular (LV) volumes with real-time three-dimensional echocardiography (3DE). METHODS AND RESULTS: Real-time 3DE was performed in six sheep during 24 haemodynamic conditions with electromagnetic flow measurements (EM), and in 29 patients with magnetic resonance imaging measurements (MRI). LV volumes were calculated by Simpson's rule with five 3DE methods (i.e. apical biplane, four-plane, six-plane, nine-plane (in which the angle between each long axis plane was 90 degrees, 45 degrees, 30 degrees or 20 degrees, respectively) and standard short axis views (SAX)). Real-time 3DE correlated well with EM for LV stroke volumes in animals (r=0.68-0.95) and with MRI for absolute volumes in patients (r-values=0.93-0.98). However, agreement between MRI and apical nine-plane, six-plane, and SAX methods in patients was better than those with apical four-plane and bi-plane methods (mean difference = -15, -18, -13, vs. -31 and -48 ml for end-diastolic volume, respectively, Pmethods of real-time 3DE correlated well with reference standards for calculating LV volumes. Balancing accuracy and required time for these LV volume measurements, the apical six-plane method is recommended for clinical use.

  8. Optimal and Scalable Caching for 5G Using Reinforcement Learning of Space-Time Popularities

    Science.gov (United States)

    Sadeghi, Alireza; Sheikholeslami, Fatemeh; Giannakis, Georgios B.

    2018-02-01

    Small basestations (SBs) equipped with caching units have potential to handle the unprecedented demand growth in heterogeneous networks. Through low-rate, backhaul connections with the backbone, SBs can prefetch popular files during off-peak traffic hours, and service them to the edge at peak periods. To intelligently prefetch, each SB must learn what and when to cache, while taking into account SB memory limitations, the massive number of available contents, the unknown popularity profiles, as well as the space-time popularity dynamics of user file requests. In this work, local and global Markov processes model user requests, and a reinforcement learning (RL) framework is put forth for finding the optimal caching policy when the transition probabilities involved are unknown. Joint consideration of global and local popularity demands along with cache-refreshing costs allow for a simple, yet practical asynchronous caching approach. The novel RL-based caching relies on a Q-learning algorithm to implement the optimal policy in an online fashion, thus enabling the cache control unit at the SB to learn, track, and possibly adapt to the underlying dynamics. To endow the algorithm with scalability, a linear function approximation of the proposed Q-learning scheme is introduced, offering faster convergence as well as reduced complexity and memory requirements. Numerical tests corroborate the merits of the proposed approach in various realistic settings.

  9. Adjustment to subtle time constraints and power law learning in rapid serial visual presentation

    Directory of Open Access Journals (Sweden)

    Jacqueline Chakyung Shin

    2015-11-01

    Full Text Available We investigated whether attention could be modulated through the implicit learning of temporal information in a rapid serial visual presentation (RSVP task. Participants identified two target letters among numeral distractors. The stimulus-onset asynchrony immediately following the first target (SOA1 varied at three levels (70, 98, and 126 ms randomly between trials or fixed within blocks of trials. Practice over three consecutive days resulted in a continuous improvement in the identification rate for both targets and attenuation of the attentional blink (AB, a decrement in target (T2 identification when presented 200-400 ms after another target (T1. Blocked SOA1s led to a faster rate of improvement in RSVP performance and more target order reversals relative to random SOA1s, suggesting that the implicit learning of SOA1 positively affected performance. The results also reveal power law learning curves for individual target identification as well as the reduction in the AB decrement. These learning curves reflect the spontaneous emergence of skill through subtle attentional modulations rather than general attentional distribution. Together, the results indicate that implicit temporal learning could improve high level and rapid cognitive processing and highlights the sensitivity and adaptability of the attentional system to subtle constraints in stimulus timing.

  10. Learning from Experience, Volume 3: Lessons from the United Kingdom’s Astute Submarine Program

    Science.gov (United States)

    2011-01-01

    Growing future program managers and technical personnel within the MOD and the Royal Navy requires planning and implementation far in advance of...recognize problems that were growing in the program. At the same time, the program’s payment clauses were tied to production metrics such as length of...Ursula Laid down Launch Commission Unicorn Osiris Opportune Valiant Valiant Spartan Swiftsure Splendid Trafalgar Triumph Renown Repulse Vanguard

  11. Time, dose and volume factors in interstitial brachytherapy combined with external irradiation for oral tongue carcinoma

    International Nuclear Information System (INIS)

    Yorozu, Atsunori

    1996-01-01

    This is a retrospective analysis of 136 patients with squamous cell carcinoma of stages I and II of the oral tongue who were treated with interstitial brachytherapy alone or in combination with external irradiation between 1976 and 1991. Control of the primary lesion and the occurrence of late complications were analyzed with respect to dose, time and tumor size with the Cox hazard model. The 5-year survival rates for stages I and II were 84.5% and 75.6%. The 5-year primary control rate was 91.3% for stage I and 77.3% for stage II (p 50 Gy compared with a brachytherapy dose 30 mm. Late complications should be reduced by using a spacer, improvements in dental and oral hygiene, and a sophisticated implant method. (author)

  12. Multi-GPU-based acceleration of the explicit time domain volume integral equation solver using MPI-OpenACC

    KAUST Repository

    Feki, Saber

    2013-07-01

    An explicit marching-on-in-time (MOT)-based time-domain volume integral equation (TDVIE) solver has recently been developed for characterizing transient electromagnetic wave interactions on arbitrarily shaped dielectric bodies (A. Al-Jarro et al., IEEE Trans. Antennas Propag., vol. 60, no. 11, 2012). The solver discretizes the spatio-temporal convolutions of the source fields with the background medium\\'s Green function using nodal discretization in space and linear interpolation in time. The Green tensor, which involves second order spatial and temporal derivatives, is computed using finite differences on the temporal and spatial grid. A predictor-corrector algorithm is used to maintain the stability of the MOT scheme. The simplicity of the discretization scheme permits the computation of the discretized spatio-temporal convolutions on the fly during time marching; no \\'interaction\\' matrices are pre-computed or stored resulting in a memory efficient scheme. As a result, most often the applicability of this solver to the characterization of wave interactions on electrically large structures is limited by the computation time but not the memory. © 2013 IEEE.

  13. Enabling Real-Time Volume Rendering of Functional Magnetic Resonance Imaging on an iOS Device.

    Science.gov (United States)

    Holub, Joseph; Winer, Eliot

    2017-12-01

    Powerful non-invasive imaging technologies like computed tomography (CT), ultrasound, and magnetic resonance imaging (MRI) are used daily by medical professionals to diagnose and treat patients. While 2D slice viewers have long been the standard, many tools allowing 3D representations of digital medical data are now available. The newest imaging advancement, functional MRI (fMRI) technology, has changed medical imaging from viewing static to dynamic physiology (4D) over time, particularly to study brain activity. Add this to the rapid adoption of mobile devices for everyday work and the need to visualize fMRI data on tablets or smartphones arises. However, there are few mobile tools available to visualize 3D MRI data, let alone 4D fMRI data. Building volume rendering tools on mobile devices to visualize 3D and 4D medical data is challenging given the limited computational power of the devices. This paper describes research that explored the feasibility of performing real-time 3D and 4D volume raycasting on a tablet device. The prototype application was tested on a 9.7" iPad Pro using two different fMRI datasets of brain activity. The results show that mobile raycasting is able to achieve between 20 and 40 frames per second for traditional 3D datasets, depending on the sampling interval, and up to 9 frames per second for 4D data. While the prototype application did not always achieve true real-time interaction, these results clearly demonstrated that visualizing 3D and 4D digital medical data is feasible with a properly constructed software framework.

  14. Editorial volume 3 - issue 3: The future of the Learning Management System

    OpenAIRE

    Yngve Nordkvelle

    2007-01-01

    Time Magazine argued in 2006 that the person of the year truly was “You”. This was in deed a significant gesture to the fact that digital technologies change the way people interact and live their lives. What made “You” a candidate for “Person of the year”, was that the development of the Internet had made it possible for anyone to publish and express your personality on the Web; or rather of “Web 2.0”. In 2007, the notion of “Web 2.0” has been on headlines for many conferences and convention...

  15. Touchscreen Facilitates Young Children’s Transfer of Learning to Tell Time

    Directory of Open Access Journals (Sweden)

    Fuxing Wang

    2016-11-01

    Full Text Available Young children are devoting increasing time to playing on handheld touchscreen devices (e.g., iPads. Though thousands of touchscreen apps are claimed to be educational, there is a lack of sufficient evidence examining the impact of touchscreens on children’s learning outcomes. In the present study, the two questions we focused on were (a whether using a touchscreen was helpful in teaching children to tell time, and (b to what extent young children could transfer what they had learned on the touchscreen to other media. A pre- and posttest design was adopted. After learning to read the time on the iPad touchscreen for 10 minutes, three groups of 5- to 6-year-old children (N = 65 were respectively tested with an iPad touchscreen, a toy clock or a drawing of a clock on paper. The results revealed that posttest scores in the iPad touchscreen test group were significantly higher than those at pretest, indicating that the touchscreen itself could provide support for young children’s learning. Similarly, regardless of being tested with a toy clock or paper drawing, children’s posttest performance was also better than pretest, suggesting that children could transfer what they had learned on an iPad touchscreen to other media. However, comparison among groups showed that children tested with the paper drawing underperformed those tested with the other two media. The theoretical and practical implications of the results, as well as limitations of the present study, are discussed.

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

    Directory of Open Access Journals (Sweden)

    Alexandru D. Iordan

    2018-01-01

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

  17. A comparison of the stochastic and machine learning approaches in hydrologic time series forecasting

    Science.gov (United States)

    Kim, T.; Joo, K.; Seo, J.; Heo, J. H.

    2016-12-01

    Hydrologic time series forecasting is an essential task in water resources management and it becomes more difficult due to the complexity of runoff process. Traditional stochastic models such as ARIMA family has been used as a standard approach in time series modeling and forecasting of hydrological variables. Due to the nonlinearity in hydrologic time series data, machine learning approaches has been studied with the advantage of discovering relevant features in a nonlinear relation among variables. This study aims to compare the predictability between the traditional stochastic model and the machine learning approach. Seasonal ARIMA model was used as the traditional time series model, and Random Forest model which consists of decision tree and ensemble method using multiple predictor approach was applied as the machine learning approach. In the application, monthly inflow data from 1986 to 2015 of Chungju dam in South Korea were used for modeling and forecasting. In order to evaluate the performances of the used models, one step ahead and multi-step ahead forecasting was applied. Root mean squared error and mean absolute error of two models were compared.

  18. EEG Eye State Identification Using Incremental Attribute Learning with Time-Series Classification

    Directory of Open Access Journals (Sweden)

    Ting Wang

    2014-01-01

    Full Text Available Eye state identification is a kind of common time-series classification problem which is also a hot spot in recent research. Electroencephalography (EEG is widely used in eye state classification to detect human's cognition state. Previous research has validated the feasibility of machine learning and statistical approaches for EEG eye state classification. This paper aims to propose a novel approach for EEG eye state identification using incremental attribute learning (IAL based on neural networks. IAL is a novel machine learning strategy which gradually imports and trains features one by one. Previous studies have verified that such an approach is applicable for solving a number of pattern recognition problems. However, in these previous works, little research on IAL focused on its application to time-series problems. Therefore, it is still unknown whether IAL can be employed to cope with time-series problems like EEG eye state classification. Experimental results in this study demonstrates that, with proper feature extraction and feature ordering, IAL can not only efficiently cope with time-series classification problems, but also exhibit better classification performance in terms of classification error rates in comparison with conventional and some other approaches.

  19. Approximation of Gas Volume in a Seafloor Sediment using Time Domain Reflectometry in the Okhotsk Sea

    Science.gov (United States)

    Aoki, S.; Noborio, K.; Matsumoto, R.

    2013-12-01

    Global warming has accelerated in recent decades as the concentration of carbon dioxide has increased in the atmosphere due to fossil fuel burning. In addition, increases in consuming fossil fuels have led to their depletion in recent years. One practical measure to meet these two challenges is the conversion of energy resources to natural gas that has less environmental impact. Gas hydrates that contain natural gas have been discovered in the sea around Japan. They are expected to serve as a new non-conventional natural gas resource. To understand the mechanism of gas hydrate accumulation, the amount of free gas in sediments should be known. However, it is difficult to measure this non-destructively without affecting other properties. In this study we examined a technique for measuring the amount of free gas using Time Domain Reflectometry (TDR). TDR was a method of measuring the dielectric constant of the soil. This method is based on the relationship between the volumetric water content and dielectric constant, to estimate the volumetric water content indirectly. TDR has commonly been used to measure the moisture content of soil such as cultivation and paddy. In our study, we used TDR to estimate the gas ratio in the sea-bottom sediment obtained from the Sea of Okhotsk. Measurement by the TDR method was difficult in a high electrical conductivity solution such as seawater. Therefore, we blunted the measurement sensitivity by coating TDR probe with plastic, which makes it possible to measure. We found that the gas phase rates differed depending on the depth and location, so gas phase existed up to about 10%.

  20. Influencing Work-Related Learning: The Role of Job Characteristics and Self-Directed Learning Orientation in Part-Time Vocational Education

    Science.gov (United States)

    Gijbels, David; Raemdonck, Isabel; Vervecken, Dries

    2010-01-01

    Based on the Demand-Control-Support (DCS) model, the present paper aims to investigate the influence of job characteristics such as job demands, job control, social support at work and self-directed learning orientation on the work-related learning behaviour of workers. The present study was conducted in a centre for part-time vocational education…

  1. Real-time, adaptive machine learning for non-stationary, near chaotic gasoline engine combustion time series.

    Science.gov (United States)

    Vaughan, Adam; Bohac, Stanislav V

    2015-10-01

    Fuel efficient Homogeneous Charge Compression Ignition (HCCI) engine combustion timing predictions must contend with non-linear chemistry, non-linear physics, period doubling bifurcation(s), turbulent mixing, model parameters that can drift day-to-day, and air-fuel mixture state information that cannot typically be resolved on a cycle-to-cycle basis, especially during transients. In previous work, an abstract cycle-to-cycle mapping function coupled with ϵ-Support Vector Regression was shown to predict experimentally observed cycle-to-cycle combustion timing over a wide range of engine conditions, despite some of the aforementioned difficulties. The main limitation of the previous approach was that a partially acasual randomly sampled training dataset was used to train proof of concept offline predictions. The objective of this paper is to address this limitation by proposing a new online adaptive Extreme Learning Machine (ELM) extension named Weighted Ring-ELM. This extension enables fully causal combustion timing predictions at randomly chosen engine set points, and is shown to achieve results that are as good as or better than the previous offline method. The broader objective of this approach is to enable a new class of real-time model predictive control strategies for high variability HCCI and, ultimately, to bring HCCI's low engine-out NOx and reduced CO2 emissions to production engines. Copyright © 2015 Elsevier Ltd. All rights reserved.

  2. Taxi-Out Time Prediction for Departures at Charlotte Airport Using Machine Learning Techniques

    Science.gov (United States)

    Lee, Hanbong; Malik, Waqar; Jung, Yoon C.

    2016-01-01

    Predicting the taxi-out times of departures accurately is important for improving airport efficiency and takeoff time predictability. In this paper, we attempt to apply machine learning techniques to actual traffic data at Charlotte Douglas International Airport for taxi-out time prediction. To find the key factors affecting aircraft taxi times, surface surveillance data is first analyzed. From this data analysis, several variables, including terminal concourse, spot, runway, departure fix and weight class, are selected for taxi time prediction. Then, various machine learning methods such as linear regression, support vector machines, k-nearest neighbors, random forest, and neural networks model are applied to actual flight data. Different traffic flow and weather conditions at Charlotte airport are also taken into account for more accurate prediction. The taxi-out time prediction results show that linear regression and random forest techniques can provide the most accurate prediction in terms of root-mean-square errors. We also discuss the operational complexity and uncertainties that make it difficult to predict the taxi times accurately.

  3. Sensitivity analysis of machine-learning models of hydrologic time series

    Science.gov (United States)

    O'Reilly, A. M.

    2017-12-01

    Sensitivity analysis traditionally has been applied to assessing model response to perturbations in model parameters, where the parameters are those model input variables adjusted during calibration. Unlike physics-based models where parameters represent real phenomena, the equivalent of parameters for machine-learning models are simply mathematical "knobs" that are automatically adjusted during training/testing/verification procedures. Thus the challenge of extracting knowledge of hydrologic system functionality from machine-learning models lies in their very nature, leading to the label "black box." Sensitivity analysis of the forcing-response behavior of machine-learning models, however, can provide understanding of how the physical phenomena represented by model inputs affect the physical phenomena represented by model outputs.As part of a previous study, hybrid spectral-decomposition artificial neural network (ANN) models were developed to simulate the observed behavior of hydrologic response contained in multidecadal datasets of lake water level, groundwater level, and spring flow. Model inputs used moving window averages (MWA) to represent various frequencies and frequency-band components of time series of rainfall and groundwater use. Using these forcing time series, the MWA-ANN models were trained to predict time series of lake water level, groundwater level, and spring flow at 51 sites in central Florida, USA. A time series of sensitivities for each MWA-ANN model was produced by perturbing forcing time-series and computing the change in response time-series per unit change in perturbation. Variations in forcing-response sensitivities are evident between types (lake, groundwater level, or spring), spatially (among sites of the same type), and temporally. Two generally common characteristics among sites are more uniform sensitivities to rainfall over time and notable increases in sensitivities to groundwater usage during significant drought periods.

  4. Real-time cerebellar neuroprosthetic system based on a spiking neural network model of motor learning

    Science.gov (United States)

    Xu, Tao; Xiao, Na; Zhai, Xiaolong; Chan, Pak Kwan; Tin, Chung

    2018-02-01

    Objective. Damage to the brain, as a result of various medical conditions, impacts the everyday life of patients and there is still no complete cure to neurological disorders. Neuroprostheses that can functionally replace the damaged neural circuit have recently emerged as a possible solution to these problems. Here we describe the development of a real-time cerebellar neuroprosthetic system to substitute neural function in cerebellar circuitry for learning delay eyeblink conditioning (DEC). Approach. The system was empowered by a biologically realistic spiking neural network (SNN) model of the cerebellar neural circuit, which considers the neuronal population and anatomical connectivity of the network. The model simulated synaptic plasticity critical for learning DEC. This SNN model was carefully implemented on a field programmable gate array (FPGA) platform for real-time simulation. This hardware system was interfaced in in vivo experiments with anesthetized rats and it used neural spikes recorded online from the animal to learn and trigger conditioned eyeblink in the animal during training. Main results. This rat-FPGA hybrid system was able to process neuronal spikes in real-time with an embedded cerebellum model of ~10 000 neurons and reproduce learning of DEC with different inter-stimulus intervals. Our results validated that the system performance is physiologically relevant at both the neural (firing pattern) and behavioral (eyeblink pattern) levels. Significance. This integrated system provides the sufficient computation power for mimicking the cerebellar circuit in real-time. The system interacts with the biological system naturally at the spike level and can be generalized for including other neural components (neuron types and plasticity) and neural functions for potential neuroprosthetic applications.

  5. Kinesthetic Astronomy: Significant Upgrades to the Sky Time Lesson that Support Student Learning

    Science.gov (United States)

    Morrow, C. A.; Zawaski, M.

    2004-12-01

    This paper will report on a significant upgrade to the first in a series of innovative, experiential lessons we call Kinesthetic Astronomy. The Sky Time lesson reconnects students with the astronomical meaning of the day, year, and seasons. Like all Kinesthetic Astronomy lessons, it teaches basic astronomical concepts through choreographed bodily movements and positions that provide educational sensory experiences. They are intended for sixth graders up through adult learners in both formal and informal educational settings. They emphasize astronomical concepts and phenomenon that people can readily encounter in their "everyday" lives such as time, seasons, and sky motions of the Sun, Moon, stars, and planets. Kinesthetic Astronomy lesson plans are fully aligned with national science education standards, both in content and instructional practice. Our lessons offer a complete learning cycle with written assessment opportunities now embedded throughout the lesson. We have substantially strengthened the written assessment options for the Sky Time lesson to help students translate their kinesthetic and visual learning into the verbal-linguistic and mathematical-logical realms of expression. Field testing with non-science undergraduates, middle school science teachers and students, Junior Girl Scouts, museum education staff, and outdoor educators has been providing evidence that Kinesthetic Astronomy techniques allow learners to achieve a good grasp of concepts that are much more difficult to learn in more conventional ways such as via textbooks or even computer animation. Field testing of the Sky Time lesson has also led us to significant changes from the previous version to support student learning. We will report on the nature of these changes.

  6. Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics

    Science.gov (United States)

    Wehmeyer, Christoph; Noé, Frank

    2018-06-01

    Inspired by the success of deep learning techniques in the physical and chemical sciences, we apply a modification of an autoencoder type deep neural network to the task of dimension reduction of molecular dynamics data. We can show that our time-lagged autoencoder reliably finds low-dimensional embeddings for high-dimensional feature spaces which capture the slow dynamics of the underlying stochastic processes—beyond the capabilities of linear dimension reduction techniques.

  7. Real-time cerebellar neuroprosthetic system based on a spiking neural network model of motor learning.

    Science.gov (United States)

    Xu, Tao; Xiao, Na; Zhai, Xiaolong; Kwan Chan, Pak; Tin, Chung

    2018-02-01

    Damage to the brain, as a result of various medical conditions, impacts the everyday life of patients and there is still no complete cure to neurological disorders. Neuroprostheses that can functionally replace the damaged neural circuit have recently emerged as a possible solution to these problems. Here we describe the development of a real-time cerebellar neuroprosthetic system to substitute neural function in cerebellar circuitry for learning delay eyeblink conditioning (DEC). The system was empowered by a biologically realistic spiking neural network (SNN) model of the cerebellar neural circuit, which considers the neuronal population and anatomical connectivity of the network. The model simulated synaptic plasticity critical for learning DEC. This SNN model was carefully implemented on a field programmable gate array (FPGA) platform for real-time simulation. This hardware system was interfaced in in vivo experiments with anesthetized rats and it used neural spikes recorded online from the animal to learn and trigger conditioned eyeblink in the animal during training. This rat-FPGA hybrid system was able to process neuronal spikes in real-time with an embedded cerebellum model of ~10 000 neurons and reproduce learning of DEC with different inter-stimulus intervals. Our results validated that the system performance is physiologically relevant at both the neural (firing pattern) and behavioral (eyeblink pattern) levels. This integrated system provides the sufficient computation power for mimicking the cerebellar circuit in real-time. The system interacts with the biological system naturally at the spike level and can be generalized for including other neural components (neuron types and plasticity) and neural functions for potential neuroprosthetic applications.

  8. A theoretical study for the real-time assessment of external gamma exposure using equivalent-volume numerical integration

    International Nuclear Information System (INIS)

    Han, Moon Hee

    1995-02-01

    An approximate method for estimating gamma external dose due to an arbitrary distribution of radioactive material has been developed. For the assessment of external gamma dose, the space over which radioactive material is distributed has been assumed to be composed of hexagonal cells. The evaluation of three-dimensional integration over the space is an extremely time-consuming task. Hence, a different approach has been used for the study, i.e., a equivalent-volume spherical approach in which a regular hexahedron is modeled as a equivalent-volume sphere to simplify the integration. For the justification of the current approach, two case studies have been performed: a comparison with a point source approximation and a comparison of external dose rate with the Monte Carlo integration. These comparisons show that the current approach gives reasonable results in a physical sense. Computing times of the developed and Monte Carlo integration method on VAX system have been compared as a function of the number of hexagonal cells. This comparison shows that CPU times for both methods are comparable in the region of small number of cells, but in the region of large number, Monte Carlo integration needs much more computing times. The proposed method is shown to have an accuracy equivalent to Monte Carlo method with an advantage of much shorter calculation time. Then, the method developed here evaluates early off-site consequences of a nuclear accident. An accident consequence assessment model has been integrated using Gaussian puff model which is used to obtain the distribution of radioactive material in the air and on the ground. For this work, the real meteorological data measured at Kori site for 10 years (1976 - 1985) have been statistically analyzed for obtaining site-specific conditions. The short-term external gamma exposures have been assessed for several site-specific meteorological conditions. The results show that the extent and the pattern of short-term external

  9. KOMPARASI KEMAMPUAN KOMUNIKASI MATEMATIS SISWA DENGAN MODEL LEARNING CYCLE DAN TIME TOKEN

    Directory of Open Access Journals (Sweden)

    Arin Ayundhita

    2014-11-01

    Full Text Available Tujuan penelitian ini untuk mengetahui apakah model pembelajaran Learning Cycle 5E dan model pembelajaran Time Token pada siswa kelas VIII materi keliling dan luas lingkaran dapat mencapai ketuntasan belajar dan untuk mengetahui manakah yang lebih baik antara model pembelajaran Learning Cycle 5E dan model pembelajaran Time Token. Populasi dalam penelitian ini adalah siswa kelas VIII SMP Negeri 1 Sine Kabupaten Ngawi tahun pelajaran 2013/2014. Dengan menggunakan teknik cluster random sampling, terpilih sampel yaitu siswa kelas VIII A sebagai kelas eksperimen 1 dan kelas VIII E sebagai kelas eksperimen 2. Pengumpulan data dilakukan dengan metode dokumentasi, tes, dan observasi. Analisis data menggunakan uji proporsi dan uji perbedaan dua rata-rata. Dari hasil uji ketuntasan belajar diperoleh siswa kelas eksperimen 1 mencapai ketuntasan belajar klasikal sementara kelas eksperimen 2 belum mencapai ketuntasan belajar klasikal. Dari hasil uji perbedaan rata-rata satu pihak diperoleh rata-rata kemampuan komunikasi matematis siswa kelas eksperimen 1 lebih baik daripada rata-rata kemampuan komunikasi matematis siswa kelas eksperimen 2. Simpulan yang diperoleh adalah model pembelajaran Learning Cycle 5E lebih baik dari pembelajaran dengan model Time Token.

  10. 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.

  11. Incorporating Real-time Earthquake Information into Large Enrollment Natural Disaster Course Learning

    Science.gov (United States)

    Furlong, K. P.; Benz, H.; Hayes, G. P.; Villasenor, A.

    2010-12-01

    Although most would agree that the occurrence of natural disaster events such as earthquakes, volcanic eruptions, and floods can provide effective learning opportunities for natural hazards-based courses, implementing compelling materials into the large-enrollment classroom environment can be difficult. These natural hazard events derive much of their learning potential from their real-time nature, and in the modern 24/7 news-cycle where all but the most devastating events are quickly out of the public eye, the shelf life for an event is quite limited. To maximize the learning potential of these events requires that both authoritative information be available and course materials be generated as the event unfolds. Although many events such as hurricanes, flooding, and volcanic eruptions provide some precursory warnings, and thus one can prepare background materials to place the main event into context, earthquakes present a particularly confounding situation of providing no warning, but where context is critical to student learning. Attempting to implement real-time materials into large enrollment classes faces the additional hindrance of limited internet access (for students) in most lecture classrooms. In Earth 101 Natural Disasters: Hollywood vs Reality, taught as a large enrollment (150+ students) general education course at Penn State, we are collaborating with the USGS’s National Earthquake Information Center (NEIC) to develop efficient means to incorporate their real-time products into learning activities in the lecture hall environment. Over time (and numerous events) we have developed a template for presenting USGS-produced real-time information in lecture mode. The event-specific materials can be quickly incorporated and updated, along with key contextual materials, to provide students with up-to-the-minute current information. In addition, we have also developed in-class activities, such as student determination of population exposure to severe ground

  12. Off-Policy Reinforcement Learning: Optimal Operational Control for Two-Time-Scale Industrial Processes.

    Science.gov (United States)

    Li, Jinna; Kiumarsi, Bahare; Chai, Tianyou; Lewis, Frank L; Fan, Jialu

    2017-12-01

    Industrial flow lines are composed of unit processes operating on a fast time scale and performance measurements known as operational indices measured at a slower time scale. This paper presents a model-free optimal solution to a class of two time-scale industrial processes using off-policy reinforcement learning (RL). First, the lower-layer unit process control loop with a fast sampling period and the upper-layer operational index dynamics at a slow time scale are modeled. Second, a general optimal operational control problem is formulated to optimally prescribe the set-points for the unit industrial process. Then, a zero-sum game off-policy RL algorithm is developed to find the optimal set-points by using data measured in real-time. Finally, a simulation experiment is employed for an industrial flotation process to show the effectiveness of the proposed method.

  13. Integral reinforcement learning for continuous-time input-affine nonlinear systems with simultaneous invariant explorations.

    Science.gov (United States)

    Lee, Jae Young; Park, Jin Bae; Choi, Yoon Ho

    2015-05-01

    This paper focuses on a class of reinforcement learning (RL) algorithms, named integral RL (I-RL), that solve continuous-time (CT) nonlinear optimal control problems with input-affine system dynamics. First, we extend the concepts of exploration, integral temporal difference, and invariant admissibility to the target CT nonlinear system that is governed by a control policy plus a probing signal called an exploration. Then, we show input-to-state stability (ISS) and invariant admissibility of the closed-loop systems with the policies generated by integral policy iteration (I-PI) or invariantly admissible PI (IA-PI) method. Based on these, three online I-RL algorithms named explorized I-PI and integral Q -learning I, II are proposed, all of which generate the same convergent sequences as I-PI and IA-PI under the required excitation condition on the exploration. All the proposed methods are partially or completely model free, and can simultaneously explore the state space in a stable manner during the online learning processes. ISS, invariant admissibility, and convergence properties of the proposed methods are also investigated, and related with these, we show the design principles of the exploration for safe learning. Neural-network-based implementation methods for the proposed schemes are also presented in this paper. Finally, several numerical simulations are carried out to verify the effectiveness of the proposed methods.

  14. Enhancement of Online Robotics Learning Using Real-Time 3D Visualization Technology

    Directory of Open Access Journals (Sweden)

    Richard Chiou

    2010-06-01

    Full Text Available This paper discusses a real-time e-Lab Learning system based on the integration of 3D visualization technology with a remote robotic laboratory. With the emergence and development of the Internet field, online learning is proving to play a significant role in the upcoming era. In an effort to enhance Internet-based learning of robotics and keep up with the rapid progression of technology, a 3- Dimensional scheme of viewing the robotic laboratory has been introduced in addition to the remote controlling of the robots. The uniqueness of the project lies in making this process Internet-based, and remote robot operated and visualized in 3D. This 3D system approach provides the students with a more realistic feel of the 3D robotic laboratory even though they are working remotely. As a result, the 3D visualization technology has been tested as part of a laboratory in the MET 205 Robotics and Mechatronics class and has received positive feedback by most of the students. This type of research has introduced a new level of realism and visual communications to online laboratory learning in a remote classroom.

  15. Learning motion concepts using real-time microcomputer-based laboratory tools

    Science.gov (United States)

    Thornton, Ronald K.; Sokoloff, David R.

    1990-09-01

    Microcomputer-based laboratory (MBL) tools have been developed which interface to Apple II and Macintosh computers. Students use these tools to collect physical data that are graphed in real time and then can be manipulated and analyzed. The MBL tools have made possible discovery-based laboratory curricula that embody results from educational research. These curricula allow students to take an active role in their learning and encourage them to construct physical knowledge from observation of the physical world. The curricula encourage collaborative learning by taking advantage of the fact that MBL tools present data in an immediately understandable graphical form. This article describes one of the tools—the motion detector (hardware and software)—and the kinematics curriculum. The effectiveness of this curriculum compared to traditional college and university methods for helping students learn basic kinematics concepts has been evaluated by pre- and post-testing and by observation. There is strong evidence for significantly improved learning and retention by students who used the MBL materials, compared to those taught in lecture.

  16. Multi-layer network utilizing rewarded spike time dependent plasticity to learn a foraging task.

    Directory of Open Access Journals (Sweden)

    Pavel Sanda

    2017-09-01

    Full Text Available Neural networks with a single plastic layer employing reward modulated spike time dependent plasticity (STDP are capable of learning simple foraging tasks. Here we demonstrate advanced pattern discrimination and continuous learning in a network of spiking neurons with multiple plastic layers. The network utilized both reward modulated and non-reward modulated STDP and implemented multiple mechanisms for homeostatic regulation of synaptic efficacy, including heterosynaptic plasticity, gain control, output balancing, activity normalization of rewarded STDP and hard limits on synaptic strength. We found that addition of a hidden layer of neurons employing non-rewarded STDP created neurons that responded to the specific combinations of inputs and thus performed basic classification of the input patterns. When combined with a following layer of neurons implementing rewarded STDP, the network was able to learn, despite the absence of labeled training data, discrimination between rewarding patterns and the patterns designated as punishing. Synaptic noise allowed for trial-and-error learning that helped to identify the goal-oriented strategies which were effective in task solving. The study predicts a critical set of properties of the spiking neuronal network with STDP that was sufficient to solve a complex foraging task involving pattern classification and decision making.

  17. Does diet mediate associations of volume and bouts of sedentary time with cardiometabolic health indicators in adolescents?

    Science.gov (United States)

    Fletcher, Elly A; Carson, Valerie; McNaughton, Sarah A; Dunstan, David W; Healy, Genevieve N; Salmon, Jo

    2017-03-01

    Examine the mediating role of diet in the relationship between volume and duration of sedentary time with cardiometabolic health in adolescents. Adolescents (12-19 years) participating in the 2003/04 and 2005/06 U.S. National Health and Nutrition Examination Survey (NHANES) were examined. Cardiometabolic health indicators were body mass index z-scores (zBMI) (n = 1,797) and metabolic syndrome (MetS) (n = 812). An ActiGraph hip-worn accelerometer was used to derive total sedentary time and usual sedentary bout duration. Dietary intake was assessed using two 24-hour dietary recalls. Mediation analyses were conducted to examine five dietary mediators [total energy intake, discretionary foods, sugar-sweetened beverages (SSB), fruits and vegetables, and dietary quality] of the relationship between total sedentary time and usual sedentary bout duration with zBMI and MetS. Total sedentary time was inversely associated with zBMI (β = -1.33; 95% CI -2.53 to -0.13) but attenuated after adjusting for moderate-to-vigorous physical activity. No significant associations were observed between usual sedentary bout duration with zBMI or either sedentary measure with MetS. None of the five dietary variables mediated any of the relationships examined. Further studies are needed to explore associations of specific time periods (e.g., after school) and bout durations with both cardiometabolic health indicators and dietary behaviors. © 2017 The Authors. Obesity published by Wiley Periodicals, Inc. on behalf of The Obesity Society (TOS).

  18. Volume Tracking: A new method for quantitative assessment and visualization of intracardiac blood flow from three-dimensional, time-resolved, three-component magnetic resonance velocity mapping

    International Nuclear Information System (INIS)

    Töger, Johannes; Carlsson, Marcus; Söderlind, Gustaf; Arheden, Håkan; Heiberg, Einar

    2011-01-01

    Functional and morphological changes of the heart influence blood flow patterns. Therefore, flow patterns may carry diagnostic and prognostic information. Three-dimensional, time-resolved, three-directional phase contrast cardiovascular magnetic resonance (4D PC-CMR) can image flow patterns with unique detail, and using new flow visualization methods may lead to new insights. The aim of this study is to present and validate a novel visualization method with a quantitative potential for blood flow from 4D PC-CMR, called Volume Tracking, and investigate if Volume Tracking complements particle tracing, the most common visualization method used today. Eight healthy volunteers and one patient with a large apical left ventricular aneurysm underwent 4D PC-CMR flow imaging of the whole heart. Volume Tracking and particle tracing visualizations were compared visually side-by-side in a visualization software package. To validate Volume Tracking, the number of particle traces that agreed with the Volume Tracking visualizations was counted and expressed as a percentage of total released particles in mid-diastole and end-diastole respectively. Two independent observers described blood flow patterns in the left ventricle using Volume Tracking visualizations. Volume Tracking was feasible in all eight healthy volunteers and in the patient. Visually, Volume Tracking and particle tracing are complementary methods, showing different aspects of the flow. When validated against particle tracing, on average 90.5% and 87.8% of the particles agreed with the Volume Tracking surface in mid-diastole and end-diastole respectively. Inflow patterns in the left ventricle varied between the subjects, with excellent agreement between observers. The left ventricular inflow pattern in the patient differed from the healthy subjects. Volume Tracking is a new visualization method for blood flow measured by 4D PC-CMR. Volume Tracking complements and provides incremental information compared to particle

  19. Investigating the Relationship Between Self-Directed Learning Readiness and Time Management Skills in Turkish Undergraduate Nursing Students.

    Science.gov (United States)

    Ertuğ, Nurcan; Faydali, Saide

    The aims of this study were to determine self-directed learning and time management skills of undergraduate nursing students and to investigate the relationship between the concepts. The use of self-directed learning has increased as an educational strategy in recent years. This descriptive and correlational study was conducted with 383 undergraduate nursing students in Turkey. Data were collected using a sociodemographic questionnaire, the Self-Directed Learning Readiness Scale, and Time Management Questionnaire. Mean scores were as follows: self-directed learning readiness, 159.12 (SD = 20.8); time management, 87.75 (SD = 12.1). A moderate positive correlation was found between self-directed learning readiness and time management values. Time management scores were 78.42 when self-directed learning readiness was ≤149 and 90.82 when self-directed learning readiness was ≥ 150, with a statistically significant difference (p = .000). Level of self-directed learning and academic achievement were higher in students who managed their time well.

  20. Measuring tongue volumes and visualizing the chewing and swallowing process using real-time TrueFISP imaging - initial clinical experience in healthy volunteers and patients with acromegaly

    International Nuclear Information System (INIS)

    Ajaj, W.; Goyen, M.; Herrmann, B.; Massing, S.; Goehde, S.; Lauenstein, T.; Ruehm, S.G.

    2005-01-01

    This study assessed both two-dimensional (2D) TrueFISP imaging for quantifying tongue volume and real-time TrueFISP imaging for evaluating chewing and swallowing in healthy volunteers and patients with acromegaly. In 50 healthy volunteers, tongue volumes were measured using a 2D TrueFISP sequence. Chewing and swallowing were visualized using a real-time TrueFISP sequence. Ten patients with acromegaly were examined twice with the same magnetic resonance imaging protocol: once prior to therapy and a second time 6 months after therapy. Prior to therapy, healthy volunteers had an average tongue volume of 140 ml for men and 90 ml for women, and patients with acromegaly had an average tongue volume of 180 ml for men and 145 ml for women. However, 6 months after therapy the mean tongue volumes in patients with acromegaly had decreased to 154 ml in the men and to 125 ml in the women. The chewing and swallowing process was normal in all volunteers. Prior to therapy, just two patients showed a chewing and swallowing pathology, which disappeared after therapy. Patients with acromegaly had larger tongue volumes than healthy volunteers, and TrueFISP imaging proved feasible for visualizing chewing and swallowing in real time and is capable of detecting possible pathologies. Furthermore, TrueFISP imaging can be used to monitor therapeutic approaches in patients with acromegaly. (orig.)

  1. Measuring tongue volumes and visualizing the chewing and swallowing process using real-time TrueFISP imaging - initial clinical experience in healthy volunteers and patients with acromegaly

    Energy Technology Data Exchange (ETDEWEB)

    Ajaj, W.; Goyen, M.; Herrmann, B.; Massing, S.; Goehde, S.; Lauenstein, T.; Ruehm, S.G. [University Hospital, Department of Diagnostic and Interventional Radiology, Essen (Germany)

    2005-05-01

    This study assessed both two-dimensional (2D) TrueFISP imaging for quantifying tongue volume and real-time TrueFISP imaging for evaluating chewing and swallowing in healthy volunteers and patients with acromegaly. In 50 healthy volunteers, tongue volumes were measured using a 2D TrueFISP sequence. Chewing and swallowing were visualized using a real-time TrueFISP sequence. Ten patients with acromegaly were examined twice with the same magnetic resonance imaging protocol: once prior to therapy and a second time 6 months after therapy. Prior to therapy, healthy volunteers had an average tongue volume of 140 ml for men and 90 ml for women, and patients with acromegaly had an average tongue volume of 180 ml for men and 145 ml for women. However, 6 months after therapy the mean tongue volumes in patients with acromegaly had decreased to 154 ml in the men and to 125 ml in the women. The chewing and swallowing process was normal in all volunteers. Prior to therapy, just two patients showed a chewing and swallowing pathology, which disappeared after therapy. Patients with acromegaly had larger tongue volumes than healthy volunteers, and TrueFISP imaging proved feasible for visualizing chewing and swallowing in real time and is capable of detecting possible pathologies. Furthermore, TrueFISP imaging can be used to monitor therapeutic approaches in patients with acromegaly. (orig.)

  2. Non-invasive assessment of distribution volume ratios and binding potential: tissue heterogeneity and interindividually averaged time-activity curves

    Energy Technology Data Exchange (ETDEWEB)

    Reimold, M.; Mueller-Schauenburg, W.; Dohmen, B.M.; Bares, R. [Department of Nuclear Medicine, University of Tuebingen, Otfried-Mueller-Strasse 14, 72076, Tuebingen (Germany); Becker, G.A. [Nuclear Medicine, University of Leipzig, Leipzig (Germany); Reischl, G. [Radiopharmacy, University of Tuebingen, Tuebingen (Germany)

    2004-04-01

    Due to the stochastic nature of radioactive decay, any measurement of radioactivity concentration requires spatial averaging. In pharmacokinetic analysis of time-activity curves (TAC), such averaging over heterogeneous tissues may introduce a systematic error (heterogeneity error) but may also improve the accuracy and precision of parameter estimation. In addition to spatial averaging (inevitable due to limited scanner resolution and intended in ROI analysis), interindividual averaging may theoretically be beneficial, too. The aim of this study was to investigate the effect of such averaging on the binding potential (BP) calculated with Logan's non-invasive graphical analysis and the ''simplified reference tissue method'' (SRTM) proposed by Lammertsma and Hume, on the basis of simulated and measured positron emission tomography data [{sup 11}C]d-threo-methylphenidate (dMP) and [{sup 11}C]raclopride (RAC) PET. dMP was not quantified with SRTM since the low k {sub 2} (washout rate constant from the first tissue compartment) introduced a high noise sensitivity. Even for considerably different shapes of TAC (dMP PET in parkinsonian patients and healthy controls, [{sup 11}C]raclopride in patients with and without haloperidol medication) and a high variance in the rate constants (e.g. simulated standard deviation of K {sub 1}=25%), the BP obtained from average TAC was close to the mean BP (<5%). However, unfavourably distributed parameters, especially a correlated large variance in two or more parameters, may lead to larger errors. In Monte Carlo simulations, interindividual averaging before quantification reduced the variance from the SRTM (beyond a critical signal to noise ratio) and the bias in Logan's method. Interindividual averaging may further increase accuracy when there is an error term in the reference tissue assumption E=DV {sub 2}-DV ' (DV {sub 2} = distribution volume of the first tissue compartment, DV &apos

  3. Time to Engage? Texting to Support and Enhance First Year Undergraduate Learning

    Directory of Open Access Journals (Sweden)

    Geraldine Jones

    2009-04-01

    Full Text Available In this paper we discuss a case study investigating how the academic and personal development of first year students on an undergraduate sports education degree can be supported and enhanced with mobile SMS communication. SMS-based technologies were introduced in response to students’ particular needs (in transition to Higher Education and characteristics (‘digital natives’. Despite being unaccustomed to using their mobile phones for academic study, students willingly participated in SMS communication with their tutor via a texting management service. Drawing on evidence from two student surveys, focus groups and a tutor’s journal, we illustrate the potential that mobile SMS communication has to link and establish continuity between face to face teaching sessions and online learning activities in the Virtual Learning Environment (VLE. Many students perceived the SMS communication to have had a positive impact on their management of study time. We link our findings with the existing literature and argue that mobile text based communication has the potential to support the development of time management skills, an important component of self regulatory learning, a skill which has been shown to be key in making a successful transition.

  4. Uncertainties in global radiation time series forecasting using machine learning: The multilayer perceptron case

    International Nuclear Information System (INIS)

    Voyant, Cyril; Notton, Gilles; Darras, Christophe; Fouilloy, Alexis; Motte, Fabrice

    2017-01-01

    As global solar radiation forecasting is a very important challenge, several methods are devoted to this goal with different levels of accuracy and confidence. In this study we propose to better understand how the uncertainty is propagated in the context of global radiation time series forecasting using machine learning. Indeed we propose to decompose the error considering four kinds of uncertainties: the error due to the measurement, the variability of time series, the machine learning uncertainty and the error related to the horizon. All these components of the error allow to determinate a global uncertainty generating prediction bands related to the prediction efficiency. We also have defined a reliability index which could be very interesting for the grid manager in order to estimate the validity of predictions. We have experimented this method on a multilayer perceptron which is a popular machine learning technique. We have shown that the global error and its components are essential to quantify in order to estimate the reliability of the model outputs. The described method has been successfully applied to four meteorological stations in Mediterranean area. - Highlights: • Solar irradiation predictions require confidence bands. • There are a lot of kinds of uncertainties to take into account in order to propose prediction bands. • the ranking of different kinds of uncertainties is essential to propose an operational tool for the grid managers.

  5. Large-scale machine learning and evaluation platform for real-time traffic surveillance

    Science.gov (United States)

    Eichel, Justin A.; Mishra, Akshaya; Miller, Nicholas; Jankovic, Nicholas; Thomas, Mohan A.; Abbott, Tyler; Swanson, Douglas; Keller, Joel

    2016-09-01

    In traffic engineering, vehicle detectors are trained on limited datasets, resulting in poor accuracy when deployed in real-world surveillance applications. Annotating large-scale high-quality datasets is challenging. Typically, these datasets have limited diversity; they do not reflect the real-world operating environment. There is a need for a large-scale, cloud-based positive and negative mining process and a large-scale learning and evaluation system for the application of automatic traffic measurements and classification. The proposed positive and negative mining process addresses the quality of crowd sourced ground truth data through machine learning review and human feedback mechanisms. The proposed learning and evaluation system uses a distributed cloud computing framework to handle data-scaling issues associated with large numbers of samples and a high-dimensional feature space. The system is trained using AdaBoost on 1,000,000 Haar-like features extracted from 70,000 annotated video frames. The trained real-time vehicle detector achieves an accuracy of at least 95% for 1/2 and about 78% for 19/20 of the time when tested on ˜7,500,000 video frames. At the end of 2016, the dataset is expected to have over 1 billion annotated video frames.

  6. From brain synapses to systems for learning and memory: Object recognition, spatial navigation, timed conditioning, and movement control.

    Science.gov (United States)

    Grossberg, Stephen

    2015-09-24

    This article provides an overview of neural models of synaptic learning and memory whose expression in adaptive behavior depends critically on the circuits and systems in which the synapses are embedded. It reviews Adaptive Resonance Theory, or ART, models that use excitatory matching and match-based learning to achieve fast category learning and whose learned memories are dynamically stabilized by top-down expectations, attentional focusing, and memory search. ART clarifies mechanistic relationships between consciousness, learning, expectation, attention, resonance, and synchrony. ART models are embedded in ARTSCAN architectures that unify processes of invariant object category learning, recognition, spatial and object attention, predictive remapping, and eye movement search, and that clarify how conscious object vision and recognition may fail during perceptual crowding and parietal neglect. The generality of learned categories depends upon a vigilance process that is regulated by acetylcholine via the nucleus basalis. Vigilance can get stuck at too high or too low values, thereby causing learning problems in autism and medial temporal amnesia. Similar synaptic learning laws support qualitatively different behaviors: Invariant object category learning in the inferotemporal cortex; learning of grid cells and place cells in the entorhinal and hippocampal cortices during spatial navigation; and learning of time cells in the entorhinal-hippocampal system during adaptively timed conditioning, including trace conditioning. Spatial and temporal processes through the medial and lateral entorhinal-hippocampal system seem to be carried out with homologous circuit designs. Variations of a shared laminar neocortical circuit design have modeled 3D vision, speech perception, and cognitive working memory and learning. A complementary kind of inhibitory matching and mismatch learning controls movement. This article is part of a Special Issue entitled SI: Brain and Memory

  7. CORRELATION OF INTEREST TO LEARN AND USE TIME LEARNING WITH LEARNING ACHIEVEMENT AUTOMOTIVE ELECTRICAL IN CLASS XII LIGHT VEHICLE ENGINEERING SMK PIRI I YOGYAKARTA ACADEMIC YEAR 2013/2014

    Directory of Open Access Journals (Sweden)

    Ari Pujiatmoko

    2014-06-01

    Full Text Available The purpose of this study were: 1 to determine whether there is a correlation between students' interest in learning and the learning achievement of automotive electrical, 2 to determine whether there is a correlation between the use of time studying the learning achievement of automotive electrical, 3 to determine whether there is a correlation between student interest and use the time to learn and the learning achievement of students of class XII automotive electrical TKR SMK PIRI 1 Yogyakarta academic year 2013/2014.  This research was conducted in class XII TKR SMK PIRI 1 Yogyakarta academic year 2013/2014. This study is an ex-post facto. This study used two independent variables and the interest in learning the use of learning time, while the dependent variable is the electrical automotive learning achievement. This study is a population study by the respondent amounted to 100 students. Techniques of data collection using questionnaire techniques and engineering documentation. Research instrument in this study is a questionnaire interest in learning, inquiry learning time management and documentation of student achievement. Trials using the instrument validity and reliability test. The analysis technique used is the prerequisite test for normality, linearity, and multicollinearity. Then test hypotheses using partial correlation analysis techniques and correlation.  The results showed that: 1 students' interest to have a strong positive correlation with school performance automotive electrical ρ value of 0.737; 2 the use of learning time have a low positive correlation with school performance automotive electrical ρ value of 0.275; 3 interest student learning and the use of study time has a very strong positive correlation with learning achievement of students of class XII automotive electrical TKR SMK PIRI I Yogyakarta academic year 2013/2014 as evidenced by the value of R = 0.811.

  8. Time-Contrastive Learning Based DNN Bottleneck Features for Text-Dependent Speaker Verification

    DEFF Research Database (Denmark)

    Sarkar, Achintya Kumar; Tan, Zheng-Hua

    2017-01-01

    In this paper, we present a time-contrastive learning (TCL) based bottleneck (BN) feature extraction method for speech signals with an application to text-dependent (TD) speaker verification (SV). It is well-known that speech signals exhibit quasi-stationary behavior in and only in a short interval......, and the TCL method aims to exploit this temporal structure. More specifically, it trains deep neural networks (DNNs) to discriminate temporal events obtained by uniformly segmenting speech signals, in contrast to existing DNN based BN feature extraction methods that train DNNs using labeled data...... to discriminate speakers or pass-phrases or phones or a combination of them. In the context of speaker verification, speech data of fixed pass-phrases are used for TCL-BN training, while the pass-phrases used for TCL-BN training are excluded from being used for SV, so that the learned features can be considered...

  9. Robust Monotonically Convergent Iterative Learning Control for Discrete-Time Systems via Generalized KYP Lemma

    Directory of Open Access Journals (Sweden)

    Jian Ding

    2014-01-01

    Full Text Available This paper addresses the problem of P-type iterative learning control for a class of multiple-input multiple-output linear discrete-time systems, whose aim is to develop robust monotonically convergent control law design over a finite frequency range. It is shown that the 2 D iterative learning control processes can be taken as 1 D state space model regardless of relative degree. With the generalized Kalman-Yakubovich-Popov lemma applied, it is feasible to describe the monotonically convergent conditions with the help of linear matrix inequality technique and to develop formulas for the control gain matrices design. An extension to robust control law design against systems with structured and polytopic-type uncertainties is also considered. Two numerical examples are provided to validate the feasibility and effectiveness of the proposed method.

  10. Reliability of perfusion MR imaging in symptomatic carotid occlusive disease. Cerebral blood volume, mean transit time and time-to-peak

    International Nuclear Information System (INIS)

    Kim, J.H.; Lee, E.J.; Lee, S.J.; Choi, N.C.; Lim, B.H.; Shin, T.

    2002-01-01

    Purpose: Perfusion MR imaging offers an easy quantitative evaluation of relative regional cerebral blood volume (rrCBV), relative mean transit time (rMTT) and time-to-peak (TTP). The purpose of this study was to investigate the reliability of these parameters in assessing the hemodynamic disturbance of carotid occlusive disease in comparison with normative data. Material and Methods: Dynamic contrast-enhanced T2*-weighted perfusion MR imaging was performed in 19 patients with symptomatic unilateral internal carotid artery occlusion and 20 control subjects. The three parameters were calculated from the concentration-time curve fitted by gamma-variate function. Lesion-to-contralateral ratios of each parameter were compared between patients and control subjects. Results: Mean±SD of rrCBV, rMTT and TTP ratios of patients were 1.089±0.118, 1.054±0.031 and 1.062±0.039, respectively, and those of control subjects were 1.002±0.045, 1.000±0.006, 1.001±0.006, respectively. The rMTT and TTP ratios of all patients were greater than 2SDs of control data, whereas in only 6 patients (32%), rrCBV ratios were greater than 2SDs of control data. The three parameter ratios of the patients were significantly high compared with those of control subjects, respectively (p<0.01 for rrCBV ratios, p<0.0001 for rMTT ratios, and p<0.0001 for TTP ratios). Conclusion: Our results indicate that rMTT and TTP of patients, in contrast to rrCBV, are distributed in narrow ranges minimally overlapped with control data. The rMTT and TTP could be more reliable parameters than rrCBV in assessing the hemodynamic disturbance in carotid occlusive disease

  11. Statistical Learning and Adaptive Decision-Making Underlie Human Response Time Variability in Inhibitory Control

    Directory of Open Access Journals (Sweden)

    Ning eMa

    2015-08-01

    Full Text Available Response time (RT is an oft-reported behavioral measure in psychological and neurocognitive experiments, but the high level of observed trial-to-trial variability in this measure has often limited its usefulness. Here, we combine computational modeling and psychophysics to examine the hypothesis that fluctuations in this noisy measure reflect dynamic computations in human statistical learning and corresponding cognitive adjustments. We present data from the stop-signal task, in which subjects respond to a go stimulus on each trial, unless instructed not to by a subsequent, infrequently presented stop signal. We model across-trial learning of stop signal frequency, P(stop, and stop-signal onset time, SSD (stop-signal delay, with a Bayesian hidden Markov model, and within-trial decision-making with an optimal stochastic control model. The combined model predicts that RT should increase with both expected P(stop and SSD. The human behavioral data (n=20 bear out this prediction, showing P(stop and SSD both to be significant, independent predictors of RT, with P(stop being a more prominent predictor in 75% of the subjects, and SSD being more prominent in the remaining 25%. The results demonstrate that humans indeed readily internalize environmental statistics and adjust their cognitive/behavioral strategy accordingly, and that subtle patterns in RT variability can serve as a valuable tool for validating models of statistical learning and decision-making. More broadly, the modeling tools presented in this work can be generalized to a large body of behavioral paradigms, in order to extract insights about cognitive and neural processing from apparently quite noisy behavioral measures. We also discuss how this behaviorally validated model can then be used to conduct model-based analysis of neural data, in order to help identify specific brain areas for representing and encoding key computational quantities in learning and decision-making.

  12. Statistical learning and adaptive decision-making underlie human response time variability in inhibitory control.

    Science.gov (United States)

    Ma, Ning; Yu, Angela J

    2015-01-01

    Response time (RT) is an oft-reported behavioral measure in psychological and neurocognitive experiments, but the high level of observed trial-to-trial variability in this measure has often limited its usefulness. Here, we combine computational modeling and psychophysics to examine the hypothesis that fluctuations in this noisy measure reflect dynamic computations in human statistical learning and corresponding cognitive adjustments. We present data from the stop-signal task (SST), in which subjects respond to a go stimulus on each trial, unless instructed not to by a subsequent, infrequently presented stop signal. We model across-trial learning of stop signal frequency, P(stop), and stop-signal onset time, SSD (stop-signal delay), with a Bayesian hidden Markov model, and within-trial decision-making with an optimal stochastic control model. The combined model predicts that RT should increase with both expected P(stop) and SSD. The human behavioral data (n = 20) bear out this prediction, showing P(stop) and SSD both to be significant, independent predictors of RT, with P(stop) being a more prominent predictor in 75% of the subjects, and SSD being more prominent in the remaining 25%. The results demonstrate that humans indeed readily internalize environmental statistics and adjust their cognitive/behavioral strategy accordingly, and that subtle patterns in RT variability can serve as a valuable tool for validating models of statistical learning and decision-making. More broadly, the modeling tools presented in this work can be generalized to a large body of behavioral paradigms, in order to extract insights about cognitive and neural processing from apparently quite noisy behavioral measures. We also discuss how this behaviorally validated model can then be used to conduct model-based analysis of neural data, in order to help identify specific brain areas for representing and encoding key computational quantities in learning and decision-making.

  13. Generation and Validation of Spatial Distribution of Hourly Wind Speed Time-Series using Machine Learning

    International Nuclear Information System (INIS)

    Veronesi, F; Grassi, S

    2016-01-01

    Wind resource assessment is a key aspect of wind farm planning since it allows to estimate the long term electricity production. Moreover, wind speed time-series at high resolution are helpful to estimate the temporal changes of the electricity generation and indispensable to design stand-alone systems, which are affected by the mismatch of supply and demand. In this work, we present a new generalized statistical methodology to generate the spatial distribution of wind speed time-series, using Switzerland as a case study. This research is based upon a machine learning model and demonstrates that statistical wind resource assessment can successfully be used for estimating wind speed time-series. In fact, this method is able to obtain reliable wind speed estimates and propagate all the sources of uncertainty (from the measurements to the mapping process) in an efficient way, i.e. minimizing computational time and load. This allows not only an accurate estimation, but the creation of precise confidence intervals to map the stochasticity of the wind resource for a particular site. The validation shows that machine learning can minimize the bias of the wind speed hourly estimates. Moreover, for each mapped location this method delivers not only the mean wind speed, but also its confidence interval, which are crucial data for planners. (paper)

  14. Generation and Validation of Spatial Distribution of Hourly Wind Speed Time-Series using Machine Learning

    Science.gov (United States)

    Veronesi, F.; Grassi, S.

    2016-09-01

    Wind resource assessment is a key aspect of wind farm planning since it allows to estimate the long term electricity production. Moreover, wind speed time-series at high resolution are helpful to estimate the temporal changes of the electricity generation and indispensable to design stand-alone systems, which are affected by the mismatch of supply and demand. In this work, we present a new generalized statistical methodology to generate the spatial distribution of wind speed time-series, using Switzerland as a case study. This research is based upon a machine learning model and demonstrates that statistical wind resource assessment can successfully be used for estimating wind speed time-series. In fact, this method is able to obtain reliable wind speed estimates and propagate all the sources of uncertainty (from the measurements to the mapping process) in an efficient way, i.e. minimizing computational time and load. This allows not only an accurate estimation, but the creation of precise confidence intervals to map the stochasticity of the wind resource for a particular site. The validation shows that machine learning can minimize the bias of the wind speed hourly estimates. Moreover, for each mapped location this method delivers not only the mean wind speed, but also its confidence interval, which are crucial data for planners.

  15. A new method to study changes in microvascular blood volume in muscle and adipose tissue: Real time imaging in humans and rat

    DEFF Research Database (Denmark)

    Sjøberg, Kim Anker; Rattigan, Stephen; Hiscock, Natalie J

    2011-01-01

    We employed and evaluated a new application of contrast enhanced ultrasound for real time imaging of changes in microvascular blood volume (MVB) in tissues in females, males and rat. Continuous real time imaging was performed using contrast enhanced ultrasound to quantify infused gas filled micro...

  16. Why are they late? Timing abilities and executive control among students with learning disabilities.

    Science.gov (United States)

    Grinblat, Nufar; Rosenblum, Sara

    2016-12-01

    While a deficient ability to perform daily tasks on time has been reported among students with learning disabilities (LD), the underlying mechanism behind their 'being late' is still unclear. This study aimed to evaluate the organization in time, time estimation abilities, actual performance time pertaining to specific daily activities, as well as the executive functions of students with LD in comparison to those of controls, and to assess the relationships between these domains among each group. The participants were 27 students with LD, aged 20-30, and 32 gender and age-matched controls who completed the Time Organization and Participation Scale (TOPS) and the Behavioral Rating Inventory of Executive Function-Adult version (BRIEF-A). In addition, their ability to estimate the time needed to complete the task of preparing a cup of coffee as well as their actual performance time were evaluated. The results indicated that in comparison to controls, students with LD showed significantly inferior organization in time (TOPS) and executive function abilities (BRIEF-A). Furthermore, their time estimation abilities were significantly inferior and they required significantly more time to prepare a cup of coffee. Regression analysis identified the variables that predicted organization in time and task performance time among each group. The significance of the results for both theoretical and clinical implications are discussed. What this paper adds? This study examines the underlying mechanism of the phenomena of being late among students with LD. Following a recent call for using ecologically valid assessments, the functional daily ability of students with LD to prepare a cup of coffee and to organize time were investigated. Furthermore, their time estimation and executive control abilities were examined as a possible underlying mechanism for their lateness. Although previous studies have indicated executive control deficits among students with LD, to our knowledge, this

  17. Using Just-in-Time Information to Support Scientific Discovery Learning in a Computer-Based Simulation

    Science.gov (United States)

    Hulshof, Casper D.; de Jong, Ton

    2006-01-01

    Students encounter many obstacles during scientific discovery learning with computer-based simulations. It is hypothesized that an effective type of support, that does not interfere with the scientific discovery learning process, should be delivered on a "just-in-time" base. This study explores the effect of facilitating access to…

  18. A Virtual Learning Environment for Part-Time MASW Students: An Evaluation of the WebCT

    Science.gov (United States)

    Chan, Charles C.; Tsui, Ming-sum; Chan, Mandy Y. C.; Hong, Joe H.

    2008-01-01

    This study aims to evaluate the perception of a cohort of social workers studying for a part-time master's program in social work in using the popular Web-based learning platform--World Wide Web Course Tools (WebCT) as a complimentary method of teaching and learning. It was noted that social work profession began incorporating computer technology…

  19. Night-Time Vehicle Detection Algorithm Based on Visual Saliency and Deep Learning

    Directory of Open Access Journals (Sweden)

    Yingfeng Cai

    2016-01-01

    Full Text Available Night vision systems get more and more attention in the field of automotive active safety field. In this area, a number of researchers have proposed far-infrared sensor based night-time vehicle detection algorithm. However, existing algorithms have low performance in some indicators such as the detection rate and processing time. To solve this problem, we propose a far-infrared image vehicle detection algorithm based on visual saliency and deep learning. Firstly, most of the nonvehicle pixels will be removed with visual saliency computation. Then, vehicle candidate will be generated by using prior information such as camera parameters and vehicle size. Finally, classifier trained with deep belief networks will be applied to verify the candidates generated in last step. The proposed algorithm is tested in around 6000 images and achieves detection rate of 92.3% and processing time of 25 Hz which is better than existing methods.

  20. Discrete-Time Stable Generalized Self-Learning Optimal Control With Approximation Errors.

    Science.gov (United States)

    Wei, Qinglai; Li, Benkai; Song, Ruizhuo

    2018-04-01

    In this paper, a generalized policy iteration (GPI) algorithm with approximation errors is developed for solving infinite horizon optimal control problems for nonlinear systems. The developed stable GPI algorithm provides a general structure of discrete-time iterative adaptive dynamic programming algorithms, by which most of the discrete-time reinforcement learning algorithms can be described using the GPI structure. It is for the first time that approximation errors are explicitly considered in the GPI algorithm. The properties of the stable GPI algorithm with approximation errors are analyzed. The admissibility of the approximate iterative control law can be guaranteed if the approximation errors satisfy the admissibility criteria. The convergence of the developed algorithm is established, which shows that the iterative value function is convergent to a finite neighborhood of the optimal performance index function, if the approximate errors satisfy the convergence criterion. Finally, numerical examples and comparisons are presented.

  1. Machine learning methods as a tool to analyse incomplete or irregularly sampled radon time series data.

    Science.gov (United States)

    Janik, M; Bossew, P; Kurihara, O

    2018-07-15

    Machine learning is a class of statistical techniques which has proven to be a powerful tool for modelling the behaviour of complex systems, in which response quantities depend on assumed controls or predictors in a complicated way. In this paper, as our first purpose, we propose the application of machine learning to reconstruct incomplete or irregularly sampled data of time series indoor radon ( 222 Rn). The physical assumption underlying the modelling is that Rn concentration in the air is controlled by environmental variables such as air temperature and pressure. The algorithms "learn" from complete sections of multivariate series, derive a dependence model and apply it to sections where the controls are available, but not the response (Rn), and in this way complete the Rn series. Three machine learning techniques are applied in this study, namely random forest, its extension called the gradient boosting machine and deep learning. For a comparison, we apply the classical multiple regression in a generalized linear model version. Performance of the models is evaluated through different metrics. The performance of the gradient boosting machine is found to be superior to that of the other techniques. By applying learning machines, we show, as our second purpose, that missing data or periods of Rn series data can be reconstructed and resampled on a regular grid reasonably, if data of appropriate physical controls are available. The techniques also identify to which degree the assumed controls contribute to imputing missing Rn values. Our third purpose, though no less important from the viewpoint of physics, is identifying to which degree physical, in this case environmental variables, are relevant as Rn predictors, or in other words, which predictors explain most of the temporal variability of Rn. We show that variables which contribute most to the Rn series reconstruction, are temperature, relative humidity and day of the year. The first two are physical

  2. Automated business process management – in times of digital transformation using machine learning or artificial intelligence

    Directory of Open Access Journals (Sweden)

    Paschek Daniel

    2017-01-01

    Full Text Available The continuous optimization of business processes is still a challenge for companies. In times of digital transformation, faster changing internal and external framework conditions and new customer expectations for fastest delivery and best quality of goods and many more, companies should set up their internal process at the best way. But what to do if framework conditions changed unexpectedly? The purpose of the paper is to analyse how the digital transformation will impact the Business Process Management (BPM while using methods like machine learning or artificial intelligence. Therefore, the core components will be explained, compared and set up in relation. To identify application areas interviews and analysis will be held up with digital companies. The finding of the paper will be recommendation for action in the field of BPM and process optimization through machine learning and artificial intelligence. The Approach of optimizing and management processes via machine learning and artificial intelligence will support companies to decide which tool will be the best for automated BPM.

  3. Rapid tomographic reconstruction based on machine learning for time-resolved combustion diagnostics

    Science.gov (United States)

    Yu, Tao; Cai, Weiwei; Liu, Yingzheng

    2018-04-01

    Optical tomography has attracted surged research efforts recently due to the progress in both the imaging concepts and the sensor and laser technologies. The high spatial and temporal resolutions achievable by these methods provide unprecedented opportunity for diagnosis of complicated turbulent combustion. However, due to the high data throughput and the inefficiency of the prevailing iterative methods, the tomographic reconstructions which are typically conducted off-line are computationally formidable. In this work, we propose an efficient inversion method based on a machine learning algorithm, which can extract useful information from the previous reconstructions and build efficient neural networks to serve as a surrogate model to rapidly predict the reconstructions. Extreme learning machine is cited here as an example for demonstrative purpose simply due to its ease of implementation, fast learning speed, and good generalization performance. Extensive numerical studies were performed, and the results show that the new method can dramatically reduce the computational time compared with the classical iterative methods. This technique is expected to be an alternative to existing methods when sufficient training data are available. Although this work is discussed under the context of tomographic absorption spectroscopy, we expect it to be useful also to other high speed tomographic modalities such as volumetric laser-induced fluorescence and tomographic laser-induced incandescence which have been demonstrated for combustion diagnostics.

  4. Optimizing Earth Data Search Ranking using Deep Learning and Real-time User Behaviour

    Science.gov (United States)

    Jiang, Y.; Yang, C. P.; Armstrong, E. M.; Huang, T.; Moroni, D. F.; McGibbney, L. J.; Greguska, F. R., III

    2017-12-01

    Finding Earth science data has been a challenging problem given both the quantity of data available and the heterogeneity of the data across a wide variety of domains. Current search engines in most geospatial data portals tend to induce end users to focus on one single data characteristic dimension (e.g., term frequency-inverse document frequency (TF-IDF) score, popularity, release date, etc.). This approach largely fails to take account of users' multidimensional preferences for geospatial data, and hence may likely result in a less than optimal user experience in discovering the most applicable dataset out of a vast range of available datasets. With users interacting with search engines, sufficient information is already hidden in the log files. Compared with explicit feedback data, information that can be derived/extracted from log files is virtually free and substantially more timely. In this dissertation, I propose an online deep learning framework that can quickly update the learning function based on real-time user clickstream data. The contributions of this framework include 1) a log processor that can ingest, process and create training data from web logs in a real-time manner; 2) a query understanding module to better interpret users' search intent using web log processing results and metadata; 3) a feature extractor that identifies ranking features representing users' multidimensional interests of geospatial data; and 4) a deep learning based ranking algorithm that can be trained incrementally using user behavior data. The search ranking results will be evaluated using precision at K and normalized discounted cumulative gain (NDCG).

  5. Online Learning Algorithm for Time Series Forecasting Suitable for Low Cost Wireless Sensor Networks Nodes

    Directory of Open Access Journals (Sweden)

    Juan Pardo

    2015-04-01

    Full Text Available Time series forecasting is an important predictive methodology which can be applied to a wide range of problems. Particularly, forecasting the indoor temperature permits an improved utilization of the HVAC (Heating, Ventilating and Air Conditioning systems in a home and thus a better energy efficiency. With such purpose the paper describes how to implement an Artificial Neural Network (ANN algorithm in a low cost system-on-chip to develop an autonomous intelligent wireless sensor network. The present paper uses a Wireless Sensor Networks (WSN to monitor and forecast the indoor temperature in a smart home, based on low resources and cost microcontroller technology as the 8051MCU. An on-line learning approach, based on Back-Propagation (BP algorithm for ANNs, has been developed for real-time time series learning. It performs the model training with every new data that arrive to the system, without saving enormous quantities of data to create a historical database as usual, i.e., without previous knowledge. Consequently to validate the approach a simulation study through a Bayesian baseline model have been tested in order to compare with a database of a real application aiming to see the performance and accuracy. The core of the paper is a new algorithm, based on the BP one, which has been described in detail, and the challenge was how to implement a computational demanding algorithm in a simple architecture with very few hardware resources.

  6. Online Learning Algorithm for Time Series Forecasting Suitable for Low Cost Wireless Sensor Networks Nodes

    Science.gov (United States)

    Pardo, Juan; Zamora-Martínez, Francisco; Botella-Rocamora, Paloma

    2015-01-01

    Time series forecasting is an important predictive methodology which can be applied to a wide range of problems. Particularly, forecasting the indoor temperature permits an improved utilization of the HVAC (Heating, Ventilating and Air Conditioning) systems in a home and thus a better energy efficiency. With such purpose the paper describes how to implement an Artificial Neural Network (ANN) algorithm in a low cost system-on-chip to develop an autonomous intelligent wireless sensor network. The present paper uses a Wireless Sensor Networks (WSN) to monitor and forecast the indoor temperature in a smart home, based on low resources and cost microcontroller technology as the 8051MCU. An on-line learning approach, based on Back-Propagation (BP) algorithm for ANNs, has been developed for real-time time series learning. It performs the model training with every new data that arrive to the system, without saving enormous quantities of data to create a historical database as usual, i.e., without previous knowledge. Consequently to validate the approach a simulation study through a Bayesian baseline model have been tested in order to compare with a database of a real application aiming to see the performance and accuracy. The core of the paper is a new algorithm, based on the BP one, which has been described in detail, and the challenge was how to implement a computational demanding algorithm in a simple architecture with very few hardware resources. PMID:25905698

  7. Changes in recognition memory over time: an ERP investigation into vocabulary learning.

    Directory of Open Access Journals (Sweden)

    Shekeila D Palmer

    Full Text Available Although it seems intuitive to assume that recognition memory fades over time when information is not reinforced, some aspects of word learning may benefit from a period of consolidation. In the present study, event-related potentials (ERP were used to examine changes in recognition memory responses to familiar and newly learned (novel words over time. Native English speakers were taught novel words associated with English translations, and subsequently performed a Recognition Memory task in which they made old/new decisions in response to both words (trained word vs. untrained word, and novel words (trained novel word vs. untrained novel word. The Recognition task was performed 45 minutes after training (Day 1 and then repeated the following day (Day 2 with no additional training session in between. For familiar words, the late parietal old/new effect distinguished old from new items on both Day 1 and Day 2, although response to trained items was significantly weaker on Day 2. For novel words, the LPC again distinguished old from new items on both days, but the effect became significantly larger on Day 2. These data suggest that while recognition memory for familiar items may fade over time, recognition of novel items, conscious recollection in particular may benefit from a period of consolidation.

  8. Online learning algorithm for time series forecasting suitable for low cost wireless sensor networks nodes.

    Science.gov (United States)

    Pardo, Juan; Zamora-Martínez, Francisco; Botella-Rocamora, Paloma

    2015-04-21

    Time series forecasting is an important predictive methodology which can be applied to a wide range of problems. Particularly, forecasting the indoor temperature permits an improved utilization of the HVAC (Heating, Ventilating and Air Conditioning) systems in a home and thus a better energy efficiency. With such purpose the paper describes how to implement an Artificial Neural Network (ANN) algorithm in a low cost system-on-chip to develop an autonomous intelligent wireless sensor network. The present paper uses a Wireless Sensor Networks (WSN) to monitor and forecast the indoor temperature in a smart home, based on low resources and cost microcontroller technology as the 8051MCU. An on-line learning approach, based on Back-Propagation (BP) algorithm for ANNs, has been developed for real-time time series learning. It performs the model training with every new data that arrive to the system, without saving enormous quantities of data to create a historical database as usual, i.e., without previous knowledge. Consequently to validate the approach a simulation study through a Bayesian baseline model have been tested in order to compare with a database of a real application aiming to see the performance and accuracy. The core of the paper is a new algorithm, based on the BP one, which has been described in detail, and the challenge was how to implement a computational demanding algorithm in a simple architecture with very few hardware resources.

  9. Using machine learning to identify structural breaks in single-group interrupted time series designs.

    Science.gov (United States)

    Linden, Ariel; Yarnold, Paul R

    2016-12-01

    Single-group interrupted time series analysis (ITSA) is a popular evaluation methodology in which a single unit of observation is being studied, the outcome variable is serially ordered as a time series and the intervention is expected to 'interrupt' the level and/or trend of the time series, subsequent to its introduction. Given that the internal validity of the design rests on the premise that the interruption in the time series is associated with the introduction of the treatment, treatment effects may seem less plausible if a parallel trend already exists in the time series prior to the actual intervention. Thus, sensitivity analyses should focus on detecting structural breaks in the time series before the intervention. In this paper, we introduce a machine-learning algorithm called optimal discriminant analysis (ODA) as an approach to determine if structural breaks can be identified in years prior to the initiation of the intervention, using data from California's 1988 voter-initiated Proposition 99 to reduce smoking rates. The ODA analysis indicates that numerous structural breaks occurred prior to the actual initiation of Proposition 99 in 1989, including perfect structural breaks in 1983 and 1985, thereby casting doubt on the validity of treatment effects estimated for the actual intervention when using a single-group ITSA design. Given the widespread use of ITSA for evaluating observational data and the increasing use of machine-learning techniques in traditional research, we recommend that structural break sensitivity analysis is routinely incorporated in all research using the single-group ITSA design. © 2016 John Wiley & Sons, Ltd.

  10. On the best learning algorithm for web services response time prediction

    DEFF Research Database (Denmark)

    Madsen, Henrik; Albu, Razvan-Daniel; Popentiu-Vladicescu, Florin

    2013-01-01

    In this article we will examine the effect of different learning algorithms, while training the MLP (Multilayer Perceptron) with the intention of predicting web services response time. Web services do not necessitate a user interface. This may seem contradictory to most people's concept of what...... an application is. A Web service is better imagined as an application "segment," or better as a program enabler. Performance is an important quality aspect of Web services because of their distributed nature. Predicting the response of web services during their operation is very important....

  11. Volume doubling time of lung cancer detected in idiopathic interstitial pneumonia. Comparison with that in chronic obstructive pulmonary disease

    International Nuclear Information System (INIS)

    Kim, Cherry; Lee, Sang Min; Choe, Jooae; Chae, Eun Jin; Do, Kyung-Hyun; Seo, Joon Beom

    2018-01-01

    To assess the volume doubling time (VDT) of lung cancers in IIP compared with COPD. A total of 61 patients (32 with IIP and 29 with COPD) were identified. A radiologist performed three-dimensional manual segmentation for lung cancers. VDTs were calculated and compared between two groups. Logistic regression was performed to identify factors associated with rapid tumour growth (VDT < 90 days). The median VDT of lung cancers in IIP (78.2 days) was significantly shorter than that in COPD (126.1 days; p=0.004). Squamous cell carcinoma (SqCC) was the most frequent subtype, followed by small cell lung cancer (SCLC) in IIP. In COPD, SqCC was the most frequent subtype, followed by adenocarcinoma. Rapid tumour growth was observed in 20 cancers from IIP, and in nine cancers from COPD (p=0.021). SCLC was significantly correlated with rapid tumour growth (p=0.038). Multivariate analysis revealed that the presence of IIP was the single independent predictor of rapid tumour growth (p = 0.016; odds ratio, 3.7). Lung cancers in IIP showed more rapid growth, with median VDT < 90 days. Therefore, a shorter follow-up interval (<90 days) may be necessary when CT surveillance is considered in IIP patients with suspected lung cancer. (orig.)

  12. Retroperitoneal laparoscopic nephrectomy: the effect of the learning curve, and concentrating expertise, on operating times.

    Science.gov (United States)

    Skinner, Adrian; Maoate, Kiki; Beasley, Spencer

    2010-05-01

    Laparoscopic nephrectomy is an accepted alternative to open nephrectomy. We analyzed our first 80 procedures of laparoscopic nephrectomy to evaluate the effect of experience and configuration of service on operative times. A retrospective review of 80 consecutive children who underwent retroperitoneal laparoscopic nephrectomy or heminephrectomy during an 11-year period from 1997 at Christchurch Hospital (Christchurch, New Zealand) was conducted. Operative times, in relation to the experience of the surgeon for this procedure, were analyzed. Four surgeons, assisted by an annually rotating trainee registrar, performed the procedure in 26 girls and 54 boys (range, 8 months to 15 years). Operating times ranged from 38 to 225 minutes (mean, 104). The average operative time fell from 105 to 90 minutes. One surgeon performed 40% of the procedures and assisted with a further 55%. The operative times for all surgeons showed a tendency to reduce, but this was not marked. Most procedures were performed by two surgeons working together, although one surgeon was involved in the majority of cases. The lead surgeon is often assisted by a fellow consultant colleague. Operative times were influenced by experience, but not markedly so. The shorter operative times and minimal "learning curve," compared with other reported series, may, in part, be due to the involvement of two surgeons experienced in laparoscopy for the majority of cases.

  13. Scheduling with Learning Effects and/or Time-Dependent Processing Times to Minimize the Weighted Number of Tardy Jobs on a Single Machine

    Directory of Open Access Journals (Sweden)

    Jianbo Qian

    2013-01-01

    Full Text Available We consider single machine scheduling problems with learning/deterioration effects and time-dependent processing times, with due date assignment consideration, and our objective is to minimize the weighted number of tardy jobs. By reducing all versions of the problem to an assignment problem, we solve them in O(n4 time. For some important special cases, the time complexity can be improved to be O(n2 using dynamic programming techniques.

  14. Scaling of volume to surface ratio and doubling time in growing unicellular organisms: Do cells appear quantum-mechanical systems?

    International Nuclear Information System (INIS)

    Atanasov, Atanas Todorov

    2014-01-01

    The scaling of physical and biological characteristics of the living organisms is a basic method for searching of new biophysical laws. In series of previous studies the author showed that in Poikilotherms, Mammals and Aves, the volume to surface ratio V×S −1 (m) of organisms is proportional to their generation time T gt (s) via growth rate v (m s −1 ): V×S −1  = v gr ×T r . The power and the correlation coefficients are near to 1.0. Aim of this study is: i) to prove with experimental data the validity of the above equation for Unicellular organisms and ii) to show that perhaps, the cells are quantum-mechanical systems. The data for body mass M (kg), density ρ (kg/m 3 ), minimum and maximum doubling time T dt (s) for 50 unicellular organisms are assembled from scientific sources, and the computer program ‘Statistics’ is used for calculations. In result i) the analytical relationship from type: V×S −1  = 4.46⋅10 −11 ×T dt was found, where v gr  = 4.46×10 −11 m/s and ii) it is shown that the products between cell mass M, cell length expressed by V/S ratio and growth rate v gr satisfied the Heisenberg uncertainty principle i.e. the inequalities V/S×M×v gr >h/2π and T dt ×M×v gr 2 >h/2π are valid, where h= 6.626×10 −34 J⋅s is the Planck constant. This rise the question: do cells appear quantum-mechanical systems?

  15. Scaling of volume to surface ratio and doubling time in growing unicellular organisms: Do cells appear quantum-mechanical systems?

    Science.gov (United States)

    Atanasov, Atanas Todorov

    2014-10-01

    The scaling of physical and biological characteristics of the living organisms is a basic method for searching of new biophysical laws. In series of previous studies the author showed that in Poikilotherms, Mammals and Aves, the volume to surface ratio V×S-1 (m) of organisms is proportional to their generation time Tgt(s) via growth rate v (m s-1): V×S-1 = vgr×Tr. The power and the correlation coefficients are near to 1.0. Aim of this study is: i) to prove with experimental data the validity of the above equation for Unicellular organisms and ii) to show that perhaps, the cells are quantum-mechanical systems. The data for body mass M (kg), density ρ (kg/m3), minimum and maximum doubling time Tdt (s) for 50 unicellular organisms are assembled from scientific sources, and the computer program `Statistics' is used for calculations. In result i) the analytical relationship from type: V×S-1 = 4.46ṡ10-11×Tdt was found, where vgr = 4.46×10-11 m/s and ii) it is shown that the products between cell mass M, cell length expressed by V/S ratio and growth rate vgr satisfied the Heisenberg uncertainty principle i.e. the inequalities V/S×M×vgr>h/2π and Tdt×M×vgr2>h/2π are valid, where h= 6.626×10-34 Jṡs is the Planck constant. This rise the question: do cells appear quantum-mechanical systems?

  16. Scaling of volume to surface ratio and doubling time in growing unicellular organisms: Do cells appear quantum-mechanical systems?

    Energy Technology Data Exchange (ETDEWEB)

    Atanasov, Atanas Todorov, E-mail: atanastod@abv.bg [Department of Physics and Biophysics, Faculty of Medicine, Trakia University, 11 Armeiska Str., 6000 Stara Zagora (Bulgaria)

    2014-10-06

    The scaling of physical and biological characteristics of the living organisms is a basic method for searching of new biophysical laws. In series of previous studies the author showed that in Poikilotherms, Mammals and Aves, the volume to surface ratio V×S{sup −1} (m) of organisms is proportional to their generation time T{sub gt}(s) via growth rate v (m s{sup −1}): V×S{sup −1} = v{sub gr}×T{sup r}. The power and the correlation coefficients are near to 1.0. Aim of this study is: i) to prove with experimental data the validity of the above equation for Unicellular organisms and ii) to show that perhaps, the cells are quantum-mechanical systems. The data for body mass M (kg), density ρ (kg/m{sup 3}), minimum and maximum doubling time T{sub dt} (s) for 50 unicellular organisms are assembled from scientific sources, and the computer program ‘Statistics’ is used for calculations. In result i) the analytical relationship from type: V×S{sup −1} = 4.46⋅10{sup −11}×T{sub dt} was found, where v{sub gr} = 4.46×10{sup −11} m/s and ii) it is shown that the products between cell mass M, cell length expressed by V/S ratio and growth rate v{sub gr} satisfied the Heisenberg uncertainty principle i.e. the inequalities V/S×M×v{sub gr}>h/2π and T{sub dt}×M×v{sub gr}{sup 2}>h/2π are valid, where h= 6.626×10{sup −34} J⋅s is the Planck constant. This rise the question: do cells appear quantum-mechanical systems?.

  17. 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.

  18. Discrete-time online learning control for a class of unknown nonaffine nonlinear systems using reinforcement learning.

    Science.gov (United States)

    Yang, Xiong; Liu, Derong; Wang, Ding; Wei, Qinglai

    2014-07-01

    In this paper, a reinforcement-learning-based direct adaptive control is developed to deliver a desired tracking performance for a class of discrete-time (DT) nonlinear systems with unknown bounded disturbances. We investigate multi-input-multi-output unknown nonaffine nonlinear DT systems and employ two neural networks (NNs). By using Implicit Function Theorem, an action NN is used to generate the control signal and it is also designed to cancel the nonlinearity of unknown DT systems, for purpose of utilizing feedback linearization methods. On the other hand, a critic NN is applied to estimate the cost function, which satisfies the recursive equations derived from heuristic dynamic programming. The weights of both the action NN and the critic NN are directly updated online instead of offline training. By utilizing Lyapunov's direct method, the closed-loop tracking errors and the NN estimated weights are demonstrated to be uniformly ultimately bounded. Two numerical examples are provided to show the effectiveness of the present approach. Copyright © 2014 Elsevier Ltd. All rights reserved.

  19. A Call to Action for Research in Digital Learning: Learning without Limits of Time, Place, Path, Pace…or Evidence

    Science.gov (United States)

    Cavanaugh, Cathy; Sessums, Christopher; Drexler, Wendy

    2015-01-01

    This essay is a call for rethinking our approach to research in digital learning. It plots a path founded in social trends and advances in education. A brief review of these trends and advances is followed by discussion of what flattened research might look like at scale. Scaling research in digital learning is crucial to advancing understanding…

  20. Unsupervised Learning of Digit Recognition Using Spike-Timing-Dependent Plasticity

    Directory of Open Access Journals (Sweden)

    Peter U. Diehl

    2015-08-01

    Full Text Available In order to understand how the mammalian neocortex is performing computations, two things are necessary; we need to have a good understanding of the available neuronal processing units and mechanisms, and we need to gain a better understanding of how those mechanisms are combined to build functioning systems. Therefore, in recent years there is an increasing interest in how spiking neural networks (SNN can be used to perform complex computations or solve pattern recognition tasks. However, it remains a challenging task to design SNNs which use biologically plausible mechanisms (especially for learning new patterns, since most of such SNN architectures rely on training in a rate-based network and subsequent conversion to a SNN. We present a SNN for digit recognition which is based on mechanisms with increased biological plausibility, i.e. conductance-based instead of current-based synapses, spike-timing-dependent plasticity with time-dependent weight change, lateral inhibition, and an adaptive spiking threshold. Unlike most other systems, we do not use a teaching signal and do not present any class labels to the network. Using this unsupervised learning scheme, our architecture achieves 95% accuracy on the MNIST benchmark, which is better than previous SNN implementations without supervision. The fact that we used no domain-specific knowledge points toward the general applicability of our network design. Also, the performance of our network scales well with the number of neurons used and shows similar performance for four different learning rules, indicating robustness of the full combination of mechanisms, which suggests applicability in heterogeneous biological neural networks.

  1. Inference of time-delayed gene regulatory networks based on dynamic Bayesian network hybrid learning method.

    Science.gov (United States)

    Yu, Bin; Xu, Jia-Meng; Li, Shan; Chen, Cheng; Chen, Rui-Xin; Wang, Lei; Zhang, Yan; Wang, Ming-Hui

    2017-10-06

    Gene regulatory networks (GRNs) research reveals complex life phenomena from the perspective of gene interaction, which is an important research field in systems biology. Traditional Bayesian networks have a high computational complexity, and the network structure scoring model has a single feature. Information-based approaches cannot identify the direction of regulation. In order to make up for the shortcomings of the above methods, this paper presents a novel hybrid learning method (DBNCS) based on dynamic Bayesian network (DBN) to construct the multiple time-delayed GRNs for the first time, combining the comprehensive score (CS) with the DBN model. DBNCS algorithm first uses CMI2NI (conditional mutual inclusive information-based network inference) algorithm for network structure profiles learning, namely the construction of search space. Then the redundant regulations are removed by using the recursive optimization algorithm (RO), thereby reduce the false positive rate. Secondly, the network structure profiles are decomposed into a set of cliques without loss, which can significantly reduce the computational complexity. Finally, DBN model is used to identify the direction of gene regulation within the cliques and search for the optimal network structure. The performance of DBNCS algorithm is evaluated by the benchmark GRN datasets from DREAM challenge as well as the SOS DNA repair network in Escherichia coli , and compared with other state-of-the-art methods. The experimental results show the rationality of the algorithm design and the outstanding performance of the GRNs.

  2. Two Machine Learning Approaches for Short-Term Wind Speed Time-Series Prediction.

    Science.gov (United States)

    Ak, Ronay; Fink, Olga; Zio, Enrico

    2016-08-01

    The increasing liberalization of European electricity markets, the growing proportion of intermittent renewable energy being fed into the energy grids, and also new challenges in the patterns of energy consumption (such as electric mobility) require flexible and intelligent power grids capable of providing efficient, reliable, economical, and sustainable energy production and distribution. From the supplier side, particularly, the integration of renewable energy sources (e.g., wind and solar) into the grid imposes an engineering and economic challenge because of the limited ability to control and dispatch these energy sources due to their intermittent characteristics. Time-series prediction of wind speed for wind power production is a particularly important and challenging task, wherein prediction intervals (PIs) are preferable results of the prediction, rather than point estimates, because they provide information on the confidence in the prediction. In this paper, two different machine learning approaches to assess PIs of time-series predictions are considered and compared: 1) multilayer perceptron neural networks trained with a multiobjective genetic algorithm and 2) extreme learning machines combined with the nearest neighbors approach. The proposed approaches are applied for short-term wind speed prediction from a real data set of hourly wind speed measurements for the region of Regina in Saskatchewan, Canada. Both approaches demonstrate good prediction precision and provide complementary advantages with respect to different evaluation criteria.

  3. 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.

  4. A real-time standard parts inspection based on deep learning

    Science.gov (United States)

    Xu, Kuan; Li, XuDong; Jiang, Hongzhi; Zhao, Huijie

    2017-10-01

    Since standard parts are necessary components in mechanical structure like bogie and connector. These mechanical structures will be shattered or loosen if standard parts are lost. So real-time standard parts inspection systems are essential to guarantee their safety. Researchers would like to take inspection systems based on deep learning because it works well in image with complex backgrounds which is common in standard parts inspection situation. A typical inspection detection system contains two basic components: feature extractors and object classifiers. For the object classifier, Region Proposal Network (RPN) is one of the most essential architectures in most state-of-art object detection systems. However, in the basic RPN architecture, the proposals of Region of Interest (ROI) have fixed sizes (9 anchors for each pixel), they are effective but they waste much computing resources and time. In standard parts detection situations, standard parts have given size, thus we can manually choose sizes of anchors based on the ground-truths through machine learning. The experiments prove that we could use 2 anchors to achieve almost the same accuracy and recall rate. Basically, our standard parts detection system could reach 15fps on NVIDIA GTX1080 (GPU), while achieving detection accuracy 90.01% mAP.

  5. Hybrid and Blended Learning: Modifying Pedagogy across Path, Pace, Time, and Place

    Science.gov (United States)

    O'Byrne, W. Ian; Pytash, Kristine E.

    2015-01-01

    Hybrid or blended learning is defined as a pedagogical approach that includes a combination of face-to-face instruction with computer-mediated instruction. The terms "blended learning", "hybrid learning", and "mixed-mode learning" are used interchangeably in current research; however, in the United States,…

  6. Learning Over Time: Using Rapid Prototyping Generative Analysis Experts and Reduction of Scope to Operationalize Design

    Science.gov (United States)

    2010-05-04

    during the Vietnam Conflict. 67 David A. Kolb , Experiential Learning : Experience as the Source of Learning and Development. (Upper Saddle River, NJ...Essentials for Military Applications. Newport Paper #10. Newport: Newport War College Press. 1996. Kolb , David A. Experiential Learning : Experience... learning over analysis. A broad review of design theory suggests that four techniques - rapid prototyping, generative analysis, use of experts, and

  7. Hybrid machine learning technique for forecasting Dhaka stock market timing decisions.

    Science.gov (United States)

    Banik, Shipra; Khodadad Khan, A F M; Anwer, Mohammad

    2014-01-01

    Forecasting stock market has been a difficult job for applied researchers owing to nature of facts which is very noisy and time varying. However, this hypothesis has been featured by several empirical experiential studies and a number of researchers have efficiently applied machine learning techniques to forecast stock market. This paper studied stock prediction for the use of investors. It is always true that investors typically obtain loss because of uncertain investment purposes and unsighted assets. This paper proposes a rough set model, a neural network model, and a hybrid neural network and rough set model to find optimal buy and sell of a share on Dhaka stock exchange. Investigational findings demonstrate that our proposed hybrid model has higher precision than the single rough set model and the neural network model. We believe this paper findings will help stock investors to decide about optimal buy and/or sell time on Dhaka stock exchange.

  8. Technical Note: Deep learning based MRAC using rapid ultra-short echo time imaging.

    Science.gov (United States)

    Jang, Hyungseok; Liu, Fang; Zhao, Gengyan; Bradshaw, Tyler; McMillan, Alan B

    2018-05-15

    In this study, we explore the feasibility of a novel framework for MR-based attenuation correction for PET/MR imaging based on deep learning via convolutional neural networks, which enables fully automated and robust estimation of a pseudo CT image based on ultrashort echo time (UTE), fat, and water images obtained by a rapid MR acquisition. MR images for MRAC are acquired using dual echo ramped hybrid encoding (dRHE), where both UTE and out-of-phase echo images are obtained within a short single acquisition (35 sec). Tissue labeling of air, soft tissue, and bone in the UTE image is accomplished via a deep learning network that was pre-trained with T1-weighted MR images. UTE images are used as input to the network, which was trained using labels derived from co-registered CT images. The tissue labels estimated by deep learning are refined by a conditional random field based correction. The soft tissue labels are further separated into fat and water components using the two-point Dixon method. The estimated bone, air, fat, and water images are then assigned appropriate Hounsfield units, resulting in a pseudo CT image for PET attenuation correction. To evaluate the proposed MRAC method, PET/MR imaging of the head was performed on 8 human subjects, where Dice similarity coefficients of the estimated tissue labels and relative PET errors were evaluated through comparison to a registered CT image. Dice coefficients for air (within the head), soft tissue, and bone labels were 0.76±0.03, 0.96±0.006, and 0.88±0.01. In PET quantification, the proposed MRAC method produced relative PET errors less than 1% within most brain regions. The proposed MRAC method utilizing deep learning with transfer learning and an efficient dRHE acquisition enables reliable PET quantification with accurate and rapid pseudo CT generation. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

  9. Interactive Learning Modules: Enabling Near Real-Time Oceanographic Data Use In Undergraduate Education

    Science.gov (United States)

    Kilb, D. L.; Fundis, A. T.; Risien, C. M.

    2012-12-01

    The focus of the Education and Public Engagement (EPE) component of the NSF's Ocean Observatories Initiative (OOI) is to provide a new layer of cyber-interactivity for undergraduate educators to bring near real-time data from the global ocean into learning environments. To accomplish this, we are designing six online services including: 1) visualization tools, 2) a lesson builder, 3) a concept map builder, 4) educational web services (middleware), 5) collaboration tools and 6) an educational resource database. Here, we report on our Fall 2012 release that includes the first four of these services: 1) Interactive visualization tools allow users to interactively select data of interest, display the data in various views (e.g., maps, time-series and scatter plots) and obtain statistical measures such as mean, standard deviation and a regression line fit to select data. Specific visualization tools include a tool to compare different months of data, a time series explorer tool to investigate the temporal evolution of select data parameters (e.g., sea water temperature or salinity), a glider profile tool that displays ocean glider tracks and associated transects, and a data comparison tool that allows users to view the data either in scatter plot view comparing one parameter with another, or in time series view. 2) Our interactive lesson builder tool allows users to develop a library of online lesson units, which are collaboratively editable and sharable and provides starter templates designed from learning theory knowledge. 3) Our interactive concept map tool allows the user to build and use concept maps, a graphical interface to map the connection between concepts and ideas. This tool also provides semantic-based recommendations, and allows for embedding of associated resources such as movies, images and blogs. 4) Education web services (middleware) will provide an educational resource database API.

  10. Lessons Learned from Real-Time, Event-Based Internet Science Communications

    Science.gov (United States)

    Phillips, T.; Myszka, E.; Gallagher, D. L.; Adams, M. L.; Koczor, R. J.; Whitaker, Ann F. (Technical Monitor)

    2001-01-01

    For the last several years the Science Directorate at Marshall Space Flight Center has carried out a diverse program of Internet-based science communication. The Directorate's Science Roundtable includes active researchers, NASA public relations, educators, and administrators. The Science@NASA award-winning family of Web sites features science, mathematics, and space news. The program includes extended stories about NASA science, a curriculum resource for teachers tied to national education standards, on-line activities for students, and webcasts of real-time events. The focus of sharing science activities in real-time has been to involve and excite students and the public about science. Events have involved meteor showers, solar eclipses, natural very low frequency radio emissions, and amateur balloon flights. In some cases, broadcasts accommodate active feedback and questions from Internet participants. Through these projects a pattern has emerged in the level of interest or popularity with the public. The pattern differentiates projects that include science from those that do not, All real-time, event-based Internet activities have captured public interest at a level not achieved through science stories or educator resource material exclusively. The worst event-based activity attracted more interest than the best written science story. One truly rewarding lesson learned through these projects is that the public recognizes the importance and excitement of being part of scientific discovery. Flying a camera to 100,000 feet altitude isn't as interesting to the public as searching for viable life-forms at these oxygen-poor altitudes. The details of these real-time, event-based projects and lessons learned will be discussed.

  11. Calculation of upper esophageal sphincter restitution time from high resolution manometry data using machine learning.

    Science.gov (United States)

    Jungheim, Michael; Busche, Andre; Miller, Simone; Schilling, Nicolas; Schmidt-Thieme, Lars; Ptok, Martin

    2016-10-15

    After swallowing, the upper esophageal sphincter (UES) needs a certain amount of time to return from maximum pressure to the resting condition. Disturbances of sphincter function not only during the swallowing process but also in this phase of pressure restitution may lead to globus sensation or dysphagia. Since UES pressures do not decrease in a linear or asymptotic manner, it is difficult to determine the exact time when the resting pressure is reached, even when using high resolution manometry (HRM). To overcome this problem a Machine Learning model was established to objectively determine the UES restitution time (RT) and moreover to collect physiological data on sphincter function after swallowing. HRM-data of 15 healthy participants performing 10 swallows each were included. After manual annotation of the RT interval by two swallowing experts, data were transferred to the Machine Learning model, which applied a sequence labeling modeling approach based on logistic regression to learn and objectivize the characteristics of all swallows. Individually computed RT values were then compared with the annotated values. Estimates of the RT were generated by the Machine Learning model for all 150 swallows. When annotated by swallowing experts mean RT of 11.16s±5.7 (SD) and 10.04s±5.74 were determined respectively, compared to model-generated values from 8.91s±3.71 to 10.87s±4.68 depending on model selection. The correlation score for the annotated RT of both examiners was 0.76 and 0.63 to 0.68 for comparison of model predicted values. Restitution time represents an important physiologic swallowing parameter not previously considered in HRM-studies of the UES, especially since disturbances of UES restitution may increase the risk of aspiration. The data presented here show that it takes approximately 9 to 11s for the UES to come to rest after swallowing. Based on maximal RT values, we demonstrate that an interval of 25-30s in between swallows is necessary until the

  12. Time-place learning and memory persist in mice lacking functional Per1 and Per2 clock genes.

    Science.gov (United States)

    Mulder, C; Van Der Zee, E A; Hut, R A; Gerkema, M P

    2013-12-01

    With time-place learning, animals link a stimulus with the location and the time of day. This ability may optimize resource localization and predator avoidance in daily changing environments. Time-place learning is a suitable task to study the interaction of the circadian system and memory. Previously, we showed that time-place learning in mice depends on the circadian system and Cry1 and/or Cry2 clock genes. We questioned whether time-place learning is Cry specific or also depends on other core molecular clock genes. Here, we show that Per1/Per2 double mutant mice, despite their arrhythmic phenotype, acquire time-place learning similar to wild-type mice. As well as an established role in circadian rhythms, Per genes have also been implicated in the formation and storage of memory. We found no deficiencies in short-term spatial working memory in Per mutant mice compared to wild-type mice. Moreover, both Per mutant and wild-type mice showed similar long-term memory for contextual features of a paradigm (a mild foot shock), measured in trained mice after a 2-month nontesting interval. In contrast, time-place associations were lost in both wild-type and mutant mice after these 2 months, suggesting a lack of maintained long-term memory storage for this type of information. Taken together, Cry-dependent time-place learning does not require Per genes, and Per mutant mice showed no PER-specific short-term or long-term memory deficiencies. These results limit the functional role of Per clock genes in the circadian regulation of time-place learning and memory.

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

    Science.gov (United States)

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

    2017-12-01

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

  14. Travel time tomography with local image regularization by sparsity constrained dictionary learning

    Science.gov (United States)

    Bianco, M.; Gerstoft, P.

    2017-12-01

    We propose a regularization approach for 2D seismic travel time tomography which models small rectangular groups of slowness pixels, within an overall or `global' slowness image, as sparse linear combinations of atoms from a dictionary. The groups of slowness pixels are referred to as patches and a dictionary corresponds to a collection of functions or `atoms' describing the slowness in each patch. These functions could for example be wavelets.The patch regularization is incorporated into the global slowness image. The global image models the broad features, while the local patch images incorporate prior information from the dictionary. Further, high resolution slowness within patches is permitted if the travel times from the global estimates support it. The proposed approach is formulated as an algorithm, which is repeated until convergence is achieved: 1) From travel times, find the global slowness image with a minimum energy constraint on the pixel variance relative to a reference. 2) Find the patch level solutions to fit the global estimate as a sparse linear combination of dictionary atoms.3) Update the reference as the weighted average of the patch level solutions.This approach relies on the redundancy of the patches in the seismic image. Redundancy means that the patches are repetitions of a finite number of patterns, which are described by the dictionary atoms. Redundancy in the earth's structure was demonstrated in previous works in seismics where dictionaries of wavelet functions regularized inversion. We further exploit redundancy of the patches by using dictionary learning algorithms, a form of unsupervised machine learning, to estimate optimal dictionaries from the data in parallel with the inversion. We demonstrate our approach on densely, but irregularly sampled synthetic seismic images.

  15. Large volume recycling of oceanic lithosphere over short time scales: geochemical constraints from the Caribbean Large Igneous Province

    Science.gov (United States)

    Hauff, F.; Hoernle, K.; Tilton, G.; Graham, D. W.; Kerr, A. C.

    2000-01-01

    isochron diagrams suggests that the age of separation of enriched and depleted components from the depleted MORB source mantle could have been ≤500 Ma before CLIP formation and interpreted to reflect the recycling time of the CLIP source. Mantle plume heads may provide a mechanism for transporting large volumes of possibly young recycled oceanic lithosphere residing in the lower mantle back into the shallow MORB source mantle.

  16. Volume Tracking: A new method for quantitative assessment and visualization of intracardiac blood flow from three-dimensional, time-resolved, three-component magnetic resonance velocity mapping

    Directory of Open Access Journals (Sweden)

    Arheden Håkan

    2011-04-01

    Full Text Available Abstract Background Functional and morphological changes of the heart influence blood flow patterns. Therefore, flow patterns may carry diagnostic and prognostic information. Three-dimensional, time-resolved, three-directional phase contrast cardiovascular magnetic resonance (4D PC-CMR can image flow patterns with unique detail, and using new flow visualization methods may lead to new insights. The aim of this study is to present and validate a novel visualization method with a quantitative potential for blood flow from 4D PC-CMR, called Volume Tracking, and investigate if Volume Tracking complements particle tracing, the most common visualization method used today. Methods Eight healthy volunteers and one patient with a large apical left ventricular aneurysm underwent 4D PC-CMR flow imaging of the whole heart. Volume Tracking and particle tracing visualizations were compared visually side-by-side in a visualization software package. To validate Volume Tracking, the number of particle traces that agreed with the Volume Tracking visualizations was counted and expressed as a percentage of total released particles in mid-diastole and end-diastole respectively. Two independent observers described blood flow patterns in the left ventricle using Volume Tracking visualizations. Results Volume Tracking was feasible in all eight healthy volunteers and in the patient. Visually, Volume Tracking and particle tracing are complementary methods, showing different aspects of the flow. When validated against particle tracing, on average 90.5% and 87.8% of the particles agreed with the Volume Tracking surface in mid-diastole and end-diastole respectively. Inflow patterns in the left ventricle varied between the subjects, with excellent agreement between observers. The left ventricular inflow pattern in the patient differed from the healthy subjects. Conclusion Volume Tracking is a new visualization method for blood flow measured by 4D PC-CMR. Volume Tracking

  17. Executive Functions, Time Organization and Quality of Life among Adults with Learning Disabilities.

    Directory of Open Access Journals (Sweden)

    Kineret Sharfi

    Full Text Available This study compared the executive functions, organization in time and perceived quality of life (QoL of 55 adults with learning disabilities (LD with those of 55 matched controls (mean age 30 years. Furthermore, relationships and predictive relationships between these variables among the group with LD were examined.All participants completed the Behavioral Rating Inventory of Executive Functions (BRIEF-A, the Time Organization and Participation (TOPS, A-C and the World Health Organization Quality of Life (WHOQOL questionnaires. Chi-square tests, independent t-tests and MANOVA were used to examine group differences in each of the subscales scores and ratings of each instrument. Pearson correlations and regression predictive models were used to examine the relationships between the variables in the group with LD.Adults with LD had significantly poorer executive functions (BRIEF-A, deficient organization in time abilities (TOPS A-B, accompanied with negative emotional response (TOPS- C, and lower perceived QoL (physical, psychological, social and environmental in comparison to adults without LD. Regression analysis revealed that Initiation (BRIEF-A significantly predicted approximately 15% of the participants' organization in time abilities (TOPS A, B scores beyond group membership. Furthermore, initiation, emotional control (BRIEF-A subscales and emotional responses following unsuccessful organization of time (TOPS-C together accounted for 39% of the variance of psychological QoL beyond the contribution of group membership.Deficits in initiation and emotional executive functions as well as organization in time abilities and emotional responses to impairments in organizing time affect the QoL of adults with LD and thus should be considered in further research as well as in clinical applications.

  18. Executive Functions, Time Organization and Quality of Life among Adults with Learning Disabilities.

    Science.gov (United States)

    Sharfi, Kineret; Rosenblum, Sara

    2016-01-01

    This study compared the executive functions, organization in time and perceived quality of life (QoL) of 55 adults with learning disabilities (LD) with those of 55 matched controls (mean age 30 years). Furthermore, relationships and predictive relationships between these variables among the group with LD were examined. All participants completed the Behavioral Rating Inventory of Executive Functions (BRIEF-A), the Time Organization and Participation (TOPS, A-C) and the World Health Organization Quality of Life (WHOQOL) questionnaires. Chi-square tests, independent t-tests and MANOVA were used to examine group differences in each of the subscales scores and ratings of each instrument. Pearson correlations and regression predictive models were used to examine the relationships between the variables in the group with LD. Adults with LD had significantly poorer executive functions (BRIEF-A), deficient organization in time abilities (TOPS A-B), accompanied with negative emotional response (TOPS- C), and lower perceived QoL (physical, psychological, social and environmental) in comparison to adults without LD. Regression analysis revealed that Initiation (BRIEF-A) significantly predicted approximately 15% of the participants' organization in time abilities (TOPS A, B scores) beyond group membership. Furthermore, initiation, emotional control (BRIEF-A subscales) and emotional responses following unsuccessful organization of time (TOPS-C) together accounted for 39% of the variance of psychological QoL beyond the contribution of group membership. Deficits in initiation and emotional executive functions as well as organization in time abilities and emotional responses to impairments in organizing time affect the QoL of adults with LD and thus should be considered in further research as well as in clinical applications.

  19. Real-time lexical comprehension in young children learning American Sign Language.

    Science.gov (United States)

    MacDonald, Kyle; LaMarr, Todd; Corina, David; Marchman, Virginia A; Fernald, Anne

    2018-04-16

    When children interpret spoken language in real time, linguistic information drives rapid shifts in visual attention to objects in the visual world. This language-vision interaction can provide insights into children's developing efficiency in language comprehension. But how does language influence visual attention when the linguistic signal and the visual world are both processed via the visual channel? Here, we measured eye movements during real-time comprehension of a visual-manual language, American Sign Language (ASL), by 29 native ASL-learning children (16-53 mos, 16 deaf, 13 hearing) and 16 fluent deaf adult signers. All signers showed evidence of rapid, incremental language comprehension, tending to initiate an eye movement before sign offset. Deaf and hearing ASL-learners showed similar gaze patterns, suggesting that the in-the-moment dynamics of eye movements during ASL processing are shaped by the constraints of processing a visual language in real time and not by differential access to auditory information in day-to-day life. Finally, variation in children's ASL processing was positively correlated with age and vocabulary size. Thus, despite competition for attention within a single modality, the timing and accuracy of visual fixations during ASL comprehension reflect information processing skills that are important for language acquisition regardless of language modality. © 2018 John Wiley & Sons Ltd.

  20. Procedural learning is impaired in dyslexia: Evidence from a meta-analysis of serial reaction time studies☆

    Science.gov (United States)

    Lum, Jarrad A.G.; Ullman, Michael T.; Conti-Ramsden, Gina

    2013-01-01

    A number of studies have investigated procedural learning in dyslexia using serial reaction time (SRT) tasks. Overall, the results have been mixed, with evidence of both impaired and intact learning reported. We undertook a systematic search of studies that examined procedural learning using SRT tasks, and synthesized the data using meta-analysis. A total of 14 studies were identified, representing data from 314 individuals with dyslexia and 317 typically developing control participants. The results indicate that, on average, individuals with dyslexia have worse procedural learning abilities than controls, as indexed by sequence learning on the SRT task. The average weighted standardized mean difference (the effect size) was found to be 0.449 (CI95: .204, .693), and was significant (p dyslexia. PMID:23920029

  1. A Novel Real-Time Speech Summarizer System for the Learning of Sustainability

    Directory of Open Access Journals (Sweden)

    Hsiu-Wen Wang

    2015-04-01

    Full Text Available As the number of speech and video documents increases on the Internet and portable devices proliferate, speech summarization becomes increasingly essential. Relevant research in this domain has typically focused on broadcasts and news; however, the automatic summarization methods used in the past may not apply to other speech domains (e.g., speech in lectures. Therefore, this study explores the lecture speech domain. The features used in previous research were analyzed and suitable features were selected following experimentation; subsequently, a three-phase real-time speech summarizer for the learning of sustainability (RTSSLS was proposed. Phase One involved selecting independent features (e.g., centrality, resemblance to the title, sentence length, term frequency, and thematic words and calculating the independent feature scores; Phase Two involved calculating the dependent features, such as the position compared with the independent feature scores; and Phase Three involved comparing these feature scores to obtain weighted averages of the function-scores, determine the highest-scoring sentence, and provide a summary. In practical results, the accuracies of macro-average and micro-average for the RTSSLS were 70% and 73%, respectively. Therefore, using a RTSSLS can enable users to acquire key speech information for the learning of sustainability.

  2. Distributed Cerebellar Motor Learning; a Spike-Timing-Dependent Plasticity Model

    Directory of Open Access Journals (Sweden)

    Niceto Rafael Luque

    2016-03-01

    Full Text Available Deep cerebellar nuclei neurons receive both inhibitory (GABAergic synaptic currents from Purkinje cells (within the cerebellar cortex and excitatory (glutamatergic synaptic currents from mossy fibres. Those two deep cerebellar nucleus inputs are thought to be also adaptive, embedding interesting properties in the framework of accurate movements. We show that distributed spike-timing-dependent plasticity mechanisms (STDP located at different cerebellar sites (parallel fibres to Purkinje cells, mossy fibres to deep cerebellar nucleus cells, and Purkinje cells to deep cerebellar nucleus cells in close-loop simulations provide an explanation for the complex learning properties of the cerebellum in motor learning. Concretely, we propose a new mechanistic cerebellar spiking model. In this new model, deep cerebellar nuclei embed a dual functionality: deep cerebellar nuclei acting as a gain adaptation mechanism and as a facilitator for the slow memory consolidation at mossy fibres to deep cerebellar nucleus synapses. Equipping the cerebellum with excitatory (e-STDP and inhibitory (i-STDP mechanisms at deep cerebellar nuclei afferents allows the accommodation of synaptic memories that were formed at parallel fibres to Purkinje cells synapses and then transferred to mossy fibres to deep cerebellar nucleus synapses. These adaptive mechanisms also contribute to modulate the deep-cerebellar-nucleus-output firing rate (output gain modulation towards optimising its working range.

  3. Real-time Stereoscopic 3D for E-Robotics Learning

    Directory of Open Access Journals (Sweden)

    Richard Y. Chiou

    2011-02-01

    Full Text Available Following the design and testing of a successful 3-Dimensional surveillance system, this 3D scheme has been implemented into online robotics learning at Drexel University. A real-time application, utilizing robot controllers, programmable logic controllers and sensors, has been developed in the “MET 205 Robotics and Mechatronics” class to provide the students with a better robotic education. The integration of the 3D system allows the students to precisely program the robot and execute functions remotely. Upon the students’ recommendation, polarization has been chosen to be the main platform behind the 3D robotic system. Stereoscopic calculations are carried out for calibration purposes to display the images with the highest possible comfort-level and 3D effect. The calculations are further validated by comparing the results with students’ evaluations. Due to the Internet-based feature, multiple clients have the opportunity to perform the online automation development. In the future, students, in different universities, will be able to cross-control robotic components of different types around the world. With the development of this 3D ERobotics interface, automation resources and robotic learning can be shared and enriched regardless of location.

  4. A model of human motor sequence learning explains facilitation and interference effects based on spike-timing dependent plasticity.

    Directory of Open Access Journals (Sweden)

    Quan Wang

    2017-08-01

    Full Text Available The ability to learn sequential behaviors is a fundamental property of our brains. Yet a long stream of studies including recent experiments investigating motor sequence learning in adult human subjects have produced a number of puzzling and seemingly contradictory results. In particular, when subjects have to learn multiple action sequences, learning is sometimes impaired by proactive and retroactive interference effects. In other situations, however, learning is accelerated as reflected in facilitation and transfer effects. At present it is unclear what the underlying neural mechanism are that give rise to these diverse findings. Here we show that a recently developed recurrent neural network model readily reproduces this diverse set of findings. The self-organizing recurrent neural network (SORN model is a network of recurrently connected threshold units that combines a simplified form of spike-timing dependent plasticity (STDP with homeostatic plasticity mechanisms ensuring network stability, namely intrinsic plasticity (IP and synaptic normalization (SN. When trained on sequence learning tasks modeled after recent experiments we find that it reproduces the full range of interference, facilitation, and transfer effects. We show how these effects are rooted in the network's changing internal representation of the different sequences across learning and how they depend on an interaction of training schedule and task similarity. Furthermore, since learning in the model is based on fundamental neuronal plasticity mechanisms, the model reveals how these plasticity mechanisms are ultimately responsible for the network's sequence learning abilities. In particular, we find that all three plasticity mechanisms are essential for the network to learn effective internal models of the different training sequences. This ability to form effective internal models is also the basis for the observed interference and facilitation effects. This suggests that

  5. Effect of tumor dose, volume and overall treatment time on local control after radiochemotherapy including MRI guided brachytherapy of locally advanced cervical cancer

    DEFF Research Database (Denmark)

    Tanderup, Kari; Fokdal, Lars Ulrik; Sturdza, Alina

    2016-01-01

    -center patient series (retroEMBRACE). Materials and methods This study analyzed 488 locally advanced cervical cancer patients treated with external beam radiotherapy ± chemotherapy combined with IGABT. Brachytherapy contouring and reporting was according to ICRU/GEC-ESTRO recommendations. The Cox Proportional...... Hazards model was applied to analyze the effect on local control of dose-volume metrics as well as overall treatment time (OTT), dose rate, chemotherapy, and tumor histology. Results With a median follow up of 46 months, 43 local failures were observed. Dose (D90) to the High Risk Clinical Target Volume...

  6. Real-time learning of predictive recognition categories that chunk sequences of items stored in working memory

    Directory of Open Access Journals (Sweden)

    Stephen eGrossberg

    2014-10-01

    Full Text Available How are sequences of events that are temporarily stored in a cognitive working memory unitized, or chunked, through learning? Such sequential learning is needed by the brain in order to enable language, spatial understanding, and motor skills to develop. In particular, how does the brain learn categories, or list chunks, that become selectively tuned to different temporal sequences of items in lists of variable length as they are stored in working memory, and how does this learning process occur in real time? The present article introduces a neural model that simulates learning of such list chunks. In this model, sequences of items are temporarily stored in an Item-and-Order, or competitive queuing, working memory before learning categorizes them using a categorization network, called a Masking Field, which is a self-similar, multiple-scale, recurrent on-center off-surround network that can weigh the evidence for variable-length sequences of items as they are stored in the working memory through time. A Masking Field hereby activates the learned list chunks that represent the most predictive item groupings at any time, while suppressing less predictive chunks. In a network with a given number of input items, all possible ordered sets of these item sequences, up to a fixed length, can be learned with unsupervised or supervised learning. The self-similar multiple-scale properties of Masking Fields interacting with an Item-and-Order working memory provide a natural explanation of George Miller's Magical Number Seven and Nelson Cowan's Magical Number Four. The article explains why linguistic, spatial, and action event sequences may all be stored by Item-and-Order working memories that obey similar design principles, and thus how the current results may apply across modalities. Item-and-Order properties may readily be extended to Item-Order-Rank working memories in which the same item can be stored in multiple list positions, or ranks, as in the list

  7. Fast Mapping Across Time: Memory Processes Support Children's Retention of Learned Words

    Directory of Open Access Journals (Sweden)

    Haley eVlach

    2012-02-01

    Full Text Available Children's remarkable ability to map linguistic labels to objects in the world is referred to as fast mapping. The current study examined children's (N = 216 and adults’ (N = 54 retention of fast-mapped words over time (immediately, after a 1 week delay, and after a 1 month delay. The fast mapping literature often characterizes children's retention of words as consistently high across timescales. However, the current study demonstrates that learners forget word mappings at a rapid rate. Moreover, these patterns of forgetting parallel forgetting functions of domain general memory processes. Memory processes are critical to children's word learning and the role of one such process, forgetting, is discussed in detail—forgetting supports both word mapping and the generalization of words and categories.

  8. A combination of HARMONIE short time direct normal irradiance forecasts and machine learning: The #hashtdim procedure

    Science.gov (United States)

    Gastón, Martín; Fernández-Peruchena, Carlos; Körnich, Heiner; Landelius, Tomas

    2017-06-01

    The present work describes the first approach of a new procedure to forecast Direct Normal Irradiance (DNI): the #hashtdim that treats to combine ground information and Numerical Weather Predictions. The system is centered in generate predictions for the very short time. It combines the outputs from the Numerical Weather Prediction Model HARMONIE with an adaptive methodology based on Machine Learning. The DNI predictions are generated with 15-minute and hourly temporal resolutions and presents 3-hourly updates. Each update offers forecasts to the next 12 hours, the first nine hours are generated with 15-minute temporal resolution meanwhile the last three hours present hourly temporal resolution. The system is proved over a Spanish emplacement with BSRN operative station in south of Spain (PSA station). The #hashtdim has been implemented in the framework of the Direct Normal Irradiance Nowcasting methods for optimized operation of concentrating solar technologies (DNICast) project, under the European Union's Seventh Programme for research, technological development and demonstration framework.

  9. Surgical training and the European Working Time Directive: The role of informal workplace learning.

    Science.gov (United States)

    Giles, James A

    2010-01-01

    The introduction of European Working Time Directive, limiting doctors' working hours to 48 per week, has caused recent controversy within the profession. The Royal College of Surgeons of England in particular has been one of the loudest critics of the legislation. One of the main concerns is regarding the negative impact on training hours for those embarking on surgical careers. Simulation technology has been suggested as a method to overcome this reduction in hospital training hours, and research suggests that this is a good substitute for operative training in a theatre. However, modern educational theory emphasises the power of informal workplace learning in postgraduate education, and the essential role of experience in training future surgeons. Copyright 2010 Surgical Associates Ltd. Published by Elsevier Ltd. All rights reserved.

  10. Real-time dosimetry system in catheterisation laboratory: utility as a learning tool in radiation protection

    International Nuclear Information System (INIS)

    Pinto Monedero, M.; Rodriguez Cobo, C.; Pifarre Martinez, X.; Ruiz Martin, J.; Barros Candelero, J.M.; Goicolea Ruigomez, J.; Diaz Blaires, G.; Garcia Lunar, I.

    2015-01-01

    Document available in abstract form only. Full text of publication follows: Workers at the catheter laboratory are among the most exposed to ionising radiation in hospitals. However, it is difficult to be certain of the radiation doses received by the staff, as personal dosemeters are often misused, and thus, the dose history is not reliable. Moreover, the information provided by personal dosemeters corresponds to the monthly accumulated dose, so corrective actions tends to be delayed. The purpose of this work is, on the one hand, to use a real-time dosimetry system to establish the occupational doses per procedure of workers at the catheter laboratories and, on the other hand, to evaluate its utility as a learning tool for radiation protection purposes with the simultaneous video recording of the interventions. (authors)

  11. A Just-in-Time Learning based Monitoring and Classification Method for Hyper/Hypocalcemia Diagnosis.

    Science.gov (United States)

    Peng, Xin; Tang, Yang; He, Wangli; Du, Wenli; Qian, Feng

    2017-01-20

    This study focuses on the classification and pathological status monitoring of hyper/hypo-calcemia in the calcium regulatory system. By utilizing the Independent Component Analysis (ICA) mixture model, samples from healthy patients are collected, diagnosed, and subsequently classified according to their underlying behaviors, characteristics, and mechanisms. Then, a Just-in-Time Learning (JITL) has been employed in order to estimate the diseased status dynamically. In terms of JITL, for the purpose of the construction of an appropriate similarity index to identify relevant datasets, a novel similarity index based on the ICA mixture model is proposed in this paper to improve online model quality. The validity and effectiveness of the proposed approach have been demonstrated by applying it to the calcium regulatory system under various hypocalcemic and hypercalcemic diseased conditions.

  12. Fast Mapping Across Time: Memory Processes Support Children's Retention of Learned Words.

    Science.gov (United States)

    Vlach, Haley A; Sandhofer, Catherine M

    2012-01-01

    Children's remarkable ability to map linguistic labels to referents in the world is commonly called fast mapping. The current study examined children's (N = 216) and adults' (N = 54) retention of fast-mapped words over time (immediately, after a 1-week delay, and after a 1-month delay). The fast mapping literature often characterizes children's retention of words as consistently high across timescales. However, the current study demonstrates that learners forget word mappings at a rapid rate. Moreover, these patterns of forgetting parallel forgetting functions of domain-general memory processes. Memory processes are critical to children's word learning and the role of one such process, forgetting, is discussed in detail - forgetting supports extended mapping by promoting the memory and generalization of words and categories.

  13. Reinforcement learning for adaptive optimal control of unknown continuous-time nonlinear systems with input constraints

    Science.gov (United States)

    Yang, Xiong; Liu, Derong; Wang, Ding

    2014-03-01

    In this paper, an adaptive reinforcement learning-based solution is developed for the infinite-horizon optimal control problem of constrained-input continuous-time nonlinear systems in the presence of nonlinearities with unknown structures. Two different types of neural networks (NNs) are employed to approximate the Hamilton-Jacobi-Bellman equation. That is, an recurrent NN is constructed to identify the unknown dynamical system, and two feedforward NNs are used as the actor and the critic to approximate the optimal control and the optimal cost, respectively. Based on this framework, the action NN and the critic NN are tuned simultaneously, without the requirement for the knowledge of system drift dynamics. Moreover, by using Lyapunov's direct method, the weights of the action NN and the critic NN are guaranteed to be uniformly ultimately bounded, while keeping the closed-loop system stable. To demonstrate the effectiveness of the present approach, simulation results are illustrated.

  14. A system for accurate and automated injection of hyperpolarized substrate with minimal dead time and scalable volumes over a large range

    Science.gov (United States)

    Reynolds, Steven; Bucur, Adriana; Port, Michael; Alizadeh, Tooba; Kazan, Samira M.; Tozer, Gillian M.; Paley, Martyn N. J.

    2014-02-01

    Over recent years hyperpolarization by dissolution dynamic nuclear polarization has become an established technique for studying metabolism in vivo in animal models. Temporal signal plots obtained from the injected metabolite and daughter products, e.g. pyruvate and lactate, can be fitted to compartmental models to estimate kinetic rate constants. Modeling and physiological parameter estimation can be made more robust by consistent and reproducible injections through automation. An injection system previously developed by us was limited in the injectable volume to between 0.6 and 2.4 ml and injection was delayed due to a required syringe filling step. An improved MR-compatible injector system has been developed that measures the pH of injected substrate, uses flow control to reduce dead volume within the injection cannula and can be operated over a larger volume range. The delay time to injection has been minimized by removing the syringe filling step by use of a peristaltic pump. For 100 μl to 10.000 ml, the volume range typically used for mice to rabbits, the average delivered volume was 97.8% of the demand volume. The standard deviation of delivered volumes was 7 μl for 100 μl and 20 μl for 10.000 ml demand volumes (mean S.D. was 9 ul in this range). In three repeat injections through a fixed 0.96 mm O.D. tube the coefficient of variation for the area under the curve was 2%. For in vivo injections of hyperpolarized pyruvate in tumor-bearing rats, signal was first detected in the input femoral vein cannula at 3-4 s post-injection trigger signal and at 9-12 s in tumor tissue. The pH of the injected pyruvate was 7.1 ± 0.3 (mean ± S.D., n = 10). For small injection volumes, e.g. less than 100 μl, the internal diameter of the tubing contained within the peristaltic pump could be reduced to improve accuracy. Larger injection volumes are limited only by the size of the receiving vessel connected to the pump.

  15. A compact time-of-flight SANS instrument optimised for measurements of small sample volumes at the European Spallation Source

    Energy Technology Data Exchange (ETDEWEB)

    Kynde, Søren, E-mail: kynde@nbi.ku.dk [Niels Bohr Institute, University of Copenhagen (Denmark); Hewitt Klenø, Kaspar [Niels Bohr Institute, University of Copenhagen (Denmark); Nagy, Gergely [SINQ, Paul Scherrer Institute (Switzerland); Mortensen, Kell; Lefmann, Kim [Niels Bohr Institute, University of Copenhagen (Denmark); Kohlbrecher, Joachim, E-mail: Joachim.kohlbrecher@psi.ch [SINQ, Paul Scherrer Institute (Switzerland); Arleth, Lise, E-mail: arleth@nbi.ku.dk [Niels Bohr Institute, University of Copenhagen (Denmark)

    2014-11-11

    The high flux at European Spallation Source (ESS) will allow for performing experiments with relatively small beam-sizes while maintaining a high intensity of the incoming beam. The pulsed nature of the source makes the facility optimal for time-of-flight small-angle neutron scattering (ToF-SANS). We find that a relatively compact SANS instrument becomes the optimal choice in order to obtain the widest possible q-range in a single setting and the best possible exploitation of the neutrons in each pulse and hence obtaining the highest possible flux at the sample position. The instrument proposed in the present article is optimised for performing fast measurements of small scattering volumes, typically down to 2×2×2 mm{sup 3}, while covering a broad q-range from about 0.005 1/Å to 0.5 1/Å in a single instrument setting. This q-range corresponds to that available at a typical good BioSAXS instrument and is relevant for a wide set of biomacromolecular samples. A central advantage of covering the whole q-range in a single setting is that each sample has to be loaded only once. This makes it convenient to use the fully automated high-throughput flow-through sample changers commonly applied at modern synchrotron BioSAXS-facilities. The central drawback of choosing a very compact instrument is that the resolution in terms of δλ/λ obtained with the short wavelength neutrons becomes worse than what is usually the standard at state-of-the-art SANS instruments. Our McStas based simulations of the instrument performance for a set of characteristic biomacromolecular samples show that the resulting smearing effects still have relatively minor effects on the obtained data and can be compensated for in the data analysis. However, in cases where a better resolution is required in combination with the large simultaneous q-range characteristic of the instrument, we show that this can be obtained by inserting a set of choppers.

  16. "Our Journey through Time": An Oral History Project Carried out by Young People with Learning Disabilities

    Science.gov (United States)

    Bentley, Sarah; Nicholls, Rickie; Price, Maxine; Wilkinson, Aaron; Purcell, Matthew; Woodhall, Martin; Walmsley, Jan

    2011-01-01

    We are five young people with learning disabilities who found out about the history of hospitals for people with learning disabilities in our area, and made a film about the project. The project taught us what life had been like for some people with learning disabilities only 30 years ago. It was very different to our lives; we have more choice,…

  17. Infant Statistical-Learning Ability Is Related to Real-Time Language Processing

    Science.gov (United States)

    Lany, Jill; Shoaib, Amber; Thompson, Abbie; Estes, Katharine Graf

    2018-01-01

    Infants are adept at learning statistical regularities in artificial language materials, suggesting that the ability to learn statistical structure may support language development. Indeed, infants who perform better on statistical learning tasks tend to be more advanced in parental reports of infants' language skills. Work with adults suggests…

  18. Evaluation of Multiple Kernel Learning Algorithms for Crop Mapping Using Satellite Image Time-Series Data

    Science.gov (United States)

    Niazmardi, S.; Safari, A.; Homayouni, S.

    2017-09-01

    Crop mapping through classification of Satellite Image Time-Series (SITS) data can provide very valuable information for several agricultural applications, such as crop monitoring, yield estimation, and crop inventory. However, the SITS data classification is not straightforward. Because different images of a SITS data have different levels of information regarding the classification problems. Moreover, the SITS data is a four-dimensional data that cannot be classified using the conventional classification algorithms. To address these issues in this paper, we presented a classification strategy based on Multiple Kernel Learning (MKL) algorithms for SITS data classification. In this strategy, initially different kernels are constructed from different images of the SITS data and then they are combined into a composite kernel using the MKL algorithms. The composite kernel, once constructed, can be used for the classification of the data using the kernel-based classification algorithms. We compared the computational time and the classification performances of the proposed classification strategy using different MKL algorithms for the purpose of crop mapping. The considered MKL algorithms are: MKL-Sum, SimpleMKL, LPMKL and Group-Lasso MKL algorithms. The experimental tests of the proposed strategy on two SITS data sets, acquired by SPOT satellite sensors, showed that this strategy was able to provide better performances when compared to the standard classification algorithm. The results also showed that the optimization method of the used MKL algorithms affects both the computational time and classification accuracy of this strategy.

  19. Machine Learning-based Transient Brokers for Real-time Classification of the LSST Alert Stream

    Science.gov (United States)

    Narayan, Gautham; Zaidi, Tayeb; Soraisam, Monika; ANTARES Collaboration

    2018-01-01

    The number of transient events discovered by wide-field time-domain surveys already far outstrips the combined followup resources of the astronomical community. This number will only increase as we progress towards the commissioning of the Large Synoptic Survey Telescope (LSST), breaking the community's current followup paradigm. Transient brokers - software to sift through, characterize, annotate and prioritize events for followup - will be a critical tool for managing alert streams in the LSST era. Developing the algorithms that underlie the brokers, and obtaining simulated LSST-like datasets prior to LSST commissioning, to train and test these algorithms are formidable, though not insurmountable challenges. The Arizona-NOAO Temporal Analysis and Response to Events System (ANTARES) is a joint project of the National Optical Astronomy Observatory and the Department of Computer Science at the University of Arizona. We have been developing completely automated methods to characterize and classify variable and transient events from their multiband optical photometry. We describe the hierarchical ensemble machine learning algorithm we are developing, and test its performance on sparse, unevenly sampled, heteroskedastic data from various existing observational campaigns, as well as our progress towards incorporating these into a real-time event broker working on live alert streams from time-domain surveys.

  20. A study of rumen water volume, rate of flow of water and rumen dry matter turnover time measurement by using 51Cr-labelled EDTA

    International Nuclear Information System (INIS)

    Krishna, G.; Ekern, A.

    1974-01-01

    Two fistulated adult sheep were infused with 100 μVi 51 Cr-EDTA, four hours after morning feeding, so as to calculate fumen water volume, and rate of flow of water from reticulo-rumen. The average figure of rumen water volume obtained was 2.191 litre, rate of flow of water expressed as volume per cent per hour was 7.55. The biological half-life of marker 51 Cr-EDTA in rumen was 9.34 hours. The percent recovery of infused dosage of 51 Cr-EDTA through faeces and urine was 66 and 5 during the period of four days after infusion. Dry matter turnover time in the rumen was 0.483 days. (author)