Using iterative learning to improve understanding during the informed consent process in a South African psychiatric genomics study.

PubMed

Campbell, Megan M; Susser, Ezra; Mall, Sumaya; Mqulwana, Sibonile G; Mndini, Michael M; Ntola, Odwa A; Nagdee, Mohamed; Zingela, Zukiswa; Van Wyk, Stephanus; Stein, Dan J

2017-01-01

Obtaining informed consent is a great challenge in global health research. There is a need for tools that can screen for and improve potential research participants' understanding of the research study at the time of recruitment. Limited empirical research has been conducted in low and middle income countries, evaluating informed consent processes in genomics research. We sought to investigate the quality of informed consent obtained in a South African psychiatric genomics study. A Xhosa language version of the University of California, San Diego Brief Assessment of Capacity to Consent Questionnaire (UBACC) was used to screen for capacity to consent and improve understanding through iterative learning in a sample of 528 Xhosa people with schizophrenia and 528 controls. We address two questions: firstly, whether research participants' understanding of the research study improved through iterative learning; and secondly, what were predictors for better understanding of the research study at the initial screening? During screening 290 (55%) cases and 172 (33%) controls scored below the 14.5 cut-off for acceptable understanding of the research study elements, however after iterative learning only 38 (7%) cases and 13 (2.5%) controls continued to score below this cut-off. Significant variables associated with increased understanding of the consent included the psychiatric nurse recruiter conducting the consent screening, higher participant level of education, and being a control. The UBACC proved an effective tool to improve understanding of research study elements during consent, for both cases and controls. The tool holds utility for complex studies such as those involving genomics, where iterative learning can be used to make significant improvements in understanding of research study elements. The UBACC may be particularly important in groups with severe mental illness and lower education levels. Study recruiters play a significant role in managing the quality of the informed consent process.

Application of a repetitive process setting to design of monotonically convergent iterative learning control

NASA Astrophysics Data System (ADS)

Boski, Marcin; Paszke, Wojciech

2015-11-01

This paper deals with the problem of designing an iterative learning control algorithm for discrete linear systems using repetitive process stability theory. The resulting design produces a stabilizing output feedback controller in the time domain and a feedforward controller that guarantees monotonic convergence in the trial-to-trial domain. The results are also extended to limited frequency range design specification. New design procedure is introduced in terms of linear matrix inequality (LMI) representations, which guarantee the prescribed performances of ILC scheme. A simulation example is given to illustrate the theoretical developments.

Quantum-Inspired Multidirectional Associative Memory With a Self-Convergent Iterative Learning.

PubMed

Masuyama, Naoki; Loo, Chu Kiong; Seera, Manjeevan; Kubota, Naoyuki

2018-04-01

Quantum-inspired computing is an emerging research area, which has significantly improved the capabilities of conventional algorithms. In general, quantum-inspired hopfield associative memory (QHAM) has demonstrated quantum information processing in neural structures. This has resulted in an exponential increase in storage capacity while explaining the extensive memory, and it has the potential to illustrate the dynamics of neurons in the human brain when viewed from quantum mechanics perspective although the application of QHAM is limited as an autoassociation. We introduce a quantum-inspired multidirectional associative memory (QMAM) with a one-shot learning model, and QMAM with a self-convergent iterative learning model (IQMAM) based on QHAM in this paper. The self-convergent iterative learning enables the network to progressively develop a resonance state, from inputs to outputs. The simulation experiments demonstrate the advantages of QMAM and IQMAM, especially the stability to recall reliability.

Eliminating Unpredictable Variation through Iterated Learning

ERIC Educational Resources Information Center

Smith, Kenny; Wonnacott, Elizabeth

2010-01-01

Human languages may be shaped not only by the (individual psychological) processes of language acquisition, but also by population-level processes arising from repeated language learning and use. One prevalent feature of natural languages is that they avoid unpredictable variation. The current work explores whether linguistic predictability might…

A Strategic Planning Process Model for Distance Education

ERIC Educational Resources Information Center

Pisel, Kenneth P.

2008-01-01

As more institutions seek to implement or expand distance learning programs, it becomes critical to integrate distance learning programs into broader strategic visions and plans. Using the informed opinion from a panel of peer-nominated experts via iterative Delphi questionnaires, a 10-phased strategic planning process model for distance education…

Computer-Supported Team-Based Learning: The Impact of Motivation, Enjoyment and Team Contributions on Learning Outcomes

ERIC Educational Resources Information Center

Gomez, Elizabeth Avery; Wu, Dezhi; Passerini, Katia

2010-01-01

The benefits of teamwork and collaboration have long been advocated by many educational theories, such as constructivist and social learning models. Among the various applications of collaborative learning, the iterative team-based learning (TBL) process proposed by Michaelsen, Fink, and Knight (2002) has been successfully used in the classroom…

Technology Education Using a Novel Approach in e-Learning-Towards Optimizing the Quality of Learning Outcomes

NASA Astrophysics Data System (ADS)

Malkawi, M. I.; Hawarey, M. M.

2012-04-01

Ever since the advent of the new era in presenting taught material in Electronic Form, international bodies, academic institutions, public sectors, as well as specialized entities in the private sector, globally, have all persevered to exploit the power of Distance Learning and e-Learning to disseminate the knowledge in Science and Art using the ubiquitous World Wide Web and its supporting Internet and Internetworking. Many Science & Education-sponsoring bodies, like UNESCO, the European Community, and the World Bank have been keen at funding multinational Distance Learning projects, many of which were directed at an educated audience in certain technical areas. Many countries around the Middle East have found a number of interested European partners to launch funding requests, and were generally successful in their solicitation efforts for the needed funds from these funding bodies. Albeit their intricacies in generating a wealth of knowledge in electronic form, many of the e-Learning schemas developed thus far, have only pursued their goals in the most conventional of ways; In essence, there had been little innovation introduced to gain anything, if any, above traditional classroom lecturing, other than, of course, the gained advantage of the simultaneous online testing and evaluation of the learned material by the examinees. In a sincere effort to change the way in which people look at the merits of e-Learning, and seek the most out of it, we shall propose a novel approach aimed at optimizing the learning outcomes of presented materials. In this paper we propose what shall henceforth be called as Iterative e-Learning. In Iterative e-Learning, as the name implies, a student uses some form of electronic media to access course material in a specific subject. At the end of each phase (Section, Chapter, Session, etc.) on a specific topic, the student is assessed online of how much he/she would have achieved before he/she would move on. If the student fails, due to some delinquency on a particular topic, the online process of e-Learning would take the student at some more detailed and deeper level on the subject matter where he/she had failed; once the student bridges the gap, to this end, then the ongoing e-Learning process would carry him/her further up the next level of the subject matter he/she is pursuing. This process is carried on at all levels of learning: section, chapter, and course level. A student may not progress to the next course level before he/she would pass the entire course at 80% or more. If in the process of repeating some section, chapter, or a whole course, then the student shall be required to score a higher percentage than the mere 80% he was required to attain the first time around; say 5% more per iteration he/she makes. Here, students going through Iterative e-Learning shall be allowed to move on to the next level of learning sooner than others if the time that takes them to learn a particular topic is shorter than would normally require an average student to expend, provided, of course, they make it through all the required assessment phases. Unlike the traditional ways of classroom or online lecturing, a student going through Iterative e-Learning is expected to achieve a quality of learning never before achieved via standard pedagogical methodologies. With Iterative e-Learning, it is expected that poorly accredited academic institutions will be able, for the first time, to produce the quality of graduates who are more capable of competing for highly paying jobs globally, and to be of the quality of contributing in more industry-supported economies.

The Iterated Classification Game: A New Model of the Cultural Transmission of Language

PubMed Central

Swarup, Samarth; Gasser, Les

2010-01-01

The Iterated Classification Game (ICG) combines the Classification Game with the Iterated Learning Model (ILM) to create a more realistic model of the cultural transmission of language through generations. It includes both learning from parents and learning from peers. Further, it eliminates some of the chief criticisms of the ILM: that it does not study grounded languages, that it does not include peer learning, and that it builds in a bias for compositional languages. We show that, over the span of a few generations, a stable linguistic system emerges that can be acquired very quickly by each generation, is compositional, and helps the agents to solve the classification problem with which they are faced. The ICG also leads to a different interpretation of the language acquisition process. It suggests that the role of parents is to initialize the linguistic system of the child in such a way that subsequent interaction with peers results in rapid convergence to the correct language. PMID:20190877

Design of robust iterative learning control schemes for systems with polytopic uncertainties and sector-bounded nonlinearities

NASA Astrophysics Data System (ADS)

Boski, Marcin; Paszke, Wojciech

2017-01-01

This paper deals with designing of iterative learning control schemes for uncertain systems with static nonlinearities. More specifically, the nonlinear part is supposed to be sector bounded and system matrices are assumed to range in the polytope of matrices. For systems with such nonlinearities and uncertainties the repetitive process setting is exploited to develop a linear matrix inequality based conditions for computing the feedback and feedforward (learning) controllers. These controllers guarantee acceptable dynamics along the trials and ensure convergence of the trial-to-trial error dynamics, respectively. Numerical examples illustrate the theoretical results and confirm effectiveness of the designed control scheme.

Discrete-Time Deterministic $Q$ -Learning: A Novel Convergence Analysis.

PubMed

Wei, Qinglai; Lewis, Frank L; Sun, Qiuye; Yan, Pengfei; Song, Ruizhuo

2017-05-01

In this paper, a novel discrete-time deterministic Q -learning algorithm is developed. In each iteration of the developed Q -learning algorithm, the iterative Q function is updated for all the state and control spaces, instead of updating for a single state and a single control in traditional Q -learning algorithm. A new convergence criterion is established to guarantee that the iterative Q function converges to the optimum, where the convergence criterion of the learning rates for traditional Q -learning algorithms is simplified. During the convergence analysis, the upper and lower bounds of the iterative Q function are analyzed to obtain the convergence criterion, instead of analyzing the iterative Q function itself. For convenience of analysis, the convergence properties for undiscounted case of the deterministic Q -learning algorithm are first developed. Then, considering the discounted factor, the convergence criterion for the discounted case is established. Neural networks are used to approximate the iterative Q function and compute the iterative control law, respectively, for facilitating the implementation of the deterministic Q -learning algorithm. Finally, simulation results and comparisons are given to illustrate the performance of the developed algorithm.

Dynamic adaptive learning for decision-making supporting systems

NASA Astrophysics Data System (ADS)

He, Haibo; Cao, Yuan; Chen, Sheng; Desai, Sachi; Hohil, Myron E.

2008-03-01

This paper proposes a novel adaptive learning method for data mining in support of decision-making systems. Due to the inherent characteristics of information ambiguity/uncertainty, high dimensionality and noisy in many homeland security and defense applications, such as surveillances, monitoring, net-centric battlefield, and others, it is critical to develop autonomous learning methods to efficiently learn useful information from raw data to help the decision making process. The proposed method is based on a dynamic learning principle in the feature spaces. Generally speaking, conventional approaches of learning from high dimensional data sets include various feature extraction (principal component analysis, wavelet transform, and others) and feature selection (embedded approach, wrapper approach, filter approach, and others) methods. However, very limited understandings of adaptive learning from different feature spaces have been achieved. We propose an integrative approach that takes advantages of feature selection and hypothesis ensemble techniques to achieve our goal. Based on the training data distributions, a feature score function is used to provide a measurement of the importance of different features for learning purpose. Then multiple hypotheses are iteratively developed in different feature spaces according to their learning capabilities. Unlike the pre-set iteration steps in many of the existing ensemble learning approaches, such as adaptive boosting (AdaBoost) method, the iterative learning process will automatically stop when the intelligent system can not provide a better understanding than a random guess in that particular subset of feature spaces. Finally, a voting algorithm is used to combine all the decisions from different hypotheses to provide the final prediction results. Simulation analyses of the proposed method on classification of different US military aircraft databases show the effectiveness of this method.

Online Learner Self-Regulation: Learning Presence Viewed through Quantitative Content- and Social Network Analysis

ERIC Educational Resources Information Center

Shea, Peter; Hayes, Suzanne; Smith, Sedef Uzuner; Vickers, Jason; Bidjerano, Temi; Gozza-Cohen, Mary; Jian, Shou-Bang; Pickett, Alexandra M.; Wilde, Jane; Tseng, Chi-Hua

2013-01-01

This paper presents an extension of an ongoing study of online learning framed within the community of inquiry (CoI) model (Garrison, Anderson, & Archer, 2001) in which we further examine a new construct labeled as "learning presence." We use learning presence to refer to the iterative processes of forethought and planning,…

Using Design-Based Research in Informal Environments

ERIC Educational Resources Information Center

Reisman, Molly

2008-01-01

Design-Based Research (DBR) has been a tool of the learning sciences since the early 1990s, used as a way to improve and study learning environments. Using an iterative process of design with the goal of reining theories of learning, researchers and educators now use DBR seek to identify "how" to make a learning environment work. They then draw…

Solving ill-posed inverse problems using iterative deep neural networks

NASA Astrophysics Data System (ADS)

Adler, Jonas; Öktem, Ozan

2017-12-01

We propose a partially learned approach for the solution of ill-posed inverse problems with not necessarily linear forward operators. The method builds on ideas from classical regularisation theory and recent advances in deep learning to perform learning while making use of prior information about the inverse problem encoded in the forward operator, noise model and a regularising functional. The method results in a gradient-like iterative scheme, where the ‘gradient’ component is learned using a convolutional network that includes the gradients of the data discrepancy and regulariser as input in each iteration. We present results of such a partially learned gradient scheme on a non-linear tomographic inversion problem with simulated data from both the Sheep-Logan phantom as well as a head CT. The outcome is compared against filtered backprojection and total variation reconstruction and the proposed method provides a 5.4 dB PSNR improvement over the total variation reconstruction while being significantly faster, giving reconstructions of 512 × 512 pixel images in about 0.4 s using a single graphics processing unit (GPU).

Photoacoustic image reconstruction via deep learning

NASA Astrophysics Data System (ADS)

Antholzer, Stephan; Haltmeier, Markus; Nuster, Robert; Schwab, Johannes

2018-02-01

Applying standard algorithms to sparse data problems in photoacoustic tomography (PAT) yields low-quality images containing severe under-sampling artifacts. To some extent, these artifacts can be reduced by iterative image reconstruction algorithms which allow to include prior knowledge such as smoothness, total variation (TV) or sparsity constraints. These algorithms tend to be time consuming as the forward and adjoint problems have to be solved repeatedly. Further, iterative algorithms have additional drawbacks. For example, the reconstruction quality strongly depends on a-priori model assumptions about the objects to be recovered, which are often not strictly satisfied in practical applications. To overcome these issues, in this paper, we develop direct and efficient reconstruction algorithms based on deep learning. As opposed to iterative algorithms, we apply a convolutional neural network, whose parameters are trained before the reconstruction process based on a set of training data. For actual image reconstruction, a single evaluation of the trained network yields the desired result. Our presented numerical results (using two different network architectures) demonstrate that the proposed deep learning approach reconstructs images with a quality comparable to state of the art iterative reconstruction methods.

Iterative deep convolutional encoder-decoder network for medical image segmentation.

PubMed

Jung Uk Kim; Hak Gu Kim; Yong Man Ro

2017-07-01

In this paper, we propose a novel medical image segmentation using iterative deep learning framework. We have combined an iterative learning approach and an encoder-decoder network to improve segmentation results, which enables to precisely localize the regions of interest (ROIs) including complex shapes or detailed textures of medical images in an iterative manner. The proposed iterative deep convolutional encoder-decoder network consists of two main paths: convolutional encoder path and convolutional decoder path with iterative learning. Experimental results show that the proposed iterative deep learning framework is able to yield excellent medical image segmentation performances for various medical images. The effectiveness of the proposed method has been proved by comparing with other state-of-the-art medical image segmentation methods.

e-Learning Application for Machine Maintenance Process using Iterative Method in XYZ Company

NASA Astrophysics Data System (ADS)

Nurunisa, Suaidah; Kurniawati, Amelia; Pramuditya Soesanto, Rayinda; Yunan Kurnia Septo Hediyanto, Umar

2016-02-01

XYZ Company is a company based on manufacturing part for airplane, one of the machine that is categorized as key facility in the company is Millac 5H6P. As a key facility, the machines should be assured to work well and in peak condition, therefore, maintenance process is needed periodically. From the data gathering, it is known that there are lack of competency from the maintenance staff to maintain different type of machine which is not assigned by the supervisor, this indicate that knowledge which possessed by maintenance staff are uneven. The purpose of this research is to create knowledge-based e-learning application as a realization from externalization process in knowledge transfer process to maintain the machine. The application feature are adjusted for maintenance purpose using e-learning framework for maintenance process, the content of the application support multimedia for learning purpose. QFD is used in this research to understand the needs from user. The application is built using moodle with iterative method for software development cycle and UML Diagram. The result from this research is e-learning application as sharing knowledge media for maintenance staff in the company. From the test, it is known that the application make maintenance staff easy to understand the competencies.

Design and Implementation of a Learning Analytics Toolkit for Teachers

ERIC Educational Resources Information Center

Dyckhoff, Anna Lea; Zielke, Dennis; Bultmann, Mareike; Chatti, Mohamed Amine; Schroeder, Ulrik

2012-01-01

Learning Analytics can provide powerful tools for teachers in order to support them in the iterative process of improving the effectiveness of their courses and to collaterally enhance their students' performance. In this paper, we present the theoretical background, design, implementation, and evaluation details of eLAT, a Learning Analytics…

Ask-the-expert: Active Learning Based Knowledge Discovery Using the Expert

NASA Technical Reports Server (NTRS)

Das, Kamalika; Avrekh, Ilya; Matthews, Bryan; Sharma, Manali; Oza, Nikunj

2017-01-01

Often the manual review of large data sets, either for purposes of labeling unlabeled instances or for classifying meaningful results from uninteresting (but statistically significant) ones is extremely resource intensive, especially in terms of subject matter expert (SME) time. Use of active learning has been shown to diminish this review time significantly. However, since active learning is an iterative process of learning a classifier based on a small number of SME-provided labels at each iteration, the lack of an enabling tool can hinder the process of adoption of these technologies in real-life, in spite of their labor-saving potential. In this demo we present ASK-the-Expert, an interactive tool that allows SMEs to review instances from a data set and provide labels within a single framework. ASK-the-Expert is powered by an active learning algorithm for training a classifier in the backend. We demonstrate this system in the context of an aviation safety application, but the tool can be adopted to work as a simple review and labeling tool as well, without the use of active learning.

Ask-the-Expert: Active Learning Based Knowledge Discovery Using the Expert

NASA Technical Reports Server (NTRS)

Das, Kamalika

2017-01-01

Often the manual review of large data sets, either for purposes of labeling unlabeled instances or for classifying meaningful results from uninteresting (but statistically significant) ones is extremely resource intensive, especially in terms of subject matter expert (SME) time. Use of active learning has been shown to diminish this review time significantly. However, since active learning is an iterative process of learning a classifier based on a small number of SME-provided labels at each iteration, the lack of an enabling tool can hinder the process of adoption of these technologies in real-life, in spite of their labor-saving potential. In this demo we present ASK-the-Expert, an interactive tool that allows SMEs to review instances from a data set and provide labels within a single framework. ASK-the-Expert is powered by an active learning algorithm for training a classifier in the back end. We demonstrate this system in the context of an aviation safety application, but the tool can be adopted to work as a simple review and labeling tool as well, without the use of active learning.

Proposing a new iterative learning control algorithm based on a non-linear least square formulation - Minimising draw-in errors

NASA Astrophysics Data System (ADS)

Endelt, B.

2017-09-01

Forming operation are subject to external disturbances and changing operating conditions e.g. new material batch, increasing tool temperature due to plastic work, material properties and lubrication is sensitive to tool temperature. It is generally accepted that forming operations are not stable over time and it is not uncommon to adjust the process parameters during the first half hour production, indicating that process instability is gradually developing over time. Thus, in-process feedback control scheme might not-be necessary to stabilize the process and an alternative approach is to apply an iterative learning algorithm, which can learn from previously produced parts i.e. a self learning system which gradually reduces error based on historical process information. What is proposed in the paper is a simple algorithm which can be applied to a wide range of sheet-metal forming processes. The input to the algorithm is the final flange edge geometry and the basic idea is to reduce the least-square error between the current flange geometry and a reference geometry using a non-linear least square algorithm. The ILC scheme is applied to a square deep-drawing and the Numisheet’08 S-rail benchmark problem, the numerical tests shows that the proposed control scheme is able control and stabilise both processes.

Learning to Teach Elementary Science Through Iterative Cycles of Enactment in Culturally and Linguistically Diverse Contexts

NASA Astrophysics Data System (ADS)

Bottoms, SueAnn I.; Ciechanowski, Kathryn M.; Hartman, Brian

2015-12-01

Iterative cycles of enactment embedded in culturally and linguistically diverse contexts provide rich opportunities for preservice teachers (PSTs) to enact core practices of science. This study is situated in the larger Families Involved in Sociocultural Teaching and Science, Technology, Engineering and Mathematics (FIESTAS) project, which weaves together cycles of enactment, core practices in science education and culturally relevant pedagogies. The theoretical foundation draws upon situated learning theory and communities of practice. Using video analysis by PSTs and course artifacts, the authors studied how the iterative process of these cycles guided PSTs development as teachers of elementary science. Findings demonstrate how PSTs were drawing on resources to inform practice, purposefully noticing their practice, renegotiating their roles in teaching, and reconsidering "professional blindness" through cultural practice.

Defining and Building an Enriched Learning and Information Environment.

ERIC Educational Resources Information Center

Goodrum, David A.; And Others

1993-01-01

Discusses the development of an Enriched Learning and Information Environment (ELIE). Highlights include technology-based and theory-based frameworks for defining ELIEs; a socio-technical definition; a conceptual prototype; a participatory design process, including iterative design through rapid prototyping; and design issues for technology…

Fostering Self-Regulated Learning in a Blended Environment Using Group Awareness and Peer Assistance as External Scaffolds

ERIC Educational Resources Information Center

Lin, J-W.; Lai, Y-C.; Lai, Y-C.; Chang, L-C.

2016-01-01

Most systems for training self-regulated learning (SRL) behaviour focus on the provision of a learner-centred environment. Such systems repeat the training process and place learners alone to experience that process iteratively. According to the relevant literature, external scaffolds are more promising for effective SRL training. In this work,…

Learning Objects: A User-Centered Design Process

ERIC Educational Resources Information Center

Branon, Rovy F., III

2011-01-01

Design research systematically creates or improves processes, products, and programs through an iterative progression connecting practice and theory (Reinking, 2008; van den Akker, 2006). Developing a new instructional systems design (ISD) processes through design research is necessary when new technologies emerge that challenge existing practices…

Intelligent process mapping through systematic improvement of heuristics

NASA Technical Reports Server (NTRS)

Ieumwananonthachai, Arthur; Aizawa, Akiko N.; Schwartz, Steven R.; Wah, Benjamin W.; Yan, Jerry C.

1992-01-01

The present system for automatic learning/evaluation of novel heuristic methods applicable to the mapping of communication-process sets on a computer network has its basis in the testing of a population of competing heuristic methods within a fixed time-constraint. The TEACHER 4.1 prototype learning system implemented or learning new postgame analysis heuristic methods iteratively generates and refines the mappings of a set of communicating processes on a computer network. A systematic exploration of the space of possible heuristic methods is shown to promise significant improvement.

Terminal iterative learning control based station stop control of a train

NASA Astrophysics Data System (ADS)

Hou, Zhongsheng; Wang, Yi; Yin, Chenkun; Tang, Tao

2011-07-01

The terminal iterative learning control (TILC) method is introduced for the first time into the field of train station stop control and three TILC-based algorithms are proposed in this study. The TILC-based train station stop control approach utilises the terminal stop position error in previous braking process to update the current control profile. The initial braking position, or the braking force, or their combination is chosen as the control input, and corresponding learning law is developed. The terminal stop position error of each algorithm is guaranteed to converge to a small region related with the initial offset of braking position with rigorous analysis. The validity of the proposed algorithms is verified by illustrative numerical examples.

Getting Results: Small Changes, Big Cohorts and Technology

ERIC Educational Resources Information Center

Kenney, Jacqueline L.

2012-01-01

This paper presents an example of constructive alignment in practice. Integrated technology supports were deployed to increase the consistency between learning objectives, activities and assessment and to foster student-centred, higher-order learning processes in the unit. Modifications took place over nine iterations of a second-year Marketing…

Robust design of feedback feed-forward iterative learning control based on 2D system theory for linear uncertain systems

NASA Astrophysics Data System (ADS)

Li, Zhifu; Hu, Yueming; Li, Di

2016-08-01

For a class of linear discrete-time uncertain systems, a feedback feed-forward iterative learning control (ILC) scheme is proposed, which is comprised of an iterative learning controller and two current iteration feedback controllers. The iterative learning controller is used to improve the performance along the iteration direction and the feedback controllers are used to improve the performance along the time direction. First of all, the uncertain feedback feed-forward ILC system is presented by an uncertain two-dimensional Roesser model system. Then, two robust control schemes are proposed. One can ensure that the feedback feed-forward ILC system is bounded-input bounded-output stable along time direction, and the other can ensure that the feedback feed-forward ILC system is asymptotically stable along time direction. Both schemes can guarantee the system is robust monotonically convergent along the iteration direction. Third, the robust convergent sufficient conditions are given, which contains a linear matrix inequality (LMI). Moreover, the LMI can be used to determine the gain matrix of the feedback feed-forward iterative learning controller. Finally, the simulation results are presented to demonstrate the effectiveness of the proposed schemes.

Multi-criteria Integrated Resource Assessment (MIRA)

EPA Pesticide Factsheets

MIRA is an approach that facilitates stakeholder engagement for collaborative multi-objective decision making. MIRA is designed to facilitate and support an inclusive, explicit, transparent, iterative learning-based decision process.

Adaptive Management for Urban Watersheds: The Slavic Village Pilot Project

EPA Science Inventory

Adaptive management is an environmental management strategy that uses an iterative process of decision-making to reduce the uncertainty in environmental management via system monitoring. A central tenet of adaptive management is that management involves a learning process that ca...

Explaining Cooperation in Groups: Testing Models of Reciprocity and Learning

ERIC Educational Resources Information Center

Biele, Guido; Rieskamp, Jorg; Czienskowski, Uwe

2008-01-01

What are the cognitive processes underlying cooperation in groups? This question is addressed by examining how well a reciprocity model, two learning models, and social value orientation can predict cooperation in two iterated n-person social dilemmas with continuous contributions. In the first of these dilemmas, the public goods game,…

Modeling Peer Assessment as Agent Negotiation in a Computer Supported Collaborative Learning Environment

ERIC Educational Resources Information Center

Lai, K. Robert; Lan, Chung Hsien

2006-01-01

This work presents a novel method for modeling collaborative learning as multi-issue agent negotiation using fuzzy constraints. Agent negotiation is an iterative process, through which, the proposed method aggregates student marks to reduce personal bias. In the framework, students define individual fuzzy membership functions based on their…

Is This a Meaningful Learning Experience? Interactive Critical Self-Inquiry as Investigation

ERIC Educational Resources Information Center

Allard, Andrea C.; Gallant, Andrea

2012-01-01

What conditions enable educators to engage in meaningful learning experiences with peers and beginning practitioners? This article documents a self-study on our actions-in-practice in a peer mentoring project. The investigation involved an iterative process to improve our knowledge as teacher educators, reflective practitioners, and researchers.…

Developing a Multi-Year Learning Progression for Carbon Cycling in Socio-Ecological Systems

ERIC Educational Resources Information Center

Mohan, Lindsey; Chen, Jing; Anderson, Charles W.

2009-01-01

This study reports on our steps toward achieving a conceptually coherent and empirically validated learning progression for carbon cycling in socio-ecological systems. It describes an iterative process of designing and analyzing assessment and interview data from students in upper elementary through high school. The product of our development…

Detangling the Interrelationships between Self- Regulation and Ill-Structured Problem Solving in Problem-Based Learning

ERIC Educational Resources Information Center

Ge, Xun; Law, Victor; Huang, Kun

2016-01-01

One of the goals for problem-based learning (PBL) is to promote self-regulation. Although self-regulation has been studied extensively, its interrelationships with ill-structured problem solving have been unclear. In order to clarify the interrelationships, this article proposes a conceptual framework illustrating the iterative processes among…

Engineering Design Theory: Applying the Success of the Modern World to Campaign Creation

DTIC Science & Technology

2009-05-21

and school of thought) to the simple methods of design.6 This progression is analogous to Peter Senge’s levels of learning disciplines.7 Senge...iterative learning and adaptive action that develops and employs critical and creative thinking , enabling leaders to apply the necessary logic to...overcome mental rigidity and develop group insight, the Army must learn to utilize group learning and thinking , through a fluid and creative open process

An iterative learning strategy for the auto-tuning of the feedforward and feedback controller in type-1 diabetes.

PubMed

Fravolini, M L; Fabietti, P G

2014-01-01

This paper proposes a scheme for the control of the blood glucose in subjects with type-1 diabetes mellitus based on the subcutaneous (s.c.) glucose measurement and s.c. insulin administration. The tuning of the controller is based on an iterative learning strategy that exploits the repetitiveness of the daily feeding habit of a patient. The control consists of a mixed feedback and feedforward contribution whose parameters are tuned through an iterative learning process that is based on the day-by-day automated analysis of the glucose response to the infusion of exogenous insulin. The scheme does not require any a priori information on the patient insulin/glucose response, on the meal times and on the amount of ingested carbohydrates (CHOs). Thanks to the learning mechanism the scheme is able to improve its performance over time. A specific logic is also introduced for the detection and prevention of possible hypoglycaemia events. The effectiveness of the methodology has been validated using long-term simulation studies applied to a set of nine in silico patients considering realistic uncertainties on the meal times and on the quantities of ingested CHOs.

Learner Centred Design for a Hybrid Interaction Application

ERIC Educational Resources Information Center

Wood, Simon; Romero, Pablo

2010-01-01

Learner centred design methods highlight the importance of involving the stakeholders of the learning process (learners, teachers, educational researchers) at all stages of the design of educational applications and of refining the design through an iterative prototyping process. These methods have been used successfully when designing systems…

Eliciting design patterns for e-learning systems

NASA Astrophysics Data System (ADS)

Retalis, Symeon; Georgiakakis, Petros; Dimitriadis, Yannis

2006-06-01

Design pattern creation, especially in the e-learning domain, is a highly complex process that has not been sufficiently studied and formalized. In this paper, we propose a systematic pattern development cycle, whose most important aspects focus on reverse engineering of existing systems in order to elicit features that are cross-validated through the use of appropriate, authentic scenarios. However, an iterative pattern process is proposed that takes advantage of multiple data sources, thus emphasizing a holistic view of the teaching learning processes. The proposed schema of pattern mining has been extensively validated for Asynchronous Network Supported Collaborative Learning (ANSCL) systems, as well as for other types of tools in a variety of scenarios, with promising results.

Using Analytics to Transform a Problem-Based Case Library: An Educational Design Research Approach

ERIC Educational Resources Information Center

Schmidt, Matthew; Tawfik, Andrew A.

2018-01-01

This article describes the iterative design, development, and evaluation of a case-based learning environment focusing on an ill-structured sales management problem. We discuss our processes and situate them within the broader framework of educational design research. The learning environment evolved over the course of three design phases. A…

The Iterative Design of a Mobile Learning Application to Support Scientific Inquiry

ERIC Educational Resources Information Center

Marty, Paul F.; Mendenhall, Anne; Douglas, Ian; Southerland, Sherry A.; Sampson, Victor; Kazmer, Michelle M.; Alemanne, Nicole; Clark, Amanda; Schellinger, Jennifer

2013-01-01

The ubiquity of mobile devices makes them well suited for field-based learning experiences that require students to gather data as part of the process of developing scientific inquiry practices. The usefulness of these devices, however, is strongly influenced by the nature of the applications students use to collect data in the field. To…

PID-based error signal modeling

NASA Astrophysics Data System (ADS)

Yohannes, Tesfay

1997-10-01

This paper introduces a PID based signal error modeling. The error modeling is based on the betterment process. The resulting iterative learning algorithm is introduced and a detailed proof is provided for both linear and nonlinear systems.

An open-closed-loop iterative learning control approach for nonlinear switched systems with application to freeway traffic control

NASA Astrophysics Data System (ADS)

Sun, Shu-Ting; Li, Xiao-Dong; Zhong, Ren-Xin

2017-10-01

For nonlinear switched discrete-time systems with input constraints, this paper presents an open-closed-loop iterative learning control (ILC) approach, which includes a feedforward ILC part and a feedback control part. Under a given switching rule, the mathematical induction is used to prove the convergence of ILC tracking error in each subsystem. It is demonstrated that the convergence of ILC tracking error is dependent on the feedforward control gain, but the feedback control can speed up the convergence process of ILC by a suitable selection of feedback control gain. A switched freeway traffic system is used to illustrate the effectiveness of the proposed ILC law.

Data-driven model reference control of MIMO vertical tank systems with model-free VRFT and Q-Learning.

PubMed

Radac, Mircea-Bogdan; Precup, Radu-Emil; Roman, Raul-Cristian

2018-02-01

This paper proposes a combined Virtual Reference Feedback Tuning-Q-learning model-free control approach, which tunes nonlinear static state feedback controllers to achieve output model reference tracking in an optimal control framework. The novel iterative Batch Fitted Q-learning strategy uses two neural networks to represent the value function (critic) and the controller (actor), and it is referred to as a mixed Virtual Reference Feedback Tuning-Batch Fitted Q-learning approach. Learning convergence of the Q-learning schemes generally depends, among other settings, on the efficient exploration of the state-action space. Handcrafting test signals for efficient exploration is difficult even for input-output stable unknown processes. Virtual Reference Feedback Tuning can ensure an initial stabilizing controller to be learned from few input-output data and it can be next used to collect substantially more input-state data in a controlled mode, in a constrained environment, by compensating the process dynamics. This data is used to learn significantly superior nonlinear state feedback neural networks controllers for model reference tracking, using the proposed Batch Fitted Q-learning iterative tuning strategy, motivating the original combination of the two techniques. The mixed Virtual Reference Feedback Tuning-Batch Fitted Q-learning approach is experimentally validated for water level control of a multi input-multi output nonlinear constrained coupled two-tank system. Discussions on the observed control behavior are offered. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Iterative Design and Testing for the Development of a Game-Based Chlamydia Awareness Intervention: A Pilot Study.

PubMed

Jiang, Rui; McKanna, James; Calabrese, Samantha; Seif El-Nasr, Magy

2017-08-01

Herein we describe a methodology for developing a game-based intervention to raise awareness of Chlamydia and other sexually transmitted infections among youth in Boston's underserved communities. We engaged in three design-based experiments. These utilized mixed methods, including playtesting and assessment methods, to examine the overall effectiveness of the game. In this case, effectiveness is defined as (1) engaging the target group, (2) increasing knowledge about Chlamydia, and (3) changing attitudes toward Chlamydia testing. These three experiments were performed using participants from different communities and with slightly different versions of the game, as we iterated through the design/feedback process. Overall, participants who played the game showed a significant increase in participants' knowledge of Chlamydia compared with those in the control group (P = 0.0002). The version of the game, including elements specifically targeting systemic thinking, showed significant improvement in participants' intent to get tested compared with the version of the game without such elements (Stage 2: P > 0.05; Stage 3: P = 0.0045). Furthermore, during both Stage 2 and Stage 3, participants showed high levels of enjoyment, mood, and participation and moderate levels of game engagement and social engagement. During Stage 3, however, participants' game engagement (P = 0.0003), social engagement (P = 0.0003), and participation (P = 0.0003) were significantly higher compared with those of Stage 2. Thus, we believe that motivation improvements from Stage 2 to 3 were also effective. Finally, participants' overall learning effectiveness was correlated with their prepositive affect (r = 0.52) and their postproblem hierarchy (r = -0.54). The game improved considerably from its initial conception through three stages of iterative design and feedback. Our assessment methods for each stage targeted and integrated learning, health, and engagement outcomes. Lessons learned through this iterative design process are a great contribution to the games for health community, especially in targeting the development of health and learning goals through game design.

Self-Regulated Learning: The Continuous-Change Conceptual Framework and a Vision of New Paradigm, Technology System, and Pedagogical Support

ERIC Educational Resources Information Center

Huh, Yeol; Reigeluth, Charles M.

2017-01-01

A modified conceptual framework called the Continuous-Change Framework for self-regulated learning (SRL) is presented. Common elements and limitations among the past frameworks are discussed in relation to the modified conceptual framework. The iterative nature of the goal setting process and overarching presence of self-efficacy and motivational…

Convergence of Proximal Iteratively Reweighted Nuclear Norm Algorithm for Image Processing.

PubMed

Sun, Tao; Jiang, Hao; Cheng, Lizhi

2017-08-25

The nonsmooth and nonconvex regularization has many applications in imaging science and machine learning research due to its excellent recovery performance. A proximal iteratively reweighted nuclear norm algorithm has been proposed for the nonsmooth and nonconvex matrix minimizations. In this paper, we aim to investigate the convergence of the algorithm. With the Kurdyka-Łojasiewicz property, we prove the algorithm globally converges to a critical point of the objective function. The numerical results presented in this paper coincide with our theoretical findings.

Imaging complex objects using learning tomography

NASA Astrophysics Data System (ADS)

Lim, JooWon; Goy, Alexandre; Shoreh, Morteza Hasani; Unser, Michael; Psaltis, Demetri

2018-02-01

Optical diffraction tomography (ODT) can be described using the scattering process through an inhomogeneous media. An inherent nonlinearity exists relating the scattering medium and the scattered field due to multiple scattering. Multiple scattering is often assumed to be negligible in weakly scattering media. This assumption becomes invalid as the sample gets more complex resulting in distorted image reconstructions. This issue becomes very critical when we image a complex sample. Multiple scattering can be simulated using the beam propagation method (BPM) as the forward model of ODT combined with an iterative reconstruction scheme. The iterative error reduction scheme and the multi-layer structure of BPM are similar to neural networks. Therefore we refer to our imaging method as learning tomography (LT). To fairly assess the performance of LT in imaging complex samples, we compared LT with the conventional iterative linear scheme using Mie theory which provides the ground truth. We also demonstrate the capacity of LT to image complex samples using experimental data of a biological cell.

Learning Contexts for Young Children in Chile: Process Quality Assessment in Preschool Centres

ERIC Educational Resources Information Center

Herrera, Maria Olivia; Mathiesen, Maria Elena; Merino, Jose Manuel; Recart, Isidora

2005-01-01

ITERS (Infant and Toddler Environment Rating Scale), ECERS (Early Childhood Environment Rating Scale) and SACERS (School Age Care Environment Rating Scale) are used to measure process quality. The psychometric characteristics of the three scales are established, and high reliability and adequate validity are observed. The global quality process…

Intelligent tuning method of PID parameters based on iterative learning control for atomic force microscopy.

PubMed

Liu, Hui; Li, Yingzi; Zhang, Yingxu; Chen, Yifu; Song, Zihang; Wang, Zhenyu; Zhang, Suoxin; Qian, Jianqiang

2018-01-01

Proportional-integral-derivative (PID) parameters play a vital role in the imaging process of an atomic force microscope (AFM). Traditional parameter tuning methods require a lot of manpower and it is difficult to set PID parameters in unattended working environments. In this manuscript, an intelligent tuning method of PID parameters based on iterative learning control is proposed to self-adjust PID parameters of the AFM according to the sample topography. This method gets enough information about the output signals of PID controller and tracking error, which will be used to calculate the proper PID parameters, by repeated line scanning until convergence before normal scanning to learn the topography. Subsequently, the appropriate PID parameters are obtained by fitting method and then applied to the normal scanning process. The feasibility of the method is demonstrated by the convergence analysis. Simulations and experimental results indicate that the proposed method can intelligently tune PID parameters of the AFM for imaging different topographies and thus achieve good tracking performance. Copyright © 2017 Elsevier Ltd. All rights reserved.

Manager Perspectives on Communication and Public ...

EPA Pesticide Factsheets

We argue that public engagement is crucial to achieving lasting ecological success in aquatic restoration efforts, and that the most effective public engagement mechanisms are what we term iterative mechanisms. Here we look to a particular social-ecological system – the restoration community in Rhode Island, U.S.A. and the rivers, wetlands, marshes, and estuaries, and their related species, that they work to protect – to better understand land managers’ perspectives on public engagement in restoration processes. Adopting an inductive approach to critical discourse analysis of interviews with 27 local, state, and federal restoration managers and the forms of public interaction they described, we identify three distinct models of public engagement in natural resources management employed by managers: unidirectional; bidirectional; and iterative. While unidirectional and bidirectional mechanisms can help managers achieve short-term ecological successes, we suggest that adopting an iterative approach can improve the quality of stakeholder and learning interactions and, subsequently, foster lasting ecological successes. We argue that managers can design deliberately for public engagement mechanisms that are best suited to projects in particular social-ecological systems in order to create restoration projects that achieve ecological, learning, and stakeholder successes. We attempt to synthesize the lessons learned from efforts at public engagement in restoratio

Interdisciplinary Research: Performance and Policy Issues.

ERIC Educational Resources Information Center

Rossini, Frederick A.; Porter, Alan L.

1981-01-01

Successful interdisciplinary research performance, it is suggested, depends on such structural and process factors as leadership, team characteristics, study bounding, iteration, communication patterns, and epistemological factors. Appropriate frameworks for socially organizing the development of knowledge such as common group learning, modeling,…

How children perceive fractals: Hierarchical self-similarity and cognitive development

PubMed Central

Martins, Maurício Dias; Laaha, Sabine; Freiberger, Eva Maria; Choi, Soonja; Fitch, W. Tecumseh

2014-01-01

The ability to understand and generate hierarchical structures is a crucial component of human cognition, available in language, music, mathematics and problem solving. Recursion is a particularly useful mechanism for generating complex hierarchies by means of self-embedding rules. In the visual domain, fractals are recursive structures in which simple transformation rules generate hierarchies of infinite depth. Research on how children acquire these rules can provide valuable insight into the cognitive requirements and learning constraints of recursion. Here, we used fractals to investigate the acquisition of recursion in the visual domain, and probed for correlations with grammar comprehension and general intelligence. We compared second (n = 26) and fourth graders (n = 26) in their ability to represent two types of rules for generating hierarchical structures: Recursive rules, on the one hand, which generate new hierarchical levels; and iterative rules, on the other hand, which merely insert items within hierarchies without generating new levels. We found that the majority of fourth graders, but not second graders, were able to represent both recursive and iterative rules. This difference was partially accounted by second graders’ impairment in detecting hierarchical mistakes, and correlated with between-grade differences in grammar comprehension tasks. Empirically, recursion and iteration also differed in at least one crucial aspect: While the ability to learn recursive rules seemed to depend on the previous acquisition of simple iterative representations, the opposite was not true, i.e., children were able to acquire iterative rules before they acquired recursive representations. These results suggest that the acquisition of recursion in vision follows learning constraints similar to the acquisition of recursion in language, and that both domains share cognitive resources involved in hierarchical processing. PMID:24955884

Optimization Control of the Color-Coating Production Process for Model Uncertainty

PubMed Central

He, Dakuo; Wang, Zhengsong; Yang, Le; Mao, Zhizhong

2016-01-01

Optimized control of the color-coating production process (CCPP) aims at reducing production costs and improving economic efficiency while meeting quality requirements. However, because optimization control of the CCPP is hampered by model uncertainty, a strategy that considers model uncertainty is proposed. Previous work has introduced a mechanistic model of CCPP based on process analysis to simulate the actual production process and generate process data. The partial least squares method is then applied to develop predictive models of film thickness and economic efficiency. To manage the model uncertainty, the robust optimization approach is introduced to improve the feasibility of the optimized solution. Iterative learning control is then utilized to further refine the model uncertainty. The constrained film thickness is transformed into one of the tracked targets to overcome the drawback that traditional iterative learning control cannot address constraints. The goal setting of economic efficiency is updated continuously according to the film thickness setting until this reaches its desired value. Finally, fuzzy parameter adjustment is adopted to ensure that the economic efficiency and film thickness converge rapidly to their optimized values under the constraint conditions. The effectiveness of the proposed optimization control strategy is validated by simulation results. PMID:27247563

Optimization Control of the Color-Coating Production Process for Model Uncertainty.

PubMed

He, Dakuo; Wang, Zhengsong; Yang, Le; Mao, Zhizhong

2016-01-01

Optimized control of the color-coating production process (CCPP) aims at reducing production costs and improving economic efficiency while meeting quality requirements. However, because optimization control of the CCPP is hampered by model uncertainty, a strategy that considers model uncertainty is proposed. Previous work has introduced a mechanistic model of CCPP based on process analysis to simulate the actual production process and generate process data. The partial least squares method is then applied to develop predictive models of film thickness and economic efficiency. To manage the model uncertainty, the robust optimization approach is introduced to improve the feasibility of the optimized solution. Iterative learning control is then utilized to further refine the model uncertainty. The constrained film thickness is transformed into one of the tracked targets to overcome the drawback that traditional iterative learning control cannot address constraints. The goal setting of economic efficiency is updated continuously according to the film thickness setting until this reaches its desired value. Finally, fuzzy parameter adjustment is adopted to ensure that the economic efficiency and film thickness converge rapidly to their optimized values under the constraint conditions. The effectiveness of the proposed optimization control strategy is validated by simulation results.

Co-Mentoring: The Iterative Process of Learning about Self and "Becoming" Leaders

ERIC Educational Resources Information Center

Allison, Valerie A.; Ramirez, Laurie A.

2016-01-01

Two pre-tenured faculty members at dissimilar institutions found themselves in similar positions--both were assigned to administrative positions that they did not seek. This self-study is an investigation of their processes of becoming leaders and how they aligned and/or conflicted with their espoused beliefs. A review of the literature that…

Kernel-based least squares policy iteration for reinforcement learning.

PubMed

Xu, Xin; Hu, Dewen; Lu, Xicheng

2007-07-01

In this paper, we present a kernel-based least squares policy iteration (KLSPI) algorithm for reinforcement learning (RL) in large or continuous state spaces, which can be used to realize adaptive feedback control of uncertain dynamic systems. By using KLSPI, near-optimal control policies can be obtained without much a priori knowledge on dynamic models of control plants. In KLSPI, Mercer kernels are used in the policy evaluation of a policy iteration process, where a new kernel-based least squares temporal-difference algorithm called KLSTD-Q is proposed for efficient policy evaluation. To keep the sparsity and improve the generalization ability of KLSTD-Q solutions, a kernel sparsification procedure based on approximate linear dependency (ALD) is performed. Compared to the previous works on approximate RL methods, KLSPI makes two progresses to eliminate the main difficulties of existing results. One is the better convergence and (near) optimality guarantee by using the KLSTD-Q algorithm for policy evaluation with high precision. The other is the automatic feature selection using the ALD-based kernel sparsification. Therefore, the KLSPI algorithm provides a general RL method with generalization performance and convergence guarantee for large-scale Markov decision problems (MDPs). Experimental results on a typical RL task for a stochastic chain problem demonstrate that KLSPI can consistently achieve better learning efficiency and policy quality than the previous least squares policy iteration (LSPI) algorithm. Furthermore, the KLSPI method was also evaluated on two nonlinear feedback control problems, including a ship heading control problem and the swing up control of a double-link underactuated pendulum called acrobot. Simulation results illustrate that the proposed method can optimize controller performance using little a priori information of uncertain dynamic systems. It is also demonstrated that KLSPI can be applied to online learning control by incorporating an initial controller to ensure online performance.

Automated Knowledge Discovery From Simulators

NASA Technical Reports Server (NTRS)

Burl, Michael; DeCoste, Dennis; Mazzoni, Dominic; Scharenbroich, Lucas; Enke, Brian; Merline, William

2007-01-01

A computational method, SimLearn, has been devised to facilitate efficient knowledge discovery from simulators. Simulators are complex computer programs used in science and engineering to model diverse phenomena such as fluid flow, gravitational interactions, coupled mechanical systems, and nuclear, chemical, and biological processes. SimLearn uses active-learning techniques to efficiently address the "landscape characterization problem." In particular, SimLearn tries to determine which regions in "input space" lead to a given output from the simulator, where "input space" refers to an abstraction of all the variables going into the simulator, e.g., initial conditions, parameters, and interaction equations. Landscape characterization can be viewed as an attempt to invert the forward mapping of the simulator and recover the inputs that produce a particular output. Given that a single simulation run can take days or weeks to complete even on a large computing cluster, SimLearn attempts to reduce costs by reducing the number of simulations needed to effect discoveries. Unlike conventional data-mining methods that are applied to static predefined datasets, SimLearn involves an iterative process in which a most informative dataset is constructed dynamically by using the simulator as an oracle. On each iteration, the algorithm models the knowledge it has gained through previous simulation trials and then chooses which simulation trials to run next. Running these trials through the simulator produces new data in the form of input-output pairs. The overall process is embodied in an algorithm that combines support vector machines (SVMs) with active learning. SVMs use learning from examples (the examples are the input-output pairs generated by running the simulator) and a principle called maximum margin to derive predictors that generalize well to new inputs. In SimLearn, the SVM plays the role of modeling the knowledge that has been gained through previous simulation trials. Active learning is used to determine which new input points would be most informative if their output were known. The selected input points are run through the simulator to generate new information that can be used to refine the SVM. The process is then repeated. SimLearn carefully balances exploration (semi-randomly searching around the input space) versus exploitation (using the current state of knowledge to conduct a tightly focused search). During each iteration, SimLearn uses not one, but an ensemble of SVMs. Each SVM in the ensemble is characterized by different hyper-parameters that control various aspects of the learned predictor - for example, whether the predictor is constrained to be very smooth (nearby points in input space lead to similar output predictions) or whether the predictor is allowed to be "bumpy." The various SVMs will have different preferences about which input points they would like to run through the simulator next. SimLearn includes a formal mechanism for balancing the ensemble SVM preferences so that a single choice can be made for the next set of trials.

Development of a public health reporting data warehouse: lessons learned.

PubMed

Rizi, Seyed Ali Mussavi; Roudsari, Abdul

2013-01-01

Data warehouse projects are perceived to be risky and prone to failure due to many organizational and technical challenges. However, often iterative and lengthy processes of implementation of data warehouses at an enterprise level provide an opportunity for formative evaluation of these solutions. This paper describes lessons learned from successful development and implementation of the first phase of an enterprise data warehouse to support public health surveillance at British Columbia Centre for Disease Control. Iterative and prototyping approach to development, overcoming technical challenges of extraction and integration of data from large scale clinical and ancillary systems, a novel approach to record linkage, flexible and reusable modeling of clinical data, and securing senior management support at the right time were the main factors that contributed to the success of the data warehousing project.

Automating Rule Strengths in Expert Systems.

DTIC Science & Technology

1987-05-01

systems were designed in an incremental, iterative way. One of the most easily identifiable phases in this process, sometimes called tuning, consists...attenuators. The designer of the knowledge-based system must determine (synthesize) or adjust (xfine, if estimates of the values are given) these...values. We consider two ways in which the designer can learn the values. We call the first model of learning the complete case and the second model the

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

NASA Technical Reports Server (NTRS)

Joglekar, Umesh Dwarkanath

1989-01-01

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

A Faculty Professional Development Model That Improves Student Learning, Encourages Active-Learning Instructional Practices, and Works for Faculty at Multiple Institutions.

PubMed

Pelletreau, Karen N; Knight, Jennifer K; Lemons, Paula P; McCourt, Jill S; Merrill, John E; Nehm, Ross H; Prevost, Luanna B; Urban-Lurain, Mark; Smith, Michelle K

2018-06-01

Helping faculty develop high-quality instruction that positively affects student learning can be complicated by time limitations, a lack of resources, and inexperience using student data to make iterative improvements. We describe a community of 16 faculty from five institutions who overcame these challenges and collaboratively designed, taught, iteratively revised, and published an instructional unit about the potential effect of mutations on DNA replication, transcription, and translation. The unit was taught to more than 2000 students in 18 courses, and student performance improved from preassessment to postassessment in every classroom. This increase occurred even though faculty varied in their instructional practices when they were teaching identical materials. We present information on how this faculty group was organized and facilitated, how members used student data to positively affect learning, and how they increased their use of active-learning instructional practices in the classroom as a result of participation. We also interviewed faculty to learn more about the most useful components of the process. We suggest that this professional development model can be used for geographically separated faculty who are interested in working together on a known conceptual difficulty to improve student learning and explore active-learning instructional practices.

Reinforcement learning for partially observable dynamic processes: adaptive dynamic programming using measured output data.

PubMed

Lewis, F L; Vamvoudakis, Kyriakos G

2011-02-01

Approximate dynamic programming (ADP) is a class of reinforcement learning methods that have shown their importance in a variety of applications, including feedback control of dynamical systems. ADP generally requires full information about the system internal states, which is usually not available in practical situations. In this paper, we show how to implement ADP methods using only measured input/output data from the system. Linear dynamical systems with deterministic behavior are considered herein, which are systems of great interest in the control system community. In control system theory, these types of methods are referred to as output feedback (OPFB). The stochastic equivalent of the systems dealt with in this paper is a class of partially observable Markov decision processes. We develop both policy iteration and value iteration algorithms that converge to an optimal controller that requires only OPFB. It is shown that, similar to Q -learning, the new methods have the important advantage that knowledge of the system dynamics is not needed for the implementation of these learning algorithms or for the OPFB control. Only the order of the system, as well as an upper bound on its "observability index," must be known. The learned OPFB controller is in the form of a polynomial autoregressive moving-average controller that has equivalent performance with the optimal state variable feedback gain.

Sparsity-constrained PET image reconstruction with learned dictionaries

NASA Astrophysics Data System (ADS)

Tang, Jing; Yang, Bao; Wang, Yanhua; Ying, Leslie

2016-09-01

PET imaging plays an important role in scientific and clinical measurement of biochemical and physiological processes. Model-based PET image reconstruction such as the iterative expectation maximization algorithm seeking the maximum likelihood solution leads to increased noise. The maximum a posteriori (MAP) estimate removes divergence at higher iterations. However, a conventional smoothing prior or a total-variation (TV) prior in a MAP reconstruction algorithm causes over smoothing or blocky artifacts in the reconstructed images. We propose to use dictionary learning (DL) based sparse signal representation in the formation of the prior for MAP PET image reconstruction. The dictionary to sparsify the PET images in the reconstruction process is learned from various training images including the corresponding MR structural image and a self-created hollow sphere. Using simulated and patient brain PET data with corresponding MR images, we study the performance of the DL-MAP algorithm and compare it quantitatively with a conventional MAP algorithm, a TV-MAP algorithm, and a patch-based algorithm. The DL-MAP algorithm achieves improved bias and contrast (or regional mean values) at comparable noise to what the other MAP algorithms acquire. The dictionary learned from the hollow sphere leads to similar results as the dictionary learned from the corresponding MR image. Achieving robust performance in various noise-level simulation and patient studies, the DL-MAP algorithm with a general dictionary demonstrates its potential in quantitative PET imaging.

Iterating between lessons on concepts and procedures can improve mathematics knowledge.

PubMed

Rittle-Johnson, Bethany; Koedinger, Kenneth

2009-09-01

Knowledge of concepts and procedures seems to develop in an iterative fashion, with increases in one type of knowledge leading to increases in the other type of knowledge. This suggests that iterating between lessons on concepts and procedures may improve learning. The purpose of the current study was to evaluate the instructional benefits of an iterative lesson sequence compared to a concepts-before-procedures sequence for students learning decimal place-value concepts and arithmetic procedures. In two classroom experiments, sixth-grade students from two schools participated (N=77 and 26). Students completed six decimal lessons on an intelligent-tutoring systems. In the iterative condition, lessons cycled between concept and procedure lessons. In the concepts-first condition, all concept lessons were presented before introducing the procedure lessons. In both experiments, students in the iterative condition gained more knowledge of arithmetic procedures, including ability to transfer the procedures to problems with novel features. Knowledge of concepts was fairly comparable across conditions. Finally, pre-test knowledge of one type predicted gains in knowledge of the other type across experiments. An iterative sequencing of lessons seems to facilitate learning and transfer, particularly of mathematical procedures. The findings support an iterative perspective for the development of knowledge of concepts and procedures.

Development of an Online Smartphone-Based eLearning Nutrition Education Program for Low-Income Individuals.

PubMed

Stotz, Sarah; Lee, Jung Sun

2018-01-01

The objective of this report was to describe the development process of an innovative smartphone-based electronic learning (eLearning) nutrition education program targeted to Supplemental Nutrition Assistance Program-Education-eligible individuals, entitled Food eTalk. Lessons learned from the Food eTalk development process suggest that it is critical to include all key team members from the program's inception using effective inter-team communication systems, understand the unique resources needed, budget ample time for development, and employ an iterative development and evaluation model. These lessons have implications for researchers and funding agencies in developing an innovative evidence-based eLearning nutrition education program to an increasingly technology-savvy, low-income audience. Copyright © 2016 Society for Nutrition Education and Behavior. Published by Elsevier Inc. All rights reserved.

Action Research: Enhancing Classroom Practice and Fulfilling Educational Responsibilities

ERIC Educational Resources Information Center

Young, Mark R.; Rapp, Eve; Murphy, James W.

2010-01-01

Action Research is an applied scholarly paradigm resulting in action for continuous improvement in our teaching and learning techniques offering faculty immediate classroom payback and providing documentation of meeting our educational responsibilities as required by AACSB standards. This article reviews the iterative action research process of…

A clustering-based graph Laplacian framework for value function approximation in reinforcement learning.

PubMed

Xu, Xin; Huang, Zhenhua; Graves, Daniel; Pedrycz, Witold

2014-12-01

In order to deal with the sequential decision problems with large or continuous state spaces, feature representation and function approximation have been a major research topic in reinforcement learning (RL). In this paper, a clustering-based graph Laplacian framework is presented for feature representation and value function approximation (VFA) in RL. By making use of clustering-based techniques, that is, K-means clustering or fuzzy C-means clustering, a graph Laplacian is constructed by subsampling in Markov decision processes (MDPs) with continuous state spaces. The basis functions for VFA can be automatically generated from spectral analysis of the graph Laplacian. The clustering-based graph Laplacian is integrated with a class of approximation policy iteration algorithms called representation policy iteration (RPI) for RL in MDPs with continuous state spaces. Simulation and experimental results show that, compared with previous RPI methods, the proposed approach needs fewer sample points to compute an efficient set of basis functions and the learning control performance can be improved for a variety of parameter settings.

Learning control system design based on 2-D theory - An application to parallel link manipulator

NASA Technical Reports Server (NTRS)

Geng, Z.; Carroll, R. L.; Lee, J. D.; Haynes, L. H.

1990-01-01

An approach to iterative learning control system design based on two-dimensional system theory is presented. A two-dimensional model for the iterative learning control system which reveals the connections between learning control systems and two-dimensional system theory is established. A learning control algorithm is proposed, and the convergence of learning using this algorithm is guaranteed by two-dimensional stability. The learning algorithm is applied successfully to the trajectory tracking control problem for a parallel link robot manipulator. The excellent performance of this learning algorithm is demonstrated by the computer simulation results.

The elements of a commercial human spaceflight safety reporting system

NASA Astrophysics Data System (ADS)

Christensen, Ian

2017-10-01

In its report on the SpaceShipTwo accident the National Transportation Safety Board (NTSB) included in its recommendations that the Federal Aviation Administration (FAA) ;in collaboration with the commercial spaceflight industry, continue work to implement a database of lessons learned from commercial space mishap investigations and encourage commercial space industry members to voluntarily submit lessons learned.; In its official response to the NTSB the FAA supported this recommendation and indicated it has initiated an iterative process to put into place a framework for a cooperative safety data sharing process including the sharing of lessons learned, and trends analysis. Such a framework is an important element of an overall commercial human spaceflight safety system.

Teaching Engineering Design Through Paper Rockets

ERIC Educational Resources Information Center

Welling, Jonathan; Wright, Geoffrey A.

2018-01-01

The paper rocket activity described in this article effectively teaches the engineering design process (EDP) by engaging students in a problem-based learning activity that encourages iterative design. For example, the first rockets the students build typically only fly between 30 and 100 feet. As students test and evaluate their rocket designs,…

Visualizing Community: Understanding Narrative Inquiry as Action Research

ERIC Educational Resources Information Center

Caine, Vera

2010-01-01

Throughout the school year I invited children in a Grade Two/Three learning strategies classroom to participate in a visual narrative inquiry. The intention was to explore children's knowledge of community in artful ways; the children photographed and wrote in what was often an iterative process, where writing/talking and photographing…

The child's perspective as a guiding principle: Young children as co-designers in the design of an interactive application meant to facilitate participation in healthcare situations.

PubMed

Stålberg, Anna; Sandberg, Anette; Söderbäck, Maja; Larsson, Thomas

2016-06-01

During the last decade, interactive technology has entered mainstream society. Its many users also include children, even the youngest ones, who use the technology in different situations for both fun and learning. When designing technology for children, it is crucial to involve children in the process in order to arrive at an age-appropriate end product. In this study we describe the specific iterative process by which an interactive application was developed. This application is intended to facilitate young children's, three-to five years old, participation in healthcare situations. We also describe the specific contributions of the children, who tested the prototypes in a preschool, a primary health care clinic and an outpatient unit at a hospital, during the development process. The iterative phases enabled the children to be involved at different stages of the process and to evaluate modifications and improvements made after each prior iteration. The children contributed their own perspectives (the child's perspective) on the usability, content and graphic design of the application, substantially improving the software and resulting in an age-appropriate product. Copyright © 2016 Elsevier Inc. All rights reserved.

Distant Supervision with Transductive Learning for Adverse Drug Reaction Identification from Electronic Medical Records

PubMed Central

Ikeda, Mitsuru

2017-01-01

Information extraction and knowledge discovery regarding adverse drug reaction (ADR) from large-scale clinical texts are very useful and needy processes. Two major difficulties of this task are the lack of domain experts for labeling examples and intractable processing of unstructured clinical texts. Even though most previous works have been conducted on these issues by applying semisupervised learning for the former and a word-based approach for the latter, they face with complexity in an acquisition of initial labeled data and ignorance of structured sequence of natural language. In this study, we propose automatic data labeling by distant supervision where knowledge bases are exploited to assign an entity-level relation label for each drug-event pair in texts, and then, we use patterns for characterizing ADR relation. The multiple-instance learning with expectation-maximization method is employed to estimate model parameters. The method applies transductive learning to iteratively reassign a probability of unknown drug-event pair at the training time. By investigating experiments with 50,998 discharge summaries, we evaluate our method by varying large number of parameters, that is, pattern types, pattern-weighting models, and initial and iterative weightings of relations for unlabeled data. Based on evaluations, our proposed method outperforms the word-based feature for NB-EM (iEM), MILR, and TSVM with F1 score of 11.3%, 9.3%, and 6.5% improvement, respectively. PMID:29090077

Learning multimodal dictionaries.

PubMed

Monaci, Gianluca; Jost, Philippe; Vandergheynst, Pierre; Mailhé, Boris; Lesage, Sylvain; Gribonval, Rémi

2007-09-01

Real-world phenomena involve complex interactions between multiple signal modalities. As a consequence, humans are used to integrate at each instant perceptions from all their senses in order to enrich their understanding of the surrounding world. This paradigm can be also extremely useful in many signal processing and computer vision problems involving mutually related signals. The simultaneous processing of multimodal data can, in fact, reveal information that is otherwise hidden when considering the signals independently. However, in natural multimodal signals, the statistical dependencies between modalities are in general not obvious. Learning fundamental multimodal patterns could offer deep insight into the structure of such signals. In this paper, we present a novel model of multimodal signals based on their sparse decomposition over a dictionary of multimodal structures. An algorithm for iteratively learning multimodal generating functions that can be shifted at all positions in the signal is proposed, as well. The learning is defined in such a way that it can be accomplished by iteratively solving a generalized eigenvector problem, which makes the algorithm fast, flexible, and free of user-defined parameters. The proposed algorithm is applied to audiovisual sequences and it is able to discover underlying structures in the data. The detection of such audio-video patterns in audiovisual clips allows to effectively localize the sound source on the video in presence of substantial acoustic and visual distractors, outperforming state-of-the-art audiovisual localization algorithms.

Is there a need for a specific educational scholarship for using e-learning in medical education?

PubMed

Sandars, John; Goh, Poh Sun

2016-10-01

We propose the need for a specific educational scholarship when using e-learning in medical education. Effective e-learning has additional factors that require specific critical attention, including the design and delivery of e-learning. An important aspect is the recognition that e-learning is a complex intervention, with several interconnecting components that have to be aligned. This alignment requires an essential iterative development process with usability testing. Effectiveness of e-learning in one context may not be fully realized in another context unless there is further consideration of applicability and scalability. We recommend a participatory approach for an educational scholarship for using e-learning in medical education, such as by action research or design-based research.

2009 Space Shuttle Probabilistic Risk Assessment Overview

NASA Technical Reports Server (NTRS)

Hamlin, Teri L.; Canga, Michael A.; Boyer, Roger L.; Thigpen, Eric B.

2010-01-01

Loss of a Space Shuttle during flight has severe consequences, including loss of a significant national asset; loss of national confidence and pride; and, most importantly, loss of human life. The Shuttle Probabilistic Risk Assessment (SPRA) is used to identify risk contributors and their significance; thus, assisting management in determining how to reduce risk. In 2006, an overview of the SPRA Iteration 2.1 was presented at PSAM 8 [1]. Like all successful PRAs, the SPRA is a living PRA and has undergone revisions since PSAM 8. The latest revision to the SPRA is Iteration 3. 1, and it will not be the last as the Shuttle program progresses and more is learned. This paper discusses the SPRA scope, overall methodology, and results, as well as provides risk insights. The scope, assumptions, uncertainties, and limitations of this assessment provide risk-informed perspective to aid management s decision-making process. In addition, this paper compares the Iteration 3.1 analysis and results to the Iteration 2.1 analysis and results presented at PSAM 8.

Teaching Assistant Competencies in Canada: Building a Framework for Practice Together

ERIC Educational Resources Information Center

Korpan, Cynthia; Sheffield, Suzanne Le-May; Verwoord, Roselynn

2015-01-01

This paper examines the stages of development for a framework of teaching assistant (TA) competencies initiated by the Teaching Assistant and Graduate Student Advancement (TAGSA) special interest group (SIG) of the Society of Teaching and Learning in Higher Education (STLHE). TAGSA initiated an iterative consultative process to inform the creation…

Negotiating Meaning in Cross-National Studies of Mathematics Teaching: Kissing Frogs to Find Princes

ERIC Educational Resources Information Center

Andrews, Paul

2007-01-01

This paper outlines the iterative processes by which a multinational team of researchers developed a low-inference framework for the analysis of video recordings of mathematics lessons drawn from Flemish Belgium, England, Finland, Hungary and Spain. Located within a theoretical framework concerning learning as the negotiation of meaning, we…

Ground Truth Creation for Complex Clinical NLP Tasks - an Iterative Vetting Approach and Lessons Learned.

PubMed

Liang, Jennifer J; Tsou, Ching-Huei; Devarakonda, Murthy V

2017-01-01

Natural language processing (NLP) holds the promise of effectively analyzing patient record data to reduce cognitive load on physicians and clinicians in patient care, clinical research, and hospital operations management. A critical need in developing such methods is the "ground truth" dataset needed for training and testing the algorithms. Beyond localizable, relatively simple tasks, ground truth creation is a significant challenge because medical experts, just as physicians in patient care, have to assimilate vast amounts of data in EHR systems. To mitigate potential inaccuracies of the cognitive challenges, we present an iterative vetting approach for creating the ground truth for complex NLP tasks. In this paper, we present the methodology, and report on its use for an automated problem list generation task, its effect on the ground truth quality and system accuracy, and lessons learned from the effort.

Machine learning in motion control

NASA Technical Reports Server (NTRS)

Su, Renjeng; Kermiche, Noureddine

1989-01-01

The existing methodologies for robot programming originate primarily from robotic applications to manufacturing, where uncertainties of the robots and their task environment may be minimized by repeated off-line modeling and identification. In space application of robots, however, a higher degree of automation is required for robot programming because of the desire of minimizing the human intervention. We discuss a new paradigm of robotic programming which is based on the concept of machine learning. The goal is to let robots practice tasks by themselves and the operational data are used to automatically improve their motion performance. The underlying mathematical problem is to solve the problem of dynamical inverse by iterative methods. One of the key questions is how to ensure the convergence of the iterative process. There have been a few small steps taken into this important approach to robot programming. We give a representative result on the convergence problem.

Experimentation in software engineering

NASA Technical Reports Server (NTRS)

Basili, V. R.; Selby, R. W.; Hutchens, D. H.

1986-01-01

Experimentation in software engineering supports the advancement of the field through an iterative learning process. In this paper, a framework for analyzing most of the experimental work performed in software engineering over the past several years is presented. A variety of experiments in the framework is described and their contribution to the software engineering discipline is discussed. Some useful recommendations for the application of the experimental process in software engineering are included.

Feasibility of Active Machine Learning for Multiclass Compound Classification.

PubMed

Lang, Tobias; Flachsenberg, Florian; von Luxburg, Ulrike; Rarey, Matthias

2016-01-25

A common task in the hit-to-lead process is classifying sets of compounds into multiple, usually structural classes, which build the groundwork for subsequent SAR studies. Machine learning techniques can be used to automate this process by learning classification models from training compounds of each class. Gathering class information for compounds can be cost-intensive as the required data needs to be provided by human experts or experiments. This paper studies whether active machine learning can be used to reduce the required number of training compounds. Active learning is a machine learning method which processes class label data in an iterative fashion. It has gained much attention in a broad range of application areas. In this paper, an active learning method for multiclass compound classification is proposed. This method selects informative training compounds so as to optimally support the learning progress. The combination with human feedback leads to a semiautomated interactive multiclass classification procedure. This method was investigated empirically on 15 compound classification tasks containing 86-2870 compounds in 3-38 classes. The empirical results show that active learning can solve these classification tasks using 10-80% of the data which would be necessary for standard learning techniques.

Observer-based distributed adaptive iterative learning control for linear multi-agent systems

NASA Astrophysics Data System (ADS)

Li, Jinsha; Liu, Sanyang; Li, Junmin

2017-10-01

This paper investigates the consensus problem for linear multi-agent systems from the viewpoint of two-dimensional systems when the state information of each agent is not available. Observer-based fully distributed adaptive iterative learning protocol is designed in this paper. A local observer is designed for each agent and it is shown that without using any global information about the communication graph, all agents achieve consensus perfectly for all undirected connected communication graph when the number of iterations tends to infinity. The Lyapunov-like energy function is employed to facilitate the learning protocol design and property analysis. Finally, simulation example is given to illustrate the theoretical analysis.

Increasing High School Student Interest in Science: An Action Research Study

NASA Astrophysics Data System (ADS)

Vartuli, Cindy A.

An action research study was conducted to determine how to increase student interest in learning science and pursuing a STEM career. The study began by exploring 10th-grade student and teacher perceptions of student interest in science in order to design an instructional strategy for stimulating student interest in learning and pursuing science. Data for this study included responses from 270 students to an on-line science survey and interviews with 11 students and eight science teachers. The action research intervention included two iterations of the STEM Career Project. The first iteration introduced four chemistry classes to the intervention. The researcher used student reflections and a post-project survey to determine if the intervention had influence on the students' interest in pursuing science. The second iteration was completed by three science teachers who had implemented the intervention with their chemistry classes, using student reflections and post-project surveys, as a way to make further procedural refinements and improvements to the intervention and measures. Findings from the exploratory phase of the study suggested students generally had interest in learning science but increasing that interest required including personally relevant applications and laboratory experiences. The intervention included a student-directed learning module in which students investigated three STEM careers and presented information on one of their chosen careers. The STEM Career Project enabled students to explore career possibilities in order to increase their awareness of STEM careers. Findings from the first iteration of the intervention suggested a positive influence on student interest in learning and pursuing science. The second iteration included modifications to the intervention resulting in support for the findings of the first iteration. Results of the second iteration provided modifications that would allow the project to be used for different academic levels. Insights from conducting the action research study provided the researcher with effective ways to make positive changes in her own teaching praxis and the tools used to improve student awareness of STEM career options.

A Distributed Learning Method for ℓ1-Regularized Kernel Machine over Wireless Sensor Networks

PubMed Central

Ji, Xinrong; Hou, Cuiqin; Hou, Yibin; Gao, Fang; Wang, Shulong

2016-01-01

In wireless sensor networks, centralized learning methods have very high communication costs and energy consumption. These are caused by the need to transmit scattered training examples from various sensor nodes to the central fusion center where a classifier or a regression machine is trained. To reduce the communication cost, a distributed learning method for a kernel machine that incorporates ℓ1 norm regularization (ℓ1-regularized) is investigated, and a novel distributed learning algorithm for the ℓ1-regularized kernel minimum mean squared error (KMSE) machine is proposed. The proposed algorithm relies on in-network processing and a collaboration that transmits the sparse model only between single-hop neighboring nodes. This paper evaluates the proposed algorithm with respect to the prediction accuracy, the sparse rate of model, the communication cost and the number of iterations on synthetic and real datasets. The simulation results show that the proposed algorithm can obtain approximately the same prediction accuracy as that obtained by the batch learning method. Moreover, it is significantly superior in terms of the sparse rate of model and communication cost, and it can converge with fewer iterations. Finally, an experiment conducted on a wireless sensor network (WSN) test platform further shows the advantages of the proposed algorithm with respect to communication cost. PMID:27376298

Adaptive iterative learning control of a class of nonlinear time-delay systems with unknown backlash-like hysteresis input and control direction.

PubMed

Wei, Jianming; Zhang, Youan; Sun, Meimei; Geng, Baoliang

2017-09-01

This paper presents an adaptive iterative learning control scheme for a class of nonlinear systems with unknown time-varying delays and control direction preceded by unknown nonlinear backlash-like hysteresis. Boundary layer function is introduced to construct an auxiliary error variable, which relaxes the identical initial condition assumption of iterative learning control. For the controller design, integral Lyapunov function candidate is used, which avoids the possible singularity problem by introducing hyperbolic tangent funciton. After compensating for uncertainties with time-varying delays by combining appropriate Lyapunov-Krasovskii function with Young's inequality, an adaptive iterative learning control scheme is designed through neural approximation technique and Nussbaum function method. On the basis of the hyperbolic tangent function's characteristics, the system output is proved to converge to a small neighborhood of the desired trajectory by constructing Lyapunov-like composite energy function (CEF) in two cases, while keeping all the closed-loop signals bounded. Finally, a simulation example is presented to verify the effectiveness of the proposed approach. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

A Fast Reduced Kernel Extreme Learning Machine.

PubMed

Deng, Wan-Yu; Ong, Yew-Soon; Zheng, Qing-Hua

2016-04-01

In this paper, we present a fast and accurate kernel-based supervised algorithm referred to as the Reduced Kernel Extreme Learning Machine (RKELM). In contrast to the work on Support Vector Machine (SVM) or Least Square SVM (LS-SVM), which identifies the support vectors or weight vectors iteratively, the proposed RKELM randomly selects a subset of the available data samples as support vectors (or mapping samples). By avoiding the iterative steps of SVM, significant cost savings in the training process can be readily attained, especially on Big datasets. RKELM is established based on the rigorous proof of universal learning involving reduced kernel-based SLFN. In particular, we prove that RKELM can approximate any nonlinear functions accurately under the condition of support vectors sufficiency. Experimental results on a wide variety of real world small instance size and large instance size applications in the context of binary classification, multi-class problem and regression are then reported to show that RKELM can perform at competitive level of generalized performance as the SVM/LS-SVM at only a fraction of the computational effort incurred. Copyright © 2015 Elsevier Ltd. All rights reserved.

Young children make their gestural communication systems more language-like: segmentation and linearization of semantic elements in motion events.

PubMed

Clay, Zanna; Pople, Sally; Hood, Bruce; Kita, Sotaro

2014-08-01

Research on Nicaraguan Sign Language, created by deaf children, has suggested that young children use gestures to segment the semantic elements of events and linearize them in ways similar to those used in signed and spoken languages. However, it is unclear whether this is due to children's learning processes or to a more general effect of iterative learning. We investigated whether typically developing children, without iterative learning, segment and linearize information. Gestures produced in the absence of speech to express a motion event were examined in 4-year-olds, 12-year-olds, and adults (all native English speakers). We compared the proportions of gestural expressions that segmented semantic elements into linear sequences and that encoded them simultaneously. Compared with adolescents and adults, children reshaped the holistic stimuli by segmenting and recombining their semantic features into linearized sequences. A control task on recognition memory ruled out the possibility that this was due to different event perception or memory. Young children spontaneously bring fundamental properties of language into their communication system. © The Author(s) 2014.

Lessons from a Space Analog on Adaptation for Long-Duration Exploration Missions.

PubMed

Anglin, Katlin M; Kring, Jason P

2016-04-01

Exploration missions to asteroids and Mars will bring new challenges associated with communication delays and more autonomy for crews. Mission safety and success will rely on how well the entire system, from technology to the human elements, is adaptable and resilient to disruptive, novel, or potentially catastrophic events. The recent NASA Extreme Environment Missions Operations (NEEMO) 20 mission highlighted this need and produced valuable "lessons learned" that will inform future research on team adaptation and resilience. A team of NASA, industry, and academic members used an iterative process to design a tripod shaped structure, called the CORAL Tower, for two astronauts to assemble underwater with minimal tools. The team also developed assembly procedures, administered training to the crew, and provided support during the mission. During the design, training, and assembly of the Tower, the team learned first-hand how adaptation in extreme environments depends on incremental testing, thorough procedures and contingency plans that predict possible failure scenarios, and effective team adaptation and resiliency for the crew and support personnel. Findings from NEEMO 20 provide direction on the design and testing process for future space systems and crews to maximize adaptation. This experience also underscored the need for more research on team adaptation, particularly how input and process factors affect adaption outcomes, the team adaptation iterative process, and new ways to measure the adaptation process.

Conceptual design of data acquisition and control system for two Rf driver based negative ion source for fusion R&D

NASA Astrophysics Data System (ADS)

Soni, Jigensh; Yadav, R. K.; Patel, A.; Gahlaut, A.; Mistry, H.; Parmar, K. G.; Mahesh, V.; Parmar, D.; Prajapati, B.; Singh, M. J.; Bandyopadhyay, M.; Bansal, G.; Pandya, K.; Chakraborty, A.

2013-02-01

Twin Source - An Inductively coupled two RF driver based 180 kW, 1 MHz negative ion source experimental setup is initiated at IPR, Gandhinagar, under Indian program, with the objective of understanding the physics and technology of multi-driver coupling. Twin Source [1] (TS) also provides an intermediate platform between operational ROBIN [2] [5] and eight RF drivers based Indian test facility -INTF [3]. A twin source experiment requires a central system to provide control, data acquisition and communication interface, referred as TS-CODAC, for which a software architecture similar to ITER CODAC core system has been decided for implementation. The Core System is a software suite for ITER plant system manufacturers to use as a template for the development of their interface with CODAC. The ITER approach, in terms of technology, has been adopted for the TS-CODAC so as to develop necessary expertise for developing and operating a control system based on the ITER guidelines as similar configuration needs to be implemented for the INTF. This cost effective approach will provide an opportunity to evaluate and learn ITER CODAC technology, documentation, information technology and control system processes, on an operational machine. Conceptual design of the TS-CODAC system has been completed. For complete control of the system, approximately 200 Nos. control signals and 152 acquisition signals are needed. In TS-CODAC, control loop time required is within the range of 5ms - 10 ms, therefore for the control system, PLC (Siemens S-7 400) has been chosen as suggested in the ITER slow controller catalog. For the data acquisition, the maximum sampling interval required is 100 micro second, and therefore National Instruments (NI) PXIe system and NI 6259 digitizer cards have been selected as suggested in the ITER fast controller catalog. This paper will present conceptual design of TS -CODAC system based on ITER CODAC Core software and applicable plant system integration processes.

Enhancement Process of Didactic Strategies in a Degree Course for Pre-Service Teachers

ERIC Educational Resources Information Center

Garcias, Adolfina Pérez; Marín, Victoria I.

2017-01-01

This paper presents a study on the enhancement of didactic strategies based on the idea of personal learning environments (PLE). It was conducted through three iterative cycles during three consecutive academic years according to the phases of design-based research applied to teaching in a university course for pre-service teachers in the…

Assessing Children's Understanding of Length Measurement: A Focus on Three Key Concepts

ERIC Educational Resources Information Center

Bush, Heidi

2009-01-01

In this article, the author presents three different tasks that can be used to assess students' understanding of the concept of length. Three important measurement concepts for students to understand are transitive reasoning, use of identical units, and iteration. In any teaching and learning process it is important to acknowledge students'…

Stimulating Students' Use of External Representations for a Distance Education Time Machine Design

ERIC Educational Resources Information Center

Baaki, John; Luo, Tian

2017-01-01

As faculty members in an instructional design and technology (IDT) program, we wanted to help our graduate students better understand and experience how designers design in the real world. We aimed to design a reflective and collaborative learning environment where we sparked students to engage in reflection, ideation, and the iterative process of…

Researchers Apply Lesson Study: A Cycle of Lesson Planning, Implementation, and Revision

ERIC Educational Resources Information Center

Regan, Kelley S.; Evmenova, Anya S.; Kurz, Leigh Ann; Hughes, Melissa D.; Sacco, Donna; Ahn, Soo Y.; MacVittie, Nichole; Good, Kevin; Boykin, Andrea; Schwartzer, Jessica; Chirinos, David S.

2016-01-01

Scripted lesson plans and/or professional development alone may not be sufficient to encourage teachers to reflect on the quality of their teaching and improve their teaching. One learning tool that teachers may use to improve their teaching is Lesson Study (LS). LS is a collaborative process involving educators, based on concepts of iteration and…

Complex Adaptive Systems and the Origins of Adaptive Structure: What Experiments Can Tell Us

ERIC Educational Resources Information Center

Cornish, Hannah; Tamariz, Monica; Kirby, Simon

2009-01-01

Language is a product of both biological and cultural evolution. Clues to the origins of key structural properties of language can be found in the process of cultural transmission between learners. Recent experiments have shown that iterated learning by human participants in the laboratory transforms an initially unstructured artificial language…

Shifting Relations with the More-than-Human: Six Threshold Concepts for Transformative Sustainability Learning

ERIC Educational Resources Information Center

Barrett, M. J.; Harmin, Matthew; Maracle, Bryan; Patterson, Molly; Thomson, Christina; Flowers, Michelle; Bors, Kirk

2017-01-01

Using the iterative process of action research, we identify six portals of understanding, called threshold concepts, which can be used as curricular guideposts to disrupt the socially constituted separation, and hierarchy, between humans and the more-than-human. The threshold concepts identified in this study provide focal points for a curriculum…

Iterative near-term ecological forecasting: Needs, opportunities, and challenges

USGS Publications Warehouse

Dietze, Michael C.; Fox, Andrew; Beck-Johnson, Lindsay; Betancourt, Julio L.; Hooten, Mevin B.; Jarnevich, Catherine S.; Keitt, Timothy H.; Kenney, Melissa A.; Laney, Christine M.; Larsen, Laurel G.; Loescher, Henry W.; Lunch, Claire K.; Pijanowski, Bryan; Randerson, James T.; Read, Emily; Tredennick, Andrew T.; Vargas, Rodrigo; Weathers, Kathleen C.; White, Ethan P.

2018-01-01

Two foundational questions about sustainability are “How are ecosystems and the services they provide going to change in the future?” and “How do human decisions affect these trajectories?” Answering these questions requires an ability to forecast ecological processes. Unfortunately, most ecological forecasts focus on centennial-scale climate responses, therefore neither meeting the needs of near-term (daily to decadal) environmental decision-making nor allowing comparison of specific, quantitative predictions to new observational data, one of the strongest tests of scientific theory. Near-term forecasts provide the opportunity to iteratively cycle between performing analyses and updating predictions in light of new evidence. This iterative process of gaining feedback, building experience, and correcting models and methods is critical for improving forecasts. Iterative, near-term forecasting will accelerate ecological research, make it more relevant to society, and inform sustainable decision-making under high uncertainty and adaptive management. Here, we identify the immediate scientific and societal needs, opportunities, and challenges for iterative near-term ecological forecasting. Over the past decade, data volume, variety, and accessibility have greatly increased, but challenges remain in interoperability, latency, and uncertainty quantification. Similarly, ecologists have made considerable advances in applying computational, informatic, and statistical methods, but opportunities exist for improving forecast-specific theory, methods, and cyberinfrastructure. Effective forecasting will also require changes in scientific training, culture, and institutions. The need to start forecasting is now; the time for making ecology more predictive is here, and learning by doing is the fastest route to drive the science forward.

Iterative near-term ecological forecasting: Needs, opportunities, and challenges.

PubMed

Dietze, Michael C; Fox, Andrew; Beck-Johnson, Lindsay M; Betancourt, Julio L; Hooten, Mevin B; Jarnevich, Catherine S; Keitt, Timothy H; Kenney, Melissa A; Laney, Christine M; Larsen, Laurel G; Loescher, Henry W; Lunch, Claire K; Pijanowski, Bryan C; Randerson, James T; Read, Emily K; Tredennick, Andrew T; Vargas, Rodrigo; Weathers, Kathleen C; White, Ethan P

2018-02-13

Two foundational questions about sustainability are "How are ecosystems and the services they provide going to change in the future?" and "How do human decisions affect these trajectories?" Answering these questions requires an ability to forecast ecological processes. Unfortunately, most ecological forecasts focus on centennial-scale climate responses, therefore neither meeting the needs of near-term (daily to decadal) environmental decision-making nor allowing comparison of specific, quantitative predictions to new observational data, one of the strongest tests of scientific theory. Near-term forecasts provide the opportunity to iteratively cycle between performing analyses and updating predictions in light of new evidence. This iterative process of gaining feedback, building experience, and correcting models and methods is critical for improving forecasts. Iterative, near-term forecasting will accelerate ecological research, make it more relevant to society, and inform sustainable decision-making under high uncertainty and adaptive management. Here, we identify the immediate scientific and societal needs, opportunities, and challenges for iterative near-term ecological forecasting. Over the past decade, data volume, variety, and accessibility have greatly increased, but challenges remain in interoperability, latency, and uncertainty quantification. Similarly, ecologists have made considerable advances in applying computational, informatic, and statistical methods, but opportunities exist for improving forecast-specific theory, methods, and cyberinfrastructure. Effective forecasting will also require changes in scientific training, culture, and institutions. The need to start forecasting is now; the time for making ecology more predictive is here, and learning by doing is the fastest route to drive the science forward.

Culture: copying, compression, and conventionality.

PubMed

Tamariz, Mónica; Kirby, Simon

2015-01-01

Through cultural transmission, repeated learning by new individuals transforms cultural information, which tends to become increasingly compressible (Kirby, Cornish, & Smith, ; Smith, Tamariz, & Kirby, ). Existing diffusion chain studies include in their design two processes that could be responsible for this tendency: learning (storing patterns in memory) and reproducing (producing the patterns again). This paper manipulates the presence of learning in a simple iterated drawing design experiment. We find that learning seems to be the causal factor behind the increase in compressibility observed in the transmitted information, while reproducing is a source of random heritable innovations. Only a theory invoking these two aspects of cultural learning will be able to explain human culture's fundamental balance between stability and innovation. Copyright © 2014 Cognitive Science Society, Inc.

Fast and Epsilon-Optimal Discretized Pursuit Learning Automata.

PubMed

Zhang, JunQi; Wang, Cheng; Zhou, MengChu

2015-10-01

Learning automata (LA) are powerful tools for reinforcement learning. A discretized pursuit LA is the most popular one among them. During an iteration its operation consists of three basic phases: 1) selecting the next action; 2) finding the optimal estimated action; and 3) updating the state probability. However, when the number of actions is large, the learning becomes extremely slow because there are too many updates to be made at each iteration. The increased updates are mostly from phases 1 and 3. A new fast discretized pursuit LA with assured ε -optimality is proposed to perform both phases 1 and 3 with the computational complexity independent of the number of actions. Apart from its low computational complexity, it achieves faster convergence speed than the classical one when operating in stationary environments. This paper can promote the applications of LA toward the large-scale-action oriented area that requires efficient reinforcement learning tools with assured ε -optimality, fast convergence speed, and low computational complexity for each iteration.

Finding the Optimal Guidance for Enhancing Anchored Instruction

ERIC Educational Resources Information Center

Zydney, Janet Mannheimer; Bathke, Arne; Hasselbring, Ted S.

2014-01-01

This study investigated the effect of different methods of guidance with anchored instruction on students' mathematical problem-solving performance. The purpose of this research was to iteratively design a learning environment to find the optimal level of guidance. Two iterations of the software were compared. The first iteration used explicit…

Gaming the System: Developing an Educational Game for Securing Principles of Arterial Blood Gases.

PubMed

Boyd, Cory Ann; Warren, Jonah; Glendon, Mary Ann

2016-01-01

This article describes the development process for creating a digital educational mini game prototype designed to provide practice opportunities for learning fundamental principles of arterial blood gases. Mini games generally take less than an hour to play and focus on specific subject matter. An interdisciplinary team of faculty from two universities mentored student game developers to design a digital educational mini game prototype. Sixteen accelerated bachelor of science in nursing students collaborated with game development students and playtested the game prototype during the last semester of their senior year in nursing school. Playtesting is a form of feedback that supports an iterative design process that is critical to game development. A 10-question survey was coupled with group discussions addressing five broad themes of an archetypical digital educational mini game to yield feedback on game design, play, and content. Four rounds of playtesting and incorporating feedback supported the iterative process. Accelerated bachelor of science in nursing student playtester feedback suggests that the digital educational mini game prototype has potential for offering an engaging, playful game experience that will support securing the fundamental principles of arterial blood gases. Next steps are to test the digital educational mini game for teaching and learning effectiveness. Copyright © 2016 Elsevier Inc. All rights reserved.

Low-cost autonomous perceptron neural network inspired by quantum computation

NASA Astrophysics Data System (ADS)

Zidan, Mohammed; Abdel-Aty, Abdel-Haleem; El-Sadek, Alaa; Zanaty, E. A.; Abdel-Aty, Mahmoud

2017-11-01

Achieving low cost learning with reliable accuracy is one of the important goals to achieve intelligent machines to save time, energy and perform learning process over limited computational resources machines. In this paper, we propose an efficient algorithm for a perceptron neural network inspired by quantum computing composite from a single neuron to classify inspirable linear applications after a single training iteration O(1). The algorithm is applied over a real world data set and the results are outer performs the other state-of-the art algorithms.

Virtual patients: practical advice for clinical authors using Labyrinth.

PubMed

Begg, Michael

2010-09-01

Labyrinth is a tool originally developed in the University of Edinburgh's Learning Technology Section for authoring and delivering branching case scenarios. The scenarios can incorporate game-informed elements such as scoring, randomising, avatars and counters. Labyrinth has grown more popular internationally since a version of the build was made available on the open source network Source Forge. This paper offers help and advice for clinical educators interested in creating cases. Labyrinth is increasingly recognised as a tool offering great potential for delivering cases that promote rich, situated learning opportunities for learners. There are, however, significant challenges to generating such cases, not least of which is the challenge for potential authors in approaching the process of constructing narrative-rich, context-sensitive cases in an unfamiliar authoring environment. This paper offers a brief overview of the principles informing Labyrinth cases (game-informed learning), and offers some practical advice to better prepare educators with little or no prior experience. Labyrinth has continued to grow and develop, from its roots as a research and development environment to one that is optimised for use by non-technical clinical educators. The process becomes increasingly iterative and better informed as the teaching community push the software further. The positive implications of providing practical advice and concept insight to new case authors is that it ideally leads to a broader base of users who will inform future iterations of the software. © Blackwell Publishing Ltd 2010.

Staying on the Journey: Maintaining a Change Momentum with PB4L "School-Wide"

ERIC Educational Resources Information Center

Boyd, Sally

2016-01-01

How do schools maintain momentum with change and enter new cycles of growth when they are attempting to do things differently? This article draws on a two-year evaluation of the "Positive Behaviour for Learning School-Wide" initiative to identify key factors that enabled schools to engage in a long-term and iterative change process.…

The interactive evolution of human communication systems.

PubMed

Fay, Nicolas; Garrod, Simon; Roberts, Leo; Swoboda, Nik

2010-04-01

This paper compares two explanations of the process by which human communication systems evolve: iterated learning and social collaboration. It then reports an experiment testing the social collaboration account. Participants engaged in a graphical communication task either as a member of a community, where they interacted with seven different partners drawn from the same pool, or as a member of an isolated pair, where they interacted with the same partner across the same number of games. Participants' horizontal, pair-wise interactions led "bottom up" to the creation of an effective and efficient shared sign system in the community condition. Furthermore, the community-evolved sign systems were as effective and efficient as the local sign systems developed by isolated pairs. Finally, and as predicted by a social collaboration account, and not by an iterated learning account, interaction was critical to the creation of shared sign systems, with different isolated pairs establishing different local sign systems and different communities establishing different global sign systems. Copyright © 2010 Cognitive Science Society, Inc.

Teaching and learning recursive programming: a review of the research literature

NASA Astrophysics Data System (ADS)

McCauley, Renée; Grissom, Scott; Fitzgerald, Sue; Murphy, Laurie

2015-01-01

Hundreds of articles have been published on the topics of teaching and learning recursion, yet fewer than 50 of them have published research results. This article surveys the computing education research literature and presents findings on challenges students encounter in learning recursion, mental models students develop as they learn recursion, and best practices in introducing recursion. Effective strategies for introducing the topic include using different contexts such as recurrence relations, programming examples, fractal images, and a description of how recursive methods are processed using a call stack. Several studies compared the efficacy of introducing iteration before recursion and vice versa. The paper concludes with suggestions for future research into how students learn and understand recursion, including a look at the possible impact of instructor attitude and newer pedagogies.

A Fast Optimization Method for General Binary Code Learning.

PubMed

Shen, Fumin; Zhou, Xiang; Yang, Yang; Song, Jingkuan; Shen, Heng; Tao, Dacheng

2016-09-22

Hashing or binary code learning has been recognized to accomplish efficient near neighbor search, and has thus attracted broad interests in recent retrieval, vision and learning studies. One main challenge of learning to hash arises from the involvement of discrete variables in binary code optimization. While the widely-used continuous relaxation may achieve high learning efficiency, the pursued codes are typically less effective due to accumulated quantization error. In this work, we propose a novel binary code optimization method, dubbed Discrete Proximal Linearized Minimization (DPLM), which directly handles the discrete constraints during the learning process. Specifically, the discrete (thus nonsmooth nonconvex) problem is reformulated as minimizing the sum of a smooth loss term with a nonsmooth indicator function. The obtained problem is then efficiently solved by an iterative procedure with each iteration admitting an analytical discrete solution, which is thus shown to converge very fast. In addition, the proposed method supports a large family of empirical loss functions, which is particularly instantiated in this work by both a supervised and an unsupervised hashing losses, together with the bits uncorrelation and balance constraints. In particular, the proposed DPLM with a supervised `2 loss encodes the whole NUS-WIDE database into 64-bit binary codes within 10 seconds on a standard desktop computer. The proposed approach is extensively evaluated on several large-scale datasets and the generated binary codes are shown to achieve very promising results on both retrieval and classification tasks.

Model-Free Primitive-Based Iterative Learning Control Approach to Trajectory Tracking of MIMO Systems With Experimental Validation.

PubMed

Radac, Mircea-Bogdan; Precup, Radu-Emil; Petriu, Emil M

2015-11-01

This paper proposes a novel model-free trajectory tracking of multiple-input multiple-output (MIMO) systems by the combination of iterative learning control (ILC) and primitives. The optimal trajectory tracking solution is obtained in terms of previously learned solutions to simple tasks called primitives. The library of primitives that are stored in memory consists of pairs of reference input/controlled output signals. The reference input primitives are optimized in a model-free ILC framework without using knowledge of the controlled process. The guaranteed convergence of the learning scheme is built upon a model-free virtual reference feedback tuning design of the feedback decoupling controller. Each new complex trajectory to be tracked is decomposed into the output primitives regarded as basis functions. The optimal reference input for the control system to track the desired trajectory is next recomposed from the reference input primitives. This is advantageous because the optimal reference input is computed straightforward without the need to learn from repeated executions of the tracking task. In addition, the optimization problem specific to trajectory tracking of square MIMO systems is decomposed in a set of optimization problems assigned to each separate single-input single-output control channel that ensures a convenient model-free decoupling. The new model-free primitive-based ILC approach is capable of planning, reasoning, and learning. A case study dealing with the model-free control tuning for a nonlinear aerodynamic system is included to validate the new approach. The experimental results are given.

Learning to improve iterative repair scheduling

NASA Technical Reports Server (NTRS)

Zweben, Monte; Davis, Eugene

1992-01-01

This paper presents a general learning method for dynamically selecting between repair heuristics in an iterative repair scheduling system. The system employs a version of explanation-based learning called Plausible Explanation-Based Learning (PEBL) that uses multiple examples to confirm conjectured explanations. The basic approach is to conjecture contradictions between a heuristic and statistics that measure the quality of the heuristic. When these contradictions are confirmed, a different heuristic is selected. To motivate the utility of this approach we present an empirical evaluation of the performance of a scheduling system with respect to two different repair strategies. We show that the scheduler that learns to choose between the heuristics outperforms the same scheduler with any one of two heuristics alone.

Quantum learning of classical stochastic processes: The completely positive realization problem

NASA Astrophysics Data System (ADS)

Monràs, Alex; Winter, Andreas

2016-01-01

Among several tasks in Machine Learning, a specially important one is the problem of inferring the latent variables of a system and their causal relations with the observed behavior. A paradigmatic instance of this is the task of inferring the hidden Markov model underlying a given stochastic process. This is known as the positive realization problem (PRP), [L. Benvenuti and L. Farina, IEEE Trans. Autom. Control 49(5), 651-664 (2004)] and constitutes a central problem in machine learning. The PRP and its solutions have far-reaching consequences in many areas of systems and control theory, and is nowadays an important piece in the broad field of positive systems theory. We consider the scenario where the latent variables are quantum (i.e., quantum states of a finite-dimensional system) and the system dynamics is constrained only by physical transformations on the quantum system. The observable dynamics is then described by a quantum instrument, and the task is to determine which quantum instrument — if any — yields the process at hand by iterative application. We take as a starting point the theory of quasi-realizations, whence a description of the dynamics of the process is given in terms of linear maps on state vectors and probabilities are given by linear functionals on the state vectors. This description, despite its remarkable resemblance with the hidden Markov model, or the iterated quantum instrument, is however devoid of any stochastic or quantum mechanical interpretation, as said maps fail to satisfy any positivity conditions. The completely positive realization problem then consists in determining whether an equivalent quantum mechanical description of the same process exists. We generalize some key results of stochastic realization theory, and show that the problem has deep connections with operator systems theory, giving possible insight to the lifting problem in quotient operator systems. Our results have potential applications in quantum machine learning, device-independent characterization and reverse-engineering of stochastic processes and quantum processors, and more generally, of dynamical processes with quantum memory [M. Guţă, Phys. Rev. A 83(6), 062324 (2011); M. Guţă and N. Yamamoto, e-print arXiv:1303.3771(2013)].

Analog Design for Digital Deployment of a Serious Leadership Game

NASA Technical Reports Server (NTRS)

Maxwell, Nicholas; Lang, Tristan; Herman, Jeffrey L.; Phares, Richard

2012-01-01

This paper presents the design, development, and user testing of a leadership development simulation. The authors share lessons learned from using a design process for a board game to allow for quick and inexpensive revision cycles during the development of a serious leadership development game. The goal of this leadership simulation is to accelerate the development of leadership capacity in high-potential mid-level managers (GS-15 level) in a federal government agency. Simulation design included a mixed-method needs analysis, using both quantitative and qualitative approaches to determine organizational leadership needs. Eight design iterations were conducted, including three user testing phases. Three re-design iterations followed initial development, enabling game testing as part of comprehensive instructional events. Subsequent design, development and testing processes targeted digital application to a computer- and tablet-based environment. Recommendations include pros and cons of development and learner testing of an initial analog simulation prior to full digital simulation development.

Adaptive management: Chapter 1

USGS Publications Warehouse

Allen, Craig R.; Garmestani, Ahjond S.; Allen, Craig R.; Garmestani, Ahjond S.

2015-01-01

Adaptive management is an approach to natural resource management that emphasizes learning through management where knowledge is incomplete, and when, despite inherent uncertainty, managers and policymakers must act. Unlike a traditional trial and error approach, adaptive management has explicit structure, including a careful elucidation of goals, identification of alternative management objectives and hypotheses of causation, and procedures for the collection of data followed by evaluation and reiteration. The process is iterative, and serves to reduce uncertainty, build knowledge and improve management over time in a goal-oriented and structured process.

Adaptive management

USGS Publications Warehouse

Allen, Craig R.; Garmestani, Ahjond S.

2015-01-01

Adaptive management is an approach to natural resource management that emphasizes learning through management where knowledge is incomplete, and when, despite inherent uncertainty, managers and policymakers must act. Unlike a traditional trial and error approach, adaptive management has explicit structure, including a careful elucidation of goals, identification of alternative management objectives and hypotheses of causation, and procedures for the collection of data followed by evaluation and reiteration. The process is iterative, and serves to reduce uncertainty, build knowledge and improve management over time in a goal-oriented and structured process.

Iterating between Lessons on Concepts and Procedures Can Improve Mathematics Knowledge

ERIC Educational Resources Information Center

Rittle-Johnson, Bethany; Koedinger, Kenneth

2009-01-01

Background: Knowledge of concepts and procedures seems to develop in an iterative fashion, with increases in one type of knowledge leading to increases in the other type of knowledge. This suggests that iterating between lessons on concepts and procedures may improve learning. Aims: The purpose of the current study was to evaluate the…

A review of assertions about the processes and outcomes of social learning in natural resource management.

PubMed

Cundill, G; Rodela, R

2012-12-30

Social learning has become a central theme in natural resource management. This growing interest is underpinned by a number of assertions about the outcomes of social learning, and about the processes that support these outcomes. Yet researchers and practitioners who seek to engage with social learning through the natural resource management literature often become disorientated by the myriad processes and outcomes that are identified. We trace the roots of current assertions about the processes and outcomes of social learning in natural resource management, and assess the extent to which there is an emerging consensus on these assertions. Results suggest that, on the one hand, social learning is described as taking place through deliberative interactions amongst multiple stakeholders. During these interactions, it is argued that participants learn to work together and build relationships that allow for collective action. On the other hand, social learning is described as occurring through deliberate experimentation and reflective practice. During these iterative cycles of action, monitoring and reflection, participants learn how to cope with uncertainty when managing complex systems. Both of these processes, and their associated outcomes, are referred to as social learning. Where, therefore, should researchers and practitioners focus their attention? Results suggest that there is an emerging consensus that processes that support social learning involve sustained interaction between stakeholders, on-going deliberation and the sharing of knowledge in a trusting environment. There is also an emerging consensus that the key outcome of such learning is improved decision making underpinned by a growing awareness of human-environment interactions, better relationships and improved problem-solving capacities for participants. Copyright © 2012 Elsevier Ltd. All rights reserved.

Proof Rules for Automated Compositional Verification through Learning

NASA Technical Reports Server (NTRS)

Barringer, Howard; Giannakopoulou, Dimitra; Pasareanu, Corina S.

2003-01-01

Compositional proof systems not only enable the stepwise development of concurrent processes but also provide a basis to alleviate the state explosion problem associated with model checking. An assume-guarantee style of specification and reasoning has long been advocated to achieve compositionality. However, this style of reasoning is often non-trivial, typically requiring human input to determine appropriate assumptions. In this paper, we present novel assume- guarantee rules in the setting of finite labelled transition systems with blocking communication. We show how these rules can be applied in an iterative and fully automated fashion within a framework based on learning.

Design for success: Identifying a process for transitioning to an intensive online course delivery model in health professions education.

PubMed

McDonald, Paige L; Harwood, Kenneth J; Butler, Joan T; Schlumpf, Karen S; Eschmann, Carson W; Drago, Daniela

2018-12-01

Intensive courses (ICs), or accelerated courses, are gaining popularity in medical and health professions education, particularly as programs adopt e-learning models to negotiate challenges of flexibility, space, cost, and time. In 2014, the Department of Clinical Research and Leadership (CRL) at the George Washington University School of Medicine and Health Sciences began the process of transitioning two online 15-week graduate programs to an IC model. Within a year, a third program also transitioned to this model. A literature review yielded little guidance on the process of transitioning from 15-week, traditional models of delivery to IC models, particularly in online learning environments. Correspondingly, this paper describes the process by which CRL transitioned three online graduate programs to an IC model and details best practices for course design and facilitation resulting from our iterative redesign process. Finally, we present lessons-learned for the benefit of other medical and health professions' programs contemplating similar transitions. CRL: Department of Clinical Research and Leadership; HSCI: Health Sciences; IC: Intensive course; PD: Program director; QM: Quality Matters.

Design for success: Identifying a process for transitioning to an intensive online course delivery model in health professions education

PubMed Central

McDonald, Paige L.; Harwood, Kenneth J.; Butler, Joan T.; Schlumpf, Karen S.; Eschmann, Carson W.; Drago, Daniela

2018-01-01

ABSTRACT Intensive courses (ICs), or accelerated courses, are gaining popularity in medical and health professions education, particularly as programs adopt e-learning models to negotiate challenges of flexibility, space, cost, and time. In 2014, the Department of Clinical Research and Leadership (CRL) at the George Washington University School of Medicine and Health Sciences began the process of transitioning two online 15-week graduate programs to an IC model. Within a year, a third program also transitioned to this model. A literature review yielded little guidance on the process of transitioning from 15-week, traditional models of delivery to IC models, particularly in online learning environments. Correspondingly, this paper describes the process by which CRL transitioned three online graduate programs to an IC model and details best practices for course design and facilitation resulting from our iterative redesign process. Finally, we present lessons-learned for the benefit of other medical and health professionsʼ programs contemplating similar transitions. Abbreviations: CRL: Department of Clinical Research and Leadership; HSCI: Health Sciences; IC: Intensive course; PD: Program director; QM: Quality Matters PMID:29277143

A Primer for Developing Measures of Science Content Knowledge for Small-Scale Research and Instructional Use

PubMed Central

Bass, Kristin M.; Drits-Esser, Dina; Stark, Louisa A.

2016-01-01

The credibility of conclusions made about the effectiveness of educational interventions depends greatly on the quality of the assessments used to measure learning gains. This essay, intended for faculty involved in small-scale projects, courses, or educational research, provides a step-by-step guide to the process of developing, scoring, and validating high-quality content knowledge assessments. We illustrate our discussion with examples from our assessments of high school students’ understanding of concepts in cell biology and epigenetics. Throughout, we emphasize the iterative nature of the development process, the importance of creating instruments aligned to the learning goals of an intervention or curricula, and the importance of collaborating with other content and measurement specialists along the way. PMID:27055776

Discrete-Time Stable Generalized Self-Learning Optimal Control With Approximation Errors.

PubMed

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.

How to build a course in mathematical-biological modeling: content and processes for knowledge and skill.

PubMed

Hoskinson, Anne-Marie

2010-01-01

Biological problems in the twenty-first century are complex and require mathematical insight, often resulting in mathematical models of biological systems. Building mathematical-biological models requires cooperation among biologists and mathematicians, and mastery of building models. A new course in mathematical modeling presented the opportunity to build both content and process learning of mathematical models, the modeling process, and the cooperative process. There was little guidance from the literature on how to build such a course. Here, I describe the iterative process of developing such a course, beginning with objectives and choosing content and process competencies to fulfill the objectives. I include some inductive heuristics for instructors seeking guidance in planning and developing their own courses, and I illustrate with a description of one instructional model cycle. Students completing this class reported gains in learning of modeling content, the modeling process, and cooperative skills. Student content and process mastery increased, as assessed on several objective-driven metrics in many types of assessments.

How to Build a Course in Mathematical–Biological Modeling: Content and Processes for Knowledge and Skill

PubMed Central

2010-01-01

Biological problems in the twenty-first century are complex and require mathematical insight, often resulting in mathematical models of biological systems. Building mathematical–biological models requires cooperation among biologists and mathematicians, and mastery of building models. A new course in mathematical modeling presented the opportunity to build both content and process learning of mathematical models, the modeling process, and the cooperative process. There was little guidance from the literature on how to build such a course. Here, I describe the iterative process of developing such a course, beginning with objectives and choosing content and process competencies to fulfill the objectives. I include some inductive heuristics for instructors seeking guidance in planning and developing their own courses, and I illustrate with a description of one instructional model cycle. Students completing this class reported gains in learning of modeling content, the modeling process, and cooperative skills. Student content and process mastery increased, as assessed on several objective-driven metrics in many types of assessments. PMID:20810966

Designing Serious Computer Games for People With Moderate and Advanced Dementia: Interdisciplinary Theory-Driven Pilot Study.

PubMed

Tziraki, Chariklia; Berenbaum, Rakel; Gross, Daniel; Abikhzer, Judith; Ben-David, Boaz M

2017-07-31

The field of serious games for people with dementia (PwD) is mostly driven by game-design principals typically applied to games created by and for younger individuals. Little has been done developing serious games to help PwD maintain cognition and to support functionality. We aimed to create a theory-based serious game for PwD, with input from a multi-disciplinary team familiar with aging, dementia, and gaming theory, as well as direct input from end users (the iterative process). Targeting enhanced self-efficacy in daily activities, the goal was to generate a game that is acceptable, accessible and engaging for PwD. The theory-driven game development was based on the following learning theories: learning in context, errorless learning, building on capacities, and acknowledging biological changes-all with the aim to boost self-efficacy. The iterative participatory process was used for game screen development with input of 34 PwD and 14 healthy community dwelling older adults, aged over 65 years. Development of game screens was informed by the bio-psychological aging related disabilities (ie, motor, visual, and perception) as well as remaining neuropsychological capacities (ie, implicit memory) of PwD. At the conclusion of the iterative development process, a prototype game with 39 screens was used for a pilot study with 24 PwD and 14 healthy community dwelling older adults. The game was played twice weekly for 10 weeks. Quantitative analysis showed that the average speed of successful screen completion was significantly longer for PwD compared with healthy older adults. Both PwD and controls showed an equivalent linear increase in the speed for task completion with practice by the third session (P<.02). Most important, the rate of improved processing speed with practice was not statistically different between PwD and controls. This may imply that some form of learning occurred for PwD at a nonsignificantly different rate than for controls. Qualitative results indicate that PwD found the game engaging and fun. Healthy older adults found the game too easy. Increase in self-reported self-efficacy was documented with PwD only. Our study demonstrated that PwD's speed improved with practice at the same rate as healthy older adults. This implies that when tasks are designed to match PwD's abilities, learning ensues. In addition, this pilot study of a serious game, designed for PwD, was accessible, acceptable, and enjoyable for end users. Games designed based on learning theories and input of end users and a multi-disciplinary team familiar with dementia and aging may have the potential of maintaining capacity and improving functionality of PwD. A larger longer study is needed to confirm our findings and evaluate the use of these games in assessing cognitive status and functionality. ©Chariklia Tziraki, Rakel Berenbaum, Daniel Gross, Judith Abikhzer, Boaz M Ben-David. Originally published in JMIR Serious Games (http://games.jmir.org), 31.07.2017.

Designing Serious Computer Games for People With Moderate and Advanced Dementia: Interdisciplinary Theory-Driven Pilot Study

PubMed Central

Gross, Daniel; Abikhzer, Judith

2017-01-01

Background The field of serious games for people with dementia (PwD) is mostly driven by game-design principals typically applied to games created by and for younger individuals. Little has been done developing serious games to help PwD maintain cognition and to support functionality. Objectives We aimed to create a theory-based serious game for PwD, with input from a multi-disciplinary team familiar with aging, dementia, and gaming theory, as well as direct input from end users (the iterative process). Targeting enhanced self-efficacy in daily activities, the goal was to generate a game that is acceptable, accessible and engaging for PwD. Methods The theory-driven game development was based on the following learning theories: learning in context, errorless learning, building on capacities, and acknowledging biological changes—all with the aim to boost self-efficacy. The iterative participatory process was used for game screen development with input of 34 PwD and 14 healthy community dwelling older adults, aged over 65 years. Development of game screens was informed by the bio-psychological aging related disabilities (ie, motor, visual, and perception) as well as remaining neuropsychological capacities (ie, implicit memory) of PwD. At the conclusion of the iterative development process, a prototype game with 39 screens was used for a pilot study with 24 PwD and 14 healthy community dwelling older adults. The game was played twice weekly for 10 weeks. Results Quantitative analysis showed that the average speed of successful screen completion was significantly longer for PwD compared with healthy older adults. Both PwD and controls showed an equivalent linear increase in the speed for task completion with practice by the third session (P<.02). Most important, the rate of improved processing speed with practice was not statistically different between PwD and controls. This may imply that some form of learning occurred for PwD at a nonsignificantly different rate than for controls. Qualitative results indicate that PwD found the game engaging and fun. Healthy older adults found the game too easy. Increase in self-reported self-efficacy was documented with PwD only. Conclusions Our study demonstrated that PwD’s speed improved with practice at the same rate as healthy older adults. This implies that when tasks are designed to match PwD’s abilities, learning ensues. In addition, this pilot study of a serious game, designed for PwD, was accessible, acceptable, and enjoyable for end users. Games designed based on learning theories and input of end users and a multi-disciplinary team familiar with dementia and aging may have the potential of maintaining capacity and improving functionality of PwD. A larger longer study is needed to confirm our findings and evaluate the use of these games in assessing cognitive status and functionality. PMID:28760730

Fundamental concepts of problem-based learning for the new facilitator.

PubMed Central

Kanter, S L

1998-01-01

Problem-based learning (PBL) is a powerful small group learning tool that should be part of the armamentarium of every serious educator. Classic PBL uses ill-structured problems to simulate the conditions that occur in the real environment. Students play an active role and use an iterative process of seeking new information based on identified learning issues, restructuring the information in light of the new knowledge, gathering additional information, and so forth. Faculty play a facilitatory role, not a traditional instructional role, by posing metacognitive questions to students. These questions serve to assist in organizing, generalizing, and evaluating knowledge; to probe for supporting evidence; to explore faulty reasoning; to stimulate discussion of attitudes; and to develop self-directed learning and self-assessment skills. Professional librarians play significant roles in the PBL environment extending from traditional service provider to resource person to educator. Students and faculty usually find the learning experience productive and enjoyable. PMID:9681175

Interactive learning in 2×2 normal form games by neural network agents

NASA Astrophysics Data System (ADS)

Spiliopoulos, Leonidas

2012-11-01

This paper models the learning process of populations of randomly rematched tabula rasa neural network (NN) agents playing randomly generated 2×2 normal form games of all strategic classes. This approach has greater external validity than the existing models in the literature, each of which is usually applicable to narrow subsets of classes of games (often a single game) and/or to fixed matching protocols. The learning prowess of NNs with hidden layers was impressive as they learned to play unique pure strategy equilibria with near certainty, adhered to principles of dominance and iterated dominance, and exhibited a preference for risk-dominant equilibria. In contrast, perceptron NNs were found to perform significantly worse than hidden layer NN agents and human subjects in experimental studies.

Developing a holistic policy and intervention framework for global mental health.

PubMed

Khenti, Akwatu; Fréel, Stéfanie; Trainor, Ruth; Mohamoud, Sirad; Diaz, Pablo; Suh, Erica; Bobbili, Sireesha J; Sapag, Jaime C

2016-02-01

There are significant gaps in the accessibility and quality of mental health services around the globe. A wide range of institutions are addressing the challenges, but there is limited reflection and evaluation on the various approaches, how they compare with each other, and conclusions regarding the most effective approach for particular settings. This article presents a framework for global mental health capacity building that could potentially serve as a promising or best practice in the field. The framework is the outcome of a decade of collaborative global health work at the Centre for Addiction and Mental Health (CAMH) (Ontario, Canada). The framework is grounded in scientific evidence, relevant learning and behavioural theories and the underlying principles of health equity and human rights. Grounded in CAMH's research, programme evaluation and practical experience in developing and implementing mental health capacity building interventions, this article presents the iterative learning process and impetus that formed the basis of the framework. A developmental evaluation (Patton M.2010. Developmental Evaluation: Applying Complexity Concepts to Enhance Innovation and Use. New York: Guilford Press.) approach was used to build the framework, as global mental health collaboration occurs in complex or uncertain environments and evolving learning systems. A multilevel framework consists of five central components: (1) holistic health, (2) cultural and socioeconomic relevance, (3) partnerships, (4) collaborative action-based education and learning and (5) sustainability. The framework's practical application is illustrated through the presentation of three international case studies and four policy implications. Lessons learned, limitations and future opportunities are also discussed. The holistic policy and intervention framework for global mental health reflects an iterative learning process that can be applied and scaled up across different settings through appropriate modifications. © The Author 2015. Published by Oxford University Press in association with The London School of Hygiene and Tropical Medicine.

Engaging Students In Modeling Instruction for Introductory Physics

NASA Astrophysics Data System (ADS)

Brewe, Eric

2016-05-01

Teaching introductory physics is arguably one of the most important things that a physics department does. It is the primary way that students from other science disciplines engage with physics and it is the introduction to physics for majors. Modeling instruction is an active learning strategy for introductory physics built on the premise that science proceeds through the iterative process of model construction, development, deployment, and revision. We describe the role that participating in authentic modeling has in learning and then explore how students engage in this process in the classroom. In this presentation, we provide a theoretical background on models and modeling and describe how these theoretical elements are enacted in the introductory university physics classroom. We provide both quantitative and video data to link the development of a conceptual model to the design of the learning environment and to student outcomes. This work is supported in part by DUE #1140706.

Using a Systematic Approach and Theoretical Framework to Design a Curriculum for the Shaping Healthy Choices Program.

PubMed

Linnell, Jessica D; Zidenberg-Cherr, Sheri; Briggs, Marilyn; Scherr, Rachel E; Brian, Kelley M; Hillhouse, Carol; Smith, Martin H

2016-01-01

To examine the use of a systematic approach and theoretical framework to develop an inquiry-based, garden-enhanced nutrition curriculum for the Shaping Healthy Choices Program. Curriculum development occurred in 3 steps: identification of learning objectives, determination of evidence of learning, and activity development. Curriculum activities were further refined through pilot-testing, which was conducted in 2 phases. Formative data collected during pilot-testing resulted in improvements to activities. Using a systematic, iterative process resulted in a curriculum called Discovering Healthy Choices, which has a strong foundation in Social Cognitive Theory and constructivist learning theory. Furthermore, the Backward Design method provided the design team with a systematic approach to ensure activities addressed targeted learning objectives and overall Shaping Healthy Choices Program goals. The process by which a nutrition curriculum is developed may have a direct effect on student outcomes. Processes by which nutrition curricula are designed and learning objectives are selected, and how theory and pedagogy are applied should be further investigated so that effective approaches to developing garden-enhanced nutrition interventions can be determined and replicated. Copyright © 2016 Society for Nutrition Education and Behavior. Published by Elsevier Inc. All rights reserved.

Powerful Design Principles and Processes: Lessons from a Case of Ambitious Civics Education Curriculum Planning. A Response to "Reinventing the High School Government Course: Rigor, Simulations, and Learning from Text"

ERIC Educational Resources Information Center

Dinkelman, Todd

2016-01-01

In "Reinventing the High School Government Course," the authors presented the latest iteration of an ambitious AP government course developed over a seven-year design-based implementation research project. Chiefly addressed to curriculum developers and civics teachers, the article elaborates key design principles, provides a description…

Reinforcement learning produces dominant strategies for the Iterated Prisoner's Dilemma.

PubMed

Harper, Marc; Knight, Vincent; Jones, Martin; Koutsovoulos, Georgios; Glynatsi, Nikoleta E; Campbell, Owen

2017-01-01

We present tournament results and several powerful strategies for the Iterated Prisoner's Dilemma created using reinforcement learning techniques (evolutionary and particle swarm algorithms). These strategies are trained to perform well against a corpus of over 170 distinct opponents, including many well-known and classic strategies. All the trained strategies win standard tournaments against the total collection of other opponents. The trained strategies and one particular human made designed strategy are the top performers in noisy tournaments also.

Investigating and improving introductory physics students’ understanding of the electric field and superposition principle

NASA Astrophysics Data System (ADS)

Li, Jing; Singh, Chandralekha

2017-09-01

We discuss an investigation of the difficulties that students in a university introductory physics course have with the electric field and superposition principle and how that research was used as a guide in the development and evaluation of a research-validated tutorial on these topics to help students learn these concepts better. The tutorial uses a guided enquiry-based approach to learning and involved an iterative process of development and evaluation. During its development, we obtained feedback both from physics instructors who regularly teach introductory physics in which these concepts are taught and from students for whom the tutorial is intended. The iterative process continued and the feedback was incorporated in the later versions of the tutorial until the researchers were satisfied with the performance of a diverse group of introductory physics students on the post-test after they worked on the tutorial in an individual one-on-one interview situation. Then the final version of the tutorial was administered in several sections of the university physics course after traditional instruction in relevant concepts. We discuss the performance of students in individual interviews and on the pre-test administered before the tutorial (but after traditional lecture-based instruction) and on the post-test administered after the tutorial. We also compare student performance in sections of the class in which students worked on the tutorial with other similar sections of the class in which students only learned via traditional instruction. We find that students performed significantly better in the sections of the class in which the tutorial was used compared to when students learned the material via only lecture-based instruction.

Model-Free control performance improvement using virtual reference feedback tuning and reinforcement Q-learning

NASA Astrophysics Data System (ADS)

Radac, Mircea-Bogdan; Precup, Radu-Emil; Roman, Raul-Cristian

2017-04-01

This paper proposes the combination of two model-free controller tuning techniques, namely linear virtual reference feedback tuning (VRFT) and nonlinear state-feedback Q-learning, referred to as a new mixed VRFT-Q learning approach. VRFT is first used to find stabilising feedback controller using input-output experimental data from the process in a model reference tracking setting. Reinforcement Q-learning is next applied in the same setting using input-state experimental data collected under perturbed VRFT to ensure good exploration. The Q-learning controller learned with a batch fitted Q iteration algorithm uses two neural networks, one for the Q-function estimator and one for the controller, respectively. The VRFT-Q learning approach is validated on position control of a two-degrees-of-motion open-loop stable multi input-multi output (MIMO) aerodynamic system (AS). Extensive simulations for the two independent control channels of the MIMO AS show that the Q-learning controllers clearly improve performance over the VRFT controllers.

Multi Agent Systems with Symbiotic Learning and Evolution using GNP

NASA Astrophysics Data System (ADS)

Eguchi, Toru; Hirasawa, Kotaro; Hu, Jinglu; Murata, Junichi

Recently, various attempts relevant to Multi Agent Systems (MAS) which is one of the most promising systems based on Distributed Artificial Intelligence have been studied to control large and complicated systems efficiently. In these trends of MAS, Multi Agent Systems with Symbiotic Learning and Evolution named Masbiole has been proposed. In Masbiole, symbiotic phenomena among creatures are considered in the process of learning and evolution of MAS. So we can expect more flexible and sophisticated solutions than conventional MAS. In this paper, we apply Masbiole to Iterative Prisoner’s Dilemma Games (IPD Games) using Genetic Network Programming (GNP) which is a newly developed evolutionary computation method for constituting agents. Some characteristics of Masbiole using GNP in IPD Games are clarified.

Community Based Learning and Civic Engagement: Informal Learning among Adult Volunteers in Community Organizations

ERIC Educational Resources Information Center

Mundel, Karsten; Schugurensky, Daniel

2008-01-01

Many iterations of community based learning employ models, such as consciousness raising groups, cultural circles, and participatory action research. In all of them, learning is a deliberate part of an explicit educational activity. This article explores another realm of community learning: the informal learning that results from volunteering in…

Reinforcement learning produces dominant strategies for the Iterated Prisoner’s Dilemma

PubMed Central

Jones, Martin; Koutsovoulos, Georgios; Glynatsi, Nikoleta E.; Campbell, Owen

2017-01-01

We present tournament results and several powerful strategies for the Iterated Prisoner’s Dilemma created using reinforcement learning techniques (evolutionary and particle swarm algorithms). These strategies are trained to perform well against a corpus of over 170 distinct opponents, including many well-known and classic strategies. All the trained strategies win standard tournaments against the total collection of other opponents. The trained strategies and one particular human made designed strategy are the top performers in noisy tournaments also. PMID:29228001

Comparisons of Observed Process Quality in German and American Infant/Toddler Programs

ERIC Educational Resources Information Center

Tietze, Wolfgang; Cryer, Debby

2004-01-01

Observed process quality in infant/toddler classrooms was compared in Germany (n = 75) and the USA (n = 219). Process quality was assessed with the Infant/Toddler Environment Rating Scale(ITERS) and parent attitudes about ITERS content with the ITERS Parent Questionnaire (ITERSPQ). The ITERS had comparable reliabilities in the two countries and…

From Innovation to Impact at Scale: Lessons Learned from a Cluster of Research-Community Partnerships

PubMed Central

Schindler, Holly S.; Fisher, Philip A.; Shonkoff, Jack P.

2017-01-01

This paper presents a description of how an interdisciplinary network of academic researchers, community-based programs, parents, and state agencies have joined together to design, test, and scale a suite of innovative intervention strategies rooted in new knowledge about the biology of adversity. Through a process of co-creation, collective pilot-testing, and the support of a measurement and evaluation hub, the Washington State Innovation Cluster is using rapid cycle, iterative learning to elucidate differential impacts of interventions designed to build child and caregiver capacities and address the developmental consequences of socioeconomic disadvantage. Key characteristics of the Innovation Cluster model are described and an example is presented of a video-coaching intervention that has been implemented, adapted, and evaluated through this distinctive, collaborative process. PMID:28777436

Developing and Evaluating an Eighth Grade Curriculum Unit That Links Foundational Chemistry to Biological Growth. Paper #1: Selecting Core Ideas and Practices -- An Iterative Process

ERIC Educational Resources Information Center

Roseman, Jo Ellen; Herrmann-Abell, Cari; Flanagan, Jean; Kruse, Rebecca; Howes, Elaine; Carlson, Janet; Roth, Kathy; Bourdelat-Parks, Brooke

2013-01-01

Researchers at AAAS and BSCS have developed a six-week unit that aims to help middle school students learn important chemistry ideas that can be used to explain growth and repair in animals and plants. By integrating core physical and life science ideas and engaging students in the science practices of modeling and constructing explanations, the…

Q-Learning-Based Adjustable Fixed-Phase Quantum Grover Search Algorithm

NASA Astrophysics Data System (ADS)

Guo, Ying; Shi, Wensha; Wang, Yijun; Hu, Jiankun

2017-02-01

We demonstrate that the rotation phase can be suitably chosen to increase the efficiency of the phase-based quantum search algorithm, leading to a dynamic balance between iterations and success probabilities of the fixed-phase quantum Grover search algorithm with Q-learning for a given number of solutions. In this search algorithm, the proposed Q-learning algorithm, which is a model-free reinforcement learning strategy in essence, is used for performing a matching algorithm based on the fraction of marked items λ and the rotation phase α. After establishing the policy function α = π(λ), we complete the fixed-phase Grover algorithm, where the phase parameter is selected via the learned policy. Simulation results show that the Q-learning-based Grover search algorithm (QLGA) enables fewer iterations and gives birth to higher success probabilities. Compared with the conventional Grover algorithms, it avoids the optimal local situations, thereby enabling success probabilities to approach one.

An Extensible Information Grid for Risk Management

NASA Technical Reports Server (NTRS)

Maluf, David A.; Bell, David G.

2003-01-01

This paper describes recent work on developing an extensible information grid for risk management at NASA - a RISK INFORMATION GRID. This grid is being developed by integrating information grid technology with risk management processes for a variety of risk related applications. To date, RISK GRID applications are being developed for three main NASA processes: risk management - a closed-loop iterative process for explicit risk management, program/project management - a proactive process that includes risk management, and mishap management - a feedback loop for learning from historical risks that escaped other processes. This is enabled through an architecture involving an extensible database, structuring information with XML, schemaless mapping of XML, and secure server-mediated communication using standard protocols.

Hard exudates segmentation based on learned initial seeds and iterative graph cut.

PubMed

Kusakunniran, Worapan; Wu, Qiang; Ritthipravat, Panrasee; Zhang, Jian

2018-05-01

(Background and Objective): The occurrence of hard exudates is one of the early signs of diabetic retinopathy which is one of the leading causes of the blindness. Many patients with diabetic retinopathy lose their vision because of the late detection of the disease. Thus, this paper is to propose a novel method of hard exudates segmentation in retinal images in an automatic way. (Methods): The existing methods are based on either supervised or unsupervised learning techniques. In addition, the learned segmentation models may often cause miss-detection and/or fault-detection of hard exudates, due to the lack of rich characteristics, the intra-variations, and the similarity with other components in the retinal image. Thus, in this paper, the supervised learning based on the multilayer perceptron (MLP) is only used to identify initial seeds with high confidences to be hard exudates. Then, the segmentation is finalized by unsupervised learning based on the iterative graph cut (GC) using clusters of initial seeds. Also, in order to reduce color intra-variations of hard exudates in different retinal images, the color transfer (CT) is applied to normalize their color information, in the pre-processing step. (Results): The experiments and comparisons with the other existing methods are based on the two well-known datasets, e_ophtha EX and DIARETDB1. It can be seen that the proposed method outperforms the other existing methods in the literature, with the sensitivity in the pixel-level of 0.891 for the DIARETDB1 dataset and 0.564 for the e_ophtha EX dataset. The cross datasets validation where the training process is performed on one dataset and the testing process is performed on another dataset is also evaluated in this paper, in order to illustrate the robustness of the proposed method. (Conclusions): This newly proposed method integrates the supervised learning and unsupervised learning based techniques. It achieves the improved performance, when compared with the existing methods in the literature. The robustness of the proposed method for the scenario of cross datasets could enhance its practical usage. That is, the trained model could be more practical for unseen data in the real-world situation, especially when the capturing environments of training and testing images are not the same. Copyright © 2018 Elsevier B.V. All rights reserved.

The ATLAS Public Web Pages: Online Management of HEP External Communication Content

NASA Astrophysics Data System (ADS)

Goldfarb, S.; Marcelloni, C.; Eli Phoboo, A.; Shaw, K.

2015-12-01

The ATLAS Education and Outreach Group is in the process of migrating its public online content to a professionally designed set of web pages built on the Drupal [1] content management system. Development of the front-end design passed through several key stages, including audience surveys, stakeholder interviews, usage analytics, and a series of fast design iterations, called sprints. Implementation of the web site involves application of the html design using Drupal templates, refined development iterations, and the overall population of the site with content. We present the design and development processes and share the lessons learned along the way, including the results of the data-driven discovery studies. We also demonstrate the advantages of selecting a back-end supported by content management, with a focus on workflow. Finally, we discuss usage of the new public web pages to implement outreach strategy through implementation of clearly presented themes, consistent audience targeting and messaging, and the enforcement of a well-defined visual identity.

Distance Metric Learning via Iterated Support Vector Machines.

PubMed

Zuo, Wangmeng; Wang, Faqiang; Zhang, David; Lin, Liang; Huang, Yuchi; Meng, Deyu; Zhang, Lei

2017-07-11

Distance metric learning aims to learn from the given training data a valid distance metric, with which the similarity between data samples can be more effectively evaluated for classification. Metric learning is often formulated as a convex or nonconvex optimization problem, while most existing methods are based on customized optimizers and become inefficient for large scale problems. In this paper, we formulate metric learning as a kernel classification problem with the positive semi-definite constraint, and solve it by iterated training of support vector machines (SVMs). The new formulation is easy to implement and efficient in training with the off-the-shelf SVM solvers. Two novel metric learning models, namely Positive-semidefinite Constrained Metric Learning (PCML) and Nonnegative-coefficient Constrained Metric Learning (NCML), are developed. Both PCML and NCML can guarantee the global optimality of their solutions. Experiments are conducted on general classification, face verification and person re-identification to evaluate our methods. Compared with the state-of-the-art approaches, our methods can achieve comparable classification accuracy and are efficient in training.

Quantum learning of classical stochastic processes: The completely positive realization problem

DOE Office of Scientific and Technical Information (OSTI.GOV)

Monràs, Alex; Centre for Quantum Technologies, National University of Singapore, 3 Science Drive 2, Singapore 117543; Winter, Andreas

2016-01-15

Among several tasks in Machine Learning, a specially important one is the problem of inferring the latent variables of a system and their causal relations with the observed behavior. A paradigmatic instance of this is the task of inferring the hidden Markov model underlying a given stochastic process. This is known as the positive realization problem (PRP), [L. Benvenuti and L. Farina, IEEE Trans. Autom. Control 49(5), 651–664 (2004)] and constitutes a central problem in machine learning. The PRP and its solutions have far-reaching consequences in many areas of systems and control theory, and is nowadays an important piece inmore » the broad field of positive systems theory. We consider the scenario where the latent variables are quantum (i.e., quantum states of a finite-dimensional system) and the system dynamics is constrained only by physical transformations on the quantum system. The observable dynamics is then described by a quantum instrument, and the task is to determine which quantum instrument — if any — yields the process at hand by iterative application. We take as a starting point the theory of quasi-realizations, whence a description of the dynamics of the process is given in terms of linear maps on state vectors and probabilities are given by linear functionals on the state vectors. This description, despite its remarkable resemblance with the hidden Markov model, or the iterated quantum instrument, is however devoid of any stochastic or quantum mechanical interpretation, as said maps fail to satisfy any positivity conditions. The completely positive realization problem then consists in determining whether an equivalent quantum mechanical description of the same process exists. We generalize some key results of stochastic realization theory, and show that the problem has deep connections with operator systems theory, giving possible insight to the lifting problem in quotient operator systems. Our results have potential applications in quantum machine learning, device-independent characterization and reverse-engineering of stochastic processes and quantum processors, and more generally, of dynamical processes with quantum memory [M. Guţă, Phys. Rev. A 83(6), 062324 (2011); M. Guţă and N. Yamamoto, e-print http://arxiv.org/abs/1303.3771 (2013)].« less

Developing an Assessment Process for a Master's of Science Degree in a Pharmaceutical Sciences Program.

PubMed

Bloom, Timothy J; Hall, Julie M; Liu, Qinfeng; Stagner, William C; Adams, Michael L

2016-09-25

Objective. To develop a program-level assessment process for a master's of science degree in a pharmaceutical sciences (MSPS) program. Design. Program-level goals were created and mapped to course learning objectives. Embedded assessment tools were created by each course director and used to gather information related to program-level goals. Initial assessment iterations involved a subset of offered courses, and course directors met with the department assessment committee to review the quality of the assessment tools as well as the data collected with them. Insights from these discussions were used to improve the process. When all courses were used for collecting program-level assessment data, a modified system of guided reflection was used to reduce demands on committee members. Assessment. The first two iterations of collecting program-level assessment revealed problems with both the assessment tools and the program goals themselves. Course directors were inconsistent in the Bloom's Taxonomy level at which they assessed student achievement of program goals. Moreover, inappropriate mapping of program goals to course learning objectives were identified. These issues led to unreliable measures of how well students were doing with regard to program-level goals. Peer discussions between course directors and the assessment committee led to modification of program goals as well as improved assessment data collection tools. Conclusion. By starting with a subset of courses and using course-embedded assessment tools, a program-level assessment process was created with little difficulty. Involving all faculty members and avoiding comparisons between courses made obtaining faculty buy-in easier. Peer discussion often resulted in consensus on how to improve assessment tools.

Density control in ITER: an iterative learning control and robust control approach

NASA Astrophysics Data System (ADS)

Ravensbergen, T.; de Vries, P. C.; Felici, F.; Blanken, T. C.; Nouailletas, R.; Zabeo, L.

2018-01-01

Plasma density control for next generation tokamaks, such as ITER, is challenging because of multiple reasons. The response of the usual gas valve actuators in future, larger fusion devices, might be too slow for feedback control. Both pellet fuelling and the use of feedforward-based control may help to solve this problem. Also, tight density limits arise during ramp-up, due to operational limits related to divertor detachment and radiative collapses. As the number of shots available for controller tuning will be limited in ITER, in this paper, iterative learning control (ILC) is proposed to determine optimal feedforward actuator inputs based on tracking errors, obtained in previous shots. This control method can take the actuator and density limits into account and can deal with large actuator delays. However, a purely feedforward-based density control may not be sufficient due to the presence of disturbances and shot-to-shot differences. Therefore, robust control synthesis is used to construct a robustly stabilizing feedback controller. In simulations, it is shown that this combined controller strategy is able to achieve good tracking performance in the presence of shot-to-shot differences, tight constraints, and model mismatches.

New Standards Put the Spotlight on Professional Learning

ERIC Educational Resources Information Center

Mizell, Hayes; Hord, Shirley; Killion, Joellen; Hirsh, Stephanie

2011-01-01

Learning Forward introduces new Standards for Professional Learning. This is the third iteration of standards outlining the characteristics of professional learning that lead to effective teaching practices, supportive leadership, and improved student results. The standards are not a prescription for how education leaders and public officials…

Service-Learning in the Environmental Sciences for Teaching Sustainability Science

NASA Astrophysics Data System (ADS)

Truebe, S.; Strong, A. L.

2016-12-01

Understanding and developing effective strategies for the use of community-engaged learning (service-learning) approaches in the environmental geosciences is an important research need in curricular and pedagogical innovation for sustainability. In 2015, we designed and implemented a new community-engaged learning practicum course through the Earth Systems Program in the School of Earth, Energy and Environmental Sciences at Stanford University focused on regional open space management and land stewardship. Undergraduate and graduate students partnered with three different regional land trust and environmental stewardship organizations to conduct quarter-long research projects ranging from remote sensing studies of historical land use, to fire ecology, to ranchland management, to volunteer retention strategies. Throughout the course, students reflected on the decision-making processes and stewardship actions of the organizations. Two iterations of the course were run in Winter and Fall 2015. Using coded and analyzed pre- and post-course student surveys from the two course iterations, we evaluate undergraduate and graduate student learning outcomes and changes in perceptions and understanding of sustainability science. We find that engagement with community partners to conduct research projects on a wide variety of aspects of open space management, land management, and environmental stewardship (1) increased an understanding of trade-offs inherent in sustainability and resource management and (2) altered student perceptions of the role of scientific information and research in environmental management and decision-making. Furthermore, students initially conceived of open space as purely ecological/biophysical, but by the end of the course, (3) their understanding was of open space as a coupled human/ecological system. This shift is crucial for student development as sustainability scientists.

Regression analysis as a design optimization tool

NASA Technical Reports Server (NTRS)

Perley, R.

1984-01-01

The optimization concepts are described in relation to an overall design process as opposed to a detailed, part-design process where the requirements are firmly stated, the optimization criteria are well established, and a design is known to be feasible. The overall design process starts with the stated requirements. Some of the design criteria are derived directly from the requirements, but others are affected by the design concept. It is these design criteria that define the performance index, or objective function, that is to be minimized within some constraints. In general, there will be multiple objectives, some mutually exclusive, with no clear statement of their relative importance. The optimization loop that is given adjusts the design variables and analyzes the resulting design, in an iterative fashion, until the objective function is minimized within the constraints. This provides a solution, but it is only the beginning. In effect, the problem definition evolves as information is derived from the results. It becomes a learning process as we determine what the physics of the system can deliver in relation to the desirable system characteristics. As with any learning process, an interactive capability is a real attriubute for investigating the many alternatives that will be suggested as learning progresses.

Theorising Teaching and Learning: Pre-Service Teachers' Theoretical Awareness of Learning

ERIC Educational Resources Information Center

Brante, Göran; Holmqvist Olander, Mona; Holmquist, Per-Ola; Palla, Marta

2015-01-01

We examine pre-service teachers' theoretical learning during one five-week training module, and their educators' learning about better lecture design to foster student learning. The study is iterative: interventions (one per group) were implemented sequentially in student groups A-C, the results of the previous intervention serving as the baseline…

Upper limb stroke rehabilitation: the effectiveness of Stimulation Assistance through Iterative Learning (SAIL).

PubMed

Meadmore, Katie L; Cai, Zhonglun; Tong, Daisy; Hughes, Ann-Marie; Freeman, Chris T; Rogers, Eric; Burridge, Jane H

2011-01-01

A novel system has been developed which combines robotic therapy with electrical stimulation (ES) for upper limb stroke rehabilitation. This technology, termed SAIL: Stimulation Assistance through Iterative Learning, employs advanced model-based iterative learning control (ILC) algorithms to precisely assist participant's completion of 3D tracking tasks with their impaired arm. Data is reported from a preliminary study with unimpaired participants, and also from a single hemiparetic stroke participant with reduced upper limb function who has used the system in a clinical trial. All participants completed tasks which involved moving their (impaired) arm to follow an image of a slowing moving sphere along a trajectory. The participants' arm was supported by a robot and ES was applied to the triceps brachii and anterior deltoid muscles. During each task, the same tracking trajectory was repeated 6 times and ILC was used to compute the stimulation signals to be applied on the next iteration. Unimpaired participants took part in a single, one hour training session and the stroke participant undertook 18, 1 hour treatment sessions composed of tracking tasks varying in length, orientation and speed. The results reported describe changes in tracking ability and demonstrate feasibility of the SAIL system for upper limb rehabilitation. © 2011 IEEE

Intelligent model-based OPC

NASA Astrophysics Data System (ADS)

Huang, W. C.; Lai, C. M.; Luo, B.; Tsai, C. K.; Chih, M. H.; Lai, C. W.; Kuo, C. C.; Liu, R. G.; Lin, H. T.

2006-03-01

Optical proximity correction is the technique of pre-distorting mask layouts so that the printed patterns are as close to the desired shapes as possible. For model-based optical proximity correction, a lithographic model to predict the edge position (contour) of patterns on the wafer after lithographic processing is needed. Generally, segmentation of edges is performed prior to the correction. Pattern edges are dissected into several small segments with corresponding target points. During the correction, the edges are moved back and forth from the initial drawn position, assisted by the lithographic model, to finally settle on the proper positions. When the correction converges, the intensity predicted by the model in every target points hits the model-specific threshold value. Several iterations are required to achieve the convergence and the computation time increases with the increase of the required iterations. An artificial neural network is an information-processing paradigm inspired by biological nervous systems, such as how the brain processes information. It is composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problems. A neural network can be a powerful data-modeling tool that is able to capture and represent complex input/output relationships. The network can accurately predict the behavior of a system via the learning procedure. A radial basis function network, a variant of artificial neural network, is an efficient function approximator. In this paper, a radial basis function network was used to build a mapping from the segment characteristics to the edge shift from the drawn position. This network can provide a good initial guess for each segment that OPC has carried out. The good initial guess reduces the required iterations. Consequently, cycle time can be shortened effectively. The optimization of the radial basis function network for this system was practiced by genetic algorithm, which is an artificially intelligent optimization method with a high probability to obtain global optimization. From preliminary results, the required iterations were reduced from 5 to 2 for a simple dumbbell-shape layout.

Control x-ray deformable mirrors with few measurements

NASA Astrophysics Data System (ADS)

Huang, Lei; Xue, Junpeng; Idir, Mourad

2016-09-01

After years of development from a concept to early experimental stage, X-ray Deformable Mirrors (XDMs) are used in many synchrotron/free-electron laser facilities as a standard x-ray optics tool. XDM is becoming an integral part of the present and future large x-ray and EUV projects and will be essential in exploiting the full potential of the new sources currently under construction. The main objective of using XDMs is to correct wavefront errors or to enable variable focus beam sizes at the sample. Due to the coupling among the N actuators of a DM, it is usually necessary to perform a calibration or training process to drive the DM into the target shape. Commonly, in order to optimize the actuators settings to minimize slope/height errors, an initial measurement need to be collected, with all actuators set to 0, and then either N or 2N measurements are necessary learn each actuator behavior sequentially. In total, it means that N+1 or 2N+1 scans are required to perform this learning process. When the actuators number N is important and the actuator response or the necessary metrology is slow then this learning process can be time consuming. In this work, we present a fast and accurate method to drive an x-ray active bimorph mirror to a target shape with only 3 or 4 measurements. Instead of sequentially measuring and calculating the influence functions of all actuators and then predicting the voltages needed for any desired shape, the metrology data are directly used to "guide" the mirror from its current status towards the particular target slope/height via iterative compensations. The feedback for the iteration process is the discrepancy in curvature calculated by using B-spline fitting of the measured height/slope data. In this paper, the feasibility of this simple and effective approach is demonstrated with experiments.

Being an honest broker of hydrology: Uncovering, communicating and addressing model error in a climate change streamflow dataset

NASA Astrophysics Data System (ADS)

Chegwidden, O.; Nijssen, B.; Pytlak, E.

2017-12-01

Any model simulation has errors, including errors in meteorological data, process understanding, model structure, and model parameters. These errors may express themselves as bias, timing lags, and differences in sensitivity between the model and the physical world. The evaluation and handling of these errors can greatly affect the legitimacy, validity and usefulness of the resulting scientific product. In this presentation we will discuss a case study of handling and communicating model errors during the development of a hydrologic climate change dataset for the Pacific Northwestern United States. The dataset was the result of a four-year collaboration between the University of Washington, Oregon State University, the Bonneville Power Administration, the United States Army Corps of Engineers and the Bureau of Reclamation. Along the way, the partnership facilitated the discovery of multiple systematic errors in the streamflow dataset. Through an iterative review process, some of those errors could be resolved. For the errors that remained, honest communication of the shortcomings promoted the dataset's legitimacy. Thoroughly explaining errors also improved ways in which the dataset would be used in follow-on impact studies. Finally, we will discuss the development of the "streamflow bias-correction" step often applied to climate change datasets that will be used in impact modeling contexts. We will describe the development of a series of bias-correction techniques through close collaboration among universities and stakeholders. Through that process, both universities and stakeholders learned about the others' expectations and workflows. This mutual learning process allowed for the development of methods that accommodated the stakeholders' specific engineering requirements. The iterative revision process also produced a functional and actionable dataset while preserving its scientific merit. We will describe how encountering earlier techniques' pitfalls allowed us to develop improved methods for scientists and practitioners alike.

Closed-loop control of artificial pancreatic Beta -cell in type 1 diabetes mellitus using model predictive iterative learning control.

PubMed

Wang, Youqing; Dassau, Eyal; Doyle, Francis J

2010-02-01

A novel combination of iterative learning control (ILC) and model predictive control (MPC), referred to here as model predictive iterative learning control (MPILC), is proposed for glycemic control in type 1 diabetes mellitus. MPILC exploits two key factors: frequent glucose readings made possible by continuous glucose monitoring technology; and the repetitive nature of glucose-meal-insulin dynamics with a 24-h cycle. The proposed algorithm can learn from an individual's lifestyle, allowing the control performance to be improved from day to day. After less than 10 days, the blood glucose concentrations can be kept within a range of 90-170 mg/dL. Generally, control performance under MPILC is better than that under MPC. The proposed methodology is robust to random variations in meal timings within +/-60 min or meal amounts within +/-75% of the nominal value, which validates MPILC's superior robustness compared to run-to-run control. Moreover, to further improve the algorithm's robustness, an automatic scheme for setpoint update that ensures safe convergence is proposed. Furthermore, the proposed method does not require user intervention; hence, the algorithm should be of particular interest for glycemic control in children and adolescents.

An iterative learning control method with application for CNC machine tools

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kim, D.I.; Kim, S.

1996-01-01

A proportional, integral, and derivative (PID) type iterative learning controller is proposed for precise tracking control of industrial robots and computer numerical controller (CNC) machine tools performing repetitive tasks. The convergence of the output error by the proposed learning controller is guaranteed under a certain condition even when the system parameters are not known exactly and unknown external disturbances exist. As the proposed learning controller is repeatedly applied to the industrial robot or the CNC machine tool with the path-dependent repetitive task, the distance difference between the desired path and the actual tracked or machined path, which is one ofmore » the most significant factors in the evaluation of control performance, is progressively reduced. The experimental results demonstrate that the proposed learning controller can improve machining accuracy when the CNC machine tool performs repetitive machining tasks.« less

Abducting Economics

PubMed Central

Singer, Burton

2018-01-01

Abduction is the process of generating and choosing models, hypotheses and data analyzed in response to surprising findings. All good empirical economists abduct. Explanations usually evolve as studies evolve. The abductive approach challenges economists to step outside the framework of received notions about the “identification problem” that rigidly separates the act of model and hypothesis creation from the act of inference from data. It asks the analyst to engage models and data in an iterative dynamic process, using multiple models and sources of data in a back and forth where both models and data are augmented as learning evolves. PMID:29430020

A review of active learning approaches to experimental design for uncovering biological networks

PubMed Central

2017-01-01

Various types of biological knowledge describe networks of interactions among elementary entities. For example, transcriptional regulatory networks consist of interactions among proteins and genes. Current knowledge about the exact structure of such networks is highly incomplete, and laboratory experiments that manipulate the entities involved are conducted to test hypotheses about these networks. In recent years, various automated approaches to experiment selection have been proposed. Many of these approaches can be characterized as active machine learning algorithms. Active learning is an iterative process in which a model is learned from data, hypotheses are generated from the model to propose informative experiments, and the experiments yield new data that is used to update the model. This review describes the various models, experiment selection strategies, validation techniques, and successful applications described in the literature; highlights common themes and notable distinctions among methods; and identifies likely directions of future research and open problems in the area. PMID:28570593

Integrating Concept Mapping into Information Systems Education for Meaningful Learning and Assessment

ERIC Educational Resources Information Center

Wei, Wei; Yue, Kwok-Bun

2017-01-01

Concept map (CM) is a theoretically sound yet easy to learn tool and can be effectively used to represent knowledge. Even though many disciplines have adopted CM as a teaching and learning tool to improve learning effectiveness, its application in IS curriculum is sparse. Meaningful learning happens when one iteratively integrates new concepts and…

Fast machine-learning online optimization of ultra-cold-atom experiments.

PubMed

Wigley, P B; Everitt, P J; van den Hengel, A; Bastian, J W; Sooriyabandara, M A; McDonald, G D; Hardman, K S; Quinlivan, C D; Manju, P; Kuhn, C C N; Petersen, I R; Luiten, A N; Hope, J J; Robins, N P; Hush, M R

2016-05-16

We apply an online optimization process based on machine learning to the production of Bose-Einstein condensates (BEC). BEC is typically created with an exponential evaporation ramp that is optimal for ergodic dynamics with two-body s-wave interactions and no other loss rates, but likely sub-optimal for real experiments. Through repeated machine-controlled scientific experimentation and observations our 'learner' discovers an optimal evaporation ramp for BEC production. In contrast to previous work, our learner uses a Gaussian process to develop a statistical model of the relationship between the parameters it controls and the quality of the BEC produced. We demonstrate that the Gaussian process machine learner is able to discover a ramp that produces high quality BECs in 10 times fewer iterations than a previously used online optimization technique. Furthermore, we show the internal model developed can be used to determine which parameters are essential in BEC creation and which are unimportant, providing insight into the optimization process of the system.

Fast machine-learning online optimization of ultra-cold-atom experiments

PubMed Central

Wigley, P. B.; Everitt, P. J.; van den Hengel, A.; Bastian, J. W.; Sooriyabandara, M. A.; McDonald, G. D.; Hardman, K. S.; Quinlivan, C. D.; Manju, P.; Kuhn, C. C. N.; Petersen, I. R.; Luiten, A. N.; Hope, J. J.; Robins, N. P.; Hush, M. R.

2016-01-01

We apply an online optimization process based on machine learning to the production of Bose-Einstein condensates (BEC). BEC is typically created with an exponential evaporation ramp that is optimal for ergodic dynamics with two-body s-wave interactions and no other loss rates, but likely sub-optimal for real experiments. Through repeated machine-controlled scientific experimentation and observations our ‘learner’ discovers an optimal evaporation ramp for BEC production. In contrast to previous work, our learner uses a Gaussian process to develop a statistical model of the relationship between the parameters it controls and the quality of the BEC produced. We demonstrate that the Gaussian process machine learner is able to discover a ramp that produces high quality BECs in 10 times fewer iterations than a previously used online optimization technique. Furthermore, we show the internal model developed can be used to determine which parameters are essential in BEC creation and which are unimportant, providing insight into the optimization process of the system. PMID:27180805

Perceptron Genetic to Recognize Openning Strategy Ruy Lopez

NASA Astrophysics Data System (ADS)

Azmi, Zulfian; Mawengkang, Herman

2018-01-01

The application of Perceptron method is not effective for coding on hardware based systems because it is not real time learning. With Genetic algorithm approach in calculating and searching the best weight (fitness value) system will do learning only one iteration. And the results of this analysis were tested in the case of the introduction of the opening pattern of chess Ruy Lopez. The Analysis with Perceptron Model with Algorithm Approach Genetics from group Artificial Neural Network for open Ruy Lopez. The data is processed with base open chess, with step eight a position white Pion from end open chess. Using perceptron method have many input and one output process many weight and refraction until output equal goal. Data trained and test with software Matlab and system can recognize the chess opening Ruy Lopez or Not open Ruy Lopez with Real time.

Design of a Braille Learning Application for Visually Impaired Students in Bangladesh.

PubMed

Nahar, Lutfun; Jaafar, Azizah; Ahamed, Eistiak; Kaish, A B M A

2015-01-01

Visually impaired students (VIS) are unable to get visual information, which has made their learning process complicated. This paper discusses the overall situation of VIS in Bangladesh and identifies major challenges that they are facing in getting education. The Braille system is followed to educate blind students in Bangladesh. However, lack of Braille based educational resources and technological solutions have made the learning process lengthy and complicated for VIS. As a developing country, Bangladesh cannot afford for the costly Braille related technological tools for VIS. Therefore, a mobile phone based Braille application, "mBRAILLE", for Android platform is designed to provide an easy Braille learning technology for VIS in Bangladesh. The proposed design is evaluated by experts in assistive technology for students with disabilities, and advanced learners of Braille. The application aims to provide a Bangla and English Braille learning platform for VIS. In this paper, we depict iterative (participatory) design of the application along with a preliminary evaluation with 5 blind subjects, and 1 sighted and 2 blind experts. The results show that the design scored an overall satisfaction level of 4.53 out of 5 by all respondents, indicating that our design is ready for the next step of development.

Experiments on Learning by Back Propagation.

ERIC Educational Resources Information Center

Plaut, David C.; And Others

This paper describes further research on a learning procedure for layered networks of deterministic, neuron-like units, described by Rumelhart et al. The units, the way they are connected, the learning procedure, and the extension to iterative networks are presented. In one experiment, a network learns a set of filters, enabling it to discriminate…

MAP: an iterative experimental design methodology for the optimization of catalytic search space structure modeling.

PubMed

Baumes, Laurent A

2006-01-01

One of the main problems in high-throughput research for materials is still the design of experiments. At early stages of discovery programs, purely exploratory methodologies coupled with fast screening tools should be employed. This should lead to opportunities to find unexpected catalytic results and identify the "groups" of catalyst outputs, providing well-defined boundaries for future optimizations. However, very few new papers deal with strategies that guide exploratory studies. Mostly, traditional designs, homogeneous covering, or simple random samplings are exploited. Typical catalytic output distributions exhibit unbalanced datasets for which an efficient learning is hardly carried out, and interesting but rare classes are usually unrecognized. Here is suggested a new iterative algorithm for the characterization of the search space structure, working independently of learning processes. It enhances recognition rates by transferring catalysts to be screened from "performance-stable" space zones to "unsteady" ones which necessitate more experiments to be well-modeled. The evaluation of new algorithm attempts through benchmarks is compulsory due to the lack of past proofs about their efficiency. The method is detailed and thoroughly tested with mathematical functions exhibiting different levels of complexity. The strategy is not only empirically evaluated, the effect or efficiency of sampling on future Machine Learning performances is also quantified. The minimum sample size required by the algorithm for being statistically discriminated from simple random sampling is investigated.

Development of a multimedia educational programme for first-time hearing aid users: a participatory design.

PubMed

Ferguson, Melanie; Leighton, Paul; Brandreth, Marian; Wharrad, Heather

2018-05-02

To develop content for a series of interactive video tutorials (or reusable learning objects, RLOs) for first-time adult hearing aid users, to enhance knowledge of hearing aids and communication. RLO content was based on an electronically-delivered Delphi review, workshops, and iterative peer-review and feedback using a mixed-methods participatory approach. An expert panel of 33 hearing healthcare professionals, and workshops involving 32 hearing aid users and 11 audiologists. This ensured that social, emotional and practical experiences of the end-user alongside clinical validity were captured. Content for evidence-based, self-contained RLOs based on pedagogical principles was developed for delivery via DVD for television, PC or internet. Content was developed based on Delphi review statements about essential information that reached consensus (≥90%), visual representations of relevant concepts relating to hearing aids and communication, and iterative peer-review and feedback of content. This participatory approach recognises and involves key stakeholders in the design process to create content for a user-friendly multimedia educational intervention, to supplement the clinical management of first-time hearing aid users. We propose participatory methodologies are used in the development of content for e-learning interventions in hearing-related research and clinical practice.

Active Learning with a Human in The Loop

DTIC Science & Technology

2012-11-01

handwrit - ten digits (LeCun et al. [1998]). In the red curve the model is built iteratively: at each iteration the twenty examples with the lowest...continuum. The most we can say about MUC annotation is that it’s simple enough that other tasks are likely to impose a heavier load on the user for

Learning to Teach Elementary Science through Iterative Cycles of Enactment in Culturally and Linguistically Diverse Contexts

ERIC Educational Resources Information Center

Bottoms, SueAnn I.; Ciechanowski, Kathryn M.; Hartman, Brian

2015-01-01

Iterative cycles of enactment embedded in culturally and linguistically diverse contexts provide rich opportunities for preservice teachers (PSTs) to enact core practices of science. This study is situated in the larger Families Involved in Sociocultural Teaching and Science, Technology, Engineering and Mathematics (FIESTAS) project, which weaves…

Slaying the Great Green Dragon: Learning and Modelling Iterable Ordered Optional Adjuncts

ERIC Educational Resources Information Center

Fowlie, Meaghan

2017-01-01

Adjuncts and arguments exhibit different syntactic behaviours, but modelling this difference in minimalist syntax is challenging: on the one hand, adjuncts differ from arguments in that they are optional, transparent, and iterable, but on the other hand they are often strictly ordered, reflecting the kind of strict selection seen in argument…

Model-based iterative learning control of Parkinsonian state in thalamic relay neuron

NASA Astrophysics Data System (ADS)

Liu, Chen; Wang, Jiang; Li, Huiyan; Xue, Zhiqin; Deng, Bin; Wei, Xile

2014-09-01

Although the beneficial effects of chronic deep brain stimulation on Parkinson's disease motor symptoms are now largely confirmed, the underlying mechanisms behind deep brain stimulation remain unclear and under debate. Hence, the selection of stimulation parameters is full of challenges. Additionally, due to the complexity of neural system, together with omnipresent noises, the accurate model of thalamic relay neuron is unknown. Thus, the iterative learning control of the thalamic relay neuron's Parkinsonian state based on various variables is presented. Combining the iterative learning control with typical proportional-integral control algorithm, a novel and efficient control strategy is proposed, which does not require any particular knowledge on the detailed physiological characteristics of cortico-basal ganglia-thalamocortical loop and can automatically adjust the stimulation parameters. Simulation results demonstrate the feasibility of the proposed control strategy to restore the fidelity of thalamic relay in the Parkinsonian condition. Furthermore, through changing the important parameter—the maximum ionic conductance densities of low-threshold calcium current, the dominant characteristic of the proposed method which is independent of the accurate model can be further verified.

Introducing soft systems methodology plus (SSM+): why we need it and what it can contribute.

PubMed

Braithwaite, Jeffrey; Hindle, Don; Iedema, Rick; Westbrook, Johanna I

2002-01-01

There are many complicated and seemingly intractable problems in the health care sector. Past ways to address them have involved political responses, economic restructuring, biomedical and scientific studies, and managerialist or business-oriented tools. Few methods have enabled us to develop a systematic response to problems. Our version of soft systems methodology, SSM+, seems to improve problem solving processes by providing an iterative, staged framework that emphasises collaborative learning and systems redesign involving both technical and cultural fixes.

Proceedings from the U.S. Army Corps of Engineers (USACE) and the National Oceanic and Atmospheric Administration (NOAA) Engineering With Nature Workshop

DTIC Science & Technology

2017-03-01

opportunities emerged. It will be essential to capture and share lessons learned as the two organizations plan and implement selected EWN projects...their top five or six opportunities and subsequently selected the two highest priorities. Each of the three breakout groups then worked together to...will ensure agency buy-in, establish local reference sites, and promote EWN principles. Site selection will include an iterative process that factors

Towards a Better Distributed Framework for Learning Big Data

DTIC Science & Technology

2017-06-14

UNLIMITED: PB Public Release 13. SUPPLEMENTARY NOTES 14. ABSTRACT This work aimed at solving issues in distributed machine learning. The PI’s team proposed...communication load. Finally, the team proposed the parallel least-squares policy iteration (parallel LSPI) to parallelize a reinforcement policy learning. 15

A linear recurrent kernel online learning algorithm with sparse updates.

PubMed

Fan, Haijin; Song, Qing

2014-02-01

In this paper, we propose a recurrent kernel algorithm with selectively sparse updates for online learning. The algorithm introduces a linear recurrent term in the estimation of the current output. This makes the past information reusable for updating of the algorithm in the form of a recurrent gradient term. To ensure that the reuse of this recurrent gradient indeed accelerates the convergence speed, a novel hybrid recurrent training is proposed to switch on or off learning the recurrent information according to the magnitude of the current training error. Furthermore, the algorithm includes a data-dependent adaptive learning rate which can provide guaranteed system weight convergence at each training iteration. The learning rate is set as zero when the training violates the derived convergence conditions, which makes the algorithm updating process sparse. Theoretical analyses of the weight convergence are presented and experimental results show the good performance of the proposed algorithm in terms of convergence speed and estimation accuracy. Copyright © 2013 Elsevier Ltd. All rights reserved.

Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View

PubMed Central

2016-01-01

Background As more and more researchers are turning to big data for new opportunities of biomedical discoveries, machine learning models, as the backbone of big data analysis, are mentioned more often in biomedical journals. However, owing to the inherent complexity of machine learning methods, they are prone to misuse. Because of the flexibility in specifying machine learning models, the results are often insufficiently reported in research articles, hindering reliable assessment of model validity and consistent interpretation of model outputs. Objective To attain a set of guidelines on the use of machine learning predictive models within clinical settings to make sure the models are correctly applied and sufficiently reported so that true discoveries can be distinguished from random coincidence. Methods A multidisciplinary panel of machine learning experts, clinicians, and traditional statisticians were interviewed, using an iterative process in accordance with the Delphi method. Results The process produced a set of guidelines that consists of (1) a list of reporting items to be included in a research article and (2) a set of practical sequential steps for developing predictive models. Conclusions A set of guidelines was generated to enable correct application of machine learning models and consistent reporting of model specifications and results in biomedical research. We believe that such guidelines will accelerate the adoption of big data analysis, particularly with machine learning methods, in the biomedical research community. PMID:27986644

Improving Group Work Practices in Teaching Life Sciences: Trialogical Learning

NASA Astrophysics Data System (ADS)

Tammeorg, Priit; Mykkänen, Anna; Rantamäki, Tomi; Lakkala, Minna; Muukkonen, Hanni

2017-08-01

Trialogical learning, a collaborative and iterative knowledge creation process using real-life artefacts or problems, familiarizes students with working life environments and aims to teach skills required in the professional world. We target one of the major limitation factors for optimal trialogical learning in university settings, inefficient group work. We propose a course design combining effective group working practices with trialogical learning principles in life sciences. We assess the usability of our design in (a) a case study on crop science education and (b) a questionnaire for university teachers in life science fields. Our approach was considered useful and supportive of the learning process by all the participants in the case study: the students, the stakeholders and the facilitator. Correspondingly, a group of university teachers expressed that the trialogical approach and the involvement of stakeholders could promote efficient learning. In our case in life sciences, we identified the key issues in facilitating effective group work to be the design of meaningful tasks and the allowance of sufficient time to take action based on formative feedback. Even though trialogical courses can be time consuming, the experience of applying knowledge in real-life cases justifies using the approach, particularly for students just about to enter their professional careers.

Electronic patient-reported data capture as a foundation of rapid learning cancer care.

PubMed

Abernethy, Amy P; Ahmad, Asif; Zafar, S Yousuf; Wheeler, Jane L; Reese, Jennifer Barsky; Lyerly, H Kim

2010-06-01

"Rapid learning healthcare" presents a new infrastructure to support comparative effectiveness research. By leveraging heterogeneous datasets (eg, clinical, administrative, genomic, registry, and research), health information technology, and sophisticated iterative analyses, rapid learning healthcare provides a real-time framework in which clinical studies can evaluate the relative impact of therapeutic approaches on a diverse array of measures. This article describes an effort, at 1 academic medical center, to demonstrate what rapid learning healthcare might look like in operation. The article describes the process of developing and testing the components of this new model of integrated clinical/research function, with the pilot site being an academic oncology clinic and with electronic patient-reported outcomes (ePROs) being the foundational dataset. Steps included: feasibility study of the ePRO system; validation study of ePRO collection across 3 cancers; linking ePRO and other datasets; implementation; stakeholder alignment and buy in, and; demonstration through use cases. Two use cases are presented; participants were metastatic breast cancer (n = 65) and gastrointestinal cancer (n = 113) patients at 2 academic medical centers. (1) Patient-reported symptom data were collected with tablet computers; patients with breast and gastrointestinal cancer indicated high levels of sexual distress, which prompted multidisciplinary response, design of an intervention, and successful application for funding to study the intervention's impact. (2) The system evaluated the longitudinal impact of a psychosocial care program provided to patients with breast cancer. Participants used tablet computers to complete PRO surveys; data indicated significant impact on psychosocial outcomes, notably distress and despair, despite advanced disease. Results return to the clinic, allowing iterative update and evaluation. An ePRO-based rapid learning cancer clinic is feasible, providing real-time research-quality data to support comparative effectiveness research.

Why and how Mastering an Incremental and Iterative Software Development Process

NASA Astrophysics Data System (ADS)

Dubuc, François; Guichoux, Bernard; Cormery, Patrick; Mescam, Jean Christophe

2004-06-01

One of the key issues regularly mentioned in the current software crisis of the space domain is related to the software development process that must be performed while the system definition is not yet frozen. This is especially true for complex systems like launchers or space vehicles.Several more or less mature solutions are under study by EADS SPACE Transportation and are going to be presented in this paper. The basic principle is to develop the software through an iterative and incremental process instead of the classical waterfall approach, with the following advantages:- It permits systematic management and incorporation of requirements changes over the development cycle with a minimal cost. As far as possible the most dimensioning requirements are analyzed and developed in priority for validating very early the architecture concept without the details.- A software prototype is very quickly available. It improves the communication between system and software teams, as it enables to check very early and efficiently the common understanding of the system requirements.- It allows the software team to complete a whole development cycle very early, and thus to become quickly familiar with the software development environment (methodology, technology, tools...). This is particularly important when the team is new, or when the environment has changed since the previous development. Anyhow, it improves a lot the learning curve of the software team.These advantages seem very attractive, but mastering efficiently an iterative development process is not so easy and induces a lot of difficulties such as:- How to freeze one configuration of the system definition as a development baseline, while most of thesystem requirements are completely and naturally unstable?- How to distinguish stable/unstable and dimensioning/standard requirements?- How to plan the development of each increment?- How to link classical waterfall development milestones with an iterative approach: when should theclassical reviews be performed: Software Specification Review? Preliminary Design Review? CriticalDesign Review? Code Review? Etc...Several solutions envisaged or already deployed by EADS SPACE Transportation will be presented, both from a methodological and technological point of view:- How the MELANIE EADS ST internal methodology improves the concurrent engineering activitiesbetween GNC, software and simulation teams in a very iterative and reactive way.- How the CMM approach can help by better formalizing Requirements Management and Planningprocesses.- How the Automatic Code Generation with "certified" tools (SCADE) can still dramatically shorten thedevelopment cycle.Then the presentation will conclude by showing an evaluation of the cost and planning reduction based on a pilot application by comparing figures on two similar projects: one with the classical waterfall process, the other one with an iterative and incremental approach.

Scalable Iterative Classification for Sanitizing Large-Scale Datasets

PubMed Central

Li, Bo; Vorobeychik, Yevgeniy; Li, Muqun; Malin, Bradley

2017-01-01

Cheap ubiquitous computing enables the collection of massive amounts of personal data in a wide variety of domains. Many organizations aim to share such data while obscuring features that could disclose personally identifiable information. Much of this data exhibits weak structure (e.g., text), such that machine learning approaches have been developed to detect and remove identifiers from it. While learning is never perfect, and relying on such approaches to sanitize data can leak sensitive information, a small risk is often acceptable. Our goal is to balance the value of published data and the risk of an adversary discovering leaked identifiers. We model data sanitization as a game between 1) a publisher who chooses a set of classifiers to apply to data and publishes only instances predicted as non-sensitive and 2) an attacker who combines machine learning and manual inspection to uncover leaked identifying information. We introduce a fast iterative greedy algorithm for the publisher that ensures a low utility for a resource-limited adversary. Moreover, using five text data sets we illustrate that our algorithm leaves virtually no automatically identifiable sensitive instances for a state-of-the-art learning algorithm, while sharing over 93% of the original data, and completes after at most 5 iterations. PMID:28943741

Generating Knowledge in a Learning Study--From the Perspective of a Teacher Researcher

ERIC Educational Resources Information Center

Thorsten, Anja

2017-01-01

The purpose of this article is to discuss and describe how a clinical research method can be used to generate knowledge about teaching and learning. This will be addressed from a teacher researcher's perspective, taking a conducted Learning Study as the departure. Learning Study is an interventionist, iterative and collaborative research approach,…

A Fast, Minimalist Search Tool for Remote Sensing Data

NASA Astrophysics Data System (ADS)

Lynnes, C. S.; Macharrie, P. G.; Elkins, M.; Joshi, T.; Fenichel, L. H.

2005-12-01

We present a tool that emphasizes speed and simplicity in searching remotely sensed Earth Science data. The tool, nicknamed "Mirador" (Spanish for a scenic overlook), provides only four freetext search form fields, for Keywords, Location, Data Start and Data Stop. This contrasts with many current Earth Science search tools that offer highly structured interfaces in order to ensure precise, non-zero results. The disadvantages of the structured approach lie in its complexity and resultant learning curve, as well as the time it takes to formulate and execute the search, thus discouraging iterative discovery. On the other hand, the success of the basic Google search interface shows that many users are willing to forgo high search precision if the search process is fast enough to enable rapid iteration. Therefore, we employ several methods to increase the speed of search formulation and execution. Search formulation is expedited by the minimalist search form, with only one required field. Also, a gazetteer enables the use of geographic terms as shorthand for latitude/longitude coordinates. The search execution is accelerated by initially presenting dataset results (returned from a Google Mini appliance) with an estimated number of "hits" for each dataset based on the user's space-time constraints. The more costly file-level search is executed against a PostGres database only when the user "drills down", and then covering only the fraction of the time period needed to return the next page of results. The simplicity of the search form makes the tool easy to learn and use, and the speed of the searches enables an iterative form of data discovery.

Teachers Supporting Teachers in Urban Schools: What Iterative Research Designs Can Teach Us.

PubMed

Shernoff, Elisa S; Maríñez-Lora, Ane M; Frazier, Stacy L; Jakobsons, Lara J; Atkins, Marc S; Bonner, Deborah

2011-12-01

Despite alarming rates and negative consequences associated with urban teacher attrition, mentoring programs often fail to target the strongest predictors of attrition: effectiveness around classroom management and engaging learners; and connectedness to colleagues. Using a mixed-method iterative development framework, we highlight the process of developing and evaluating the feasibility of a multi-component professional development model for urban early career teachers. The model includes linking novices with peer-nominated key opinion leader teachers and an external coach who work together to (1) provide intensive support in evidence-based practices for classroom management and engaging learners, and (2) connect new teachers with their larger network of colleagues. Fidelity measures and focus group data illustrated varying attendance rates throughout the school year and that although seminars and professional learning communities were delivered as intended, adaptations to enhance the relevance, authenticity, level, and type of instrumental support were needed. Implications for science and practice are discussed.

Cluster analysis of polymers using laser-induced breakdown spectroscopy with K-means

NASA Astrophysics Data System (ADS)

Yangmin, GUO; Yun, TANG; Yu, DU; Shisong, TANG; Lianbo, GUO; Xiangyou, LI; Yongfeng, LU; Xiaoyan, ZENG

2018-06-01

Laser-induced breakdown spectroscopy (LIBS) combined with K-means algorithm was employed to automatically differentiate industrial polymers under atmospheric conditions. The unsupervised learning algorithm K-means were utilized for the clustering of LIBS dataset measured from twenty kinds of industrial polymers. To prevent the interference from metallic elements, three atomic emission lines (C I 247.86 nm , H I 656.3 nm, and O I 777.3 nm) and one molecular line C–N (0, 0) 388.3 nm were used. The cluster analysis results were obtained through an iterative process. The Davies–Bouldin index was employed to determine the initial number of clusters. The average relative standard deviation values of characteristic spectral lines were used as the iterative criterion. With the proposed approach, the classification accuracy for twenty kinds of industrial polymers achieved 99.6%. The results demonstrated that this approach has great potential for industrial polymers recycling by LIBS.

The Effect of Iteration on the Design Performance of Primary School Children

ERIC Educational Resources Information Center

Looijenga, Annemarie; Klapwijk, Remke; de Vries, Marc J.

2015-01-01

Iteration during the design process is an essential element. Engineers optimize their design by iteration. Research on iteration in Primary Design Education is however scarce; possibly teachers believe they do not have enough time for iteration in daily classroom practices. Spontaneous playing behavior of children indicates that iteration fits in…

Virtual reality cataract surgery training: learning curves and concurrent validity.

PubMed

Selvander, Madeleine; Åsman, Peter

2012-08-01

To investigate initial learning curves on a virtual reality (VR) eye surgery simulator and whether achieved skills are transferable between tasks. Thirty-five medical students were randomized to complete ten iterations on either the VR Caspulorhexis module (group A) or the Cataract navigation training module (group B) and then two iterations on the other module. Learning curves were compared between groups. The second Capsulorhexis video was saved and evaluated with the performance rating tool Objective Structured Assessment of Cataract Surgical Skill (OSACSS). The students' stereoacuity was examined. Both groups demonstrated significant improvements in performance over the 10 iterations: group A for all parameters analysed including score (p < 0.0001), time (p < 0.0001) and corneal damage (p = 0.0003), group B for time (p < 0.0001), corneal damage (p < 0.0001) but not for score (p = 0.752). Training on one module did not improve performance on the other. Capsulorhexis score correlated significantly with evaluation of the videos using the OSACSS performance rating tool. For stereoacuity < and ≥120 seconds of arc, sum of both modules' second iteration score was 73.5 and 41.0, respectively (p = 0.062). An initial rapid improvement in performance on a simulator with repeated practice was shown. For capsulorhexis, 10 iterations with only simulator feedback are not enough to reach a plateau for overall score. Skills transfer between modules was not found suggesting benefits from training on both modules. Stereoacuity may be of importance in the recruitment and training of new cataract surgeons. Additional studies are needed to investigate this further. Concurrent validity was found for Capsulorhexis module. © 2010 The Authors. Acta Ophthalmologica © 2010 Acta Ophthalmologica Scandinavica Foundation.

"Ask Ernö": a self-learning tool for assignment and prediction of nuclear magnetic resonance spectra.

PubMed

Castillo, Andrés M; Bernal, Andrés; Dieden, Reiner; Patiny, Luc; Wist, Julien

2016-01-01

We present "Ask Ernö", a self-learning system for the automatic analysis of NMR spectra, consisting of integrated chemical shift assignment and prediction tools. The output of the automatic assignment component initializes and improves a database of assigned protons that is used by the chemical shift predictor. In turn, the predictions provided by the latter facilitate improvement of the assignment process. Iteration on these steps allows Ask Ernö to improve its ability to assign and predict spectra without any prior knowledge or assistance from human experts. This concept was tested by training such a system with a dataset of 2341 molecules and their (1)H-NMR spectra, and evaluating the accuracy of chemical shift predictions on a test set of 298 partially assigned molecules (2007 assigned protons). After 10 iterations, Ask Ernö was able to decrease its prediction error by 17 %, reaching an average error of 0.265 ppm. Over 60 % of the test chemical shifts were predicted within 0.2 ppm, while only 5 % still presented a prediction error of more than 1 ppm. Ask Ernö introduces an innovative approach to automatic NMR analysis that constantly learns and improves when provided with new data. Furthermore, it completely avoids the need for manually assigned spectra. This system has the potential to be turned into a fully autonomous tool able to compete with the best alternatives currently available.Graphical abstractSelf-learning loop. Any progress in the prediction (forward problem) will improve the assignment ability (reverse problem) and vice versa.

Infant/Toddler Environment Rating Scale (ITERS-3). Third Edition

ERIC Educational Resources Information Center

Harms, Thelma; Cryer, Debby; Clifford, Richard M.; Yazejian, Noreen

2017-01-01

Building on extensive feedback from the field as well as vigorous new research on how best to support infant and toddler development and learning, the authors have revised and updated the widely used "Infant/Toddler Environment Rating Scale." ITERS-3 is the next-generation assessment tool for use in center-based child care programs for…

ITER Central Solenoid Module Fabrication

DOE Office of Scientific and Technical Information (OSTI.GOV)

Smith, John

The fabrication of the modules for the ITER Central Solenoid (CS) has started in a dedicated production facility located in Poway, California, USA. The necessary tools have been designed, built, installed, and tested in the facility to enable the start of production. The current schedule has first module fabrication completed in 2017, followed by testing and subsequent shipment to ITER. The Central Solenoid is a key component of the ITER tokamak providing the inductive voltage to initiate and sustain the plasma current and to position and shape the plasma. The design of the CS has been a collaborative effort betweenmore » the US ITER Project Office (US ITER), the international ITER Organization (IO) and General Atomics (GA). GA’s responsibility includes: completing the fabrication design, developing and qualifying the fabrication processes and tools, and then completing the fabrication of the seven 110 tonne CS modules. The modules will be shipped separately to the ITER site, and then stacked and aligned in the Assembly Hall prior to insertion in the core of the ITER tokamak. A dedicated facility in Poway, California, USA has been established by GA to complete the fabrication of the seven modules. Infrastructure improvements included thick reinforced concrete floors, a diesel generator for backup power, along with, cranes for moving the tooling within the facility. The fabrication process for a single module requires approximately 22 months followed by five months of testing, which includes preliminary electrical testing followed by high current (48.5 kA) tests at 4.7K. The production of the seven modules is completed in a parallel fashion through ten process stations. The process stations have been designed and built with most stations having completed testing and qualification for carrying out the required fabrication processes. The final qualification step for each process station is achieved by the successful production of a prototype coil. Fabrication of the first ITER module is in progress. The seven modules will be individually shipped to Cadarache, France upon their completion. This paper describes the processes and status of the fabrication of the CS Modules for ITER.« less

Including information technology project management in the nursing informatics curriculum.

PubMed

Sockolow, Paulina; Bowles, Kathryn H

2008-01-01

Project management is a critical skill for nurse informaticists who are in prominent roles developing and implementing clinical information systems. It should be included in the nursing informatics curriculum, as evidenced by its inclusion in informatics competencies and surveys of important skills for informaticists. The University of Pennsylvania School of Nursing includes project management in two of the four courses in the master's level informatics minor. Course content includes the phases of the project management process; the iterative unified process methodology; and related systems analysis and project management skills. During the introductory course, students learn about the project plan, requirements development, project feasibility, and executive summary documents. In the capstone course, students apply the system development life cycle and project management skills during precepted informatics projects. During this in situ experience, students learn, the preceptors benefit, and the institution better prepares its students for the real world.

Learning outcomes for communication skills across the health professions: a systematic literature review and qualitative synthesis

PubMed Central

Denniston, Charlotte; Molloy, Elizabeth; Woodward-Kron, Robyn; Keating, Jennifer L

2017-01-01

Objective The aim of this study was to identify and analyse communication skills learning outcomes via a systematic review and present results in a synthesised list. Summarised results inform educators and researchers in communication skills teaching and learning across health professions. Design Systematic review and qualitative synthesis. Methods A systematic search of five databases (MEDLINE, PsycINFO, ERIC, CINAHL plus and Scopus), from first records until August 2016, identified published learning outcomes for communication skills in health professions education. Extracted data were analysed through an iterative process of qualitative synthesis. This process was guided by principles of person centredness and an a priori decision guide. Results 168 papers met the eligibility criteria; 1669 individual learning outcomes were extracted and refined using qualitative synthesis. A final refined set of 205 learning outcomes were constructed and are presented in 4 domains that include: (1) knowledge (eg, describe the importance of communication in healthcare), (2) content skills (eg, explore a healthcare seeker's motivation for seeking healthcare),( 3) process skills (eg, respond promptly to a communication partner's questions) and (4) perceptual skills (eg, reflect on own ways of expressing emotion). Conclusions This study provides a list of 205 communication skills learning outcomes that provide a foundation for further research and educational design in communication education across the health professions. Areas for future investigation include greater patient involvement in communication skills education design and further identification of learning outcomes that target knowledge and perceptual skills. This work may also prompt educators to be cognisant of the quality and scope of the learning outcomes they design and their application as goals for learning. PMID:28389493

Learning outcomes for communication skills across the health professions: a systematic literature review and qualitative synthesis.

PubMed

Denniston, Charlotte; Molloy, Elizabeth; Nestel, Debra; Woodward-Kron, Robyn; Keating, Jennifer L

2017-04-07

The aim of this study was to identify and analyse communication skills learning outcomes via a systematic review and present results in a synthesised list. Summarised results inform educators and researchers in communication skills teaching and learning across health professions. Systematic review and qualitative synthesis. A systematic search of five databases (MEDLINE, PsycINFO, ERIC, CINAHL plus and Scopus), from first records until August 2016, identified published learning outcomes for communication skills in health professions education. Extracted data were analysed through an iterative process of qualitative synthesis. This process was guided by principles of person centredness and an a priori decision guide. 168 papers met the eligibility criteria; 1669 individual learning outcomes were extracted and refined using qualitative synthesis. A final refined set of 205 learning outcomes were constructed and are presented in 4 domains that include: (1) knowledge (eg, describe the importance of communication in healthcare), (2) content skills (eg, explore a healthcare seeker's motivation for seeking healthcare),( 3) process skills (eg, respond promptly to a communication partner's questions) and (4) perceptual skills (eg, reflect on own ways of expressing emotion). This study provides a list of 205 communication skills learning outcomes that provide a foundation for further research and educational design in communication education across the health professions. Areas for future investigation include greater patient involvement in communication skills education design and further identification of learning outcomes that target knowledge and perceptual skills. This work may also prompt educators to be cognisant of the quality and scope of the learning outcomes they design and their application as goals for learning. 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/.

Deep Learning: A Primer for Radiologists.

PubMed

Chartrand, Gabriel; Cheng, Phillip M; Vorontsov, Eugene; Drozdzal, Michal; Turcotte, Simon; Pal, Christopher J; Kadoury, Samuel; Tang, An

2017-01-01

Deep learning is a class of machine learning methods that are gaining success and attracting interest in many domains, including computer vision, speech recognition, natural language processing, and playing games. Deep learning methods produce a mapping from raw inputs to desired outputs (eg, image classes). Unlike traditional machine learning methods, which require hand-engineered feature extraction from inputs, deep learning methods learn these features directly from data. With the advent of large datasets and increased computing power, these methods can produce models with exceptional performance. These models are multilayer artificial neural networks, loosely inspired by biologic neural systems. Weighted connections between nodes (neurons) in the network are iteratively adjusted based on example pairs of inputs and target outputs by back-propagating a corrective error signal through the network. For computer vision tasks, convolutional neural networks (CNNs) have proven to be effective. Recently, several clinical applications of CNNs have been proposed and studied in radiology for classification, detection, and segmentation tasks. This article reviews the key concepts of deep learning for clinical radiologists, discusses technical requirements, describes emerging applications in clinical radiology, and outlines limitations and future directions in this field. Radiologists should become familiar with the principles and potential applications of deep learning in medical imaging. © RSNA, 2017.

Becoming an expert carer: the process of family carers learning to manage technical health procedures at home.

PubMed

McDonald, Janet; McKinlay, Eileen; Keeling, Sally; Levack, William

2016-09-01

To describe the learning process of family carers who manage technical health procedures (such as enteral tube feeding, intravenous therapy, dialysis or tracheostomy care) at home. Increasingly, complex procedures are being undertaken at home but little attention has been paid to the experiences of family carers who manage such procedures. Grounded theory, following Charmaz's constructivist approach. Interviews with 26 family carers who managed technical health procedures and 15 health professionals who taught carers such procedures. Data collection took place in New Zealand over 19 months during 2011-2013. Grounded theory procedures of iterative data collection, coding and analysis were followed, with the gradual development of theoretical ideas. The learning journey comprised three phases: (1) an initial, concentrated period of training; (2) novice carers taking responsibility for day-to-day care of procedures while continuing their learning; and (3) with time, experience and ongoing self-directed learning, the development of expertise. Teaching and support by health professionals (predominantly nurses) was focussed on the initial phase, but carers' learning continued throughout, developed through their own experience and using additional sources of information (notably the Internet and other carers). Further work is needed to determine the best educational process for carers, including where to locate training, who should teach them, optimal teaching methods and how structured or individualized teaching should be. Supporting carers well also benefits patient care. © 2016 John Wiley & Sons Ltd.

Oxytocin attenuates trust as a subset of more general reinforcement learning, with altered reward circuit functional connectivity in males.

PubMed

Ide, Jaime S; Nedic, Sanja; Wong, Kin F; Strey, Shmuel L; Lawson, Elizabeth A; Dickerson, Bradford C; Wald, Lawrence L; La Camera, Giancarlo; Mujica-Parodi, Lilianne R

2018-07-01

Oxytocin (OT) is an endogenous neuropeptide that, while originally thought to promote trust, has more recently been found to be context-dependent. Here we extend experimental paradigms previously restricted to de novo decision-to-trust, to a more realistic environment in which social relationships evolve in response to iterative feedback over twenty interactions. In a randomized, double blind, placebo-controlled within-subject/crossover experiment of human adult males, we investigated the effects of a single dose of intranasal OT (40 IU) on Bayesian expectation updating and reinforcement learning within a social context, with associated brain circuit dynamics. Subjects participated in a neuroeconomic task (Iterative Trust Game) designed to probe iterative social learning while their brains were scanned using ultra-high field (7T) fMRI. We modeled each subject's behavior using Bayesian updating of belief-states ("willingness to trust") as well as canonical measures of reinforcement learning (learning rate, inverse temperature). Behavioral trajectories were then used as regressors within fMRI activation and connectivity analyses to identify corresponding brain network functionality affected by OT. Behaviorally, OT reduced feedback learning, without bias with respect to positive versus negative reward. Neurobiologically, reduced learning under OT was associated with muted communication between three key nodes within the reward circuit: the orbitofrontal cortex, amygdala, and lateral (limbic) habenula. Our data suggest that OT, rather than inspiring feelings of generosity, instead attenuates the brain's encoding of prediction error and therefore its ability to modulate pre-existing beliefs. This effect may underlie OT's putative role in promoting what has typically been reported as 'unjustified trust' in the face of information that suggests likely betrayal, while also resolving apparent contradictions with regard to OT's context-dependent behavioral effects. Copyright © 2018 Elsevier Inc. All rights reserved.

Advancing Personalized Learning through the Iterative Application of Innovation Science

ERIC Educational Resources Information Center

Redding, Sam; Twyman, Janet; Murphy, Marilyn

2016-01-01

The promise of personalized learning excites many educators, and schools are wondering how best to introduce it and how they know when they have achieved it. Rather than thinking of personalized learning as an "it" (i.e., a program that is either present or not), we might think of it as an approach to teaching and learning that has many…

An analysis dictionary learning algorithm under a noisy data model with orthogonality constraint.

PubMed

Zhang, Ye; Yu, Tenglong; Wang, Wenwu

2014-01-01

Two common problems are often encountered in analysis dictionary learning (ADL) algorithms. The first one is that the original clean signals for learning the dictionary are assumed to be known, which otherwise need to be estimated from noisy measurements. This, however, renders a computationally slow optimization process and potentially unreliable estimation (if the noise level is high), as represented by the Analysis K-SVD (AK-SVD) algorithm. The other problem is the trivial solution to the dictionary, for example, the null dictionary matrix that may be given by a dictionary learning algorithm, as discussed in the learning overcomplete sparsifying transform (LOST) algorithm. Here we propose a novel optimization model and an iterative algorithm to learn the analysis dictionary, where we directly employ the observed data to compute the approximate analysis sparse representation of the original signals (leading to a fast optimization procedure) and enforce an orthogonality constraint on the optimization criterion to avoid the trivial solutions. Experiments demonstrate the competitive performance of the proposed algorithm as compared with three baselines, namely, the AK-SVD, LOST, and NAAOLA algorithms.

Osteoarthritis Severity Determination using Self Organizing Map Based Gabor Kernel

NASA Astrophysics Data System (ADS)

Anifah, L.; Purnomo, M. H.; Mengko, T. L. R.; Purnama, I. K. E.

2018-02-01

The number of osteoarthritis patients in Indonesia is enormous, so early action is needed in order for this disease to be handled. The aim of this paper to determine osteoarthritis severity based on x-ray image template based on gabor kernel. This research is divided into 3 stages, the first step is image processing that is using gabor kernel. The second stage is the learning stage, and the third stage is the testing phase. The image processing stage is by normalizing the image dimension to be template to 50 □ 200 image. Learning stage is done with parameters initial learning rate of 0.5 and the total number of iterations of 1000. The testing stage is performed using the weights generated at the learning stage. The testing phase has been done and the results were obtained. The result shows KL-Grade 0 has an accuracy of 36.21%, accuracy for KL-Grade 2 is 40,52%, while accuracy for KL-Grade 2 and KL-Grade 3 are 15,52%, and 25,86%. The implication of this research is expected that this research as decision support system for medical practitioners in determining KL-Grade on X-ray images of knee osteoarthritis.

We Are the Game Changers: An Open Gaming Literacy Programme

ERIC Educational Resources Information Center

Arnab, Sylvester; Morini, Luca; Green, Kate; Masters, Alex; Bellamy-Woods, Tyrone

2017-01-01

This paper discusses the first iteration of Game Changers Programme hosted by Coventry University's Disruptive Media Learning Lab (DMLL), an open game design initiative. The Programme had the goal of facilitating new models of teaching and learning, new practices in cross-faculty learning/ collaboration to make game design and development more…

Complexity-Based Learning and Teaching: A Case Study in Higher Education

ERIC Educational Resources Information Center

Fabricatore, Carlo; López, María Ximena

2014-01-01

This paper presents a learning and teaching strategy based on complexity science and explores its impacts on a higher education game design course. The strategy aimed at generating conditions fostering individual and collective learning in educational complex adaptive systems, and led the design of the course through an iterative and adaptive…

Rapid Prototyping of Mobile Learning Games

ERIC Educational Resources Information Center

Federley, Maija; Sorsa, Timo; Paavilainen, Janne; Boissonnier, Kimo; Seisto, Anu

2014-01-01

This position paper presents the first results of an on-going project, in which we explore rapid prototyping method to efficiently produce digital learning solutions that are commercially viable. In this first phase, rapid game prototyping and an iterative approach was tested as a quick and efficient way to create learning games and to evaluate…

Online Learning Tools for Middle School Science: Lessons Learned from a Design-Based Research Project

ERIC Educational Resources Information Center

Terrazas-Arellanes, Fatima E.; Knox, Carolyn; Strycker, Lisa A.; Walden, Emily D.

2017-01-01

This article reports on how design-based research methodology was used to guide a line of intervention research that developed, implemented, revised, and evaluated online learning science curricula for middle school students, including general education students and English language learners (primarily of Hispanic origin). The iterative,…

A General Iterative Shrinkage and Thresholding Algorithm for Non-convex Regularized Optimization Problems.

PubMed

Gong, Pinghua; Zhang, Changshui; Lu, Zhaosong; Huang, Jianhua Z; Ye, Jieping

2013-01-01

Non-convex sparsity-inducing penalties have recently received considerable attentions in sparse learning. Recent theoretical investigations have demonstrated their superiority over the convex counterparts in several sparse learning settings. However, solving the non-convex optimization problems associated with non-convex penalties remains a big challenge. A commonly used approach is the Multi-Stage (MS) convex relaxation (or DC programming), which relaxes the original non-convex problem to a sequence of convex problems. This approach is usually not very practical for large-scale problems because its computational cost is a multiple of solving a single convex problem. In this paper, we propose a General Iterative Shrinkage and Thresholding (GIST) algorithm to solve the nonconvex optimization problem for a large class of non-convex penalties. The GIST algorithm iteratively solves a proximal operator problem, which in turn has a closed-form solution for many commonly used penalties. At each outer iteration of the algorithm, we use a line search initialized by the Barzilai-Borwein (BB) rule that allows finding an appropriate step size quickly. The paper also presents a detailed convergence analysis of the GIST algorithm. The efficiency of the proposed algorithm is demonstrated by extensive experiments on large-scale data sets.

MO-B-BRB-01: Optimize Treatment Planning Process in Clinical Environment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Feng, W.

The radiotherapy treatment planning process has evolved over the years with innovations in treatment planning, treatment delivery and imaging systems. Treatment modality and simulation technologies are also rapidly improving and affecting the planning process. For example, Image-guided-radiation-therapy has been widely adopted for patient setup, leading to margin reduction and isocenter repositioning after simulation. Stereotactic Body radiation therapy (SBRT) and Radiosurgery (SRS) have gradually become the standard of care for many treatment sites, which demand a higher throughput for the treatment plans even if the number of treatments per day remains the same. Finally, simulation, planning and treatment are traditionally sequentialmore » events. However, with emerging adaptive radiotherapy, they are becoming more tightly intertwined, leading to iterative processes. Enhanced efficiency of planning is therefore becoming more critical and poses serious challenge to the treatment planning process; Lean Six Sigma approaches are being utilized increasingly to balance the competing needs for speed and quality. In this symposium we will discuss the treatment planning process and illustrate effective techniques for managing workflow. Topics will include: Planning techniques: (a) beam placement, (b) dose optimization, (c) plan evaluation (d) export to RVS. Planning workflow: (a) import images, (b) Image fusion, (c) contouring, (d) plan approval (e) plan check (f) chart check, (g) sequential and iterative process Influence of upstream and downstream operations: (a) simulation, (b) immobilization, (c) motion management, (d) QA, (e) IGRT, (f) Treatment delivery, (g) SBRT/SRS (h) adaptive planning Reduction of delay between planning steps with Lean systems due to (a) communication, (b) limited resource, (b) contour, (c) plan approval, (d) treatment. Optimizing planning processes: (a) contour validation (b) consistent planning protocol, (c) protocol/template sharing, (d) semi-automatic plan evaluation, (e) quality checklist for error prevention, (f) iterative process, (g) balance of speed and quality Learning Objectives: Gain familiarity with the workflow of modern treatment planning process. Understand the scope and challenges of managing modern treatment planning processes. Gain familiarity with Lean Six Sigma approaches and their implementation in the treatment planning workflow.« less

MO-B-BRB-00: Optimizing the Treatment Planning Process

DOE Office of Scientific and Technical Information (OSTI.GOV)

NONE

The radiotherapy treatment planning process has evolved over the years with innovations in treatment planning, treatment delivery and imaging systems. Treatment modality and simulation technologies are also rapidly improving and affecting the planning process. For example, Image-guided-radiation-therapy has been widely adopted for patient setup, leading to margin reduction and isocenter repositioning after simulation. Stereotactic Body radiation therapy (SBRT) and Radiosurgery (SRS) have gradually become the standard of care for many treatment sites, which demand a higher throughput for the treatment plans even if the number of treatments per day remains the same. Finally, simulation, planning and treatment are traditionally sequentialmore » events. However, with emerging adaptive radiotherapy, they are becoming more tightly intertwined, leading to iterative processes. Enhanced efficiency of planning is therefore becoming more critical and poses serious challenge to the treatment planning process; Lean Six Sigma approaches are being utilized increasingly to balance the competing needs for speed and quality. In this symposium we will discuss the treatment planning process and illustrate effective techniques for managing workflow. Topics will include: Planning techniques: (a) beam placement, (b) dose optimization, (c) plan evaluation (d) export to RVS. Planning workflow: (a) import images, (b) Image fusion, (c) contouring, (d) plan approval (e) plan check (f) chart check, (g) sequential and iterative process Influence of upstream and downstream operations: (a) simulation, (b) immobilization, (c) motion management, (d) QA, (e) IGRT, (f) Treatment delivery, (g) SBRT/SRS (h) adaptive planning Reduction of delay between planning steps with Lean systems due to (a) communication, (b) limited resource, (b) contour, (c) plan approval, (d) treatment. Optimizing planning processes: (a) contour validation (b) consistent planning protocol, (c) protocol/template sharing, (d) semi-automatic plan evaluation, (e) quality checklist for error prevention, (f) iterative process, (g) balance of speed and quality Learning Objectives: Gain familiarity with the workflow of modern treatment planning process. Understand the scope and challenges of managing modern treatment planning processes. Gain familiarity with Lean Six Sigma approaches and their implementation in the treatment planning workflow.« less

MO-B-BRB-03: Systems Engineering Tools for Treatment Planning Process Optimization in Radiation Medicine

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kapur, A.

The radiotherapy treatment planning process has evolved over the years with innovations in treatment planning, treatment delivery and imaging systems. Treatment modality and simulation technologies are also rapidly improving and affecting the planning process. For example, Image-guided-radiation-therapy has been widely adopted for patient setup, leading to margin reduction and isocenter repositioning after simulation. Stereotactic Body radiation therapy (SBRT) and Radiosurgery (SRS) have gradually become the standard of care for many treatment sites, which demand a higher throughput for the treatment plans even if the number of treatments per day remains the same. Finally, simulation, planning and treatment are traditionally sequentialmore » events. However, with emerging adaptive radiotherapy, they are becoming more tightly intertwined, leading to iterative processes. Enhanced efficiency of planning is therefore becoming more critical and poses serious challenge to the treatment planning process; Lean Six Sigma approaches are being utilized increasingly to balance the competing needs for speed and quality. In this symposium we will discuss the treatment planning process and illustrate effective techniques for managing workflow. Topics will include: Planning techniques: (a) beam placement, (b) dose optimization, (c) plan evaluation (d) export to RVS. Planning workflow: (a) import images, (b) Image fusion, (c) contouring, (d) plan approval (e) plan check (f) chart check, (g) sequential and iterative process Influence of upstream and downstream operations: (a) simulation, (b) immobilization, (c) motion management, (d) QA, (e) IGRT, (f) Treatment delivery, (g) SBRT/SRS (h) adaptive planning Reduction of delay between planning steps with Lean systems due to (a) communication, (b) limited resource, (b) contour, (c) plan approval, (d) treatment. Optimizing planning processes: (a) contour validation (b) consistent planning protocol, (c) protocol/template sharing, (d) semi-automatic plan evaluation, (e) quality checklist for error prevention, (f) iterative process, (g) balance of speed and quality Learning Objectives: Gain familiarity with the workflow of modern treatment planning process. Understand the scope and challenges of managing modern treatment planning processes. Gain familiarity with Lean Six Sigma approaches and their implementation in the treatment planning workflow.« less

Analysis of the “naming game” with learning errors in communications

NASA Astrophysics Data System (ADS)

Lou, Yang; Chen, Guanrong

2015-07-01

Naming game simulates the process of naming an objective by a population of agents organized in a certain communication network. By pair-wise iterative interactions, the population reaches consensus asymptotically. We study naming game with communication errors during pair-wise conversations, with error rates in a uniform probability distribution. First, a model of naming game with learning errors in communications (NGLE) is proposed. Then, a strategy for agents to prevent learning errors is suggested. To that end, three typical topologies of communication networks, namely random-graph, small-world and scale-free networks, are employed to investigate the effects of various learning errors. Simulation results on these models show that 1) learning errors slightly affect the convergence speed but distinctively increase the requirement for memory of each agent during lexicon propagation; 2) the maximum number of different words held by the population increases linearly as the error rate increases; 3) without applying any strategy to eliminate learning errors, there is a threshold of the learning errors which impairs the convergence. The new findings may help to better understand the role of learning errors in naming game as well as in human language development from a network science perspective.

Analysis of the "naming game" with learning errors in communications.

PubMed

Lou, Yang; Chen, Guanrong

2015-07-16

Naming game simulates the process of naming an objective by a population of agents organized in a certain communication network. By pair-wise iterative interactions, the population reaches consensus asymptotically. We study naming game with communication errors during pair-wise conversations, with error rates in a uniform probability distribution. First, a model of naming game with learning errors in communications (NGLE) is proposed. Then, a strategy for agents to prevent learning errors is suggested. To that end, three typical topologies of communication networks, namely random-graph, small-world and scale-free networks, are employed to investigate the effects of various learning errors. Simulation results on these models show that 1) learning errors slightly affect the convergence speed but distinctively increase the requirement for memory of each agent during lexicon propagation; 2) the maximum number of different words held by the population increases linearly as the error rate increases; 3) without applying any strategy to eliminate learning errors, there is a threshold of the learning errors which impairs the convergence. The new findings may help to better understand the role of learning errors in naming game as well as in human language development from a network science perspective.

Building a Mobile HIV Prevention App for Men Who Have Sex With Men: An Iterative and Community-Driven Process

PubMed Central

McDougal, Sarah J; Sullivan, Patrick S; Stekler, Joanne D; Stephenson, Rob

2015-01-01

Background Gay, bisexual, and other men who have sex with men (MSM) account for a disproportionate burden of new HIV infections in the United States. Mobile technology presents an opportunity for innovative interventions for HIV prevention. Some HIV prevention apps currently exist; however, it is challenging to encourage users to download these apps and use them regularly. An iterative research process that centers on the community’s needs and preferences may increase the uptake, adherence, and ultimate effectiveness of mobile apps for HIV prevention. Objective The aim of this paper is to provide a case study to illustrate how an iterative community approach to a mobile HIV prevention app can lead to changes in app content to appropriately address the needs and the desires of the target community. Methods In this three-phase study, we conducted focus group discussions (FGDs) with MSM and HIV testing counselors in Atlanta, Seattle, and US rural regions to learn preferences for building a mobile HIV prevention app. We used data from these groups to build a beta version of the app and theater tested it in additional FGDs. A thematic data analysis examined how this approach addressed preferences and concerns expressed by the participants. Results There was an increased willingness to use the app during theater testing than during the first phase of FGDs. Many concerns that were identified in phase one (eg, disagreements about reminders for HIV testing, concerns about app privacy) were considered in building the beta version. Participants perceived these features as strengths during theater testing. However, some disagreements were still present, especially regarding the tone and language of the app. Conclusions These findings highlight the benefits of using an interactive and community-driven process to collect data on app preferences when building a mobile HIV prevention app. Through this process, we learned how to be inclusive of the larger MSM population without marginalizing some app users. Though some issues in phase one were able to be addressed, disagreements still occurred in theater testing. If the app is going to address a large and diverse risk group, we cannot include niche functionality that may offend some of the target population. PMID:27227136

Learning in context: identifying gaps in research on the transfer of medical communication skills to the clinical workplace.

PubMed

van den Eertwegh, Valerie; van Dulmen, Sandra; van Dalen, Jan; Scherpbier, Albert J J A; van der Vleuten, Cees P M

2013-02-01

In order to reduce the inconsistencies of findings and the apparent low transfer of communication skills from training to medical practice, this narrative review identifies some main gaps in research on medical communication skills training and presents insights from theories on learning and transfer to broaden the view for future research. Relevant literature was identified using Pubmed, GoogleScholar, Cochrane database, and Web of Science; and analyzed using an iterative procedure. Research findings on the effectiveness of medical communication training still show inconsistencies and variability. Contemporary theories on learning based on a constructivist paradigm offer the following insights: acquisition of knowledge and skills should be viewed as an ongoing process of exchange between the learner and his environment, so called lifelong learning. This process can neither be atomized nor separated from the context in which it occurs. Four contemporary approaches are presented as examples. The following shift in focus for future research is proposed: beyond isolated single factor effectiveness studies toward constructivist, non-reductionistic studies integrating the context. Future research should investigate how constructivist approaches can be used in the medical context to increase effective learning and transition of communication skills. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Iterative learning control with applications in energy generation, lasers and health care.

PubMed

Rogers, E; Tutty, O R

2016-09-01

Many physical systems make repeated executions of the same finite time duration task. One example is a robot in a factory or warehouse whose task is to collect an object in sequence from a location, transfer it over a finite duration, place it at a specified location or on a moving conveyor and then return for the next one and so on. Iterative learning control was especially developed for systems with this mode of operation and this paper gives an overview of this control design method using relatively recent relevant applications in wind turbines, free-electron lasers and health care, as exemplars to demonstrate its applicability.

Optimism as a Prior Belief about the Probability of Future Reward

PubMed Central

Kalra, Aditi; Seriès, Peggy

2014-01-01

Optimists hold positive a priori beliefs about the future. In Bayesian statistical theory, a priori beliefs can be overcome by experience. However, optimistic beliefs can at times appear surprisingly resistant to evidence, suggesting that optimism might also influence how new information is selected and learned. Here, we use a novel Pavlovian conditioning task, embedded in a normative framework, to directly assess how trait optimism, as classically measured using self-report questionnaires, influences choices between visual targets, by learning about their association with reward progresses. We find that trait optimism relates to an a priori belief about the likelihood of rewards, but not losses, in our task. Critically, this positive belief behaves like a probabilistic prior, i.e. its influence reduces with increasing experience. Contrary to findings in the literature related to unrealistic optimism and self-beliefs, it does not appear to influence the iterative learning process directly. PMID:24853098

CLAss-Specific Subspace Kernel Representations and Adaptive Margin Slack Minimization for Large Scale Classification.

PubMed

Yu, Yinan; Diamantaras, Konstantinos I; McKelvey, Tomas; Kung, Sun-Yuan

2018-02-01

In kernel-based classification models, given limited computational power and storage capacity, operations over the full kernel matrix becomes prohibitive. In this paper, we propose a new supervised learning framework using kernel models for sequential data processing. The framework is based on two components that both aim at enhancing the classification capability with a subset selection scheme. The first part is a subspace projection technique in the reproducing kernel Hilbert space using a CLAss-specific Subspace Kernel representation for kernel approximation. In the second part, we propose a novel structural risk minimization algorithm called the adaptive margin slack minimization to iteratively improve the classification accuracy by an adaptive data selection. We motivate each part separately, and then integrate them into learning frameworks for large scale data. We propose two such frameworks: the memory efficient sequential processing for sequential data processing and the parallelized sequential processing for distributed computing with sequential data acquisition. We test our methods on several benchmark data sets and compared with the state-of-the-art techniques to verify the validity of the proposed techniques.

Implementation of Competency-Based Pharmacy Education (CBPE)

PubMed Central

Koster, Andries; Schalekamp, Tom; Meijerman, Irma

2017-01-01

Implementation of competency-based pharmacy education (CBPE) is a time-consuming, complicated process, which requires agreement on the tasks of a pharmacist, commitment, institutional stability, and a goal-directed developmental perspective of all stakeholders involved. In this article the main steps in the development of a fully-developed competency-based pharmacy curriculum (bachelor, master) are described and tips are given for a successful implementation. After the choice for entering into CBPE is made and a competency framework is adopted (step 1), intended learning outcomes are defined (step 2), followed by analyzing the required developmental trajectory (step 3) and the selection of appropriate assessment methods (step 4). Designing the teaching-learning environment involves the selection of learning activities, student experiences, and instructional methods (step 5). Finally, an iterative process of evaluation and adjustment of individual courses, and the curriculum as a whole, is entered (step 6). Successful implementation of CBPE requires a system of effective quality management and continuous professional development as a teacher. In this article suggestions for the organization of CBPE and references to more detailed literature are given, hoping to facilitate the implementation of CBPE. PMID:28970422

Single-hidden-layer feed-forward quantum neural network based on Grover learning.

PubMed

Liu, Cheng-Yi; Chen, Chein; Chang, Ching-Ter; Shih, Lun-Min

2013-09-01

In this paper, a novel single-hidden-layer feed-forward quantum neural network model is proposed based on some concepts and principles in the quantum theory. By combining the quantum mechanism with the feed-forward neural network, we defined quantum hidden neurons and connected quantum weights, and used them as the fundamental information processing unit in a single-hidden-layer feed-forward neural network. The quantum neurons make a wide range of nonlinear functions serve as the activation functions in the hidden layer of the network, and the Grover searching algorithm outstands the optimal parameter setting iteratively and thus makes very efficient neural network learning possible. The quantum neuron and weights, along with a Grover searching algorithm based learning, result in a novel and efficient neural network characteristic of reduced network, high efficient training and prospect application in future. Some simulations are taken to investigate the performance of the proposed quantum network and the result show that it can achieve accurate learning. Copyright © 2013 Elsevier Ltd. All rights reserved.

Enhancing Teacher Preparation and Improving Faculty Teaching Skills: Lessons Learned from Implementing ``Science That Matters'' a Standards Based Interdisciplinary Science Course Sequence

NASA Astrophysics Data System (ADS)

Potter, Robert; Meisels, Gerry

2005-06-01

In a highly collaborative process we developed an introductory science course sequence to improve science literacy especially among future elementary and middle school education majors. The materials and course features were designed using the results of research on teaching and learning to provide a rigorous, relevant and engaging, standard based science experience. More than ten years of combined planning, development, implementation and assessment of this college science course sequence for nonmajors/future teachers has provided significant insights and success in achieving our goal. This paper describes the history and iterative nature of our ongoing improvements, changes in faculty instructional practice, strategies used to overcome student resistance, significant student learning outcomes, support structures for faculty, and the essential and informative role of assessment in improving the outcomes. Our experience with diverse institutions, students and faculty provides the basis for the lessons we have learned and should be of help to others involved in advancing science education.

MO-B-BRB-02: Maintain the Quality of Treatment Planning for Time-Constraint Cases

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chang, J.

The radiotherapy treatment planning process has evolved over the years with innovations in treatment planning, treatment delivery and imaging systems. Treatment modality and simulation technologies are also rapidly improving and affecting the planning process. For example, Image-guided-radiation-therapy has been widely adopted for patient setup, leading to margin reduction and isocenter repositioning after simulation. Stereotactic Body radiation therapy (SBRT) and Radiosurgery (SRS) have gradually become the standard of care for many treatment sites, which demand a higher throughput for the treatment plans even if the number of treatments per day remains the same. Finally, simulation, planning and treatment are traditionally sequentialmore » events. However, with emerging adaptive radiotherapy, they are becoming more tightly intertwined, leading to iterative processes. Enhanced efficiency of planning is therefore becoming more critical and poses serious challenge to the treatment planning process; Lean Six Sigma approaches are being utilized increasingly to balance the competing needs for speed and quality. In this symposium we will discuss the treatment planning process and illustrate effective techniques for managing workflow. Topics will include: Planning techniques: (a) beam placement, (b) dose optimization, (c) plan evaluation (d) export to RVS. Planning workflow: (a) import images, (b) Image fusion, (c) contouring, (d) plan approval (e) plan check (f) chart check, (g) sequential and iterative process Influence of upstream and downstream operations: (a) simulation, (b) immobilization, (c) motion management, (d) QA, (e) IGRT, (f) Treatment delivery, (g) SBRT/SRS (h) adaptive planning Reduction of delay between planning steps with Lean systems due to (a) communication, (b) limited resource, (b) contour, (c) plan approval, (d) treatment. Optimizing planning processes: (a) contour validation (b) consistent planning protocol, (c) protocol/template sharing, (d) semi-automatic plan evaluation, (e) quality checklist for error prevention, (f) iterative process, (g) balance of speed and quality Learning Objectives: Gain familiarity with the workflow of modern treatment planning process. Understand the scope and challenges of managing modern treatment planning processes. Gain familiarity with Lean Six Sigma approaches and their implementation in the treatment planning workflow.« less

The Iterative Design Process in Research and Development: A Work Experience Paper

NASA Technical Reports Server (NTRS)

Sullivan, George F. III

2013-01-01

The iterative design process is one of many strategies used in new product development. Top-down development strategies, like waterfall development, place a heavy emphasis on planning and simulation. The iterative process, on the other hand, is better suited to the management of small to medium scale projects. Over the past four months, I have worked with engineers at Johnson Space Center on a multitude of electronics projects. By describing the work I have done these last few months, analyzing the factors that have driven design decisions, and examining the testing and verification process, I will demonstrate that iterative design is the obvious choice for research and development projects.

How can we make progress with decision support systems in landscape and river basin management? Lessons learned from a comparative analysis of four different decision support systems.

PubMed

Volk, Martin; Lautenbach, Sven; van Delden, Hedwig; Newham, Lachlan T H; Seppelt, Ralf

2010-12-01

This article analyses the benefits and shortcomings of the recently developed decision support systems (DSS) FLUMAGIS, Elbe-DSS, CatchMODS, and MedAction. The analysis elaborates on the following aspects: (i) application area/decision problem, (ii) stakeholder interaction/users involved, (iii) structure of DSS/model structure, (iv) usage of the DSS, and finally (v) most important shortcomings. On the basis of this analysis, we formulate four criteria that we consider essential for the successful use of DSS in landscape and river basin management. The criteria relate to (i) system quality, (ii) user support and user training, (iii) perceived usefulness and (iv) user satisfaction. We can show that the availability of tools and technologies for DSS in landscape and river basin management is good to excellent. However, our investigations indicate that several problems have to be tackled. First of all, data availability and homogenisation, uncertainty analysis and uncertainty propagation and problems with model integration require further attention. Furthermore, the appropriate and methodological stakeholder interaction and the definition of 'what end-users really need and want' have been documented as general shortcomings of all four examples of DSS. Thus, we propose an iterative development process that enables social learning of the different groups involved in the development process, because it is easier to design a DSS for a group of stakeholders who actively participate in an iterative process. We also identify two important lines of further development in DSS: the use of interactive visualization tools and the methodology of optimization to inform scenario elaboration and evaluate trade-offs among environmental measures and management alternatives.

How Can We Make Progress with Decision Support Systems in Landscape and River Basin Management? Lessons Learned from a Comparative Analysis of Four Different Decision Support Systems

NASA Astrophysics Data System (ADS)

Volk, Martin; Lautenbach, Sven; van Delden, Hedwig; Newham, Lachlan T. H.; Seppelt, Ralf

2010-12-01

This article analyses the benefits and shortcomings of the recently developed decision support systems (DSS) FLUMAGIS, Elbe-DSS, CatchMODS, and MedAction. The analysis elaborates on the following aspects: (i) application area/decision problem, (ii) stakeholder interaction/users involved, (iii) structure of DSS/model structure, (iv) usage of the DSS, and finally (v) most important shortcomings. On the basis of this analysis, we formulate four criteria that we consider essential for the successful use of DSS in landscape and river basin management. The criteria relate to (i) system quality, (ii) user support and user training, (iii) perceived usefulness and (iv) user satisfaction. We can show that the availability of tools and technologies for DSS in landscape and river basin management is good to excellent. However, our investigations indicate that several problems have to be tackled. First of all, data availability and homogenisation, uncertainty analysis and uncertainty propagation and problems with model integration require further attention. Furthermore, the appropriate and methodological stakeholder interaction and the definition of `what end-users really need and want' have been documented as general shortcomings of all four examples of DSS. Thus, we propose an iterative development process that enables social learning of the different groups involved in the development process, because it is easier to design a DSS for a group of stakeholders who actively participate in an iterative process. We also identify two important lines of further development in DSS: the use of interactive visualization tools and the methodology of optimization to inform scenario elaboration and evaluate trade-offs among environmental measures and management alternatives.

Drawing Analogies to Deepen Learning

ERIC Educational Resources Information Center

Fava, Michelle

2017-01-01

This article offers examples of how drawing can facilitate thinking skills that promote analogical reasoning to enable deeper learning. The instructional design applies cognitive principles, briefly described here. The workshops were developed iteratively, through feedback from student and teacher participants. Elements of the UK National…

Socio-Technical Dimensions of an Outdoor Mobile Learning Environment: A Three-Phase Design-Based Research Investigation

ERIC Educational Resources Information Center

Land, Susan M.; Zimmerman, Heather Toomey

2015-01-01

This design-based research project examines three iterations of Tree Investigators, a learning environment designed to support science learning outdoors at an arboretum and nature center using mobile devices (iPads). Researchers coded videorecords and artifacts created by children and parents (n = 53) to understand how both social and…

Contextual EFL Learning in a 3D Virtual Environment

ERIC Educational Resources Information Center

Lan, Yu-Ju

2015-01-01

The purposes of the current study are to develop virtually immersive EFL learning contexts for EFL learners in Taiwan to pre- and review English materials beyond the regular English class schedule. A 2-iteration action research lasting for one semester was conducted to evaluate the effects of virtual contexts on learners' EFL learning. 132…

Towards Collaboration as Learning: Evaluation of an Open CPD Opportunity for HE Teachers

ERIC Educational Resources Information Center

Nerantzi, Chrissi; Gossman, Peter

2015-01-01

Flexible, Distance and Online Learning (FDOL) is an open online course offered as an informal cross-institutional collaboration based on a postgraduate module in the context of teacher education in higher education. The second iteration, FDOL132, was offered in 2013 using a problem-based learning (PBL) design (FISh) to foster collaborative…

Reflections on the Use of Iterative, Agile and Collaborative Approaches for Blended Flipped Learning Development

ERIC Educational Resources Information Center

Owen, Hazel; Dunham, Nicola

2015-01-01

E-learning experiences are widely becoming common practice in many schools, tertiary institutions and other organisations. However despite this increased use of technology to enhance learning and the associated investment involved the result does not always equate to more engaged, knowledgeable and skilled learners. We have observed two key…

Factors Contributing to Cognitive Absorption and Grounded Learning Effectiveness in a Competitive Business Marketing Simulation

ERIC Educational Resources Information Center

Baker, David Scott; Underwood, James, III; Thakur, Ramendra

2017-01-01

This study aimed to establish a pedagogical positioning of a business marketing simulation as a grounded learning teaching tool and empirically assess the dimensions of cognitive absorption related to grounded learning effectiveness in an iterative business simulation environment. The method/design and sample consisted of a field study survey…

The Iterative Development and Use of an Online Problem-Based Learning Module for Preservice and Inservice Teachers

ERIC Educational Resources Information Center

Rillero, Peter; Camposeco, Laurie

2018-01-01

Teachers' problem-based learning knowledge, abilities, and attitudes are important factors in successful K--12 PBL implementations. This article describes the development and use of a free, online module entitled "Design a Problem-Based Learning Experience." The module production, aligned with theories of andragogy, was a partnership…

Exploiting Multi-Step Sample Trajectories for Approximate Value Iteration

DTIC Science & Technology

2013-09-01

WORK UNIT NUMBER IH 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) AFRL/ RISC 525 Brooks Road, Rome NY 13441-4505 Binghamton University...S) AND ADDRESS(ES) Air Force Research Laboratory/Information Directorate Rome Research Site/ RISC 525 Brooks Road Rome NY 13441-4505 10. SPONSOR...iteration methods for reinforcement learning (RL) generalize experience from limited samples across large state-action spaces. The function approximators

A machine learning heuristic to identify biologically relevant and minimal biomarker panels from omics data

PubMed Central

2015-01-01

Background Investigations into novel biomarkers using omics techniques generate large amounts of data. Due to their size and numbers of attributes, these data are suitable for analysis with machine learning methods. A key component of typical machine learning pipelines for omics data is feature selection, which is used to reduce the raw high-dimensional data into a tractable number of features. Feature selection needs to balance the objective of using as few features as possible, while maintaining high predictive power. This balance is crucial when the goal of data analysis is the identification of highly accurate but small panels of biomarkers with potential clinical utility. In this paper we propose a heuristic for the selection of very small feature subsets, via an iterative feature elimination process that is guided by rule-based machine learning, called RGIFE (Rule-guided Iterative Feature Elimination). We use this heuristic to identify putative biomarkers of osteoarthritis (OA), articular cartilage degradation and synovial inflammation, using both proteomic and transcriptomic datasets. Results and discussion Our RGIFE heuristic increased the classification accuracies achieved for all datasets when no feature selection is used, and performed well in a comparison with other feature selection methods. Using this method the datasets were reduced to a smaller number of genes or proteins, including those known to be relevant to OA, cartilage degradation and joint inflammation. The results have shown the RGIFE feature reduction method to be suitable for analysing both proteomic and transcriptomics data. Methods that generate large ‘omics’ datasets are increasingly being used in the area of rheumatology. Conclusions Feature reduction methods are advantageous for the analysis of omics data in the field of rheumatology, as the applications of such techniques are likely to result in improvements in diagnosis, treatment and drug discovery. PMID:25923811

Web-Based Virtual Patients in Nursing Education: Development and Validation of Theory-Anchored Design and Activity Models

PubMed Central

2014-01-01

Background Research has shown that nursing students find it difficult to translate and apply their theoretical knowledge in a clinical context. Virtual patients (VPs) have been proposed as a learning activity that can support nursing students in their learning of scientific knowledge and help them integrate theory and practice. Although VPs are increasingly used in health care education, they still lack a systematic consistency that would allow their reuse outside of their original context. There is therefore a need to develop a model for the development and implementation of VPs in nursing education. Objective The aim of this study was to develop and evaluate a virtual patient model optimized to the learning and assessment needs in nursing education. Methods The process of modeling started by reviewing theoretical frameworks reported in the literature and used by practitioners when designing learning and assessment activities. The Outcome-Present State Test (OPT) model was chosen as the theoretical framework. The model was then, in an iterative manner, developed and optimized to the affordances of virtual patients. Content validation was performed with faculty both in terms of the relevance of the chosen theories but also its applicability in nursing education. The virtual patient nursing model was then instantiated in two VPs. The students’ perceived usefulness of the VPs was investigated using a questionnaire. The result was analyzed using descriptive statistics. Results A virtual patient Nursing Design Model (vpNDM) composed of three layers was developed. Layer 1 contains the patient story and ways of interacting with the data, Layer 2 includes aspects of the iterative process of clinical reasoning, and finally Layer 3 includes measurable outcomes. A virtual patient Nursing Activity Model (vpNAM) was also developed as a guide when creating VP-centric learning activities. The students perceived the global linear VPs as a relevant learning activity for the integration of theory and practice. Conclusions Virtual patients that are adapted to the nursing paradigm can support nursing students’ development of clinical reasoning skills. The proposed virtual patient nursing design and activity models will allow the systematic development of different types of virtual patients from a common model and thereby create opportunities for sharing pedagogical designs across technical solutions. PMID:24727709

Keeping the Bootcamp Fun Alive!

EPA Science Inventory

This product is a blog post that outlines a course conducted to build on skills learned in a Software Carpentry Bootcamp co-hosted by AED researcher, Jeff Hollister. The post provides details on the course and some lessons learned that will be implemented in future iterations of...

"There Was a Lot of Learning Going on" Using a Digital Medium to Support Learning in a Professional Course for New HE Lecturers

ERIC Educational Resources Information Center

Chesney, Sarah; Marcangelo, Caroline

2010-01-01

This small scale action research study investigated the experiences of learners over two iterations as they completed a patchwork text assignment within the digital medium of a personal learning system (PLS). The aim was to investigate the extent to which using a PLS can facilitate formative and collaborative feedback to assist student learning. A…

Online Pairwise Learning Algorithms.

PubMed

Ying, Yiming; Zhou, Ding-Xuan

2016-04-01

Pairwise learning usually refers to a learning task that involves a loss function depending on pairs of examples, among which the most notable ones are bipartite ranking, metric learning, and AUC maximization. In this letter we study an online algorithm for pairwise learning with a least-square loss function in an unconstrained setting of a reproducing kernel Hilbert space (RKHS) that we refer to as the Online Pairwise lEaRning Algorithm (OPERA). In contrast to existing works (Kar, Sriperumbudur, Jain, & Karnick, 2013 ; Wang, Khardon, Pechyony, & Jones, 2012 ), which require that the iterates are restricted to a bounded domain or the loss function is strongly convex, OPERA is associated with a non-strongly convex objective function and learns the target function in an unconstrained RKHS. Specifically, we establish a general theorem that guarantees the almost sure convergence for the last iterate of OPERA without any assumptions on the underlying distribution. Explicit convergence rates are derived under the condition of polynomially decaying step sizes. We also establish an interesting property for a family of widely used kernels in the setting of pairwise learning and illustrate the convergence results using such kernels. Our methodology mainly depends on the characterization of RKHSs using its associated integral operators and probability inequalities for random variables with values in a Hilbert space.

Application of a simple cerebellar model to geologic surface mapping

USGS Publications Warehouse

Hagens, A.; Doveton, J.H.

1991-01-01

Neurophysiological research into the structure and function of the cerebellum has inspired computational models that simulate information processing associated with coordination and motor movement. The cerebellar model arithmetic computer (CMAC) has a design structure which makes it readily applicable as an automated mapping device that "senses" a surface, based on a sample of discrete observations of surface elevation. The model operates as an iterative learning process, where cell weights are continuously modified by feedback to improve surface representation. The storage requirements are substantially less than those of a conventional memory allocation, and the model is extended easily to mapping in multidimensional space, where the memory savings are even greater. ?? 1991.

From PBL tutoring to PBL coaching in undergraduate medical education: an interpretative phenomenological analysis study

PubMed Central

Wang, Qing; Li, Huiping; Pang, Weiguo

2016-01-01

Background Coaching psychology is of increasing interest to medical educators for its potential benefits as a facilitative method in problem-based learning (PBL). However, the field lacks empirical studies that explore the lived experiences of students and tutors in the PBL coaching process. This study aimed to elicit knowledge regarding medical students’ and tutors’ experiences and perceptions of PBL coaching in the context of Chinese undergraduate medical education. Methods The qualitative methodology of interpretative phenomenological analysis (IPA) was employed. Participants comprised third year medical students (n=20) and PBL tutors (n=5) who have adopted a coaching approach in PBL for a semester. Semi-structured interviews were utilized to obtain a comprehensive understanding of their experiences of PBL coaching. Data analysis followed an iterative four-stage scheme of Biggerstaff and Thompson. Results Six main themes emerged from diverse experiences and interpretations: 1) mindsets of coaching and learning, 2) the development of learning dispositions and capacities, 3) student group collaboration, 4) tutor–student relationships, 5) personal and professional development, and 6) challenges and difficulties in implementation. Conclusions It could be concluded that PBL coaching is a dynamic, facilitative process that makes a particular contribution to the learning process from psychological, emotional, and social perspectives, whilst it demonstrates significant overlaps with PBL tutoring in terms of supporting students’ cognitive activities in PBL. Further research is needed to identify the barriers and challenges for medical educators to implement coaching in the PBL process. PMID:27396900

From PBL tutoring to PBL coaching in undergraduate medical education: an interpretative phenomenological analysis study.

PubMed

Wang, Qing; Li, Huiping; Pang, Weiguo

2016-01-01

Coaching psychology is of increasing interest to medical educators for its potential benefits as a facilitative method in problem-based learning (PBL). However, the field lacks empirical studies that explore the lived experiences of students and tutors in the PBL coaching process. This study aimed to elicit knowledge regarding medical students' and tutors' experiences and perceptions of PBL coaching in the context of Chinese undergraduate medical education. The qualitative methodology of interpretative phenomenological analysis (IPA) was employed. Participants comprised third year medical students (n=20) and PBL tutors (n=5) who have adopted a coaching approach in PBL for a semester. Semi-structured interviews were utilized to obtain a comprehensive understanding of their experiences of PBL coaching. Data analysis followed an iterative four-stage scheme of Biggerstaff and Thompson. Six main themes emerged from diverse experiences and interpretations: 1) mindsets of coaching and learning, 2) the development of learning dispositions and capacities, 3) student group collaboration, 4) tutor-student relationships, 5) personal and professional development, and 6) challenges and difficulties in implementation. It could be concluded that PBL coaching is a dynamic, facilitative process that makes a particular contribution to the learning process from psychological, emotional, and social perspectives, whilst it demonstrates significant overlaps with PBL tutoring in terms of supporting students' cognitive activities in PBL. Further research is needed to identify the barriers and challenges for medical educators to implement coaching in the PBL process.

From PBL tutoring to PBL coaching in undergraduate medical education: an interpretative phenomenological analysis study.

PubMed

Wang, Qing; Li, Huiping; Pang, Weiguo

2016-01-01

Background Coaching psychology is of increasing interest to medical educators for its potential benefits as a facilitative method in problem-based learning (PBL). However, the field lacks empirical studies that explore the lived experiences of students and tutors in the PBL coaching process. This study aimed to elicit knowledge regarding medical students' and tutors' experiences and perceptions of PBL coaching in the context of Chinese undergraduate medical education. Methods The qualitative methodology of interpretative phenomenological analysis (IPA) was employed. Participants comprised third year medical students (n=20) and PBL tutors (n=5) who have adopted a coaching approach in PBL for a semester. Semi-structured interviews were utilized to obtain a comprehensive understanding of their experiences of PBL coaching. Data analysis followed an iterative four-stage scheme of Biggerstaff and Thompson. Results Six main themes emerged from diverse experiences and interpretations: 1) mindsets of coaching and learning, 2) the development of learning dispositions and capacities, 3) student group collaboration, 4) tutor-student relationships, 5) personal and professional development, and 6) challenges and difficulties in implementation. Conclusions It could be concluded that PBL coaching is a dynamic, facilitative process that makes a particular contribution to the learning process from psychological, emotional, and social perspectives, whilst it demonstrates significant overlaps with PBL tutoring in terms of supporting students' cognitive activities in PBL. Further research is needed to identify the barriers and challenges for medical educators to implement coaching in the PBL process.

Realization of Comfortable Massage by Using Iterative Learning Control Based on EEG

NASA Astrophysics Data System (ADS)

Teramae, Tatsuya; Kushida, Daisuke; Takemori, Fumiaki; Kitamura, Akira

Recently the massage chair is used by a lot of people because they are able to use it easily at home. However a present massage chair only realizes the massage motion. Moreover the massage chair can not consider the user’s condition and massage force. On the other hand, the professional masseur is according to presume the mental condition by patient’s reaction. Then this paper proposes the method of applying masseur’s procedure for the massage chair using iterative learning control based on EEG. And massage force is estimated by acceleration sensor. The realizability of the proposed method is verified by the experimental works using the massage chair.

Iterative learning control with applications in energy generation, lasers and health care

PubMed Central

Tutty, O. R.

2016-01-01

Many physical systems make repeated executions of the same finite time duration task. One example is a robot in a factory or warehouse whose task is to collect an object in sequence from a location, transfer it over a finite duration, place it at a specified location or on a moving conveyor and then return for the next one and so on. Iterative learning control was especially developed for systems with this mode of operation and this paper gives an overview of this control design method using relatively recent relevant applications in wind turbines, free-electron lasers and health care, as exemplars to demonstrate its applicability. PMID:27713654

How do medical educators design a curriculum that facilitates student learning about professionalism?

PubMed

Langendyk, Vicki; Mason, Glenn; Wang, Shaoyu

2016-02-04

This study analyses the ways in which curriculum reform facilitated student learning about professionalism. Design-based research provided the structure for an iterative approach to curriculum change which we undertook over a 3 year period. The learning environment of the Personal and Professional Development Theme (PPD) was analysed through the sociocultural lens of Activity Theory. Lave and Wenger's and Mezirow's learning theories informed curriculum reform to support student development of a patient-centred and critically reflective professional identity. The renewed pedagogical outcomes were aligned with curriculum content, learning and teaching processes and assessment, and intense staff education was undertaken. We analysed qualitative data from tutor interviews and free-response student surveys to evaluate the impact of curriculum reform. Students' and tutors' reflections on learning in PPD converged on two principle themes--'Developing a philosophy of medicine' and 'Becoming an ethical doctor'--which corresponded to the overarching PPD theme aims of communicative learning. Students and tutors emphasised the importance of the unique learning environment of PPD tutorials for nurturing personal development and the positive impact of the renewed assessment programme on learning. A theory-led approach to curriculum reform resulted in student engagement in the PPD curriculum and facilitated a change in student perspective about the epistemological foundation of medicine.

An iterated Laplacian based semi-supervised dimensionality reduction for classification of breast cancer on ultrasound images.

PubMed

Liu, Xiao; Shi, Jun; Zhou, Shichong; Lu, Minhua

2014-01-01

The dimensionality reduction is an important step in ultrasound image based computer-aided diagnosis (CAD) for breast cancer. A newly proposed l2,1 regularized correntropy algorithm for robust feature selection (CRFS) has achieved good performance for noise corrupted data. Therefore, it has the potential to reduce the dimensions of ultrasound image features. However, in clinical practice, the collection of labeled instances is usually expensive and time costing, while it is relatively easy to acquire the unlabeled or undetermined instances. Therefore, the semi-supervised learning is very suitable for clinical CAD. The iterated Laplacian regularization (Iter-LR) is a new regularization method, which has been proved to outperform the traditional graph Laplacian regularization in semi-supervised classification and ranking. In this study, to augment the classification accuracy of the breast ultrasound CAD based on texture feature, we propose an Iter-LR-based semi-supervised CRFS (Iter-LR-CRFS) algorithm, and then apply it to reduce the feature dimensions of ultrasound images for breast CAD. We compared the Iter-LR-CRFS with LR-CRFS, original supervised CRFS, and principal component analysis. The experimental results indicate that the proposed Iter-LR-CRFS significantly outperforms all other algorithms.

Development of hierarchical structures for actions and motor imagery: a constructivist view from synthetic neuro-robotics study.

PubMed

Nishimoto, Ryunosuke; Tani, Jun

2009-07-01

The current paper shows a neuro-robotics experiment on developmental learning of goal-directed actions. The robot was trained to predict visuo-proprioceptive flow of achieving a set of goal-directed behaviors through iterative tutor training processes. The learning was conducted by employing a dynamic neural network model which is characterized by their multiple time-scale dynamics. The experimental results showed that functional hierarchical structures emerge through stages of developments where behavior primitives are generated in earlier stages and their sequences of achieving goals appear in later stages. It was also observed that motor imagery is generated in earlier stages compared to actual behaviors. Our claim that manipulatable inner representation should emerge through the sensory-motor interactions is corresponded to Piaget's constructivist view.

Transforming paper-based assessment forms to a digital format: Exemplified by the Housing Enabler prototype app.

PubMed

Svarre, Tanja; Lunn, Tine Bieber Kirkegaard; Helle, Tina

2017-11-01

The aim of this paper is to provide the reader with an overall impression of the stepwise user-centred design approach including the specific methods used and lessons learned when transforming paper-based assessment forms into a prototype app, taking the Housing Enabler as an example. Four design iterations were performed, building on a domain study, workshops, expert evaluation and controlled and realistic usability tests. The user-centred design process involved purposefully selected participants with different Housing Enabler knowledge and housing adaptation experience. The design iterations resulted in the development of a Housing Enabler prototype app. The prototype app has several features and options that are new compared with the original paper-based Housing Enabler assessment form. These new features include a user friendly overview of the assessment form; easy navigation by swiping back and forth between items; onsite data analysis; and ranking of the accessibility score, photo documentation and a data export facility. Based on the presented stepwise approach, a high-fidelity Housing Enabler prototype app was successfully developed. The development process has emphasized the importance of combining design participants' knowledge and experiences, and has shown that methods should seem relevant to participants to increase their engagement.

Fostering learners' interaction with content: A learner-centered mobile device interface

NASA Astrophysics Data System (ADS)

Abdous, M.

2015-12-01

With the ever-increasing omnipresence of mobile devices in student life, leveraging smart devices to foster students' interaction with course content is critical. Following a learner-centered design iterative approach, we designed a mobile interface that may enable learners to access and interact with online course content efficiently and intuitively. Our design process leveraged recent technologies, such as bootstrap, Google's Material Design, HTML5, and JavaScript to design an intuitive, efficient, and portable mobile interface with a variety of built-in features, including context sensitive bookmarking, searching, progress tracking, captioning, and transcript display. The mobile interface also offers students the ability to ask context-related questions and to complete self-checks as they watch audio/video presentations. Our design process involved ongoing iterative feedback from learners, allowing us to refine and tweak the interface to provide learners with a unified experience across platforms and devices. The innovative combination of technologies built around well-structured and well-designed content seems to provide an effective learning experience to mobile learners. Early feedback indicates a high level of satisfaction with the interface's efficiency, intuitiveness, and robustness from both students and faculty.

Applying the scientific method to small catchment studies: Areview of the Panola Mountain experience

USGS Publications Warehouse

Hooper, R.P.

2001-01-01

A hallmark of the scientific method is its iterative application to a problem to increase and refine the understanding of the underlying processes controlling it. A successful iterative application of the scientific method to catchment science (including the fields of hillslope hydrology and biogeochemistry) has been hindered by two factors. First, the scale at which controlled experiments can be performed is much smaller than the scale of the phenomenon of interest. Second, computer simulation models generally have not been used as hypothesis-testing tools as rigorously as they might have been. Model evaluation often has gone only so far as evaluation of goodness of fit, rather than a full structural analysis, which is more useful when treating the model as a hypothesis. An iterative application of a simple mixing model to the Panola Mountain Research Watershed is reviewed to illustrate the increase in understanding gained by this approach and to discern general principles that may be applicable to other studies. The lessons learned include the need for an explicitly stated conceptual model of the catchment, the definition of objective measures of its applicability, and a clear linkage between the scale of observations and the scale of predictions. Published in 2001 by John Wiley & Sons. Ltd.

Learning to See the Infinite: Measuring Visual Literacy Skills in a 1st-Year Seminar Course

ERIC Educational Resources Information Center

Palmer, Michael S.; Matthews, Tatiana

2015-01-01

Visual literacy was a stated learning objective for the fall 2009 iteration of a first-year seminar course. To help students develop visual literacy skills, they received formal instruction throughout the semester and completed a series of carefully designed learning activities. The effects of these interventions were measured using a one-group…

Integrating a MOOC into the Postgraduate ELT Curriculum: Reflecting on Students' Beliefs with a MOOC Blend

ERIC Educational Resources Information Center

Orsini-Jones, Marina; Gafaro, Barbara Conde; Altamimi, Shooq

2017-01-01

This chapter builds on the outcomes of a blended learning action-research project in its third iteration (academic year 2015-16). The FutureLearn Massive Open Online Course (MOOC) "Understanding Language: Learning and Teaching" was integrated into the curriculum of the Master of Arts (MA) in English Language Teaching (ELT) at Coventry…

Situated Cognition and Learning Environments: Implications for Teachers On- and Offline in the New Digital Media Age

ERIC Educational Resources Information Center

Gomez, Kimberley; Lee, Ung-Sang

2015-01-01

John Seely Brown suggested that learning environments should be spaces in which all work is public, is subject to iterative critique by instructors and peers, and in which social interaction is primary. In such spaces, students and teachers engage in a situated cognition approach to teaching and learning where "cognitive accomplishments rely…

Quantitative Reasoning in Environmental Science: A Learning Progression

ERIC Educational Resources Information Center

Mayes, Robert Lee; Forrester, Jennifer Harris; Christus, Jennifer Schuttlefield; Peterson, Franziska Isabel; Bonilla, Rachel; Yestness, Nissa

2014-01-01

The ability of middle and high school students to reason quantitatively within the context of environmental science was investigated. A quantitative reasoning (QR) learning progression was created with three progress variables: quantification act, quantitative interpretation, and quantitative modeling. An iterative research design was used as it…

Teachers Supporting Teachers in Urban Schools: What Iterative Research Designs Can Teach Us

PubMed Central

Shernoff, Elisa S.; Maríñez-Lora, Ane M.; Frazier, Stacy L.; Jakobsons, Lara J.; Atkins, Marc S.; Bonner, Deborah

2012-01-01

Despite alarming rates and negative consequences associated with urban teacher attrition, mentoring programs often fail to target the strongest predictors of attrition: effectiveness around classroom management and engaging learners; and connectedness to colleagues. Using a mixed-method iterative development framework, we highlight the process of developing and evaluating the feasibility of a multi-component professional development model for urban early career teachers. The model includes linking novices with peer-nominated key opinion leader teachers and an external coach who work together to (1) provide intensive support in evidence-based practices for classroom management and engaging learners, and (2) connect new teachers with their larger network of colleagues. Fidelity measures and focus group data illustrated varying attendance rates throughout the school year and that although seminars and professional learning communities were delivered as intended, adaptations to enhance the relevance, authenticity, level, and type of instrumental support were needed. Implications for science and practice are discussed. PMID:23275682

Performance improvement of robots using a learning control scheme

NASA Technical Reports Server (NTRS)

Krishna, Ramuhalli; Chiang, Pen-Tai; Yang, Jackson C. S.

1987-01-01

Many applications of robots require that the same task be repeated a number of times. In such applications, the errors associated with one cycle are also repeated every cycle of the operation. An off-line learning control scheme is used here to modify the command function which would result in smaller errors in the next operation. The learning scheme is based on a knowledge of the errors and error rates associated with each cycle. Necessary conditions for the iterative scheme to converge to zero errors are derived analytically considering a second order servosystem model. Computer simulations show that the errors are reduced at a faster rate if the error rate is included in the iteration scheme. The results also indicate that the scheme may increase the magnitude of errors if the rate information is not included in the iteration scheme. Modification of the command input using a phase and gain adjustment is also proposed to reduce the errors with one attempt. The scheme is then applied to a computer model of a robot system similar to PUMA 560. Improved performance of the robot is shown by considering various cases of trajectory tracing. The scheme can be successfully used to improve the performance of actual robots within the limitations of the repeatability and noise characteristics of the robot.

Multi-Innovation Gradient Iterative Locally Weighted Learning Identification for A Nonlinear Ship Maneuvering System

NASA Astrophysics Data System (ADS)

Bai, Wei-wei; Ren, Jun-sheng; Li, Tie-shan

2018-06-01

This paper explores a highly accurate identification modeling approach for the ship maneuvering motion with fullscale trial. A multi-innovation gradient iterative (MIGI) approach is proposed to optimize the distance metric of locally weighted learning (LWL), and a novel non-parametric modeling technique is developed for a nonlinear ship maneuvering system. This proposed method's advantages are as follows: first, it can avoid the unmodeled dynamics and multicollinearity inherent to the conventional parametric model; second, it eliminates the over-learning or underlearning and obtains the optimal distance metric; and third, the MIGI is not sensitive to the initial parameter value and requires less time during the training phase. These advantages result in a highly accurate mathematical modeling technique that can be conveniently implemented in applications. To verify the characteristics of this mathematical model, two examples are used as the model platforms to study the ship maneuvering.

Intelligent cooperation: A framework of pedagogic practice in the operating room.

PubMed

Sutkin, Gary; Littleton, Eliza B; Kanter, Steven L

2018-04-01

Surgeons who work with trainees must address their learning needs without compromising patient safety. We used a constructivist grounded theory approach to examine videos of five teaching surgeries. Attending surgeons were interviewed afterward while watching cued videos of their cases. Codes were iteratively refined into major themes, and then constructed into a larger framework. We present a novel framework, Intelligent Cooperation, which accounts for the highly adaptive, iterative features of surgical teaching in the operating room. Specifically, we define Intelligent Cooperation as a sequence of coordinated exchanges between attending and trainee that accomplishes small surgical steps while simultaneously uncovering the trainee's learning needs. Intelligent Cooperation requires the attending to accurately determine learning needs, perform real-time needs assessment, provide critical scaffolding, and work with the learner to accomplish the next step in the surgery. This is achieved through intense, coordinated verbal and physical cooperation. Copyright © 2017 Elsevier Inc. All rights reserved.

Off-Policy Integral Reinforcement Learning Method to Solve Nonlinear Continuous-Time Multiplayer Nonzero-Sum Games.

PubMed

Song, Ruizhuo; Lewis, Frank L; Wei, Qinglai

2017-03-01

This paper establishes an off-policy integral reinforcement learning (IRL) method to solve nonlinear continuous-time (CT) nonzero-sum (NZS) games with unknown system dynamics. The IRL algorithm is presented to obtain the iterative control and off-policy learning is used to allow the dynamics to be completely unknown. Off-policy IRL is designed to do policy evaluation and policy improvement in the policy iteration algorithm. Critic and action networks are used to obtain the performance index and control for each player. The gradient descent algorithm makes the update of critic and action weights simultaneously. The convergence analysis of the weights is given. The asymptotic stability of the closed-loop system and the existence of Nash equilibrium are proved. The simulation study demonstrates the effectiveness of the developed method for nonlinear CT NZS games with unknown system dynamics.

High resolution x-ray CMT: Reconstruction methods

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brown, J.K.

This paper qualitatively discusses the primary characteristics of methods for reconstructing tomographic images from a set of projections. These reconstruction methods can be categorized as either {open_quotes}analytic{close_quotes} or {open_quotes}iterative{close_quotes} techniques. Analytic algorithms are derived from the formal inversion of equations describing the imaging process, while iterative algorithms incorporate a model of the imaging process and provide a mechanism to iteratively improve image estimates. Analytic reconstruction algorithms are typically computationally more efficient than iterative methods; however, analytic algorithms are available for a relatively limited set of imaging geometries and situations. Thus, the framework of iterative reconstruction methods is better suited formore » high accuracy, tomographic reconstruction codes.« less

Using concept maps and goal-setting to support the development of self-regulated learning in a problem-based learning curriculum.

PubMed

Thomas, Lisa; Bennett, Sue; Lockyer, Lori

2016-09-01

Problem-based learning (PBL) in medical education focuses on preparing independent learners for continuing, self-directed, professional development beyond the classroom. Skills in self-regulated learning (SRL) are important for success in PBL and ongoing professional practice. However, the development of SRL skills is often left to chance. This study presents the investigated outcomes for students when support for the development of SRL was embedded in a PBL medical curriculum. This investigation involved design, delivery and testing of SRL support, embedded into the first phase of a four-year, graduate-entry MBBS degree. The intervention included concept mapping and goal-setting activities through iterative processes of planning, monitoring and reflecting on learning. A mixed-methods approach was used to collect data from seven students to develop case studies of engagement with, and outcomes from, the SRL support. The findings indicate that students who actively engaged with support for SRL demonstrated increases in cognitive and metacognitive functioning. Students also reported a greater sense of confidence in and control over their approaches to learning in PBL. This study advances understanding about how the development of SRL can be integrated into PBL.

Cross-domain active learning for video concept detection

NASA Astrophysics Data System (ADS)

Li, Huan; Li, Chao; Shi, Yuan; Xiong, Zhang; Hauptmann, Alexander G.

2011-08-01

As video data from a variety of different domains (e.g., news, documentaries, entertainment) have distinctive data distributions, cross-domain video concept detection becomes an important task, in which one can reuse the labeled data of one domain to benefit the learning task in another domain with insufficient labeled data. In this paper, we approach this problem by proposing a cross-domain active learning method which iteratively queries labels of the most informative samples in the target domain. Traditional active learning assumes that the training (source domain) and test data (target domain) are from the same distribution. However, it may fail when the two domains have different distributions because querying informative samples according to a base learner that initially learned from source domain may no longer be helpful for the target domain. In our paper, we use the Gaussian random field model as the base learner which has the advantage of exploring the distributions in both domains, and adopt uncertainty sampling as the query strategy. Additionally, we present an instance weighting trick to accelerate the adaptability of the base learner, and develop an efficient model updating method which can significantly speed up the active learning process. Experimental results on TRECVID collections highlight the effectiveness.

Exploring emerging learning needs: a UK-wide consultation on environmental sustainability learning objectives for medical education.

PubMed

Walpole, Sarah C; Mortimer, Frances; Inman, Alice; Braithwaite, Isobel; Thompson, Trevor

2015-12-24

This study aimed to engage wide-ranging stakeholders and develop consensus learning objectives for undergraduate and postgraduate medical education. A UK-wide consultation garnered opinions of healthcare students, healthcare educators and other key stakeholders about environmental sustainability in medical education. The policy Delphi approach informed this study. Draft learning objectives were revised iteratively during three rounds of consultation: online questionnaire or telephone interview, face-to-face seminar and email consultation. Twelve draft learning objectives were developed based on review of relevant literature. In round one, 64 participants' median ratings of the learning objectives were 3.5 for relevance and 3.0 for feasibility on a Likert scale of one to four. Revisions were proposed, e.g. to highlight relevance to public health and professionalism. Thirty three participants attended round two. Conflicting opinions were explored. Added content areas included health benefits of sustainable behaviours. To enhance usability, restructuring provided three overarching learning objectives, each with subsidiary points. All participants from rounds one and two were contacted in round three, and no further edits were required. This is the first attempt to define consensus learning objectives for medical students about environmental sustainability. Allowing a wide range of stakeholders to comment on multiple iterations of the document stimulated their engagement with the issues raised and ownership of the resulting learning objectives.

Cognitive Model of Trust Dynamics Predicts Human Behavior within and between Two Games of Strategic Interaction with Computerized Confederate Agents

PubMed Central

Collins, Michael G.; Juvina, Ion; Gluck, Kevin A.

2016-01-01

When playing games of strategic interaction, such as iterated Prisoner's Dilemma and iterated Chicken Game, people exhibit specific within-game learning (e.g., learning a game's optimal outcome) as well as transfer of learning between games (e.g., a game's optimal outcome occurring at a higher proportion when played after another game). The reciprocal trust players develop during the first game is thought to mediate transfer of learning effects. Recently, a computational cognitive model using a novel trust mechanism has been shown to account for human behavior in both games, including the transfer between games. We present the results of a study in which we evaluate the model's a priori predictions of human learning and transfer in 16 different conditions. The model's predictive validity is compared against five model variants that lacked a trust mechanism. The results suggest that a trust mechanism is necessary to explain human behavior across multiple conditions, even when a human plays against a non-human agent. The addition of a trust mechanism to the other learning mechanisms within the cognitive architecture, such as sequence learning, instance-based learning, and utility learning, leads to better prediction of the empirical data. It is argued that computational cognitive modeling is a useful tool for studying trust development, calibration, and repair. PMID:26903892

Research on material removal accuracy analysis and correction of removal function during ion beam figuring

NASA Astrophysics Data System (ADS)

Wu, Weibin; Dai, Yifan; Zhou, Lin; Xu, Mingjin

2016-09-01

Material removal accuracy has a direct impact on the machining precision and efficiency of ion beam figuring. By analyzing the factors suppressing the improvement of material removal accuracy, we conclude that correcting the removal function deviation and reducing the removal material amount during each iterative process could help to improve material removal accuracy. Removal function correcting principle can effectively compensate removal function deviation between actual figuring and simulated processes, while experiments indicate that material removal accuracy decreases with a long machining time, so a small amount of removal material in each iterative process is suggested. However, more clamping and measuring steps will be introduced in this way, which will also generate machining errors and suppress the improvement of material removal accuracy. On this account, a free-measurement iterative process method is put forward to improve material removal accuracy and figuring efficiency by using less measuring and clamping steps. Finally, an experiment on a φ 100-mm Zerodur planar is preformed, which shows that, in similar figuring time, three free-measurement iterative processes could improve the material removal accuracy and the surface error convergence rate by 62.5% and 17.6%, respectively, compared with a single iterative process.

Designing the Architecture of Hierachical Neural Networks Model Attention, Learning and Goal-Oriented Behavior

DTIC Science & Technology

1993-12-31

19,23,25,26,27,28,32,33,35,41]) - A new cost function is postulated and an algorithm that employs this cost function is proposed for the learning of...updates the controller parameters from time to time [53]. The learning control algorithm consist of updating the parameter estimates as used in the...proposed cost function with the other learning type algorithms , such as based upon learning of iterative tasks [Kawamura-85], variable structure

Accelerating Learning By Neural Networks

NASA Technical Reports Server (NTRS)

Toomarian, Nikzad; Barhen, Jacob

1992-01-01

Electronic neural networks made to learn faster by use of terminal teacher forcing. Method of supervised learning involves addition of teacher forcing functions to excitations fed as inputs to output neurons. Initially, teacher forcing functions are strong enough to force outputs to desired values; subsequently, these functions decay with time. When learning successfully completed, terminal teacher forcing vanishes, and dynamics or neural network become equivalent to those of conventional neural network. Simulated neural network with terminal teacher forcing learned to produce close approximation of circular trajectory in 400 iterations.

Video-Based Analyses of Motivation and Interaction in Science Classrooms

NASA Astrophysics Data System (ADS)

Moeller Andersen, Hanne; Nielsen, Birgitte Lund

2013-04-01

An analytical framework for examining students' motivation was developed and used for analyses of video excerpts from science classrooms. The framework was developed in an iterative process involving theories on motivation and video excerpts from a 'motivational event' where students worked in groups. Subsequently, the framework was used for an analysis of students' motivation in the whole class situation. A cross-case analysis was carried out illustrating characteristics of students' motivation dependent on the context. This research showed that students' motivation to learn science is stimulated by a range of different factors, with autonomy, relatedness and belonging apparently being the main sources of motivation. The teacher's combined use of questions, uptake and high level evaluation was very important for students' learning processes and motivation, especially students' self-efficacy. By coding and analysing video excerpts from science classrooms, we were able to demonstrate that the analytical framework helped us gain new insights into the effect of teachers' communication and other elements on students' motivation.

Linking departmental priorities to knowledge management: the experiences of Santa Cruz County's Human Services Department.

PubMed

Lindberg, Arley

2012-01-01

Federal welfare reform, local service collaborations, and the evolution of statewide information systems inspired agency interest in evidence-informed practice and knowledge sharing systems. Four agency leaders, including the Director, Deputy Director, Director of Planning and Evaluation, and Staff Development Program Manager championed the development of a learning organization based on knowledge management throughout the agency. Internal department restructuring helped to strengthen the Planning and Evaluation, Staff Development, and Personnel units, which have become central to supporting knowledge sharing activities. The Four Pillars of Knowledge framework was designed to capture agency directions in relationship to future knowledge management goals. Featuring People, Practice, Technology and Budget, the framework links the agency's services, mission and goals to the process of becoming a learning organization. Built through an iterative process, the framework was created by observing existing activities in each department rather than being designed from the top down. Knowledge management can help the department to fulfill its mission despite reduced resources. Copyright © Taylor & Francis Group, LLC

Adaptive Management: From More Talk to Real Action

NASA Astrophysics Data System (ADS)

Williams, Byron K.; Brown, Eleanor D.

2014-02-01

The challenges currently facing resource managers are large-scale and complex, and demand new approaches to balance development and conservation goals. One approach that shows considerable promise for addressing these challenges is adaptive management, which by now is broadly seen as a natural, intuitive, and potentially effective way to address decision-making in the face of uncertainties. Yet the concept of adaptive management continues to evolve, and its record of success remains limited. In this article, we present an operational framework for adaptive decision-making, and describe the challenges and opportunities in applying it to real-world problems. We discuss the key elements required for adaptive decision-making, and their integration into an iterative process that highlights and distinguishes technical and social learning. We illustrate the elements and processes of the framework with some successful on-the-ground examples of natural resource management. Finally, we address some of the difficulties in applying learning-based management, and finish with a discussion of future directions and strategic challenges.

Efficient full-chip SRAF placement using machine learning for best accuracy and improved consistency

NASA Astrophysics Data System (ADS)

Wang, Shibing; Baron, Stanislas; Kachwala, Nishrin; Kallingal, Chidam; Sun, Dezheng; Shu, Vincent; Fong, Weichun; Li, Zero; Elsaid, Ahmad; Gao, Jin-Wei; Su, Jing; Ser, Jung-Hoon; Zhang, Quan; Chen, Been-Der; Howell, Rafael; Hsu, Stephen; Luo, Larry; Zou, Yi; Zhang, Gary; Lu, Yen-Wen; Cao, Yu

2018-03-01

Various computational approaches from rule-based to model-based methods exist to place Sub-Resolution Assist Features (SRAF) in order to increase process window for lithography. Each method has its advantages and drawbacks, and typically requires the user to make a trade-off between time of development, accuracy, consistency and cycle time. Rule-based methods, used since the 90 nm node, require long development time and struggle to achieve good process window performance for complex patterns. Heuristically driven, their development is often iterative and involves significant engineering time from multiple disciplines (Litho, OPC and DTCO). Model-based approaches have been widely adopted since the 20 nm node. While the development of model-driven placement methods is relatively straightforward, they often become computationally expensive when high accuracy is required. Furthermore these methods tend to yield less consistent SRAFs due to the nature of the approach: they rely on a model which is sensitive to the pattern placement on the native simulation grid, and can be impacted by such related grid dependency effects. Those undesirable effects tend to become stronger when more iterations or complexity are needed in the algorithm to achieve required accuracy. ASML Brion has developed a new SRAF placement technique on the Tachyon platform that is assisted by machine learning and significantly improves the accuracy of full chip SRAF placement while keeping consistency and runtime under control. A Deep Convolutional Neural Network (DCNN) is trained using the target wafer layout and corresponding Continuous Transmission Mask (CTM) images. These CTM images have been fully optimized using the Tachyon inverse mask optimization engine. The neural network generated SRAF guidance map is then used to place SRAF on full-chip. This is different from our existing full-chip MB-SRAF approach which utilizes a SRAF guidance map (SGM) of mask sensitivity to improve the contrast of optical image at the target pattern edges. In this paper, we demonstrate that machine learning assisted SRAF placement can achieve a superior process window compared to the SGM model-based SRAF method, while keeping the full-chip runtime affordable, and maintain consistency of SRAF placement . We describe the current status of this machine learning assisted SRAF technique and demonstrate its application to full chip mask synthesis and discuss how it can extend the computational lithography roadmap.

Origami: An Active Learning Exercise for Scrum Project Management

ERIC Educational Resources Information Center

Sibona, Christopher; Pourreza, Saba; Hill, Stephen

2018-01-01

Scrum is a popular project management model for iterative delivery of software that subscribes to Agile principles. This paper describes an origami active learning exercise to teach the principles of Scrum in management information systems courses. The exercise shows students how Agile methods respond to changes in requirements during project…

Developing Preservice Elementary Teachers' Knowledge and Practices through Modeling-Centered Scientific Inquiry

ERIC Educational Resources Information Center

Schwarz, Christina

2009-01-01

Preservice elementary teachers face many challenges in learning how to teach science effectively, such as engaging students in science, organizing instruction, and developing a productive learning community. This paper reports on several iterative cycles of design-based research aimed at fostering preservice teachers' principled reasoning around…

LATUX: An Iterative Workflow for Designing, Validating, and Deploying Learning Analytics Visualizations

ERIC Educational Resources Information Center

Martinez-Maldonado, Roberto; Pardo, Abelardo; Mirriahi, Negin; Yacef, Kalina; Kay, Judy; Clayphan, Andrew

2015-01-01

Designing, validating, and deploying learning analytics tools for instructors or students is a challenge that requires techniques and methods from different disciplines, such as software engineering, human-computer interaction, computer graphics, educational design, and psychology. Whilst each has established its own design methodologies, we now…

Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View.

PubMed

Luo, Wei; Phung, Dinh; Tran, Truyen; Gupta, Sunil; Rana, Santu; Karmakar, Chandan; Shilton, Alistair; Yearwood, John; Dimitrova, Nevenka; Ho, Tu Bao; Venkatesh, Svetha; Berk, Michael

2016-12-16

As more and more researchers are turning to big data for new opportunities of biomedical discoveries, machine learning models, as the backbone of big data analysis, are mentioned more often in biomedical journals. However, owing to the inherent complexity of machine learning methods, they are prone to misuse. Because of the flexibility in specifying machine learning models, the results are often insufficiently reported in research articles, hindering reliable assessment of model validity and consistent interpretation of model outputs. To attain a set of guidelines on the use of machine learning predictive models within clinical settings to make sure the models are correctly applied and sufficiently reported so that true discoveries can be distinguished from random coincidence. A multidisciplinary panel of machine learning experts, clinicians, and traditional statisticians were interviewed, using an iterative process in accordance with the Delphi method. The process produced a set of guidelines that consists of (1) a list of reporting items to be included in a research article and (2) a set of practical sequential steps for developing predictive models. A set of guidelines was generated to enable correct application of machine learning models and consistent reporting of model specifications and results in biomedical research. We believe that such guidelines will accelerate the adoption of big data analysis, particularly with machine learning methods, in the biomedical research community. ©Wei Luo, Dinh Phung, Truyen Tran, Sunil Gupta, Santu Rana, Chandan Karmakar, Alistair Shilton, John Yearwood, Nevenka Dimitrova, Tu Bao Ho, Svetha Venkatesh, Michael Berk. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 16.12.2016.

Accurate Micro-Tool Manufacturing by Iterative Pulsed-Laser Ablation

NASA Astrophysics Data System (ADS)

Warhanek, Maximilian; Mayr, Josef; Dörig, Christian; Wegener, Konrad

2017-12-01

Iterative processing solutions, including multiple cycles of material removal and measurement, are capable of achieving higher geometric accuracy by compensating for most deviations manifesting directly on the workpiece. Remaining error sources are the measurement uncertainty and the repeatability of the material-removal process including clamping errors. Due to the lack of processing forces, process fluids and wear, pulsed-laser ablation has proven high repeatability and can be realized directly on a measuring machine. This work takes advantage of this possibility by implementing an iterative, laser-based correction process for profile deviations registered directly on an optical measurement machine. This way efficient iterative processing is enabled, which is precise, applicable for all tool materials including diamond and eliminates clamping errors. The concept is proven by a prototypical implementation on an industrial tool measurement machine and a nanosecond fibre laser. A number of measurements are performed on both the machine and the processed workpieces. Results show production deviations within 2 μm diameter tolerance.

Analysis of Artificial Neural Network in Erosion Modeling: A Case Study of Serang Watershed

NASA Astrophysics Data System (ADS)

Arif, N.; Danoedoro, P.; Hartono

2017-12-01

Erosion modeling is an important measuring tool for both land users and decision makers to evaluate land cultivation and thus it is necessary to have a model to represent the actual reality. Erosion models are a complex model because of uncertainty data with different sources and processing procedures. Artificial neural networks can be relied on for complex and non-linear data processing such as erosion data. The main difficulty in artificial neural network training is the determination of the value of each network input parameters, i.e. hidden layer, momentum, learning rate, momentum, and RMS. This study tested the capability of artificial neural network application in the prediction of erosion risk with some input parameters through multiple simulations to get good classification results. The model was implemented in Serang Watershed, Kulonprogo, Yogyakarta which is one of the critical potential watersheds in Indonesia. The simulation results showed the number of iterations that gave a significant effect on the accuracy compared to other parameters. A small number of iterations can produce good accuracy if the combination of other parameters was right. In this case, one hidden layer was sufficient to produce good accuracy. The highest training accuracy achieved in this study was 99.32%, occurred in ANN 14 simulation with combination of network input parameters of 1 HL; LR 0.01; M 0.5; RMS 0.0001, and the number of iterations of 15000. The ANN training accuracy was not influenced by the number of channels, namely input dataset (erosion factors) as well as data dimensions, rather it was determined by changes in network parameters.

Physiotherapists use a great variety of motor learning options in neurological rehabilitation, from which they choose through an iterative process: a retrospective think-aloud study.

PubMed

Kleynen, Melanie; Moser, Albine; Haarsma, Frederike A; Beurskens, Anna J; Braun, Susy M

2017-08-01

The goal of this study was to examine which motor learning options are applied by experienced physiotherapists in neurological rehabilitation, and how they choose between the different options. A descriptive qualitative approach was used. A purposive sample of five expert physiotherapists from the neurological ward of a rehabilitation center participated. Data were collected using nine videotaped therapy situations. During retrospective think-aloud interviews, the physiotherapists were instructed to constantly "think aloud" while they were watching their own videos. Five "operators" were identified: "act", "know", "observe", "assess" and "argue". The "act" operator consisted of 34 motor learning options, which were clustered into "instruction", "feedback" and "organization". The "know", "observe", "assess" and "argue" operators explained how therapists chose one of these options. The four operators seem to be interrelated and together lead to a decision to apply a particular motor learning option. Results show that the participating physiotherapists used a great variety of motor learning options in their treatment sessions. Further, the decision-making process with regard to these motor learning options was identified. Results may support future intervention studies that match the content and process of therapy in daily practice. The study should be repeated with other physiotherapists. Implications for Rehabilitation The study provided insight into the way experienced therapist handle the great variety of possible motor learning options, including concrete ideas on how to operationalize these options in specific situations. Despite differences in patients' abilities, it seems that therapists use the same underlying clinical reasoning process when choosing a particular motor learning option. Participating physiotherapists used more than the in guidelines suggested motor learning options and considered more than the suggested factors, hence adding practice based options of motor learning to the recommended ones in the guidelines. A think-aloud approach can be considered for peer-to-peer and student coaching to enhance discussion on the motor learning options applied and the underlying choices and to encourage research by practicing clinicians.

Simulation Learning PC Screen-Based vs. High Fidelity

DTIC Science & Technology

2011-08-01

D., Burgess, L., Berg, B . and Connolly, K . (2009). Teaching mass casualty triage skills using iterative multimanikin simulations. Prehospital...SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT 18. NUMBER OF PAGES 19a. NAME OF RESPONSIBLE PERSON USAMRMC a. REPORT U b . ABSTRACT U...learning PC screen-based vs. high fidelity – progress chart Attachment B . Approved Protocol - Simulation Learning: PC-Screen Based (PCSB) versus High

Asthma in the community: Designing instruction to help students explore scientific dilemmas that impact their lives

NASA Astrophysics Data System (ADS)

Tate, Erika Dawn

School science instruction that connects to students' diverse home, cultural, or linguistic experiences can encourage lifelong participation in the scientific dilemmas that impact students' lives. This dissertation seeks effective ways to support high school students as they learn complex science topics and use their knowledge to transform their personal and community environments. Applying the knowledge integration perspective, I collaborated with education, science, and community partners to design a technology enhanced science module, Improving Your Community's Asthma Problem. This exemplar community science curriculum afforded students the opportunity to (a) investigate a local community health issue, (b) interact with relevant evidence related to physiology, clinical management, and environmental risks, and (c) construct an integrated understanding of the asthma problem in their community. To identify effective instructional scaffolds that engage students in the knowledge integration process and prepare them to participate in community science, I conducted 2 years of research that included 5 schools, 10 teachers, and over 500 students. This dissertation reports on four studies that analyzed student responses on pre-, post-, and embedded assessments. Researching across four design stages, the iterative design study investigated how to best embed the visualizations of the physiological processes breathing, asthma attack, and the allergic immune response in an inquiry activity and informed evidence-based revisions to the module. The evaluation study investigated the impact of this revised Asthma module across multiple classrooms and differences in students' prior knowledge. Combining evidence of student learning from the iterative and evaluation studies with classroom observations and teacher interviews, the longitudinal study explored the impact of teacher practices on student learning in years 1 and 2. In the final chapter, I studied how the Asthma module and students' local community influenced students as they integrated their ideas related to perspectives, evidence use, the consideration of tradeoffs, and localization to construct explanations and decision justifications regarding their community's asthma problem. In the end, this dissertation offers evidence that informs the future design of community science instruction that successfully engages students in the knowledge integration process and has implications for creating multiple opportunities for students to meaningfully participate in community science.

A new iterative triclass thresholding technique in image segmentation.

PubMed

Cai, Hongmin; Yang, Zhong; Cao, Xinhua; Xia, Weiming; Xu, Xiaoyin

2014-03-01

We present a new method in image segmentation that is based on Otsu's method but iteratively searches for subregions of the image for segmentation, instead of treating the full image as a whole region for processing. The iterative method starts with Otsu's threshold and computes the mean values of the two classes as separated by the threshold. Based on the Otsu's threshold and the two mean values, the method separates the image into three classes instead of two as the standard Otsu's method does. The first two classes are determined as the foreground and background and they will not be processed further. The third class is denoted as a to-be-determined (TBD) region that is processed at next iteration. At the succeeding iteration, Otsu's method is applied on the TBD region to calculate a new threshold and two class means and the TBD region is again separated into three classes, namely, foreground, background, and a new TBD region, which by definition is smaller than the previous TBD regions. Then, the new TBD region is processed in the similar manner. The process stops when the Otsu's thresholds calculated between two iterations is less than a preset threshold. Then, all the intermediate foreground and background regions are, respectively, combined to create the final segmentation result. Tests on synthetic and real images showed that the new iterative method can achieve better performance than the standard Otsu's method in many challenging cases, such as identifying weak objects and revealing fine structures of complex objects while the added computational cost is minimal.

Flutter optimization in fighter aircraft design

NASA Technical Reports Server (NTRS)

Triplett, W. E.

1984-01-01

The efficient design of aircraft structure involves a series of compromises among various engineering disciplines. These compromises are necessary to ensure the best overall design. To effectively reconcile the various technical constraints requires a number of design iterations, with the accompanying long elapsed time. Automated procedures can reduce the elapsed time, improve productivity and hold the promise of optimum designs which may be missed by batch processing. Several examples are given of optimization applications including aeroelastic constraints. Particular attention is given to the success or failure of each example and the lessons learned. The specific applications are shown. The final two applications were made recently.

Analysis Resilient Algorithm on Artificial Neural Network Backpropagation

NASA Astrophysics Data System (ADS)

Saputra, Widodo; Tulus; Zarlis, Muhammad; Widia Sembiring, Rahmat; Hartama, Dedy

2017-12-01

Prediction required by decision makers to anticipate future planning. Artificial Neural Network (ANN) Backpropagation is one of method. This method however still has weakness, for long training time. This is a reason to improve a method to accelerate the training. One of Artificial Neural Network (ANN) Backpropagation method is a resilient method. Resilient method of changing weights and bias network with direct adaptation process of weighting based on local gradient information from every learning iteration. Predicting data result of Istanbul Stock Exchange training getting better. Mean Square Error (MSE) value is getting smaller and increasing accuracy.

Design Approaches to Support Preservice Teachers in Scientific Modeling

NASA Astrophysics Data System (ADS)

Kenyon, Lisa; Davis, Elizabeth A.; Hug, Barbara

2011-02-01

Engaging children in scientific practices is hard for beginning teachers. One such scientific practice with which beginning teachers may have limited experience is scientific modeling. We have iteratively designed preservice teacher learning experiences and materials intended to help teachers achieve learning goals associated with scientific modeling. Our work has taken place across multiple years at three university sites, with preservice teachers focused on early childhood, elementary, and middle school teaching. Based on results from our empirical studies supporting these design decisions, we discuss design features of our modeling instruction in each iteration. Our results suggest some successes in supporting preservice teachers in engaging students in modeling practice. We propose design principles that can guide science teacher educators in incorporating modeling in teacher education.

Curriculum reform and evolution: Innovative content and processes at one US medical school.

PubMed

Fischel, Janet E; Olvet, Doreen M; Iuli, Richard J; Lu, Wei-Hsin; Chandran, Latha

2018-03-11

Curriculum reform in medical schools continues to be an ever-present and challenging activity in medical education. This paper describes one school's experiences with specific curricular innovations that were developed or adapted and targeted to meet a clear set of curricular goals during the curriculum reform process. Those goals included: (a) promoting active learning and learner engagement; (b) establishing early professional identity; and (c) developing physician competencies in an integrated and contextual manner while allowing for individualized learning experiences for the millennial student. Six specific innovations championed by the school are described in detail. These included Themes in Medical Education, Translational Pillars, Stony Brook Teaching Families, Transition Courses, Educational Continuous Quality Improvement Processes, and our Career Advising Program. Development of the ideas and design of the innovations were done by faculty and student teams. We discuss successes and ongoing challenges with these innovations which are currently in the fourth year of implementation. Our curriculum reform has emphasized the iterative process of curriculum building. Based on our experience, we discuss general and practical guidelines for curriculum innovation in its three phases: setting the stage, implementation, and monitoring for the achievement of intended goals.

A Randomized Crossover Design to Assess Learning Impact and Student Preference for Active and Passive Online Learning Modules.

PubMed

Prunuske, Amy J; Henn, Lisa; Brearley, Ann M; Prunuske, Jacob

Medical education increasingly involves online learning experiences to facilitate the standardization of curriculum across time and space. In class, delivering material by lecture is less effective at promoting student learning than engaging students in active learning experience and it is unclear whether this difference also exists online. We sought to evaluate medical student preferences for online lecture or online active learning formats and the impact of format on short- and long-term learning gains. Students participated online in either lecture or constructivist learning activities in a first year neurologic sciences course at a US medical school. In 2012, students selected which format to complete and in 2013, students were randomly assigned in a crossover fashion to the modules. In the first iteration, students strongly preferred the lecture modules and valued being told "what they need to know" rather than figuring it out independently. In the crossover iteration, learning gains and knowledge retention were found to be equivalent regardless of format, and students uniformly demonstrated a strong preference for the lecture format, which also on average took less time to complete. When given a choice for online modules, students prefer passive lecture rather than completing constructivist activities, and in the time-limited environment of medical school, this choice results in similar performance on multiple-choice examinations with less time invested. Instructors need to look more carefully at whether assessments and learning strategies are helping students to obtain self-directed learning skills and to consider strategies to help students learn to value active learning in an online environment.

Development of the Learning Health System Researcher Core Competencies.

PubMed

Forrest, Christopher B; Chesley, Francis D; Tregear, Michelle L; Mistry, Kamila B

2017-08-04

To develop core competencies for learning health system (LHS) researchers to guide the development of training programs. Data were obtained from literature review, expert interviews, a modified Delphi process, and consensus development meetings. The competencies were developed from August to December 2016 using qualitative methods. The literature review formed the basis for the initial draft of a competency domain framework. Key informant semi-structured interviews, a modified Delphi survey, and three expert panel (n = 19 members) consensus development meetings produced the final set of competencies. The iterative development process yielded seven competency domains: (1) systems science; (2) research questions and standards of scientific evidence; (3) research methods; (4) informatics; (5) ethics of research and implementation in health systems; (6) improvement and implementation science; and (7) engagement, leadership, and research management. A total of 33 core competencies were prioritized across these seven domains. The real-world milieu of LHS research, the embeddedness of the researcher within the health system, and engagement of stakeholders are distinguishing characteristics of this emerging field. The LHS researcher core competencies can be used to guide the development of learning objectives, evaluation methods, and curricula for training programs. © Health Research and Educational Trust.

Design principles for simulation games for learning clinical reasoning: A design-based research approach.

PubMed

Koivisto, J-M; Haavisto, E; Niemi, H; Haho, P; Nylund, S; Multisilta, J

2018-01-01

Nurses sometimes lack the competence needed for recognising deterioration in patient conditions and this is often due to poor clinical reasoning. There is a need to develop new possibilities for learning this crucial competence area. In addition, educators need to be future oriented; they need to be able to design and adopt new pedagogical innovations. The purpose of the study is to describe the development process and to generate principles for the design of nursing simulation games. A design-based research methodology is applied in this study. Iterative cycles of analysis, design, development, testing and refinement were conducted via collaboration among researchers, educators, students, and game designers. The study facilitated the generation of reusable design principles for simulation games to guide future designers when designing and developing simulation games for learning clinical reasoning. This study makes a major contribution to research on simulation game development in the field of nursing education. The results of this study provide important insights into the significance of involving nurse educators in the design and development process of educational simulation games for the purpose of nursing education. Copyright © 2017 Elsevier Ltd. All rights reserved.

[MicroRNA Target Prediction Based on Support Vector Machine Ensemble Classification Algorithm of Under-sampling Technique].

PubMed

Chen, Zhiru; Hong, Wenxue

2016-02-01

Considering the low accuracy of prediction in the positive samples and poor overall classification effects caused by unbalanced sample data of MicroRNA (miRNA) target, we proposes a support vector machine (SVM)-integration of under-sampling and weight (IUSM) algorithm in this paper, an under-sampling based on the ensemble learning algorithm. The algorithm adopts SVM as learning algorithm and AdaBoost as integration framework, and embeds clustering-based under-sampling into the iterative process, aiming at reducing the degree of unbalanced distribution of positive and negative samples. Meanwhile, in the process of adaptive weight adjustment of the samples, the SVM-IUSM algorithm eliminates the abnormal ones in negative samples with robust sample weights smoothing mechanism so as to avoid over-learning. Finally, the prediction of miRNA target integrated classifier is achieved with the combination of multiple weak classifiers through the voting mechanism. The experiment revealed that the SVM-IUSW, compared with other algorithms on unbalanced dataset collection, could not only improve the accuracy of positive targets and the overall effect of classification, but also enhance the generalization ability of miRNA target classifier.

Judging adaptive management practices of U.S. agencies.

PubMed

Fischman, Robert L; Ruhl, J B

2016-04-01

All U.S. federal agencies administering environmental laws purport to practice adaptive management (AM), but little is known about how they actually implement this conservation tool. A gap between the theory and practice of AM is revealed in judicial decisions reviewing agency adaptive management plans. We analyzed all U.S. federal court opinions published through 1 January 2015 to identify the agency AM practices courts found most deficient. The shortcomings included lack of clear objectives and processes, monitoring thresholds, and defined actions triggered by thresholds. This trio of agency shortcuts around critical, iterative steps characterizes what we call AM-lite. Passive AM differs from active AM in its relative lack of management interventions through experimental strategies. In contrast, AM-lite is a distinctive form of passive AM that fails to provide for the iterative steps necessary to learn from management. Courts have developed a sophisticated understanding of AM and often offer instructive rather than merely critical opinions. The role of the judiciary is limited by agency discretion under U.S. administrative law. But courts have overturned some agency AM-lite practices and insisted on more rigorous analyses to ensure that the promised benefits of structured learning and fine-tuned management have a reasonable likelihood of occurring. Nonetheless, there remains a mismatch in U.S. administrative law between the flexibility demanded by adaptive management and the legal objectives of transparency, public participation, and finality. © 2015 Society for Conservation Biology.

Diverse power iteration embeddings: Theory and practice

DOE PAGES

Huang, Hao; Yoo, Shinjae; Yu, Dantong; ...

2015-11-09

Manifold learning, especially spectral embedding, is known as one of the most effective learning approaches on high dimensional data, but for real-world applications it raises a serious computational burden in constructing spectral embeddings for large datasets. To overcome this computational complexity, we propose a novel efficient embedding construction, Diverse Power Iteration Embedding (DPIE). DPIE shows almost the same effectiveness of spectral embeddings and yet is three order of magnitude faster than spectral embeddings computed from eigen-decomposition. Our DPIE is unique in that (1) it finds linearly independent embeddings and thus shows diverse aspects of dataset; (2) the proposed regularized DPIEmore » is effective if we need many embeddings; (3) we show how to efficiently orthogonalize DPIE if one needs; and (4) Diverse Power Iteration Value (DPIV) provides the importance of each DPIE like an eigen value. As a result, such various aspects of DPIE and DPIV ensure that our algorithm is easy to apply to various applications, and we also show the effectiveness and efficiency of DPIE on clustering, anomaly detection, and feature selection as our case studies.« less

Networked iterative learning control design for discrete-time systems with stochastic communication delay in input and output channels

NASA Astrophysics Data System (ADS)

Liu, Jian; Ruan, Xiaoe

2017-07-01

This paper develops two kinds of derivative-type networked iterative learning control (NILC) schemes for repetitive discrete-time systems with stochastic communication delay occurred in input and output channels and modelled as 0-1 Bernoulli-type stochastic variable. In the two schemes, the delayed signal of the current control input is replaced by the synchronous input utilised at the previous iteration, whilst for the delayed signal of the system output the one scheme substitutes it by the synchronous predetermined desired trajectory and the other takes it by the synchronous output at the previous operation, respectively. In virtue of the mathematical expectation, the tracking performance is analysed which exhibits that for both the linear time-invariant and nonlinear affine systems the two kinds of NILCs are convergent under the assumptions that the probabilities of communication delays are adequately constrained and the product of the input-output coupling matrices is full-column rank. Last, two illustrative examples are presented to demonstrate the effectiveness and validity of the proposed NILC schemes.

Development, Evaluation and Use of a Student Experience Survey in Undergraduate Science Laboratories: The Advancing Science by Enhancing Learning in the Laboratory Student Laboratory Learning Experience Survey

NASA Astrophysics Data System (ADS)

Barrie, Simon C.; Bucat, Robert B.; Buntine, Mark A.; Burke da Silva, Karen; Crisp, Geoffrey T.; George, Adrian V.; Jamie, Ian M.; Kable, Scott H.; Lim, Kieran F.; Pyke, Simon M.; Read, Justin R.; Sharma, Manjula D.; Yeung, Alexandra

2015-07-01

Student experience surveys have become increasingly popular to probe various aspects of processes and outcomes in higher education, such as measuring student perceptions of the learning environment and identifying aspects that could be improved. This paper reports on a particular survey for evaluating individual experiments that has been developed over some 15 years as part of a large national Australian study pertaining to the area of undergraduate laboratories-Advancing Science by Enhancing Learning in the Laboratory. This paper reports on the development of the survey instrument and the evaluation of the survey using student responses to experiments from different institutions in Australia, New Zealand and the USA. A total of 3153 student responses have been analysed using factor analysis. Three factors, motivation, assessment and resources, have been identified as contributing to improved student attitudes to laboratory activities. A central focus of the survey is to provide feedback to practitioners to iteratively improve experiments. Implications for practitioners and researchers are also discussed.

Residential roof condition assessment system using deep learning

NASA Astrophysics Data System (ADS)

Wang, Fan; Kerekes, John P.; Xu, Zhuoyi; Wang, Yandong

2018-01-01

The emergence of high resolution (HR) and ultra high resolution (UHR) airborne remote sensing imagery is enabling humans to move beyond traditional land cover analysis applications to the detailed characterization of surface objects. A residential roof condition assessment method using techniques from deep learning is presented. The proposed method operates on individual roofs and divides the task into two stages: (1) roof segmentation, followed by (2) condition classification of the segmented roof regions. As the first step in this process, a self-tuning method is proposed to segment the images into small homogeneous areas. The segmentation is initialized with simple linear iterative clustering followed by deep learned feature extraction and region merging, with the optimal result selected by an unsupervised index, Q. After the segmentation, a pretrained residual network is fine-tuned on the augmented roof segments using a proposed k-pixel extension technique for classification. The effectiveness of the proposed algorithm was demonstrated on both HR and UHR imagery collected by EagleView over different study sites. The proposed algorithm has yielded promising results and has outperformed traditional machine learning methods using hand-crafted features.

A learning-based agent for home neurorehabilitation.

PubMed

Lydakis, Andreas; Meng, Yuanliang; Munroe, Christopher; Wu, Yi-Ning; Begum, Momotaz

2017-07-01

This paper presents the iterative development of an artificially intelligent system to promote home-based neurorehabilitation. Although proper, structured practice of rehabilitation exercises at home is the key to successful recovery of motor functions, there is no home-program out there which can monitor a patient's exercise-related activities and provide corrective feedback in real time. To this end, we designed a Learning from Demonstration (LfD) based home-rehabilitation framework that combines advanced robot learning algorithms with commercially available wearable technologies. The proposed system uses exercise-related motion information and electromyography signals (EMG) of a patient to train a Markov Decision Process (MDP). The trained MDP model can enable an agent to serve as a coach for a patient. On a system level, this is the first initiative, to the best of our knowledge, to employ LfD in an health-care application to enable lay users to program an intelligent system. From a rehabilitation research perspective, this is a completely novel initiative to employ machine learning to provide interactive corrective feedback to a patient in home settings.

See, reflect, learn more: qualitative analysis of breaking bad news reflective narratives.

PubMed

Karnieli-Miller, Orit; Palombo, Michal; Meitar, Dafna

2018-05-01

Breaking bad news (BBN) is a challenge that requires multiple professional competencies. BBN teaching often includes didactic and group role-playing sessions. Both are useful and important, but exclude another critical component of students' learning: day-to-day role-model observation in the clinics. Given the importance of observation and the potential benefit of reflective writing in teaching, we have incorporated reflective writing into our BBN course. The aim of this study was to enhance our understanding of the learning potential in reflective writing about BBN encounters and the ability to identify components that inhibit this learning. This was a systematic qualitative immersion/crystallization analysis of 166 randomly selected BBN narratives written by 83 senior medical students. We analysed the narratives in an iterative consensus-building process to identify the issues discussed, the lessons learned and the enhanced understanding of BBN. Having previously been unaware of, not invited to or having avoided BBN encounters, the mandatory assignment led students to search for or ask their mentors to join them in BBN encounters. Observation and reflective writing enhanced students' awareness that 'bad news' is relative and subjective, while shedding light on patients', families', physicians' and their own experiences and needs, revealing the importance of the different components of the BBN protocol. We identified diversity among the narratives and the extent of students' learning. Narrative writing provided students with an opportunity for a deliberative learning process. This led to deeper understanding of BBN encounters, of how to apply the newly taught protocol, or of the need for it. This process connected the formal and informal or hidden curricula. To maximise learning through reflective writing, students should be encouraged to write in detail about a recent observed encounter, analyse it according to the protocol, address different participants' behaviours and emotions, and identify dilemmas and clear lessons learned. © 2018 John Wiley & Sons Ltd and The Association for the Study of Medical Education.

Iteration and Prototyping in Creating Technical Specifications.

ERIC Educational Resources Information Center

Flynt, John P.

1994-01-01

Claims that the development process for computer software can be greatly aided by the writers of specifications if they employ basic iteration and prototyping techniques. Asserts that computer software configuration management practices provide ready models for iteration and prototyping. (HB)

Exploring the factors influencing clinical students' self-regulated learning.

PubMed

Berkhout, Joris J; Helmich, Esther; Teunissen, Pim W; van den Berg, Joost W; van der Vleuten, Cees P M; Jaarsma, A Debbie C

2015-06-01

The importance of self-regulated learning (SRL) has been broadly recognised by medical education institutions and regulatory bodies. Supporting the development of SRL skills has proven difficult because self-regulation is a complex interactive process and we know relatively little about the factors influencing this process in real practice settings. The aim of our study was therefore to identify factors that support or hamper medical students' SRL in a clinical context. We conducted a constructivist grounded theory study using semi-structured interviews with 17 medical students from two universities enrolled in clerkships. Participants were purposively sampled to ensure variety in age, gender, experience and current clerkship. The Day Reconstruction Method was used to help participants remember their activities of the previous day. The interviews were transcribed verbatim and analysed iteratively using constant comparison and open, axial and interpretive coding. Self-regulated learning by students in the clinical environment was influenced by the specific goals perceived by students, the autonomy they experienced, the learning opportunities they were given or created themselves, and the anticipated outcomes of an activity. All of these factors were affected by personal, contextual and social attributes. Self-regulated learning of medical students in the clinical environment is different for every individual. The factors influencing this process are affected by personal, social and contextual attributes. Some of these are similar to those known from previous research in classroom settings, but others are unique to the clinical environment and include the facilities available, the role of patients, and social relationships pertaining to peers and other hospital staff. To better support students' SRL, we believe it is important to increase students' metacognitive awareness and to offer students more tailored learning opportunities. © 2015 John Wiley & Sons Ltd.

ENERGY-NET (Energy, Environment and Society Learning Network): Best Practices to Enhance Informal Geoscience Learning

NASA Astrophysics Data System (ADS)

Rossi, R.; Elliott, E. M.; Bain, D.; Crowley, K. J.; Steiner, M. A.; Divers, M. T.; Hopkins, K. G.; Giarratani, L.; Gilmore, M. E.

2014-12-01

While energy links all living and non-living systems, the integration of energy, the environment, and society is often not clearly represented in 9 - 12 classrooms and informal learning venues. However, objective public learning that integrates these components is essential for improving public environmental literacy. ENERGY-NET (Energy, Environment and Society Learning Network) is a National Science Foundation funded initiative that uses an Earth Systems Science framework to guide experimental learning for high school students and to improve public learning opportunities regarding the energy-environment-society nexus in a Museum setting. One of the primary objectives of the ENERGY-NET project is to develop a rich set of experimental learning activities that are presented as exhibits at the Carnegie Museum of Natural History in Pittsburgh, Pennsylvania (USA). Here we detail the evolution of the ENERGY-NET exhibit building process and the subsequent evolution of exhibit content over the past three years. While preliminary plans included the development of five "exploration stations" (i.e., traveling activity carts) per calendar year, the opportunity arose to create a single, larger topical exhibit per semester, which was assumed to have a greater impact on museum visitors. Evaluative assessments conducted to date reveal important practices to be incorporated into ongoing exhibit development: 1) Undergraduate mentors and teen exhibit developers should receive additional content training to allow richer exhibit materials. 2) The development process should be distributed over as long a time period as possible and emphasize iteration. This project can serve as a model for other collaborations between geoscience departments and museums. In particular, these practices may streamline development of public presentations and increase the effectiveness of experimental learning activities.

Evolving Deep Networks Using HPC

DOE Office of Scientific and Technical Information (OSTI.GOV)

Young, Steven R.; Rose, Derek C.; Johnston, Travis

While a large number of deep learning networks have been studied and published that produce outstanding results on natural image datasets, these datasets only make up a fraction of those to which deep learning can be applied. These datasets include text data, audio data, and arrays of sensors that have very different characteristics than natural images. As these “best” networks for natural images have been largely discovered through experimentation and cannot be proven optimal on some theoretical basis, there is no reason to believe that they are the optimal network for these drastically different datasets. Hyperparameter search is thus oftenmore » a very important process when applying deep learning to a new problem. In this work we present an evolutionary approach to searching the possible space of network hyperparameters and construction that can scale to 18, 000 nodes. This approach is applied to datasets of varying types and characteristics where we demonstrate the ability to rapidly find best hyperparameters in order to enable practitioners to quickly iterate between idea and result.« less

Peer assisted learning as a formal instructional tool.

PubMed

Naqi, Syed Asghar

2014-03-01

To explore the utility of peer assisted learning (PAL) in medical schools as a formal instructional tool. Grounded theory approach. King Edward Medical University, Lahore, from July 2011 to December 2011. A study was designed using semi-structured in-depth interviews to collect data from final year medical students (n=6), residents (n=4) and faculty members (n=3), selected on the basis of non-probability purposive sampling. The qualitative data thus generated was first translated in English and transcribed and organized into major categories by using a coding framework. Participants were interviewed two more times to further explore their perceptions and experiences related to emergent categories. An iterative process was employed using grounded theory analysis technique to eventually generate theory. PAL was perceived as rewarding in terms of fostering higher order thinking, effective teaching skills and in improving self efficacy among learners. PAL can offer learning opportunity to medical students, residents and faculty members. It can improve depth of their knowledge and skills.

Residents' responses to medical error: coping, learning, and change.

PubMed

Engel, Kirsten G; Rosenthal, Marilynn; Sutcliffe, Kathleen M

2006-01-01

To explore the significant emotional challenges facing resident physicians in the setting of medical mishaps, as well as their approaches to coping with these difficult experiences. Twenty-six resident physicians were randomly selected from a single teaching hospital and participated in in-depth qualitative interviews. Transcripts were analyzed iteratively and themes identified. Residents expressed intense emotional responses to error events. Poor patient outcomes and greater perceived personal responsibility were associated with more intense reactions and greater personal anguish. For the great majority of residents, their ability to cope with these events was dependent on a combination of reassurance and opportunities for learning. Interactions with medical colleagues and supervisory physicians were critical to this coping process. Medical mishaps have a profound impact on resident physicians by eliciting intense emotional responses. It is critical that resident training programs recognize the personal and professional significance of these experiences for young physicians. Moreover, resident education must support the development of constructive coping skills by facilitating candid discussion and learning subsequent to these events.

DART system analysis.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Boggs, Paul T.; Althsuler, Alan; Larzelere, Alex R.

2005-08-01

The Design-through-Analysis Realization Team (DART) is chartered with reducing the time Sandia analysts require to complete the engineering analysis process. The DART system analysis team studied the engineering analysis processes employed by analysts in Centers 9100 and 8700 at Sandia to identify opportunities for reducing overall design-through-analysis process time. The team created and implemented a rigorous analysis methodology based on a generic process flow model parameterized by information obtained from analysts. They also collected data from analysis department managers to quantify the problem type and complexity distribution throughout Sandia's analyst community. They then used this information to develop a communitymore » model, which enables a simple characterization of processes that span the analyst community. The results indicate that equal opportunity for reducing analysis process time is available both by reducing the ''once-through'' time required to complete a process step and by reducing the probability of backward iteration. In addition, reducing the rework fraction (i.e., improving the engineering efficiency of subsequent iterations) offers approximately 40% to 80% of the benefit of reducing the ''once-through'' time or iteration probability, depending upon the process step being considered. Further, the results indicate that geometry manipulation and meshing is the largest portion of an analyst's effort, especially for structural problems, and offers significant opportunity for overall time reduction. Iteration loops initiated late in the process are more costly than others because they increase ''inner loop'' iterations. Identifying and correcting problems as early as possible in the process offers significant opportunity for time savings.« less

Clinical Training at Remote Sites Using Mobile Technology: An India-USA Partnership

ERIC Educational Resources Information Center

Vyas, R.; Albright, S.; Walker, D.; Zachariah, A.; Lee, M. Y.

2010-01-01

Christian Medical College (CMC), India, and Tufts University School of Medicine, USA, have developed an "institutional hub and spokes" model (campus-based e-learning supporting m-learning in the field) to facilitate clinical education and training at remote secondary hospital sites across India. Iterative research, design, development,…

Who Are with Us: MOOC Learners on a FutureLearn Course

ERIC Educational Resources Information Center

Liyanagunawardena, Tharindu Rekha; Lundqvist, Karsten Øster; Williams, Shirley Ann

2015-01-01

Massive open online courses (MOOCs) attract learners with a variety of backgrounds. Engaging them using game development was trialled in a beginner's programming course, "Begin programming: build your first mobile game," on FutureLearn platform. The course has completed two iterations: first in autumn 2013 and second in spring 2014 with…

Rules, Roles and Tools: Activity Theory and the Comparative Study of E-Learning

ERIC Educational Resources Information Center

Benson, Angela; Lawler, Cormac; Whitworth, Andrew

2008-01-01

Activity theory (AT) is a powerful tool for investigating "artefacts in use", ie, the ways technologies interrelate with their local context. AT reveals the interfaces between e-learning at the macro- (strategy, policy, "campus-wide" solutions) and the micro-organisational levels (everyday working practice, iterative change, individual…

Design Principles for "Thriving in Our Digital World": A High School Computer Science Course

ERIC Educational Resources Information Center

Veletsianos, George; Beth, Bradley; Lin, Calvin; Russell, Gregory

2016-01-01

"Thriving in Our Digital World" is a technology-enhanced dual enrollment course introducing high school students to computer science through project- and problem-based learning. This article describes the evolution of the course and five lessons learned during the design, development, implementation, and iteration of the course from its…

Multimodal and Adaptive Learning Management: An Iterative Design

ERIC Educational Resources Information Center

Squires, David R.; Orey, Michael A.

2015-01-01

The purpose of this study is to measure the outcome of a comprehensive learning management system implemented at a Spinal Cord Injury (SCI) hospital in the Southeast United States. Specifically this SCI hospital has been experiencing an evident volume of patients returning seeking more information about the nature of their injuries. Recognizing…

Learner-Controlled Scaffolding Linked to Open-Ended Problems in a Digital Learning Environment

ERIC Educational Resources Information Center

Edson, Alden Jack

2017-01-01

This exploratory study reports on how students activated learner-controlled scaffolding and navigated through sequences of connected problems in a digital learning environment. A design experiment was completed to (re)design, iteratively develop, test, and evaluate a digital version of an instructional unit focusing on binomial distributions and…

User Acceptance of a Haptic Interface for Learning Anatomy

ERIC Educational Resources Information Center

Yeom, Soonja; Choi-Lundberg, Derek; Fluck, Andrew; Sale, Arthur

2013-01-01

Visualizing the structure and relationships in three dimensions (3D) of organs is a challenge for students of anatomy. To provide an alternative way of learning anatomy engaging multiple senses, we are developing a force-feedback (haptic) interface for manipulation of 3D virtual organs, using design research methodology, with iterations of system…

Challenges and Opportunities for Teacher Professional Development in Interactive Use of Technology in African Schools

ERIC Educational Resources Information Center

Hennessy, Sara; Haßler, Bjoern; Hofmann, Riikka

2015-01-01

This article examines the supporting and constraining factors influencing professional learning about interactive teaching and mobile digital technology use in low-resourced basic schools in sub-Saharan Africa. It draws on a case study of iterative development and refinement of a school-based, peer-facilitated professional learning programme…

Students' Socio-Scientific Reasoning in an Astrobiological Context during Work with a Digital Learning Environment

ERIC Educational Resources Information Center

Hansson, Lena; Redfors, Andreas; Rosberg, Maria

2011-01-01

In a European project--CoReflect--researchers in seven countries are developing, implementing and evaluating teaching sequences using a web-based platform (STOCHASMOS). The interactive web-based inquiry materials support collaborative and reflective work. The learning environments will be iteratively tested and refined, during different phases of…

Adaptive management: The U.S. Department of the Interior technical guide

USGS Publications Warehouse

Williams, B K; Szaro, Robert C.; Shapiro, Carl D.

2009-01-01

The purpose of this technical guide is to present an operational definition of adaptive management, identify the conditions in which adaptive management should be considered, and describe the process of using adaptive management for managing natural resources. The guide is not an exhaustive discussion of adaptive management, nor does it include detailed specifications for individual projects. However, it should aid U.S. Department of the Interior (DOI) managers and practitioners in determining when and how to apply adaptive management. Adaptive management is framed within the context of structured decision making, with an emphasis on uncertainty about resource responses to management actions and the value of reducing that uncertainty to improve management. Though learning plays a key role in adaptive management, it is seen here as a means to an end, namely good management, and not an end in itself. The operational definition used in the guide is adopted from the National Research Council, which characterizes adaptive management as an iterative learning process producing improved understanding and improved management over time: Adaptive management [is a decision process that] promotes flexible decision making that can be adjusted in the face of uncertainties as outcomes from management actions and other events become better understood. Careful monitoring of these outcomes both advances scientific understanding and helps adjust policies or operations as part of an iterative learning process. Adaptive management also recognizes the importance of natural variability in contributing to ecological resilience and productivity. It is not a ‘trial and error’ process, but rather emphasizes learning while doing. Adaptive management does not represent an end in itself, but rather a means to more effective decisions and enhanced benefits. Its true measure is in how well it helps meet environmental, social, and economic goals, increases scientific knowledge, and reduces tensions among stakeholders. Adaptive management as defined here involves ongoing, real-time learning and knowledge creation, both in a substantive sense and in terms of the adaptive process itself. It is described in what follows in a series of 9 steps, as summarized in section 4.1, involving stakeholder involvement, management objectives, management alternatives, predictive models, monitoring plans, decision making, monitoring responses to management, assessment, and adjustment to management actions. An adaptive approach actively engages stakeholders in all phases of a project over its timeframe, facilitating mutual learning and reinforcing the commitment to learning-based management. Adaptive management in DOI is implemented within a legal context that includes statutory authorities such as the National Environmental Policy Act (NEPA), the Endangered Species Act, and the Federal Advisory Committee Act. For many important problems now facing the resource management community, adaptive management holds great promise in reducing the uncertainties that limit the effective management of natural resource systems. For many conservation and management problems, utilizing management itself in an experimental context may be the only feasible way to gain the system understanding needed to improve management. Though it is commonly thought that an adaptive approach can produce results quickly at low cost, the opposite is more likely to be true. An initial investment of time and effort will increase the likelihood of better decision making and resource stewardship in the future, but patience, flexibility, and support are needed over the life of an adaptive management project. For these reasons it is important to carefully consider the potential use of an adaptive approach, and to engage in careful planning and evaluation when adaptive management is used.

Learning Biological Networks via Bootstrapping with Optimized GO-based Gene Similarity

DOE Office of Scientific and Technical Information (OSTI.GOV)

Taylor, Ronald C.; Sanfilippo, Antonio P.; McDermott, Jason E.

2010-08-02

Microarray gene expression data provide a unique information resource for learning biological networks using "reverse engineering" methods. However, there are a variety of cases in which we know which genes are involved in a given pathology of interest, but we do not have enough experimental evidence to support the use of fully-supervised/reverse-engineering learning methods. In this paper, we explore a novel semi-supervised approach in which biological networks are learned from a reference list of genes and a partial set of links for these genes extracted automatically from PubMed abstracts, using a knowledge-driven bootstrapping algorithm. We show how new relevant linksmore » across genes can be iteratively derived using a gene similarity measure based on the Gene Ontology that is optimized on the input network at each iteration. We describe an application of this approach to the TGFB pathway as a case study and show how the ensuing results prove the feasibility of the approach as an alternate or complementary technique to fully supervised methods.« less

Implementation of a Curriculum-Integrated Computer Game for Introducing Scientific Argumentation

NASA Astrophysics Data System (ADS)

Wallon, Robert C.; Jasti, Chandana; Lauren, Hillary Z. G.; Hug, Barbara

2017-11-01

Argumentation has been emphasized in recent US science education reform efforts (NGSS Lead States 2013; NRC 2012), and while existing studies have investigated approaches to introducing and supporting argumentation (e.g., McNeill and Krajcik in Journal of Research in Science Teaching, 45(1), 53-78, 2008; Kang et al. in Science Education, 98(4), 674-704, 2014), few studies have investigated how game-based approaches may be used to introduce argumentation to students. In this paper, we report findings from a design-based study of a teacher's use of a computer game intended to introduce the claim, evidence, reasoning (CER) framework (McNeill and Krajcik 2012) for scientific argumentation. We studied the implementation of the game over two iterations of development in a high school biology teacher's classes. The results of this study include aspects of enactment of the activities and student argument scores. We found the teacher used the game in aspects of explicit instruction of argumentation during both iterations, although the ways in which the game was used differed. Also, students' scores in the second iteration were significantly higher than the first iteration. These findings support the notion that students can learn argumentation through a game, especially when used in conjunction with explicit instruction and support in student materials. These findings also highlight the importance of analyzing classroom implementation in studies of game-based learning.

How do medical educators design a curriculum that facilitates student learning about professionalism?

PubMed Central

Mason, Glenn; Wang, Shaoyu

2016-01-01

Objectives This study analyses the ways in which curriculum reform facilitated student learning about professionalism. Methods Design-based research provided the structure for an iterative approach to curriculum change which we undertook over a 3 year period. The learning environment of the Personal and Professional Development Theme (PPD) was analysed through the sociocultural lens of Activity Theory. Lave and Wenger’s and Mezirow’s learning theories informed curriculum reform to support student development of a patient-centred and critically reflective professional identity. The renewed pedagogical outcomes were aligned with curriculum content, learning and teaching processes and assessment, and intense staff education was undertaken. We analysed qualitative data from tutor interviews and free-response student surveys to evaluate the impact of curriculum reform. Results Students’ and tutors’ reflections on learning in PPD converged on two principle themes - ‘Developing a philosophy of medicine’ and ‘Becoming an ethical doctor’- which corresponded to the overarching PPD theme aims of communicative learning. Students and tutors emphasised the importance of the unique learning environment of PPD tutorials for nurturing personal development and the positive impact of the renewed assessment programme on learning. Conclusions A theory-led approach to curriculum reform resulted in student engagement in the PPD curriculum and facilitated a change in student perspective about the epistemological foundation of medicine. PMID:26845777

Deformable Image Registration based on Similarity-Steered CNN Regression.

PubMed

Cao, Xiaohuan; Yang, Jianhua; Zhang, Jun; Nie, Dong; Kim, Min-Jeong; Wang, Qian; Shen, Dinggang

2017-09-01

Existing deformable registration methods require exhaustively iterative optimization, along with careful parameter tuning, to estimate the deformation field between images. Although some learning-based methods have been proposed for initiating deformation estimation, they are often template-specific and not flexible in practical use. In this paper, we propose a convolutional neural network (CNN) based regression model to directly learn the complex mapping from the input image pair (i.e., a pair of template and subject) to their corresponding deformation field. Specifically, our CNN architecture is designed in a patch-based manner to learn the complex mapping from the input patch pairs to their respective deformation field. First, the equalized active-points guided sampling strategy is introduced to facilitate accurate CNN model learning upon a limited image dataset. Then, the similarity-steered CNN architecture is designed, where we propose to add the auxiliary contextual cue, i.e., the similarity between input patches, to more directly guide the learning process. Experiments on different brain image datasets demonstrate promising registration performance based on our CNN model. Furthermore, it is found that the trained CNN model from one dataset can be successfully transferred to another dataset, although brain appearances across datasets are quite variable.

Deep Unfolding for Topic Models.

PubMed

Chien, Jen-Tzung; Lee, Chao-Hsi

2018-02-01

Deep unfolding provides an approach to integrate the probabilistic generative models and the deterministic neural networks. Such an approach is benefited by deep representation, easy interpretation, flexible learning and stochastic modeling. This study develops the unsupervised and supervised learning of deep unfolded topic models for document representation and classification. Conventionally, the unsupervised and supervised topic models are inferred via the variational inference algorithm where the model parameters are estimated by maximizing the lower bound of logarithm of marginal likelihood using input documents without and with class labels, respectively. The representation capability or classification accuracy is constrained by the variational lower bound and the tied model parameters across inference procedure. This paper aims to relax these constraints by directly maximizing the end performance criterion and continuously untying the parameters in learning process via deep unfolding inference (DUI). The inference procedure is treated as the layer-wise learning in a deep neural network. The end performance is iteratively improved by using the estimated topic parameters according to the exponentiated updates. Deep learning of topic models is therefore implemented through a back-propagation procedure. Experimental results show the merits of DUI with increasing number of layers compared with variational inference in unsupervised as well as supervised topic models.

Learning theory and its application to the use of social media in medical education.

PubMed

Flynn, Leslie; Jalali, Alireza; Moreau, Katherine A

2015-10-01

There is rapidly increasing pressure to employ social media in medical education, but a review of the literature demonstrates that its value and role are uncertain. To determine if medical educators have a conceptual framework that informs their use of social media and whether this framework can be mapped to learning theory. Thirty-six participants engaged in an iterative, consensus building process that identified their conceptual framework and determined if it aligned with one or more learning theories. The results show that the use of social media by the participants could be traced to two dominant theories-Connectivism and Constructivism. They also suggest that many medical educators may not be fully informed of these theories. Medical educators' use of social media can be traced to learning theories, but these theories may not be explicitly utilised in instructional design. It is recommended that formal education (faculty development) around learning theory would further enhance the use of social media in medical education. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Devil is in the details: Using logic models to investigate program process.

PubMed

Peyton, David J; Scicchitano, Michael

2017-12-01

Theory-based logic models are commonly developed as part of requirements for grant funding. As a tool to communicate complex social programs, theory based logic models are an effective visual communication. However, after initial development, theory based logic models are often abandoned and remain in their initial form despite changes in the program process. This paper examines the potential benefits of committing time and resources to revising the initial theory driven logic model and developing detailed logic models that describe key activities to accurately reflect the program and assist in effective program management. The authors use a funded special education teacher preparation program to exemplify the utility of drill down logic models. The paper concludes with lessons learned from the iterative revision process and suggests how the process can lead to more flexible and calibrated program management. Copyright © 2017 Elsevier Ltd. All rights reserved.

Principles of Temporal Processing Across the Cortical Hierarchy.

PubMed

Himberger, Kevin D; Chien, Hsiang-Yun; Honey, Christopher J

2018-05-02

The world is richly structured on multiple spatiotemporal scales. In order to represent spatial structure, many machine-learning models repeat a set of basic operations at each layer of a hierarchical architecture. These iterated spatial operations - including pooling, normalization and pattern completion - enable these systems to recognize and predict spatial structure, while robust to changes in the spatial scale, contrast and noisiness of the input signal. Because our brains also process temporal information that is rich and occurs across multiple time scales, might the brain employ an analogous set of operations for temporal information processing? Here we define a candidate set of temporal operations, and we review evidence that they are implemented in the mammalian cerebral cortex in a hierarchical manner. We conclude that multiple consecutive stages of cortical processing can be understood to perform temporal pooling, temporal normalization and temporal pattern completion. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

A STRICTLY CONTRACTIVE PEACEMAN-RACHFORD SPLITTING METHOD FOR CONVEX PROGRAMMING.

PubMed

Bingsheng, He; Liu, Han; Wang, Zhaoran; Yuan, Xiaoming

2014-07-01

In this paper, we focus on the application of the Peaceman-Rachford splitting method (PRSM) to a convex minimization model with linear constraints and a separable objective function. Compared to the Douglas-Rachford splitting method (DRSM), another splitting method from which the alternating direction method of multipliers originates, PRSM requires more restrictive assumptions to ensure its convergence, while it is always faster whenever it is convergent. We first illustrate that the reason for this difference is that the iterative sequence generated by DRSM is strictly contractive, while that generated by PRSM is only contractive with respect to the solution set of the model. With only the convexity assumption on the objective function of the model under consideration, the convergence of PRSM is not guaranteed. But for this case, we show that the first t iterations of PRSM still enable us to find an approximate solution with an accuracy of O (1/ t ). A worst-case O (1/ t ) convergence rate of PRSM in the ergodic sense is thus established under mild assumptions. After that, we suggest attaching an underdetermined relaxation factor with PRSM to guarantee the strict contraction of its iterative sequence and thus propose a strictly contractive PRSM. A worst-case O (1/ t ) convergence rate of this strictly contractive PRSM in a nonergodic sense is established. We show the numerical efficiency of the strictly contractive PRSM by some applications in statistical learning and image processing.

Creating Learning Experiences that Promote Informal Science Education: Designing Conservation-Focused Interactive Zoo Exhibits through Action Research

NASA Astrophysics Data System (ADS)

Kalenda, Peter

Research on exhibit design over the past twenty years has started to identify many different methods to increase the learning that occurs in informal education environments. This study utilized relevant research on exhibit design to create and study the effectiveness of a mobile interactive exhibit at the Seneca Park Zoo that promotes socialization, engagement in science, and conservation-related practices among guests. This study will serve as one component of a major redesign project at the Seneca Park Zoo for their Rocky Coasts exhibit. This action research study targeted the following question, "How can interactive exhibits be designed to promote socialization, engagement in science, and real-world conservation-related practices (RCPs) among zoo guests?" Specific research questions included: 1. In what ways did guests engage with the exhibit? 2. In what ways were guests impacted by the exhibit? a) What evidence exists, if any, of guests learning science content from the exhibit? b) What evidence exists, if any, of guests being emotionally affected by the exhibit? c) What evidence exists, if any, of guests changing their RCPs after visiting the exhibit? Data were collected through zoo guest surveys completed by zoo guests comparing multiple exhibits, interviews with guests before and after they used the prototype exhibit, observations and audio recordings of guests using the prototype exhibit, and follow-up phone interviews with guests who volunteered to participate. Data were analyzed collaboratively with members of the zoo's exhibit Redesign Team using grounded theory qualitative data analysis techniques to find patterns and trends among data. Initial findings from data analysis were used to develop shifts in the exhibit in order to increase visitor engagement and learning. This process continued for two full action research spirals, which resulted in three iterations of the prototype exhibit. The overall findings of this study highlight the ways in which guests engaged with and were impacted by this exhibit. Findings revealed the importance of the location of interactives and signage as well as a range of readability concerns for visitor engagement. In addition, findings highlight the roles of parents in informal learning environments, and the impact of exhibit design on dwell time and questioning. This study demonstrates the value and importance of utilizing an iterative design process informed by action research when creating learning experiences in zoos. This study also reinforces how difficult it can be to both influence and measure the shifting of guests' RCPs.

Fuzzy adaptive iterative learning coordination control of second-order multi-agent systems with imprecise communication topology structure

NASA Astrophysics Data System (ADS)

Chen, Jiaxi; Li, Junmin

2018-02-01

In this paper, we investigate the perfect consensus problem for second-order linearly parameterised multi-agent systems (MAS) with imprecise communication topology structure. Takagi-Sugeno (T-S) fuzzy models are presented to describe the imprecise communication topology structure of leader-following MAS, and a distributed adaptive iterative learning control protocol is proposed with the dynamic of leader unknown to any of the agent. The proposed protocol guarantees that the follower agents can track the leader perfectly on [0,T] for the consensus problem. Under alignment condition, a sufficient condition of the consensus for closed-loop MAS is given based on Lyapunov stability theory. Finally, a numerical example and a multiple pendulum system are given to illustrate the effectiveness of the proposed algorithm.

Nonlinear Motion Tracking by Deep Learning Architecture

NASA Astrophysics Data System (ADS)

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

2018-03-01

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

Iterative quantization: a Procrustean approach to learning binary codes for large-scale image retrieval.

PubMed

Gong, Yunchao; Lazebnik, Svetlana; Gordo, Albert; Perronnin, Florent

2013-12-01

This paper addresses the problem of learning similarity-preserving binary codes for efficient similarity search in large-scale image collections. We formulate this problem in terms of finding a rotation of zero-centered data so as to minimize the quantization error of mapping this data to the vertices of a zero-centered binary hypercube, and propose a simple and efficient alternating minimization algorithm to accomplish this task. This algorithm, dubbed iterative quantization (ITQ), has connections to multiclass spectral clustering and to the orthogonal Procrustes problem, and it can be used both with unsupervised data embeddings such as PCA and supervised embeddings such as canonical correlation analysis (CCA). The resulting binary codes significantly outperform several other state-of-the-art methods. We also show that further performance improvements can result from transforming the data with a nonlinear kernel mapping prior to PCA or CCA. Finally, we demonstrate an application of ITQ to learning binary attributes or "classemes" on the ImageNet data set.

Effects of iterative learning based signal control strategies on macroscopic fundamental diagrams of urban road networks

NASA Astrophysics Data System (ADS)

Yan, Fei; Tian, Fuli; Shi, Zhongke

2016-10-01

Urban traffic flows are inherently repeated on a daily or weekly basis. This repeatability can help improve the traffic conditions if it is used properly by the control system. In this paper, we propose a novel iterative learning control (ILC) strategy for traffic signals of urban road networks using the repeatability feature of traffic flow. To improve the control robustness, the ILC strategy is further integrated with an error feedback control law in a complementary manner. Theoretical analysis indicates that the ILC-based traffic signal control methods can guarantee the asymptotic learning convergence, despite the presence of modeling uncertainties and exogenous disturbances. Finally, the impacts of the ILC-based signal control strategies on the network macroscopic fundamental diagram (MFD) are examined. The results show that the proposed ILC-based control strategies can homogenously distribute the network accumulation by controlling the vehicle numbers in each link to the desired levels under different traffic demands, which can result in the network with high capacity and mobility.

A Modularized Efficient Framework for Non-Markov Time Series Estimation

NASA Astrophysics Data System (ADS)

Schamberg, Gabriel; Ba, Demba; Coleman, Todd P.

2018-06-01

We present a compartmentalized approach to finding the maximum a-posteriori (MAP) estimate of a latent time series that obeys a dynamic stochastic model and is observed through noisy measurements. We specifically consider modern signal processing problems with non-Markov signal dynamics (e.g. group sparsity) and/or non-Gaussian measurement models (e.g. point process observation models used in neuroscience). Through the use of auxiliary variables in the MAP estimation problem, we show that a consensus formulation of the alternating direction method of multipliers (ADMM) enables iteratively computing separate estimates based on the likelihood and prior and subsequently "averaging" them in an appropriate sense using a Kalman smoother. As such, this can be applied to a broad class of problem settings and only requires modular adjustments when interchanging various aspects of the statistical model. Under broad log-concavity assumptions, we show that the separate estimation problems are convex optimization problems and that the iterative algorithm converges to the MAP estimate. As such, this framework can capture non-Markov latent time series models and non-Gaussian measurement models. We provide example applications involving (i) group-sparsity priors, within the context of electrophysiologic specrotemporal estimation, and (ii) non-Gaussian measurement models, within the context of dynamic analyses of learning with neural spiking and behavioral observations.

Mediating the Message: The Team Approach to Developing Interdisciplinary Science Exhibitions

NASA Astrophysics Data System (ADS)

Stauffer, B. W.; Starrs, S. K.

2005-05-01

Museum exhibition developers can take advantage of a wide range of methods and media for delivering scientific information to a general audience. But, determining what information to convey and which medium is the best means of conveying it can be an arduous process. How do you design an exhibition so a visiting fifth grade school group learns basic scientific concepts while an amateur naturalist finds enough rich content to warrant coming back in a few months? How much or how little media should be included? What forms of media are most appropriate? Answering these questions requires intensive and iterative collaboration and compromise among a team of educators, scientists and designers. The National Museum of Natural History's Forces of Change Program uses a unique team approach that includes scientific, exhibit design, and education experts to create interdisciplinary science exhibitions. Exhibit topics have explored the dynamics of a grasslands ecosystem, global impacts of El Nino, climate change in the Arctic, the functions of the atmosphere, and soil composition. Exhibition-related products include publications, scavenger hunts, interactive computer kiosks, educational CD-ROMs, animated cartoons, web sites, and school group activities. Team members will describe the team process and the iterative discussions involved in developing these products so they are as scientifically sound and engaging as possible.

Organisation of workplace learning: a case study of paediatric residents' and consultants' beliefs and practices.

PubMed

Skipper, Mads; Nøhr, Susanne Backman; Jacobsen, Tine Klitgaard; Musaeus, Peter

2016-08-01

Several studies have examined how doctors learn in the workplace, but research is needed linking workplace learning with the organisation of doctors' daily work. This study examined residents' and consultants' attitudes and beliefs regarding workplace learning and contextual and organisational factors influencing the organisation and planning of medical specialist training. An explorative case study in three paediatric departments in Denmark including 9 days of field observations and focus group interviews with 9 consultants responsible for medical education and 16 residents. The study aimed to identify factors in work organisation facilitating and hindering residents' learning. Data were coded through an iterative process guided by thematic analysis. Findings illustrate three main themes: (1) Learning beliefs about patient care and apprenticeship learning as inseparable in medical practice. Beliefs about training and patient care expressed in terms of training versus production caused a potential conflict. (2) Learning context. Continuity over time in tasks and care for patients is important, but continuity is challenged by the organisation of daily work routines. (3) Organisational culture and regulations were found to be encouraging as well inhibiting to a successful organisation of the work in regards to learning. Our findings stress the importance of consultants' and residents' beliefs about workplace learning as these agents handle the potential conflict between patient care and training of health professionals. The structuring of daily work tasks is a key factor in workplace learning as is an understanding of underlying relations and organisational culture in the clinical departments.

Fast learning method for convolutional neural networks using extreme learning machine and its application to lane detection.

PubMed

Kim, Jihun; Kim, Jonghong; Jang, Gil-Jin; Lee, Minho

2017-03-01

Deep learning has received significant attention recently as a promising solution to many problems in the area of artificial intelligence. Among several deep learning architectures, convolutional neural networks (CNNs) demonstrate superior performance when compared to other machine learning methods in the applications of object detection and recognition. We use a CNN for image enhancement and the detection of driving lanes on motorways. In general, the process of lane detection consists of edge extraction and line detection. A CNN can be used to enhance the input images before lane detection by excluding noise and obstacles that are irrelevant to the edge detection result. However, training conventional CNNs requires considerable computation and a big dataset. Therefore, we suggest a new learning algorithm for CNNs using an extreme learning machine (ELM). The ELM is a fast learning method used to calculate network weights between output and hidden layers in a single iteration and thus, can dramatically reduce learning time while producing accurate results with minimal training data. A conventional ELM can be applied to networks with a single hidden layer; as such, we propose a stacked ELM architecture in the CNN framework. Further, we modify the backpropagation algorithm to find the targets of hidden layers and effectively learn network weights while maintaining performance. Experimental results confirm that the proposed method is effective in reducing learning time and improving performance. Copyright © 2016 Elsevier Ltd. All rights reserved.

Using Iterative Plan-Do-Study-Act Cycles to Improve Teaching Pedagogy.

PubMed

Murray, Elizabeth J

2018-01-15

Most students entering nursing programs today are members of Generation Y or the Millennial generation, and they learn differently than previous generations. Nurse educators must consider implementing innovative teaching strategies that appeal to the newest generation of learners. The Plan-Do-Study-Act cycle is a framework that can be helpful when planning, assessing, and continually improving teaching pedagogy. This article describes the use of iterative Plan-Do-Study-Act cycles to implement a change in teaching pedagogy.

Exploring the Impact of Global Studies Experiences on Undergraduate Student Development: Some Curricular Considerations

ERIC Educational Resources Information Center

Stephens, Christopher J.; Morford, Z. Harrison; Cihon, Traci M.; Forand, Elissa Hamilton; Neri-Hernández, Lucero

2018-01-01

In this manuscript, the authors detail the initial evaluations of the effects of participation in two iterations of an interdisciplinary learning community with a short-term study abroad opportunity on undergraduate student learning and behavior. The results suggest that the CHE (Cultural, Historical, and Environmental) log tool may be useful in…

Design and Facilitation of Problem-Based Learning in Graduate Teacher Education: An MA TESOL Case

ERIC Educational Resources Information Center

Caswell, Cynthia Ann

2016-01-01

This exploratory, evaluative case study introduces a new context for problem-based learning (PBL) involving an iterative, modular approach to curriculum-wide delivery of PBL in an MA TESOL program. The introduction to the curriculum context provides an overview of the design and delivery features particular to the situation. The delivery approach…

Using Learning Trajectories for Teacher Learning to Structure Professional Development

ERIC Educational Resources Information Center

Bargagliotti, Anna E.; Anderson, Celia Rousseau

2017-01-01

As a result of the increased focus on data literacy and data science across the world, there has been a large demand for professional development in statistics. However, exactly how these professional development opportunities should be structured remains an open question. The purpose of this paper is to describe the first iteration of a design…

Shaping Aspirations, Awareness, Academics, and Action: Outcomes of Summer Enrichment Programs for English-Learning Secondary Students

ERIC Educational Resources Information Center

Matthews, Paul H.; Mellom, Paula J.

2012-01-01

Mixed-method evaluation of two iterations of month-long summer enrichment programs for English-learning secondary students investigated impacts on participants' beliefs about school and academic achievement, and on actual course choices, test outcomes, and graduation rates. Students (N = 85) from one ethnically diverse, high-poverty high school in…

Introducing 12 Year-Olds to Elementary Particles

ERIC Educational Resources Information Center

Wiener, Gerfried J.; Schmeling, Sascha M.; Hopf, Martin

2017-01-01

We present a new learning unit, which introduces 12 year-olds to the subatomic structure of matter. The learning unit was iteratively developed as a design-based research project using the technique of probing acceptance. We give a brief overview of the unit's final version, discuss its key ideas and main concepts, and conclude by highlighting the…

Strong Convergence of Iteration Processes for Infinite Family of General Extended Mappings

NASA Astrophysics Data System (ADS)

Hussein Maibed, Zena

2018-05-01

The aim of this paper, we introduce a concept of general extended mapping which is independent of nonexpansive mapping and give an iteration process of families of quasi nonexpansive and of general extended mappings. Also, the existence of common fixed point are studied for these process in the Hilbert spaces.

A WEB based approach in biomedical engineering design education.

PubMed

Enderle, J D; Browne, A F; Hallowell, M B

1997-01-01

As part of the accreditation process for university engineering programs, students are required to complete a minimum number of design credits in their course of study, typically at the senior level. Many call this the capstone course. Engineering design is a course or series of courses that bring together concepts and principles that students learn in their field of study--it involves the integration and extension of material learned in their major toward a specific project. Most often, the student is exposed to system-wide analysis, critique and evaluation for the first time. Design is an iterative, decision making process in which the student optimally applies previously learned material to meet a stated objective. At the University of Connecticut, students work in teams of 3-4 members and work on externally sponsored projects. To facilitate working with sponsors, a WEB based approach is used for reporting the progress on projects. Students are responsible for creating their own WEB sites that support both html and pdf formats. Students provide the following deliverables: weekly progress reports, project statement, specifications, project proposal, interim report, and final report. A senior design homepage also provides links to data books and other resources for use by students. We are also planning distance learning experiences between two campuses so students can work on projects that involve the use of video conferencing.

Transition to intensive care nursing: establishing a starting point.

PubMed

Boyle, Martin; Butcher, Rand; Conyers, Vicki; Kendrick, Tina; MacNamara, Mary; Lang, Susie

2008-11-01

There is a shortage of intensive care (IC) nurses. A supported transition to IC nursing has been identified as a key strategy for recruitment and retention. In 2004 a discussion document relating to transition of IC nurses was presented to the New South Wales (NSW) Chief Nursing Officer (CNO). A workshop was held with key stakeholders and a Steering Group was established to develop a state-wide transition to IC nursing program. To survey orientation programs and educational resources and develop definitions, goals, learning objectives and clinical competencies relating to transition to IC nursing practice. A questionnaire and a draft document of definitions, target group, goals, learning objectives and clinical competencies for IC transition was distributed to 43 NSW IC units (ICUs). An iterative process of anonymous feedback and modification was undertaken to establish agreement on content. Responses were received from 29 units (return rate of 67%). The survey of educational resources indicated ICUs had access to educational support and there was evidence of a lack of a common standard or definition for "orientation" or "transition". The definitions, target group, goals and competency statements from the draft document were accepted with minor editorial change. Seventeen learning objectives or psychomotor skills were modified and an additional 19 were added to the draft as a result of the process. This work has established valid definitions, goals, learning objectives and clinical competencies that describe transition to intensive care nursing.

Learning to care for older patients: hospitals and nursing homes as learning environments.

PubMed

Huls, Marije; de Rooij, Sophia E; Diepstraten, Annemie; Koopmans, Raymond; Helmich, Esther

2015-03-01

A significant challenge facing health care is the ageing of the population, which calls for a major response in medical education. Most clinical learning takes place within hospitals, but nursing homes may also represent suitable learning environments in which students can gain competencies in geriatric medicine. This study explores what students perceive as the main learning outcomes of a geriatric medicine clerkship in a hospital or a nursing home, and explicitly addresses factors that may stimulate or hamper the learning process. This qualitative study falls within a constructivist paradigm: it draws on socio-cultural learning theory and is guided by the principles of constructivist grounded theory. There were two phases of data collection. Firstly, a maximum variation sample of 68 students completed a worksheet, giving brief written answers on questions regarding their geriatric medicine clerkships. Secondly, focus group discussions were conducted with 19 purposively sampled students. We used template analysis, iteratively cycling between data collection and analysis, using a constant comparative process. Students described a broad range of learning outcomes and formative experiences that were largely distinct from their learning in previous clerkships with regard to specific geriatric knowledge, deliberate decision making, end-of-life care, interprofessional collaboration and communication. According to students, the nursing home differed from the hospital in three aspects: interprofessional collaboration was more prominent; the lower resources available in nursing homes stimulated students to be creative, and students reported having greater autonomy in nursing homes compared with the more extensive educational guidance provided in hospitals. In both hospitals and nursing homes, students not only learn to care for older patients, but also describe various broader learning outcomes necessary to become good doctors. The results of our study, in particular the specific benefits and challenges associated with learning in the nursing home, may further inform the implementation of geriatric medicine clerkships in hospitals and nursing homes. © 2015 John Wiley & Sons Ltd.

Knowledge 'Translation' as social learning: negotiating the uptake of research-based knowledge in practice.

PubMed

Salter, K L; Kothari, A

2016-02-29

Knowledge translation and evidence-based practice have relied on research derived from clinical trials, which are considered to be methodologically rigorous. The result is practice recommendations based on a narrow view of evidence. We discuss how, within a practice environment, in fact individuals adopt and apply new evidence derived from multiple sources through ongoing, iterative learning cycles. The discussion is presented in four sections. After elaborating on the multiple forms of evidence used in practice, in section 2 we argue that the practitioner derives contextualized knowledge through reflective practice. Then, in section 3, the focus shifts from the individual to the team with consideration of social learning and theories of practice. In section 4 we discuss the implications of integrative and negotiated knowledge exchange and generation within the practice environment. Namely, how can we promote the use of research within a team-based, contextualized knowledge environment? We suggest support for: 1) collaborative learning environments for active learning and reflection, 2) engaged scholarship approaches so that practice can inform research in a collaborative manner and 3) leveraging authoritative opinion leaders for their clinical expertise during the shared negotiation of knowledge and research. Our approach also points to implications for studying evidence-informed practice: the identification of practice change (as an outcome) ought to be supplemented with understandings of how and when social negotiation processes occur to achieve integrated knowledge. This article discusses practice knowledge as dependent on the practice context and on social learning processes, and suggests how research knowledge uptake might be supported from this vantage point.

Programmable Iterative Optical Image And Data Processing

NASA Technical Reports Server (NTRS)

Jackson, Deborah J.

1995-01-01

Proposed method of iterative optical image and data processing overcomes limitations imposed by loss of optical power after repeated passes through many optical elements - especially, beam splitters. Involves selective, timed combination of optical wavefront phase conjugation and amplification to regenerate images in real time to compensate for losses in optical iteration loops; timing such that amplification turned on to regenerate desired image, then turned off so as not to regenerate other, undesired images or spurious light propagating through loops from unwanted reflections.

Lessons learned in the deployment of a HIV counseling and testing management information system on a new project.

PubMed

Makinde, Olusesan A; Ezomike, Chioma F; Lehmann, Harold P; Ibanga, Iko J

2011-11-28

To share our experience on how we used simple but detailed processes and deployed a management information system on a new HIV counseling and testing (HCT) project in Nigeria. The procedures used in this study were adopted for their strength in identifying areas of continuous improvement as the project was implemented. We used an iterative brainstorming technique among 30 participants (volunteer counselors and project management staff) as well as iterative quality audits to identify several limitations to the success of the project and to propose solutions. We then implemented the solutions and reevaluated for performance. Findings from the evaluations were then reintroduced into the brainstorming and planning sessions. Several limitations were identified with the most prominent being the poor documentation of records at the site and the lack of a document transfer trail for audit purposes. Communication, cohesion and team focus are necessary to achieve success on any new project. Institutionalizing routine HIV behavioral surveillance using data collected at HCT will help in streamlining interventions that will be evidence-based. 2011 Wolters Kluwer Health | Lippincott Williams & Wilkins

Solving the multiple-set split equality common fixed-point problem of firmly quasi-nonexpansive operators.

PubMed

Zhao, Jing; Zong, Haili

2018-01-01

In this paper, we propose parallel and cyclic iterative algorithms for solving the multiple-set split equality common fixed-point problem of firmly quasi-nonexpansive operators. We also combine the process of cyclic and parallel iterative methods and propose two mixed iterative algorithms. Our several algorithms do not need any prior information about the operator norms. Under mild assumptions, we prove weak convergence of the proposed iterative sequences in Hilbert spaces. As applications, we obtain several iterative algorithms to solve the multiple-set split equality problem.

Struggling to be self-directed: residents' paradoxical beliefs about learning.

PubMed

Nothnagle, Melissa; Anandarajah, Gowri; Goldman, Roberta E; Reis, Shmuel

2011-12-01

Self-directed learning (SDL) skills serve as the basis for physician lifelong learning; however, residency training does not typically emphasize SDL skills. To understand residents' needs regarding SDL curricula, the authors used qualitative methods to examine the residency learning culture and residents' views of SDL. The authors conducted individual, in-depth, semistructured interviews with all 13 final-year residents at the Brown University Family Medicine Residency Program. Interviews were audio taped and transcribed verbatim. Using an iterative individual and group process, four researchers conducted a qualitative analysis of the transcripts, identifying major themes and higher-order interpretations. Major themes included resident beliefs about learning, the learning culture in residency, and developmental progress in learning. Four paradoxes emerged in the analysis: (1) Residents understand and value the concept of SDL, but they engage in limited goal setting and reflection and report lack of skills to manage their own learning, particularly in the clinical setting. (2) Despite being immersed in what aims to be a learner-centered culture, many residents still value traditional, teacher-centered approaches. (3) Residents recognize patient care as the most powerful stimulus for SDL, but they often perceive patient care and learning as competing priorities. (4) Residents desire external guidance for SDL. Graduating residents lacked confidence in their SDL skills and their ability to manage their learning, especially in clinical settings. Fostering SDL skills during residency will likely require training and guidance for SDL as well as changes in the structure and culture of residency.

Neural Basis of Strategic Decision Making

PubMed Central

Lee, Daeyeol; Seo, Hyojung

2015-01-01

Human choice behaviors during social interactions often deviate from the predictions of game theory. This might arise partly from the limitations in cognitive abilities necessary for recursive reasoning about the behaviors of others. In addition, during iterative social interactions, choices might change dynamically, as knowledge about the intentions of others and estimates for choice outcomes are incrementally updated via reinforcement learning. Some of the brain circuits utilized during social decision making might be general-purpose and contribute to isomorphic individual and social decision making. By contrast, regions in the medial prefrontal cortex and temporal parietal junction might be recruited for cognitive processes unique to social decision making. PMID:26688301

The Lessons Oscar Taught Us: Data Science and Media & Entertainment.

PubMed

Gold, Michael; McClarren, Ryan; Gaughan, Conor

2013-06-01

Farsite Group, a data science firm based in Columbus, Ohio, launched a highly visible campaign in early 2013 to use predictive analytics to forecast the winners of the 85th Annual Academy Awards. The initiative was fun and exciting for the millions of Oscar viewers, but it also illustrated how data science could be further deployed in the media and entertainment industries. This article explores the current and potential use cases for big data and predictive analytics in those industries. It further discusses how the Farsite Forecast was built, as well as how the model was iterated, how the projections performed, and what lessons were learned in the process.

Rank-Optimized Logistic Matrix Regression toward Improved Matrix Data Classification.

PubMed

Zhang, Jianguang; Jiang, Jianmin

2018-02-01

While existing logistic regression suffers from overfitting and often fails in considering structural information, we propose a novel matrix-based logistic regression to overcome the weakness. In the proposed method, 2D matrices are directly used to learn two groups of parameter vectors along each dimension without vectorization, which allows the proposed method to fully exploit the underlying structural information embedded inside the 2D matrices. Further, we add a joint [Formula: see text]-norm on two parameter matrices, which are organized by aligning each group of parameter vectors in columns. This added co-regularization term has two roles-enhancing the effect of regularization and optimizing the rank during the learning process. With our proposed fast iterative solution, we carried out extensive experiments. The results show that in comparison to both the traditional tensor-based methods and the vector-based regression methods, our proposed solution achieves better performance for matrix data classifications.

Deep learning for low-dose CT

NASA Astrophysics Data System (ADS)

Chen, Hu; Zhang, Yi; Zhou, Jiliu; Wang, Ge

2017-09-01

Given the potential risk of X-ray radiation to the patient, low-dose CT has attracted a considerable interest in the medical imaging field. Currently, the main stream low-dose CT methods include vendor-specific sinogram domain filtration and iterative reconstruction algorithms, but they need to access raw data whose formats are not transparent to most users. Due to the difficulty of modeling the statistical characteristics in the image domain, the existing methods for directly processing reconstructed images cannot eliminate image noise very well while keeping structural details. Inspired by the idea of deep learning, here we combine the autoencoder, deconvolution network, and shortcut connections into the residual encoder-decoder convolutional neural network (RED-CNN) for low-dose CT imaging. After patch-based training, the proposed RED-CNN achieves a competitive performance relative to the-state-of-art methods. Especially, our method has been favorably evaluated in terms of noise suppression and structural preservation.

Preceptors' Perceptions of Interprofessional Practice, Student Interactions, and Strategies for Interprofessional Education in Clinical Settings.

PubMed

Hudak, Nicholas M; Melcher, Betsy; Strand de Oliveira, Justine

2017-12-01

This study describes clinical preceptors' perceptions of interprofessional practice, the nature and variety of physician assistant (PA) students' interprofessional interactions during clinical training, and factors that facilitate or hinder interprofessional education (IPE) in clinical settings. This qualitative study involved interviews with preceptors that were audio-recorded, transcribed, and then analyzed through an iterative process to identify key conceptual themes. Fourteen preceptors from a variety of clinical settings participated. Four themes were identified: (1) preceptors define interprofessional practice differently; (2) students learn about teams by being a part of teams; (3) preceptors separate students to avoid diluting learning experiences; and (4) preceptors can facilitate IPE by introducing students to members of the team and role modeling team skills. The themes may inform PA educators' efforts to increase IPE in clinical settings through educational interventions with both preceptors and students.

Enhanced HMAX model with feedforward feature learning for multiclass categorization.

PubMed

Li, Yinlin; Wu, Wei; Zhang, Bo; Li, Fengfu

2015-01-01

In recent years, the interdisciplinary research between neuroscience and computer vision has promoted the development in both fields. Many biologically inspired visual models are proposed, and among them, the Hierarchical Max-pooling model (HMAX) is a feedforward model mimicking the structures and functions of V1 to posterior inferotemporal (PIT) layer of the primate visual cortex, which could generate a series of position- and scale- invariant features. However, it could be improved with attention modulation and memory processing, which are two important properties of the primate visual cortex. Thus, in this paper, based on recent biological research on the primate visual cortex, we still mimic the first 100-150 ms of visual cognition to enhance the HMAX model, which mainly focuses on the unsupervised feedforward feature learning process. The main modifications are as follows: (1) To mimic the attention modulation mechanism of V1 layer, a bottom-up saliency map is computed in the S1 layer of the HMAX model, which can support the initial feature extraction for memory processing; (2) To mimic the learning, clustering and short-term memory to long-term memory conversion abilities of V2 and IT, an unsupervised iterative clustering method is used to learn clusters with multiscale middle level patches, which are taken as long-term memory; (3) Inspired by the multiple feature encoding mode of the primate visual cortex, information including color, orientation, and spatial position are encoded in different layers of the HMAX model progressively. By adding a softmax layer at the top of the model, multiclass categorization experiments can be conducted, and the results on Caltech101 show that the enhanced model with a smaller memory size exhibits higher accuracy than the original HMAX model, and could also achieve better accuracy than other unsupervised feature learning methods in multiclass categorization task.

User-Driven Sampling Strategies in Image Exploitation

DOE PAGES

Harvey, Neal R.; Porter, Reid B.

2013-12-23

Visual analytics and interactive machine learning both try to leverage the complementary strengths of humans and machines to solve complex data exploitation tasks. These fields overlap most significantly when training is involved: the visualization or machine learning tool improves over time by exploiting observations of the human-computer interaction. This paper focuses on one aspect of the human-computer interaction that we call user-driven sampling strategies. Unlike relevance feedback and active learning sampling strategies, where the computer selects which data to label at each iteration, we investigate situations where the user selects which data is to be labeled at each iteration. User-drivenmore » sampling strategies can emerge in many visual analytics applications but they have not been fully developed in machine learning. We discovered that in user-driven sampling strategies suggest new theoretical and practical research questions for both visualization science and machine learning. In this paper we identify and quantify the potential benefits of these strategies in a practical image analysis application. We find user-driven sampling strategies can sometimes provide significant performance gains by steering tools towards local minima that have lower error than tools trained with all of the data. Furthermore, in preliminary experiments we find these performance gains are particularly pronounced when the user is experienced with the tool and application domain.« less

User-driven sampling strategies in image exploitation

NASA Astrophysics Data System (ADS)

Harvey, Neal; Porter, Reid

2013-12-01

Visual analytics and interactive machine learning both try to leverage the complementary strengths of humans and machines to solve complex data exploitation tasks. These fields overlap most significantly when training is involved: the visualization or machine learning tool improves over time by exploiting observations of the human-computer interaction. This paper focuses on one aspect of the human-computer interaction that we call user-driven sampling strategies. Unlike relevance feedback and active learning sampling strategies, where the computer selects which data to label at each iteration, we investigate situations where the user selects which data is to be labeled at each iteration. User-driven sampling strategies can emerge in many visual analytics applications but they have not been fully developed in machine learning. User-driven sampling strategies suggest new theoretical and practical research questions for both visualization science and machine learning. In this paper we identify and quantify the potential benefits of these strategies in a practical image analysis application. We find user-driven sampling strategies can sometimes provide significant performance gains by steering tools towards local minima that have lower error than tools trained with all of the data. In preliminary experiments we find these performance gains are particularly pronounced when the user is experienced with the tool and application domain.

Iterative Neighbour-Information Gathering for Ranking Nodes in Complex Networks

NASA Astrophysics Data System (ADS)

Xu, Shuang; Wang, Pei; Lü, Jinhu

2017-01-01

Designing node influence ranking algorithms can provide insights into network dynamics, functions and structures. Increasingly evidences reveal that node’s spreading ability largely depends on its neighbours. We introduce an iterative neighbourinformation gathering (Ing) process with three parameters, including a transformation matrix, a priori information and an iteration time. The Ing process iteratively combines priori information from neighbours via the transformation matrix, and iteratively assigns an Ing score to each node to evaluate its influence. The algorithm appropriates for any types of networks, and includes some traditional centralities as special cases, such as degree, semi-local, LeaderRank. The Ing process converges in strongly connected networks with speed relying on the first two largest eigenvalues of the transformation matrix. Interestingly, the eigenvector centrality corresponds to a limit case of the algorithm. By comparing with eight renowned centralities, simulations of susceptible-infected-removed (SIR) model on real-world networks reveal that the Ing can offer more exact rankings, even without a priori information. We also observe that an optimal iteration time is always in existence to realize best characterizing of node influence. The proposed algorithms bridge the gaps among some existing measures, and may have potential applications in infectious disease control, designing of optimal information spreading strategies.

Development and Evaluation of an Intuitive Operations Planning Process

DTIC Science & Technology

2006-03-01

designed to be iterative and also prescribes the way in which iterations should occur. On the other hand, participants’ perceived level of trust and...16 4. DESIGN AND METHOD OF THE EXPERIMENTAL EVALUATION OF THE INTUITIVE PLANNING PROCESS...20 4.1.3 Design

Online Bahavior Aquisition of an Agent based on Coaching as Learning Assistance

NASA Astrophysics Data System (ADS)

Hirokawa, Masakazu; Suzuki, Kenji

This paper describes a novel methodology, namely ``Coaching'', which allows humans to give a subjective evaluation to an agent in an iterative manner. This is an interactive learning method to improve the reinforcement learning by modifying a reward function dynamically according to given evaluations by a trainer and the learning situation of the agent. We demonstrate that the agent can learn different reward functions by given instructions such as ``good or bad'' by human's observation, and can also obtain a set of behavior based on the learnt reward functions through several experiments.

Creating a Learning Organization for State, Local, and Tribal Law Enforcement to Combat Violent Extremism

DTIC Science & Technology

2016-09-01

iterations in that time for the student practitioners to work through. When possible, case studies will be selected from actual counter-radicalizations...justify participation in the learning 9 organization. Those cases will be evaluated on a case -by- case basis and the need to expand the CVE mission...interested within the learning organization. The National Fire Academy Executive Fire Officer Program applied research pre -course is an example of

Visual recognition and inference using dynamic overcomplete sparse learning.

PubMed

Murray, Joseph F; Kreutz-Delgado, Kenneth

2007-09-01

We present a hierarchical architecture and learning algorithm for visual recognition and other visual inference tasks such as imagination, reconstruction of occluded images, and expectation-driven segmentation. Using properties of biological vision for guidance, we posit a stochastic generative world model and from it develop a simplified world model (SWM) based on a tractable variational approximation that is designed to enforce sparse coding. Recent developments in computational methods for learning overcomplete representations (Lewicki & Sejnowski, 2000; Teh, Welling, Osindero, & Hinton, 2003) suggest that overcompleteness can be useful for visual tasks, and we use an overcomplete dictionary learning algorithm (Kreutz-Delgado, et al., 2003) as a preprocessing stage to produce accurate, sparse codings of images. Inference is performed by constructing a dynamic multilayer network with feedforward, feedback, and lateral connections, which is trained to approximate the SWM. Learning is done with a variant of the back-propagation-through-time algorithm, which encourages convergence to desired states within a fixed number of iterations. Vision tasks require large networks, and to make learning efficient, we take advantage of the sparsity of each layer to update only a small subset of elements in a large weight matrix at each iteration. Experiments on a set of rotated objects demonstrate various types of visual inference and show that increasing the degree of overcompleteness improves recognition performance in difficult scenes with occluded objects in clutter.

Knowledge exchange systems for youth health and chronic disease prevention: a tri-provincial case study.

PubMed

Murnaghan, D; Morrison, W; Griffith, E J; Bell, B L; Duffley, L A; McGarry, K; Manske, S

2013-09-01

The research teams undertook a case study design using a common analytical framework to investigate three provincial (Prince Edward Island, New Brunswick and Manitoba) knowledge exchange systems. These three knowledge exchange systems seek to generate and enhance the use of evidence in policy development, program planning and evaluation to improve youth health and chronic disease prevention. We applied a case study design to explore the lessons learned, that is, key conditions or processes contributing to the development of knowledge exchange capacity, using a multi-data collection method to gain an in-depth understanding. Data management, synthesis and analysis activities were concurrent, iterative and ongoing. The lessons learned were organized into seven "clusters." Key findings demonstrated that knowledge exchange is a complex process requiring champions, collaborative partnerships, regional readiness and the adaptation of knowledge exchange to diverse stakeholders. Overall, knowledge exchange systems can increase the capacity to exchange and use evidence by moving beyond collecting and reporting data. Areas of influence included development of new partnerships, expanded knowledge-sharing activities, and refinement of policy and practice approaches related to youth health and chronic disease prevention.

A Primer for Developing Measures of Science Content Knowledge for Small-Scale Research and Instructional Use.

PubMed

Bass, Kristin M; Drits-Esser, Dina; Stark, Louisa A

2016-01-01

The credibility of conclusions made about the effectiveness of educational interventions depends greatly on the quality of the assessments used to measure learning gains. This essay, intended for faculty involved in small-scale projects, courses, or educational research, provides a step-by-step guide to the process of developing, scoring, and validating high-quality content knowledge assessments. We illustrate our discussion with examples from our assessments of high school students' understanding of concepts in cell biology and epigenetics. Throughout, we emphasize the iterative nature of the development process, the importance of creating instruments aligned to the learning goals of an intervention or curricula, and the importance of collaborating with other content and measurement specialists along the way. © 2016 K. M. Bass et al. CBE—Life Sciences Education © 2016 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

A Universal Tare Load Prediction Algorithm for Strain-Gage Balance Calibration Data Analysis

NASA Technical Reports Server (NTRS)

Ulbrich, N.

2011-01-01

An algorithm is discussed that may be used to estimate tare loads of wind tunnel strain-gage balance calibration data. The algorithm was originally developed by R. Galway of IAR/NRC Canada and has been described in the literature for the iterative analysis technique. Basic ideas of Galway's algorithm, however, are universally applicable and work for both the iterative and the non-iterative analysis technique. A recent modification of Galway's algorithm is presented that improves the convergence behavior of the tare load prediction process if it is used in combination with the non-iterative analysis technique. The modified algorithm allows an analyst to use an alternate method for the calculation of intermediate non-linear tare load estimates whenever Galway's original approach does not lead to a convergence of the tare load iterations. It is also shown in detail how Galway's algorithm may be applied to the non-iterative analysis technique. Hand load data from the calibration of a six-component force balance is used to illustrate the application of the original and modified tare load prediction method. During the analysis of the data both the iterative and the non-iterative analysis technique were applied. Overall, predicted tare loads for combinations of the two tare load prediction methods and the two balance data analysis techniques showed excellent agreement as long as the tare load iterations converged. The modified algorithm, however, appears to have an advantage over the original algorithm when absolute voltage measurements of gage outputs are processed using the non-iterative analysis technique. In these situations only the modified algorithm converged because it uses an exact solution of the intermediate non-linear tare load estimate for the tare load iteration.

Performance Analysis of Entropy Methods on K Means in Clustering Process

NASA Astrophysics Data System (ADS)

Dicky Syahputra Lubis, Mhd.; Mawengkang, Herman; Suwilo, Saib

2017-12-01

K Means is a non-hierarchical data clustering method that attempts to partition existing data into one or more clusters / groups. This method partitions the data into clusters / groups so that data that have the same characteristics are grouped into the same cluster and data that have different characteristics are grouped into other groups.The purpose of this data clustering is to minimize the objective function set in the clustering process, which generally attempts to minimize variation within a cluster and maximize the variation between clusters. However, the main disadvantage of this method is that the number k is often not known before. Furthermore, a randomly chosen starting point may cause two points to approach the distance to be determined as two centroids. Therefore, for the determination of the starting point in K Means used entropy method where this method is a method that can be used to determine a weight and take a decision from a set of alternatives. Entropy is able to investigate the harmony in discrimination among a multitude of data sets. Using Entropy criteria with the highest value variations will get the highest weight. Given this entropy method can help K Means work process in determining the starting point which is usually determined at random. Thus the process of clustering on K Means can be more quickly known by helping the entropy method where the iteration process is faster than the K Means Standard process. Where the postoperative patient dataset of the UCI Repository Machine Learning used and using only 12 data as an example of its calculations is obtained by entropy method only with 2 times iteration can get the desired end result.

Exploring Alignment among Learning Progressions, Teacher-Designed Formative Assessment Tasks, and Student Growth: Results of a Four-Year Study

ERIC Educational Resources Information Center

Furtak, Erin Marie; Circi, Ruhan; Heredia, Sara C.

2018-01-01

This article describes a 4-year study of experienced high school biology teachers' participation in a five-step professional development experience in which they iteratively studied student ideas with the support of a set of learning progressions, designed formative assessment activities, practiced using those activities with their students,…

For the Love of Statistics: Appreciating and Learning to Apply Experimental Analysis and Statistics through Computer Programming Activities

ERIC Educational Resources Information Center

Mascaró, Maite; Sacristán, Ana Isabel; Rufino, Marta M.

2016-01-01

For the past 4 years, we have been involved in a project that aims to enhance the teaching and learning of experimental analysis and statistics, of environmental and biological sciences students, through computational programming activities (using R code). In this project, through an iterative design, we have developed sequences of R-code-based…

Playing and Learning: An iPad Game Development & Implementation Case Study

ERIC Educational Resources Information Center

Jenson, Jennifer; de Castell, Suzanne; Muehrer, Rachel; McLaughlin-Jenkins, Erin

2016-01-01

There is a great deal of enthusiasm for the use of games in formal educational contexts; however, there is a notable and problematic lack of studies that make use of replicable study designs to empirically link games to learning (Young, et al., 2012). This paper documents the iterative design and development of an educationally focused game,…

A Machine Learning System for Analyzing Human Tactics in a Game

NASA Astrophysics Data System (ADS)

Ito, Hirotaka; Tanaka, Toshimitsu; Sugie, Noboru

In order to realize advanced man-machine interfaces, it is desired to develop a system that can infer the mental state of human users and then return appropriate responses. As the first step toward the above goal, we developed a system capable of inferring human tactics in a simple game played between the system and a human. We present a machine learning system that plays a color expectation game. The system infers the tactics of the opponent, and then decides the action based on the result. We employed a modified version of classifier system like XCS in order to design the system. In addition, three methods are proposed in order to accelerate the learning rate. They are a masking method, an iterative method, and tactics templates. The results of computer experiments confirmed that the proposed methods effectively accelerate the machine learning. The masking method and the iterative method are effective to a simple strategy that considers only a part of past information. However, study speed of these methods is not enough for the tactics that refers to a lot of past information. For the case, the tactics template was able to settle the study rapidly when the tactics is identified.

Low-rank structure learning via nonconvex heuristic recovery.

PubMed

Deng, Yue; Dai, Qionghai; Liu, Risheng; Zhang, Zengke; Hu, Sanqing

2013-03-01

In this paper, we propose a nonconvex framework to learn the essential low-rank structure from corrupted data. Different from traditional approaches, which directly utilizes convex norms to measure the sparseness, our method introduces more reasonable nonconvex measurements to enhance the sparsity in both the intrinsic low-rank structure and the sparse corruptions. We will, respectively, introduce how to combine the widely used ℓp norm (0 < p < 1) and log-sum term into the framework of low-rank structure learning. Although the proposed optimization is no longer convex, it still can be effectively solved by a majorization-minimization (MM)-type algorithm, with which the nonconvex objective function is iteratively replaced by its convex surrogate and the nonconvex problem finally falls into the general framework of reweighed approaches. We prove that the MM-type algorithm can converge to a stationary point after successive iterations. The proposed model is applied to solve two typical problems: robust principal component analysis and low-rank representation. Experimental results on low-rank structure learning demonstrate that our nonconvex heuristic methods, especially the log-sum heuristic recovery algorithm, generally perform much better than the convex-norm-based method (0 < p < 1) for both data with higher rank and with denser corruptions.

Calibration and compensation method of three-axis geomagnetic sensor based on pre-processing total least square iteration

NASA Astrophysics Data System (ADS)

Zhou, Y.; Zhang, X.; Xiao, W.

2018-04-01

As the geomagnetic sensor is susceptible to interference, a pre-processing total least square iteration method is proposed for calibration compensation. Firstly, the error model of the geomagnetic sensor is analyzed and the correction model is proposed, then the characteristics of the model are analyzed and converted into nine parameters. The geomagnetic data is processed by Hilbert transform (HHT) to improve the signal-to-noise ratio, and the nine parameters are calculated by using the combination of Newton iteration method and the least squares estimation method. The sifter algorithm is used to filter the initial value of the iteration to ensure that the initial error is as small as possible. The experimental results show that this method does not need additional equipment and devices, can continuously update the calibration parameters, and better than the two-step estimation method, it can compensate geomagnetic sensor error well.

Solving coupled groundwater flow systems using a Jacobian Free Newton Krylov method

NASA Astrophysics Data System (ADS)

Mehl, S.

2012-12-01

Jacobian Free Newton Kyrlov (JFNK) methods can have several advantages for simulating coupled groundwater flow processes versus conventional methods. Conventional methods are defined here as those based on an iterative coupling (rather than a direct coupling) and/or that use Picard iteration rather than Newton iteration. In an iterative coupling, the systems are solved separately, coupling information is updated and exchanged between the systems, and the systems are re-solved, etc., until convergence is achieved. Trusted simulators, such as Modflow, are based on these conventional methods of coupling and work well in many cases. An advantage of the JFNK method is that it only requires calculation of the residual vector of the system of equations and thus can make use of existing simulators regardless of how the equations are formulated. This opens the possibility of coupling different process models via augmentation of a residual vector by each separate process, which often requires substantially fewer changes to the existing source code than if the processes were directly coupled. However, appropriate perturbation sizes need to be determined for accurate approximations of the Frechet derivative, which is not always straightforward. Furthermore, preconditioning is necessary for reasonable convergence of the linear solution required at each Kyrlov iteration. Existing preconditioners can be used and applied separately to each process which maximizes use of existing code and robust preconditioners. In this work, iteratively coupled parent-child local grid refinement models of groundwater flow and groundwater flow models with nonlinear exchanges to streams are used to demonstrate the utility of the JFNK approach for Modflow models. Use of incomplete Cholesky preconditioners with various levels of fill are examined on a suite of nonlinear and linear models to analyze the effect of the preconditioner. Comparisons of convergence and computer simulation time are made using conventional iteratively coupled methods and those based on Picard iteration to those formulated with JFNK to gain insights on the types of nonlinearities and system features that make one approach advantageous. Results indicate that nonlinearities associated with stream/aquifer exchanges are more problematic than those resulting from unconfined flow.

Automated concept-level information extraction to reduce the need for custom software and rules development.

PubMed

D'Avolio, Leonard W; Nguyen, Thien M; Goryachev, Sergey; Fiore, Louis D

2011-01-01

Despite at least 40 years of promising empirical performance, very few clinical natural language processing (NLP) or information extraction systems currently contribute to medical science or care. The authors address this gap by reducing the need for custom software and rules development with a graphical user interface-driven, highly generalizable approach to concept-level retrieval. A 'learn by example' approach combines features derived from open-source NLP pipelines with open-source machine learning classifiers to automatically and iteratively evaluate top-performing configurations. The Fourth i2b2/VA Shared Task Challenge's concept extraction task provided the data sets and metrics used to evaluate performance. Top F-measure scores for each of the tasks were medical problems (0.83), treatments (0.82), and tests (0.83). Recall lagged precision in all experiments. Precision was near or above 0.90 in all tasks. Discussion With no customization for the tasks and less than 5 min of end-user time to configure and launch each experiment, the average F-measure was 0.83, one point behind the mean F-measure of the 22 entrants in the competition. Strong precision scores indicate the potential of applying the approach for more specific clinical information extraction tasks. There was not one best configuration, supporting an iterative approach to model creation. Acceptable levels of performance can be achieved using fully automated and generalizable approaches to concept-level information extraction. The described implementation and related documentation is available for download.

State-of-the-art Anonymization of Medical Records Using an Iterative Machine Learning Framework

PubMed Central

Szarvas, György; Farkas, Richárd; Busa-Fekete, Róbert

2007-01-01

Objective The anonymization of medical records is of great importance in the human life sciences because a de-identified text can be made publicly available for non-hospital researchers as well, to facilitate research on human diseases. Here the authors have developed a de-identification model that can successfully remove personal health information (PHI) from discharge records to make them conform to the guidelines of the Health Information Portability and Accountability Act. Design We introduce here a novel, machine learning-based iterative Named Entity Recognition approach intended for use on semi-structured documents like discharge records. Our method identifies PHI in several steps. First, it labels all entities whose tags can be inferred from the structure of the text and it then utilizes this information to find further PHI phrases in the flow text parts of the document. Measurements Following the standard evaluation method of the first Workshop on Challenges in Natural Language Processing for Clinical Data, we used token-level Precision, Recall and Fβ=1 measure metrics for evaluation. Results Our system achieved outstanding accuracy on the standard evaluation dataset of the de-identification challenge, with an F measure of 99.7534% for the best submitted model. Conclusion We can say that our system is competitive with the current state-of-the-art solutions, while we describe here several techniques that can be beneficial in other tasks that need to handle structured documents such as clinical records. PMID:17823086

MapReduce SVM Game

DOE PAGES

Vineyard, Craig M.; Verzi, Stephen J.; James, Conrad D.; ...

2015-08-10

Despite technological advances making computing devices faster, smaller, and more prevalent in today's age, data generation and collection has outpaced data processing capabilities. Simply having more compute platforms does not provide a means of addressing challenging problems in the big data era. Rather, alternative processing approaches are needed and the application of machine learning to big data is hugely important. The MapReduce programming paradigm is an alternative to conventional supercomputing approaches, and requires less stringent data passing constrained problem decompositions. Rather, MapReduce relies upon defining a means of partitioning the desired problem so that subsets may be computed independently andmore » recom- bined to yield the net desired result. However, not all machine learning algorithms are amenable to such an approach. Game-theoretic algorithms are often innately distributed, consisting of local interactions between players without requiring a central authority and are iterative by nature rather than requiring extensive retraining. Effectively, a game-theoretic approach to machine learning is well suited for the MapReduce paradigm and provides a novel, alternative new perspective to addressing the big data problem. In this paper we present a variant of our Support Vector Machine (SVM) Game classifier which may be used in a distributed manner, and show an illustrative example of applying this algorithm.« less

MapReduce SVM Game

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vineyard, Craig M.; Verzi, Stephen J.; James, Conrad D.

Despite technological advances making computing devices faster, smaller, and more prevalent in today's age, data generation and collection has outpaced data processing capabilities. Simply having more compute platforms does not provide a means of addressing challenging problems in the big data era. Rather, alternative processing approaches are needed and the application of machine learning to big data is hugely important. The MapReduce programming paradigm is an alternative to conventional supercomputing approaches, and requires less stringent data passing constrained problem decompositions. Rather, MapReduce relies upon defining a means of partitioning the desired problem so that subsets may be computed independently andmore » recom- bined to yield the net desired result. However, not all machine learning algorithms are amenable to such an approach. Game-theoretic algorithms are often innately distributed, consisting of local interactions between players without requiring a central authority and are iterative by nature rather than requiring extensive retraining. Effectively, a game-theoretic approach to machine learning is well suited for the MapReduce paradigm and provides a novel, alternative new perspective to addressing the big data problem. In this paper we present a variant of our Support Vector Machine (SVM) Game classifier which may be used in a distributed manner, and show an illustrative example of applying this algorithm.« less

Nested Krylov methods and preserving the orthogonality

NASA Technical Reports Server (NTRS)

Desturler, Eric; Fokkema, Diederik R.

1993-01-01

Recently the GMRESR inner-outer iteraction scheme for the solution of linear systems of equations was proposed by Van der Vorst and Vuik. Similar methods have been proposed by Axelsson and Vassilevski and Saad (FGMRES). The outer iteration is GCR, which minimizes the residual over a given set of direction vectors. The inner iteration is GMRES, which at each step computes a new direction vector by approximately solving the residual equation. However, the optimality of the approximation over the space of outer search directions is ignored in the inner GMRES iteration. This leads to suboptimal corrections to the solution in the outer iteration, as components of the outer iteration directions may reenter in the inner iteration process. Therefore we propose to preserve the orthogonality relations of GCR in the inner GMRES iteration. This gives optimal corrections; however, it involves working with a singular, non-symmetric operator. We will discuss some important properties, and we will show by experiments that, in terms of matrix vector products, this modification (almost) always leads to better convergence. However, because we do more orthogonalizations, it does not always give an improved performance in CPU-time. Furthermore, we will discuss efficient implementations as well as the truncation possibilities of the outer GCR process. The experimental results indicate that for such methods it is advantageous to preserve the orthogonality in the inner iteration. Of course we can also use iteration schemes other than GMRES as the inner method; methods with short recurrences like GICGSTAB are of interest.

Hierarchical extreme learning machine based reinforcement learning for goal localization

NASA Astrophysics Data System (ADS)

AlDahoul, Nouar; Zaw Htike, Zaw; Akmeliawati, Rini

2017-03-01

The objective of goal localization is to find the location of goals in noisy environments. Simple actions are performed to move the agent towards the goal. The goal detector should be capable of minimizing the error between the predicted locations and the true ones. Few regions need to be processed by the agent to reduce the computational effort and increase the speed of convergence. In this paper, reinforcement learning (RL) method was utilized to find optimal series of actions to localize the goal region. The visual data, a set of images, is high dimensional unstructured data and needs to be represented efficiently to get a robust detector. Different deep Reinforcement models have already been used to localize a goal but most of them take long time to learn the model. This long learning time results from the weights fine tuning stage that is applied iteratively to find an accurate model. Hierarchical Extreme Learning Machine (H-ELM) was used as a fast deep model that doesn’t fine tune the weights. In other words, hidden weights are generated randomly and output weights are calculated analytically. H-ELM algorithm was used in this work to find good features for effective representation. This paper proposes a combination of Hierarchical Extreme learning machine and Reinforcement learning to find an optimal policy directly from visual input. This combination outperforms other methods in terms of accuracy and learning speed. The simulations and results were analysed by using MATLAB.

WE-G-18A-04: 3D Dictionary Learning Based Statistical Iterative Reconstruction for Low-Dose Cone Beam CT Imaging

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bai, T; UT Southwestern Medical Center, Dallas, TX; Yan, H

2014-06-15

Purpose: To develop a 3D dictionary learning based statistical reconstruction algorithm on graphic processing units (GPU), to improve the quality of low-dose cone beam CT (CBCT) imaging with high efficiency. Methods: A 3D dictionary containing 256 small volumes (atoms) of 3x3x3 voxels was trained from a high quality volume image. During reconstruction, we utilized a Cholesky decomposition based orthogonal matching pursuit algorithm to find a sparse representation on this dictionary basis of each patch in the reconstructed image, in order to regularize the image quality. To accelerate the time-consuming sparse coding in the 3D case, we implemented our algorithm inmore » a parallel fashion by taking advantage of the tremendous computational power of GPU. Evaluations are performed based on a head-neck patient case. FDK reconstruction with full dataset of 364 projections is used as the reference. We compared the proposed 3D dictionary learning based method with a tight frame (TF) based one using a subset data of 121 projections. The image qualities under different resolutions in z-direction, with or without statistical weighting are also studied. Results: Compared to the TF-based CBCT reconstruction, our experiments indicated that 3D dictionary learning based CBCT reconstruction is able to recover finer structures, to remove more streaking artifacts, and is less susceptible to blocky artifacts. It is also observed that statistical reconstruction approach is sensitive to inconsistency between the forward and backward projection operations in parallel computing. Using high a spatial resolution along z direction helps improving the algorithm robustness. Conclusion: 3D dictionary learning based CBCT reconstruction algorithm is able to sense the structural information while suppressing noise, and hence to achieve high quality reconstruction. The GPU realization of the whole algorithm offers a significant efficiency enhancement, making this algorithm more feasible for potential clinical application. A high zresolution is preferred to stabilize statistical iterative reconstruction. This work was supported in part by NIH(1R01CA154747-01), NSFC((No. 61172163), Research Fund for the Doctoral Program of Higher Education of China (No. 20110201110011), China Scholarship Council.« less

Not so Complex: Iteration in the Complex Plane

ERIC Educational Resources Information Center

O'Dell, Robin S.

2014-01-01

The simple process of iteration can produce complex and beautiful figures. In this article, Robin O'Dell presents a set of tasks requiring students to use the geometric interpretation of complex number multiplication to construct linear iteration rules. When the outputs are plotted in the complex plane, the graphs trace pleasing designs…

The recruitment of new members to existing PBSGL small groups: a qualitative study.

PubMed

Park, Julia; Cunningham, David E

2018-04-23

Introduction Practice-Based Small Group Learning (PBSGL) is a learning programme widely adopted by primary healthcare professions (general practitioners, general practice nurses and pharmacists) in Scotland and other countries in the UK. PBSGL groups recruit members and decide on meeting dates and venues. Study aims To determine how groups recruit new members and discern what are the important attributes of the new members. Method A grounded theory approach was used with purposive sampling to recruit PBSGL groups to the study. Focus groups drawn from established PBSGL groups were conducted by two researchers following an iterative process, with interviews audio-recorded and transcribed, and codes and themes constructed. Data saturation was achieved. Results and conclusions Four themes were identified that affected group recruitment: group formation and purpose; group culture and ethos; experience and seniority range of group members; professional socialisation and cross-fertilisation. Groups whose main purpose was learning encouraged diverse membership, while groups that were stricter with recruitment often prioritised friendship, group safety, trust and peer support over learning. The variation in group's openness to recruitment may make it difficult for potential members to find a group and this may affect the development and expansion of the PBSGL programme.

Hypergame theory applied to cyber attack and defense

NASA Astrophysics Data System (ADS)

House, James Thomas; Cybenko, George

2010-04-01

This work concerns cyber attack and defense in the context of game theory--specifically hypergame theory. Hypergame theory extends classical game theory with the ability to deal with differences in players' expertise, differences in their understanding of game rules, misperceptions, and so forth. Each of these different sub-scenarios, or subgames, is associated with a probability--representing the likelihood that the given subgame is truly "in play" at a given moment. In order to form an optimal attack or defense policy, these probabilities must be learned if they're not known a-priori. We present hidden Markov model and maximum entropy approaches for accurately learning these probabilities through multiple iterations of both normal and modified game play. We also give a widely-applicable approach for the analysis of cases where an opponent is aware that he is being studied, and intentionally plays to spoil the process of learning and thereby obfuscate his attributes. These are considered in the context of a generic, abstract cyber attack example. We demonstrate that machine learning efficacy can be heavily dependent on the goals and styles of participant behavior. To this end detailed simulation results under various combinations of attacker and defender behaviors are presented and analyzed.

An Active Learning Framework for Hyperspectral Image Classification Using Hierarchical Segmentation

NASA Technical Reports Server (NTRS)

Zhang, Zhou; Pasolli, Edoardo; Crawford, Melba M.; Tilton, James C.

2015-01-01

Augmenting spectral data with spatial information for image classification has recently gained significant attention, as classification accuracy can often be improved by extracting spatial information from neighboring pixels. In this paper, we propose a new framework in which active learning (AL) and hierarchical segmentation (HSeg) are combined for spectral-spatial classification of hyperspectral images. The spatial information is extracted from a best segmentation obtained by pruning the HSeg tree using a new supervised strategy. The best segmentation is updated at each iteration of the AL process, thus taking advantage of informative labeled samples provided by the user. The proposed strategy incorporates spatial information in two ways: 1) concatenating the extracted spatial features and the original spectral features into a stacked vector and 2) extending the training set using a self-learning-based semi-supervised learning (SSL) approach. Finally, the two strategies are combined within an AL framework. The proposed framework is validated with two benchmark hyperspectral datasets. Higher classification accuracies are obtained by the proposed framework with respect to five other state-of-the-art spectral-spatial classification approaches. Moreover, the effectiveness of the proposed pruning strategy is also demonstrated relative to the approaches based on a fixed segmentation.

Finite Volume Element (FVE) discretization and multilevel solution of the axisymmetric heat equation

NASA Astrophysics Data System (ADS)

Litaker, Eric T.

1994-12-01

The axisymmetric heat equation, resulting from a point-source of heat applied to a metal block, is solved numerically; both iterative and multilevel solutions are computed in order to compare the two processes. The continuum problem is discretized in two stages: finite differences are used to discretize the time derivatives, resulting is a fully implicit backward time-stepping scheme, and the Finite Volume Element (FVE) method is used to discretize the spatial derivatives. The application of the FVE method to a problem in cylindrical coordinates is new, and results in stencils which are analyzed extensively. Several iteration schemes are considered, including both Jacobi and Gauss-Seidel; a thorough analysis of these schemes is done, using both the spectral radii of the iteration matrices and local mode analysis. Using this discretization, a Gauss-Seidel relaxation scheme is used to solve the heat equation iteratively. A multilevel solution process is then constructed, including the development of intergrid transfer and coarse grid operators. Local mode analysis is performed on the components of the amplification matrix, resulting in the two-level convergence factors for various combinations of the operators. A multilevel solution process is implemented by using multigrid V-cycles; the iterative and multilevel results are compared and discussed in detail. The computational savings resulting from the multilevel process are then discussed.

Implementation of team-based learning in year 1 of a PBL based medical program: a pilot study.

PubMed

Burgess, Annette; Ayton, Tom; Mellis, Craig

2016-02-04

A traditional and effective form of teaching within medical education has been Problem Based Learning (PBL). However, this method of teaching is resource intensive, normally requiring one tutor for every ten students. Team-based learning (TBL) has gained recent popularity in medical education, and can be applied to large groups of up to 100 students. TBL makes use of the advantages of small group teaching and learning, but in contrast to PBL, does not need large numbers of teachers. This study sought to explore the efficacy of using TBL in place of PBL in Year 1 of a medical program. In Year 1 of the medical program, two iterations of TBL, with 20 students, were run following four iterations of PBL within the Cardiology teaching block. Student feedback following PBL and TBL was collected by questionnaire, using closed and open ended questions. Additionally, individual and team tests were held at the beginning of each TBL class, and results of each week were compared. All students (n = 20) participated in the test in week 1, and 18/20 students participated in week 2. In total, 19/20 (95%) of students completed the questionnaires regarding their PBL and TBL experiences. The use of small groups, the readiness assurance tests, immediate feedback from an expert clinician, as well as time efficiency were all aspects of the TBL experience that students found positive. The clinical problem-solving activity, however, was considered to be less effective with TBL. There was a significant improvement (p = 0.004) in students' score from the week 1 assessment (median = 2) to the week 2 (median = 3.5) assessment. Interestingly, all teams but one (Team 1) achieved a lower score on their second week assessment than on their first. However, the lowest performing team in week 1 outperformed all other teams in week 2. Students favoured many aspects of the TBL process, particularly motivation to do the pre-reading, and better engagement in the process. Additionally, the application of TBL principles meant the sessions were not reliant upon a large teacher to student ratio. Students, however, highlighted the need for more time within TBL for clinical problem-solving.

Case-based learning facilitates critical thinking in undergraduate nutrition education: students describe the big picture.

PubMed

Harman, Tara; Bertrand, Brenda; Greer, Annette; Pettus, Arianna; Jennings, Jill; Wall-Bassett, Elizabeth; Babatunde, Oyinlola Toyin

2015-03-01

The vision of dietetics professions is based on interdependent education, credentialing, and practice. Case-based learning is a method of problem-based learning that is designed to heighten higher-order thinking. Case-based learning can assist students to connect education and specialized practice while developing professional skills for entry-level practice in nutrition and dietetics. This study examined student perspectives of their learning after immersion into case-based learning in nutrition courses. The theoretical frameworks of phenomenology and Bloom's Taxonomy of Educational Objectives triangulated the design of this qualitative study. Data were drawn from 426 written responses and three focus group discussions among 85 students from three upper-level undergraduate nutrition courses. Coding served to deconstruct the essence of respondent meaning given to case-based learning as a learning method. The analysis of the coding was the constructive stage that led to configuration of themes and theoretical practice pathways about student learning. Four leading themes emerged. Story or Scenario represents the ways that students described case-based learning, changes in student thought processes to accommodate case-based learning are illustrated in Method of Learning, higher cognitive learning that was achieved from case-based learning is represented in Problem Solving, and Future Practice details how students explained perceived professional competency gains from case-based learning. The skills that students acquired are consistent with those identified as essential to professional practice. In addition, the common concept of Big Picture was iterated throughout the themes and demonstrated that case-based learning prepares students for multifaceted problems that they are likely to encounter in professional practice. Copyright © 2015 Academy of Nutrition and Dietetics. Published by Elsevier Inc. All rights reserved.

The Role and Reprocessing of Attitudes in Fostering Employee Work Happiness: An Intervention Study.

PubMed

Williams, Paige; Kern, Margaret L; Waters, Lea

2017-01-01

This intervention study examines the iterative reprocessing of explicit and implicit attitudes as the process underlying associations between positive employee attitudes (PsyCap), perception of positive organization culture (organizational virtuousness, OV), and work happiness. Using a quasi-experimental design, a group of school staff ( N = 69) completed surveys at three time points. After the first assessment, the treatment group ( n = 51) completed a positive psychology training intervention. Results suggest that employee PsyCap, OV, and work happiness are associated with one another through both implicit and explicit attitudes. Further, the Iterative-Reprocessing Model of attitudes (IRM) provides some insights into the processes underlying these associations. By examining the role and processes through which explicit and implicit attitudes relate to wellbeing at work, the study integrates theories on attitudes, positive organizational scholarship, positive organizational behavior and positive education. It is one of the first studies to apply the theory of the IRM to explain associations amongst PsyCap, OV and work happiness, and to test the IRM theory in a field-based setting. In applying attitude theory to wellbeing research, this study provides insights to mechanisms underlying workplace wellbeing that have not been previously examined and in doing so responds to calls for researchers to learn more about the mechanisms underlying wellbeing interventions. Further, it highlights the need to understand subconscious processes in future wellbeing research and to include implicit measures in positive psychology interventions measurement programs. Practically, this research calls attention to the importance of developing both the positive attitudes of employees and the organizational culture in developing employee work happiness.

The Role and Reprocessing of Attitudes in Fostering Employee Work Happiness: An Intervention Study

PubMed Central

Williams, Paige; Kern, Margaret L.; Waters, Lea

2017-01-01

This intervention study examines the iterative reprocessing of explicit and implicit attitudes as the process underlying associations between positive employee attitudes (PsyCap), perception of positive organization culture (organizational virtuousness, OV), and work happiness. Using a quasi-experimental design, a group of school staff (N = 69) completed surveys at three time points. After the first assessment, the treatment group (n = 51) completed a positive psychology training intervention. Results suggest that employee PsyCap, OV, and work happiness are associated with one another through both implicit and explicit attitudes. Further, the Iterative-Reprocessing Model of attitudes (IRM) provides some insights into the processes underlying these associations. By examining the role and processes through which explicit and implicit attitudes relate to wellbeing at work, the study integrates theories on attitudes, positive organizational scholarship, positive organizational behavior and positive education. It is one of the first studies to apply the theory of the IRM to explain associations amongst PsyCap, OV and work happiness, and to test the IRM theory in a field-based setting. In applying attitude theory to wellbeing research, this study provides insights to mechanisms underlying workplace wellbeing that have not been previously examined and in doing so responds to calls for researchers to learn more about the mechanisms underlying wellbeing interventions. Further, it highlights the need to understand subconscious processes in future wellbeing research and to include implicit measures in positive psychology interventions measurement programs. Practically, this research calls attention to the importance of developing both the positive attitudes of employees and the organizational culture in developing employee work happiness. PMID:28154546

Implementation of a Multimodal Mobile System for Point-of-Sale Surveillance: Lessons Learned From Case Studies in Washington, DC, and New York City.

PubMed

Cantrell, Jennifer; Ganz, Ollie; Ilakkuvan, Vinu; Tacelosky, Michael; Kreslake, Jennifer; Moon-Howard, Joyce; Aidala, Angela; Vallone, Donna; Anesetti-Rothermel, Andrew; Kirchner, Thomas R

2015-01-01

In tobacco control and other fields, point-of-sale surveillance of the retail environment is critical for understanding industry marketing of products and informing public health practice. Innovations in mobile technology can improve existing, paper-based surveillance methods, yet few studies describe in detail how to operationalize the use of technology in public health surveillance. The aims of this paper are to share implementation strategies and lessons learned from 2 tobacco, point-of-sale surveillance projects to inform and prepare public health researchers and practitioners to implement new mobile technologies in retail point-of-sale surveillance systems. From 2011 to 2013, 2 point-of-sale surveillance pilot projects were conducted in Washington, DC, and New York, New York, to capture information about the tobacco retail environment and test the feasibility of a multimodal mobile data collection system, which included capabilities for audio or video recording data, electronic photographs, electronic location data, and a centralized back-end server and dashboard. We established a preimplementation field testing process for both projects, which involved a series of rapid and iterative tests to inform decisions and establish protocols around key components of the project. Important components of field testing included choosing a mobile phone that met project criteria, establishing an efficient workflow and accessible user interfaces for each component of the system, training and providing technical support to fieldworkers, and developing processes to integrate data from multiple sources into back-end systems that can be utilized in real-time. A well-planned implementation process is critical for successful use and performance of multimodal mobile surveillance systems. Guidelines for implementation include (1) the need to establish and allow time for an iterative testing framework for resolving technical and logistical challenges; (2) developing a streamlined workflow and user-friendly interfaces for data collection; (3) allowing for ongoing communication, feedback, and technology-related skill-building among all staff; and (4) supporting infrastructure for back-end data systems. Although mobile technologies are evolving rapidly, lessons learned from these case studies are essential for ensuring that the many benefits of new mobile systems for rapid point-of-sale surveillance are fully realized.

Implementation of a Multimodal Mobile System for Point-of-Sale Surveillance: Lessons Learned From Case Studies in Washington, DC, and New York City

PubMed Central

Ganz, Ollie; Ilakkuvan, Vinu; Tacelosky, Michael; Kreslake, Jennifer; Moon-Howard, Joyce; Aidala, Angela; Vallone, Donna; Anesetti-Rothermel, Andrew; Kirchner, Thomas R

2015-01-01

Background In tobacco control and other fields, point-of-sale surveillance of the retail environment is critical for understanding industry marketing of products and informing public health practice. Innovations in mobile technology can improve existing, paper-based surveillance methods, yet few studies describe in detail how to operationalize the use of technology in public health surveillance. Objective The aims of this paper are to share implementation strategies and lessons learned from 2 tobacco, point-of-sale surveillance projects to inform and prepare public health researchers and practitioners to implement new mobile technologies in retail point-of-sale surveillance systems. Methods From 2011 to 2013, 2 point-of-sale surveillance pilot projects were conducted in Washington, DC, and New York, New York, to capture information about the tobacco retail environment and test the feasibility of a multimodal mobile data collection system, which included capabilities for audio or video recording data, electronic photographs, electronic location data, and a centralized back-end server and dashboard. We established a preimplementation field testing process for both projects, which involved a series of rapid and iterative tests to inform decisions and establish protocols around key components of the project. Results Important components of field testing included choosing a mobile phone that met project criteria, establishing an efficient workflow and accessible user interfaces for each component of the system, training and providing technical support to fieldworkers, and developing processes to integrate data from multiple sources into back-end systems that can be utilized in real-time. Conclusions A well-planned implementation process is critical for successful use and performance of multimodal mobile surveillance systems. Guidelines for implementation include (1) the need to establish and allow time for an iterative testing framework for resolving technical and logistical challenges; (2) developing a streamlined workflow and user-friendly interfaces for data collection; (3) allowing for ongoing communication, feedback, and technology-related skill-building among all staff; and (4) supporting infrastructure for back-end data systems. Although mobile technologies are evolving rapidly, lessons learned from these case studies are essential for ensuring that the many benefits of new mobile systems for rapid point-of-sale surveillance are fully realized. PMID:27227138

Automatic OPC repair flow: optimized implementation of the repair recipe

NASA Astrophysics Data System (ADS)

Bahnas, Mohamed; Al-Imam, Mohamed; Word, James

2007-10-01

Virtual manufacturing that is enabled by rapid, accurate, full-chip simulation is a main pillar in achieving successful mask tape-out in the cutting-edge low-k1 lithography. It facilitates detecting printing failures before a costly and time-consuming mask tape-out and wafer print occur. The OPC verification step role is critical at the early production phases of a new process development, since various layout patterns will be suspected that they might to fail or cause performance degradation, and in turn need to be accurately flagged to be fed back to the OPC Engineer for further learning and enhancing in the OPC recipe. At the advanced phases of the process development, there is much less probability of detecting failures but still the OPC Verification step act as the last-line-of-defense for the whole RET implemented work. In recent publication the optimum approach of responding to these detected failures was addressed, and a solution was proposed to repair these defects in an automated methodology and fully integrated and compatible with the main RET/OPC flow. In this paper the authors will present further work and optimizations of this Repair flow. An automated analysis methodology for root causes of the defects and classification of them to cover all possible causes will be discussed. This automated analysis approach will include all the learning experience of the previously highlighted causes and include any new discoveries. Next, according to the automated pre-classification of the defects, application of the appropriate approach of OPC repair (i.e. OPC knob) on each classified defect location can be easily selected, instead of applying all approaches on all locations. This will help in cutting down the runtime of the OPC repair processing and reduce the needed number of iterations to reach the status of zero defects. An output report for existing causes of defects and how the tool handled them will be generated. The report will with help further learning and facilitate the enhancement of the main OPC recipe. Accordingly, the main OPC recipe can be more robust, converging faster and probably in a fewer number of iterations. This knowledge feedback loop is one of the fruitful benefits of the Automatic OPC Repair flow.

Teachers’ perceptions of aspects affecting seminar learning: a qualitative study

PubMed Central

2013-01-01

Background Many medical schools have embraced small group learning methods in their undergraduate curricula. Given increasing financial constraints on universities, active learning groups like seminars (with 25 students a group) are gaining popularity. To enhance the understanding of seminar learning and to determine how seminar learning can be optimised it is important to investigate stakeholders’ views. In this study, we qualitatively explored the views of teachers on aspects affecting seminar learning. Methods Twenty-four teachers with experience in facilitating seminars in a three-year bachelor curriculum participated in semi-structured focus group interviews. Three focus groups met twice with an interval of two weeks led by one moderator. Sessions were audio taped, transcribed verbatim and independently coded by two researchers using thematic analysis. An iterative process of data reduction resulted in emerging aspects that influence seminar learning. Results Teachers identified seven key aspects affecting seminar learning: the seminar teacher, students, preparation, group functioning, seminar goals and content, course coherence and schedule and facilities. Important components of these aspects were: the teachers’ role in developing seminars (‘ownership’), the amount and quality of preparation materials, a non-threatening learning climate, continuity of group composition, suitability of subjects for seminar teaching, the number and quality of seminar questions, and alignment of different course activities. Conclusions The results of this study contribute to the unravelling of the ‘the black box’ of seminar learning. Suggestions for ways to optimise active learning in seminars are made regarding curriculum development, seminar content, quality assurance and faculty development. PMID:23399475

Designing a Web-Based Learning Portal for Geographic Visualization and Analysis in Public Health

PubMed Central

Robinson, Anthony C.; Roth, Robert E.; MacEachren, Alan M.

2011-01-01

Interactive mapping and spatial analysis tools are underutilized by health researchers and decision-makers due to scarce training materials, few examples demonstrating the successful use of geographic visualization, and poor mechanisms for sharing results generated by geovisualization. We report here on the development of the Geovisual EXplication (G-EX) Portal, a web-based application designed to connect researchers in geovisualization and related mapping sciences to users who are working in public health and epidemiology. This paper focuses on the design and development of the G-EX Portal Learn module, a set of tools intended to disseminate learning artifacts. Initial design and development of the G-EX Portal has been guided by our past research on use and usability of geovisualization in public health. As part of the iterative design and development process, we conducted a needs assessment survey with targeted end-users that we report on here. The survey focused on users’ current learning habits, their preferred kind of learning artifacts, and issues they may have with contributing learning artifacts to web portals. Survey results showed that users desire a diverse set of learning artifacts in terms of both formats and topics covered. Results also revealed a willingness of users to contribute both learning artifacts and personal information that would help other users to evaluate the credibility of the learning artifact source. We include a detailed description of the G-EX Portal Learn module and focus on modifications to the design of the Learn module as a result from feedback we received from our survey. PMID:21937462

A STRICTLY CONTRACTIVE PEACEMAN–RACHFORD SPLITTING METHOD FOR CONVEX PROGRAMMING

PubMed Central

BINGSHENG, HE; LIU, HAN; WANG, ZHAORAN; YUAN, XIAOMING

2014-01-01

In this paper, we focus on the application of the Peaceman–Rachford splitting method (PRSM) to a convex minimization model with linear constraints and a separable objective function. Compared to the Douglas–Rachford splitting method (DRSM), another splitting method from which the alternating direction method of multipliers originates, PRSM requires more restrictive assumptions to ensure its convergence, while it is always faster whenever it is convergent. We first illustrate that the reason for this difference is that the iterative sequence generated by DRSM is strictly contractive, while that generated by PRSM is only contractive with respect to the solution set of the model. With only the convexity assumption on the objective function of the model under consideration, the convergence of PRSM is not guaranteed. But for this case, we show that the first t iterations of PRSM still enable us to find an approximate solution with an accuracy of O(1/t). A worst-case O(1/t) convergence rate of PRSM in the ergodic sense is thus established under mild assumptions. After that, we suggest attaching an underdetermined relaxation factor with PRSM to guarantee the strict contraction of its iterative sequence and thus propose a strictly contractive PRSM. A worst-case O(1/t) convergence rate of this strictly contractive PRSM in a nonergodic sense is established. We show the numerical efficiency of the strictly contractive PRSM by some applications in statistical learning and image processing. PMID:25620862

Data-Driven Property Estimation for Protective Clothing

DTIC Science & Technology

2014-09-01

reliable predictions falls under the rubric “machine learning”. Inspired by the applications of machine learning in pharmaceutical drug design and...using genetic algorithms, for instance— descriptor selection can be automated as well. A well-known structured learning technique—Artificial Neural...descriptors automatically, by iteration, e.g., using a genetic algorithm [49]. 4.2.4 Avoiding Overfitting A peril of all regression—least squares as

Through the Looking Glass: Adult Education through the Lens of the Australian Journal of Adult Learning over Fifty Years

ERIC Educational Resources Information Center

Harris, Roger; Morrison, Anne

2011-01-01

In this paper we review fifty years of articles published in Australian Journal of Adult Learning in its various iterations. We examine the different roles of the journal: to illuminate the history and trends of adult education authors; to be the flagship of the adult education profession in Australia; to reflect on significant national events;…

Status of the ITER Cryodistribution

NASA Astrophysics Data System (ADS)

Chang, H.-S.; Vaghela, H.; Patel, P.; Rizzato, A.; Cursan, M.; Henry, D.; Forgeas, A.; Grillot, D.; Sarkar, B.; Muralidhara, S.; Das, J.; Shukla, V.; Adler, E.

2017-12-01

Since the conceptual design of the ITER Cryodistribution many modifications have been applied due to both system optimization and improved knowledge of the clients’ requirements. Process optimizations in the Cryoplant resulted in component simplifications whereas increased heat load in some of the superconducting magnet systems required more complicated process configuration but also the removal of a cold box was possible due to component arrangement standardization. Another cold box, planned for redundancy, has been removed due to the Tokamak in-Cryostat piping layout modification. In this proceeding we will summarize the present design status and component configuration of the ITER Cryodistribution with all changes implemented which aim at process optimization and simplification as well as operational reliability, stability and flexibility.

Particle Swarm Optimization With Interswarm Interactive Learning Strategy.

PubMed

Qin, Quande; Cheng, Shi; Zhang, Qingyu; Li, Li; Shi, Yuhui

2016-10-01

The learning strategy in the canonical particle swarm optimization (PSO) algorithm is often blamed for being the primary reason for loss of diversity. Population diversity maintenance is crucial for preventing particles from being stuck into local optima. In this paper, we present an improved PSO algorithm with an interswarm interactive learning strategy (IILPSO) by overcoming the drawbacks of the canonical PSO algorithm's learning strategy. IILPSO is inspired by the phenomenon in human society that the interactive learning behavior takes place among different groups. Particles in IILPSO are divided into two swarms. The interswarm interactive learning (IIL) behavior is triggered when the best particle's fitness value of both the swarms does not improve for a certain number of iterations. According to the best particle's fitness value of each swarm, the softmax method and roulette method are used to determine the roles of the two swarms as the learning swarm and the learned swarm. In addition, the velocity mutation operator and global best vibration strategy are used to improve the algorithm's global search capability. The IIL strategy is applied to PSO with global star and local ring structures, which are termed as IILPSO-G and IILPSO-L algorithm, respectively. Numerical experiments are conducted to compare the proposed algorithms with eight popular PSO variants. From the experimental results, IILPSO demonstrates the good performance in terms of solution accuracy, convergence speed, and reliability. Finally, the variations of the population diversity in the entire search process provide an explanation why IILPSO performs effectively.

Outcomes from the Delphi process of the Thoracic Robotic Curriculum Development Committee.

PubMed

Veronesi, Giulia; Dorn, Patrick; Dunning, Joel; Cardillo, Giuseppe; Schmid, Ralph A; Collins, Justin; Baste, Jean-Marc; Limmer, Stefan; Shahin, Ghada M M; Egberts, Jan-Hendrik; Pardolesi, Alessandro; Meacci, Elisa; Stamenkovic, Sasha; Casali, Gianluca; Rueckert, Jens C; Taurchini, Mauro; Santelmo, Nicola; Melfi, Franca; Toker, Alper

2018-06-01

As the adoption of robotic procedures becomes more widespread, additional risk related to the learning curve can be expected. This article reports the results of a Delphi process to define procedures to optimize robotic training of thoracic surgeons and to promote safe performance of established robotic interventions as, for example, lung cancer and thymoma surgery. In June 2016, a working panel was spontaneously created by members of the European Society of Thoracic Surgeons (ESTS) and European Association for Cardio-Thoracic Surgery (EACTS) with a specialist interest in robotic thoracic surgery and/or surgical training. An e-consensus-finding exercise using the Delphi methodology was applied requiring 80% agreement to reach consensus on each question. Repeated iterations of anonymous voting continued over 3 rounds. Agreement was reached on many points: a standardized robotic training curriculum for robotic thoracic surgery should be divided into clearly defined sections as a staged learning pathway; the basic robotic curriculum should include a baseline evaluation, an e-learning module, a simulation-based training (including virtual reality simulation, Dry lab and Wet lab) and a robotic theatre (bedside) observation. Advanced robotic training should include e-learning on index procedures (right upper lobe) with video demonstration, access to video library of robotic procedures, simulation training, modular console training to index procedure, transition to full-procedure training with a proctor and final evaluation of the submitted video to certified independent examiners. Agreement was reached on a large number of questions to optimize and standardize training and education of thoracic surgeons in robotic activity. The production of the content of the learning material is ongoing.

Exploiting parallel computing with limited program changes using a network of microcomputers

NASA Technical Reports Server (NTRS)

Rogers, J. L., Jr.; Sobieszczanski-Sobieski, J.

1985-01-01

Network computing and multiprocessor computers are two discernible trends in parallel processing. The computational behavior of an iterative distributed process in which some subtasks are completed later than others because of an imbalance in computational requirements is of significant interest. The effects of asynchronus processing was studied. A small existing program was converted to perform finite element analysis by distributing substructure analysis over a network of four Apple IIe microcomputers connected to a shared disk, simulating a parallel computer. The substructure analysis uses an iterative, fully stressed, structural resizing procedure. A framework of beams divided into three substructures is used as the finite element model. The effects of asynchronous processing on the convergence of the design variables are determined by not resizing particular substructures on various iterations.

Metal-induced streak artifact reduction using iterative reconstruction algorithms in x-ray computed tomography image of the dentoalveolar region.

PubMed

Dong, Jian; Hayakawa, Yoshihiko; Kannenberg, Sven; Kober, Cornelia

2013-02-01

The objective of this study was to reduce metal-induced streak artifact on oral and maxillofacial x-ray computed tomography (CT) images by developing the fast statistical image reconstruction system using iterative reconstruction algorithms. Adjacent CT images often depict similar anatomical structures in thin slices. So, first, images were reconstructed using the same projection data of an artifact-free image. Second, images were processed by the successive iterative restoration method where projection data were generated from reconstructed image in sequence. Besides the maximum likelihood-expectation maximization algorithm, the ordered subset-expectation maximization algorithm (OS-EM) was examined. Also, small region of interest (ROI) setting and reverse processing were applied for improving performance. Both algorithms reduced artifacts instead of slightly decreasing gray levels. The OS-EM and small ROI reduced the processing duration without apparent detriments. Sequential and reverse processing did not show apparent effects. Two alternatives in iterative reconstruction methods were effective for artifact reduction. The OS-EM algorithm and small ROI setting improved the performance. Copyright © 2012 Elsevier Inc. All rights reserved.

Manifold regularized matrix completion for multi-label learning with ADMM.

PubMed

Liu, Bin; Li, Yingming; Xu, Zenglin

2018-05-01

Multi-label learning is a common machine learning problem arising from numerous real-world applications in diverse fields, e.g, natural language processing, bioinformatics, information retrieval and so on. Among various multi-label learning methods, the matrix completion approach has been regarded as a promising approach to transductive multi-label learning. By constructing a joint matrix comprising the feature matrix and the label matrix, the missing labels of test samples are regarded as missing values of the joint matrix. With the low-rank assumption of the constructed joint matrix, the missing labels can be recovered by minimizing its rank. Despite its success, most matrix completion based approaches ignore the smoothness assumption of unlabeled data, i.e., neighboring instances should also share a similar set of labels. Thus they may under exploit the intrinsic structures of data. In addition, the matrix completion problem can be less efficient. To this end, we propose to efficiently solve the multi-label learning problem as an enhanced matrix completion model with manifold regularization, where the graph Laplacian is used to ensure the label smoothness over it. To speed up the convergence of our model, we develop an efficient iterative algorithm, which solves the resulted nuclear norm minimization problem with the alternating direction method of multipliers (ADMM). Experiments on both synthetic and real-world data have shown the promising results of the proposed approach. Copyright © 2018 Elsevier Ltd. All rights reserved.

Opening up to learning spiritual care of patients: a grounded theory study of nursing students.

PubMed

Giske, Tove; Cone, Pamela H

2012-07-01

To determine undergraduate nursing students' perspectives on spiritual care and how they learn to assess and provide spiritual care to patients. Nursing is concerned with holistic care. Systematic teaching and supervision of students to prepare them to assist patients spiritually is a growing focus. However, there is limited consensus about the competences students need to develop and little is written related to students learning processes. Grounded theory was used to identify students' main concern and develop a substantive grounded theory. Data collected during semi-structured interviews at three Norwegian University Colleges in eight focus groups with 42 undergraduate nursing students were analysed through constant comparison of transcribed interviews until categories were saturated. The participants' main concern was 'How to create a professional relationship with patients and maintain rapport when spiritual concerns were recognised'. Participants resolved this by 'Opening up to learning spiritual care'. This basic social process has three iterative phases that develop as a spiral throughout the nursing programme: 'Preparing for connection', 'Connecting with and supporting patients' and 'Reflecting on experiences'. Nurses need a wide range of competences to fulfil the nursing focus on holistic patient care. Nursing education should prepare students to recognise and act on spiritual cues. A trusting relationship and respectful and sensitive communication assist students to discover what is important to patients. An educational focus on spiritual and existential themes throughout the nursing programme will assist students to integrate theoretical learning into clinical practice. Study participants reported seeing few role models in clinical settings. Making spiritual assessment and interventions more visible and explicit would facilitate student learning in clinical practice. Evaluative discussions in clinical settings that include spiritual concerns will enhance holistic care. © 2012 Blackwell Publishing Ltd.

Adaptive Management of Ecosystems

EPA Science Inventory

Adaptive management is an approach to natural resource management that emphasizes learning through management. As such, management may be treated as experiment, with replication, or management may be conducted in an iterative manner. Although the concept has resonated with many...

Introductory Statistics in the Garden

ERIC Educational Resources Information Center

Wagaman, John C.

2017-01-01

This article describes four semesters of introductory statistics courses that incorporate service learning and gardening into the curriculum with applications of the binomial distribution, least squares regression and hypothesis testing. The activities span multiple semesters and are iterative in nature.

Robust iterative learning contouring controller with disturbance observer for machine tool feed drives.

PubMed

Simba, Kenneth Renny; Bui, Ba Dinh; Msukwa, Mathew Renny; Uchiyama, Naoki

2018-04-01

In feed drive systems, particularly machine tools, a contour error is more significant than the individual axial tracking errors from the view point of enhancing precision in manufacturing and production systems. The contour error must be within the permissible tolerance of given products. In machining complex or sharp-corner products, large contour errors occur mainly owing to discontinuous trajectories and the existence of nonlinear uncertainties. Therefore, it is indispensable to design robust controllers that can enhance the tracking ability of feed drive systems. In this study, an iterative learning contouring controller consisting of a classical Proportional-Derivative (PD) controller and disturbance observer is proposed. The proposed controller was evaluated experimentally by using a typical sharp-corner trajectory, and its performance was compared with that of conventional controllers. The results revealed that the maximum contour error can be reduced by about 37% on average. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

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

PubMed

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

2018-06-01

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

A new model for graduate education and innovation in medical technology.

PubMed

Yazdi, Youseph; Acharya, Soumyadipta

2013-09-01

We describe a new model of graduate education in bioengineering innovation and design- a year long Master's degree program that educates engineers in the process of healthcare technology innovation for both advanced and low-resource global markets. Students are trained in an iterative "Spiral Innovation" approach that ensures early, staged, and repeated examination of all key elements of a successful medical device. This includes clinical immersion based problem identification and assessment (at Johns Hopkins Medicine and abroad), team based concept and business model development, and project planning based on iterative technical and business plan de-risking. The experiential, project based learning process is closely supported by several core courses in business, design, and engineering. Students in the program work on two team based projects, one focused on addressing healthcare needs in advanced markets and a second focused on low-resource settings. The program recently completed its fourth year of existence, and has graduated 61 students, who have continued on to industry or startups (one half), additional graduate education, or medical school (one third), or our own Global Health Innovation Fellowships. Over the 4 years, the program has sponsored 10 global health teams and 14 domestic/advanced market medtech teams, and launched 5 startups, of which 4 are still active. Projects have attracted over US$2.5M in follow-on awards and grants, that are supporting the continued development of over a dozen projects.

Publishing activities improves undergraduate biology education

PubMed Central

Smith, Michelle K

2018-01-01

Abstract To improve undergraduate biology education, there is an urgent need for biology instructors to publish their innovative active-learning instructional materials in peer-reviewed journals. To do this, instructors can measure student knowledge about a variety of biology concepts, iteratively design activities, explore student learning outcomes and publish the results. Creating a set of well-vetted activities, searchable through a journal interface, saves other instructors time and encourages the use of active-learning instructional practices. For authors, these publications offer new opportunities to collaborate and can provide evidence of a commitment to using active-learning instructional techniques in the classroom. PMID:29672697

Publishing activities improves undergraduate biology education.

PubMed

Smith, Michelle K

2018-06-01

To improve undergraduate biology education, there is an urgent need for biology instructors to publish their innovative active-learning instructional materials in peer-reviewed journals. To do this, instructors can measure student knowledge about a variety of biology concepts, iteratively design activities, explore student learning outcomes and publish the results. Creating a set of well-vetted activities, searchable through a journal interface, saves other instructors time and encourages the use of active-learning instructional practices. For authors, these publications offer new opportunities to collaborate and can provide evidence of a commitment to using active-learning instructional techniques in the classroom.

'It's important that we learn too': Empowering parents to facilitate participation in physical activity for children and youth with disabilities.

PubMed

Willis, Claire E; Reid, Siobhan; Elliott, Catherine; Nyquist, Astrid; Jahnsen, Reidun; Rosenberg, Michael; Girdler, Sonya

2017-09-20

The actions and behaviors of parents have been identified as key factors that influence a child's participation in physical activity. However, there is limited knowledge of how parents can be supported to embody facilitative roles. This study aimed to explore how an ecological intervention encourages parents of children with disabilities to develop as facilitators, to enable ongoing physical activity participation in a child's local environment. A qualitative design using grounded theory was employed. Forty four parents (26 mothers, 18 fathers) of 31 children with a range of disabilities (mean age 12y 6m (SD 2y 2m); 18 males) partaking in the Local Environment Model intervention at Beitostolen Healthsports Centre in Norway participated in the study. Data were derived from the triangulation of semi-structured interviews and participant observation. Data analysis was an iterative approach of constant comparison, where data collection, memo writing, open, axial and selective coding analysis, were undertaken simultaneously. Findings were consolidated into a model describing the central phenomenon and its relationship to other categories. Thematic concepts uncovered in this study describe a social process of parent learning and empowerment, comprising three primary components; (i) active ingredients of the intervention that enabled learning and empowerment to transpire, (ii) parent learning and empowerment as a process, and (iii) related outcomes. A family-centered approach, encompassing family-to-family support, may enhance physical activity participation outcomes for children and youth with disabilities.

The development and evaluation of a 'blended' enquiry based learning model for mental health nursing students: "making your experience count".

PubMed

Rigby, Lindsay; Wilson, Ian; Baker, John; Walton, Tim; Price, Owen; Dunne, Kate; Keeley, Philip

2012-04-01

To meet the demands required for safe and effective care, nurses must be able to integrate theoretical knowledge with clinical practice (Kohen and Lehman, 2008; Polit and Beck, 2008; Shirey, 2006). This should include the ability to adapt research in response to changing clinical environments and the changing needs of service users. It is through reflective practice that students develop their clinical reasoning and evaluation skills to engage in this process. This paper aims to describe the development, implementation and evaluation of a project designed to provide a structural approach to the recognition and resolution of clinical, theoretical and ethical dilemmas identified by 3rd year undergraduate mental health nursing students. This is the first paper to describe the iterative process of developing a 'blended' learning model which provides students with an opportunity to experience the process of supervision and to become more proficient in using information technology to develop and maintain their clinical skills. Three cohorts of student nurses were exposed to various combinations of face to face group supervision and a virtual learning environment (VLE) in order to apply their knowledge of good practice guidelines and evidenced-based practice to identified clinical issues. A formal qualitative evaluation using independently facilitated focus groups was conducted with each student cohort and thematically analysed (Miles & Huberman, 1994). The themes that emerged were: relevance to practice; facilitation of independent learning; and the discussion of clinical issues. The results of this study show that 'blending' face-to-face groups with an e-learning component was the most acceptable and effective form of delivery which met the needs of students' varied learning styles. Additionally, students reported that they were more aware of the importance of clinical supervision and of their role as supervisees. Copyright © 2011 Elsevier Ltd. All rights reserved.

Adaptive Comparative Judgment: A Tool to Support Students' Assessment Literacy.

PubMed

Rhind, Susan M; Hughes, Kirsty J; Yool, Donald; Shaw, Darren; Kerr, Wesley; Reed, Nicki

Comparative judgment in assessment is a process whereby repeated comparison of two items (e.g., assessment answers) can allow an accurate ranking of all the submissions to be achieved. In adaptive comparative judgment (ACJ), technology is used to automate the process and present pairs of pieces of work over iterative cycles. An online ACJ system was used to present students with work prepared by a previous cohort at the same stage of their studies. Objective marks given to the work by experienced faculty were compared to the rankings given to the work by a cohort of veterinary students (n=154). Each student was required to review and judge 20 answers provided by the previous cohort to a free-text short answer question. The time that students spent on the judgment tasks was recorded, and students were asked to reflect on their experiences after engaging with the task. There was a strong positive correlation between student ranking and faculty marking. A weak positive correlation was found between the time students spent on the judgments and their performance on the part of their own examination that contained questions in the same format. Slightly less than half of the students agreed that the exercise was a good use of their time, but 78% agreed that they had learned from the process. Qualitative data highlighted different levels of benefit from the simplest aspect of learning more about the topic to an appreciation of the more generic lessons to be learned.

SCI-U: E-learning for patient education in spinal cord injury rehabilitation

PubMed Central

Shepherd, John D.; Badger-Brown, Karla M.; Legassic, Matthew S.; Walia, Saagar; Wolfe, Dalton L.

2012-01-01

Background/objectives To develop an online patient education resource for use in spinal cord injury rehabilitation. Participants The development process involved more than 100 subject-matter experts (SMEs) (rehabilitation professionals and consumers) from across Canada. Preliminary evaluation was conducted with 25 end-users. Methods An iterative development process was coordinated by a project team; SMEs (including patients) developed the content in working groups using wiki-based tools. Multiple rounds of feedback based on early prototypes helped improve the courses during development. Results Five courses were created, each featuring more than 45 minutes of video content and hundreds of media assets. Preliminary evaluation results indicate that users were satisfied by the courses and perceived them to be effective. Conclusions This is an effective process for developing multimedia patient education resources; the involvement of patients in all parts of the process was particularly helpful. Future work will focus on implementation, integration into clinical practice and other delivery formats (smart phones, tablets). PMID:23031169

Neural Basis of Strategic Decision Making.

PubMed

Lee, Daeyeol; Seo, Hyojung

2016-01-01

Human choice behaviors during social interactions often deviate from the predictions of game theory. This might arise partly from the limitations in the cognitive abilities necessary for recursive reasoning about the behaviors of others. In addition, during iterative social interactions, choices might change dynamically as knowledge about the intentions of others and estimates for choice outcomes are incrementally updated via reinforcement learning. Some of the brain circuits utilized during social decision making might be general-purpose and contribute to isomorphic individual and social decision making. By contrast, regions in the medial prefrontal cortex (mPFC) and temporal parietal junction (TPJ) might be recruited for cognitive processes unique to social decision making. Copyright © 2015 Elsevier Ltd. All rights reserved.

Development of a Mixed Methods Investigation of Process and Outcomes of Community-Based Participatory Research.

PubMed

Lucero, Julie; Wallerstein, Nina; Duran, Bonnie; Alegria, Margarita; Greene-Moton, Ella; Israel, Barbara; Kastelic, Sarah; Magarati, Maya; Oetzel, John; Pearson, Cynthia; Schulz, Amy; Villegas, Malia; White Hat, Emily R

2018-01-01

This article describes a mixed methods study of community-based participatory research (CBPR) partnership practices and the links between these practices and changes in health status and disparities outcomes. Directed by a CBPR conceptual model and grounded in indigenous-transformative theory, our nation-wide, cross-site study showcases the value of a mixed methods approach for better understanding the complexity of CBPR partnerships across diverse community and research contexts. The article then provides examples of how an iterative, integrated approach to our mixed methods analysis yielded enriched understandings of two key constructs of the model: trust and governance. Implications and lessons learned while using mixed methods to study CBPR are provided.

Simultaneous and iterative weighted regression analysis of toxicity tests using a microplate reader.

PubMed

Galgani, F; Cadiou, Y; Gilbert, F

1992-04-01

A system is described for determination of LC50 or IC50 by an iterative process based on data obtained from a plate reader using a marine unicellular alga as a target species. The esterase activity of Tetraselmis suesica on fluorescein diacetate as a substrate was measured using a fluorescence titerplate. Simultaneous analysis of results was performed using an iterative process adopting the sigmoid function Y = y/1 (dose of toxicant/IC50)slope for dose-response relationships. IC50 (+/- SEM) was estimated (P less than 0.05). An application with phosalone as a toxicant is presented.

Experiments on water detritiation and cryogenic distillation at TLK; Impact on ITER fuel cycle subsystems interfaces

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cristescu, I.; Cristescu, I. R.; Doerr, L.

2008-07-15

The ITER Isotope Separation System (ISS) and Water Detritiation System (WDS) should be integrated in order to reduce potential chronic tritium emissions from the ISS. This is achieved by routing the top (protium) product from the ISS to a feed point near the bottom end of the WDS Liquid Phase Catalytic Exchange (LPCE) column. This provides an additional barrier against ISS emissions and should mitigate the memory effects due to process parameter fluctuations in the ISS. To support the research activities needed to characterize the performances of various components for WDS and ISS processes under various working conditions and configurationsmore » as needed for ITER design, an experimental facility called TRENTA representative of the ITER WDS and ISS protium separation column, has been commissioned and is in operation at TLK The experimental program on TRENTA facility is conducted to provide the necessary design data related to the relevant ITER operating modes. The operation availability and performances of ISS-WDS have impact on ITER fuel cycle subsystems with consequences on the design integration. The preliminary experimental data on TRENTA facility are presented. (authors)« less

Studying citizen science through adaptive management and learning feedbacks as mechanisms for improving conservation.

PubMed

Jordan, Rebecca; Gray, Steven; Sorensen, Amanda; Newman, Greg; Mellor, David; Newman, Greg; Hmelo-Silver, Cindy; LaDeau, Shannon; Biehler, Dawn; Crall, Alycia

2016-06-01

Citizen science has generated a growing interest among scientists and community groups, and citizen science programs have been created specifically for conservation. We examined collaborative science, a highly interactive form of citizen science, which we developed within a theoretically informed framework. In this essay, we focused on 2 aspects of our framework: social learning and adaptive management. Social learning, in contrast to individual-based learning, stresses collaborative and generative insight making and is well-suited for adaptive management. Adaptive-management integrates feedback loops that are informed by what is learned and is guided by iterative decision making. Participants engaged in citizen science are able to add to what they are learning through primary data collection, which can result in the real-time information that is often necessary for conservation. Our work is particularly timely because research publications consistently report a lack of established frameworks and evaluation plans to address the extent of conservation outcomes in citizen science. To illustrate how our framework supports conservation through citizen science, we examined how 2 programs enacted our collaborative science framework. Further, we inspected preliminary conservation outcomes of our case-study programs. These programs, despite their recent implementation, are demonstrating promise with regard to positive conservation outcomes. To date, they are independently earning funds to support research, earning buy-in from local partners to engage in experimentation, and, in the absence of leading scientists, are collecting data to test ideas. We argue that this success is due to citizen scientists being organized around local issues and engaging in iterative, collaborative, and adaptive learning. © 2016 Society for Conservation Biology.

Iterative-Transform Phase Retrieval Using Adaptive Diversity

NASA Technical Reports Server (NTRS)

Dean, Bruce H.

2007-01-01

A phase-diverse iterative-transform phase-retrieval algorithm enables high spatial-frequency, high-dynamic-range, image-based wavefront sensing. [The terms phase-diverse, phase retrieval, image-based, and wavefront sensing are defined in the first of the two immediately preceding articles, Broadband Phase Retrieval for Image-Based Wavefront Sensing (GSC-14899-1).] As described below, no prior phase-retrieval algorithm has offered both high dynamic range and the capability to recover high spatial-frequency components. Each of the previously developed image-based phase-retrieval techniques can be classified into one of two categories: iterative transform or parametric. Among the modifications of the original iterative-transform approach has been the introduction of a defocus diversity function (also defined in the cited companion article). Modifications of the original parametric approach have included minimizing alternative objective functions as well as implementing a variety of nonlinear optimization methods. The iterative-transform approach offers the advantage of ability to recover low, middle, and high spatial frequencies, but has disadvantage of having a limited dynamic range to one wavelength or less. In contrast, parametric phase retrieval offers the advantage of high dynamic range, but is poorly suited for recovering higher spatial frequency aberrations. The present phase-diverse iterative transform phase-retrieval algorithm offers both the high-spatial-frequency capability of the iterative-transform approach and the high dynamic range of parametric phase-recovery techniques. In implementation, this is a focus-diverse iterative-transform phaseretrieval algorithm that incorporates an adaptive diversity function, which makes it possible to avoid phase unwrapping while preserving high-spatial-frequency recovery. The algorithm includes an inner and an outer loop (see figure). An initial estimate of phase is used to start the algorithm on the inner loop, wherein multiple intensity images are processed, each using a different defocus value. The processing is done by an iterative-transform method, yielding individual phase estimates corresponding to each image of the defocus-diversity data set. These individual phase estimates are combined in a weighted average to form a new phase estimate, which serves as the initial phase estimate for either the next iteration of the iterative-transform method or, if the maximum number of iterations has been reached, for the next several steps, which constitute the outerloop portion of the algorithm. The details of the next several steps must be omitted here for the sake of brevity. The overall effect of these steps is to adaptively update the diversity defocus values according to recovery of global defocus in the phase estimate. Aberration recovery varies with differing amounts as the amount of diversity defocus is updated in each image; thus, feedback is incorporated into the recovery process. This process is iterated until the global defocus error is driven to zero during the recovery process. The amplitude of aberration may far exceed one wavelength after completion of the inner-loop portion of the algorithm, and the classical iterative transform method does not, by itself, enable recovery of multi-wavelength aberrations. Hence, in the absence of a means of off-loading the multi-wavelength portion of the aberration, the algorithm would produce a wrapped phase map. However, a special aberration-fitting procedure can be applied to the wrapped phase data to transfer at least some portion of the multi-wavelength aberration to the diversity function, wherein the data are treated as known phase values. In this way, a multiwavelength aberration can be recovered incrementally by successively applying the aberration-fitting procedure to intermediate wrapped phase maps. During recovery, as more of the aberration is transferred to the diversity function following successive iterations around the ter loop, the estimated phase ceases to wrap in places where the aberration values become incorporated as part of the diversity function. As a result, as the aberration content is transferred to the diversity function, the phase estimate resembles that of a reference flat.

Modeling the dynamics of evaluation: a multilevel neural network implementation of the iterative reprocessing model.

PubMed

Ehret, Phillip J; Monroe, Brian M; Read, Stephen J

2015-05-01

We present a neural network implementation of central components of the iterative reprocessing (IR) model. The IR model argues that the evaluation of social stimuli (attitudes, stereotypes) is the result of the IR of stimuli in a hierarchy of neural systems: The evaluation of social stimuli develops and changes over processing. The network has a multilevel, bidirectional feedback evaluation system that integrates initial perceptual processing and later developing semantic processing. The network processes stimuli (e.g., an individual's appearance) over repeated iterations, with increasingly higher levels of semantic processing over time. As a result, the network's evaluations of stimuli evolve. We discuss the implications of the network for a number of different issues involved in attitudes and social evaluation. The success of the network supports the IR model framework and provides new insights into attitude theory. © 2014 by the Society for Personality and Social Psychology, Inc.

Advanced Agent Methods in Adversarial Environment

DTIC Science & Technology

2005-11-30

2 Contents Contents 1 Introduction – Technical Statement of Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.1...37 5.4.1 Deriving Trust Observations from Coalition Cooperation Results . . . . . . . . . . . 37 Contents 3 5.4.2 Iterative Learning of...85 4 Contents A.3.5 Class Finder

Learning by Restorying

ERIC Educational Resources Information Center

Slabon, Wayne A.; Richards, Randy L.; Dennen, Vanessa P.

2014-01-01

In this paper, we introduce restorying, a pedagogical approach based on social constructivism that employs successive iterations of rewriting and discussing personal, student-generated, domain-relevant stories to promote conceptual application, critical thinking, and ill-structured problem solving skills. Using a naturalistic, qualitative case…

Support patient search on pathology reports with interactive online learning based data extraction.

PubMed

Zheng, Shuai; Lu, James J; Appin, Christina; Brat, Daniel; Wang, Fusheng

2015-01-01

Structural reporting enables semantic understanding and prompt retrieval of clinical findings about patients. While synoptic pathology reporting provides templates for data entries, information in pathology reports remains primarily in narrative free text form. Extracting data of interest from narrative pathology reports could significantly improve the representation of the information and enable complex structured queries. However, manual extraction is tedious and error-prone, and automated tools are often constructed with a fixed training dataset and not easily adaptable. Our goal is to extract data from pathology reports to support advanced patient search with a highly adaptable semi-automated data extraction system, which can adjust and self-improve by learning from a user's interaction with minimal human effort. We have developed an online machine learning based information extraction system called IDEAL-X. With its graphical user interface, the system's data extraction engine automatically annotates values for users to review upon loading each report text. The system analyzes users' corrections regarding these annotations with online machine learning, and incrementally enhances and refines the learning model as reports are processed. The system also takes advantage of customized controlled vocabularies, which can be adaptively refined during the online learning process to further assist the data extraction. As the accuracy of automatic annotation improves overtime, the effort of human annotation is gradually reduced. After all reports are processed, a built-in query engine can be applied to conveniently define queries based on extracted structured data. We have evaluated the system with a dataset of anatomic pathology reports from 50 patients. Extracted data elements include demographical data, diagnosis, genetic marker, and procedure. The system achieves F-1 scores of around 95% for the majority of tests. Extracting data from pathology reports could enable more accurate knowledge to support biomedical research and clinical diagnosis. IDEAL-X provides a bridge that takes advantage of online machine learning based data extraction and the knowledge from human's feedback. By combining iterative online learning and adaptive controlled vocabularies, IDEAL-X can deliver highly adaptive and accurate data extraction to support patient search.

Statistical mechanics of unsupervised feature learning in a restricted Boltzmann machine with binary synapses

NASA Astrophysics Data System (ADS)

Huang, Haiping

2017-05-01

Revealing hidden features in unlabeled data is called unsupervised feature learning, which plays an important role in pretraining a deep neural network. Here we provide a statistical mechanics analysis of the unsupervised learning in a restricted Boltzmann machine with binary synapses. A message passing equation to infer the hidden feature is derived, and furthermore, variants of this equation are analyzed. A statistical analysis by replica theory describes the thermodynamic properties of the model. Our analysis confirms an entropy crisis preceding the non-convergence of the message passing equation, suggesting a discontinuous phase transition as a key characteristic of the restricted Boltzmann machine. Continuous phase transition is also confirmed depending on the embedded feature strength in the data. The mean-field result under the replica symmetric assumption agrees with that obtained by running message passing algorithms on single instances of finite sizes. Interestingly, in an approximate Hopfield model, the entropy crisis is absent, and a continuous phase transition is observed instead. We also develop an iterative equation to infer the hyper-parameter (temperature) hidden in the data, which in physics corresponds to iteratively imposing Nishimori condition. Our study provides insights towards understanding the thermodynamic properties of the restricted Boltzmann machine learning, and moreover important theoretical basis to build simplified deep networks.

Experiments on individual strategy updating in iterated snowdrift game under random rematching.

PubMed

Qi, Hang; Ma, Shoufeng; Jia, Ning; Wang, Guangchao

2015-03-07

How do people actually play the iterated snowdrift games, particularly under random rematching protocol is far from well explored. Two sets of laboratory experiments on snowdrift game were conducted to investigate human strategy updating rules. Four groups of subjects were modeled by experience-weighted attraction learning theory at individual-level. Three out of the four groups (75%) passed model validation. Substantial heterogeneity is observed among the players who update their strategies in four typical types, whereas rare people behave like belief-based learners even under fixed pairing. Most subjects (63.9%) adopt the reinforcement learning (or alike) rules; but, interestingly, the performance of averaged reinforcement learners suffered. It is observed that two factors seem to benefit players in competition, i.e., the sensitivity to their recent experiences and the overall consideration of forgone payoffs. Moreover, subjects with changing opponents tend to learn faster based on their own recent experience, and display more diverse strategy updating rules than they do with fixed opponent. These findings suggest that most of subjects do apply reinforcement learning alike updating rules even under random rematching, although these rules may not improve their performance. The findings help evolutionary biology researchers to understand sophisticated human behavioral strategies in social dilemmas. Copyright © 2015 Elsevier Ltd. All rights reserved.

US NDC Modernization Iteration E1 Prototyping Report: Processing Control Framework

DOE Office of Scientific and Technical Information (OSTI.GOV)

Prescott, Ryan; Hamlet, Benjamin R.

2014-12-01

During the first iteration of the US NDC Modernization Elaboration phase (E1), the SNL US NDC modernization project team developed an initial survey of applicable COTS solutions, and established exploratory prototyping related to the processing control framework in support of system architecture definition. This report summarizes these activities and discusses planned follow-on work.

The impact of a faculty learning community on professional and personal development: the facilitator training program of the American Academy on Communication in Healthcare.

PubMed

Chou, Calvin L; Hirschmann, Krista; Fortin, Auguste H; Lichstein, Peter R

2014-07-01

Relationship-centered care attends to the entire network of human relationships essential to patient care. Few faculty development programs prepare faculty to teach principles and skills in relationship-centered care. One exception is the Facilitator Training Program (FTP), a 25-year-old training program of the American Academy on Communication in Healthcare. The authors surveyed FTP graduates to determine the efficacy of its curriculum and the most important elements for participants' learning. In 2007, surveys containing quantitative and narrative elements were distributed to 51 FTP graduates. Quantitative data were analyzed using descriptive statistics. The authors analyzed narratives using Burke's dramatistic pentad as a qualitative framework to delineate how interrelated themes interacted in the FTP. Forty-seven respondents (92%) identified two essential acts that happened in the program: an iterative learning process, leading to heightened personal awareness and group facilitation skills; and longevity of learning and effect on career. The structure of the program's learning community provided the scene, and the agents were the participants, who provided support and contributed to mutual success. Methods of developing skills in personal awareness, group facilitation, teaching, and feedback constituted agency. The purpose was to learn skills and to join a community to share common values. The FTP is a learning community that provided faculty with skills in principles of relationship-centered care. Four further features that describe elements of this successful faculty-based learning community are achievement of self-identified goals, distance learning modalities, opportunities to safely discuss workplace issues outside the workplace, and self-renewing membership.

An iterative reduced field-of-view reconstruction for periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) MRI.

PubMed

Lin, Jyh-Miin; Patterson, Andrew J; Chang, Hing-Chiu; Gillard, Jonathan H; Graves, Martin J

2015-10-01

To propose a new reduced field-of-view (rFOV) strategy for iterative reconstructions in a clinical environment. Iterative reconstructions can incorporate regularization terms to improve the image quality of periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) MRI. However, the large amount of calculations required for full FOV iterative reconstructions has posed a huge computational challenge for clinical usage. By subdividing the entire problem into smaller rFOVs, the iterative reconstruction can be accelerated on a desktop with a single graphic processing unit (GPU). This rFOV strategy divides the iterative reconstruction into blocks, based on the block-diagonal dominant structure. A near real-time reconstruction system was developed for the clinical MR unit, and parallel computing was implemented using the object-oriented model. In addition, the Toeplitz method was implemented on the GPU to reduce the time required for full interpolation. Using the data acquired from the PROPELLER MRI, the reconstructed images were then saved in the digital imaging and communications in medicine format. The proposed rFOV reconstruction reduced the gridding time by 97%, as the total iteration time was 3 s even with multiple processes running. A phantom study showed that the structure similarity index for rFOV reconstruction was statistically superior to conventional density compensation (p < 0.001). In vivo study validated the increased signal-to-noise ratio, which is over four times higher than with density compensation. Image sharpness index was improved using the regularized reconstruction implemented. The rFOV strategy permits near real-time iterative reconstruction to improve the image quality of PROPELLER images. Substantial improvements in image quality metrics were validated in the experiments. The concept of rFOV reconstruction may potentially be applied to other kinds of iterative reconstructions for shortened reconstruction duration.

Statistical Methods in Ai: Rare Event Learning Using Associative Rules and Higher-Order Statistics

NASA Astrophysics Data System (ADS)

Iyer, V.; Shetty, S.; Iyengar, S. S.

2015-07-01

Rare event learning has not been actively researched since lately due to the unavailability of algorithms which deal with big samples. The research addresses spatio-temporal streams from multi-resolution sensors to find actionable items from a perspective of real-time algorithms. This computing framework is independent of the number of input samples, application domain, labelled or label-less streams. A sampling overlap algorithm such as Brooks-Iyengar is used for dealing with noisy sensor streams. We extend the existing noise pre-processing algorithms using Data-Cleaning trees. Pre-processing using ensemble of trees using bagging and multi-target regression showed robustness to random noise and missing data. As spatio-temporal streams are highly statistically correlated, we prove that a temporal window based sampling from sensor data streams converges after n samples using Hoeffding bounds. Which can be used for fast prediction of new samples in real-time. The Data-cleaning tree model uses a nonparametric node splitting technique, which can be learned in an iterative way which scales linearly in memory consumption for any size input stream. The improved task based ensemble extraction is compared with non-linear computation models using various SVM kernels for speed and accuracy. We show using empirical datasets the explicit rule learning computation is linear in time and is only dependent on the number of leafs present in the tree ensemble. The use of unpruned trees (t) in our proposed ensemble always yields minimum number (m) of leafs keeping pre-processing computation to n × t log m compared to N2 for Gram Matrix. We also show that the task based feature induction yields higher Qualify of Data (QoD) in the feature space compared to kernel methods using Gram Matrix.

Approximate dynamic programming for optimal stationary control with control-dependent noise.

PubMed

Jiang, Yu; Jiang, Zhong-Ping

2011-12-01

This brief studies the stochastic optimal control problem via reinforcement learning and approximate/adaptive dynamic programming (ADP). A policy iteration algorithm is derived in the presence of both additive and multiplicative noise using Itô calculus. The expectation of the approximated cost matrix is guaranteed to converge to the solution of some algebraic Riccati equation that gives rise to the optimal cost value. Moreover, the covariance of the approximated cost matrix can be reduced by increasing the length of time interval between two consecutive iterations. Finally, a numerical example is given to illustrate the efficiency of the proposed ADP methodology.

Cognitive representation of "musical fractals": Processing hierarchy and recursion in the auditory domain.

PubMed

Martins, Mauricio Dias; Gingras, Bruno; Puig-Waldmueller, Estela; Fitch, W Tecumseh

2017-04-01

The human ability to process hierarchical structures has been a longstanding research topic. However, the nature of the cognitive machinery underlying this faculty remains controversial. Recursion, the ability to embed structures within structures of the same kind, has been proposed as a key component of our ability to parse and generate complex hierarchies. Here, we investigated the cognitive representation of both recursive and iterative processes in the auditory domain. The experiment used a two-alternative forced-choice paradigm: participants were exposed to three-step processes in which pure-tone sequences were built either through recursive or iterative processes, and had to choose the correct completion. Foils were constructed according to generative processes that did not match the previous steps. Both musicians and non-musicians were able to represent recursion in the auditory domain, although musicians performed better. We also observed that general 'musical' aptitudes played a role in both recursion and iteration, although the influence of musical training was somehow independent from melodic memory. Moreover, unlike iteration, recursion in audition was well correlated with its non-auditory (recursive) analogues in the visual and action sequencing domains. These results suggest that the cognitive machinery involved in establishing recursive representations is domain-general, even though this machinery requires access to information resulting from domain-specific processes. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Implementation of a deidentified federated data network for population-based cohort discovery

PubMed Central

Abend, Aaron; Mandel, Aaron; Geraghty, Estella; Gabriel, Davera; Wynden, Rob; Kamerick, Michael; Anderson, Kent; Rainwater, Julie; Tarczy-Hornoch, Peter

2011-01-01

Objective The Cross-Institutional Clinical Translational Research project explored a federated query tool and looked at how this tool can facilitate clinical trial cohort discovery by managing access to aggregate patient data located within unaffiliated academic medical centers. Methods The project adapted software from the Informatics for Integrating Biology and the Bedside (i2b2) program to connect three Clinical Translational Research Award sites: University of Washington, Seattle, University of California, Davis, and University of California, San Francisco. The project developed an iterative spiral software development model to support the implementation and coordination of this multisite data resource. Results By standardizing technical infrastructures, policies, and semantics, the project enabled federated querying of deidentified clinical datasets stored in separate institutional environments and identified barriers to engaging users for measuring utility. Discussion The authors discuss the iterative development and evaluation phases of the project and highlight the challenges identified and the lessons learned. Conclusion The common system architecture and translational processes provide high-level (aggregate) deidentified access to a large patient population (>5 million patients), and represent a novel and extensible resource. Enhancing the network for more focused disease areas will require research-driven partnerships represented across all partner sites. PMID:21873473

Implementation of a deidentified federated data network for population-based cohort discovery.

PubMed

Anderson, Nicholas; Abend, Aaron; Mandel, Aaron; Geraghty, Estella; Gabriel, Davera; Wynden, Rob; Kamerick, Michael; Anderson, Kent; Rainwater, Julie; Tarczy-Hornoch, Peter

2012-06-01

The Cross-Institutional Clinical Translational Research project explored a federated query tool and looked at how this tool can facilitate clinical trial cohort discovery by managing access to aggregate patient data located within unaffiliated academic medical centers. The project adapted software from the Informatics for Integrating Biology and the Bedside (i2b2) program to connect three Clinical Translational Research Award sites: University of Washington, Seattle, University of California, Davis, and University of California, San Francisco. The project developed an iterative spiral software development model to support the implementation and coordination of this multisite data resource. By standardizing technical infrastructures, policies, and semantics, the project enabled federated querying of deidentified clinical datasets stored in separate institutional environments and identified barriers to engaging users for measuring utility. The authors discuss the iterative development and evaluation phases of the project and highlight the challenges identified and the lessons learned. The common system architecture and translational processes provide high-level (aggregate) deidentified access to a large patient population (>5 million patients), and represent a novel and extensible resource. Enhancing the network for more focused disease areas will require research-driven partnerships represented across all partner sites.

Low-dose CT image reconstruction using gain intervention-based dictionary learning

NASA Astrophysics Data System (ADS)

Pathak, Yadunath; Arya, K. V.; Tiwari, Shailendra

2018-05-01

Computed tomography (CT) approach is extensively utilized in clinical diagnoses. However, X-ray residue in human body may introduce somatic damage such as cancer. Owing to radiation risk, research has focused on the radiation exposure distributed to patients through CT investigations. Therefore, low-dose CT has become a significant research area. Many researchers have proposed different low-dose CT reconstruction techniques. But, these techniques suffer from various issues such as over smoothing, artifacts, noise, etc. Therefore, in this paper, we have proposed a novel integrated low-dose CT reconstruction technique. The proposed technique utilizes global dictionary-based statistical iterative reconstruction (GDSIR) and adaptive dictionary-based statistical iterative reconstruction (ADSIR)-based reconstruction techniques. In case the dictionary (D) is predetermined, then GDSIR can be used and if D is adaptively defined then ADSIR is appropriate choice. The gain intervention-based filter is also used as a post-processing technique for removing the artifacts from low-dose CT reconstructed images. Experiments have been done by considering the proposed and other low-dose CT reconstruction techniques on well-known benchmark CT images. Extensive experiments have shown that the proposed technique outperforms the available approaches.

Cyclic Game Dynamics Driven by Iterated Reasoning

PubMed Central

Frey, Seth; Goldstone, Robert L.

2013-01-01

Recent theories from complexity science argue that complex dynamics are ubiquitous in social and economic systems. These claims emerge from the analysis of individually simple agents whose collective behavior is surprisingly complicated. However, economists have argued that iterated reasoning–what you think I think you think–will suppress complex dynamics by stabilizing or accelerating convergence to Nash equilibrium. We report stable and efficient periodic behavior in human groups playing the Mod Game, a multi-player game similar to Rock-Paper-Scissors. The game rewards subjects for thinking exactly one step ahead of others in their group. Groups that play this game exhibit cycles that are inconsistent with any fixed-point solution concept. These cycles are driven by a “hopping” behavior that is consistent with other accounts of iterated reasoning: agents are constrained to about two steps of iterated reasoning and learn an additional one-half step with each session. If higher-order reasoning can be complicit in complex emergent dynamics, then cyclic and chaotic patterns may be endogenous features of real-world social and economic systems. PMID:23441191

Performance analysis of model based iterative reconstruction with dictionary learning in transportation security CT

NASA Astrophysics Data System (ADS)

Haneda, Eri; Luo, Jiajia; Can, Ali; Ramani, Sathish; Fu, Lin; De Man, Bruno

2016-05-01

In this study, we implement and compare model based iterative reconstruction (MBIR) with dictionary learning (DL) over MBIR with pairwise pixel-difference regularization, in the context of transportation security. DL is a technique of sparse signal representation using an over complete dictionary which has provided promising results in image processing applications including denoising,1 as well as medical CT reconstruction.2 It has been previously reported that DL produces promising results in terms of noise reduction and preservation of structural details, especially for low dose and few-view CT acquisitions.2 A distinguishing feature of transportation security CT is that scanned baggage may contain items with a wide range of material densities. While medical CT typically scans soft tissues, blood with and without contrast agents, and bones, luggage typically contains more high density materials (i.e. metals and glass), which can produce severe distortions such as metal streaking artifacts. Important factors of security CT are the emphasis on image quality such as resolution, contrast, noise level, and CT number accuracy for target detection. While MBIR has shown exemplary performance in the trade-off of noise reduction and resolution preservation, we demonstrate that DL may further improve this trade-off. In this study, we used the KSVD-based DL3 combined with the MBIR cost-minimization framework and compared results to Filtered Back Projection (FBP) and MBIR with pairwise pixel-difference regularization. We performed a parameter analysis to show the image quality impact of each parameter. We also investigated few-view CT acquisitions where DL can show an additional advantage relative to pairwise pixel difference regularization.

Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments

PubMed Central

Wolverton, Christopher; Hattrick-Simpers, Jason; Mehta, Apurva

2018-01-01

With more than a hundred elements in the periodic table, a large number of potential new materials exist to address the technological and societal challenges we face today; however, without some guidance, searching through this vast combinatorial space is frustratingly slow and expensive, especially for materials strongly influenced by processing. We train a machine learning (ML) model on previously reported observations, parameters from physiochemical theories, and make it synthesis method–dependent to guide high-throughput (HiTp) experiments to find a new system of metallic glasses in the Co-V-Zr ternary. Experimental observations are in good agreement with the predictions of the model, but there are quantitative discrepancies in the precise compositions predicted. We use these discrepancies to retrain the ML model. The refined model has significantly improved accuracy not only for the Co-V-Zr system but also across all other available validation data. We then use the refined model to guide the discovery of metallic glasses in two additional previously unreported ternaries. Although our approach of iterative use of ML and HiTp experiments has guided us to rapid discovery of three new glass-forming systems, it has also provided us with a quantitatively accurate, synthesis method–sensitive predictor for metallic glasses that improves performance with use and thus promises to greatly accelerate discovery of many new metallic glasses. We believe that this discovery paradigm is applicable to a wider range of materials and should prove equally powerful for other materials and properties that are synthesis path–dependent and that current physiochemical theories find challenging to predict. PMID:29662953

Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ren, Fang; Ward, Logan; Williams, Travis

With more than a hundred elements in the periodic table, a large number of potential new materials exist to address the technological and societal challenges we face today; however, without some guidance, searching through this vast combinatorial space is frustratingly slow and expensive, especially for materials strongly influenced by processing. We train a machine learning (ML) model on previously reported observations, parameters from physiochemical theories, and make it synthesis method–dependent to guide high-throughput (HiTp) experiments to find a new system of metallic glasses in the Co-V-Zr ternary. Experimental observations are in good agreement with the predictions of the model, butmore » there are quantitative discrepancies in the precise compositions predicted. We use these discrepancies to retrain the ML model. The refined model has significantly improved accuracy not only for the Co-V-Zr system but also across all other available validation data. We then use the refined model to guide the discovery of metallic glasses in two additional previously unreported ternaries. Although our approach of iterative use of ML and HiTp experiments has guided us to rapid discovery of three new glass-forming systems, it has also provided us with a quantitatively accurate, synthesis method–sensitive predictor for metallic glasses that improves performance with use and thus promises to greatly accelerate discovery of many new metallic glasses. We believe that this discovery paradigm is applicable to a wider range of materials and should prove equally powerful for other materials and properties that are synthesis path–dependent and that current physiochemical theories find challenging to predict.« less

Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments

DOE PAGES

Ren, Fang; Ward, Logan; Williams, Travis; ...

2018-04-01

With more than a hundred elements in the periodic table, a large number of potential new materials exist to address the technological and societal challenges we face today; however, without some guidance, searching through this vast combinatorial space is frustratingly slow and expensive, especially for materials strongly influenced by processing. We train a machine learning (ML) model on previously reported observations, parameters from physiochemical theories, and make it synthesis method–dependent to guide high-throughput (HiTp) experiments to find a new system of metallic glasses in the Co-V-Zr ternary. Experimental observations are in good agreement with the predictions of the model, butmore » there are quantitative discrepancies in the precise compositions predicted. We use these discrepancies to retrain the ML model. The refined model has significantly improved accuracy not only for the Co-V-Zr system but also across all other available validation data. We then use the refined model to guide the discovery of metallic glasses in two additional previously unreported ternaries. Although our approach of iterative use of ML and HiTp experiments has guided us to rapid discovery of three new glass-forming systems, it has also provided us with a quantitatively accurate, synthesis method–sensitive predictor for metallic glasses that improves performance with use and thus promises to greatly accelerate discovery of many new metallic glasses. We believe that this discovery paradigm is applicable to a wider range of materials and should prove equally powerful for other materials and properties that are synthesis path–dependent and that current physiochemical theories find challenging to predict.« less

The development of participatory health research among incarcerated women in a Canadian prison

PubMed Central

Murphy, K.; Hanson, D.; Hemingway, C.; Ramsden, V.; Buxton, J.; Granger-Brown, A.; Condello, L-L.; Buchanan, M.; Espinoza-Magana, N.; Edworthy, G.; Hislop, T. G.

2009-01-01

This paper describes the development of a unique prison participatory research project, in which incarcerated women formed a research team, the research activities and the lessons learned. The participatory action research project was conducted in the main short sentence minimum/medium security women's prison located in a Western Canadian province. An ethnographic multi-method approach was used for data collection and analysis. Quantitative data was collected by surveys and analysed using descriptive statistics. Qualitative data was collected from orientation package entries, audio recordings, and written archives of research team discussions, forums and debriefings, and presentations. These data and ethnographic observations were transcribed and analysed using iterative and interpretative qualitative methods and NVivo 7 software. Up to 15 women worked each day as prison research team members; a total of 190 women participated at some time in the project between November 2005 and August 2007. Incarcerated women peer researchers developed the research processes including opportunities for them to develop leadership and technical skills. Through these processes, including data collection and analysis, nine health goals emerged. Lessons learned from the research processes were confirmed by the common themes that emerged from thematic analysis of the research activity data. Incarceration provides a unique opportunity for engagement of women as expert partners alongside academic researchers and primary care workers in participatory research processes to improve their health. PMID:25759141

Biosignals learning and synthesis using deep neural networks.

PubMed

Belo, David; Rodrigues, João; Vaz, João R; Pezarat-Correia, Pedro; Gamboa, Hugo

2017-09-25

Modeling physiological signals is a complex task both for understanding and synthesize biomedical signals. We propose a deep neural network model that learns and synthesizes biosignals, validated by the morphological equivalence of the original ones. This research could lead the creation of novel algorithms for signal reconstruction in heavily noisy data and source detection in biomedical engineering field. The present work explores the gated recurrent units (GRU) employed in the training of respiration (RESP), electromyograms (EMG) and electrocardiograms (ECG). Each signal is pre-processed, segmented and quantized in a specific number of classes, corresponding to the amplitude of each sample and fed to the model, which is composed by an embedded matrix, three GRU blocks and a softmax function. This network is trained by adjusting its internal parameters, acquiring the representation of the abstract notion of the next value based on the previous ones. The simulated signal was generated by forecasting a random value and re-feeding itself. The resulting generated signals are similar with the morphological expression of the originals. During the learning process, after a set of iterations, the model starts to grasp the basic morphological characteristics of the signal and later their cyclic characteristics. After training, these models' prediction are closer to the signals that trained them, specially the RESP and ECG. This synthesis mechanism has shown relevant results that inspire the use to characterize signals from other physiological sources.

Reducing Design Cycle Time and Cost Through Process Resequencing

NASA Technical Reports Server (NTRS)

Rogers, James L.

2004-01-01

In today's competitive environment, companies are under enormous pressure to reduce the time and cost of their design cycle. One method for reducing both time and cost is to develop an understanding of the flow of the design processes and the effects of the iterative subcycles that are found in complex design projects. Once these aspects are understood, the design manager can make decisions that take advantage of decomposition, concurrent engineering, and parallel processing techniques to reduce the total time and the total cost of the design cycle. One software tool that can aid in this decision-making process is the Design Manager's Aid for Intelligent Decomposition (DeMAID). The DeMAID software minimizes the feedback couplings that create iterative subcycles, groups processes into iterative subcycles, and decomposes the subcycles into a hierarchical structure. The real benefits of producing the best design in the least time and at a minimum cost are obtained from sequencing the processes in the subcycles.

Using Negotiated Joining to Construct and Fill Open-ended Roles in Elite Culinary Groups.

PubMed

Tan, Vaughn

2015-03-01

This qualitative study examines membership processes in groups operating in an uncertain environment that prevents them from fully predefining new members' roles. I describe how nine elite high-end, cutting-edge culinary groups in the U.S. and Europe, ranging from innovative restaurants to culinary R&D groups, use negotiated joining-a previously undocumented process-to systematically construct and fill these emergent, open-ended roles. I show that negotiated joining is a consistently patterned, iterative process that begins with a role that both aspirant and target group explicitly understand to be provisional. This provisional role is then jointly modified and constructed by the aspirant and target group through repeated iterations of proposition, validation through trial and evaluation, and selective integration of validated role components. The initially provisional role stabilizes and the aspirant achieves membership if enough role components are validated; otherwise the negotiated joining process is abandoned. Negotiated joining allows the aspirant and target group to learn if a mutually desirable role is likely and, if so, to construct such a role. In addition, the provisional roles in negotiated joining can support absorptive capacity by allowing novel role components to enter target groups through aspirants' efforts to construct stable roles for themselves, while the internal adjustment involved in integrating newly validated role components can have the unintended side effect of supporting adaptation by providing opportunities for the groups to use these novel role components to modify their role structure and goals to suit a changing and uncertain environment. Negotiated joining thus reveals role ambiguity's hitherto unexamined beneficial consequences and provides a foundation for a contingency theory of new-member acquisition.

Teaching Graduate Students How To Do Informal Science Education

NASA Astrophysics Data System (ADS)

Ackerman, S. A.; Crone, W.; Dunwoody, S. L.; Zenner, G.

2011-12-01

One of the most important skills a student needs to develop during their graduate days is the skill of communicating their scientific work with a wide array of audiences. That facility will serve them across audiences, from scientific peers to students to neighbors and the general public. Increasingly, graduate students express a need for training in skills needed to manage diverse communicative environments. In response to that need we have created a course for graduate students in STEM-related fields which provides a structured framework and experiential learning about informal science education. This course seeks to familiarize students with concepts and processes important to communicating science successfully to a variety of audiences. A semester-long course, "Informal Science Education for Scientists: A Practicum," has been co-taught by a scientist/engineer and a social scientist/humanist over several years through the Delta Program in Research, Teaching, & Learning at the University of Wisconsin-Madison. The course is project based and understanding audience is stressed throughout the class. Through development and exhibition of the group project, students experience front end, formative and summative evaluation methods. The disciplines of the participating students is broad, but includes students in the geosciences each year. After a brief description of the course and its evolution, we will present assessment and evaluation results from seven different iterations of the course showing significant gains in how informed students felt about evaluation as a tool to determine the effectiveness of their science outreach activities. Significant gains were found in the graduate students' perceptions that they were better qualified to explain a research topic to a lay audience, and in the students' confidence in using and understanding evaluation techniques to determine the effectiveness of communication strategies. There were also increases in the students' understanding of audiences and the iterative process required to design an informal education product.

Engineering design in the primary school: applying stem concepts to build an optical instrument

NASA Astrophysics Data System (ADS)

King, Donna; English, Lyn D.

2016-12-01

Internationally there is a need for research that focuses on STEM (Science, Technology, Engineering and Mathematics) education to equip students with the skills needed for a rapidly changing future. One way to do this is through designing engineering activities that reflect real-world problems and contextualise students' learning of STEM concepts. As such, this study examined the learning that occurred when fifth-grade students completed an optical engineering activity using an iterative engineering design model. Through a qualitative methodology using a case study design, we analysed multiple data sources including students' design sketches from eight focus groups. Three key findings emerged: first, the collaborative process of the first design sketch enabled students to apply core STEM concepts to model construction; second, during the construction stage students used experimentation for the positioning of lenses, mirrors and tubes resulting in a simpler 'working' model; and third, the redesign process enabled students to apply structural changes to their design. The engineering design model was useful for structuring stages of design, construction and redesign; however, we suggest a more flexible approach for advanced applications of STEM concepts in the future.

Optimize Short Term load Forcasting Anomalous Based Feed Forward Backpropagation

NASA Astrophysics Data System (ADS)

Mulyadi, Y.; Abdullah, A. G.; Rohmah, K. A.

2017-03-01

This paper contains the Short-Term Load Forecasting (STLF) using artificial neural network especially feed forward back propagation algorithm which is particularly optimized in order to getting a reduced error value result. Electrical load forecasting target is a holiday that hasn’t identical pattern and different from weekday’s pattern, in other words the pattern of holiday load is an anomalous. Under these conditions, the level of forecasting accuracy will be decrease. Hence we need a method that capable to reducing error value in anomalous load forecasting. Learning process of algorithm is supervised or controlled, then some parameters are arranged before performing computation process. Momentum constant a value is set at 0.8 which serve as a reference because it has the greatest converge tendency. Learning rate selection is made up to 2 decimal digits. In addition, hidden layer and input component are tested in several variation of number also. The test result leads to the conclusion that the number of hidden layer impact on the forecasting accuracy and test duration determined by the number of iterations when performing input data until it reaches the maximum of a parameter value.

Implementing the Science Assessment Standards: Developing and validating a set of laboratory assessment tasks in high school biology

NASA Astrophysics Data System (ADS)

Saha, Gouranga Chandra

Very often a number of factors, especially time, space and money, deter many science educators from using inquiry-based, hands-on, laboratory practical tasks as alternative assessment instruments in science. A shortage of valid inquiry-based laboratory tasks for high school biology has been cited. Driven by this need, this study addressed the following three research questions: (1) How can laboratory-based performance tasks be designed and developed that are doable by students for whom they are designed/written? (2) Do student responses to the laboratory-based performance tasks validly represent at least some of the intended process skills that new biology learning goals want students to acquire? (3) Are the laboratory-based performance tasks psychometrically consistent as individual tasks and as a set? To answer these questions, three tasks were used from the six biology tasks initially designed and developed by an iterative process of trial testing. Analyses of data from 224 students showed that performance-based laboratory tasks that are doable by all students require careful and iterative process of development. Although the students demonstrated more skill in performing than planning and reasoning, their performances at the item level were very poor for some items. Possible reasons for the poor performances have been discussed and suggestions on how to remediate the deficiencies have been made. Empirical evidences for validity and reliability of the instrument have been presented both from the classical and the modern validity criteria point of view. Limitations of the study have been identified. Finally implications of the study and directions for further research have been discussed.

The use of process mapping in healthcare quality improvement projects.

PubMed

Antonacci, Grazia; Reed, Julie E; Lennox, Laura; Barlow, James

2018-05-01

Introduction Process mapping provides insight into systems and processes in which improvement interventions are introduced and is seen as useful in healthcare quality improvement projects. There is little empirical evidence on the use of process mapping in healthcare practice. This study advances understanding of the benefits and success factors of process mapping within quality improvement projects. Methods Eight quality improvement projects were purposively selected from different healthcare settings within the UK's National Health Service. Data were gathered from multiple data-sources, including interviews exploring participants' experience of using process mapping in their projects and perceptions of benefits and challenges related to its use. These were analysed using inductive analysis. Results Eight key benefits related to process mapping use were reported by participants (gathering a shared understanding of the reality; identifying improvement opportunities; engaging stakeholders in the project; defining project's objectives; monitoring project progress; learning; increased empathy; simplicity of the method) and five factors related to successful process mapping exercises (simple and appropriate visual representation, information gathered from multiple stakeholders, facilitator's experience and soft skills, basic training, iterative use of process mapping throughout the project). Conclusions Findings highlight benefits and versatility of process mapping and provide practical suggestions to improve its use in practice.

Integrating qualitative research methods into care improvement efforts within a learning health system: addressing antibiotic overuse.

PubMed

Munoz-Plaza, Corrine E; Parry, Carla; Hahn, Erin E; Tang, Tania; Nguyen, Huong Q; Gould, Michael K; Kanter, Michael H; Sharp, Adam L

2016-08-15

Despite reports advocating for integration of research into healthcare delivery, scant literature exists describing how this can be accomplished. Examples highlighting application of qualitative research methods embedded into a healthcare system are particularly needed. This article describes the process and value of embedding qualitative research as the second phase of an explanatory, sequential, mixed methods study to improve antibiotic stewardship for acute sinusitis. Purposive sampling of providers for in-depth interviews improved understanding of unwarranted antibiotic prescribing and elicited stakeholder recommendations for improvement. Qualitative data collection, transcription and constant comparative analyses occurred iteratively. Emerging themes and sub-themes identified primary drivers of unwarranted antibiotic prescribing patterns and recommendations for improving practice. These findings informed the design of a health system intervention to improve antibiotic stewardship for acute sinusitis. Core components of the intervention are also described. Qualitative research can be effectively applied in learning healthcare systems to elucidate quantitative results and inform improvement efforts.

Robust model predictive control of nonlinear systems with unmodeled dynamics and bounded uncertainties based on neural networks.

PubMed

Yan, Zheng; Wang, Jun

2014-03-01

This paper presents a neural network approach to robust model predictive control (MPC) for constrained discrete-time nonlinear systems with unmodeled dynamics affected by bounded uncertainties. The exact nonlinear model of underlying process is not precisely known, but a partially known nominal model is available. This partially known nonlinear model is first decomposed to an affine term plus an unknown high-order term via Jacobian linearization. The linearization residue combined with unmodeled dynamics is then modeled using an extreme learning machine via supervised learning. The minimax methodology is exploited to deal with bounded uncertainties. The minimax optimization problem is reformulated as a convex minimization problem and is iteratively solved by a two-layer recurrent neural network. The proposed neurodynamic approach to nonlinear MPC improves the computational efficiency and sheds a light for real-time implementability of MPC technology. Simulation results are provided to substantiate the effectiveness and characteristics of the proposed approach.

Progressive Learning of Topic Modeling Parameters: A Visual Analytics Framework.

PubMed

El-Assady, Mennatallah; Sevastjanova, Rita; Sperrle, Fabian; Keim, Daniel; Collins, Christopher

2018-01-01

Topic modeling algorithms are widely used to analyze the thematic composition of text corpora but remain difficult to interpret and adjust. Addressing these limitations, we present a modular visual analytics framework, tackling the understandability and adaptability of topic models through a user-driven reinforcement learning process which does not require a deep understanding of the underlying topic modeling algorithms. Given a document corpus, our approach initializes two algorithm configurations based on a parameter space analysis that enhances document separability. We abstract the model complexity in an interactive visual workspace for exploring the automatic matching results of two models, investigating topic summaries, analyzing parameter distributions, and reviewing documents. The main contribution of our work is an iterative decision-making technique in which users provide a document-based relevance feedback that allows the framework to converge to a user-endorsed topic distribution. We also report feedback from a two-stage study which shows that our technique results in topic model quality improvements on two independent measures.

Learning Assumptions for Compositional Verification

NASA Technical Reports Server (NTRS)

Cobleigh, Jamieson M.; Giannakopoulou, Dimitra; Pasareanu, Corina; Clancy, Daniel (Technical Monitor)

2002-01-01

Compositional verification is a promising approach to addressing the state explosion problem associated with model checking. One compositional technique advocates proving properties of a system by checking properties of its components in an assume-guarantee style. However, the application of this technique is difficult because it involves non-trivial human input. This paper presents a novel framework for performing assume-guarantee reasoning in an incremental and fully automated fashion. To check a component against a property, our approach generates assumptions that the environment needs to satisfy for the property to hold. These assumptions are then discharged on the rest of the system. Assumptions are computed by a learning algorithm. They are initially approximate, but become gradually more precise by means of counterexamples obtained by model checking the component and its environment, alternately. This iterative process may at any stage conclude that the property is either true or false in the system. We have implemented our approach in the LTSA tool and applied it to the analysis of a NASA system.

Twostep-by-twostep PIRK-type PC methods with continuous output formulas

NASA Astrophysics Data System (ADS)

Cong, Nguyen Huu; Xuan, Le Ngoc

2008-11-01

This paper deals with parallel predictor-corrector (PC) iteration methods based on collocation Runge-Kutta (RK) corrector methods with continuous output formulas for solving nonstiff initial-value problems (IVPs) for systems of first-order differential equations. At nth step, the continuous output formulas are used not only for predicting the stage values in the PC iteration methods but also for calculating the step values at (n+2)th step. In this case, the integration processes can be proceeded twostep-by-twostep. The resulting twostep-by-twostep (TBT) parallel-iterated RK-type (PIRK-type) methods with continuous output formulas (twostep-by-twostep PIRKC methods or TBTPIRKC methods) give us a faster integration process. Fixed stepsize applications of these TBTPIRKC methods to a few widely-used test problems reveal that the new PC methods are much more efficient when compared with the well-known parallel-iterated RK methods (PIRK methods), parallel-iterated RK-type PC methods with continuous output formulas (PIRKC methods) and sequential explicit RK codes DOPRI5 and DOP853 available from the literature.

Introducing 12 year-olds to elementary particles

NASA Astrophysics Data System (ADS)

Wiener, Gerfried J.; Schmeling, Sascha M.; Hopf, Martin

2017-07-01

We present a new learning unit, which introduces 12 year-olds to the subatomic structure of matter. The learning unit was iteratively developed as a design-based research project using the technique of probing acceptance. We give a brief overview of the unit’s final version, discuss its key ideas and main concepts, and conclude by highlighting the main implications of our research, which we consider to be most promising for use in the physics classroom.

The Impact of NSF-funded Physics Education Research at the University of Washington

NASA Astrophysics Data System (ADS)

Heron, Paula

2015-03-01

It is now well known that many students who complete introductory physics courses are unable to apply fundamental concepts in situations that involve qualitative reasoning. Systematic investigations have helped researchers understand why so many students fail to develop robust and coherent conceptual frameworks, and have led to the development of new teaching practices and materials that are far more effective than conventional ones. The Physics Education Group at the University of Washington has played a leading role in raising awareness of the need to improve instruction, and in supporting physics faculty in their efforts to do so. With support from the National Science Foundation, the group has helped build a research base that instructors can draw on, and has produced practical, flexible instructional materials that promote deeper learning in physics classrooms. Both ``Tutorials in Introductory Physics'' (Pearson, 2002) and ``Physics by Inquiry'' (Wiley, 1996) have been developed in an iterative process in which ongoing assessment of student learning plays an integral role. These materials have had a widespread and significant impact on physics teaching and on student learning from kindergarten through graduate school. In this talk I will describe the role of research in curriculum development, and speculate on the next generation of tools and resources to support physics teaching and learning.

A strategy with novel evolutionary features for the iterated prisoner's dilemma.

PubMed

Li, Jiawei; Kendall, Graham

2009-01-01

In recent iterated prisoner's dilemma tournaments, the most successful strategies were those that had identification mechanisms. By playing a predetermined sequence of moves and learning from their opponents' responses, these strategies managed to identify their opponents. We believe that these identification mechanisms may be very useful in evolutionary games. In this paper one such strategy, which we call collective strategy, is analyzed. Collective strategies apply a simple but efficient identification mechanism (that just distinguishes themselves from other strategies), and this mechanism allows them to only cooperate with their group members and defect against any others. In this way, collective strategies are able to maintain a stable population in evolutionary iterated prisoner's dilemma. By means of an invasion barrier, this strategy is compared with other strategies in evolutionary dynamics in order to demonstrate its evolutionary features. We also find that this collective behavior assists the evolution of cooperation in specific evolutionary environments.

Accelerated Path-following Iterative Shrinkage Thresholding Algorithm with Application to Semiparametric Graph Estimation

PubMed Central

Zhao, Tuo; Liu, Han

2016-01-01

We propose an accelerated path-following iterative shrinkage thresholding algorithm (APISTA) for solving high dimensional sparse nonconvex learning problems. The main difference between APISTA and the path-following iterative shrinkage thresholding algorithm (PISTA) is that APISTA exploits an additional coordinate descent subroutine to boost the computational performance. Such a modification, though simple, has profound impact: APISTA not only enjoys the same theoretical guarantee as that of PISTA, i.e., APISTA attains a linear rate of convergence to a unique sparse local optimum with good statistical properties, but also significantly outperforms PISTA in empirical benchmarks. As an application, we apply APISTA to solve a family of nonconvex optimization problems motivated by estimating sparse semiparametric graphical models. APISTA allows us to obtain new statistical recovery results which do not exist in the existing literature. Thorough numerical results are provided to back up our theory. PMID:28133430

Integral reinforcement learning for continuous-time input-affine nonlinear systems with simultaneous invariant explorations.

PubMed

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.

Incrementally learning objects by touch: online discriminative and generative models for tactile-based recognition.

PubMed

Soh, Harold; Demiris, Yiannis

2014-01-01

Human beings not only possess the remarkable ability to distinguish objects through tactile feedback but are further able to improve upon recognition competence through experience. In this work, we explore tactile-based object recognition with learners capable of incremental learning. Using the sparse online infinite Echo-State Gaussian process (OIESGP), we propose and compare two novel discriminative and generative tactile learners that produce probability distributions over objects during object grasping/palpation. To enable iterative improvement, our online methods incorporate training samples as they become available. We also describe incremental unsupervised learning mechanisms, based on novelty scores and extreme value theory, when teacher labels are not available. We present experimental results for both supervised and unsupervised learning tasks using the iCub humanoid, with tactile sensors on its five-fingered anthropomorphic hand, and 10 different object classes. Our classifiers perform comparably to state-of-the-art methods (C4.5 and SVM classifiers) and findings indicate that tactile signals are highly relevant for making accurate object classifications. We also show that accurate "early" classifications are possible using only 20-30 percent of the grasp sequence. For unsupervised learning, our methods generate high quality clusterings relative to the widely-used sequential k-means and self-organising map (SOM), and we present analyses into the differences between the approaches.

Scenario-based fitted Q-iteration for adaptive control of water reservoir systems under uncertainty

NASA Astrophysics Data System (ADS)

Bertoni, Federica; Giuliani, Matteo; Castelletti, Andrea

2017-04-01

Over recent years, mathematical models have largely been used to support planning and management of water resources systems. Yet, the increasing uncertainties in their inputs - due to increased variability in the hydrological regimes - are a major challenge to the optimal operations of these systems. Such uncertainty, boosted by projected changing climate, violates the stationarity principle generally used for describing hydro-meteorological processes, which assumes time persisting statistical characteristics of a given variable as inferred by historical data. As this principle is unlikely to be valid in the future, the probability density function used for modeling stochastic disturbances (e.g., inflows) becomes an additional uncertain parameter of the problem, which can be described in a deterministic and set-membership based fashion. This study contributes a novel method for designing optimal, adaptive policies for controlling water reservoir systems under climate-related uncertainty. The proposed method, called scenario-based Fitted Q-Iteration (sFQI), extends the original Fitted Q-Iteration algorithm by enlarging the state space to include the space of the uncertain system's parameters (i.e., the uncertain climate scenarios). As a result, sFQI embeds the set-membership uncertainty of the future inflow scenarios in the action-value function and is able to approximate, with a single learning process, the optimal control policy associated to any scenario included in the uncertainty set. The method is demonstrated on a synthetic water system, consisting of a regulated lake operated for ensuring reliable water supply to downstream users. Numerical results show that the sFQI algorithm successfully identifies adaptive solutions to operate the system under different inflow scenarios, which outperform the control policy designed under historical conditions. Moreover, the sFQI policy generalizes over inflow scenarios not directly experienced during the policy design, thus alleviating the risk of mis-adaptation, namely the design of a solution fully adapted to a scenario that is different from the one that will actually realize.

Multidisciplinary systems optimization by linear decomposition

NASA Technical Reports Server (NTRS)

Sobieski, J.

1984-01-01

In a typical design process major decisions are made sequentially. An illustrated example is given for an aircraft design in which the aerodynamic shape is usually decided first, then the airframe is sized for strength and so forth. An analogous sequence could be laid out for any other major industrial product, for instance, a ship. The loops in the discipline boxes symbolize iterative design improvements carried out within the confines of a single engineering discipline, or subsystem. The loops spanning several boxes depict multidisciplinary design improvement iterations. Omitted for graphical simplicity is parallelism of the disciplinary subtasks. The parallelism is important in order to develop a broad workfront necessary to shorten the design time. If all the intradisciplinary and interdisciplinary iterations were carried out to convergence, the process could yield a numerically optimal design. However, it usually stops short of that because of time and money limitations. This is especially true for the interdisciplinary iterations.

Integrating Low-Cost Rapid Usability Testing into Agile System Development of Healthcare IT: A Methodological Perspective.

PubMed

Kushniruk, Andre W; Borycki, Elizabeth M

2015-01-01

The development of more usable and effective healthcare information systems has become a critical issue. In the software industry methodologies such as agile and iterative development processes have emerged to lead to more effective and usable systems. These approaches highlight focusing on user needs and promoting iterative and flexible development practices. Evaluation and testing of iterative agile development cycles is considered an important part of the agile methodology and iterative processes for system design and re-design. However, the issue of how to effectively integrate usability testing methods into rapid and flexible agile design cycles has remained to be fully explored. In this paper we describe our application of an approach known as low-cost rapid usability testing as it has been applied within agile system development in healthcare. The advantages of the integrative approach are described, along with current methodological considerations.

What Patients and Providers Want to Know About Complementary and Integrative Health Therapies.

PubMed

Taylor, Stephanie L; Giannitrapani, Karleen F; Yuan, Anita; Marshall, Nell

2018-01-01

We conducted a quality improvement project to determine (1) what information providers and patients most wanted to learn about complementary and integrative health (CIH) therapies and (2) in what format they wanted to receive this information. The overall aim was to develop educational materials to facilitate the CIH therapy decision-making processes. We used mixed methods to iteratively pilot test and revise provider and patient educational materials on yoga and meditation. We conducted semistructured interviews with 11 medical providers and held seven focus groups and used feedback forms with 52 outpatients. We iteratively developed and tested three versions of both provider and patient materials. Activities were conducted at four Veterans Administration medical facilities (two large medical centers and two outpatient clinics). Patients want educational materials with clearly stated basic information about: (1) what mindfulness and yoga are, (2) what a yoga/meditation class entails and how classes can be modified to suit different abilities, (3) key benefits to health and wellness, and (4) how to find classes at the hospital/clinic. Diverse media (videos, handouts, pocket guides) appealed to different Veterans. Videos should depict patients speaking to patients and demonstrating the CIH therapy. Written materials should be one to three pages with colors, and images and messages targeting a variety of patients. Providers wanted a concise (one-page) sheet in black and white font with no images listing the scientific evidence for CIH therapies from high-impact journals, organized by either type of CIH or health condition to use during patient encounters, and including practical information about how to refer patients. Providers and patients want to learn more about CIH therapies, but want the information in succinct, targeted formats. The information learned and materials developed in this study can be used by others to educate patients and providers on CIH therapies.

"They Have to Adapt to Learn": Surgeons' Perspectives on the Role of Procedural Variation in Surgical Education.

PubMed

Apramian, Tavis; Cristancho, Sayra; Watling, Chris; Ott, Michael; Lingard, Lorelei

2016-01-01

Clinical research increasingly acknowledges the existence of significant procedural variation in surgical practice. This study explored surgeons' perspectives regarding the influence of intersurgeon procedural variation on the teaching and learning of surgical residents. This qualitative study used a grounded theory-based analysis of observational and interview data. Observational data were collected in 3 tertiary care teaching hospitals in Ontario, Canada. Semistructured interviews explored potential procedural variations arising during the observations and prompts from an iteratively refined guide. Ongoing data analysis refined the theoretical framework and informed data collection strategies, as prescribed by the iterative nature of grounded theory research. Our sample included 99 hours of observation across 45 cases with 14 surgeons. Semistructured, audio-recorded interviews (n = 14) occurred immediately following observational periods. Surgeons endorsed the use of intersurgeon procedural variations to teach residents about adapting to the complexity of surgical practice and the norms of surgical culture. Surgeons suggested that residents' efforts to identify thresholds of principle and preference are crucial to professional development. Principles that emerged from the study included the following: (1) knowing what comes next, (2) choosing the right plane, (3) handling tissue appropriately, (4) recognizing the abnormal, and (5) making safe progress. Surgeons suggested that learning to follow these principles while maintaining key aspects of surgical culture, like autonomy and individuality, are important social processes in surgical education. Acknowledging intersurgeon variation has important implications for curriculum development and workplace-based assessment in surgical education. Adapting to intersurgeon procedural variations may foster versatility in surgical residents. However, the existence of procedural variations and their active use in surgeons' teaching raises questions about the lack of attention to this form of complexity in current workplace-based assessment strategies. Failure to recognize the role of such variations may threaten the implementation of competency-based medical education in surgery. Copyright © 2015 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights reserved.

The Healthcare Complaints Analysis Tool: development and reliability testing of a method for service monitoring and organisational learning

PubMed Central

Gillespie, Alex; Reader, Tom W

2016-01-01

Background Letters of complaint written by patients and their advocates reporting poor healthcare experiences represent an under-used data source. The lack of a method for extracting reliable data from these heterogeneous letters hinders their use for monitoring and learning. To address this gap, we report on the development and reliability testing of the Healthcare Complaints Analysis Tool (HCAT). Methods HCAT was developed from a taxonomy of healthcare complaints reported in a previously published systematic review. It introduces the novel idea that complaints should be analysed in terms of severity. Recruiting three groups of educated lay participants (n=58, n=58, n=55), we refined the taxonomy through three iterations of discriminant content validity testing. We then supplemented this refined taxonomy with explicit coding procedures for seven problem categories (each with four levels of severity), stage of care and harm. These combined elements were further refined through iterative coding of a UK national sample of healthcare complaints (n= 25, n=80, n=137, n=839). To assess reliability and accuracy for the resultant tool, 14 educated lay participants coded a referent sample of 125 healthcare complaints. Results The seven HCAT problem categories (quality, safety, environment, institutional processes, listening, communication, and respect and patient rights) were found to be conceptually distinct. On average, raters identified 1.94 problems (SD=0.26) per complaint letter. Coders exhibited substantial reliability in identifying problems at four levels of severity; moderate and substantial reliability in identifying stages of care (except for ‘discharge/transfer’ that was only fairly reliable) and substantial reliability in identifying overall harm. Conclusions HCAT is not only the first reliable tool for coding complaints, it is the first tool to measure the severity of complaints. It facilitates service monitoring and organisational learning and it enables future research examining whether healthcare complaints are a leading indicator of poor service outcomes. HCAT is freely available to download and use. PMID:26740496

Using a web-based, iterative education model to enhance clinical clerkships.

PubMed

Alexander, Erik K; Bloom, Nurit; Falchuk, Kenneth H; Parker, Michael

2006-10-01

Although most clinical clerkship curricula are designed to provide all students consistent exposure to defined course objectives, it is clear that individual students are diverse in their backgrounds and baseline knowledge. Ideally, the learning process should be individualized towards the strengths and weakness of each student, but, until recently, this has proved prohibitively time-consuming. The authors describe a program to develop and evaluate an iterative, Web-based educational model assessing medical students' knowledge deficits and allowing targeted teaching shortly after their identification. Beginning in 2002, a new educational model was created, validated, and applied in a prospective fashion to medical students during an internal medicine clerkship at Harvard Medical School. Using a Web-based platform, five validated questions were delivered weekly and a specific knowledge deficiency identified. Teaching targeted to the deficiency was provided to an intervention cohort of five to seven students in each clerkship, though not to controls (the remaining 7-10 students). Effectiveness of this model was assessed by performance on the following week's posttest question. Specific deficiencies were readily identified weekly using this model. Throughout the year, however, deficiencies varied unpredictably. Teaching targeted to deficiencies resulted in significantly better performance on follow-up questioning compared to the performance of those who did not receive this intervention. This model was easily applied in an additive fashion to the current curriculum, and student acceptance was high. The authors conclude that a Web-based, iterative assessment model can effectively target specific curricular needs unique to each group; focus teaching in a rapid, formative, and highly efficient manner; and may improve the efficiency of traditional clerkship teaching.

Effects of Direct Social Experience on Trust Decisions and Neural Reward Circuitry

PubMed Central

Fareri, Dominic S.; Chang, Luke J.; Delgado, Mauricio R.

2012-01-01

The human striatum is integral for reward-processing and supports learning by linking experienced outcomes with prior expectations. Recent endeavors implicate the striatum in processing outcomes of social interactions, such as social approval/rejection, as well as in learning reputations of others. Interestingly, social impressions often influence our behavior with others during interactions. Information about an interaction partner’s moral character acquired from biographical information hinders updating of expectations after interactions via top down modulation of reward circuitry. An outstanding question is whether initial impressions formed through experience similarly modulate the ability to update social impressions at the behavioral and neural level. We investigated the role of experienced social information on trust behavior and reward-related BOLD activity. Participants played a computerized ball-tossing game with three fictional partners manipulated to be perceived as good, bad, or neutral. Participants then played an iterated trust game as investors with these same partners while undergoing fMRI. Unbeknownst to participants, partner behavior in the trust game was random and unrelated to their ball-tossing behavior. Participants’ trust decisions were influenced by their prior experience in the ball-tossing game, investing less often with the bad partner compared to the good and neutral. Reinforcement learning models revealed that participants were more sensitive to updating their beliefs about good and bad partners when experiencing outcomes consistent with initial experience. Increased striatal and anterior cingulate BOLD activity for positive versus negative trust game outcomes emerged, which further correlated with model-derived prediction error learning signals. These results suggest that initial impressions formed from direct social experience can be continually shaped by consistent information through reward learning mechanisms. PMID:23087604

Using Art to Enhance the Learning of Math and Science: Developing an Educational Art-Science Kit about Fractal Patterns in Nature

NASA Astrophysics Data System (ADS)

Rao, Deepa

This study documents the development of an educational art-science kit about natural fractals, whose aim is to unite artistic and scientific inquiry in the informal learning of science and math. Throughout this research, I argue that having an arts-integrated approach can enhance the learner of science and math concepts. A guiding metaphor in this thesis is the Enlightenment-era cabinet of curiosities that represents a time when art and science were unified in the process of inquiry about the natural world. Over time, increased specialization in the practice of arts and science led to a growing divergence between the disciplines in the educational system. Recently, initiatives like STEAM are underway at the national level to integrate "Arts and Design" into the Science, Technology, Engineering, and Math (STEM) formal education agenda. Learning artifacts like science kits present an opportunity to unite artistic and scientific inquiry in informal settings. Although science kits have been introduced to promote informal learning, presently, many science kits have a gap in their design, whereby the activities consist of recipe-like instructions that do not encourage further inquiry-based learning. In the spirit of the cabinet of curiosities, this study seeks to unify visual arts and science in the process of inquiry. Drawing from educational theories of Dewey, Piaget, and Papert, I developed a novel, prototype "art-science kit" that promotes experiential, hands-on, and active learning, and encourages inquiry, exploration, creativity, and reflection through a series of art-based activities to help users learn science and math concepts. In this study, I provide an overview of the design and development process of the arts-based educational activities. Furthermore, I present the results of a pilot usability study (n=10) conducted to receive user feedback on the designed materials for use in improving future iterations of the art-science fractal kit. The fractal kit booklet that I designed can be found in the supplemental materials to this thesis.

Active learning strategies for the deduplication of electronic patient data using classification trees.

PubMed

Sariyar, M; Borg, A; Pommerening, K

2012-10-01

Supervised record linkage methods often require a clerical review to gain informative training data. Active learning means to actively prompt the user to label data with special characteristics in order to minimise the review costs. We conducted an empirical evaluation to investigate whether a simple active learning strategy using binary comparison patterns is sufficient or if string metrics together with a more sophisticated algorithm are necessary to achieve high accuracies with a small training set. Based on medical registry data with different numbers of attributes, we used active learning to acquire training sets for classification trees, which were then used to classify the remaining data. Active learning for binary patterns means that every distinct comparison pattern represents a stratum from which one item is sampled. Active learning for patterns consisting of the Levenshtein string metric values uses an iterative process where the most informative and representative examples are added to the training set. In this context, we extended the active learning strategy by Sarawagi and Bhamidipaty (2002). On the original data set, active learning based on binary comparison patterns leads to the best results. When dropping four or six attributes, using string metrics leads to better results. In both cases, not more than 200 manually reviewed training examples are necessary. In record linkage applications where only forename, name and birthday are available as attributes, we suggest the sophisticated active learning strategy based on string metrics in order to achieve highly accurate results. We recommend the simple strategy if more attributes are available, as in our study. In both cases, active learning significantly reduces the amount of manual involvement in training data selection compared to usual record linkage settings. Copyright © 2012 Elsevier Inc. All rights reserved.

Flyback CCM inverter for AC module applications: iterative learning control and convergence analysis

NASA Astrophysics Data System (ADS)

Lee, Sung-Ho; Kim, Minsung

2017-12-01

This paper presents an iterative learning controller (ILC) for an interleaved flyback inverter operating in continuous conduction mode (CCM). The flyback CCM inverter features small output ripple current, high efficiency, and low cost, and hence it is well suited for photovoltaic power applications. However, it exhibits the non-minimum phase behaviour, because its transfer function from control duty to output current has the right-half-plane (RHP) zero. Moreover, the flyback CCM inverter suffers from the time-varying grid voltage disturbance. Thus, conventional control scheme results in inaccurate output tracking. To overcome these problems, the ILC is first developed and applied to the flyback inverter operating in CCM. The ILC makes use of both predictive and current learning terms which help the system output to converge to the reference trajectory. We take into account the nonlinear averaged model and use it to construct the proposed controller. It is proven that the system output globally converges to the reference trajectory in the absence of state disturbances, output noises, or initial state errors. Numerical simulations are performed to validate the proposed control scheme, and experiments using 400-W AC module prototype are carried out to demonstrate its practical feasibility.

Improving Patients Experience in Peadiatric Emergency Waiting Room.

PubMed

Ehrler, Frederic; Siebert, Johan; Wipfli, Rolf; Duret, Cyrille; Gervaix, Alain; Lovis, Christian

2016-01-01

When visiting the emergency department, the perception of the time spent in the waiting room before the beginning of the care, may influence patients' experience. Based on models of service evaluation, highlighting the importance of informing people about their waiting process and their place in the queue, we have developed an innovative information screen aiming at improving perception of time by patients. Following an iterative process, a group of experts including computer scientists, ergonomists and caregivers designed a solution adapted to the pediatric context. The solution includes a screen displaying five lanes representing triage levels. Patients are represented by individual avatars, drawn sequentially in the appropriate line. The interface has been designed using gamification principle, aiming at increasing acceptance, lowering learning curve and improving satisfaction. Questionnaire based evaluation results revealed high satisfaction from the 278 respondents even if the informative content was not always completely clear.

Development of programmable artificial neural networks

NASA Technical Reports Server (NTRS)

Meade, Andrew J.

1993-01-01

Conventionally programmed digital computers can process numbers with great speed and precision, but do not easily recognize patterns or imprecise or contradictory data. Instead of being programmed in the conventional sense, artificial neural networks are capable of self-learning through exposure to repeated examples. However, the training of an ANN can be a time consuming and unpredictable process. A general method is being developed to mate the adaptability of the ANN with the speed and precision of the digital computer. This method was successful in building feedforward networks that can approximate functions and their partial derivatives from examples in a single iteration. The general method also allows the formation of feedforward networks that can approximate the solution to nonlinear ordinary and partial differential equations to desired accuracy without the need of examples. It is believed that continued research will produce artificial neural networks that can be used with confidence in practical scientific computing and engineering applications.

Off-site training of laparoscopic skills, a scoping review using a thematic analysis.

PubMed

Thinggaard, Ebbe; Kleif, Jakob; Bjerrum, Flemming; Strandbygaard, Jeanett; Gögenur, Ismail; Matthew Ritter, E; Konge, Lars

2016-11-01

The focus of research in simulation-based laparoscopic training has changed from examining whether simulation training works to examining how best to implement it. In laparoscopic skills training, portable and affordable box trainers allow for off-site training. Training outside simulation centers and hospitals can increase access to training, but also poses new challenges to implementation. This review aims to guide implementation of off-site training of laparoscopic skills by critically reviewing the existing literature. An iterative systematic search was carried out in MEDLINE, EMBASE, ERIC, Scopus, and PsychINFO, following a scoping review methodology. The included literature was analyzed iteratively using a thematic analysis approach. The study was reported in accordance with the STructured apprOach to the Reporting In healthcare education of Evidence Synthesis statement. From the search, 22 records were identified and included for analysis. A thematic analysis revealed the themes: access to training, protected training time, distribution of training, goal setting and testing, task design, and unsupervised training. The identified themes were based on learning theories including proficiency-based learning, deliberate practice, and self-regulated learning. Methods of instructional design vary widely in off-site training of laparoscopic skills. Implementation can be facilitated by organizing courses and training curricula following sound education theories such as proficiency-based learning and deliberate practice. Directed self-regulated learning has the potential to improve off-site laparoscopic skills training; however, further studies are needed to demonstrate the effect of this type of instructional design.

PredicT-ML: a tool for automating machine learning model building with big clinical data.

PubMed

Luo, Gang

2016-01-01

Predictive modeling is fundamental to transforming large clinical data sets, or "big clinical data," into actionable knowledge for various healthcare applications. Machine learning is a major predictive modeling approach, but two barriers make its use in healthcare challenging. First, a machine learning tool user must choose an algorithm and assign one or more model parameters called hyper-parameters before model training. The algorithm and hyper-parameter values used typically impact model accuracy by over 40 %, but their selection requires many labor-intensive manual iterations that can be difficult even for computer scientists. Second, many clinical attributes are repeatedly recorded over time, requiring temporal aggregation before predictive modeling can be performed. Many labor-intensive manual iterations are required to identify a good pair of aggregation period and operator for each clinical attribute. Both barriers result in time and human resource bottlenecks, and preclude healthcare administrators and researchers from asking a series of what-if questions when probing opportunities to use predictive models to improve outcomes and reduce costs. This paper describes our design of and vision for PredicT-ML (prediction tool using machine learning), a software system that aims to overcome these barriers and automate machine learning model building with big clinical data. The paper presents the detailed design of PredicT-ML. PredicT-ML will open the use of big clinical data to thousands of healthcare administrators and researchers and increase the ability to advance clinical research and improve healthcare.

Integrating Theory and Practice: Applying the Quality Improvement Paradigm to Product Line Engineering

NASA Technical Reports Server (NTRS)

Stark, Michael; Hennessy, Joseph F. (Technical Monitor)

2002-01-01

My assertion is that not only are product lines a relevant research topic, but that the tools used by empirical software engineering researchers can address observed practical problems. Our experience at NASA has been there are often externally proposed solutions available, but that we have had difficulties applying them in our particular context. We have also focused on return on investment issues when evaluating product lines, and while these are important, one can not attain objective data on success or failure until several applications from a product family have been deployed. The use of the Quality Improvement Paradigm (QIP) can address these issues: (1) Planning an adoption path from an organization's current state to a product line approach; (2) Constructing a development process to fit the organization's adoption path; (3) Evaluation of product line development processes as the project is being developed. The QIP consists of the following six steps: (1) Characterize the project and its environment; (2) Set quantifiable goals for successful project performance; (3) Choose the appropriate process models, supporting methods, and tools for the project; (4) Execute the process, analyze interim results, and provide real-time feedback for corrective action; (5) Analyze the results of completed projects and recommend improvements; and (6) Package the lessons learned as updated and refined process models. A figure shows the QIP in detail. The iterative nature of the QIP supports an incremental development approach to product lines, and the project learning and feedback provide the necessary early evaluations.

Functional electrical stimulation mediated by iterative learning control and 3D robotics reduces motor impairment in chronic stroke

PubMed Central

2012-01-01

Background Novel stroke rehabilitation techniques that employ electrical stimulation (ES) and robotic technologies are effective in reducing upper limb impairments. ES is most effective when it is applied to support the patients’ voluntary effort; however, current systems fail to fully exploit this connection. This study builds on previous work using advanced ES controllers, and aims to investigate the feasibility of Stimulation Assistance through Iterative Learning (SAIL), a novel upper limb stroke rehabilitation system which utilises robotic support, ES, and voluntary effort. Methods Five hemiparetic, chronic stroke participants with impaired upper limb function attended 18, 1 hour intervention sessions. Participants completed virtual reality tracking tasks whereby they moved their impaired arm to follow a slowly moving sphere along a specified trajectory. To do this, the participants’ arm was supported by a robot. ES, mediated by advanced iterative learning control (ILC) algorithms, was applied to the triceps and anterior deltoid muscles. Each movement was repeated 6 times and ILC adjusted the amount of stimulation applied on each trial to improve accuracy and maximise voluntary effort. Participants completed clinical assessments (Fugl-Meyer, Action Research Arm Test) at baseline and post-intervention, as well as unassisted tracking tasks at the beginning and end of each intervention session. Data were analysed using t-tests and linear regression. Results From baseline to post-intervention, Fugl-Meyer scores improved, assisted and unassisted tracking performance improved, and the amount of ES required to assist tracking reduced. Conclusions The concept of minimising support from ES using ILC algorithms was demonstrated. The positive results are promising with respect to reducing upper limb impairments following stroke, however, a larger study is required to confirm this. PMID:22676920

Functional electrical stimulation mediated by iterative learning control and 3D robotics reduces motor impairment in chronic stroke.

PubMed

Meadmore, Katie L; Hughes, Ann-Marie; Freeman, Chris T; Cai, Zhonglun; Tong, Daisy; Burridge, Jane H; Rogers, Eric

2012-06-07

Novel stroke rehabilitation techniques that employ electrical stimulation (ES) and robotic technologies are effective in reducing upper limb impairments. ES is most effective when it is applied to support the patients' voluntary effort; however, current systems fail to fully exploit this connection. This study builds on previous work using advanced ES controllers, and aims to investigate the feasibility of Stimulation Assistance through Iterative Learning (SAIL), a novel upper limb stroke rehabilitation system which utilises robotic support, ES, and voluntary effort. Five hemiparetic, chronic stroke participants with impaired upper limb function attended 18, 1 hour intervention sessions. Participants completed virtual reality tracking tasks whereby they moved their impaired arm to follow a slowly moving sphere along a specified trajectory. To do this, the participants' arm was supported by a robot. ES, mediated by advanced iterative learning control (ILC) algorithms, was applied to the triceps and anterior deltoid muscles. Each movement was repeated 6 times and ILC adjusted the amount of stimulation applied on each trial to improve accuracy and maximise voluntary effort. Participants completed clinical assessments (Fugl-Meyer, Action Research Arm Test) at baseline and post-intervention, as well as unassisted tracking tasks at the beginning and end of each intervention session. Data were analysed using t-tests and linear regression. From baseline to post-intervention, Fugl-Meyer scores improved, assisted and unassisted tracking performance improved, and the amount of ES required to assist tracking reduced. The concept of minimising support from ES using ILC algorithms was demonstrated. The positive results are promising with respect to reducing upper limb impairments following stroke, however, a larger study is required to confirm this.

Sparse Representation with Spatio-Temporal Online Dictionary Learning for Efficient Video Coding.

PubMed

Dai, Wenrui; Shen, Yangmei; Tang, Xin; Zou, Junni; Xiong, Hongkai; Chen, Chang Wen

2016-07-27

Classical dictionary learning methods for video coding suer from high computational complexity and interfered coding eciency by disregarding its underlying distribution. This paper proposes a spatio-temporal online dictionary learning (STOL) algorithm to speed up the convergence rate of dictionary learning with a guarantee of approximation error. The proposed algorithm incorporates stochastic gradient descents to form a dictionary of pairs of 3-D low-frequency and highfrequency spatio-temporal volumes. In each iteration of the learning process, it randomly selects one sample volume and updates the atoms of dictionary by minimizing the expected cost, rather than optimizes empirical cost over the complete training data like batch learning methods, e.g. K-SVD. Since the selected volumes are supposed to be i.i.d. samples from the underlying distribution, decomposition coecients attained from the trained dictionary are desirable for sparse representation. Theoretically, it is proved that the proposed STOL could achieve better approximation for sparse representation than K-SVD and maintain both structured sparsity and hierarchical sparsity. It is shown to outperform batch gradient descent methods (K-SVD) in the sense of convergence speed and computational complexity, and its upper bound for prediction error is asymptotically equal to the training error. With lower computational complexity, extensive experiments validate that the STOL based coding scheme achieves performance improvements than H.264/AVC or HEVC as well as existing super-resolution based methods in ratedistortion performance and visual quality.

Adaptive management of urban watersheds

NASA Astrophysics Data System (ADS)

Garmestani, A.; Shuster, W.; Green, O. O.

2013-12-01

Consent decree settlements for violations of the Clean Water Act (1972) increasingly include provisions for redress of combined sewer overflow activity through hybrid approaches that incorporate the best of both gray (e.g., storage tunnels) and green infrastructure (e.g., rain gardens). Adaptive management is an environmental management strategy that uses an iterative process of decision-making to improve environmental management via system monitoring. A central tenet of adaptive management is that management involves a learning process that can help regulated communities achieve environmental quality objectives. We are using an adaptive management approach to guide a green infrastructure retrofit of a neighborhood in the Slavic Village Development Corporation area (Cleveland, Ohio). We are in the process of gathering hydrologic and ecosystem services data and will use this data as a basis for collaboration with area citizens on a plan to use green infrastructure to contain stormflows. Monitoring data provides researchers with feedback on the impact of green infrastructure implementation and suggest where improvements can be made.

A relational learning approach to Structure-Activity Relationships in drug design toxicity studies.

PubMed

Camacho, Rui; Pereira, Max; Costa, Vítor Santos; Fonseca, Nuno A; Adriano, Carlos; Simões, Carlos J V; Brito, Rui M M

2011-09-16

It has been recognized that the development of new therapeutic drugs is a complex and expensive process. A large number of factors affect the activity in vivo of putative candidate molecules and the propensity for causing adverse and toxic effects is recognized as one of the major hurdles behind the current "target-rich, lead-poor" scenario. Structure-Activity Relationship (SAR) studies, using relational Machine Learning (ML) algorithms, have already been shown to be very useful in the complex process of rational drug design. Despite the ML successes, human expertise is still of the utmost importance in the drug development process. An iterative process and tight integration between the models developed by ML algorithms and the know-how of medicinal chemistry experts would be a very useful symbiotic approach. In this paper we describe a software tool that achieves that goal--iLogCHEM. The tool allows the use of Relational Learners in the task of identifying molecules or molecular fragments with potential to produce toxic effects, and thus help in stream-lining drug design in silico. It also allows the expert to guide the search for useful molecules without the need to know the details of the algorithms used. The models produced by the algorithms may be visualized using a graphical interface, that is of common use amongst researchers in structural biology and medicinal chemistry. The graphical interface enables the expert to provide feedback to the learning system. The developed tool has also facilities to handle the similarity bias typical of large chemical databases. For that purpose the user can filter out similar compounds when assembling a data set. Additionally, we propose ways of providing background knowledge for Relational Learners using the results of Graph Mining algorithms. Copyright 2011 The Author(s). Published by Journal of Integrative Bioinformatics.

A Consensus Statement on Practical Skills in Medical School – a position paper by the GMA Committee on Practical Skills

PubMed Central

Schnabel, Kai P.; Boldt, Patrick D.; Breuer, Georg; Fichtner, Andreas; Karsten, Gudrun; Kujumdshiev, Sandy; Schmidts, Michael; Stosch, Christoph

2011-01-01

Introduction: Encouraged by the change in licensing regulations the practical professional skills in Germany received a higher priority and are taught in medical schools therefore increasingly. This created the need to standardize the process more and more. On the initiative of the German skills labs the German Medical Association Committee for practical skills was established and developed a competency-based catalogue of learning objectives, whose origin and structure is described here. Goal of the catalogue is to define the practical skills in undergraduate medical education and to give the medical schools a rational planning basis for the necessary resources to teach them. Methods: Building on already existing German catalogues of learning objectives a multi-iterative process of condensation was performed, which corresponds to the development of S1 guidelines, in order to get a broad professional and political support. Results: 289 different practical learning goals were identified and assigned to twelve different organ systems with three overlapping areas to other fields of expertise and one area of across organ system skills. They were three depths and three different chronological dimensions assigned and the objectives were matched with the Swiss and the Austrian equivalent. Discussion: This consensus statement may provide the German faculties with a basis for planning the teaching of practical skills and is an important step towards a national standard of medical learning objectives. Looking ahead: The consensus statement may have a formative effect on the medical schools to teach practical skills and plan the resources accordingly. PMID:22205916

Exemplary Care and Learning Sites: A Model for Achieving Continual Improvement in Care and Learning in the Clinical Setting

PubMed Central

Ogrinc, Greg; Hoffman, Kimberly G.; Stevenson, Katherine M.; Shalaby, Marc; Beard, Albertine S.; Thörne, Karin E.; Coleman, Mary T.; Baum, Karyn D.

2016-01-01

Problem Current models of health care quality improvement do not explicitly describe the role of health professions education. The authors propose the Exemplary Care and Learning Site (ECLS) model as an approach to achieving continual improvement in care and learning in the clinical setting. Approach From 2008–2012, an iterative, interactive process was used to develop the ECLS model and its core elements—patients and families informing process changes; trainees engaging both in care and the improvement of care; leaders knowing, valuing, and practicing improvement; data transforming into useful information; and health professionals competently engaging both in care improvement and teaching about care improvement. In 2012–2013, a three-part feasibility test of the model, including a site self-assessment, an independent review of each site’s ratings, and implementation case stories, was conducted at six clinical teaching sites (in the United States and Sweden). Outcomes Site leaders reported the ECLS model provided a systematic approach toward improving patient (and population) outcomes, system performance, and professional development. Most sites found it challenging to incorporate the patients and families element. The trainee element was strong at four sites. The leadership and data elements were self-assessed as the most fully developed. The health professionals element exhibited the greatest variability across sites. Next Steps The next test of the model should be prospective, linked to clinical and educa tional outcomes, to evaluate whether it helps care delivery teams, educators, and patients and families take action to achieve better patient (and population) outcomes, system performance, and professional development. PMID:26760058

Unsupervised method for automatic construction of a disease dictionary from a large free text collection.

PubMed

Xu, Rong; Supekar, Kaustubh; Morgan, Alex; Das, Amar; Garber, Alan

2008-11-06

Concept specific lexicons (e.g. diseases, drugs, anatomy) are a critical source of background knowledge for many medical language-processing systems. However, the rapid pace of biomedical research and the lack of constraints on usage ensure that such dictionaries are incomplete. Focusing on disease terminology, we have developed an automated, unsupervised, iterative pattern learning approach for constructing a comprehensive medical dictionary of disease terms from randomized clinical trial (RCT) abstracts, and we compared different ranking methods for automatically extracting con-textual patterns and concept terms. When used to identify disease concepts from 100 randomly chosen, manually annotated clinical abstracts, our disease dictionary shows significant performance improvement (F1 increased by 35-88%) over available, manually created disease terminologies.