Translating Data into Information to Improve Teaching and Learning

ERIC Educational Resources Information Center

Bernhardt, Victoria L.

2007-01-01

In these times of high-stakes accountability, all professional educators must learn how to gather, analyze, and use data to improve teaching and learning, becoming efficient generators and consumers of data. The intended audiences for this book are school and district administrators and teacher leaders who recognize the need to use data to…

A hybrid fuzzy logic and extreme learning machine for improving efficiency of circulating water systems in power generation plant

NASA Astrophysics Data System (ADS)

Aziz, Nur Liyana Afiqah Abdul; Siah Yap, Keem; Afif Bunyamin, Muhammad

2013-06-01

This paper presents a new approach of the fault detection for improving efficiency of circulating water system (CWS) in a power generation plant using a hybrid Fuzzy Logic System (FLS) and Extreme Learning Machine (ELM) neural network. The FLS is a mathematical tool for calculating the uncertainties where precision and significance are applied in the real world. It is based on natural language which has the ability of "computing the word". The ELM is an extremely fast learning algorithm for neural network that can completed the training cycle in a very short time. By combining the FLS and ELM, new hybrid model, i.e., FLS-ELM is developed. The applicability of this proposed hybrid model is validated in fault detection in CWS which may help to improve overall efficiency of power generation plant, hence, consuming less natural recourses and producing less pollutions.

Perceptual learning improves visual performance in juvenile amblyopia.

PubMed

Li, Roger W; Young, Karen G; Hoenig, Pia; Levi, Dennis M

2005-09-01

To determine whether practicing a position-discrimination task improves visual performance in children with amblyopia and to determine the mechanism(s) of improvement. Five children (age range, 7-10 years) with amblyopia practiced a positional acuity task in which they had to judge which of three pairs of lines was misaligned. Positional noise was produced by distributing the individual patches of each line segment according to a Gaussian probability function. Observers were trained at three noise levels (including 0), with each observer performing between 3000 and 4000 responses in 7 to 10 sessions. Trial-by-trial feedback was provided. Four of the five observers showed significant improvement in positional acuity. In those four observers, on average, positional acuity with no noise improved by approximately 32% and with high noise by approximately 26%. A position-averaging model was used to parse the improvement into an increase in efficiency or a decrease in equivalent input noise. Two observers showed increased efficiency (51% and 117% improvements) with no significant change in equivalent input noise across sessions. The other two observers showed both a decrease in equivalent input noise (18% and 29%) and an increase in efficiency (17% and 71%). All five observers showed substantial improvement in Snellen acuity (approximately 26%) after practice. Perceptual learning can improve visual performance in amblyopic children. The improvement can be parsed into two important factors: decreased equivalent input noise and increased efficiency. Perceptual learning techniques may add an effective new method to the armamentarium of amblyopia treatments.

The Framework of Intervention Engine Based on Learning Analytics

ERIC Educational Resources Information Center

Sahin, Muhittin; Yurdugül, Halil

2017-01-01

Learning analytics primarily deals with the optimization of learning environments and the ultimate goal of learning analytics is to improve learning and teaching efficiency. Studies on learning analytics seem to have been made in the form of adaptation engine and intervention engine. Adaptation engine studies are quite widespread, but intervention…

There is No Free Lunch: Tradeoffs in the Utility of Learned Knowledge

NASA Technical Reports Server (NTRS)

Kedar, Smadar T.; McKusick, Kathleen B.

1992-01-01

With the recent introduction of learning in integrated systems, there is a need to measure the utility of learned knowledge for these more complex systems. A difficulty arrises when there are multiple, possibly conflicting, utility metrics to be measured. In this paper, we present schemes which trade off conflicting utility metrics in order to achieve some global performance objectives. In particular, we present a case study of a multi-strategy machine learning system, mutual theory refinement, which refines world models for an integrated reactive system, the Entropy Reduction Engine. We provide experimental results on the utility of learned knowledge in two conflicting metrics - improved accuracy and degraded efficiency. We then demonstrate two ways to trade off these metrics. In each, some learned knowledge is either approximated or dynamically 'forgotten' so as to improve efficiency while degrading accuracy only slightly.

Quantum-Enhanced Machine Learning

NASA Astrophysics Data System (ADS)

Dunjko, Vedran; Taylor, Jacob M.; Briegel, Hans J.

2016-09-01

The emerging field of quantum machine learning has the potential to substantially aid in the problems and scope of artificial intelligence. This is only enhanced by recent successes in the field of classical machine learning. In this work we propose an approach for the systematic treatment of machine learning, from the perspective of quantum information. Our approach is general and covers all three main branches of machine learning: supervised, unsupervised, and reinforcement learning. While quantum improvements in supervised and unsupervised learning have been reported, reinforcement learning has received much less attention. Within our approach, we tackle the problem of quantum enhancements in reinforcement learning as well, and propose a systematic scheme for providing improvements. As an example, we show that quadratic improvements in learning efficiency, and exponential improvements in performance over limited time periods, can be obtained for a broad class of learning problems.

Improving the Efficiency of Virtual Reality Training by Integrating Partly Observational Learning

ERIC Educational Resources Information Center

Yuviler-Gavish, Nirit; Rodríguez, Jorge; Gutiérrez, Teresa; Sánchez, Emilio; Casado, Sara

2014-01-01

The current study hypothesized that integrating partly observational learning into virtual reality training systems (VRTS) can enhance training efficiency for procedural tasks. A common approach in designing VRTS is the enactive approach, which stresses the importance of physical actions within the environment to enhance perception and improve…

Inter-Individual Differences in the Initial 80 Minutes of Motor Learning of Handrim Wheelchair Propulsion

PubMed Central

Vegter, Riemer J. K.; Lamoth, Claudine J.; de Groot, Sonja; Veeger, Dirkjan H. E. J.; van der Woude, Lucas H. V.

2014-01-01

Handrim wheelchair propulsion is a cyclic skill that needs to be learned during rehabilitation. Yet it is unclear how inter-individual differences in motor learning impact wheelchair propulsion practice. Therefore we studied how early-identified motor learning styles in novice able-bodied participants impact the outcome of a low-intensity wheelchair-practice intervention. Over a 12-minute pre-test, 39 participants were split in two groups based on a relative 10% increase in mechanical efficiency. Following the pretest the participants continued one of four different low-intensity wheelchair practice interventions, yet all performed in the same trial-setup with a total 80-minute dose at 1.11 m/s at 0.20 W/kg. Instead of focusing on the effect of the different interventions, we focused on differences in motor learning between participants over the intervention. Twenty-six participants started the pretest with a lower mechanical efficiency and a less optimal propulsion technique, but showed a fast improvement during the first 12 minutes and this effect continued over the 80 minutes of practice. Eventually these initially fast improvers benefitted more from the given practice indicated by a better propulsion technique (like reduced frequency and increased stroke angle) and a higher mechanical efficiency. The initially fast improvers also had a higher intra-individual variability in the pre and posttest, which possibly relates to the increased motor learning of the initially fast improvers. Further exploration of the common characteristics of different types of learners will help to better tailor rehabilitation to the needs of wheelchair-dependent persons and improve our understanding of cyclic motor learning processes. PMID:24586992

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

PubMed

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

2018-04-12

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

Evaluation of Energy Efficiency Improvements to Portable Classrooms in Florida.

ERIC Educational Resources Information Center

Callahan, Michael P.; Parker, Danny S.; Sherwin, John R.; Anello, Michael T.

Findings are presented from a 2-year experiment exploring ways to reduce energy costs and improve the learning environment in Florida's 25,000 portable classrooms. Improvements were made in two highly instrumented portable classrooms in the following areas: installation of a T8 lighting system with electronic ballasts; a high efficiency heat pump…

Spitzer observatory operations: increasing efficiency in mission operations

NASA Astrophysics Data System (ADS)

Scott, Charles P.; Kahr, Bolinda E.; Sarrel, Marc A.

2006-06-01

This paper explores the how's and why's of the Spitzer Mission Operations System's (MOS) success, efficiency, and affordability in comparison to other observatory-class missions. MOS exploits today's flight, ground, and operations capabilities, embraces automation, and balances both risk and cost. With operational efficiency as the primary goal, MOS maintains a strong control process by translating lessons learned into efficiency improvements, thereby enabling the MOS processes, teams, and procedures to rapidly evolve from concept (through thorough validation) into in-flight implementation. Operational teaming, planning, and execution are designed to enable re-use. Mission changes, unforeseen events, and continuous improvement have often times forced us to learn to fly anew. Collaborative spacecraft operations and remote science and instrument teams have become well integrated, and worked together to improve and optimize each human, machine, and software-system element. Adaptation to tighter spacecraft margins has facilitated continuous operational improvements via automated and autonomous software coupled with improved human analysis. Based upon what we now know and what we need to improve, adapt, or fix, the projected mission lifetime continues to grow - as does the opportunity for numerous scientific discoveries.

A visual tracking method based on improved online multiple instance learning

NASA Astrophysics Data System (ADS)

He, Xianhui; Wei, Yuxing

2016-09-01

Visual tracking is an active research topic in the field of computer vision and has been well studied in the last decades. The method based on multiple instance learning (MIL) was recently introduced into the tracking task, which can solve the problem that template drift well. However, MIL method has relatively poor performance in running efficiency and accuracy, due to its strong classifiers updating strategy is complicated, and the speed of the classifiers update is not always same with the change of the targets' appearance. In this paper, we present a novel online effective MIL (EMIL) tracker. A new update strategy for strong classifier was proposed to improve the running efficiency of MIL method. In addition, to improve the t racking accuracy and stability of the MIL method, a new dynamic mechanism for learning rate renewal of the classifier and variable search window were proposed. Experimental results show that our method performs good performance under the complex scenes, with strong stability and high efficiency.

Applying Quality Management Process-Improvement Principles to Learning in Reading Courses: An Improved Learning and Retention Method.

ERIC Educational Resources Information Center

Hahn, William G.; Bart, Barbara D.

2003-01-01

Business students were taught a total quality management-based outlining process for course readings and a tally method to measure learning efficiency. Comparison of 233 who used the process and 99 who did not showed that the group means of users' test scores were 12.4 points higher than those of nonusers. (Contains 25 references.) (SK)

Designing Ensemble Based Security Framework for M-Learning System

ERIC Educational Resources Information Center

Mahalingam, Sheila; Abdollah, Mohd Faizal; bin Sahibuddin, Shahrin

2014-01-01

Mobile Learning has a potential to improve efficiency in the education sector and expand educational opportunities to underserved remote area in higher learning institutions. However there are multi challenges in different altitude faced when introducing and implementing m-learning. Despite the evolution of technology changes in education,…

LEARN: Playful Techniques To Accelerate Learning.

ERIC Educational Resources Information Center

Richards, Regina G.

The methods outlined in this guide offer teachers a variety of ways to stimulate interest, enhance concentration, increase understanding, and improve memory in their students. Chapter 1 discusses the LEARN (Learning Efficiently And Remembering Mnemonics) system, a set of strategies that help students use a variety of processing styles to a greater…

Collaborative E-Learning Using Semantic Course Blog

ERIC Educational Resources Information Center

Lu, Lai-Chen; Yeh, Ching-Long

2008-01-01

Collaborative e-learning delivers many enhancements to e-learning technology; it enables students to collaborate with each other and improves their learning efficiency. Semantic blog combines semantic Web and blog technology that users can import, export, view, navigate, and query the blog. We developed a semantic course blog for collaborative…

Assessment of the Effectiveness of an Online Learning System in Improving Student Test Performance

ERIC Educational Resources Information Center

Buttner, E. Holly; Black, Aprille Noe

2014-01-01

Colleges and universities, particularly public institutions, are facing higher enrollments and declining resources from state and federal governments. In this resource-constrained environment, faculty are seeking more efficient and effective teaching strategies to improve student learning and test performance. The authors assessed an online…

Does Competition Improve Public School Efficiency? A Spatial Analysis

ERIC Educational Resources Information Center

Misra, Kaustav; Grimes, Paul W.; Rogers, Kevin E.

2012-01-01

Advocates for educational reform frequently call for policies to increase competition between schools because it is argued that market forces naturally lead to greater efficiencies, including improved student learning, when schools face competition. Researchers examining this issue are confronted with difficulties in defining reasonable measures…

Learners' Ensemble Based Security Conceptual Model for M-Learning System in Malaysian Higher Learning Institution

ERIC Educational Resources Information Center

Mahalingam, Sheila; Abdollah, Faizal Mohd; Sahib, Shahrin

2014-01-01

M-Learning has a potential to improve efficiency in the education sector and has a tendency to grow advance and transform the learning environment in the future. Yet there are challenges in many areas faced when introducing and implementing m-learning. The learner centered attribute in mobile learning implies deployment in untrustworthy learning…

The Effects of Locus of Control on University Students' Mobile Learning Adoption

ERIC Educational Resources Information Center

Hsia, Jung-Wen

2016-01-01

Since mobile devices have become cheaper, easily accessible, powerful, and popular and the cost of wireless access has declined gradually, mobile learning (m-learning) has begun to spread rapidly. To further improve the effectiveness and efficiency of m-learning for university students, it is critical to understand whether they use m-learning.…

Capacity to improve fine motor skills in Williams syndrome.

PubMed

Berencsi, A; Gombos, F; Kovács, I

2016-10-01

Individuals with Williams syndrome (WS) are known to have difficulties in carrying out fine motor movements; however, a detailed behavioural profile of WS in this domain is still missing. It is also unknown how great the capacity to improve these skills with focused and extensive practice is. We studied initial performance and learning capacity in a sequential finger tapping (FT) task in WS and in typical development. Improvement in the FT task has been shown to be sleep dependent. WS subjects participating in the current study have also participated in earlier polysomnography studies, although not directly related to learning. WS participants presented with great individual variability. In addition to generally poor initial performance, learning capacity was also greatly limited in WS. We found indications that reduced sleep efficiency might contribute to this limitation. Estimating motor learning capacity and the depth of sleep disorder in a larger sample of WS individuals might reveal important relationships between sleep and learning, and contribute to efficient intervention methods improving skill acquisition in WS. © 2016 The Authors. Journal of Intellectual Disability Research published by MENCAP and International Association of the Scientific Study of Intellectual and Developmental Disabilities and John Wiley & Sons Ltd.

Does Competition Improve Public School Efficiency? A Spatial Analysis

ERIC Educational Resources Information Center

Misra, Kaustav

2010-01-01

Proponents of educational reform often call for policies to increase competition between schools. It is argued that market forces naturally lead to greater efficiencies, including improved student learning, when schools face competition. In many parts of the country, public schools experience significant competition from private schools; however,…

Learning classification models with soft-label information.

PubMed

Nguyen, Quang; Valizadegan, Hamed; Hauskrecht, Milos

2014-01-01

Learning of classification models in medicine often relies on data labeled by a human expert. Since labeling of clinical data may be time-consuming, finding ways of alleviating the labeling costs is critical for our ability to automatically learn such models. In this paper we propose a new machine learning approach that is able to learn improved binary classification models more efficiently by refining the binary class information in the training phase with soft labels that reflect how strongly the human expert feels about the original class labels. Two types of methods that can learn improved binary classification models from soft labels are proposed. The first relies on probabilistic/numeric labels, the other on ordinal categorical labels. We study and demonstrate the benefits of these methods for learning an alerting model for heparin induced thrombocytopenia. The experiments are conducted on the data of 377 patient instances labeled by three different human experts. The methods are compared using the area under the receiver operating characteristic curve (AUC) score. Our AUC results show that the new approach is capable of learning classification models more efficiently compared to traditional learning methods. The improvement in AUC is most remarkable when the number of examples we learn from is small. A new classification learning framework that lets us learn from auxiliary soft-label information provided by a human expert is a promising new direction for learning classification models from expert labels, reducing the time and cost needed to label data.

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

ERIC Educational Resources Information Center

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

2008-01-01

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

Mobile Learning According to Students of Computer Engineering and Computer Education: A Comparison of Attitudes

ERIC Educational Resources Information Center

Gezgin, Deniz Mertkan; Adnan, Muge; Acar Guvendir, Meltem

2018-01-01

Mobile learning has started to perform an increasingly significant role in improving learning outcomes in education. Successful and efficient implementation of m-learning in higher education, as with all educational levels, depends on users' acceptance of this technology. This study focuses on investigating the attitudes of undergraduate students…

Supporting Adults to Address Their Literacy Needs Using E-Learning

ERIC Educational Resources Information Center

Fletcher, Jo; Nicholas, Karen; Davis, Niki

2011-01-01

Many adults need help with literacy learning. This is extremely challenging for the tertiary education sector and workplace-situated learning organisations. E-learning may be an effective and efficient way to improve the delivery of teaching of basic skills to learners. Our research study included five embedded case studies within one tertiary…

Actualizing the Learning Community.

ERIC Educational Resources Information Center

Braman, Dave

Where conditions are right, continuing education (CE) staff working in true collaboration with campus-based credit staff can meet the learning needs of the community and improve instructional quality with greater resource efficiency. CE staff must become learning strategists who bring ideas from their marketplace experience to the instructional…

Larval antlions show a cognitive ability/hunting efficiency trade-off connected with the level of behavioural asymmetry.

PubMed

Miler, Krzysztof; Kuszewska, Karolina; Zuber, Gabriela; Woyciechowski, Michal

2018-05-14

Recently, antlion larvae with greater behavioural asymmetry were shown to have improved learning abilities. However, a major evolutionary question that remained unanswered was why this asymmetry does not increase in all individuals during development. Here, we show that a trade-off exists between learning ability of larvae and their hunting efficiency. Larvae with greater asymmetry learn better than those with less, but the latter are better able to sense vibrational signals used to detect prey and can capture prey more quickly. Both traits, learning ability and hunting efficiency, present obvious fitness advantages; the trade-off between them may explain why behavioural asymmetry, which presumably stems from brain lateralization, is relatively rare in natural antlion populations.

Time and learning efficiency in Internet-based learning: a systematic review and meta-analysis.

PubMed

Cook, David A; Levinson, Anthony J; Garside, Sarah

2010-12-01

Authors have claimed that Internet-based instruction promotes greater learning efficiency than non-computer methods. determine, through a systematic synthesis of evidence in health professions education, how Internet-based instruction compares with non-computer instruction in time spent learning, and what features of Internet-based instruction are associated with improved learning efficiency. we searched databases including MEDLINE, CINAHL, EMBASE, and ERIC from 1990 through November 2008. STUDY SELECTION AND DATA ABSTRACTION we included all studies quantifying learning time for Internet-based instruction for health professionals, compared with other instruction. Reviewers worked independently, in duplicate, to abstract information on interventions, outcomes, and study design. we identified 20 eligible studies. Random effects meta-analysis of 8 studies comparing Internet-based with non-Internet instruction (positive numbers indicating Internet longer) revealed pooled effect size (ES) for time -0.10 (p = 0.63). Among comparisons of two Internet-based interventions, providing feedback adds time (ES 0.67, p =0.003, two studies), and greater interactivity generally takes longer (ES 0.25, p = 0.089, five studies). One study demonstrated that adapting to learner prior knowledge saves time without significantly affecting knowledge scores. Other studies revealed that audio narration, video clips, interactive models, and animations increase learning time but also facilitate higher knowledge and/or satisfaction. Across all studies, time correlated positively with knowledge outcomes (r = 0.53, p = 0.021). on average, Internet-based instruction and non-computer instruction require similar time. Instructional strategies to enhance feedback and interactivity typically prolong learning time, but in many cases also enhance learning outcomes. Isolated examples suggest potential for improving efficiency in Internet-based instruction.

Energy Efficiency in Water and Wastewater Facilities

EPA Pesticide Factsheets

Learn how water and wastewater facilities can lead by example and achieve multiple benefits by improving energy efficiency of their new, existing, and renovated buildings and their day-to-day operations.

What China can learn from international policy experiences to improve industrial energy efficiency and reduce CO 2 emissions?

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

Liu, Xu; Shen, Bo; Price, Lynn

China’s industrial sector dominates the country’s total energy consumption and energy efficiency in the industry sector is crucial to help China reach its energy and CO 2 emissions reduction goals. There are many energy efficiency policies in China, but the motivation and willingness of enterprises to improve energy efficiency has weakened. This report first identifies barriers that enterprises face to be self-motivated to implement energy efficiency measures. Then, this report reviews international policies and programs to improve energy efficiency and evaluates how these policies helped to address the identified barriers. Lastly, this report draws conclusions and provides recommendations to Chinamore » in developing policies and programs to motivate enterprises to improve energy efficiency.« less

Challenge-based instruction in biomedical engineering: a scalable method to increase the efficiency and effectiveness of teaching and learning in biomedical engineering.

PubMed

Harris, Thomas R; Brophy, Sean P

2005-09-01

Vanderbilt University, Northwestern University, the University of Texas and the Harvard/MIT Health Sciences Technology Program have collaborated since 1999 to develop means to improve bioengineering education. This effort, funded by the National Science Foundation as the VaNTH Engineering Research Center in Bioengineering Educational Technologies, has sought a synthesis of learning science, learning technology, assessment and the domains of bioengineering in order to improve learning by bioengineering students. Research has shown that bioengineering educational materials may be designed to emphasize challenges that engage the student and, when coupled with a learning cycle and appropriate technologies, can lead to improvements in instruction.

Implementation and Use of Simulated Students for Test and Validation of New Adaptive Educational Systems: A Practical Insight

ERIC Educational Resources Information Center

Dorça, Fabiano

2015-01-01

Studies attest that learning is facilitated if teaching strategies are in accordance with students learning styles, making learning process more effective and considerably improving students performances. In this context, one major research point--and a challenge--is to efficiently discover students' learning styles. But, the test and validation…

Using Mobile Communication Technology in High School Education: Motivation, Pressure, and Learning Performance

ERIC Educational Resources Information Center

Rau, Pei-Luen Patrick; Gao, Qin; Wu, Li-Mei

2008-01-01

Motivation and pressure are considered two factors impacting vocational senior high school student learning. New communication technology, especially mobile communication technology, is supposed to be effective in encouraging interaction between the student and the instructor and improving learning efficiency. Social presence and information…

Does Proactive Personality Matter in Mobile Learning?

ERIC Educational Resources Information Center

Huang, Rui-Ting; Tang, Tzy-Wen; Lee, Yi Ping; Yang, Fang-Ying

2017-01-01

Increasing attention has been paid to mobile learning studies. However, there is still a dearth of studies investigating the moderating effect of proactive personality on mobile learning achievements. Accordingly, the primary purpose of this study is not only to investigate the key elements that could improve the effectiveness and efficiency of…

Efficiency of goal-oriented communicating agents in different graph topologies: A study with Internet crawlers

NASA Astrophysics Data System (ADS)

Lőrincz, András; Lázár, Katalin A.; Palotai, Zsolt

2007-05-01

To what extent does the communication make a goal-oriented community efficient in different topologies? In order to gain insight into this problem, we study the influence of learning method as well as that of the topology of the environment on the communication efficiency of crawlers in quest of novel information in different topics on the Internet. Individual crawlers employ selective learning, function approximation-based reinforcement learning (RL), and their combination. Selective learning, in effect, modifies the starting URL lists of the crawlers, whilst RL alters the URL orderings. Real data have been collected from the web and scale-free worlds, scale-free small world (SFSW), and random world environments (RWEs) have been created by link reorganization. In our previous experiments [ Zs. Palotai, Cs. Farkas, A. Lőrincz, Is selection optimal in scale-free small worlds?, ComPlexUs 3 (2006) 158-168], the crawlers searched for novel, genuine documents and direct communication was not possible. Herein, our finding is reproduced: selective learning performs the best and RL the worst in SFSW, whereas the combined, i.e., selective learning coupled with RL is the best-by a slight margin-in scale-free worlds. This effect is demonstrated to be more pronounced when the crawlers search for different topic-specific documents: the relative performance of the combined learning algorithm improves in all worlds, i.e., in SFSW, in SFW, and in RWE. If the tasks are more complex and the work sharing is enforced by the environment then the combined learning algorithm becomes at least equal, even superior to both the selective and the RL algorithms in most cases, irrespective of the efficiency of communication. Furthermore, communication improves the performance by a large margin and adaptive communication is advantageous in the majority of the cases.

[Application of mind map in teaching of medical parasitology].

PubMed

Zhou, Hong-Chang; Shao, Sheng-Wen; Xu, Bo-Ying

2012-12-30

To improve the teaching quality of medical parasitology, mind map, a simple and effective learning method, was introduced. The mind map of each chapter was drawn by teacher and distributed to students before the class. It was helpful for teacher to straighten out the teaching idea, and for students to grasp the important learning points, perfect the class notes and improve learning efficiency. The divergent characteristics of mind map can also help to develop the students' innovation ability.

QML-AiNet: An immune network approach to learning qualitative differential equation models

PubMed Central

Pang, Wei; Coghill, George M.

2015-01-01

In this paper, we explore the application of Opt-AiNet, an immune network approach for search and optimisation problems, to learning qualitative models in the form of qualitative differential equations. The Opt-AiNet algorithm is adapted to qualitative model learning problems, resulting in the proposed system QML-AiNet. The potential of QML-AiNet to address the scalability and multimodal search space issues of qualitative model learning has been investigated. More importantly, to further improve the efficiency of QML-AiNet, we also modify the mutation operator according to the features of discrete qualitative model space. Experimental results show that the performance of QML-AiNet is comparable to QML-CLONALG, a QML system using the clonal selection algorithm (CLONALG). More importantly, QML-AiNet with the modified mutation operator can significantly improve the scalability of QML and is much more efficient than QML-CLONALG. PMID:25648212

QML-AiNet: An immune network approach to learning qualitative differential equation models.

PubMed

Pang, Wei; Coghill, George M

2015-02-01

In this paper, we explore the application of Opt-AiNet, an immune network approach for search and optimisation problems, to learning qualitative models in the form of qualitative differential equations. The Opt-AiNet algorithm is adapted to qualitative model learning problems, resulting in the proposed system QML-AiNet. The potential of QML-AiNet to address the scalability and multimodal search space issues of qualitative model learning has been investigated. More importantly, to further improve the efficiency of QML-AiNet, we also modify the mutation operator according to the features of discrete qualitative model space. Experimental results show that the performance of QML-AiNet is comparable to QML-CLONALG, a QML system using the clonal selection algorithm (CLONALG). More importantly, QML-AiNet with the modified mutation operator can significantly improve the scalability of QML and is much more efficient than QML-CLONALG.

Utilization of Intelligent Software Agent Features for Improving E-Learning Efforts: A Comprehensive Investigation

ERIC Educational Resources Information Center

Farzaneh, Mandana; Vanani, Iman Raeesi; Sohrabi, Babak

2012-01-01

E-learning is one of the most important learning approaches within which intelligent software agents can be efficiently used so as to automate and facilitate the process of learning. The aim of this paper is to illustrate a comprehensive categorization of intelligent software agent features, which is valuable for being deployed in the virtual…

The neuroscience of learning.

PubMed

Collins, John W

2007-10-01

Significant advances have been made in understanding the neurophysiological basis of learning, including the discovery of mirror neurons and the role of cyclic adenosine monophosphate (cAMP) responsive element binding (CREB) protein in learning. Mirror neurons help us visually compare an observed activity with a remembered action in our memory, an ability that helps us imitate and learn through watching. Long-term potentiation, the Hebb rule, and CREB protein are associated with the formation of long-term memories. Conversely, protein phosphatase 1 and glucocorticoids are neurophysiological phenomena that limit what can be learned and cause forgetfulness. Gardner's theory of multiple intelligences contends that different areas of the brain are responsible for different competencies that we all possess to varying degrees. These multiple intelligences can be used as strategies for improved learning. Repeating material, using mnemonics, and avoiding overwhelming stress are other strategies for improving learning. Imaging studies have shown that practice with resultant learning results in significantly less use of brain areas, indicating that the brain becomes more efficient. Experts have advantages over novices, including increased cognitive processing efficiency. Nurses are in a unique position to use their understanding of neurophysiological principles to implement better educational strategies to provide quality education to patients and others.

Using learning curves on energy-efficient technologies to estimate future energy savings and emission reduction potentials in the U.S. iron and steel industry

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

Karali, Nihan; Park, Won Young; McNeil, Michael A.

Increasing concerns on non-sustainable energy use and climate change spur a growing research interest in energy efficiency potentials in various critical areas such as industrial production. This paper focuses on learning curve aspects of energy efficiency measures in the U.S iron and steel sector. A number of early-stage efficient technologies (i.e., emerging or demonstration technologies) are technically feasible and have the potential to make a significant contribution to energy saving and CO 2 emissions reduction, but fall short economically to be included. However, they may also have the cost effective potential for significant cost reduction and/or performance improvement in themore » future under learning effects such as ‘learning-by-doing’. The investigation is carried out using ISEEM, a technology oriented, linear optimization model. We investigated how steel demand is balanced with/without the availability learning curve, compared to a Reference scenario. The retrofit (or investment in some cases) costs of energy efficient technologies decline in the scenario where learning curve is applied. The analysis also addresses market penetration of energy efficient technologies, energy saving, and CO 2 emissions in the U.S. iron and steel sector with/without learning impact. Accordingly, the study helps those who use energy models better manage the price barriers preventing unrealistic diffusion of energy-efficiency technologies, better understand the market and learning system involved, predict future achievable learning rates more accurately, and project future savings via energy-efficiency technologies with presence of learning. We conclude from our analysis that, most of the existing energy efficiency technologies that are currently used in the U.S. iron and steel sector are cost effective. Penetration levels increases through the years, even though there is no price reduction. However, demonstration technologies are not economically feasible in the U.S. iron and steel sector with the current cost structure. In contrast, some of the demonstration technologies are adapted in the mid-term and their penetration levels increase as the prices go down with learning curve. We also observe large penetration of 225kg pulverized coal injection with the presence of learning.« less

Do Learning Activities Improve Students' Ability to Construct Explanatory Models with a Prism Foil Problem?

ERIC Educational Resources Information Center

Gojkošek, Mihael; Sliško, Josip; Planinšic, Gorazd

2013-01-01

The transfer of knowledge is considered to be a fundamental goal of education; therefore, knowing and understanding the conditions that influence the efficiency of the transfer from learning activity to problem solving play a decisive role in the improvement of science education. In this article, the results of a study of 196 high school students'…

Serious games and blended learning; effects on performance and motivation in medical education.

PubMed

Dankbaar, Mary

2017-02-01

More efficient, flexible training models are needed in medical education. Information technology offers the tools to design and develop effective and more efficient training. The aims of this thesis were: 1) Compare the effectiveness of blended versus classroom training for the acquisition of knowledge; 2) Investigate the effectiveness and critical design features of serious games for performance improvement and motivation. Five empirical studies were conducted to answer the research questions and a descriptive study on an evaluation framework to assess serious games was performed. The results of the research studies indicated that: 1) For knowledge acquisition, blended learning is equally effective and attractive for learners as classroom learning; 2) A serious game with realistic, interactive cases improved complex cognitive skills for residents, with limited self-study time. Although the same game was motivating for inexperienced medical students and stimulated them to study longer, it did not improve their cognitive skills, compared with what they learned from an instructional e‑module. This indicates an 'expertise reversal effect', where a rich learning environment is effective for experts, but may be contra-productive for novices (interaction of prior knowledge and complexity of format). A blended design is equally effective and attractive as classroom training. Blended learning facilitates adaptation to the learners' knowledge level, flexibility in time and scalability of learning. Games may support skills learning, provided task complexity matches the learner's competency level. More design-based research is needed on the effects of task complexity and other design features on performance improvement, for both novices and experts.

Social Web Content Enhancement in a Distance Learning Environment: Intelligent Metadata Generation for Resources

ERIC Educational Resources Information Center

García-Floriano, Andrés; Ferreira-Santiago, Angel; Yáñez-Márquez, Cornelio; Camacho-Nieto, Oscar; Aldape-Pérez, Mario; Villuendas-Rey, Yenny

2017-01-01

Social networking potentially offers improved distance learning environments by enabling the exchange of resources between learners. The existence of properly classified content results in an enhanced distance learning experience in which appropriate materials can be retrieved efficiently; however, for this to happen, metadata needs to be present.…

Making Online Learning Accessible for Students with Disabilities

ERIC Educational Resources Information Center

Hashey, Andrew I.; Stahl, Skip

2014-01-01

The growing presence of K-12 online education programs is a trend that promises to increase flexibility, improve efficiency, and foster engagement in learning. Students with disabilities can benefit from dynamic online educational environments, but only to the extent that they can access and participate in the learning process. As students with…

User-Centred Design for Chinese-Oriented Spoken English Learning System

ERIC Educational Resources Information Center

Yu, Ping; Pan, Yingxin; Li, Chen; Zhang, Zengxiu; Shi, Qin; Chu, Wenpei; Liu, Mingzhuo; Zhu, Zhiting

2016-01-01

Oral production is an important part in English learning. Lack of a language environment with efficient instruction and feedback is a big issue for non-native speakers' English spoken skill improvement. A computer-assisted language learning system can provide many potential benefits to language learners. It allows adequate instructions and instant…

Enhancing E-Learning Quality through the Application of the AKUE Procedure Model

ERIC Educational Resources Information Center

Bremer, C.

2012-01-01

The paper describes the procedure model AKUE, which aims at the improvement and assurance of quality and cost efficiency in the context of the introduction of e-learning and the development of digital learning material. AKUE divides the whole planning and implementation process into four different phases: analysis, conception, implementation, and…

Re-Conceptualizing Emotion and Motivation to Learn in Classroom Contexts

ERIC Educational Resources Information Center

Meyer, Debra K.; Turner, Julianne C.

2006-01-01

To better inform and improve classroom teaching and learning, now more than ever before, educational researchers need to effectively and efficiently describe essential components of positive learning environments. In this article, we discuss how our research findings about motivation in classrooms have led to a closer examination of emotions. We…

How teachers can help learners build storage and retrieval strength.

PubMed

Desy, Janeve; Busche, Kevin; Cusano, Ronald; Veale, Pamela; Coderre, Sylvain; McLaughlin, Kevin

2018-04-01

To be an effective teacher, content expertise is necessary but alone does not guarantee optimal learning outcomes for students. In this article, the authors discuss ways in which medical teachers can shape the learning of their students and enable them to become more efficient and effective learners. Using Bjork and Bjork's new theory of disuse as their framework, the authors discuss strategies to improve storage strength of to-be-learned information and strategies to improve retrieval strength of learned information. Strategies to improve storage strength include optimizing cognitive load, providing causal explanations, and giving effective feedback. Strategies to improve retrieval strength include situated cognition and various types of retrieval practice. Adopting these teaching strategies should hopefully help teachers improve the learning outcomes of their students, but there is still a need for further research into the science of learning and the science of instruction, including comparative effectiveness of different teaching strategies and how best to translate findings from the psychology literature into medical education.

Training directionally selective motion pathways can significantly improve reading efficiency

NASA Astrophysics Data System (ADS)

Lawton, Teri

2004-06-01

This study examined whether perceptual learning at early levels of visual processing would facilitate learning at higher levels of processing. This was examined by determining whether training the motion pathways by practicing leftright movement discrimination, as found previously, would improve the reading skills of inefficient readers significantly more than another computer game, a word discrimination game, or the reading program offered by the school. This controlled validation study found that practicing left-right movement discrimination 5-10 minutes twice a week (rapidly) for 15 weeks doubled reading fluency, and significantly improved all reading skills by more than one grade level, whereas inefficient readers in the control groups barely improved on these reading skills. In contrast to previous studies of perceptual learning, these experiments show that perceptual learning of direction discrimination significantly improved reading skills determined at higher levels of cognitive processing, thereby being generalized to a new task. The deficits in reading performance and attentional focus experienced by the person who struggles when reading are suggested to result from an information overload, resulting from timing deficits in the direction-selectivity network proposed by Russell De Valois et al. (2000), that following practice on direction discrimination goes away. This study found that practicing direction discrimination rapidly transitions the inefficient 7-year-old reader to an efficient reader.

IRB Process Improvements: A Machine Learning Analysis.

PubMed

Shoenbill, Kimberly; Song, Yiqiang; Cobb, Nichelle L; Drezner, Marc K; Mendonca, Eneida A

2017-06-01

Clinical research involving humans is critically important, but it is a lengthy and expensive process. Most studies require institutional review board (IRB) approval. Our objective is to identify predictors of delays or accelerations in the IRB review process and apply this knowledge to inform process change in an effort to improve IRB efficiency, transparency, consistency and communication. We analyzed timelines of protocol submissions to determine protocol or IRB characteristics associated with different processing times. Our evaluation included single variable analysis to identify significant predictors of IRB processing time and machine learning methods to predict processing times through the IRB review system. Based on initial identified predictors, changes to IRB workflow and staffing procedures were instituted and we repeated our analysis. Our analysis identified several predictors of delays in the IRB review process including type of IRB review to be conducted, whether a protocol falls under Veteran's Administration purview and specific staff in charge of a protocol's review. We have identified several predictors of delays in IRB protocol review processing times using statistical and machine learning methods. Application of this knowledge to process improvement efforts in two IRBs has led to increased efficiency in protocol review. The workflow and system enhancements that are being made support our four-part goal of improving IRB efficiency, consistency, transparency, and communication.

Learning to detect and combine the features of an object

PubMed Central

Suchow, Jordan W.; Pelli, Denis G.

2013-01-01

To recognize an object, it is widely supposed that we first detect and then combine its features. Familiar objects are recognized effortlessly, but unfamiliar objects—like new faces or foreign-language letters—are hard to distinguish and must be learned through practice. Here, we describe a method that separates detection and combination and reveals how each improves as the observer learns. We dissociate the steps by two independent manipulations: For each step, we do or do not provide a bionic crutch that performs it optimally. Thus, the two steps may be performed solely by the human, solely by the crutches, or cooperatively, when the human takes one step and a crutch takes the other. The crutches reveal a double dissociation between detecting and combining. Relative to the two-step ideal, the human observer’s overall efficiency for unconstrained identification equals the product of the efficiencies with which the human performs the steps separately. The two-step strategy is inefficient: Constraining the ideal to take two steps roughly halves its identification efficiency. In contrast, we find that humans constrained to take two steps perform just as well as when unconstrained, which suggests that they normally take two steps. Measuring threshold contrast (the faintness of a barely identifiable letter) as it improves with practice, we find that detection is inefficient and learned slowly. Combining is learned at a rate that is 4× higher and, after 1,000 trials, 7× more efficient. This difference explains much of the diversity of rates reported in perceptual learning studies, including effects of complexity and familiarity. PMID:23267067

Do emergency medicine residents and faculty have similar learning styles when assessed with the Kolb learning style assessment tool?

PubMed

Fredette, Jenna; O'Brien, Corinne; Poole, Christy; Nomura, Jason

2015-04-01

Experiential learning theory and the Kolb Learning Style Inventory (Kolb LSI) have influenced educators worldwide for decades. Knowledge of learning styles can create efficient learning environments, increase information retention, and improve learner satisfaction. Learning styles have been examined in medicine previously, but not specifically with Emergency Medicine (EM) residents and attendings. Using the Kolb LSI, the learning styles of Emergency Medicine residents and attendings were assessed. The findings showed that the majority of EM residents and attendings shared the accommodating learning style. This result was different than prior studies that found the majority of medical professionals had a converging learning style and other studies that found attendings often have different learning styles than residents. The issue of learning styles among emergency medical residents and attendings is important because learning style knowledge may have an impact on how a residency program structures curriculum and how EM residents are successfully, efficiently, and creatively educated.

The Implementation of Lesson Study to Strengthen Students: Understanding Participation and Application Capabilities in History Education Research Method on Topic Research and Development

ERIC Educational Resources Information Center

Towaf, Siti Malikhah

2016-01-01

Learning can be observed from three-dimensions called: effectiveness, efficiency, and attractiveness of learning. Careful study carried out by analyzing the learning elements of the system are: input, process, and output. Lesson study is an activity designed and implemented as an effort to improve learning in a variety of dimensions. "Lesson…

Using input feature information to improve ultraviolet retrieval in neural networks

NASA Astrophysics Data System (ADS)

Sun, Zhibin; Chang, Ni-Bin; Gao, Wei; Chen, Maosi; Zempila, Melina

2017-09-01

In neural networks, the training/predicting accuracy and algorithm efficiency can be improved significantly via accurate input feature extraction. In this study, some spatial features of several important factors in retrieving surface ultraviolet (UV) are extracted. An extreme learning machine (ELM) is used to retrieve the surface UV of 2014 in the continental United States, using the extracted features. The results conclude that more input weights can improve the learning capacities of neural networks.

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

PubMed

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

2011-01-01

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

Relationship between Student's Self-Directed-Learning Readiness and Academic Self-Efficacy and Achievement Motivation in Students

ERIC Educational Resources Information Center

Saeid, Nasim; Eslaminejad, Tahere

2017-01-01

Self-directed learning readiness to expand and enhance learning, This is an important goal of higher education, Besides his academic self-efficacy can be improved efficiency and Achievement Motivation, so understanding how to use these strategies by students is very important. Because the purpose this study is determination of relationship between…

Incidental Learning Speeds Visual Search by Lowering Response Thresholds, Not by Improving Efficiency: Evidence from Eye Movements

ERIC Educational Resources Information Center

Hout, Michael C.; Goldinger, Stephen D.

2012-01-01

When observers search for a target object, they incidentally learn the identities and locations of "background" objects in the same display. This learning can facilitate search performance, eliciting faster reaction times for repeated displays. Despite these findings, visual search has been successfully modeled using architectures that maintain no…

Challenges of Blended E-Learning Tools in Mathematics: Students' Perspectives University of Uyo

ERIC Educational Resources Information Center

Umoh, Joseph B.; Akpan, Ekemini T.

2014-01-01

An in-depth knowledge of pedagogical approaches can help improve the formulation of effective and efficient pedagogy, tools and technology to support and enhance the teaching and learning of Mathematics in higher institutions. This study investigated students' perceptions of the challenges of blended e-learning tools in the teaching and learning…

The Role of Leadership in Starting and Operating Blended Learning Charter Schools: A Multisite Case Study

ERIC Educational Resources Information Center

Agostini, Michael Eric

2013-01-01

Heavily utilizing both instructional technology and face-to-face instruction within a bricks-and-mortar school environment, blended learning charter schools are gaining attention as a cost-effective school design. As educators turn to these blended learning school models to improve both the operational efficiency and student outcomes of America's…

An Efficient Approach to Improve the Usability of e-Learning Resources: The Role of Heuristic Evaluation

ERIC Educational Resources Information Center

Davids, Mogamat Razeen; Chikte, Usuf M. E.; Halperin, Mitchell L.

2013-01-01

Optimizing the usability of e-learning materials is necessary to maximize their potential educational impact, but this is often neglected when time and other resources are limited, leading to the release of materials that cannot deliver the desired learning outcomes. As clinician-teachers in a resource-constrained environment, we investigated…

Improving the Science Excursion: An Educational Technologist's View

ERIC Educational Resources Information Center

Balson, M.

1973-01-01

Analyzes the nature of the learning process and attempts to show how the three components of a reinforcement contingency, the stimulus, the response and the reinforcement can be utilized to increase the efficiency of a typical science learning experience, the excursion. (JR)

Optimizing Chemical Reactions with Deep Reinforcement Learning.

PubMed

Zhou, Zhenpeng; Li, Xiaocheng; Zare, Richard N

2017-12-27

Deep reinforcement learning was employed to optimize chemical reactions. Our model iteratively records the results of a chemical reaction and chooses new experimental conditions to improve the reaction outcome. This model outperformed a state-of-the-art blackbox optimization algorithm by using 71% fewer steps on both simulations and real reactions. Furthermore, we introduced an efficient exploration strategy by drawing the reaction conditions from certain probability distributions, which resulted in an improvement on regret from 0.062 to 0.039 compared with a deterministic policy. Combining the efficient exploration policy with accelerated microdroplet reactions, optimal reaction conditions were determined in 30 min for the four reactions considered, and a better understanding of the factors that control microdroplet reactions was reached. Moreover, our model showed a better performance after training on reactions with similar or even dissimilar underlying mechanisms, which demonstrates its learning ability.

Effect of two layouts on high technology AAC navigation and content location by people with aphasia.

PubMed

Wallace, Sarah E; Hux, Karen

2014-03-01

Navigating high-technology augmentative and alternative communication (AAC) devices with dynamic displays can be challenging for people with aphasia. The purpose of this study was to determine which of two AAC interfaces two people with aphasia could use most efficiently and accurately. The researchers used a BCB'C' alternating treatment design to provide device-use instruction to two people with severe aphasia regarding two personalised AAC interfaces that had different navigation layouts but identical content. One interface had static buttons for homepage and go-back features, and the other interface had static buttons in a navigation ring layout. Throughout treatment, the researchers monitored participants' mastery patterns regarding navigation efficiency and accuracy when locating target messages. Participants' accuracy and efficiency improved with both interfaces given intervention; however, the navigation ring layout appeared more transparent and better facilitated navigation than the homepage layout. People with aphasia can learn to navigate computerised devices; however, interface layout can substantially affect the efficiency and accuracy with which they locate messages. Given intervention incorporating errorless learning principles, people with chronic aphasia can learn to navigate across multiple device levels to locate target sentences. Both navigation ring and homepage interfaces may be used by people with aphasia. Some people with aphasia may be more consistent and efficient in finding target sentences using the navigation ring interface than the homepage interface. Additionally, the navigation ring interface may be more transparent and easier for people with aphasia to master--that is, they may require fewer intervention sessions to learn to navigate the navigation ring interface. Generalisation of learning may result from use of the navigation ring interface. Specifically, people with aphasia may improve navigation with the homepage interface as a result of instruction on the navigation interface, but not vice versa.

Meeting the Challenge: Providing High-Quality School Environments through Energy Performance Contracting.

ERIC Educational Resources Information Center

Birr, David

2000-01-01

Energy performance contracting allows schools to pay for needed new energy equipment and modernization improvements with savings from reduced utility and maintenance costs. Improved energy efficiency reduces demand for burning fossil fuels, which reduces air pollution, leading to improved learning environments and budgets (through improved average…

Validating YouTube Factors Affecting Learning Performance

NASA Astrophysics Data System (ADS)

Pratama, Yoga; Hartanto, Rudy; Suning Kusumawardani, Sri

2018-03-01

YouTube is often used as a companion medium or a learning supplement. One of the educational places that often uses is Jogja Audio School (JAS) which focuses on music production education. Music production is a difficult material to learn, especially at the audio mastering. With tutorial contents from YouTube, students find it easier to learn and understand audio mastering and improved their learning performance. This study aims to validate the role of YouTube as a medium of learning in improving student’s learning performance by looking at the factors that affect student learning performance. The sample involves 100 respondents from JAS at audio mastering level. The results showed that student learning performance increases seen from factors that have a significant influence of motivation, instructional content, and YouTube usefulness. Overall findings suggest that YouTube has a important role to student learning performance in music production education and as an innovative and efficient learning medium.

Short term effects of methylphenidate on the cognitive, learning and academic performance of children with attention deficit disorder in the laboratory and the classroom.

PubMed

Douglas, V I; Barr, R G; O'Neill, M E; Britton, B G

1986-03-01

Sixteen children meeting diagnostic criteria for Attention Deficit Disorder with Hyperactivity (ADD-H) were tested on methylphenidate (0.3 mg/kg) and placebo on cognitive, learning, academic and behavioral measures in a double-blind study. Assessments were carried out in the laboratory and in the children's regular classrooms. Results indicate methylphenidate-induced improvements on a majority of the measures. Drug-induced changes reflected increased output, accuracy and efficiency and improved learning acquisition. There was also evidence of increased effort and self-correcting behaviours. It is argued that reviewers have underestimated the potential of stimulants to improve the performance of ADD-H children on academic, learning and cognitive tasks.

Improving Students' Ability to Intuitively Infer Resistance from Magnitude of Current and Potential Difference Information: A Functional Learning Approach

ERIC Educational Resources Information Center

Chasseigne, Gerard; Giraudeau, Caroline; Lafon, Peggy; Mullet, Etienne

2011-01-01

The study examined the knowledge of the functional relations between potential difference, magnitude of current, and resistance among seventh graders, ninth graders, 11th graders (in technical schools), and college students. It also tested the efficiency of a learning device named "functional learning" derived from cognitive psychology on the…

Students' Perception of a Flipped Classroom Approach to Facilitating Online Project-Based Learning in Marketing Research Courses

ERIC Educational Resources Information Center

Shih, Wen-Ling; Tsai, Chun-Yen

2017-01-01

This study investigated students' perception of a flipped classroom approach to facilitating online project-based learning (FC-OPBL) in a marketing research course at a technical university. This combined strategy was aimed at improving teaching quality and learning efficiency. Sixty-seven students taking a marketing research course were surveyed.…

Effects of Cues and Real Objects on Learning in a Mobile Device Supported Environment

ERIC Educational Resources Information Center

Liu, Tzu-Chien; Lin, Yi-Chun; Paas, Fred

2013-01-01

This study investigated whether arrow-line cues can improve the effectiveness and efficiency of learning in a mobile device supported learning environment on leaf morphology of plants, either with or without the use of real plants. A cued and un-cued condition, in which primary school students used text and pictures on a tablet PC, were compared…

Measurement of Usability for Multimedia Interactive Learning Based on Website in Mathematics for SMK

NASA Astrophysics Data System (ADS)

Sukardjo, Moch.; Sugiyanta, Lipur

2018-04-01

Web usability, if evaluation done correctly, can significantly improve the quality of the website. Website containing multimedia for education shoud apply user interfaces that are both easy to learn and easy to use. Multimedia has big role in changing the mindset of a person in learning. Using multimedia, learners get easy to obtain information, adjust information and empower information. Therefore, multimedia is utilized by teachers in developing learning techniques to improve student learning outcomes. For students with self-directed learning, multimedia provides the ease and completeness of the courses in such a way that students can complete the learning independently both at school and at home without the guidance of teachers. The learning independence takes place in how students choose, absorb information, and follow the evaluation quickly and efficiently. The 2013 Curriculum 2013 for Vocational High School (SMK) requires teachers to create engaging teaching and learning activities that students enjoy in the classroom (also called invitation learning environment). The creation of learning activity environment is still problem for most teachers. Various researches reveal that teaching and learning activities will be more effective and easy when assisted by visual tools. Using multimedia, learning material can be presented more attractively that help students understand the material easily. The opposite is found in the learning activity environment who only rely on ordinary lectures. Usability is a quality level of multimedia with easy to learn, easy to use and encourages users to use it. The website Multimedia Interactive Learning for Mathematics SMK Class X is targeted object. Usability website in Multimedia Interactive Learning for Mathematics SMK Class X is important indicators to measure effectiveness, efficiency, and student satisfaction to access the functionality of website. This usability measurement should be done carefully before the design is implemented thoroughly. The only way to get test with high quality results is to start testing at the beginning of the design process and continuously testing each of the next steps. This research performs usability testing on of website by using WAMMI criterion (Website Analysis and Measurement Inventory) and will be focused on how convenience using the website application. Components of Attractiveness, Controllability, Efficiency, Helpfulness, and Learnability are applied. The website in Multimedia Interactive Learning for Mathematics SMK Class X can be in accordance with the purpose to be accepted by student to improve student learning outcomes. The results show that WAMMI method show the usability value of Multimedia Mathematics SMK Class X is about from 70% to 90%.

Incidental learning speeds visual search by lowering response thresholds, not by improving efficiency: evidence from eye movements.

PubMed

Hout, Michael C; Goldinger, Stephen D

2012-02-01

When observers search for a target object, they incidentally learn the identities and locations of "background" objects in the same display. This learning can facilitate search performance, eliciting faster reaction times for repeated displays. Despite these findings, visual search has been successfully modeled using architectures that maintain no history of attentional deployments; they are amnesic (e.g., Guided Search Theory). In the current study, we asked two questions: 1) under what conditions does such incidental learning occur? And 2) what does viewing behavior reveal about the efficiency of attentional deployments over time? In two experiments, we tracked eye movements during repeated visual search, and we tested incidental memory for repeated nontarget objects. Across conditions, the consistency of search sets and spatial layouts were manipulated to assess their respective contributions to learning. Using viewing behavior, we contrasted three potential accounts for faster searching with experience. The results indicate that learning does not result in faster object identification or greater search efficiency. Instead, familiar search arrays appear to allow faster resolution of search decisions, whether targets are present or absent.

The Business of Learning

ERIC Educational Resources Information Center

Bers, Trudy; Gelfman, Arnold; Knapp, Jolene

2008-01-01

This article describes several community colleges that are taking a more business-like approach, trimming costs, improving efficiencies, and pursuing next-generation innovation--all while keeping the focus squarely where it should be: on learning. At Florida Keys Community College (FKCC), John Keho says his college is taking some strong--though…

Perceptual Learning as a potential treatment for amblyopia: a mini-review

PubMed Central

Levi, Dennis M.; Li, Roger W.

2009-01-01

Amblyopia is a developmental abnormality that results from physiological alterations in the visual cortex and impairs form vision. It is a consequence of abnormal binocular visual experience during the “sensitive period” early in life. While amblyopia can often be reversed when treated early, conventional treatment is generally not undertaken in older children and adults. A number of studies over the last twelve years or so suggest that Perceptual Learning (PL) may provide an important new method for treating amblyopia. The aim of this mini-review is to provide a critical review and “meta-analysis” of perceptual learning in adults and children with amblyopia, with a view to extracting principles that might make PL more effective and efficient. Specifically we evaluate: What factors influence the outcome of perceptual learning?Specificity and generalization – two sides of the coin.Do the improvements last?How does PL improve visual function?Should PL be part of the treatment armamentarium? A review of the extant studies makes it clear that practicing a visual task results in a long-lasting improvement in performance in an amblyopic eye. The improvement is generally strongest for the trained eye, task, stimulus and orientation, but appears to have a broader spatial frequency bandwidth than in normal vision. Importantly, practicing on a variety of different tasks and stimuli seems to transfer to improved visual acuity. Perceptual learning operates via a reduction of internal neural noise and/or through more efficient use of the stimulus information by retuning the weighting of the information. The success of PL raises the question of whether it should become a standard part of the armamentarium for the clinical treatment of amblyopia, and suggests several important principles for effective perceptual learning in amblyopia. PMID:19250947

Optimizing Chemical Reactions with Deep Reinforcement Learning

PubMed Central

2017-01-01

Deep reinforcement learning was employed to optimize chemical reactions. Our model iteratively records the results of a chemical reaction and chooses new experimental conditions to improve the reaction outcome. This model outperformed a state-of-the-art blackbox optimization algorithm by using 71% fewer steps on both simulations and real reactions. Furthermore, we introduced an efficient exploration strategy by drawing the reaction conditions from certain probability distributions, which resulted in an improvement on regret from 0.062 to 0.039 compared with a deterministic policy. Combining the efficient exploration policy with accelerated microdroplet reactions, optimal reaction conditions were determined in 30 min for the four reactions considered, and a better understanding of the factors that control microdroplet reactions was reached. Moreover, our model showed a better performance after training on reactions with similar or even dissimilar underlying mechanisms, which demonstrates its learning ability. PMID:29296675

SemiBoost: boosting for semi-supervised learning.

PubMed

Mallapragada, Pavan Kumar; Jin, Rong; Jain, Anil K; Liu, Yi

2009-11-01

Semi-supervised learning has attracted a significant amount of attention in pattern recognition and machine learning. Most previous studies have focused on designing special algorithms to effectively exploit the unlabeled data in conjunction with labeled data. Our goal is to improve the classification accuracy of any given supervised learning algorithm by using the available unlabeled examples. We call this as the Semi-supervised improvement problem, to distinguish the proposed approach from the existing approaches. We design a metasemi-supervised learning algorithm that wraps around the underlying supervised algorithm and improves its performance using unlabeled data. This problem is particularly important when we need to train a supervised learning algorithm with a limited number of labeled examples and a multitude of unlabeled examples. We present a boosting framework for semi-supervised learning, termed as SemiBoost. The key advantages of the proposed semi-supervised learning approach are: 1) performance improvement of any supervised learning algorithm with a multitude of unlabeled data, 2) efficient computation by the iterative boosting algorithm, and 3) exploiting both manifold and cluster assumption in training classification models. An empirical study on 16 different data sets and text categorization demonstrates that the proposed framework improves the performance of several commonly used supervised learning algorithms, given a large number of unlabeled examples. We also show that the performance of the proposed algorithm, SemiBoost, is comparable to the state-of-the-art semi-supervised learning algorithms.

Student Peer Review as a Tool for Efficiently Achieving Subject-Specific and Generic Learning Outcomes: Examples in Botany at the Faculty of Agriculture, University of Belgrade

ERIC Educational Resources Information Center

Quarrie, Sofija Pekic

2007-01-01

Several teachers at the Faculty of Agriculture at the University of Belgrade recognised the need to improve teaching methods in order to actively involve students in the teaching process, help them learn more effectively, and reduce the low exam pass rate. This led to a purpose-designed course on improving academic skills, after which the author…

Improving Learning Object Quality: Moodle HEODAR Implementation

ERIC Educational Resources Information Center

Munoz, Carlos; Garcia-Penalvo, Francisco J.; Morales, Erla Mariela; Conde, Miguel Angel; Seoane, Antonio M.

2012-01-01

Automation toward efficiency is the aim of most intelligent systems in an educational context in which results calculation automation that allows experts to spend most of their time on important tasks, not on retrieving, ordering, and interpreting information. In this paper, the authors provide a tool that easily evaluates Learning Objects quality…

Light, Colour & Air Quality: Important Elements of the Learning Environment?

ERIC Educational Resources Information Center

Hathaway, Warren E.

1987-01-01

Reviews and evaluates studies of the effects of light, color, and air quality on the learning environment. Concludes that studies suggest a role for light in establishing and maintaining physiological functions and balances and a need for improved air quality in airtight, energy efficient buildings. (JHZ)

Team-Based Learning in a Physical Therapy Gross Anatomy Course

ERIC Educational Resources Information Center

Killins, Anita M.

2015-01-01

As medical knowledge grows exponentially and healthcare systems continue to utilize interdisciplinary care, it is essential that physical therapy (PT) graduates be prepared to practice efficiently and effectively on healthcare teams. Team-based learning (TBL) is a teaching pedagogy used in medicine to improve academic performance and teamwork…

Learning Styles in Engineering Education: The Quest to Improve Didactic Practices

ERIC Educational Resources Information Center

Holvikivi, Jaana

2007-01-01

This article discusses a dilemma that engineering educators encounter when attempting to develop pedagogical methods: that of finding efficient and scientifically valid didactic practices. The multitude of methods offered by educational consultants is perplexing. Moreover, the popularity of commercially offered solutions such as learning styles…

Establishing the Learning Curve of Robotic Sacral Colpopexy in a Start-up Robotics Program.

PubMed

Sharma, Shefali; Calixte, Rose; Finamore, Peter S

2016-01-01

To determine the learning curve of the following segments of a robotic sacral colpopexy: preoperative setup, operative time, postoperative transition, and room turnover. A retrospective cohort study to determine the number of cases needed to reach points of efficiency in the various segments of a robotic sacral colpopexy (Canadian Task Force II-2). A university-affiliated community hospital. Women who underwent robotic sacral colpopexy at our institution from 2009 to 2013 comprise the study population. Patient characteristics and operative reports were extracted from a patient database that has been maintained since the inception of the robotics program at Winthrop University Hospital and electronic medical records. Based on additional procedures performed, 4 groups of patients were created (A-D). Learning curves for each of the segment times of interest were created using penalized basis spline (B-spline) regression. Operative time was further analyzed using an inverse curve and sequential grouping. A total of 176 patients were eligible. Nonparametric tests detected no difference in procedure times between the 4 groups (A-D) of patients. The preoperative and postoperative points of efficiency were 108 and 118 cases, respectively. The operative points of proficiency and efficiency were 25 and 36 cases, respectively. Operative time was further analyzed using an inverse curve that revealed that after 11 cases the surgeon had reached 90% of the learning plateau. Sequential grouping revealed no significant improvement in operative time after 60 cases. Turnover time could not be assessed because of incomplete data. There is a difference in the operative time learning curve for robotic sacral colpopexy depending on the statistical analysis used. The learning curve of the operative segment showed an improvement in operative time between 25 and 36 cases when using B-spline regression. When the data for operative time was fit to an inverse curve, a learning rate of 11 cases was appreciated. Using sequential grouping to describe the data, no improvement in operative time was seen after 60 cases. Ultimately, we believe that efficiency in operative time is attained after 30 to 60 cases when performing robotic sacral colpopexy. The learning curve for preoperative setup and postoperative transition, which is reflective of anesthesia and nursing staff, was approximately 110 cases. Copyright © 2016 AAGL. Published by Elsevier Inc. All rights reserved.

Metacognitive Training at Home: Does It Improve Older Adults' Learning?

PubMed Central

Bailey, Heather; Dunlosky, John; Hertzog, Christopher

2010-01-01

Background Previous research has described the success of an intervention aimed at improving older adults' ability to regulate their learning. This metacognitive approach involves teaching older adults to allocate their study time more efficiently by testing themselves and restudying items that are less well learned. Objective Although this type of memory intervention has shown promise, training older adults to test themselves in the laboratory can be very time-intensive. Thus, the purpose of the present study is to transport the self-testing training method from the laboratory to home use. Methods A standard intervention design was used that included a pretraining session, multiple training sessions, and a posttraining session. Participants were randomly assigned to either the training group (n = 29) or the waiting list control group (n = 27). Moreover, we screened participants for whether they used the self-testing strategy during their pretraining test session. Results Compared to the performance of the control group, the training group displayed significant gains, which demonstrates that older adults can benefit from training themselves to use these skills at home. Moreover, the results of the present study indicate that this metacognitive approach can effectively improve older adults' learning, even in those who spontaneously self-test prior to training. Conclusions Training metacognitive skills, such as self-testing and efficient study allocation, can improve the ability to learn new information in healthy older adults. More importantly, older adult clients can be supplied with an at-home training manual, which will ease the burden on practitioners. PMID:20016124

The new and computationally efficient MIL-SOM algorithm: potential benefits for visualization and analysis of a large-scale high-dimensional clinically acquired geographic data.

PubMed

Oyana, Tonny J; Achenie, Luke E K; Heo, Joon

2012-01-01

The objective of this paper is to introduce an efficient algorithm, namely, the mathematically improved learning-self organizing map (MIL-SOM) algorithm, which speeds up the self-organizing map (SOM) training process. In the proposed MIL-SOM algorithm, the weights of Kohonen's SOM are based on the proportional-integral-derivative (PID) controller. Thus, in a typical SOM learning setting, this improvement translates to faster convergence. The basic idea is primarily motivated by the urgent need to develop algorithms with the competence to converge faster and more efficiently than conventional techniques. The MIL-SOM algorithm is tested on four training geographic datasets representing biomedical and disease informatics application domains. Experimental results show that the MIL-SOM algorithm provides a competitive, better updating procedure and performance, good robustness, and it runs faster than Kohonen's SOM.

The New and Computationally Efficient MIL-SOM Algorithm: Potential Benefits for Visualization and Analysis of a Large-Scale High-Dimensional Clinically Acquired Geographic Data

PubMed Central

Oyana, Tonny J.; Achenie, Luke E. K.; Heo, Joon

2012-01-01

The objective of this paper is to introduce an efficient algorithm, namely, the mathematically improved learning-self organizing map (MIL-SOM) algorithm, which speeds up the self-organizing map (SOM) training process. In the proposed MIL-SOM algorithm, the weights of Kohonen's SOM are based on the proportional-integral-derivative (PID) controller. Thus, in a typical SOM learning setting, this improvement translates to faster convergence. The basic idea is primarily motivated by the urgent need to develop algorithms with the competence to converge faster and more efficiently than conventional techniques. The MIL-SOM algorithm is tested on four training geographic datasets representing biomedical and disease informatics application domains. Experimental results show that the MIL-SOM algorithm provides a competitive, better updating procedure and performance, good robustness, and it runs faster than Kohonen's SOM. PMID:22481977

A sparse structure learning algorithm for Gaussian Bayesian Network identification from high-dimensional data.

PubMed

Huang, Shuai; Li, Jing; Ye, Jieping; Fleisher, Adam; Chen, Kewei; Wu, Teresa; Reiman, Eric

2013-06-01

Structure learning of Bayesian Networks (BNs) is an important topic in machine learning. Driven by modern applications in genetics and brain sciences, accurate and efficient learning of large-scale BN structures from high-dimensional data becomes a challenging problem. To tackle this challenge, we propose a Sparse Bayesian Network (SBN) structure learning algorithm that employs a novel formulation involving one L1-norm penalty term to impose sparsity and another penalty term to ensure that the learned BN is a Directed Acyclic Graph--a required property of BNs. Through both theoretical analysis and extensive experiments on 11 moderate and large benchmark networks with various sample sizes, we show that SBN leads to improved learning accuracy, scalability, and efficiency as compared with 10 existing popular BN learning algorithms. We apply SBN to a real-world application of brain connectivity modeling for Alzheimer's disease (AD) and reveal findings that could lead to advancements in AD research.

A Sparse Structure Learning Algorithm for Gaussian Bayesian Network Identification from High-Dimensional Data

PubMed Central

Huang, Shuai; Li, Jing; Ye, Jieping; Fleisher, Adam; Chen, Kewei; Wu, Teresa; Reiman, Eric

2014-01-01

Structure learning of Bayesian Networks (BNs) is an important topic in machine learning. Driven by modern applications in genetics and brain sciences, accurate and efficient learning of large-scale BN structures from high-dimensional data becomes a challenging problem. To tackle this challenge, we propose a Sparse Bayesian Network (SBN) structure learning algorithm that employs a novel formulation involving one L1-norm penalty term to impose sparsity and another penalty term to ensure that the learned BN is a Directed Acyclic Graph (DAG)—a required property of BNs. Through both theoretical analysis and extensive experiments on 11 moderate and large benchmark networks with various sample sizes, we show that SBN leads to improved learning accuracy, scalability, and efficiency as compared with 10 existing popular BN learning algorithms. We apply SBN to a real-world application of brain connectivity modeling for Alzheimer’s disease (AD) and reveal findings that could lead to advancements in AD research. PMID:22665720

A professional learning community model: a case study of primary teachers community in west Bandung

NASA Astrophysics Data System (ADS)

Sari, A.; Suryadi, D.; Syaodih, E.

2018-05-01

The purpose of this study is to provide an alternative model of professional learning community for primary school teachers in improving the knowledge and professional skills. This study is a qualitative research with case study method with data collection is an interview, observation and document and triangulation technique for validation data that focuses on thirteen people 5th grade elementary school teacher. The results showed that by joining a professional learning community, teachers can share both experience and knowledge to other colleagues so that they can be able to continue to improve and enhance the quality of their learning. This happens because of the reflection done together before, during and after the learning activities. It was also revealed that by learning in a professional learning community, teachers can learn in their own way, according to need, and can collaborate with their colleagues in improving the effectiveness of learning. Based on the implementation of professional learning community primary school teachers can be concluded that teachers can develop the curriculum, the students understand the development, overcome learning difficulties faced by students and can make learning design more effective and efficient.

How does informational heterogeneity affect the quality of forecasts?

NASA Astrophysics Data System (ADS)

Gualdi, S.; De Martino, A.

2010-01-01

We investigate a toy model of inductive interacting agents aiming to forecast a continuous, exogenous random variable E. Private information on E is spread heterogeneously across agents. Herding turns out to be the preferred forecasting mechanism when heterogeneity is maximal. However in such conditions aggregating information efficiently is hard even in the presence of learning, as the herding ratio rises significantly above the efficient market expectation of 1 and remarkably close to the empirically observed values. We also study how different parameters (interaction range, learning rate, cost of information and score memory) may affect this scenario and improve efficiency in the hard phase.

Data Mining for Efficient and Accurate Large Scale Retrieval of Geophysical Parameters

NASA Astrophysics Data System (ADS)

Obradovic, Z.; Vucetic, S.; Peng, K.; Han, B.

2004-12-01

Our effort is devoted to developing data mining technology for improving efficiency and accuracy of the geophysical parameter retrievals by learning a mapping from observation attributes to the corresponding parameters within the framework of classification and regression. We will describe a method for efficient learning of neural network-based classification and regression models from high-volume data streams. The proposed procedure automatically learns a series of neural networks of different complexities on smaller data stream chunks and then properly combines them into an ensemble predictor through averaging. Based on the idea of progressive sampling the proposed approach starts with a very simple network trained on a very small chunk and then gradually increases the model complexity and the chunk size until the learning performance no longer improves. Our empirical study on aerosol retrievals from data obtained with the MISR instrument mounted at Terra satellite suggests that the proposed method is successful in learning complex concepts from large data streams with near-optimal computational effort. We will also report on a method that complements deterministic retrievals by constructing accurate predictive algorithms and applying them on appropriately selected subsets of observed data. The method is based on developing more accurate predictors aimed to catch global and local properties synthesized in a region. The procedure starts by learning the global properties of data sampled over the entire space, and continues by constructing specialized models on selected localized regions. The global and local models are integrated through an automated procedure that determines the optimal trade-off between the two components with the objective of minimizing the overall mean square errors over a specific region. Our experimental results on MISR data showed that the combined model can increase the retrieval accuracy significantly. The preliminary results on various large heterogeneous spatial-temporal datasets provide evidence that the benefits of the proposed methodology for efficient and accurate learning exist beyond the area of retrieval of geophysical parameters.

Utilization of Portable Radios to Improve Ophthalmology Clinic Efficiency in an Academic Setting.

PubMed

Davis, Alexander S; Elkeeb, Ahmed M; Vizzeri, Gianmarco; Godley, Bernard F

2016-03-01

Improvement in clinic efficiency in the ambulatory setting is often looked at as an area for development of lean management strategies to deliver a higher quality of healthcare while reducing errors, costs, and delays. To examine the benefits of improving team communication and its impact on clinic flow and efficiency, we describe a time-motion study performed in an academic outpatient Ophthalmology clinic and its objective and subjective results. Compared to clinic encounters without the use of the portable radios, objective data demonstrated an overall significant decreases in mean workup time (15.18 vs. 13.10), room wait (13.10 vs. 10.47), and decreased the total time needed with an MD per encounter (9.45 vs. 6.63). Subjectively, significant improvements were seen in careprovider scores for patient flow (60.78 vs. 84.29), getting assistance (61.89 vs. 88.57), moving patient charts (54.44 vs. 85.71), teamwork (69.56 vs. 91.0), communications (62.33 vs. 90.43), providing quality patient care (76.22 vs. 89.57), and receiving input on the ability to see walk-in patients (80.11 vs. 90.43). For academic purposes, an improvement in engagement in patient care and learning opportunities was noted by the clinic resident-in-training during the pilot study. Portable radios in our pilot study were preferred over the previous method of communication and demonstrates significant improvements in certain areas of clinical efficiency, subjective perception of teamwork and communications, and academic learning.

Measuring and Benchmarking Technical Efficiency of Public Hospitals in Tianjin, China

PubMed Central

Li, Hao; Dong, Siping

2015-01-01

China has long been stuck in applying traditional data envelopment analysis (DEA) models to measure technical efficiency of public hospitals without bias correction of efficiency scores. In this article, we have introduced the Bootstrap-DEA approach from the international literature to analyze the technical efficiency of public hospitals in Tianjin (China) and tried to improve the application of this method for benchmarking and inter-organizational learning. It is found that the bias corrected efficiency scores of Bootstrap-DEA differ significantly from those of the traditional Banker, Charnes, and Cooper (BCC) model, which means that Chinese researchers need to update their DEA models for more scientific calculation of hospital efficiency scores. Our research has helped shorten the gap between China and the international world in relative efficiency measurement and improvement of hospitals. It is suggested that Bootstrap-DEA be widely applied into afterward research to measure relative efficiency and productivity of Chinese hospitals so as to better serve for efficiency improvement and related decision making. PMID:26396090

Future applications of artificial intelligence to Mission Control Centers

NASA Technical Reports Server (NTRS)

Friedland, Peter

1991-01-01

Future applications of artificial intelligence to Mission Control Centers are presented in the form of the viewgraphs. The following subject areas are covered: basic objectives of the NASA-wide AI program; inhouse research program; constraint-based scheduling; learning and performance improvement for scheduling; GEMPLAN multi-agent planner; planning, scheduling, and control; Bayesian learning; efficient learning algorithms; ICARUS (an integrated architecture for learning); design knowledge acquisition and retention; computer-integrated documentation; and some speculation on future applications.

Energy Efficiency in Water and Wastewater Facilities

EPA Pesticide Factsheets

Learn how local governments have achieved sustained energy improvements at their water and wastewater facilities through equipment upgrades, operational modifications, and modifications to facility buildings.

77 FR 261 - Notice of Request for Extension of a Currently Approved Information Collection

Federal Register 2010, 2011, 2012, 2013, 2014

2012-01-04

... small businesses to become more energy efficient and to use renewable energy technologies and resources... colleges and universities or other institutions of higher learning; rural electric cooperatives; public... to improve the energy efficiency of the operations of the agricultural producers and rural small...

Improving the Efficiency of Dialogue in Tutoring

ERIC Educational Resources Information Center

Kopp, Kristopher J.; Britt, M. Anne; Millis, Keith; Graesser, Arthur C.

2012-01-01

The current studies investigated the efficient use of dialogue in intelligent tutoring systems that use natural language interaction. Such dialogues can be relatively time-consuming. This work addresses the question of how much dialogue is needed to produce significant learning gains. In Experiment 1, a full dialogue condition and a read-only…

Repetition Suppression in the Left Inferior Frontal Gyrus Predicts Tone Learning Performance.

PubMed

Asaridou, Salomi S; Takashima, Atsuko; Dediu, Dan; Hagoort, Peter; McQueen, James M

2016-06-01

Do individuals differ in how efficiently they process non-native sounds? To what extent do these differences relate to individual variability in sound-learning aptitude? We addressed these questions by assessing the sound-learning abilities of Dutch native speakers as they were trained on non-native tone contrasts. We used fMRI repetition suppression to the non-native tones to measure participants' neuronal processing efficiency before and after training. Although all participants improved in tone identification with training, there was large individual variability in learning performance. A repetition suppression effect to tone was found in the bilateral inferior frontal gyri (IFGs) before training. No whole-brain effect was found after training; a region-of-interest analysis, however, showed that, after training, repetition suppression to tone in the left IFG correlated positively with learning. That is, individuals who were better in learning the non-native tones showed larger repetition suppression in this area. Crucially, this was true even before training. These findings add to existing evidence that the left IFG plays an important role in sound learning and indicate that individual differences in learning aptitude stem from differences in the neuronal efficiency with which non-native sounds are processed. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

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

PubMed Central

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

2011-01-01

Background We introduced a series of computer-supported workshops in our undergraduate statistics courses, in the hope that it would help students to gain a deeper understanding of statistical concepts. This raised questions about the appropriate design of the Virtual Learning Environment (VLE) in which such an approach had to be implemented. Therefore, we investigated two competing software design models for VLEs. In the first system, all learning features were a function of the classical VLE. The second system was designed from the perspective that learning features should be a function of the course's core content (statistical analyses), which required us to develop a specific–purpose Statistical Learning Environment (SLE) based on Reproducible Computing and newly developed Peer Review (PR) technology. Objectives The main research question is whether the second VLE design improved learning efficiency as compared to the standard type of VLE design that is commonly used in education. As a secondary objective we provide empirical evidence about the usefulness of PR as a constructivist learning activity which supports non-rote learning. Finally, this paper illustrates that it is possible to introduce a constructivist learning approach in large student populations, based on adequately designed educational technology, without subsuming educational content to technological convenience. Methods Both VLE systems were tested within a two-year quasi-experiment based on a Reliable Nonequivalent Group Design. This approach allowed us to draw valid conclusions about the treatment effect of the changed VLE design, even though the systems were implemented in successive years. The methodological aspects about the experiment's internal validity are explained extensively. Results The effect of the design change is shown to have substantially increased the efficiency of constructivist, computer-assisted learning activities for all cohorts of the student population under investigation. The findings demonstrate that a content–based design outperforms the traditional VLE–based design. PMID:21998652

Students' perceptions of clinical teaching and learning strategies: a Pakistani perspective.

PubMed

Khan, Basnama Ayaz; Ali, Fauziya; Vazir, Nilofar; Barolia, Rubina; Rehan, Seema

2012-01-01

The complexity of the health care environment is increasing with the explosion of technology, coupled with the issues of patients' access, equity, time efficiency, and cost containment. Nursing education must focus on means that enable students to develop the processes of active learning, problem-solving, and critical thinking, in order to enable them to deal with the complexities. This study aims at identifying the nursing students' perceptions about the effectiveness of utilized teaching and learning strategies of clinical education, in improving students' knowledge, skills, and attitudes. A descriptive cross sectional study design was utilized using both qualitative and quantitative approaches. Data were collected from 74 students, using a questionnaire that was developed for the purpose of the study and analyzed using descriptive and non-parametric statistics. The findings revealed that demonstration was the most effective strategy for improving students' skills; reflection, for improving attitudes; and problem based learning and concept map for improving their knowledge. Students' responses to open-ended questions confirmed the effectiveness of these strategies in improving their learning outcomes. Recommendations have been provided based on the findings. Copyright © 2011 Elsevier Ltd. All rights reserved.

Transforming Public Health Systems: Using Data to Drive Organizational Capacity for Quality Improvement and Efficiency

PubMed Central

Steinwachs, Donald M.

2014-01-01

Introduction: This paper examines the organization, services, and priorities of public health agencies and their capacity to be learning public health systems (LPHS). An LPHS uses data to measure population health and health risks and to evaluate its services and programs, and then integrates its own research with advances in scientific knowledge to innovate and improve its efficiency and effectiveness. Public Health Agencies and Impact for LPHS: Public health agencies’ (PHA) organizational characteristics vary across states, as does their funding per capita. Variations in organization, services provided, and expenditures per capita may reflect variations in community needs or may be associated with unmet needs. The status of legal statutes defining responsibilities and authorities and their relationships to other public and private agencies also vary. Little information is available on the efficiency and effectiveness of state and local PHAs, in part due to a lack of information infrastructure to capture uniform data on services provided. There are almost no data on the relationship of quality of services, staff performance, and resources to population health outcomes. By building a capacity to collect and analyze data on population health within and across communities, and by becoming a continuous learning PHA, the allocation of resources can more closely match population health needs and improve health outcomes. Accreditation of every PHA is an important first step toward becoming a learning PHA. Conclusions: Public Health Services and Systems Research (PHSSR) is beginning to shed light on some of these issues, particularly by investigating variation across PHAs. As this emerging discipline grows, there is a need to enhance the collection and use of data in support of building organized, effective, and efficient LPHSs with the PHA capacity to continually improve the public’s health. PMID:25995990

An efficient approach to improve the usability of e-learning resources: the role of heuristic evaluation.

PubMed

Davids, Mogamat Razeen; Chikte, Usuf M E; Halperin, Mitchell L

2013-09-01

Optimizing the usability of e-learning materials is necessary to maximize their potential educational impact, but this is often neglected when time and other resources are limited, leading to the release of materials that cannot deliver the desired learning outcomes. As clinician-teachers in a resource-constrained environment, we investigated whether heuristic evaluation of our multimedia e-learning resource by a panel of experts would be an effective and efficient alternative to testing with end users. We engaged six inspectors, whose expertise included usability, e-learning, instructional design, medical informatics, and the content area of nephrology. They applied a set of commonly used heuristics to identify usability problems, assigning severity scores to each problem. The identification of serious problems was compared with problems previously found by user testing. The panel completed their evaluations within 1 wk and identified a total of 22 distinct usability problems, 11 of which were considered serious. The problems violated the heuristics of visibility of system status, user control and freedom, match with the real world, intuitive visual layout, consistency and conformity to standards, aesthetic and minimalist design, error prevention and tolerance, and help and documentation. Compared with user testing, heuristic evaluation found most, but not all, of the serious problems. Combining heuristic evaluation and user testing, with each involving a small number of participants, may be an effective and efficient way of improving the usability of e-learning materials. Heuristic evaluation should ideally be used first to identify the most obvious problems and, once these are fixed, should be followed by testing with typical end users.

Using Deep Learning for Compound Selectivity Prediction.

PubMed

Zhang, Ruisheng; Li, Juan; Lu, Jingjing; Hu, Rongjing; Yuan, Yongna; Zhao, Zhili

2016-01-01

Compound selectivity prediction plays an important role in identifying potential compounds that bind to the target of interest with high affinity. However, there is still short of efficient and accurate computational approaches to analyze and predict compound selectivity. In this paper, we propose two methods to improve the compound selectivity prediction. We employ an improved multitask learning method in Neural Networks (NNs), which not only incorporates both activity and selectivity for other targets, but also uses a probabilistic classifier with a logistic regression. We further improve the compound selectivity prediction by using the multitask learning method in Deep Belief Networks (DBNs) which can build a distributed representation model and improve the generalization of the shared tasks. In addition, we assign different weights to the auxiliary tasks that are related to the primary selectivity prediction task. In contrast to other related work, our methods greatly improve the accuracy of the compound selectivity prediction, in particular, using the multitask learning in DBNs with modified weights obtains the best performance.

Implementing the Flipped Classroom in Teacher Education: Evidence from Turkey

ERIC Educational Resources Information Center

Kurt, Gökçe

2017-01-01

The flipped classroom, a form of blended learning, is an emerging instructional strategy reversing a traditional lecture-based teaching model to improve the quality and efficiency of the teaching and learning process. The present article reports a study that focused on the implementation of the flipped approach in a higher education institution in…

A Systematic Approach to Faculty Development--Capability Improvement for Blended Learning

ERIC Educational Resources Information Center

Badawood, Ashraf; Steenkamp, Annette Lerine; Al-Werfalli, Daw

2013-01-01

Blended learning (BL) provides an efficient and effective instructional experience. However, adopting a BL approach poses some challenges to faculty; the most important obstacle found in this research is faculty's lack of knowledge regarding the use of technology in their teaching. This challenge prompted the research project focused on improving…

Science Teaching Based on Cognitive Load Theory: Engaged Students, but Cognitive Deficiencies

ERIC Educational Resources Information Center

Meissner, Barbara; Bogner, Franz X.

2012-01-01

To improve science learning under demanding conditions, we designed an out-of-school lesson in compliance with cognitive load theory (CLT). We extracted student clusters based on individual effectiveness, and compared instructional efficiency, mental effort, and persistence of learning. The present study analyses students' engagement. 50.0% of our…

Alternative Fuels Data Center: Availability of Hybrid and Plug-In Electric

Science.gov Websites

AddThis.com... More in this section... Electricity Basics Benefits & Considerations Stations Vehicles electricity to improve fuel efficiency. Pre-Owned Vehicles Learn about buying and selling pre-owned and plug-in electric vehicles. Learn more about the benefits and considerations of electricity as a

Designing a Programmatic Digital Learning Environment: Lessons from Prototyping

ERIC Educational Resources Information Center

Gal, Diane; Lewis, Mark

2018-01-01

Promoted as a way to enhance learning and improve efficiencies, the steady rise of technology adoption across higher education has created both new opportunities and new challenges. Borrowing principles of design thinking and related user- or learner-centered design practices, this descriptive case study offers an example of how institutions of…

An Artificial Intelligence Approach to the Symbolic Factorization of Multivariable Polynomials. Technical Report No. CS74019-R.

ERIC Educational Resources Information Center

Claybrook, Billy G.

A new heuristic factorization scheme uses learning to improve the efficiency of determining the symbolic factorization of multivariable polynomials with interger coefficients and an arbitrary number of variables and terms. The factorization scheme makes extensive use of artificial intelligence techniques (e.g., model-building, learning, and…

An Analysis of Learning To Plan as a Search Problem.

ERIC Educational Resources Information Center

Gratch, Jonathan; DeJong, Gerald

Increasingly, machine learning is entertained as a mechanism for improving the efficiency of planning systems. Research in this area has generated an impressive battery of techniques and a growing body of empirical successes. Unfortunately the formal properties of these systems are not well understood. This is highlighted by a growing corpus of…

Improving Student Learning: A Strategic Planning Framework for an Integrated Student Information System in Charlotte-Mecklenburg Schools

ERIC Educational Resources Information Center

Ngoma, Sylvester

2010-01-01

There is growing recognition that an electronic Student Information System (SIS) affects student learning. Given the strategic importance of SIS in supporting school administration and enhancing student performance, school districts are increasingly interested in acquiring the most effective and efficient Student Information Systems for their…

Idm@ti Network: An Innovative Proposal for Improving Teaching and Learning in Spanish Universities

ERIC Educational Resources Information Center

Salan, Nuria; Cabedo, Luis; Segarra, Mercedes; Guraya, Teresa; Lopez, Pascal; Sales, David; Gamez, Jose

2017-01-01

IdM@ti network members concurred in the diagnosis of the difficulties and opportunities arising from Bologna process implementation and teaching methodologies improvement in Materials Science and Engineering (MSE) teaching. This network has been created with the aim of improving efficiency of underway and future collaborations.The main objectives…

Learning optimal eye movements to unusual faces

PubMed Central

Peterson, Matthew F.; Eckstein, Miguel P.

2014-01-01

Eye movements, which guide the fovea’s high resolution and computational power to relevant areas of the visual scene, are integral to efficient, successful completion of many visual tasks. How humans modify their eye movements through experience with their perceptual environments, and its functional role in learning new tasks, has not been fully investigated. Here, we used a face identification task where only the mouth discriminated exemplars to assess if, how, and when eye movement modulation may mediate learning. By interleaving trials of unconstrained eye movements with trials of forced fixation, we attempted to separate the contributions of eye movements and covert mechanisms to performance improvements. Without instruction, a majority of observers substantially increased accuracy and learned to direct their initial eye movements towards the optimal fixation point. The proximity of an observer’s default face identification eye movement behavior to the new optimal fixation point and the observer’s peripheral processing ability were predictive of performance gains and eye movement learning. After practice in a subsequent condition in which observers were directed to fixate different locations along the face, including the relevant mouth region, all observers learned to make eye movements to the optimal fixation point. In this fully learned state, augmented fixation strategy accounted for 43% of total efficiency improvements while covert mechanisms accounted for the remaining 57%. The findings suggest a critical role for eye movement planning to perceptual learning, and elucidate factors that can predict when and how well an observer can learn a new task with unusual exemplars. PMID:24291712

An English Vocabulary Learning System Based on Fuzzy Theory and Memory Cycle

NASA Astrophysics Data System (ADS)

Wang, Tzone I.; Chiu, Ti Kai; Huang, Liang Jun; Fu, Ru Xuan; Hsieh, Tung-Cheng

This paper proposes an English Vocabulary Learning System based on the Fuzzy Theory and the Memory Cycle Theory to help a learner to memorize vocabularies easily. By using fuzzy inferences and personal memory cycles, it is possible to find an article that best suits a learner. After reading an article, a quiz is provided for the learner to improve his/her memory of the vocabulary in the article. Early researches use just explicit response (ex. quiz exam) to update memory cycles of newly learned vocabulary; apart from that approach, this paper proposes a methodology that also modify implicitly the memory cycles of learned word. By intensive reading of articles recommended by our approach, a learner learns new words quickly and reviews learned words implicitly as well, and by which the vocabulary ability of the learner improves efficiently.

Acquisition of Motor and Cognitive Skills through Repetition in Typically Developing Children

PubMed Central

Magallón, Sara; Narbona, Juan; Crespo-Eguílaz, Nerea

2016-01-01

Background Procedural memory allows acquisition, consolidation and use of motor skills and cognitive routines. Automation of procedures is achieved through repeated practice. In children, improvement in procedural skills is a consequence of natural neurobiological development and experience. Methods The aim of the present research was to make a preliminary evaluation and description of repetition-based improvement of procedures in typically developing children (TDC). Ninety TDC children aged 6–12 years were asked to perform two procedural learning tasks. In an assembly learning task, which requires predominantly motor skills, we measured the number of assembled pieces in 60 seconds. In a mirror drawing learning task, which requires more cognitive functions, we measured time spent and efficiency. Participants were tested four times for each task: three trials were consecutive and the fourth trial was performed after a 10-minute nonverbal interference task. The influence of repeated practice on performance was evaluated by means of the analysis of variance with repeated measures and the paired-sample test. Correlation coefficients and simple linear regression test were used to examine the relationship between age and performance. Results TDC achieved higher scores in both tasks through repetition. Older children fitted more pieces than younger ones in assembling learning and they were faster and more efficient at the mirror drawing learning task. Conclusions These findings indicate that three consecutive trials at a procedural task increased speed and efficiency, and that age affected basal performance in motor-cognitive procedures. PMID:27384671

Acquisition of Motor and Cognitive Skills through Repetition in Typically Developing Children.

PubMed

Magallón, Sara; Narbona, Juan; Crespo-Eguílaz, Nerea

2016-01-01

Procedural memory allows acquisition, consolidation and use of motor skills and cognitive routines. Automation of procedures is achieved through repeated practice. In children, improvement in procedural skills is a consequence of natural neurobiological development and experience. The aim of the present research was to make a preliminary evaluation and description of repetition-based improvement of procedures in typically developing children (TDC). Ninety TDC children aged 6-12 years were asked to perform two procedural learning tasks. In an assembly learning task, which requires predominantly motor skills, we measured the number of assembled pieces in 60 seconds. In a mirror drawing learning task, which requires more cognitive functions, we measured time spent and efficiency. Participants were tested four times for each task: three trials were consecutive and the fourth trial was performed after a 10-minute nonverbal interference task. The influence of repeated practice on performance was evaluated by means of the analysis of variance with repeated measures and the paired-sample test. Correlation coefficients and simple linear regression test were used to examine the relationship between age and performance. TDC achieved higher scores in both tasks through repetition. Older children fitted more pieces than younger ones in assembling learning and they were faster and more efficient at the mirror drawing learning task. These findings indicate that three consecutive trials at a procedural task increased speed and efficiency, and that age affected basal performance in motor-cognitive procedures.

Learning just-in-time in medical informatics.

PubMed

Sancho, J J; Sanz, F

2000-01-01

Just-in-time learning (JITL) methodology has been applied to many areas of knowledge acquisition and dissemination. The paradigm is a challenge to the traditional classroom course-oriented approach with the aim to shorten the learning time, increasing the efficiency of the learning process, improve availability and save money. The information technology tools and platforms have been heavily involved to develop and deliver JITL. This paper discusses the main characteristics of JITL with regard to its implementation to teaching Medical Informatics.

Incidental learning speeds visual search by lowering response thresholds, not by improving efficiency: Evidence from eye movements

PubMed Central

Hout, Michael C.; Goldinger, Stephen D.

2011-01-01

When observers search for a target object, they incidentally learn the identities and locations of “background” objects in the same display. This learning can facilitate search performance, eliciting faster reaction times for repeated displays (Hout & Goldinger, 2010). Despite these findings, visual search has been successfully modeled using architectures that maintain no history of attentional deployments; they are amnesic (e.g., Guided Search Theory; Wolfe, 2007). In the current study, we asked two questions: 1) under what conditions does such incidental learning occur? And 2) what does viewing behavior reveal about the efficiency of attentional deployments over time? In two experiments, we tracked eye movements during repeated visual search, and we tested incidental memory for repeated non-target objects. Across conditions, the consistency of search sets and spatial layouts were manipulated to assess their respective contributions to learning. Using viewing behavior, we contrasted three potential accounts for faster searching with experience. The results indicate that learning does not result in faster object identification or greater search efficiency. Instead, familiar search arrays appear to allow faster resolution of search decisions, whether targets are present or absent. PMID:21574743

Five Climate Control Techniques for Schools.

ERIC Educational Resources Information Center

Wilson, Maurice J.

1963-01-01

There are many reasons for air-conditioning schools and among them are--(1) the improvement of learning and teaching efficiency, (2) effective use of the educational plant for a greater part of the year, and (3) more efficient use of space through compact building design. Five climate control techniques are cited as providing optimum…

Balancing Online Teaching Activities: Strategies for Optimizing Efficiency and Effectiveness

ERIC Educational Resources Information Center

Raffo, Deana M.; Brinthaupt, Thomas M.; Gardner, Justin G.; Fisher, Lawanna S.

2015-01-01

Increased demands in professional expectations have required online faculty to learn how to balance multiple roles in an open-ended, changing, and relatively unstructured job. In this paper, we argue that being strategic about one's balance of the various facets of online teaching will improve one's teaching efficiency and effectiveness. We…

An Energy-Efficient Spectrum-Aware Reinforcement Learning-Based Clustering Algorithm for Cognitive Radio Sensor Networks

PubMed Central

Mustapha, Ibrahim; Ali, Borhanuddin Mohd; Rasid, Mohd Fadlee A.; Sali, Aduwati; Mohamad, Hafizal

2015-01-01

It is well-known that clustering partitions network into logical groups of nodes in order to achieve energy efficiency and to enhance dynamic channel access in cognitive radio through cooperative sensing. While the topic of energy efficiency has been well investigated in conventional wireless sensor networks, the latter has not been extensively explored. In this paper, we propose a reinforcement learning-based spectrum-aware clustering algorithm that allows a member node to learn the energy and cooperative sensing costs for neighboring clusters to achieve an optimal solution. Each member node selects an optimal cluster that satisfies pairwise constraints, minimizes network energy consumption and enhances channel sensing performance through an exploration technique. We first model the network energy consumption and then determine the optimal number of clusters for the network. The problem of selecting an optimal cluster is formulated as a Markov Decision Process (MDP) in the algorithm and the obtained simulation results show convergence, learning and adaptability of the algorithm to dynamic environment towards achieving an optimal solution. Performance comparisons of our algorithm with the Groupwise Spectrum Aware (GWSA)-based algorithm in terms of Sum of Square Error (SSE), complexity, network energy consumption and probability of detection indicate improved performance from the proposed approach. The results further reveal that an energy savings of 9% and a significant Primary User (PU) detection improvement can be achieved with the proposed approach. PMID:26287191

An Energy-Efficient Spectrum-Aware Reinforcement Learning-Based Clustering Algorithm for Cognitive Radio Sensor Networks.

PubMed

Mustapha, Ibrahim; Mohd Ali, Borhanuddin; Rasid, Mohd Fadlee A; Sali, Aduwati; Mohamad, Hafizal

2015-08-13

It is well-known that clustering partitions network into logical groups of nodes in order to achieve energy efficiency and to enhance dynamic channel access in cognitive radio through cooperative sensing. While the topic of energy efficiency has been well investigated in conventional wireless sensor networks, the latter has not been extensively explored. In this paper, we propose a reinforcement learning-based spectrum-aware clustering algorithm that allows a member node to learn the energy and cooperative sensing costs for neighboring clusters to achieve an optimal solution. Each member node selects an optimal cluster that satisfies pairwise constraints, minimizes network energy consumption and enhances channel sensing performance through an exploration technique. We first model the network energy consumption and then determine the optimal number of clusters for the network. The problem of selecting an optimal cluster is formulated as a Markov Decision Process (MDP) in the algorithm and the obtained simulation results show convergence, learning and adaptability of the algorithm to dynamic environment towards achieving an optimal solution. Performance comparisons of our algorithm with the Groupwise Spectrum Aware (GWSA)-based algorithm in terms of Sum of Square Error (SSE), complexity, network energy consumption and probability of detection indicate improved performance from the proposed approach. The results further reveal that an energy savings of 9% and a significant Primary User (PU) detection improvement can be achieved with the proposed approach.

A Practical Decision Guide for Integrating Digital Applications and Handheld Devices into Advanced Individual Training

DTIC Science & Technology

2013-07-01

the devices increase efficiency and make instruction easier for them. (1) Demonstrate the ability of mobile learning to improve student learning ...predictors of learning , after controlling for the effects of cognitive ability and pre-training knowledge of the subject matter. Equally as...conventional teaching. PBL is an instructional model originally developed in medical schools , in which students are given a complex problem to solve that may

Partnership between CTSI and Business Schools Can Promote Best Practices for Core Facilities and Resources

PubMed Central

Reeves, Lilith; Dunn‐Jensen, Linda M.; Baldwin, Timothy T.; Tatikonda, Mohan V.

2013-01-01

Abstract Biomedical research enterprises require a large number of core facilities and resources to supply the infrastructure necessary for translational research. Maintaining the financial viability and promoting efficiency in an academic environment can be particularly challenging for medical schools and universities. The Indiana Clinical and Translational Sciences Institute sought to improve core and service programs through a partnership with the Indiana University Kelley School of Business. The program paired teams of Masters of Business Administration students with cores and programs that self‐identified the need for assistance in project management, financial management, marketing, or resource efficiency. The projects were developed by CTSI project managers and business school faculty using service‐learning principles to ensure learning for students who also received course credit for their participation. With three years of experience, the program demonstrates a successful partnership that improves clinical research infrastructure by promoting business best practices and providing a valued learning experience for business students. PMID:23919365

Partnership between CTSI and business schools can promote best practices for core facilities and resources.

PubMed

Reeves, Lilith; Dunn-Jensen, Linda M; Baldwin, Timothy T; Tatikonda, Mohan V; Cornetta, Kenneth

2013-08-01

Biomedical research enterprises require a large number of core facilities and resources to supply the infrastructure necessary for translational research. Maintaining the financial viability and promoting efficiency in an academic environment can be particularly challenging for medical schools and universities. The Indiana Clinical and Translational Sciences Institute sought to improve core and service programs through a partnership with the Indiana University Kelley School of Business. The program paired teams of Masters of Business Administration students with cores and programs that self-identified the need for assistance in project management, financial management, marketing, or resource efficiency. The projects were developed by CTSI project managers and business school faculty using service-learning principles to ensure learning for students who also received course credit for their participation. With three years of experience, the program demonstrates a successful partnership that improves clinical research infrastructure by promoting business best practices and providing a valued learning experience for business students. © 2013 Wiley Periodicals, Inc.

Revenue Generation in the Wake of Welfare Reform: Summary of the Pilot Learning Cluster on Early Childhood Finance.

ERIC Educational Resources Information Center

Finance Project, Washington, DC.

Creating more comprehensive, community-based support systems and reforming early childhood financing systems are critical to advancing the goal of having all children enter school ready to learn. The Finance Project is a national initiative to improve effectiveness, efficiency, and equity of financing for education, children's services, and…

Styles of Self-Regulation of Learning Activities of University Students

ERIC Educational Resources Information Center

Khusainova, Rezeda M.; Ivutina, Helena P.

2016-01-01

The relevance of the study is largely due to changes in the country's education system in recent years, in particular--the transition to a two-tier system of education--undergraduate and graduate--the purpose of which is to improve learning efficiency. One feature of this system is to strengthen the role of independent educational activity of…

A Systematic Approach to Faculty Development toward Improved Capability in Tertiary Teaching in a Blended Learning Environment

ERIC Educational Resources Information Center

Badawood, Ashraf Mohammad

2011-01-01

The blended learning (BL) approach provides an efficient and effective instructional experience. However, adopting BL poses some challenge to faculty; the most important obstacle found in this research is faculty's lack of knowledge regarding the use of technology in their teaching. This challenge prompted the researcher to identify a solution…

A Study on the Methods of Assessment and Strategy of Knowledge Sharing in Computer Course

ERIC Educational Resources Information Center

Chan, Pat P. W.

2014-01-01

With the advancement of information and communication technology, collaboration and knowledge sharing through technology is facilitated which enhances the learning process and improves the learning efficiency. The purpose of this paper is to review the methods of assessment and strategy of collaboration and knowledge sharing in a computer course,…

English Cooperative Learning Mode in a Rural Junior High School in China

ERIC Educational Resources Information Center

Zhang, Haiyan; Peng, Wen; Sun, Liuhua

2017-01-01

Cooperative learning is one of the most recognized and fruitful research areas in modern education practice. It has been widely used in many countries as an effective teaching strategy to improve class efficiency and students' comprehensive language ability since the 1990's. This paper takes JA Junior High School, a rural junior high school in…

A fast learning method for large scale and multi-class samples of SVM

NASA Astrophysics Data System (ADS)

Fan, Yu; Guo, Huiming

2017-06-01

A multi-class classification SVM(Support Vector Machine) fast learning method based on binary tree is presented to solve its low learning efficiency when SVM processing large scale multi-class samples. This paper adopts bottom-up method to set up binary tree hierarchy structure, according to achieved hierarchy structure, sub-classifier learns from corresponding samples of each node. During the learning, several class clusters are generated after the first clustering of the training samples. Firstly, central points are extracted from those class clusters which just have one type of samples. For those which have two types of samples, cluster numbers of their positive and negative samples are set respectively according to their mixture degree, secondary clustering undertaken afterwards, after which, central points are extracted from achieved sub-class clusters. By learning from the reduced samples formed by the integration of extracted central points above, sub-classifiers are obtained. Simulation experiment shows that, this fast learning method, which is based on multi-level clustering, can guarantee higher classification accuracy, greatly reduce sample numbers and effectively improve learning efficiency.

Measuring and Benchmarking Technical Efficiency of Public Hospitals in Tianjin, China: A Bootstrap-Data Envelopment Analysis Approach.

PubMed

Li, Hao; Dong, Siping

2015-01-01

China has long been stuck in applying traditional data envelopment analysis (DEA) models to measure technical efficiency of public hospitals without bias correction of efficiency scores. In this article, we have introduced the Bootstrap-DEA approach from the international literature to analyze the technical efficiency of public hospitals in Tianjin (China) and tried to improve the application of this method for benchmarking and inter-organizational learning. It is found that the bias corrected efficiency scores of Bootstrap-DEA differ significantly from those of the traditional Banker, Charnes, and Cooper (BCC) model, which means that Chinese researchers need to update their DEA models for more scientific calculation of hospital efficiency scores. Our research has helped shorten the gap between China and the international world in relative efficiency measurement and improvement of hospitals. It is suggested that Bootstrap-DEA be widely applied into afterward research to measure relative efficiency and productivity of Chinese hospitals so as to better serve for efficiency improvement and related decision making. © The Author(s) 2015.

Relearning of Activities of Daily Living: A Comparison of the Effectiveness of Three Learning Methods in Patients with Dementia of the Alzheimer Type.

PubMed

Bourgeois, J; Laye, M; Lemaire, J; Leone, E; Deudon, A; Darmon, N; Giaume, C; Lafont, V; Brinck-Jensen, S; Dechamps, A; König, A; Robert, P

2016-01-01

This study examined the effectiveness of three different learning methods: trial and error learning (TE), errorless learning (EL) and learning by modeling with spaced retrieval (MR) on the relearning process of IADL in mild-to-moderately severe Alzheimer's Dementia (AD) patients (n=52), using a 6-weeks randomized controlled trial design. The participants had to relearn three IADLs. Repeated-measure analyses during pre-intervention, post-intervention and 1-month delayed sessions were performed. All three learning methods were found to have similar efficiency. However, the intervention produced greater improvements in the actual performance of the IADL tasks than on their explicit knowledge. This study confirms that the relearning of IADL is possible with AD patients through individualized interventions, and that the improvements can be maintained even after the intervention.

Motor Learning Versus StandardWalking Exercise in Older Adults with Subclinical Gait Dysfunction: A Randomized Clinical Trial

PubMed Central

Brach, Jennifer S.; Van Swearingen, Jessie M.; Perera, Subashan; Wert, David M.; Studenski, Stephanie

2013-01-01

Background Current exercise recommendationsfocus on endurance and strength, but rarely incorporate principles of motor learning. Motor learning exerciseis designed to address neurological aspects of movement. Motor learning exercise has not been evaluated in older adults with subclinical gait dysfunction. Objectives Tocompare motor learning versus standard exercise on measures of mobility and perceived function and disability. Design Single-blind randomized trial. Setting University research center. Participants Olderadults (n=40), mean age 77.1±6.0 years), who had normal walking speed (≥1.0 m/s) and impaired motor skill (Figure of 8 walk time > 8 s). Interventions The motor learning program (ML) incorporated goal-oriented stepping and walking to promote timing and coordination within the phases of the gait cycle. The standard program (S) employed endurance training by treadmill walking.Both included strength training and were offered twice weekly for one hour for 12 weeks. Measurements Primary outcomes included mobility performance (gait efficiency, motor skill in walking, gait speed, and walking endurance)and secondary outcomes included perceived function and disability (Late Life Function and Disability Instrument). Results 38 of 40 participants completed the trial (ML, n=18; S, n=20). ML improved more than Sin gait speed (0.13 vs. 0.05 m/s, p=0.008) and motor skill (−2.2 vs. −0.89 s, p<0.0001). Both groups improved in walking endurance (28.3 and 22.9m, but did not differ significantly p=0.14). Changes in gait efficiency and perceived function and disability were not different between the groups (p>0.10). Conclusion In older adults with subclinical gait dysfunction, motor learning exercise improved some parameters of mobility performance more than standard exercise. PMID:24219189

Effects of variable practice on the motor learning outcomes in manual wheelchair propulsion.

PubMed

Leving, Marika T; Vegter, Riemer J K; de Groot, Sonja; van der Woude, Lucas H V

2016-11-23

Handrim wheelchair propulsion is a cyclic skill that needs to be learned during rehabilitation. It has been suggested that more variability in propulsion technique benefits the motor learning process of wheelchair propulsion. The purpose of this study was to determine the influence of variable practice on the motor learning outcomes of wheelchair propulsion in able-bodied participants. Variable practice was introduced in the form of wheelchair basketball practice and wheelchair-skill practice. Motor learning was operationalized as improvements in mechanical efficiency and propulsion technique. Eleven Participants in the variable practice group and 12 participants in the control group performed an identical pre-test and a post-test. Pre- and post-test were performed in a wheelchair on a motor-driven treadmill (1.11 m/s) at a relative power output of 0.23 W/kg. Energy consumption and the propulsion technique variables with their respective coefficient of variation were calculated. Between the pre- and the post-test the variable practice group received 7 practice sessions. During the practice sessions participants performed one-hour of variable practice, consisting of five wheelchair-skill tasks and a 30 min wheelchair basketball game. The control group did not receive any practice between the pre- and the post-test. Comparison of the pre- and the post-test showed that the variable practice group significantly improved the mechanical efficiency (4.5 ± 0.6% → 5.7 ± 0.7%) in contrast to the control group (4.5 ± 0.6% → 4.4 ± 0.5%) (group x time interaction effect p < 0.001).With regard to propulsion technique, both groups significantly reduced the push frequency and increased the contact angle of the hand with the handrim (within group, time effect). No significant group × time interaction effects were found for propulsion technique. With regard to propulsion variability, the variable practice group increased variability when compared to the control group (interaction effect p < 0.001). Compared to a control, variable practice, resulted in an increase in mechanical efficiency and increased variability. Interestingly, the large relative improvement in mechanical efficiency was concomitant with only moderate improvements in the propulsion technique, which were similar in the control group, suggesting that other factors besides propulsion technique contributed to the lower energy expenditure.

Artificial Intelligence in Medicine and Radiation Oncology

PubMed Central

Weidlich, Vincent

2018-01-01

Artifical Intelligence (AI) was reviewed with a focus on its potential applicability to radiation oncology. The improvement of process efficiencies and the prevention of errors were found to be the most significant contributions of AI to radiation oncology. It was found that the prevention of errors is most effective when data transfer processes were automated and operational decisions were based on logical or learned evaluations by the system. It was concluded that AI could greatly improve the efficiency and accuracy of radiation oncology operations. PMID:29904616

Artificial Intelligence in Medicine and Radiation Oncology.

PubMed

Weidlich, Vincent; Weidlich, Georg A

2018-04-13

Artifical Intelligence (AI) was reviewed with a focus on its potential applicability to radiation oncology. The improvement of process efficiencies and the prevention of errors were found to be the most significant contributions of AI to radiation oncology. It was found that the prevention of errors is most effective when data transfer processes were automated and operational decisions were based on logical or learned evaluations by the system. It was concluded that AI could greatly improve the efficiency and accuracy of radiation oncology operations.

Generalized SMO algorithm for SVM-based multitask learning.

PubMed

Cai, Feng; Cherkassky, Vladimir

2012-06-01

Exploiting additional information to improve traditional inductive learning is an active research area in machine learning. In many supervised-learning applications, training data can be naturally separated into several groups, and incorporating this group information into learning may improve generalization. Recently, Vapnik proposed a general approach to formalizing such problems, known as "learning with structured data" and its support vector machine (SVM) based optimization formulation called SVM+. Liang and Cherkassky showed the connection between SVM+ and multitask learning (MTL) approaches in machine learning, and proposed an SVM-based formulation for MTL called SVM+MTL for classification. Training the SVM+MTL classifier requires the solution of a large quadratic programming optimization problem which scales as O(n(3)) with sample size n. So there is a need to develop computationally efficient algorithms for implementing SVM+MTL. This brief generalizes Platt's sequential minimal optimization (SMO) algorithm to the SVM+MTL setting. Empirical results show that, for typical SVM+MTL problems, the proposed generalized SMO achieves over 100 times speed-up, in comparison with general-purpose optimization routines.

Improving nurses' perceptions of competency in diabetes self-management education through the use of simulation and problem-based learning.

PubMed

Tschannen, Dana; Aebersold, Michelle; Sauter, Cecilia; Funnell, Martha M

2013-06-01

Nurses who provide case management can improve care practice and outcomes among patients who have type 2 diabetes through appropriate training and systems of care. This study was conducted to improve ambulatory care nurses' perceptions of competency in empowerment-based skills required for diabetes self-management education after participation in a multifaceted educational session that included problem-based learning and simulation. After participation in the multifaceted educational session, nurses (n = 21) perceived that the education provided an excellent opportunity for knowledge uptake and applicability to their respective work settings. The learning strategies provided opportunities for engagement in a safe and relaxed atmosphere. The simulation experience allowed participants to deliberately practice the competencies. These nurses considered this a very effective learning activity. Through the use of problem-based learning and simulation, nurses may be able to more efficiently and effectively develop the necessary skills to provide effective case management of chronic disease. Copyright 2013, SLACK Incorporated.

Road safety education: What works?

PubMed

Assailly, J P

2017-01-01

The objectives of the paper are: METHOD: Seminal papers, collaborative reports from traffic safety research institutes and books from experts have been used as materials. Very diverse fields of application are presented such as: the importance of emotional experience in interaction with traffic experiences; the efficiency of e-learning; the efficiency of simulators to improve hazard perception skills and calibration of one's driving competencies; the efficiency of social norms marketing at changing behaviors by correcting normative misperceptions; the usefulness of parents-based interventions to improve parental supervision; and finally the importance of multi-components programs due to their synergies. Scientific evidence collected in this paper shows that RSE may have some positive effects if good practices are adopted, if it is part of a lifelong learning process and if transmits not only knowledge but also "life-skills" (or psycho-social competences). for practice From each example, we will see the implications of the results for the implementation of RSE. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Effective in-service training design and delivery: evidence from an integrative literature review.

PubMed

Bluestone, Julia; Johnson, Peter; Fullerton, Judith; Carr, Catherine; Alderman, Jessica; BonTempo, James

2013-10-01

In-service training represents a significant financial investment for supporting continued competence of the health care workforce. An integrative review of the education and training literature was conducted to identify effective training approaches for health worker continuing professional education (CPE) and what evidence exists of outcomes derived from CPE. A literature review was conducted from multiple databases including PubMed, the Cochrane Library and Cumulative Index to Nursing and Allied Health Literature (CINAHL) between May and June 2011. The initial review of titles and abstracts produced 244 results. Articles selected for analysis after two quality reviews consisted of systematic reviews, randomized controlled trials (RCTs) and programme evaluations published in peer-reviewed journals from 2000 to 2011 in the English language. The articles analysed included 37 systematic reviews and 32 RCTs. The research questions focused on the evidence supporting educational techniques, frequency, setting and media used to deliver instruction for continuing health professional education. The evidence suggests the use of multiple techniques that allow for interaction and enable learners to process and apply information. Case-based learning, clinical simulations, practice and feedback are identified as effective educational techniques. Didactic techniques that involve passive instruction, such as reading or lecture, have been found to have little or no impact on learning outcomes. Repetitive interventions, rather than single interventions, were shown to be superior for learning outcomes. Settings similar to the workplace improved skill acquisition and performance. Computer-based learning can be equally or more effective than live instruction and more cost efficient if effective techniques are used. Effective techniques can lead to improvements in knowledge and skill outcomes and clinical practice behaviours, but there is less evidence directly linking CPE to improved clinical outcomes. Very limited quality data are available from low- to middle-income countries. Educational techniques are critical to learning outcomes. Targeted, repetitive interventions can result in better learning outcomes. Setting should be selected to support relevant and realistic practice and increase efficiency. Media should be selected based on the potential to support effective educational techniques and efficiency of instruction. CPE can lead to improved learning outcomes if effective techniques are used. Limited data indicate that there may also be an effect on improving clinical practice behaviours. The research agenda calls for well-constructed evaluations of culturally appropriate combinations of technique, setting, frequency and media, developed for and tested among all levels of health workers in low- and middle-income countries.

Effective in-service training design and delivery: evidence from an integrative literature review

PubMed Central

2013-01-01

Background In-service training represents a significant financial investment for supporting continued competence of the health care workforce. An integrative review of the education and training literature was conducted to identify effective training approaches for health worker continuing professional education (CPE) and what evidence exists of outcomes derived from CPE. Methods A literature review was conducted from multiple databases including PubMed, the Cochrane Library and Cumulative Index to Nursing and Allied Health Literature (CINAHL) between May and June 2011. The initial review of titles and abstracts produced 244 results. Articles selected for analysis after two quality reviews consisted of systematic reviews, randomized controlled trials (RCTs) and programme evaluations published in peer-reviewed journals from 2000 to 2011 in the English language. The articles analysed included 37 systematic reviews and 32 RCTs. The research questions focused on the evidence supporting educational techniques, frequency, setting and media used to deliver instruction for continuing health professional education. Results The evidence suggests the use of multiple techniques that allow for interaction and enable learners to process and apply information. Case-based learning, clinical simulations, practice and feedback are identified as effective educational techniques. Didactic techniques that involve passive instruction, such as reading or lecture, have been found to have little or no impact on learning outcomes. Repetitive interventions, rather than single interventions, were shown to be superior for learning outcomes. Settings similar to the workplace improved skill acquisition and performance. Computer-based learning can be equally or more effective than live instruction and more cost efficient if effective techniques are used. Effective techniques can lead to improvements in knowledge and skill outcomes and clinical practice behaviours, but there is less evidence directly linking CPE to improved clinical outcomes. Very limited quality data are available from low- to middle-income countries. Conclusions Educational techniques are critical to learning outcomes. Targeted, repetitive interventions can result in better learning outcomes. Setting should be selected to support relevant and realistic practice and increase efficiency. Media should be selected based on the potential to support effective educational techniques and efficiency of instruction. CPE can lead to improved learning outcomes if effective techniques are used. Limited data indicate that there may also be an effect on improving clinical practice behaviours. The research agenda calls for well-constructed evaluations of culturally appropriate combinations of technique, setting, frequency and media, developed for and tested among all levels of health workers in low- and middle-income countries. PMID:24083659

Item-specific and generalization effects on brain activation when learning Chinese characters

PubMed Central

Deng, Yuan; Booth, James R.; Chou, Tai-Li; Ding, Guo-Sheng; Peng, Dan-Ling

2009-01-01

Neural changes related to learning of the meaning of Chinese characters in English speakers were examined using functional magnetic resonance imaging (fMRI). We examined item specific learning effects for trained characters, but also the generalization of semantic knowledge to novel transfer characters that shared a semantic radical (part of a character that gives a clue to word meaning, e.g. water for lake) with trained characters. Behavioral results show that acquired semantic knowledge improves performance for both trained and transfer characters. Neuroimaging results show that the left fusiform gyrus plays a central role in the visual processing of orthographic information in characters. The left superior parietal cortex seems to play a crucial role in learning the visual–spatial aspects of the characters because it shows learning related decreases for trained characters, is correlated with behavioral improvement from early to late in learning for the trained characters, and is correlated with better long-term retention for the transfer characters. The inferior frontal gyrus seems to be associated with the efficiency of retrieving and manipulating semantic representations because there are learning related decreases for trained characters and this decrease is correlated with greater behavioral improvement from early to late in learning. PMID:18514678

A Multidisciplinary Self-Directed Learning Module Improves Knowledge of a Quality Improvement Instrument: The HEART Pathway.

PubMed

Hartman, Nicholas D; Harper, Erin N; Leppert, Lauren M; Browning, Brittany M; Askew, Kim; Manthey, David E; Mahler, Simon A

We created and tested an educational intervention to support implementation of an institution wide QI project (the HEART Pathway) designed to improve care for patients with acute chest pain. Although online learning modules have been shown effective in imparting knowledge regarding QI projects, it is unknown whether these modules are effective across specialties and healthcare professions. Participants, including nurses, advanced practice clinicians, house staff and attending physicians (N = 486), were enrolled into an online, self-directed learning course exploring the key concepts of the HEART Pathway. The module was completed by 97% of enrollees (469/486) and 90% passed on the first attempt (422/469). Out of 469 learners, 323 completed the pretest, learning module and posttest in the correct order. Mean test scores across learners improved significantly from 74% to 89% from the pretest to the posttest. Following the intervention, the HEART Pathway was used for 88% of patients presenting to our institution with acute chest pain. Our data demonstrate that this online, self-directed learning module can improve knowledge of the HEART Pathway across specialties-paving the way for more efficient and informed care for acute chest pain patients.

Sparsity-promoting orthogonal dictionary updating for image reconstruction from highly undersampled magnetic resonance data.

PubMed

Huang, Jinhong; Guo, Li; Feng, Qianjin; Chen, Wufan; Feng, Yanqiu

2015-07-21

Image reconstruction from undersampled k-space data accelerates magnetic resonance imaging (MRI) by exploiting image sparseness in certain transform domains. Employing image patch representation over a learned dictionary has the advantage of being adaptive to local image structures and thus can better sparsify images than using fixed transforms (e.g. wavelets and total variations). Dictionary learning methods have recently been introduced to MRI reconstruction, and these methods demonstrate significantly reduced reconstruction errors compared to sparse MRI reconstruction using fixed transforms. However, the synthesis sparse coding problem in dictionary learning is NP-hard and computationally expensive. In this paper, we present a novel sparsity-promoting orthogonal dictionary updating method for efficient image reconstruction from highly undersampled MRI data. The orthogonality imposed on the learned dictionary enables the minimization problem in the reconstruction to be solved by an efficient optimization algorithm which alternately updates representation coefficients, orthogonal dictionary, and missing k-space data. Moreover, both sparsity level and sparse representation contribution using updated dictionaries gradually increase during iterations to recover more details, assuming the progressively improved quality of the dictionary. Simulation and real data experimental results both demonstrate that the proposed method is approximately 10 to 100 times faster than the K-SVD-based dictionary learning MRI method and simultaneously improves reconstruction accuracy.

Machine Learning-based Intelligent Formal Reasoning and Proving System

NASA Astrophysics Data System (ADS)

Chen, Shengqing; Huang, Xiaojian; Fang, Jiaze; Liang, Jia

2018-03-01

The reasoning system can be used in many fields. How to improve reasoning efficiency is the core of the design of system. Through the formal description of formal proof and the regular matching algorithm, after introducing the machine learning algorithm, the system of intelligent formal reasoning and verification has high efficiency. The experimental results show that the system can verify the correctness of propositional logic reasoning and reuse the propositional logical reasoning results, so as to obtain the implicit knowledge in the knowledge base and provide the basic reasoning model for the construction of intelligent system.

Drosophila learn efficient paths to a food source.

PubMed

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

2016-05-01

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

Improving Efficiency and Quality of the Children's ASD Diagnostic Pathway: Lessons Learned from Practice

ERIC Educational Resources Information Center

Rutherford, Marion; Burns, Morag; Gray, Duncan; Bremner, Lynne; Clegg, Sarah; Russell, Lucy; Smith, Charlie; O'Hare, Anne

2018-01-01

The 'autism diagnosis crisis' and long waiting times for assessment are as yet unresolved, leading to undue stress and limiting access to effective support. There is therefore a significant need for evidence to support practitioners in the development of efficient services, delivering acceptable waiting times and effectively meeting guideline…

Self-Paced Prioritized Curriculum Learning With Coverage Penalty in Deep Reinforcement Learning.

PubMed

Ren, Zhipeng; Dong, Daoyi; Li, Huaxiong; Chen, Chunlin; Zhipeng Ren; Daoyi Dong; Huaxiong Li; Chunlin Chen; Dong, Daoyi; Li, Huaxiong; Chen, Chunlin; Ren, Zhipeng

2018-06-01

In this paper, a new training paradigm is proposed for deep reinforcement learning using self-paced prioritized curriculum learning with coverage penalty. The proposed deep curriculum reinforcement learning (DCRL) takes the most advantage of experience replay by adaptively selecting appropriate transitions from replay memory based on the complexity of each transition. The criteria of complexity in DCRL consist of self-paced priority as well as coverage penalty. The self-paced priority reflects the relationship between the temporal-difference error and the difficulty of the current curriculum for sample efficiency. The coverage penalty is taken into account for sample diversity. With comparison to deep Q network (DQN) and prioritized experience replay (PER) methods, the DCRL algorithm is evaluated on Atari 2600 games, and the experimental results show that DCRL outperforms DQN and PER on most of these games. More results further show that the proposed curriculum training paradigm of DCRL is also applicable and effective for other memory-based deep reinforcement learning approaches, such as double DQN and dueling network. All the experimental results demonstrate that DCRL can achieve improved training efficiency and robustness for deep reinforcement learning.

Video Self-Modeling Technique That Can Be Used in Improving the Abilities of Fluent Reading and Fluent Speaking

ERIC Educational Resources Information Center

Sen, Ülker

2016-01-01

The use of technology in the field of education makes the educational process more efficient and motivating. Technological tools are used for developing the communication skills of students and teachers in the learning process increasing the participation, supporting the peer, the realization of collaborative learning. The use of technology is…

Research on particle swarm optimization algorithm based on optimal movement probability

NASA Astrophysics Data System (ADS)

Ma, Jianhong; Zhang, Han; He, Baofeng

2017-01-01

The particle swarm optimization algorithm to improve the control precision, and has great application value training neural network and fuzzy system control fields etc.The traditional particle swarm algorithm is used for the training of feed forward neural networks,the search efficiency is low, and easy to fall into local convergence.An improved particle swarm optimization algorithm is proposed based on error back propagation gradient descent. Particle swarm optimization for Solving Least Squares Problems to meme group, the particles in the fitness ranking, optimization problem of the overall consideration, the error back propagation gradient descent training BP neural network, particle to update the velocity and position according to their individual optimal and global optimization, make the particles more to the social optimal learning and less to its optimal learning, it can avoid the particles fall into local optimum, by using gradient information can accelerate the PSO local search ability, improve the multi beam particle swarm depth zero less trajectory information search efficiency, the realization of improved particle swarm optimization algorithm. Simulation results show that the algorithm in the initial stage of rapid convergence to the global optimal solution can be near to the global optimal solution and keep close to the trend, the algorithm has faster convergence speed and search performance in the same running time, it can improve the convergence speed of the algorithm, especially the later search efficiency.

Social learning and evolution: the cultural intelligence hypothesis

PubMed Central

van Schaik, Carel P.; Burkart, Judith M.

2011-01-01

If social learning is more efficient than independent individual exploration, animals should learn vital cultural skills exclusively, and routine skills faster, through social learning, provided they actually use social learning preferentially. Animals with opportunities for social learning indeed do so. Moreover, more frequent opportunities for social learning should boost an individual's repertoire of learned skills. This prediction is confirmed by comparisons among wild great ape populations and by social deprivation and enculturation experiments. These findings shaped the cultural intelligence hypothesis, which complements the traditional benefit hypotheses for the evolution of intelligence by specifying the conditions in which these benefits can be reaped. The evolutionary version of the hypothesis argues that species with frequent opportunities for social learning should more readily respond to selection for a greater number of learned skills. Because improved social learning also improves asocial learning, the hypothesis predicts a positive interspecific correlation between social-learning performance and individual learning ability. Variation among primates supports this prediction. The hypothesis also predicts that more heavily cultural species should be more intelligent. Preliminary tests involving birds and mammals support this prediction too. The cultural intelligence hypothesis can also account for the unusual cognitive abilities of humans, as well as our unique mechanisms of skill transfer. PMID:21357223

Social learning and evolution: the cultural intelligence hypothesis.

PubMed

van Schaik, Carel P; Burkart, Judith M

2011-04-12

If social learning is more efficient than independent individual exploration, animals should learn vital cultural skills exclusively, and routine skills faster, through social learning, provided they actually use social learning preferentially. Animals with opportunities for social learning indeed do so. Moreover, more frequent opportunities for social learning should boost an individual's repertoire of learned skills. This prediction is confirmed by comparisons among wild great ape populations and by social deprivation and enculturation experiments. These findings shaped the cultural intelligence hypothesis, which complements the traditional benefit hypotheses for the evolution of intelligence by specifying the conditions in which these benefits can be reaped. The evolutionary version of the hypothesis argues that species with frequent opportunities for social learning should more readily respond to selection for a greater number of learned skills. Because improved social learning also improves asocial learning, the hypothesis predicts a positive interspecific correlation between social-learning performance and individual learning ability. Variation among primates supports this prediction. The hypothesis also predicts that more heavily cultural species should be more intelligent. Preliminary tests involving birds and mammals support this prediction too. The cultural intelligence hypothesis can also account for the unusual cognitive abilities of humans, as well as our unique mechanisms of skill transfer.

Imitation learning based on an intrinsic motivation mechanism for efficient coding

PubMed Central

Triesch, Jochen

2013-01-01

A hypothesis regarding the development of imitation learning is presented that is rooted in intrinsic motivations. It is derived from a recently proposed form of intrinsically motivated learning (IML) for efficient coding in active perception, wherein an agent learns to perform actions with its sense organs to facilitate efficient encoding of the sensory data. To this end, actions of the sense organs that improve the encoding of the sensory data trigger an internally generated reinforcement signal. Here it is argued that the same IML mechanism might also support the development of imitation when general actions beyond those of the sense organs are considered: The learner first observes a tutor performing a behavior and learns a model of the the behavior's sensory consequences. The learner then acts itself and receives an internally generated reinforcement signal reflecting how well the sensory consequences of its own behavior are encoded by the sensory model. Actions that are more similar to those of the tutor will lead to sensory signals that are easier to encode and produce a higher reinforcement signal. Through this, the learner's behavior is progressively tuned to make the sensory consequences of its actions match the learned sensory model. I discuss this mechanism in the context of human language acquisition and bird song learning where similar ideas have been proposed. The suggested mechanism also offers an account for the development of mirror neurons and makes a number of predictions. Overall, it establishes a connection between principles of efficient coding, intrinsic motivations and imitation. PMID:24204350

Developing an eLearning tool formalizing in YAWL the guidelines used in a transfusion medicine service.

PubMed

Russo, Paola; Piazza, Miriam; Leonardi, Giorgio; Roncoroni, Layla; Russo, Carlo; Spadaro, Salvatore; Quaglini, Silvana

2012-01-01

The blood transfusion is a complex activity subject to a high risk of eventually fatal errors. The development and application of computer-based systems could help reducing the error rate, playing a fundamental role in the improvement of the quality of care. This poster presents an under development eLearning tool formalizing the guidelines of the transfusion process. This system, implemented in YAWL (Yet Another Workflow Language), will be used to train the personnel in order to improve the efficiency of care and to reduce errors.

Improving performance through concept formation and conceptual clustering

NASA Technical Reports Server (NTRS)

Fisher, Douglas H.

1992-01-01

Research from June 1989 through October 1992 focussed on concept formation, clustering, and supervised learning for purposes of improving the efficiency of problem-solving, planning, and diagnosis. These projects resulted in two dissertations on clustering, explanation-based learning, and means-ends planning, and publications in conferences and workshops, several book chapters, and journals; a complete Bibliography of NASA Ames supported publications is included. The following topics are studied: clustering of explanations and problem-solving experiences; clustering and means-end planning; and diagnosis of space shuttle and space station operating modes.

Aggregative Learning Method and Its Application for Communication Quality Evaluation

NASA Astrophysics Data System (ADS)

Akhmetov, Dauren F.; Kotaki, Minoru

2007-12-01

In this paper, so-called Aggregative Learning Method (ALM) is proposed to improve and simplify the learning and classification abilities of different data processing systems. It provides a universal basis for design and analysis of mathematical models of wide class. A procedure was elaborated for time series model reconstruction and analysis for linear and nonlinear cases. Data approximation accuracy (during learning phase) and data classification quality (during recall phase) are estimated from introduced statistic parameters. The validity and efficiency of the proposed approach have been demonstrated through its application for monitoring of wireless communication quality, namely, for Fixed Wireless Access (FWA) system. Low memory and computation resources were shown to be needed for the procedure realization, especially for data classification (recall) stage. Characterized with high computational efficiency and simple decision making procedure, the derived approaches can be useful for simple and reliable real-time surveillance and control system design.

Collaborative learning using nursing student dyads in the clinical setting.

PubMed

Austria, Mary Jean; Baraki, Katie; Doig, Alexa K

2013-05-04

Formal pairing of student nurses to work collaboratively on one patient assignment is a strategy for improving the quality and efficiency of clinical instruction while better utilizing the limited resources at clinical agencies. The aim of this qualitative study was to explore the student nurse and patient experiences of collaborative learning when peer dyads are used in clinical nursing education. Interviews were conducted with 11 students and 9 patients. Students described the process of collaborative learning as information sharing, cross-checking when making clinical decisions, and group processing when assessing the outcomes of nursing interventions. Positive outcomes reported by students and patients included reduced student anxiety, increased confidence and task efficiency. Students' primary concern was reduced opportunity to perform hands-on skills which had to be negotiated within each dyad. Meeting the present and future challenges of educating nurses will require innovative models of clinical instruction such as collaborative learning using student peer dyads.

Filtering sensory information with XCSF: improving learning robustness and robot arm control performance.

PubMed

Kneissler, Jan; Stalph, Patrick O; Drugowitsch, Jan; Butz, Martin V

2014-01-01

It has been shown previously that the control of a robot arm can be efficiently learned using the XCSF learning classifier system, which is a nonlinear regression system based on evolutionary computation. So far, however, the predictive knowledge about how actual motor activity changes the state of the arm system has not been exploited. In this paper, we utilize the forward velocity kinematics knowledge of XCSF to alleviate the negative effect of noisy sensors for successful learning and control. We incorporate Kalman filtering for estimating successive arm positions, iteratively combining sensory readings with XCSF-based predictions of hand position changes over time. The filtered arm position is used to improve both trajectory planning and further learning of the forward velocity kinematics. We test the approach on a simulated kinematic robot arm model. The results show that the combination can improve learning and control performance significantly. However, it also shows that variance estimates of XCSF prediction may be underestimated, in which case self-delusional spiraling effects can hinder effective learning. Thus, we introduce a heuristic parameter, which can be motivated by theory, and which limits the influence of XCSF's predictions on its own further learning input. As a result, we obtain drastic improvements in noise tolerance, allowing the system to cope with more than 10 times higher noise levels.

Immune allied genetic algorithm for Bayesian network structure learning

NASA Astrophysics Data System (ADS)

Song, Qin; Lin, Feng; Sun, Wei; Chang, KC

2012-06-01

Bayesian network (BN) structure learning is a NP-hard problem. In this paper, we present an improved approach to enhance efficiency of BN structure learning. To avoid premature convergence in traditional single-group genetic algorithm (GA), we propose an immune allied genetic algorithm (IAGA) in which the multiple-population and allied strategy are introduced. Moreover, in the algorithm, we apply prior knowledge by injecting immune operator to individuals which can effectively prevent degeneration. To illustrate the effectiveness of the proposed technique, we present some experimental results.

[Gender-dependent effects of histone deacetylase inhibitor sodium valproate on early olfactory learning in 129Sv mice].

PubMed

Burenkova, O V; Aleksandrova, E A; Zaraĭskaia, I Iu

2013-02-01

In the brain, histone acetylation underlies both learning and the maintenance of long-term sustained effects of early experience which is further epigenetically inherited. However, the role of acetylation in learning previously has only been studied in adult animals: high level of learning could be dependent on high levels of histone H3 acetylation in the brain. The role of acetylation in the mechanisms of early learning has not been studied. In the present work, we were interested whether histone deacetylase inhibitor sodium valproate which increases the level of histone H3 acetylation will affect early olfactory discrimination learning in 8-day-old pups of 129Sv mice that are characterized by low efficiency of learning with imitation of maternal grooming. Multiple valproate injections from 3rd to 6th postnatal day had a gender-dependent effect: learning was selectively improved in male but not in female pups. In the female pups, learning improvement was observed after multiple injections of saline. Possible epigenetic mechanisms underlying these sex differences are discussed.

Improving The Quality of Education through School-Based Management: Learning from International Experiences

ERIC Educational Resources Information Center

De Grauwe, Anton

2005-01-01

School-based management is being increasingly advocated as a shortcut to more efficient management and quality improvement in education. Research, however, has been unable to prove conclusively such a linkage. Especially in developing countries, concerns remain about the possible detrimental impact of school-based management on school quality;…

Role of Institutions of Higher Learning in Enhancing Sustainable Development in Kenya

ERIC Educational Resources Information Center

Ekene, Osuji Gregory; Oluoch-Suleh, Everlyn

2015-01-01

Education brings about a change in the individual which promotes greater productivity and work efficiency. It remains a major component in the development of human resources and it accounts for much improvements in population quality and environmental resource management; hence, sustainable development. Improvement of human resources is not…

Sentiment classification technology based on Markov logic networks

NASA Astrophysics Data System (ADS)

He, Hui; Li, Zhigang; Yao, Chongchong; Zhang, Weizhe

2016-07-01

With diverse online media emerging, there is a growing concern of sentiment classification problem. At present, text sentiment classification mainly utilizes supervised machine learning methods, which feature certain domain dependency. On the basis of Markov logic networks (MLNs), this study proposed a cross-domain multi-task text sentiment classification method rooted in transfer learning. Through many-to-one knowledge transfer, labeled text sentiment classification, knowledge was successfully transferred into other domains, and the precision of the sentiment classification analysis in the text tendency domain was improved. The experimental results revealed the following: (1) the model based on a MLN demonstrated higher precision than the single individual learning plan model. (2) Multi-task transfer learning based on Markov logical networks could acquire more knowledge than self-domain learning. The cross-domain text sentiment classification model could significantly improve the precision and efficiency of text sentiment classification.

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.

Adaptive design of an X-ray magnetic circular dichroism spectroscopy experiment with Gaussian process modelling

NASA Astrophysics Data System (ADS)

Ueno, Tetsuro; Hino, Hideitsu; Hashimoto, Ai; Takeichi, Yasuo; Sawada, Masahiro; Ono, Kanta

2018-01-01

Spectroscopy is a widely used experimental technique, and enhancing its efficiency can have a strong impact on materials research. We propose an adaptive design for spectroscopy experiments that uses a machine learning technique to improve efficiency. We examined X-ray magnetic circular dichroism (XMCD) spectroscopy for the applicability of a machine learning technique to spectroscopy. An XMCD spectrum was predicted by Gaussian process modelling with learning of an experimental spectrum using a limited number of observed data points. Adaptive sampling of data points with maximum variance of the predicted spectrum successfully reduced the total data points for the evaluation of magnetic moments while providing the required accuracy. The present method reduces the time and cost for XMCD spectroscopy and has potential applicability to various spectroscopies.

A diagram retrieval method with multi-label learning

NASA Astrophysics Data System (ADS)

Fu, Songping; Lu, Xiaoqing; Liu, Lu; Qu, Jingwei; Tang, Zhi

2015-01-01

In recent years, the retrieval of plane geometry figures (PGFs) has attracted increasing attention in the fields of mathematics education and computer science. However, the high cost of matching complex PGF features leads to the low efficiency of most retrieval systems. This paper proposes an indirect classification method based on multi-label learning, which improves retrieval efficiency by reducing the scope of compare operation from the whole database to small candidate groups. Label correlations among PGFs are taken into account for the multi-label classification task. The primitive feature selection for multi-label learning and the feature description of visual geometric elements are conducted individually to match similar PGFs. The experiment results show the competitive performance of the proposed method compared with existing PGF retrieval methods in terms of both time consumption and retrieval quality.

Orthopaedic resident preparedness for closed reduction and pinning of pediatric supracondylar fractures is improved by e-learning: a multisite randomized controlled study.

PubMed

Hearty, Thomas; Maizels, Max; Pring, Maya; Mazur, John; Liu, Raymond; Sarwark, John; Janicki, Joseph

2013-09-04

There is a need to provide more efficient surgical training methods for orthopaedic residents. E-learning could possibly increase resident surgical preparedness, confidence, and comfort for surgery. Using closed reduction and pinning of pediatric supracondylar humeral fractures as the index case, we hypothesized that e-learning could increase resident knowledge acquisition for case preparation in the operating room. An e-learning surgical training module was created on the Computer Enhanced Visual Learning platform. The module provides a detailed and focused road map of the procedure utilizing a multimedia format. A multisite prospective randomized controlled study design compared residents who used a textbook for case preparation (control group) with residents who used the same textbook plus completed the e-learning module (test group). All subjects completed a sixty-question test on the theory and methods of the case. After completion of the test, the control group then completed the module as well. All subjects were surveyed on their opinion regarding the effectiveness of the module after performing an actual surgical case. Twenty-eight subjects with no previous experience in this surgery were enrolled at four academic centers. Subjects were randomized into two equal groups. The test group scored significantly better (p < 0.001) and demonstrated competence on the test compared with the control group; the mean correct test score (and standard deviation) was 90.9% ± 6.8% for the test group and 73.5% ± 6.4% for the control group. All residents surveyed (n = 27) agreed that the module is a useful supplement to traditional methods for case preparation and twenty-two of twenty-seven residents agreed that it reduced their anxiety during the case and improved their attention to surgical detail. E-learning using the Computer Enhanced Visual Learning platform significantly improved preparedness, confidence, and comfort with percutaneous closed reduction and pinning of a pediatric supracondylar humeral fracture. We believe that adapting such methods into residency training programs will improve efficiency in surgical training.

The Role of Cooperative Learning Type Team Assisted Individualization to Improve the Students' Mathematics Communication Ability in the Subject of Probability Theory

ERIC Educational Resources Information Center

Tinungki, Georgina Maria

2015-01-01

The importance of learning mathematics can not be separated from its role in all aspects of life. Communicating ideas by using mathematics language is even more practical, systematic, and efficient. In order to overcome the difficulties of students who have insufficient understanding of mathematics material, good communications should be built in…

The Role of Independent Activities in Development of Strategic Learning Competences and Increase of School Performance Level, within the Study of High School Pedagogy

ERIC Educational Resources Information Center

Anca, Monica-Iuliana; Bocos, Musata

2017-01-01

The experimental research performed by us with the purpose of exploring the possibilities of development of strategic learning competences and improvement of school performance of 11th grade students, pedagogical profile, specialisation in primary school-kindergarten teacher, falls in the category of researches aiming to make efficient certain…

Semi-supervised learning for photometric supernova classification

NASA Astrophysics Data System (ADS)

Richards, Joseph W.; Homrighausen, Darren; Freeman, Peter E.; Schafer, Chad M.; Poznanski, Dovi

2012-01-01

We present a semi-supervised method for photometric supernova typing. Our approach is to first use the non-linear dimension reduction technique diffusion map to detect structure in a data base of supernova light curves and subsequently employ random forest classification on a spectroscopically confirmed training set to learn a model that can predict the type of each newly observed supernova. We demonstrate that this is an effective method for supernova typing. As supernova numbers increase, our semi-supervised method efficiently utilizes this information to improve classification, a property not enjoyed by template-based methods. Applied to supernova data simulated by Kessler et al. to mimic those of the Dark Energy Survey, our methods achieve (cross-validated) 95 per cent Type Ia purity and 87 per cent Type Ia efficiency on the spectroscopic sample, but only 50 per cent Type Ia purity and 50 per cent efficiency on the photometric sample due to their spectroscopic follow-up strategy. To improve the performance on the photometric sample, we search for better spectroscopic follow-up procedures by studying the sensitivity of our machine-learned supernova classification on the specific strategy used to obtain training sets. With a fixed amount of spectroscopic follow-up time, we find that, despite collecting data on a smaller number of supernovae, deeper magnitude-limited spectroscopic surveys are better for producing training sets. For supernova Ia (II-P) typing, we obtain a 44 per cent (1 per cent) increase in purity to 72 per cent (87 per cent) and 30 per cent (162 per cent) increase in efficiency to 65 per cent (84 per cent) of the sample using a 25th (24.5th) magnitude-limited survey instead of the shallower spectroscopic sample used in the original simulations. When redshift information is available, we incorporate it into our analysis using a novel method of altering the diffusion map representation of the supernovae. Incorporating host redshifts leads to a 5 per cent improvement in Type Ia purity and 13 per cent improvement in Type Ia efficiency. A web service for the supernova classification method used in this paper can be found at .

Overview of Heat Addition and Efficiency Predictions for an Advanced Stirling Convertor

NASA Technical Reports Server (NTRS)

Wilson, Scott D.; Reid, Terry; Schifer, Nicholas; Briggs, Maxwell

2011-01-01

Past methods of predicting net heat input needed to be validated. Validation effort pursued with several paths including improving model inputs, using test hardware to provide validation data, and validating high fidelity models. Validation test hardware provided direct measurement of net heat input for comparison to predicted values. Predicted value of net heat input was 1.7 percent less than measured value and initial calculations of measurement uncertainty were 2.1 percent (under review). Lessons learned during validation effort were incorporated into convertor modeling approach which improved predictions of convertor efficiency.

Multimedia learning for increasing knowledge on energy efficiency and promotion of proenvironmental behavior: A study of undergraduate students in Costa Rica

NASA Astrophysics Data System (ADS)

Walsh-Zuniga, Yoselyn

Promotion of energy efficiency practices among household has been employed in many interventions with a varying degree of success, mainly on developed countries. The purpose of the study is to promote and measure knowledge of proenvironmental behavior in undergraduate students in the Costa Rica Institute of Technology. The intervention used for this purpose provided personal and altruistic information about the impact of energy consumption activities in household. People's perceptions and attitudes about behaviors that contribute and mitigate climate change were also investigated. Participants were students from undergraduate programs who are also inhabitants of the residence hall provided by the institution. The participation consisted in two surveys and a learning module. Students responded a survey before and after exposure to a learning module. Surveys focused on identifying knowledge, attitudes and intentions. The learning module provided information about three hypothetical scenarios and corresponding energy consumption estimates for each one. Participants did not significantly improve their knowledge on energy efficiency topics and did not change perceptions about the topic of climate change. Yet for both, knowledge and perceptions, participants demonstrated an average knowledge on topics associated to climate change. In addition, participants did not use technical information to explain concepts and perceptions. Another important finding was that participants wrote their responses more third-person than in first person singular or plural, meaning that, excluding themselves from the solution and the problem. Results suggest that there is an average knowledge among participants about 2.5 out of 5 points that represent a start point to design more successful interventions that promote energy efficiency behaviors. A major recommendation to improve energy efficiency behaviors is to place a greater emphasis and awareness in personal consequences of the misuse of energy in household as part of future interventions. More studies based on real consumption data along with more engaging visualizations are highly encouraged.

Efficient differentially private learning improves drug sensitivity prediction.

PubMed

Honkela, Antti; Das, Mrinal; Nieminen, Arttu; Dikmen, Onur; Kaski, Samuel

2018-02-06

Users of a personalised recommendation system face a dilemma: recommendations can be improved by learning from data, but only if other users are willing to share their private information. Good personalised predictions are vitally important in precision medicine, but genomic information on which the predictions are based is also particularly sensitive, as it directly identifies the patients and hence cannot easily be anonymised. Differential privacy has emerged as a potentially promising solution: privacy is considered sufficient if presence of individual patients cannot be distinguished. However, differentially private learning with current methods does not improve predictions with feasible data sizes and dimensionalities. We show that useful predictors can be learned under powerful differential privacy guarantees, and even from moderately-sized data sets, by demonstrating significant improvements in the accuracy of private drug sensitivity prediction with a new robust private regression method. Our method matches the predictive accuracy of the state-of-the-art non-private lasso regression using only 4x more samples under relatively strong differential privacy guarantees. Good performance with limited data is achieved by limiting the sharing of private information by decreasing the dimensionality and by projecting outliers to fit tighter bounds, therefore needing to add less noise for equal privacy. The proposed differentially private regression method combines theoretical appeal and asymptotic efficiency with good prediction accuracy even with moderate-sized data. As already the simple-to-implement method shows promise on the challenging genomic data, we anticipate rapid progress towards practical applications in many fields. This article was reviewed by Zoltan Gaspari and David Kreil.

Machine Learning for Social Services: A Study of Prenatal Case Management in Illinois.

PubMed

Pan, Ian; Nolan, Laura B; Brown, Rashida R; Khan, Romana; van der Boor, Paul; Harris, Daniel G; Ghani, Rayid

2017-06-01

To evaluate the positive predictive value of machine learning algorithms for early assessment of adverse birth risk among pregnant women as a means of improving the allocation of social services. We used administrative data for 6457 women collected by the Illinois Department of Human Services from July 2014 to May 2015 to develop a machine learning model for adverse birth prediction and improve upon the existing paper-based risk assessment. We compared different models and determined the strongest predictors of adverse birth outcomes using positive predictive value as the metric for selection. Machine learning algorithms performed similarly, outperforming the current paper-based risk assessment by up to 36%; a refined paper-based assessment outperformed the current assessment by up to 22%. We estimate that these improvements will allow 100 to 170 additional high-risk pregnant women screened for program eligibility each year to receive services that would have otherwise been unobtainable. Our analysis exhibits the potential for machine learning to move government agencies toward a more data-informed approach to evaluating risk and providing social services. Overall, such efforts will improve the efficiency of allocating resource-intensive interventions.

Today's Leaders for a Sustainable Tomorrow

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

Wood, Bryan

2013-02-27

Today's Leaders for a Sustainable Tomorrow is a collaboration of five residential environmental learning centers (Audubon Center of the North Woods, Deep Portage Learning Center, Laurentian Environmental Center, Long Lake Conservation Center and Wolf Ridge Environmental Learning Center) that together increased energy efficiency, energy conservation and renewable energy technologies through a number of different means appropriate for each unique center. For energy efficiency upgrades the centers installed envelope improvements to seal air barriers through better insulation in walls, ceilings, windows, doors as well as the installation of more energy efficient windows, doors, lighting and air ventilation systems. Through energy sub-metermore » monitoring the centers are able to accurately chart the usage of energy at each of their campuses and eliminate unnecessary energy usage. Facilities reduced their dependence on fossil fuel energy sources through the installation of renewable energy technologies including wood gasification, solar domestic hot water, solar photovoltaic, solar air heat, geothermal heating and wind power. Centers also installed energy education displays on the specific renewable energy technologies used at the center.« less

Query-based learning for aerospace applications.

PubMed

Saad, E W; Choi, J J; Vian, J L; Wunsch, D C Ii

2003-01-01

Models of real-world applications often include a large number of parameters with a wide dynamic range, which contributes to the difficulties of neural network training. Creating the training data set for such applications becomes costly, if not impossible. In order to overcome the challenge, one can employ an active learning technique known as query-based learning (QBL) to add performance-critical data to the training set during the learning phase, thereby efficiently improving the overall learning/generalization. The performance-critical data can be obtained using an inverse mapping called network inversion (discrete network inversion and continuous network inversion) followed by oracle query. This paper investigates the use of both inversion techniques for QBL learning, and introduces an original heuristic to select the inversion target values for continuous network inversion method. Efficiency and generalization was further enhanced by employing node decoupled extended Kalman filter (NDEKF) training and a causality index (CI) as a means to reduce the input search dimensionality. The benefits of the overall QBL approach are experimentally demonstrated in two aerospace applications: a classification problem with large input space and a control distribution problem.

Improving education under work-hour restrictions: comparing learning and teaching preferences of faculty, residents, and students.

PubMed

Jack, Megan C; Kenkare, Sonya B; Saville, Benjamin R; Beidler, Stephanie K; Saba, Sam C; West, Alisha N; Hanemann, Michael S; van Aalst, John A

2010-01-01

Faced with work-hour restrictions, educators are mandated to improve the efficiency of resident and medical student education. Few studies have assessed learning styles in medicine; none have compared teaching and learning preferences. Validated tools exist to study these deficiencies. Kolb describes 4 learning styles: converging (practical), diverging (imaginative), assimilating (inductive), and accommodating (active). Grasha Teaching Styles are categorized into "clusters": 1 (teacher-centered, knowledge acquisition), 2 (teacher-centered, role modeling), 3 (student-centered, problem-solving), and 4 (student-centered, facilitative). Kolb's Learning Style Inventory (HayGroup, Philadelphia, Pennsylvania) and Grasha-Riechmann's TSS were administered to surgical faculty (n = 61), residents (n = 96), and medical students (n = 183) at a tertiary academic medical center, after informed consent was obtained (IRB # 06-0612). Statistical analysis was performed using χ(2) and Fisher exact tests. Surgical residents preferred active learning (p = 0.053), whereas faculty preferred reflective learning (p < 0.01). As a result of a comparison of teaching preferences, although both groups preferred student-centered, facilitative teaching, faculty preferred teacher-centered, role-modeling instruction (p = 0.02) more often. Residents had no dominant teaching style more often than surgical faculty (p = 0.01). Medical students preferred converging learning (42%) and cluster 4 teaching (35%). Statistical significance was unchanged when corrected for gender, resident training level, and subspecialization. Significant differences exist between faculty and residents in both learning and teaching preferences; this finding suggests inefficiency in resident education, as previous research suggests that learning styles parallel teaching styles. Absence of a predominant teaching style in residents suggests these individuals are learning to be teachers. The adaptation of faculty teaching methods to account for variations in resident learning styles may promote a better learning environment and more efficient faculty-resident interaction. Additional, multi-institutional studies using these tools are needed to elucidate these findings fully. Copyright © 2010 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights reserved.

The competitive imperative of learning.

PubMed

Edmondson, Amy C

2008-01-01

Most executives believe that relentless execution--efficient, timely, consistent production and delivery of goods or services--is the surefire path to customer satisfaction and positive financial results. But this is a myth in the knowledge economy, argues Edmondson, a Harvard Business School professor. She points to General Motors, which for years has remained wedded to a well-developed competency in centralized controls and efficient execution but has steadily lost ground, posting a record $38.7 billion loss in 2007. Such an execution-as-efficiency model results in employees who are exceedingly reluctant to offer ideas or voice questions and concerns. Placing value only on getting things right the first time, organizations are unable to take the risks necessary to improve and evolve. By contrast, firms that put a premium on what Edmondson calls execution-as-learning focus not so much on how a process should be carried out as on how it should evolve. Since 1980 General Electric, for instance, has continued to reinvent itself in every field from wind energy to medical diagnostics; and it enjoyed a $22.5 billion profit in 2007. Organizations that foster execution-as-learning provide employees with psychological safety. No one is penalized for asking for help or making a mistake. These companies also employ four distinct approaches to day-to-day work: They use the best available knowledge (which is understood to be a moving target) to inform the design of specific process guidelines. They encourage employee collaboration by making information available when and where it's needed. They routinely capture data on processes to discover how work really happens. Finally, they study these data in an effort to find ways to improve execution. Taken together, these practices form the basis of a learning infrastructure that makes continual learning part of business as usual.

Nonvolatile Memory Materials for Neuromorphic Intelligent Machines.

PubMed

Jeong, Doo Seok; Hwang, Cheol Seong

2018-04-18

Recent progress in deep learning extends the capability of artificial intelligence to various practical tasks, making the deep neural network (DNN) an extremely versatile hypothesis. While such DNN is virtually built on contemporary data centers of the von Neumann architecture, physical (in part) DNN of non-von Neumann architecture, also known as neuromorphic computing, can remarkably improve learning and inference efficiency. Particularly, resistance-based nonvolatile random access memory (NVRAM) highlights its handy and efficient application to the multiply-accumulate (MAC) operation in an analog manner. Here, an overview is given of the available types of resistance-based NVRAMs and their technological maturity from the material- and device-points of view. Examples within the strategy are subsequently addressed in comparison with their benchmarks (virtual DNN in deep learning). A spiking neural network (SNN) is another type of neural network that is more biologically plausible than the DNN. The successful incorporation of resistance-based NVRAM in SNN-based neuromorphic computing offers an efficient solution to the MAC operation and spike timing-based learning in nature. This strategy is exemplified from a material perspective. Intelligent machines are categorized according to their architecture and learning type. Also, the functionality and usefulness of NVRAM-based neuromorphic computing are addressed. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Building mental models by dissecting physical models.

PubMed

Srivastava, Anveshna

2016-01-01

When students build physical models from prefabricated components to learn about model systems, there is an implicit trade-off between the physical degrees of freedom in building the model and the intensity of instructor supervision needed. Models that are too flexible, permitting multiple possible constructions require greater supervision to ensure focused learning; models that are too constrained require less supervision, but can be constructed mechanically, with little to no conceptual engagement. We propose "model-dissection" as an alternative to "model-building," whereby instructors could make efficient use of supervisory resources, while simultaneously promoting focused learning. We report empirical results from a study conducted with biology undergraduate students, where we demonstrate that asking them to "dissect" out specific conceptual structures from an already built 3D physical model leads to a significant improvement in performance than asking them to build the 3D model from simpler components. Using questionnaires to measure understanding both before and after model-based interventions for two cohorts of students, we find that both the "builders" and the "dissectors" improve in the post-test, but it is the latter group who show statistically significant improvement. These results, in addition to the intrinsic time-efficiency of "model dissection," suggest that it could be a valuable pedagogical tool. © 2015 The International Union of Biochemistry and Molecular Biology.

Effective Physics Study Habits

NASA Astrophysics Data System (ADS)

Zettili, Nouredine

2011-04-01

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

Innovative and Alternative Technologies. Instructor Guide. Working for Clean Water: An Information Program for Advisory Groups.

ERIC Educational Resources Information Center

Cole, Charles A.

Innovative and alternative methods of wastewater treatment can improve the efficiency and lower the cost of waste treatment procedures. Described in this instructor's guide is a one-hour learning session for citizens interested in improving water quality planning and decision making. Among the topics covered are the need for alternative wastewater…

Using prospective hazard analysis to assess an active shooter emergency operations plan.

PubMed

Card, Alan J; Harrison, Heidi; Ward, James; Clarkson, P John

2012-01-01

Most risk management activity in the healthcare sector is retrospective, based on learning from experience. This is feasible where the risks are routine, but emergency operations plans (EOP) guide the response to events that are both high risk and rare. Under these circumstances, it is important to get the response right the first time, but learning from experience is usually not an option. This case study presents the rationale for taking a proactive approach to improving healthcare organizations' EOP. It demonstrates how the Prospective Hazard Analysis (PHA) Toolkit can drive organizational learning and argues that this toolkit may lead to more efficient improvement than drills and exercises. © 2012 American Society for Healthcare Risk Management of the American Hospital Association.

Marginal Space Deep Learning: Efficient Architecture for Volumetric Image Parsing.

PubMed

Ghesu, Florin C; Krubasik, Edward; Georgescu, Bogdan; Singh, Vivek; Yefeng Zheng; Hornegger, Joachim; Comaniciu, Dorin

2016-05-01

Robust and fast solutions for anatomical object detection and segmentation support the entire clinical workflow from diagnosis, patient stratification, therapy planning, intervention and follow-up. Current state-of-the-art techniques for parsing volumetric medical image data are typically based on machine learning methods that exploit large annotated image databases. Two main challenges need to be addressed, these are the efficiency in scanning high-dimensional parametric spaces and the need for representative image features which require significant efforts of manual engineering. We propose a pipeline for object detection and segmentation in the context of volumetric image parsing, solving a two-step learning problem: anatomical pose estimation and boundary delineation. For this task we introduce Marginal Space Deep Learning (MSDL), a novel framework exploiting both the strengths of efficient object parametrization in hierarchical marginal spaces and the automated feature design of Deep Learning (DL) network architectures. In the 3D context, the application of deep learning systems is limited by the very high complexity of the parametrization. More specifically 9 parameters are necessary to describe a restricted affine transformation in 3D, resulting in a prohibitive amount of billions of scanning hypotheses. The mechanism of marginal space learning provides excellent run-time performance by learning classifiers in clustered, high-probability regions in spaces of gradually increasing dimensionality. To further increase computational efficiency and robustness, in our system we learn sparse adaptive data sampling patterns that automatically capture the structure of the input. Given the object localization, we propose a DL-based active shape model to estimate the non-rigid object boundary. Experimental results are presented on the aortic valve in ultrasound using an extensive dataset of 2891 volumes from 869 patients, showing significant improvements of up to 45.2% over the state-of-the-art. To our knowledge, this is the first successful demonstration of the DL potential to detection and segmentation in full 3D data with parametrized representations.

Operating Room of the Future: Advanced Technologies in Safe and Efficient Operating Rooms

DTIC Science & Technology

2008-10-01

fit” or compatibility with different tasks. Ideally, the optimal match between tasks and well-designed display alternatives will be self -apparent...hierarchical display environment. The FARO robot arm is used as an accurate and reliable tracker to control a virtual camera. The virtual camera pose is...in learning outcomes due to self -feedback, improvements in learning outcomes due to instructor feedback and synchronous versus asynchronous

Microvascular Anastomosis: Proposition of a Learning Curve.

PubMed

Mokhtari, Pooneh; Tayebi Meybodi, Ali; Benet, Arnau; Lawton, Michael T

2018-04-14

Learning to perform a microvascular anastomosis is one of the most difficult tasks in cerebrovascular surgery. Previous studies offer little regarding the optimal protocols to maximize learning efficiency. This failure stems mainly from lack of knowledge about the learning curve of this task. To delineate this learning curve and provide information about its various features including acquisition, improvement, consistency, stability, and recall. Five neurosurgeons with an average surgical experience history of 5 yr and without any experience in bypass surgery performed microscopic anastomosis on progressively smaller-caliber silastic tubes (Biomet, Palm Beach Gardens, Florida) during 24 consecutive sessions. After a 1-, 2-, and 8-wk retention interval, they performed recall test on 0.7-mm silastic tubes. The anastomoses were rated based on anastomosis patency and presence of any leaks. Improvement rate was faster during initial sessions compared to the final practice sessions. Performance decline was observed in the first session of working on a smaller-caliber tube. However, this rapidly improved during the following sessions of practice. Temporary plateaus were seen in certain segments of the curve. The retention interval between the acquisition and recall phase did not cause a regression to the prepractice performance level. Learning the fine motor task of microvascular anastomosis adapts to the basic rules of learning such as the "power law of practice." Our results also support the improvement of performance during consecutive sessions of practice. The objective evidence provided may help in developing optimized learning protocols for microvascular anastomosis.

A Convex Formulation for Learning a Shared Predictive Structure from Multiple Tasks

PubMed Central

Chen, Jianhui; Tang, Lei; Liu, Jun; Ye, Jieping

2013-01-01

In this paper, we consider the problem of learning from multiple related tasks for improved generalization performance by extracting their shared structures. The alternating structure optimization (ASO) algorithm, which couples all tasks using a shared feature representation, has been successfully applied in various multitask learning problems. However, ASO is nonconvex and the alternating algorithm only finds a local solution. We first present an improved ASO formulation (iASO) for multitask learning based on a new regularizer. We then convert iASO, a nonconvex formulation, into a relaxed convex one (rASO). Interestingly, our theoretical analysis reveals that rASO finds a globally optimal solution to its nonconvex counterpart iASO under certain conditions. rASO can be equivalently reformulated as a semidefinite program (SDP), which is, however, not scalable to large datasets. We propose to employ the block coordinate descent (BCD) method and the accelerated projected gradient (APG) algorithm separately to find the globally optimal solution to rASO; we also develop efficient algorithms for solving the key subproblems involved in BCD and APG. The experiments on the Yahoo webpages datasets and the Drosophila gene expression pattern images datasets demonstrate the effectiveness and efficiency of the proposed algorithms and confirm our theoretical analysis. PMID:23520249

Online learning control using adaptive critic designs with sparse kernel machines.

PubMed

Xu, Xin; Hou, Zhongsheng; Lian, Chuanqiang; He, Haibo

2013-05-01

In the past decade, adaptive critic designs (ACDs), including heuristic dynamic programming (HDP), dual heuristic programming (DHP), and their action-dependent ones, have been widely studied to realize online learning control of dynamical systems. However, because neural networks with manually designed features are commonly used to deal with continuous state and action spaces, the generalization capability and learning efficiency of previous ACDs still need to be improved. In this paper, a novel framework of ACDs with sparse kernel machines is presented by integrating kernel methods into the critic of ACDs. To improve the generalization capability as well as the computational efficiency of kernel machines, a sparsification method based on the approximately linear dependence analysis is used. Using the sparse kernel machines, two kernel-based ACD algorithms, that is, kernel HDP (KHDP) and kernel DHP (KDHP), are proposed and their performance is analyzed both theoretically and empirically. Because of the representation learning and generalization capability of sparse kernel machines, KHDP and KDHP can obtain much better performance than previous HDP and DHP with manually designed neural networks. Simulation and experimental results of two nonlinear control problems, that is, a continuous-action inverted pendulum problem and a ball and plate control problem, demonstrate the effectiveness of the proposed kernel ACD methods.

Learning to assign binary weights to binary descriptor

NASA Astrophysics Data System (ADS)

Huang, Zhoudi; Wei, Zhenzhong; Zhang, Guangjun

2016-10-01

Constructing robust binary local feature descriptors are receiving increasing interest due to their binary nature, which can enable fast processing while requiring significantly less memory than their floating-point competitors. To bridge the performance gap between the binary and floating-point descriptors without increasing the computational cost of computing and matching, optimal binary weights are learning to assign to binary descriptor for considering each bit might contribute differently to the distinctiveness and robustness. Technically, a large-scale regularized optimization method is applied to learn float weights for each bit of the binary descriptor. Furthermore, binary approximation for the float weights is performed by utilizing an efficient alternatively greedy strategy, which can significantly improve the discriminative power while preserve fast matching advantage. Extensive experimental results on two challenging datasets (Brown dataset and Oxford dataset) demonstrate the effectiveness and efficiency of the proposed method.

Efficiency Improvement of Action Acquisition in Two-Link Robot Arm Using Fuzzy ART with Genetic Algorithm

NASA Astrophysics Data System (ADS)

Kotani, Naoki; Taniguchi, Kenji

An efficient learning method using Fuzzy ART with Genetic Algorithm is proposed. The proposed method reduces the number of trials by using a policy acquired in other tasks because a reinforcement learning needs a lot of the number of trials until an agent acquires appropriate actions. Fuzzy ART is an incremental unsupervised learning algorithm in responce to arbitrary sequences of analog or binary input vectors. Our proposed method gives a policy by crossover or mutation when an agent observes unknown states. Selection controls the category proliferation problem of Fuzzy ART. The effectiveness of the proposed method was verified with the simulation of the reaching problem for the two-link robot arm. The proposed method achieves a reduction of both the number of trials and the number of states.

Corporate Energy Conservation Program for Alcoa North American Extrusions

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

None

2001-08-01

This case study is the latest in a series on industrial firms who are implementing energy efficient technologies and system improvements into their manufacturing processes. The case studies document the activities, savings, and lessons learned on these projects.

Indoor airPLUS Videos, Podcasts, Webinars and Interviews

EPA Pesticide Factsheets

The Webinar presentations will help you discover how Indoor airPLUS homes are designed to improve indoor air quality and increase energy efficiency and learn about the key design and construction features included in Indoor airPLUS homes.

Monitoring and regulation of learning in medical education: the need for predictive cues.

PubMed

de Bruin, Anique B H; Dunlosky, John; Cavalcanti, Rodrigo B

2017-06-01

Being able to accurately monitor learning activities is a key element in self-regulated learning in all settings, including medical schools. Yet students' ability to monitor their progress is often limited, leading to inefficient use of study time. Interventions that improve the accuracy of students' monitoring can optimise self-regulated learning, leading to higher achievement. This paper reviews findings from cognitive psychology and explores potential applications in medical education, as well as areas for future research. Effective monitoring depends on students' ability to generate information ('cues') that accurately reflects their knowledge and skills. The ability of these 'cues' to predict achievement is referred to as 'cue diagnosticity'. Interventions that improve the ability of students to elicit predictive cues typically fall into two categories: (i) self-generation of cues and (ii) generation of cues that is delayed after self-study. Providing feedback and support is useful when cues are predictive but may be too complex to be readily used. Limited evidence exists about interventions to improve the accuracy of self-monitoring among medical students or trainees. Developing interventions that foster use of predictive cues can enhance the accuracy of self-monitoring, thereby improving self-study and clinical reasoning. First, insight should be gained into the characteristics of predictive cues used by medical students and trainees. Next, predictive cue prompts should be designed and tested to improve monitoring and regulation of learning. Finally, the use of predictive cues should be explored in relation to teaching and learning clinical reasoning. Improving self-regulated learning is important to help medical students and trainees efficiently acquire knowledge and skills necessary for clinical practice. Interventions that help students generate and use predictive cues hold the promise of improved self-regulated learning and achievement. This framework is applicable to learning in several areas, including the development of clinical reasoning. © 2017 The Authors Medical Education published by Association for the Study of Medical Education and John Wiley & Sons Ltd.

"Flipping" the introductory clerkship in radiology: impact on medical student performance and perceptions.

PubMed

Belfi, Lily M; Bartolotta, Roger J; Giambrone, Ashley E; Davi, Caryn; Min, Robert J

2015-06-01

Among methods of "blended learning" (ie, combining online modules with in-class instruction), the "flipped classroom" involves student preclass review of material while reserving class time for interactive knowledge application. We integrated blended learning methodology in a "flipped" introductory clerkship in radiology, and assessed the impact of this approach on the student educational experience (performance and perception). In preparation for the "flipped clerkship," radiology faculty and residents created e-learning modules that were uploaded to an open-source website. The clerkship's 101 rising third-year medical students were exposed to different teaching methods during the course, such as blended learning, traditional lecture learning, and independent learning. Students completed precourse and postcourse knowledge assessments and surveys. Student knowledge improved overall as a result of taking the course. Blended learning achieved greater pretest to post-test improvement of high statistical significance (P value, .0060) compared to lecture learning alone. Blended learning also achieved greater pretest to post-test improvement of borderline statistical significance (P value, .0855) in comparison to independent learning alone. The difference in effectiveness of independent learning versus lecture learning was not statistically significant (P value, .2730). Student perceptions of the online modules used in blended learning portions of the course were very positive. They specifically enjoyed the self-paced interactivity and the ability to return to the modules in the future. Blended learning can be successfully applied to the introductory clerkship in radiology. This teaching method offers educators an innovative and efficient approach to medical student education in radiology. Copyright © 2015 AUR. Published by Elsevier Inc. All rights reserved.

Facilitating mathematics learning for students with upper extremity disabilities using touch-input system.

PubMed

Choi, Kup-Sze; Chan, Tak-Yin

2015-03-01

To investigate the feasibility of using tablet device as user interface for students with upper extremity disabilities to input mathematics efficiently into computer. A touch-input system using tablet device as user interface was proposed to assist these students to write mathematics. User-switchable and context-specific keyboard layouts were designed to streamline the input process. The system could be integrated with conventional computer systems only with minor software setup. A two-week pre-post test study involving five participants was conducted to evaluate the performance of the system and collect user feedback. The mathematics input efficiency of the participants was found to improve during the experiment sessions. In particular, their performance in entering trigonometric expressions by using the touch-input system was significantly better than that by using conventional mathematics editing software with keyboard and mouse. The participants rated the touch-input system positively and were confident that they could operate at ease with more practice. The proposed touch-input system provides a convenient way for the students with hand impairment to write mathematics and has the potential to facilitate their mathematics learning. Implications for Rehabilitation Students with upper extremity disabilities often face barriers to learning mathematics which is largely based on handwriting. Conventional computer user interfaces are inefficient for them to input mathematics into computer. A touch-input system with context-specific and user-switchable keyboard layouts was designed to improve the efficiency of mathematics input. Experimental results and user feedback suggested that the system has the potential to facilitate mathematics learning for the students.

Magnetic Tunnel Junction Based Long-Term Short-Term Stochastic Synapse for a Spiking Neural Network with On-Chip STDP Learning

NASA Astrophysics Data System (ADS)

Srinivasan, Gopalakrishnan; Sengupta, Abhronil; Roy, Kaushik

2016-07-01

Spiking Neural Networks (SNNs) have emerged as a powerful neuromorphic computing paradigm to carry out classification and recognition tasks. Nevertheless, the general purpose computing platforms and the custom hardware architectures implemented using standard CMOS technology, have been unable to rival the power efficiency of the human brain. Hence, there is a need for novel nanoelectronic devices that can efficiently model the neurons and synapses constituting an SNN. In this work, we propose a heterostructure composed of a Magnetic Tunnel Junction (MTJ) and a heavy metal as a stochastic binary synapse. Synaptic plasticity is achieved by the stochastic switching of the MTJ conductance states, based on the temporal correlation between the spiking activities of the interconnecting neurons. Additionally, we present a significance driven long-term short-term stochastic synapse comprising two unique binary synaptic elements, in order to improve the synaptic learning efficiency. We demonstrate the efficacy of the proposed synaptic configurations and the stochastic learning algorithm on an SNN trained to classify handwritten digits from the MNIST dataset, using a device to system-level simulation framework. The power efficiency of the proposed neuromorphic system stems from the ultra-low programming energy of the spintronic synapses.

Magnetic Tunnel Junction Based Long-Term Short-Term Stochastic Synapse for a Spiking Neural Network with On-Chip STDP Learning.

PubMed

Srinivasan, Gopalakrishnan; Sengupta, Abhronil; Roy, Kaushik

2016-07-13

Spiking Neural Networks (SNNs) have emerged as a powerful neuromorphic computing paradigm to carry out classification and recognition tasks. Nevertheless, the general purpose computing platforms and the custom hardware architectures implemented using standard CMOS technology, have been unable to rival the power efficiency of the human brain. Hence, there is a need for novel nanoelectronic devices that can efficiently model the neurons and synapses constituting an SNN. In this work, we propose a heterostructure composed of a Magnetic Tunnel Junction (MTJ) and a heavy metal as a stochastic binary synapse. Synaptic plasticity is achieved by the stochastic switching of the MTJ conductance states, based on the temporal correlation between the spiking activities of the interconnecting neurons. Additionally, we present a significance driven long-term short-term stochastic synapse comprising two unique binary synaptic elements, in order to improve the synaptic learning efficiency. We demonstrate the efficacy of the proposed synaptic configurations and the stochastic learning algorithm on an SNN trained to classify handwritten digits from the MNIST dataset, using a device to system-level simulation framework. The power efficiency of the proposed neuromorphic system stems from the ultra-low programming energy of the spintronic synapses.

An introduction to quantum machine learning

NASA Astrophysics Data System (ADS)

Schuld, Maria; Sinayskiy, Ilya; Petruccione, Francesco

2015-04-01

Machine learning algorithms learn a desired input-output relation from examples in order to interpret new inputs. This is important for tasks such as image and speech recognition or strategy optimisation, with growing applications in the IT industry. In the last couple of years, researchers investigated if quantum computing can help to improve classical machine learning algorithms. Ideas range from running computationally costly algorithms or their subroutines efficiently on a quantum computer to the translation of stochastic methods into the language of quantum theory. This contribution gives a systematic overview of the emerging field of quantum machine learning. It presents the approaches as well as technical details in an accessible way, and discusses the potential of a future theory of quantum learning.

Social learning of an associative foraging task in zebrafish

NASA Astrophysics Data System (ADS)

Zala, Sarah M.; Määttänen, Ilmari

2013-05-01

The zebrafish ( Danio rerio) is increasingly becoming an important model species for studies on the genetic and neural mechanisms controlling behaviour and cognition. Here, we utilized a conditioned place preference (CPP) paradigm to study social learning in zebrafish. We tested whether social interactions with conditioned demonstrators enhance the ability of focal naïve individuals to learn an associative foraging task. We found that the presence of conditioned demonstrators improved focal fish foraging behaviour through the process of social transmission, whereas the presence of inexperienced demonstrators interfered with the learning of the control focal fish. Our results indicate that zebrafish use social learning for finding food and that this CPP paradigm is an efficient assay to study social learning and memory in zebrafish.

Multi-Source Multi-Target Dictionary Learning for Prediction of Cognitive Decline.

PubMed

Zhang, Jie; Li, Qingyang; Caselli, Richard J; Thompson, Paul M; Ye, Jieping; Wang, Yalin

2017-06-01

Alzheimer's Disease (AD) is the most common type of dementia. Identifying correct biomarkers may determine pre-symptomatic AD subjects and enable early intervention. Recently, Multi-task sparse feature learning has been successfully applied to many computer vision and biomedical informatics researches. It aims to improve the generalization performance by exploiting the shared features among different tasks. However, most of the existing algorithms are formulated as a supervised learning scheme. Its drawback is with either insufficient feature numbers or missing label information. To address these challenges, we formulate an unsupervised framework for multi-task sparse feature learning based on a novel dictionary learning algorithm. To solve the unsupervised learning problem, we propose a two-stage Multi-Source Multi-Target Dictionary Learning (MMDL) algorithm. In stage 1, we propose a multi-source dictionary learning method to utilize the common and individual sparse features in different time slots. In stage 2, supported by a rigorous theoretical analysis, we develop a multi-task learning method to solve the missing label problem. Empirical studies on an N = 3970 longitudinal brain image data set, which involves 2 sources and 5 targets, demonstrate the improved prediction accuracy and speed efficiency of MMDL in comparison with other state-of-the-art algorithms.

The impact of mathematical models of teaching materials on square and rectangle concepts to improve students' mathematical connection ability and mathematical disposition in middle school

NASA Astrophysics Data System (ADS)

Afrizal, Irfan Mufti; Dachlan, Jarnawi Afghani

2017-05-01

The aim of this study was to determine design of mathematical models of teaching materials to improve students' mathematical connection ability and mathematical disposition in middle school through experimental studies. The design in this study was quasi-experimental with non-equivalent control group type. This study consisted of two phases, the first phase was identify students' learning obstacle on square and rectangle concepts to obtain the appropriate design of teaching materials, beside that there were internalization of the values or characters expected to appear on students through the teaching materials. Second phase was experiments on the effectiveness and efficiency of mathematical models of teaching materials to improve students' mathematical connection ability and mathematical disposition. The result of this study are 1) Students' learning obstacle that have identified was categorized as an epistemological obstacle. 2) The improvement of students' mathematical connection ability and mathematical disposition who used mathematical teaching materials is better than the students who used conventional learning.

Closed-Loop Targeted Memory Reactivation during Sleep Improves Spatial Navigation.

PubMed

Shimizu, Renee E; Connolly, Patrick M; Cellini, Nicola; Armstrong, Diana M; Hernandez, Lexus T; Estrada, Rolando; Aguilar, Mario; Weisend, Michael P; Mednick, Sara C; Simons, Stephen B

2018-01-01

Sounds associated with newly learned information that are replayed during non-rapid eye movement (NREM) sleep can improve recall in simple tasks. The mechanism for this improvement is presumed to be reactivation of the newly learned memory during sleep when consolidation takes place. We have developed an EEG-based closed-loop system to precisely deliver sensory stimulation at the time of down-state to up-state transitions during NREM sleep. Here, we demonstrate that applying this technology to participants performing a realistic navigation task in virtual reality results in a significant improvement in navigation efficiency after sleep that is accompanied by increases in the spectral power especially in the fast (12-15 Hz) sleep spindle band. Our results show promise for the application of sleep-based interventions to drive improvement in real-world tasks.

FPGA implementation of neuro-fuzzy system with improved PSO learning.

PubMed

Karakuzu, Cihan; Karakaya, Fuat; Çavuşlu, Mehmet Ali

2016-07-01

This paper presents the first hardware implementation of neuro-fuzzy system (NFS) with its metaheuristic learning ability on field programmable gate array (FPGA). Metaheuristic learning of NFS for all of its parameters is accomplished by using the improved particle swarm optimization (iPSO). As a second novelty, a new functional approach, which does not require any memory and multiplier usage, is proposed for the Gaussian membership functions of NFS. NFS and its learning using iPSO are implemented on Xilinx Virtex5 xc5vlx110-3ff1153 and efficiency of the proposed implementation tested on two dynamic system identification problems and licence plate detection problem as a practical application. Results indicate that proposed NFS implementation and membership function approximation is as effective as the other approaches available in the literature but requires less hardware resources. Copyright © 2016 Elsevier Ltd. All rights reserved.

Smart Aquarium as Physics Learning Media for Renewable Energy

NASA Astrophysics Data System (ADS)

Desnita, D.; Raihanati, R.; Susanti, D.

2018-04-01

Smart aquarium has been developed as a learning media to visualize Micro Hydro Power Generator (MHPG). Its used aquarium water circulation system and Wind Power Generation (WPG) which generated through a wheel as a source. Its also used to teach about energy changes, circular motion and wheel connection, electromagnetic impact, and AC power circuit. The output power and system efficiency was adjusted through the adjustment of water level and wind speed. Specific targets in this research are: to achieved: (i) develop green aquarium technology that’s suitable to used as a medium of physics learning, (ii) improving quality of process and learning result at a senior high school student. Research method used development research by Borg and Gall, which includes preliminary studies, design, product development, expert validation, and product feasibility test, and vinalisation. The validation test by the expert states that props feasible to use. Limited trials conducted prove that this tool can improve students science process skills.

Application of Multi-Objective Human Learning Optimization Method to Solve AC/DC Multi-Objective Optimal Power Flow Problem

NASA Astrophysics Data System (ADS)

Cao, Jia; Yan, Zheng; He, Guangyu

2016-06-01

This paper introduces an efficient algorithm, multi-objective human learning optimization method (MOHLO), to solve AC/DC multi-objective optimal power flow problem (MOPF). Firstly, the model of AC/DC MOPF including wind farms is constructed, where includes three objective functions, operating cost, power loss, and pollutant emission. Combining the non-dominated sorting technique and the crowding distance index, the MOHLO method can be derived, which involves individual learning operator, social learning operator, random exploration learning operator and adaptive strategies. Both the proposed MOHLO method and non-dominated sorting genetic algorithm II (NSGAII) are tested on an improved IEEE 30-bus AC/DC hybrid system. Simulation results show that MOHLO method has excellent search efficiency and the powerful ability of searching optimal. Above all, MOHLO method can obtain more complete pareto front than that by NSGAII method. However, how to choose the optimal solution from pareto front depends mainly on the decision makers who stand from the economic point of view or from the energy saving and emission reduction point of view.

A deep learning and novelty detection framework for rapid phenotyping in high-content screening

PubMed Central

Sommer, Christoph; Hoefler, Rudolf; Samwer, Matthias; Gerlich, Daniel W.

2017-01-01

Supervised machine learning is a powerful and widely used method for analyzing high-content screening data. Despite its accuracy, efficiency, and versatility, supervised machine learning has drawbacks, most notably its dependence on a priori knowledge of expected phenotypes and time-consuming classifier training. We provide a solution to these limitations with CellCognition Explorer, a generic novelty detection and deep learning framework. Application to several large-scale screening data sets on nuclear and mitotic cell morphologies demonstrates that CellCognition Explorer enables discovery of rare phenotypes without user training, which has broad implications for improved assay development in high-content screening. PMID:28954863

Improving the Teaching of Science through Discipline-Based Education Research: An Example from Physics

ERIC Educational Resources Information Center

McDermott, Lillian C.

2013-01-01

Research on the learning and teaching of science is an important field for scholarly inquiry by faculty in science departments. Such research has proved to be an efficient means for improving the effectiveness of instruction in physics. A basic topic in introductory physics is used to illustrate how discipline-based education research has helped…

Alcoa North American Extrusions Implements Energy Use Assessments at Multiple Facilities

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

None

2001-08-01

This case study is the latest in a series on industrial firms who are implementing energy efficient technologies and system improvements into their manufacturing processes. The case studies document the activities, savings, and lessons learned on these projects.

Seven Steps for Success: Selecting IT Consultants

ERIC Educational Resources Information Center

Moriarty, Daniel F.

2004-01-01

Information technology (IT) presents community colleges with both powerful opportunities and formidable challenges. The prospects of expedited and more efficient business processes, greater student access through distance learning, improved communication, and strengthened relationships with students can embolden the most hesitant college…

Building Measures of Instructional Differentiation from Teacher Checklists

ERIC Educational Resources Information Center

Williams, Ryan; Swanlund, Andrew; Miller, Shazia; Konstantopoulos, Spyros; van der Ploeg, Arie

2012-01-01

Differentiated instruction is commonly believed to be critical to improving the quality and efficiency of teachers' instructional repertoires (Fischer & Rose, 2001; Tomlinson, 2004). Tomlinson (2000) describes differentiation in four domains: content, process, product, and learning environment. Content differentiation involves varying…

Safe, High-Performance, Sustainable Precast School Design

ERIC Educational Resources Information Center

Finsen, Peter I.

2011-01-01

School design utilizing integrated architectural and structural precast and prestressed concrete components has gained greater acceptance recently for numerous reasons, including increasingly sophisticated owners and improved learning environments based on material benefits such as: sustainability, energy efficiency, indoor air quality, storm…

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

PubMed

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

2017-06-28

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

Assessment of two e-learning methods teaching undergraduate students cephalometry in orthodontics.

PubMed

Ludwig, B; Bister, D; Schott, T C; Lisson, J A; Hourfar, J

2016-02-01

Cephalometry is important for orthodontic diagnosis and treatment planning and is part of the core curriculum for training dentists. Training involves identifying anatomical landmarks. The aim of this investigation was to assess whether e-learning improves learning efficiency; a programme specifically designed for this purpose was compared to commercially available software. Thirty undergraduate students underwent traditional training of cephalometry consisting of lectures and tutorials. Tracing skills were tested immediately afterwards (T0). The students were then randomly allocated to three groups: 10 students served as control (CF); they were asked to improve their skills using the material provided so far. Ten students were given a program specifically designed for this study that was based on a power point presentation (PPT). The last group was given a commercially available program that included teaching elements (SW). The groups were tested at the end the six week training (T1). The test consisted of tracing 30 points on two radiographs and a point score improvement was calculated. The students were interviewed after the second test. Both e-learning groups improved more than the traditional group. Improvement scores were four for CF; 8.6 for PPT and 2.8 for SW. For PPT all participants improved and the student feedback was the best compared to the other groups. For the other groups some candidates worsened. Blended learning produced better learning outcomes compared to using a traditional teaching method alone. The easy to use Power Point based custom software produced better results than the commercially available software. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Deep imitation learning for 3D navigation tasks.

PubMed

Hussein, Ahmed; Elyan, Eyad; Gaber, Mohamed Medhat; Jayne, Chrisina

2018-01-01

Deep learning techniques have shown success in learning from raw high-dimensional data in various applications. While deep reinforcement learning is recently gaining popularity as a method to train intelligent agents, utilizing deep learning in imitation learning has been scarcely explored. Imitation learning can be an efficient method to teach intelligent agents by providing a set of demonstrations to learn from. However, generalizing to situations that are not represented in the demonstrations can be challenging, especially in 3D environments. In this paper, we propose a deep imitation learning method to learn navigation tasks from demonstrations in a 3D environment. The supervised policy is refined using active learning in order to generalize to unseen situations. This approach is compared to two popular deep reinforcement learning techniques: deep-Q-networks and Asynchronous actor-critic (A3C). The proposed method as well as the reinforcement learning methods employ deep convolutional neural networks and learn directly from raw visual input. Methods for combining learning from demonstrations and experience are also investigated. This combination aims to join the generalization ability of learning by experience with the efficiency of learning by imitation. The proposed methods are evaluated on 4 navigation tasks in a 3D simulated environment. Navigation tasks are a typical problem that is relevant to many real applications. They pose the challenge of requiring demonstrations of long trajectories to reach the target and only providing delayed rewards (usually terminal) to the agent. The experiments show that the proposed method can successfully learn navigation tasks from raw visual input while learning from experience methods fail to learn an effective policy. Moreover, it is shown that active learning can significantly improve the performance of the initially learned policy using a small number of active samples.

The research of computer multimedia assistant in college English listening

NASA Astrophysics Data System (ADS)

Zhang, Qian

2012-04-01

With the technology development of network information, there exists more and more seriously questions to our education. Computer multimedia application breaks the traditional foreign language teaching and brings new challenges and opportunities for the education. Through the multiple media application, the teaching process is full of animation, image, voice, and characters. This can improve the learning initiative and objective with great development of learning efficiency. During the traditional foreign language teaching, people use characters learning. However, through this method, the theory performance is good but the practical application is low. During the long time computer multimedia application in the foreign language teaching, many teachers still have prejudice. Therefore, the method is not obtaining the effect. After all the above, the research has significant meaning for improving the teaching quality of foreign language.

Diverse expected gradient active learning for relative attributes.

PubMed

You, Xinge; Wang, Ruxin; Tao, Dacheng

2014-07-01

The use of relative attributes for semantic understanding of images and videos is a promising way to improve communication between humans and machines. However, it is extremely labor- and time-consuming to define multiple attributes for each instance in large amount of data. One option is to incorporate active learning, so that the informative samples can be actively discovered and then labeled. However, most existing active-learning methods select samples one at a time (serial mode), and may therefore lose efficiency when learning multiple attributes. In this paper, we propose a batch-mode active-learning method, called diverse expected gradient active learning. This method integrates an informativeness analysis and a diversity analysis to form a diverse batch of queries. Specifically, the informativeness analysis employs the expected pairwise gradient length as a measure of informativeness, while the diversity analysis forces a constraint on the proposed diverse gradient angle. Since simultaneous optimization of these two parts is intractable, we utilize a two-step procedure to obtain the diverse batch of queries. A heuristic method is also introduced to suppress imbalanced multiclass distributions. Empirical evaluations of three different databases demonstrate the effectiveness and efficiency of the proposed approach.

Diverse Expected Gradient Active Learning for Relative Attributes.

PubMed

You, Xinge; Wang, Ruxin; Tao, Dacheng

2014-06-02

The use of relative attributes for semantic understanding of images and videos is a promising way to improve communication between humans and machines. However, it is extremely labor- and time-consuming to define multiple attributes for each instance in large amount of data. One option is to incorporate active learning, so that the informative samples can be actively discovered and then labeled. However, most existing active-learning methods select samples one at a time (serial mode), and may therefore lose efficiency when learning multiple attributes. In this paper, we propose a batch-mode active-learning method, called Diverse Expected Gradient Active Learning (DEGAL). This method integrates an informativeness analysis and a diversity analysis to form a diverse batch of queries. Specifically, the informativeness analysis employs the expected pairwise gradient length as a measure of informativeness, while the diversity analysis forces a constraint on the proposed diverse gradient angle. Since simultaneous optimization of these two parts is intractable, we utilize a two-step procedure to obtain the diverse batch of queries. A heuristic method is also introduced to suppress imbalanced multi-class distributions. Empirical evaluations of three different databases demonstrate the effectiveness and efficiency of the proposed approach.

Recovery Act--Class 8 Truck Freight Efficiency Improvement Project

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

Trucks, Daimler

2015-07-26

Daimler Trucks North America completed a five year, $79.6M project to develop and demonstrate a concept vehicle with at least 50% freight efficiency improvement over a weighted average of several drive cycles relative to a 2009 best-in-class baseline vehicle. DTNA chose a very fuel efficient baseline vehicle, the 2009 Freightliner Cascadia with a DD15 engine, yet successfully demonstrated a 115% freight efficiency improvement. DTNA learned a great deal about the various technologies that were incorporated into Super Truck and those that, through down-selection, were discarded. Some of the technologies competed with each other for efficiency, and notably some of themore » technologies complemented each other. For example, we found that Super Truck’s improved aerodynamic drag resulted in improved fuel savings from eCoast, relative to a similar vehicle with worse aerodynamic drag. However, some technologies were in direct competition with each other, namely the predictive technologies which use GPS and 3D digital maps to efficiently manage the vehicles kinetic energy through controls and software, versus hybrid which is a much costlier technology that essentially targets the same inefficiency. Furthermore, the benefits of a comprehensive, integrated powertrain/vehicle approach was proven, in which vast improvements in vehicle efficiency (e.g. lower aero drag and driveline losses) enabled engine strategies such as downrating and downspeeding. The joint engine and vehicle developments proved to be a multiplier-effect which resulted in large freight efficiency improvements. Although a large number of technologies made the selection process and were used on the Super Truck demonstrator vehicle, some of the technologies proved not feasible for series production.« less

Early motor learning changes in upper-limb dynamics and shoulder complex loading during handrim wheelchair propulsion.

PubMed

Vegter, Riemer J K; Hartog, Johanneke; de Groot, Sonja; Lamoth, Claudine J; Bekker, Michel J; van der Scheer, Jan W; van der Woude, Lucas H V; Veeger, Dirkjan H E J

2015-03-10

To propel in an energy-efficient manner, handrim wheelchair users must learn to control the bimanually applied forces onto the rims, preserving both speed and direction of locomotion. Previous studies have found an increase in mechanical efficiency due to motor learning associated with changes in propulsion technique, but it is unclear in what way the propulsion technique impacts the load on the shoulder complex. The purpose of this study was to evaluate mechanical efficiency, propulsion technique and load on the shoulder complex during the initial stage of motor learning. 15 naive able-bodied participants received 12-minutes uninstructed wheelchair practice on a motor driven treadmill, consisting of three 4-minute blocks separated by two minutes rest. Practice was performed at a fixed belt speed (v = 1.1 m/s) and constant low-intensity power output (0.2 W/kg). Energy consumption, kinematics and kinetics of propulsion technique were continuously measured. The Delft Shoulder Model was used to calculate net joint moments, muscle activity and glenohumeral reaction force. With practice mechanical efficiency increased and propulsion technique changed, reflected by a reduced push frequency and increased work per push, performed over a larger contact angle, with more tangentially applied force and reduced power losses before and after each push. Contrary to our expectations, the above mentioned propulsion technique changes were found together with an increased load on the shoulder complex reflected by higher net moments, a higher total muscle power and higher peak and mean glenohumeral reaction forces. It appears that the early stages of motor learning in handrim wheelchair propulsion are indeed associated with improved technique and efficiency due to optimization of the kinematics and dynamics of the upper extremity. This process goes at the cost of an increased muscular effort and mechanical loading of the shoulder complex. This seems to be associated with an unchanged stable function of the trunk and could be due to the early learning phase where participants still have to learn to effectively use the full movement amplitude available within the wheelchair-user combination. Apparently whole body energy efficiency has priority over mechanical loading in the early stages of learning to propel a handrim wheelchair.

Data Mining Student Answers with Moodle to Investigate Learning Pathways in an Introductory Geohazards Course

NASA Astrophysics Data System (ADS)

Sit, S. M.; Brudzinski, M. R.; Colella, H. V.

2012-12-01

The recent growth of online learning in higher education is primarily motivated by a desire to (a) increase the availability of learning experiences for learners who cannot, or choose not, to attend traditional face-to-face offerings, (b) assemble and disseminate instructional content more cost-efficiently, or (c) enable instructors to handle more students while maintaining a learning outcome quality that is equivalent to that of comparable face-to-face instruction. However, a less recognized incentive is that online learning also provides an opportunity for data mining, or efficient discovery of non-obvious valuable patterns from a large collection of data, that can be used to investigate learning pathways as opposed to focusing solely on assessing student outcomes. Course management systems that enable online courses provide a means to collect a vast amount of information to analyze students' behavior and the learning process in general. One of the most commonly used is Moodle (modular object-oriented developmental learning environment), a free learning management system that enables creation of powerful, flexible, and engaging online courses and experiences. In order to examine student learning pathways, the online learning modules we are constructing take advantage of Moodle capabilities to provide immediate formative feedback, verifying answers as correct or incorrect and elaborating on knowledge components to guide students towards the correct answer. By permitting multiple attempts in which credit is diminished for each incorrect answer, we provide opportunities to use data mining strategies to assess thousands of students' actions for evidence of problem solving strategies and mastery of concepts. We will show preliminary results from application of this approach to a ~90 student introductory geohazard course that is migrating toward online instruction. We hope more continuous assessment of students' performances will help generate cognitive models that can inform instructional redesign, improve overall efficiency of student learning, and, potentially, be used to create an intelligent tutoring system.

Improving maximum power point tracking of partially shaded photovoltaic system by using IPSO-BELBIC

NASA Astrophysics Data System (ADS)

Al-Alim El-Garhy, M. Abd; Mubarak, R. I.; El-Bably, M.

2017-08-01

Solar photovoltaic (PV) arrays in remote applications are often related to the rapid changes in the partial shading pattern. Rapid changes of the partial shading pattern make the tracking of maximum power point (MPP) of the global peak through the local ones too difficult. An essential need to make a fast and efficient algorithm to detect the peaks values which always vary as the sun irradiance changes. This paper presents two algorithms based on the improved particle swarm optimization technique one of them with PID controller (IPSO-PID), and the other one with Brain Emotional Learning Based Intelligent Controller (IPSO-BELBIC). These techniques improve the maximum power point (MPP) tracking capabilities for photovoltaic (PV) system under partial shading circumstances. The main aim of these improved algorithms is to accelerate the velocity of IPSO to reach to (MPP) and increase its efficiency. These algorithms also improve the tracking time under complex irradiance conditions. Based on these conditions, the tracking time of these presented techniques improves to 2 msec, with an efficiency of 100%.

Healthcare Commercialization Programs: Improving the Efficiency of Translating Healthcare Innovations From Academia Into Practice.

PubMed

Collins, John M; Reizes, Ofer; Dempsey, Michael K

2016-01-01

Academic investigators are generating a plethora of insights and technologies that have the potential to significantly improve patient care. However, to address the imperative to improve the quality, cost and access to care with ever more constrained funding, the efficiency and the consistency with which they are translated into cost effective products and/or services need to improve. Healthcare commercialization programs (HCPs) are described and proposed as an option that institutions can add to their portfolio to improve translational research. In helping teams translate specific healthcare innovations into practice, HCPs expand the skillset of investigators and enhance an institution's innovation capacity. Lessons learned are shared from configuring and delivering HCPs, which build on the fundamentals of the National Science Foundation's Innovation Corps program, to address the unique challenges in supporting healthcare innovations and innovators.

Perceptual learning through optimization of attentional weighting: human versus optimal Bayesian learner

NASA Technical Reports Server (NTRS)

Eckstein, Miguel P.; Abbey, Craig K.; Pham, Binh T.; Shimozaki, Steven S.

2004-01-01

Human performance in visual detection, discrimination, identification, and search tasks typically improves with practice. Psychophysical studies suggest that perceptual learning is mediated by an enhancement in the coding of the signal, and physiological studies suggest that it might be related to the plasticity in the weighting or selection of sensory units coding task relevant information (learning through attention optimization). We propose an experimental paradigm (optimal perceptual learning paradigm) to systematically study the dynamics of perceptual learning in humans by allowing comparisons to that of an optimal Bayesian algorithm and a number of suboptimal learning models. We measured improvement in human localization (eight-alternative forced-choice with feedback) performance of a target randomly sampled from four elongated Gaussian targets with different orientations and polarities and kept as a target for a block of four trials. The results suggest that the human perceptual learning can occur within a lapse of four trials (<1 min) but that human learning is slower and incomplete with respect to the optimal algorithm (23.3% reduction in human efficiency from the 1st-to-4th learning trials). The greatest improvement in human performance, occurring from the 1st-to-2nd learning trial, was also present in the optimal observer, and, thus reflects a property inherent to the visual task and not a property particular to the human perceptual learning mechanism. One notable source of human inefficiency is that, unlike the ideal observer, human learning relies more heavily on previous decisions than on the provided feedback, resulting in no human learning on trials following a previous incorrect localization decision. Finally, the proposed theory and paradigm provide a flexible framework for future studies to evaluate the optimality of human learning of other visual cues and/or sensory modalities.

Context aware decision support in neurosurgical oncology based on an efficient classification of endomicroscopic data.

PubMed

Li, Yachun; Charalampaki, Patra; Liu, Yong; Yang, Guang-Zhong; Giannarou, Stamatia

2018-06-13

Probe-based confocal laser endomicroscopy (pCLE) enables in vivo, in situ tissue characterisation without changes in the surgical setting and simplifies the oncological surgical workflow. The potential of this technique in identifying residual cancer tissue and improving resection rates of brain tumours has been recently verified in pilot studies. The interpretation of endomicroscopic information is challenging, particularly for surgeons who do not themselves routinely review histopathology. Also, the diagnosis can be examiner-dependent, leading to considerable inter-observer variability. Therefore, automatic tissue characterisation with pCLE would support the surgeon in establishing diagnosis as well as guide robot-assisted intervention procedures. The aim of this work is to propose a deep learning-based framework for brain tissue characterisation for context aware diagnosis support in neurosurgical oncology. An efficient representation of the context information of pCLE data is presented by exploring state-of-the-art CNN models with different tuning configurations. A novel video classification framework based on the combination of convolutional layers with long-range temporal recursion has been proposed to estimate the probability of each tumour class. The video classification accuracy is compared for different network architectures and data representation and video segmentation methods. We demonstrate the application of the proposed deep learning framework to classify Glioblastoma and Meningioma brain tumours based on endomicroscopic data. Results show significant improvement of our proposed image classification framework over state-of-the-art feature-based methods. The use of video data further improves the classification performance, achieving accuracy equal to 99.49%. This work demonstrates that deep learning can provide an efficient representation of pCLE data and accurately classify Glioblastoma and Meningioma tumours. The performance evaluation analysis shows the potential clinical value of the technique.

Learning curves for transapical transcatheter aortic valve replacement in the PARTNER-I trial: Technical performance, success, and safety.

PubMed

Suri, Rakesh M; Minha, Sa'ar; Alli, Oluseun; Waksman, Ron; Rihal, Charanjit S; Satler, Lowell P; Greason, Kevin L; Torguson, Rebecca; Pichard, Augusto D; Mack, Michael; Svensson, Lars G; Rajeswaran, Jeevanantham; Lowry, Ashley M; Ehrlinger, John; Mick, Stephanie L; Tuzcu, E Murat; Thourani, Vinod H; Makkar, Raj; Holmes, David; Leon, Martin B; Blackstone, Eugene H

2016-09-01

Introduction of hybrid techniques, such as transapical transcatheter aortic valve replacement (TA-TAVR), requires skills that a heart team must master to achieve technical efficiency: the technical performance learning curve. To date, the learning curve for TA-TAVR remains unknown. We therefore evaluated the rate at which technical performance improved, assessed change in occurrence of adverse events in relation to technical performance, and determined whether adverse events after TA-TAVR were linked to acquiring technical performance efficiency (the learning curve). From April 2007 to February 2012, 1100 patients, average age 85.0 ± 6.4 years, underwent TA-TAVR in the PARTNER-I trial. Learning curves were defined by institution-specific patient sequence number using nonlinear mixed modeling. Mean procedure time decreased from 131 to 116 minutes within 30 cases (P = .06) and device success increased to 90% by case 45 (P = .0007). Within 30 days, 354 patients experienced a major adverse event (stroke in 29, death in 96), with possibly decreased complications over time (P ∼ .08). Although longer procedure time was associated with more adverse events (P < .0001), these events were associated with change in patient risk profile, not the technical performance learning curve (P = .8). The learning curve for TA-TAVR was 30 to 45 procedures performed, and technical efficiency was achieved without compromising patient safety. Although fewer patients are now undergoing TAVR via nontransfemoral access, understanding TA-TAVR learning curves and their relationship with outcomes is important as the field moves toward next-generation devices, such as those to replace the mitral valve, delivered via the left ventricular apex. Copyright © 2016 The American Association for Thoracic Surgery. Published by Elsevier Inc. All rights reserved.

The Examination of Strength and Weakness of Online Evaluation of Faculty Members Teaching by Students in the University of Isfahan

ERIC Educational Resources Information Center

Maryam, Ansary; Alireza, Shavakhi; Reza, Nasr Ahmad; Azizollah, Arbabisarjou

2012-01-01

Evaluation of faculty members' teaching is a device for recognition of their ability in teaching, assessing, the student's learning and it can improve efficiency of faculty members in teaching. In terms of growth of computer's technologies improvement of universities and its effect on achievement and information processing, it is necessary to use…

Research on cultivating medical students' self-learning ability using teaching system integrated with learning analysis technology.

PubMed

Luo, Hong; Wu, Cheng; He, Qian; Wang, Shi-Yong; Ma, Xiu-Qiang; Wang, Ri; Li, Bing; He, Jia

2015-01-01

Along with the advancement of information technology and the era of big data education, using learning process data to provide strategic decision-making in cultivating and improving medical students' self-learning ability has become a trend in educational research. Educator Abuwen Toffler said once, the illiterates in the future may not be the people not able to read and write, but not capable to know how to learn. Serving as educational institutions cultivating medical students' learning ability, colleges and universities should not only instruct specific professional knowledge and skills, but also develop medical students' self-learning ability. In this research, we built a teaching system which can help to restore medical students' self-learning processes and analyze their learning outcomes and behaviors. To evaluate the effectiveness of the system in supporting medical students' self-learning, an experiment was conducted in 116 medical students from two grades. The results indicated that problems in self-learning process through this system was consistent with problems raised from traditional classroom teaching. Moreover, the experimental group (using this system) acted better than control group (using traditional classroom teaching) to some extent. Thus, this system can not only help medical students to develop their self-learning ability, but also enhances the ability of teachers to target medical students' questions quickly, improving the efficiency of answering questions in class.

Nature's Design Rules.

ERIC Educational Resources Information Center

Reicher, Dan

2000-01-01

Discusses school design considerations for energy-efficient schools that provide learning environments that lead to improved student performance. Design myths are addressed as are use of daylighting and designing schools that can teach students and adults about the importance of conserving energy and money. Two online resources are included. (GR)

77 FR 45944 - Final Priorities and Definitions; State Personnel Development Grants

Federal Register 2010, 2011, 2012, 2013, 2014

2012-08-02

... designed broadly to focus on the effective and efficient delivery of professional development using... development. For example, some commenters recommended including references to universal design for learning... culturally competent, provided the project is designed to improve professional development in this area...

Power Factor Study Reduces Energy Costs at Aluminum Extrusion Plant (Alcoa North American Extrusions)

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

None

2001-08-01

This case study is the latest in a series on industrial firms who are implementing energy efficient technologies and system improvements into their manufacturing processes. The case studies document the activities, savings, and lessons learned on these projects.

Resuscitation Education Science: Educational Strategies to Improve Outcomes From Cardiac Arrest: A Scientific Statement From the American Heart Association.

PubMed

Cheng, Adam; Nadkarni, Vinay M; Mancini, Mary Beth; Hunt, Elizabeth A; Sinz, Elizabeth H; Merchant, Raina M; Donoghue, Aaron; Duff, Jonathan P; Eppich, Walter; Auerbach, Marc; Bigham, Blair L; Blewer, Audrey L; Chan, Paul S; Bhanji, Farhan

2018-06-21

The formula for survival in resuscitation describes educational efficiency and local implementation as key determinants in survival after cardiac arrest. Current educational offerings in the form of standardized online and face-to-face courses are falling short, with providers demonstrating a decay of skills over time. This translates to suboptimal clinical care and poor survival outcomes from cardiac arrest. In many institutions, guidelines taught in courses are not thoughtfully implemented in the clinical environment. A current synthesis of the evidence supporting best educational and knowledge translation strategies in resuscitation is lacking. In this American Heart Association scientific statement, we provide a review of the literature describing key elements of educational efficiency and local implementation, including mastery learning and deliberate practice, spaced practice, contextual learning, feedback and debriefing, assessment, innovative educational strategies, faculty development, and knowledge translation and implementation. For each topic, we provide suggestions for improving provider performance that may ultimately optimize patient outcomes from cardiac arrest. © 2018 American Heart Association, Inc.

Multi-Source Multi-Target Dictionary Learning for Prediction of Cognitive Decline

PubMed Central

Zhang, Jie; Li, Qingyang; Caselli, Richard J.; Thompson, Paul M.; Ye, Jieping; Wang, Yalin

2017-01-01

Alzheimer’s Disease (AD) is the most common type of dementia. Identifying correct biomarkers may determine pre-symptomatic AD subjects and enable early intervention. Recently, Multi-task sparse feature learning has been successfully applied to many computer vision and biomedical informatics researches. It aims to improve the generalization performance by exploiting the shared features among different tasks. However, most of the existing algorithms are formulated as a supervised learning scheme. Its drawback is with either insufficient feature numbers or missing label information. To address these challenges, we formulate an unsupervised framework for multi-task sparse feature learning based on a novel dictionary learning algorithm. To solve the unsupervised learning problem, we propose a two-stage Multi-Source Multi-Target Dictionary Learning (MMDL) algorithm. In stage 1, we propose a multi-source dictionary learning method to utilize the common and individual sparse features in different time slots. In stage 2, supported by a rigorous theoretical analysis, we develop a multi-task learning method to solve the missing label problem. Empirical studies on an N = 3970 longitudinal brain image data set, which involves 2 sources and 5 targets, demonstrate the improved prediction accuracy and speed efficiency of MMDL in comparison with other state-of-the-art algorithms. PMID:28943731

A Matter of Time: Faster Percolator Analysis via Efficient SVM Learning for Large-Scale Proteomics.

PubMed

Halloran, John T; Rocke, David M

2018-05-04

Percolator is an important tool for greatly improving the results of a database search and subsequent downstream analysis. Using support vector machines (SVMs), Percolator recalibrates peptide-spectrum matches based on the learned decision boundary between targets and decoys. To improve analysis time for large-scale data sets, we update Percolator's SVM learning engine through software and algorithmic optimizations rather than heuristic approaches that necessitate the careful study of their impact on learned parameters across different search settings and data sets. We show that by optimizing Percolator's original learning algorithm, l 2 -SVM-MFN, large-scale SVM learning requires nearly only a third of the original runtime. Furthermore, we show that by employing the widely used Trust Region Newton (TRON) algorithm instead of l 2 -SVM-MFN, large-scale Percolator SVM learning is reduced to nearly only a fifth of the original runtime. Importantly, these speedups only affect the speed at which Percolator converges to a global solution and do not alter recalibration performance. The upgraded versions of both l 2 -SVM-MFN and TRON are optimized within the Percolator codebase for multithreaded and single-thread use and are available under Apache license at bitbucket.org/jthalloran/percolator_upgrade .

Smart-system of distance learning of visually impaired people based on approaches of artificial intelligence

NASA Astrophysics Data System (ADS)

Samigulina, Galina A.; Shayakhmetova, Assem S.

2016-11-01

Research objective is the creation of intellectual innovative technology and information Smart-system of distance learning for visually impaired people. The organization of the available environment for receiving quality education for visually impaired people, their social adaptation in society are important and topical issues of modern education.The proposed Smart-system of distance learning for visually impaired people can significantly improve the efficiency and quality of education of this category of people. The scientific novelty of proposed Smart-system is using intelligent and statistical methods of processing multi-dimensional data, and taking into account psycho-physiological characteristics of perception and awareness learning information by visually impaired people.

Machine Learning in Radiology: Applications Beyond Image Interpretation.

PubMed

Lakhani, Paras; Prater, Adam B; Hutson, R Kent; Andriole, Kathy P; Dreyer, Keith J; Morey, Jose; Prevedello, Luciano M; Clark, Toshi J; Geis, J Raymond; Itri, Jason N; Hawkins, C Matthew

2018-02-01

Much attention has been given to machine learning and its perceived impact in radiology, particularly in light of recent success with image classification in international competitions. However, machine learning is likely to impact radiology outside of image interpretation long before a fully functional "machine radiologist" is implemented in practice. Here, we describe an overview of machine learning, its application to radiology and other domains, and many cases of use that do not involve image interpretation. We hope that better understanding of these potential applications will help radiology practices prepare for the future and realize performance improvement and efficiency gains. Copyright © 2017 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Review on the administration and effectiveness of team-based learning in medical education.

PubMed

Hur, Yera; Cho, A Ra; Kim, Sun

2013-12-01

Team-based learning (TBL) is an active learning approach. In recent years, medical educators have been increasingly using TBL in their classes. We reviewed the concepts of TBL and discuss examples of international cases. Two types of TBL are administered: classic TBL and adapted TBL. Combining TBL and problem-based learning (PBL) might be a useful strategy for medical schools. TBL is an attainable and efficient educational approach in preparing large classes with regard to PBL. TBL improves student performance, team communication skills, leadership skills, problem solving skills, and cognitive conceptual structures and increases student engagement and satisfaction. This study suggests recommendations for administering TBL effectively in medical education.

Machine learning action parameters in lattice quantum chromodynamics

NASA Astrophysics Data System (ADS)

Shanahan, Phiala E.; Trewartha, Daniel; Detmold, William

2018-05-01

Numerical lattice quantum chromodynamics studies of the strong interaction are important in many aspects of particle and nuclear physics. Such studies require significant computing resources to undertake. A number of proposed methods promise improved efficiency of lattice calculations, and access to regions of parameter space that are currently computationally intractable, via multi-scale action-matching approaches that necessitate parametric regression of generated lattice datasets. The applicability of machine learning to this regression task is investigated, with deep neural networks found to provide an efficient solution even in cases where approaches such as principal component analysis fail. The high information content and complex symmetries inherent in lattice QCD datasets require custom neural network layers to be introduced and present opportunities for further development.

The African disability scooter: efficiency testing in paediatric amputees in Malawi

PubMed Central

Beckles, Verona; McCahill, Jennifer L.; Stebbins, Julie; Mkandawire, Nyengo; Church, John C. T.; Lavy, Chris

2016-01-01

Abstract Purpose: The African Disability Scooter (ADS) was developed for lower limb amputees, to improve mobility and provide access to different terrains. The aim of this study was to test the efficiency of the ADS in Africa over different terrains. Method: Eight subjects with a mean age of 12 years participated. Energy expenditure and speed were calculated over different terrains using the ADS, a prosthetic limb, and crutches. Repeated testing was completed on different days to assess learning effect. Results: Speed was significantly faster with the ADS on a level surface compared to crutch walking. This difference was maintained when using the scooter on rough terrain. Oxygen cost was halved with the scooter on level ground compared to crutch walking. There were no significant differences in oxygen consumption or heart rate. There were significant differences in oxygen cost and speed between days using the scooter over level ground, suggesting the presence of a learning effect. Conclusions: This study demonstrates that the ADS is faster and more energy efficient than crutch walking in young individuals with amputations, and should be considered as an alternative to a prosthesis where this is not available. The presence of a learning effect suggests supervision and training is required when the scooter is first issued.Implications for RehabilitationThe African Disability Scooter:is faster than crutch walking in amputees;is more energy efficient than walking with crutches;supervised use is needed when learning to use the device;is a good alternative/adjunct for mobility. PMID:25316033

A Novel Harmony Search Algorithm Based on Teaching-Learning Strategies for 0-1 Knapsack Problems

PubMed Central

Tuo, Shouheng; Yong, Longquan; Deng, Fang'an

2014-01-01

To enhance the performance of harmony search (HS) algorithm on solving the discrete optimization problems, this paper proposes a novel harmony search algorithm based on teaching-learning (HSTL) strategies to solve 0-1 knapsack problems. In the HSTL algorithm, firstly, a method is presented to adjust dimension dynamically for selected harmony vector in optimization procedure. In addition, four strategies (harmony memory consideration, teaching-learning strategy, local pitch adjusting, and random mutation) are employed to improve the performance of HS algorithm. Another improvement in HSTL method is that the dynamic strategies are adopted to change the parameters, which maintains the proper balance effectively between global exploration power and local exploitation power. Finally, simulation experiments with 13 knapsack problems show that the HSTL algorithm can be an efficient alternative for solving 0-1 knapsack problems. PMID:24574905

A novel harmony search algorithm based on teaching-learning strategies for 0-1 knapsack problems.

PubMed

Tuo, Shouheng; Yong, Longquan; Deng, Fang'an

2014-01-01

To enhance the performance of harmony search (HS) algorithm on solving the discrete optimization problems, this paper proposes a novel harmony search algorithm based on teaching-learning (HSTL) strategies to solve 0-1 knapsack problems. In the HSTL algorithm, firstly, a method is presented to adjust dimension dynamically for selected harmony vector in optimization procedure. In addition, four strategies (harmony memory consideration, teaching-learning strategy, local pitch adjusting, and random mutation) are employed to improve the performance of HS algorithm. Another improvement in HSTL method is that the dynamic strategies are adopted to change the parameters, which maintains the proper balance effectively between global exploration power and local exploitation power. Finally, simulation experiments with 13 knapsack problems show that the HSTL algorithm can be an efficient alternative for solving 0-1 knapsack problems.

Two Processes in Early Bimanual Motor Skill Learning

PubMed Central

Yeganeh Doost, Maral; Orban de Xivry, Jean-Jacques; Bihin, Benoît; Vandermeeren, Yves

2017-01-01

Most daily activities are bimanual and their efficient performance requires learning and retention of bimanual coordination. Despite in-depth knowledge of the various stages of motor skill learning in general, how new bimanual coordination control policies are established is still unclear. We designed a new cooperative bimanual task in which subjects had to move a cursor across a complex path (a circuit) as fast and as accurately as possible through coordinated bimanual movements. By looking at the transfer of the skill between different circuits and by looking at training with varying circuits, we identified two processes in early bimanual motor learning. Loss of performance due to the switch in circuit after 15 min of training amounted to 20%, which suggests that a significant portion of improvements in bimanual performance is specific to the used circuit (circuit-specific skill). In contrast, the loss of performance due to the switch in circuit was 5% after 4 min of training. This suggests that learning the new bimanual coordination control policy dominates early in the training and is independent of the used circuit. Finally, switching between two circuits throughout training did not affect the early stage of learning (i.e., the first few minutes), but did affect the later stage. Together, these results suggest that early bimanual motor skill learning includes two different processes. Learning the new bimanual coordination control policy predominates in the first minutes whereas circuit-specific skill improvements unfold later in parallel with further improvements in the bimanual coordination control policy. PMID:29326573

BlueSky Cloud Framework: An E-Learning Framework Embracing Cloud Computing

NASA Astrophysics Data System (ADS)

Dong, Bo; Zheng, Qinghua; Qiao, Mu; Shu, Jian; Yang, Jie

Currently, E-Learning has grown into a widely accepted way of learning. With the huge growth of users, services, education contents and resources, E-Learning systems are facing challenges of optimizing resource allocations, dealing with dynamic concurrency demands, handling rapid storage growth requirements and cost controlling. In this paper, an E-Learning framework based on cloud computing is presented, namely BlueSky cloud framework. Particularly, the architecture and core components of BlueSky cloud framework are introduced. In BlueSky cloud framework, physical machines are virtualized, and allocated on demand for E-Learning systems. Moreover, BlueSky cloud framework combines with traditional middleware functions (such as load balancing and data caching) to serve for E-Learning systems as a general architecture. It delivers reliable, scalable and cost-efficient services to E-Learning systems, and E-Learning organizations can establish systems through these services in a simple way. BlueSky cloud framework solves the challenges faced by E-Learning, and improves the performance, availability and scalability of E-Learning systems.

Improving the Critic Learning for Event-Based Nonlinear $H_{\\infty }$ Control Design.

PubMed

Wang, Ding; He, Haibo; Liu, Derong

2017-10-01

In this paper, we aim at improving the critic learning criterion to cope with the event-based nonlinear H ∞ state feedback control design. First of all, the H ∞ control problem is regarded as a two-player zero-sum game and the adaptive critic mechanism is used to achieve the minimax optimization under event-based environment. Then, based on an improved updating rule, the event-based optimal control law and the time-based worst-case disturbance law are obtained approximately by training a single critic neural network. The initial stabilizing control is no longer required during the implementation process of the new algorithm. Next, the closed-loop system is formulated as an impulsive model and its stability issue is handled by incorporating the improved learning criterion. The infamous Zeno behavior of the present event-based design is also avoided through theoretical analysis on the lower bound of the minimal intersample time. Finally, the applications to an aircraft dynamics and a robot arm plant are carried out to verify the efficient performance of the present novel design method.

Recurrent Neural Networks With Auxiliary Memory Units.

PubMed

Wang, Jianyong; Zhang, Lei; Guo, Quan; Yi, Zhang

2018-05-01

Memory is one of the most important mechanisms in recurrent neural networks (RNNs) learning. It plays a crucial role in practical applications, such as sequence learning. With a good memory mechanism, long term history can be fused with current information, and can thus improve RNNs learning. Developing a suitable memory mechanism is always desirable in the field of RNNs. This paper proposes a novel memory mechanism for RNNs. The main contributions of this paper are: 1) an auxiliary memory unit (AMU) is proposed, which results in a new special RNN model (AMU-RNN), separating the memory and output explicitly and 2) an efficient learning algorithm is developed by employing the technique of error flow truncation. The proposed AMU-RNN model, together with the developed learning algorithm, can learn and maintain stable memory over a long time range. This method overcomes both the learning conflict problem and gradient vanishing problem. Unlike the traditional method, which mixes the memory and output with a single neuron in a recurrent unit, the AMU provides an auxiliary memory neuron to maintain memory in particular. By separating the memory and output in a recurrent unit, the problem of learning conflicts can be eliminated easily. Moreover, by using the technique of error flow truncation, each auxiliary memory neuron ensures constant error flow during the learning process. The experiments demonstrate good performance of the proposed AMU-RNNs and the developed learning algorithm. The method exhibits quite efficient learning performance with stable convergence in the AMU-RNN learning and outperforms the state-of-the-art RNN models in sequence generation and sequence classification tasks.

Flexibility in Problem Solving: The Case of Equation Solving

ERIC Educational Resources Information Center

Star, Jon R.; Rittle-Johnson, Bethany

2008-01-01

A key learning outcome in problem-solving domains is the development of flexible knowledge, where learners know multiple strategies and adaptively choose efficient strategies. Two interventions hypothesized to improve flexibility in problem solving were experimentally evaluated: prompts to discover multiple strategies and direct instruction on…

Healthcare Commercialization Programs: Improving the Efficiency of Translating Healthcare Innovations From Academia Into Practice

PubMed Central

Reizes, Ofer; Dempsey, Michael K.

2016-01-01

Academic investigators are generating a plethora of insights and technologies that have the potential to significantly improve patient care. However, to address the imperative to improve the quality, cost and access to care with ever more constrained funding, the efficiency and the consistency with which they are translated into cost effective products and/or services need to improve. Healthcare commercialization programs (HCPs) are described and proposed as an option that institutions can add to their portfolio to improve translational research. In helping teams translate specific healthcare innovations into practice, HCPs expand the skillset of investigators and enhance an institution’s innovation capacity. Lessons learned are shared from configuring and delivering HCPs, which build on the fundamentals of the National Science Foundation’s Innovation Corps program, to address the unique challenges in supporting healthcare innovations and innovators. PMID:27766188

[Virtual reality simulation training in gynecology: review and perspectives].

PubMed

Ricard-Gauthier, Dominique; Popescu, Silvia; Benmohamed, Naida; Petignat, Patrick; Dubuisson, Jean

2016-10-26

Laparoscopic simulation has rapidly become an important tool for learning and acquiring technical skills in surgery. It is based on two different complementary pedagogic tools : the box model trainer and the virtual reality simulator. The virtual reality simulator has shown its efficiency by improving surgical skills, decreasing operating time, improving economy of movements and improving self-confidence. The main objective of this tool is the opportunity to easily organize a regular, structured and uniformed training program enabling an automated individualized feedback.

The HTM Spatial Pooler-A Neocortical Algorithm for Online Sparse Distributed Coding.

PubMed

Cui, Yuwei; Ahmad, Subutai; Hawkins, Jeff

2017-01-01

Hierarchical temporal memory (HTM) provides a theoretical framework that models several key computational principles of the neocortex. In this paper, we analyze an important component of HTM, the HTM spatial pooler (SP). The SP models how neurons learn feedforward connections and form efficient representations of the input. It converts arbitrary binary input patterns into sparse distributed representations (SDRs) using a combination of competitive Hebbian learning rules and homeostatic excitability control. We describe a number of key properties of the SP, including fast adaptation to changing input statistics, improved noise robustness through learning, efficient use of cells, and robustness to cell death. In order to quantify these properties we develop a set of metrics that can be directly computed from the SP outputs. We show how the properties are met using these metrics and targeted artificial simulations. We then demonstrate the value of the SP in a complete end-to-end real-world HTM system. We discuss the relationship with neuroscience and previous studies of sparse coding. The HTM spatial pooler represents a neurally inspired algorithm for learning sparse representations from noisy data streams in an online fashion.

MiYA, an efficient machine-learning workflow in conjunction with the YeastFab assembly strategy for combinatorial optimization of heterologous metabolic pathways in Saccharomyces cerevisiae.

PubMed

Zhou, Yikang; Li, Gang; Dong, Junkai; Xing, Xin-Hui; Dai, Junbiao; Zhang, Chong

2018-05-01

Facing boosting ability to construct combinatorial metabolic pathways, how to search the metabolic sweet spot has become the rate-limiting step. We here reported an efficient Machine-learning workflow in conjunction with YeastFab Assembly strategy (MiYA) for combinatorial optimizing the large biosynthetic genotypic space of heterologous metabolic pathways in Saccharomyces cerevisiae. Using β-carotene biosynthetic pathway as example, we first demonstrated that MiYA has the power to search only a small fraction (2-5%) of combinatorial space to precisely tune the expression level of each gene with a machine-learning algorithm of an artificial neural network (ANN) ensemble to avoid over-fitting problem when dealing with a small number of training samples. We then applied MiYA to improve the biosynthesis of violacein. Feed with initial data from a colorimetric plate-based, pre-screened pool of 24 strains producing violacein, MiYA successfully predicted, and verified experimentally, the existence of a strain that showed a 2.42-fold titer improvement in violacein production among 3125 possible designs. Furthermore, MiYA was able to largely avoid the branch pathway of violacein biosynthesis that makes deoxyviolacein, and produces very pure violacein. Together, MiYA combines the advantages of standardized building blocks and machine learning to accelerate the Design-Build-Test-Learn (DBTL) cycle for combinatorial optimization of metabolic pathways, which could significantly accelerate the development of microbial cell factories. Copyright © 2018 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.

Wishart Deep Stacking Network for Fast POLSAR Image Classification.

PubMed

Jiao, Licheng; Liu, Fang

2016-05-11

Inspired by the popular deep learning architecture - Deep Stacking Network (DSN), a specific deep model for polarimetric synthetic aperture radar (POLSAR) image classification is proposed in this paper, which is named as Wishart Deep Stacking Network (W-DSN). First of all, a fast implementation of Wishart distance is achieved by a special linear transformation, which speeds up the classification of POLSAR image and makes it possible to use this polarimetric information in the following Neural Network (NN). Then a single-hidden-layer neural network based on the fast Wishart distance is defined for POLSAR image classification, which is named as Wishart Network (WN) and improves the classification accuracy. Finally, a multi-layer neural network is formed by stacking WNs, which is in fact the proposed deep learning architecture W-DSN for POLSAR image classification and improves the classification accuracy further. In addition, the structure of WN can be expanded in a straightforward way by adding hidden units if necessary, as well as the structure of the W-DSN. As a preliminary exploration on formulating specific deep learning architecture for POLSAR image classification, the proposed methods may establish a simple but clever connection between POLSAR image interpretation and deep learning. The experiment results tested on real POLSAR image show that the fast implementation of Wishart distance is very efficient (a POLSAR image with 768000 pixels can be classified in 0.53s), and both the single-hidden-layer architecture WN and the deep learning architecture W-DSN for POLSAR image classification perform well and work efficiently.

Mutual interference between statistical summary perception and statistical learning.

PubMed

Zhao, Jiaying; Ngo, Nhi; McKendrick, Ryan; Turk-Browne, Nicholas B

2011-09-01

The visual system is an efficient statistician, extracting statistical summaries over sets of objects (statistical summary perception) and statistical regularities among individual objects (statistical learning). Although these two kinds of statistical processing have been studied extensively in isolation, their relationship is not yet understood. We first examined how statistical summary perception influences statistical learning by manipulating the task that participants performed over sets of objects containing statistical regularities (Experiment 1). Participants who performed a summary task showed no statistical learning of the regularities, whereas those who performed control tasks showed robust learning. We then examined how statistical learning influences statistical summary perception by manipulating whether the sets being summarized contained regularities (Experiment 2) and whether such regularities had already been learned (Experiment 3). The accuracy of summary judgments improved when regularities were removed and when learning had occurred in advance. In sum, calculating summary statistics impeded statistical learning, and extracting statistical regularities impeded statistical summary perception. This mutual interference suggests that statistical summary perception and statistical learning are fundamentally related.

Optimized Structure of the Traffic Flow Forecasting Model With a Deep Learning Approach.

PubMed

Yang, Hao-Fan; Dillon, Tharam S; Chen, Yi-Ping Phoebe

2017-10-01

Forecasting accuracy is an important issue for successful intelligent traffic management, especially in the domain of traffic efficiency and congestion reduction. The dawning of the big data era brings opportunities to greatly improve prediction accuracy. In this paper, we propose a novel model, stacked autoencoder Levenberg-Marquardt model, which is a type of deep architecture of neural network approach aiming to improve forecasting accuracy. The proposed model is designed using the Taguchi method to develop an optimized structure and to learn traffic flow features through layer-by-layer feature granulation with a greedy layerwise unsupervised learning algorithm. It is applied to real-world data collected from the M6 freeway in the U.K. and is compared with three existing traffic predictors. To the best of our knowledge, this is the first time that an optimized structure of the traffic flow forecasting model with a deep learning approach is presented. The evaluation results demonstrate that the proposed model with an optimized structure has superior performance in traffic flow forecasting.

Fidelity-Based Ant Colony Algorithm with Q-learning of Quantum System

NASA Astrophysics Data System (ADS)

Liao, Qin; Guo, Ying; Tu, Yifeng; Zhang, Hang

2018-03-01

Quantum ant colony algorithm (ACA) has potential applications in quantum information processing, such as solutions of traveling salesman problem, zero-one knapsack problem, robot route planning problem, and so on. To shorten the search time of the ACA, we suggest the fidelity-based ant colony algorithm (FACA) for the control of quantum system. Motivated by structure of the Q-learning algorithm, we demonstrate the combination of a FACA with the Q-learning algorithm and suggest the design of a fidelity-based ant colony algorithm with the Q-learning to improve the performance of the FACA in a spin-1/2 quantum system. The numeric simulation results show that the FACA with the Q-learning can efficiently avoid trapping into local optimal policies and increase the speed of convergence process of quantum system.

Fidelity-Based Ant Colony Algorithm with Q-learning of Quantum System

NASA Astrophysics Data System (ADS)

Liao, Qin; Guo, Ying; Tu, Yifeng; Zhang, Hang

2017-12-01

Quantum ant colony algorithm (ACA) has potential applications in quantum information processing, such as solutions of traveling salesman problem, zero-one knapsack problem, robot route planning problem, and so on. To shorten the search time of the ACA, we suggest the fidelity-based ant colony algorithm (FACA) for the control of quantum system. Motivated by structure of the Q-learning algorithm, we demonstrate the combination of a FACA with the Q-learning algorithm and suggest the design of a fidelity-based ant colony algorithm with the Q-learning to improve the performance of the FACA in a spin-1/2 quantum system. The numeric simulation results show that the FACA with the Q-learning can efficiently avoid trapping into local optimal policies and increase the speed of convergence process of quantum system.

Optimizing the number of steps in learning tasks for complex skills.

PubMed

Nadolski, Rob J; Kirschner, Paul A; van Merriënboer, Jeroen J G

2005-06-01

Carrying out whole tasks is often too difficult for novice learners attempting to acquire complex skills. The common solution is to split up the tasks into a number of smaller steps. The number of steps must be optimized for efficient and effective learning. The aim of the study is to investigate the relation between the number of steps provided to learners and the quality of their learning of complex skills. It is hypothesized that students receiving an optimized number of steps will learn better than those receiving either the whole task in only one step or those receiving a large number of steps. Participants were 35 sophomore law students studying at Dutch universities, mean age=22.8 years (SD=3.5), 63% were female. Participants were randomly assigned to 1 of 3 computer-delivered versions of a multimedia programme on how to prepare and carry out a law plea. The versions differed only in the number of learning steps provided. Videotaped plea-performance results were determined, various related learning measures were acquired and all computer actions were logged and analyzed. Participants exposed to an intermediate (i.e. optimized) number of steps outperformed all others on the compulsory learning task. No differences in performance on a transfer task were found. A high number of steps proved to be less efficient for carrying out the learning task. An intermediate number of steps is the most effective, proving that the number of steps can be optimized for improving learning.

Efficacy of plaque removal and learning effect of a powered and a manual toothbrush.

PubMed

Lazarescu, D; Boccaneala, S; Illiescu, A; De Boever, J A

2003-08-01

Subjects with high plaque and gingivitis scores can profit most from the introduction of new manual or powered tooth brushes. To improve their hygiene, not only the technical characteristics of new brushes but also the learning effect in efficient handling are of importance. : The present study compared the efficacy in plaque removal of an electric and a manual toothbrush in a general population and analysed the learning effect in efficient handling. Eighty healthy subjects, unfamiliar with electric brushes, were divided into two groups: group 1 used the Philips/Jordan HP 735 powered brush and group 2 used a manual brush, Oral-B40+. Plaque index (PI) and gingival bleeding index (GBI) were assessed at baseline and at weeks 3, 6, 12 and 18. After each evaluation, patients abstained from oral hygiene for 24 h. The next day a 3-min supervised brushing was performed. Before and after this brushing, PI was assessed for the estimation of the individual learning effect. The study was single blinded. Over the 18-week period, PI reduced gradually and statistically significantly (p<0.001) in group 1 from 2.9 (+/-0.38) to 1.5 (+/-0.24) and in group 2 from 2.9 (+/-0.34) to 2.2 (+/-0.23). From week 3 onwards, the difference between groups was statistically significant (p<0.001). The bleeding index decreased in group 1 from 28% (+/-17%) to 7% (+/-5%) (p<0.001) and in group 2 from 30% (+/-12%) to 12% (+/-6%) (p<0.001). The difference between groups was statistically significant (p<0.001) from week 6 onwards. The learning effect, expressed as the percentage of plaque reduction after 3 min of supervised brushing, was 33% for group 1 and 26% for group 2 at week 0. This percentage increased at week 18 to 64% in group 1 and 44% in group 2 (difference between groups statistically significant: p<0.001). The powered brush was significantly more efficient in removing plaque and improving gingival health than the manual brush in the group of subjects unfamiliar with electric brushes. There was also a significant learning effect that was more pronounced with the electric toothbrush.

Improving the performance of the amblyopic visual system

PubMed Central

Levi, Dennis M.; Li, Roger W.

2008-01-01

Experience-dependent plasticity is closely linked with the development of sensory function; however, there is also growing evidence for plasticity in the adult visual system. This review re-examines the notion of a sensitive period for the treatment of amblyopia in the light of recent experimental and clinical evidence for neural plasticity. One recently proposed method for improving the effectiveness and efficiency of treatment that has received considerable attention is ‘perceptual learning’. Specifically, both children and adults with amblyopia can improve their perceptual performance through extensive practice on a challenging visual task. The results suggest that perceptual learning may be effective in improving a range of visual performance and, importantly, the improvements may transfer to visual acuity. Recent studies have sought to explore the limits and time course of perceptual learning as an adjunct to occlusion and to investigate the neural mechanisms underlying the visual improvement. These findings, along with the results of new clinical trials, suggest that it might be time to reconsider our notions about neural plasticity in amblyopia. PMID:19008199

Compressed Air System Redesign Results in Increased Production at a Fuel System Plant (Caterpillar Fuel Systems Pontiac Plant)

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

None

2001-06-01

This case study is one in a series on industrial firms who are implementing energy efficient technologies and system improvements into their manufacturing processes. This case study documents the activities, savings, and lessons learned on the Caterpillar's Pontiac Plant project.

Improved System Yields $100,000 Annual Savings (Systems Analysis at Alcoa Yields Significant Savings)

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

None

1999-01-01

In another Office of Industrial Technologies Motor Challenge Success Story, Alcoa (formerly Alumax) aluminum reduced annual energy consumption by 12% and reduced both maintenance and noise levels. Order this fact sheet now to learn how your company can both increase energy efficiency and decrease pollution.

Corporate Energy Conservation Program for Alcoa North American Extrusions: Office of Industrial Technologies (OIT) Aluminum BestPractices Management Case Study

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

U.S. Department of Energy

2001-08-06

This case study is the latest in a series on industrial firms who are implementing energy efficient technologies and system improvements into their manufacturing processes. The case studies document the activities, savings, and lessons learned on these projects.

The 2 Es

ERIC Educational Resources Information Center

Kroog, Heidi; Hess, Kristin King; Ruiz-Primo, Maria Araceli

2016-01-01

What are the characteristics of formal formative assessments that are both effective in improving student learning and an efficient use of a teacher's time and efforts? That's the question that the authors explore in this article drawing on a five-year research study. First, formal formative assessment is defined as being planned in advance,…

A Foothold for Handhelds.

ERIC Educational Resources Information Center

Joyner, Amy

2003-01-01

Handheld computers provide students tremendous computing and learning power at about a 10th the cost of a regular computer. Describes the evolution of handhelds; provides some examples of their uses; and cites research indicating they are effective classroom tools that can improve efficiency and instruction. A sidebar lists handheld resources.…

Working Memory Intervention: A Reading Comprehension Approach

ERIC Educational Resources Information Center

Perry, Tracy L.; Malaia, Evguenia

2013-01-01

For any complex mental task, people rely on working memory. Working memory capacity (WMC) is one predictor of success in learning. Historically, attempts to improve verbal WM through training have not been effective. This study provided elementary students with WM consolidation efficiency training to answer the question, Can reading comprehension…

Providing Demonstrable Return-on-Investment for Organisational Learning and Training

ERIC Educational Resources Information Center

Elliott, Michael; Dawson, Ray; Edwards, Janet

2009-01-01

Purpose: The aim of this paper is to present a holistic approach to training, that clearly demonstrates cost savings with improved effectiveness and efficiencies that are aligned to business objectives. Design/methodology/approach: Extending Kirkpatrick's evaluation framework with Phillips's return-on-investment (ROI) concepts, the paper conveys a…

Organizational Problems of Nutrition in the Context of Modernization of Education

ERIC Educational Resources Information Center

Platonovaa, Raisa I.; Lebedeva, Uljana M.; Cherkashina, Anna G.; Ammosova, Liliya I.; Dokhunaeva, Alyona V.

2016-01-01

The realization of the project of regional educational systems' modernization was started in 2011. The main goal of the project is to achieve systemic positive changes in the school education, improving of learning conditions, increasing of openness, availability, efficiency of General education, introduction of modern educational technologies. In…

Learning across Lines. The Secret to More Efficient Factories.

ERIC Educational Resources Information Center

Lapre, Michael A.; Van Wassenhove, Luk N.

2002-01-01

Manufacturers' attempts to boost operating productivity rarely pay off and some do more harm than good. A study of a Belgian manufacturer illustrates characteristics that enhance productivity improvement: (1) production of process knowledge that is well understood and relevant and (2) transfer by combining conceptual and operational knowledge.…

Process-Oriented Worked Examples: Improving Transfer Performance through Enhanced Understanding

ERIC Educational Resources Information Center

van Gog, Tamara; Paas, Fred; van Merrienboer, Jeroen J. G.

2004-01-01

The research on worked examples has shown that for novices, studying worked examples is often a more effective and efficient way of learning than solving conventional problems. This theoretical paper argues that adding process-oriented information to worked examples can further enhance transfer performance, especially for complex cognitive skills…

EDUCATIONAL CASE REPORTS

PubMed Central

Ackerman, Sara L.; Boscardin, Christy; Karliner, Leah; Handley, Margaret A.; Cheng, Sarah; Gaither, Tom; Hagey, Jill; Hennein, Lauren; Malik, Faizan; Shaw, Brian; Trinidad, Norver; Zahner, Greg; Gonzales, Ralph

2016-01-01

Problem Systems-based practice focuses on the organization, financing, and delivery of medical services. The American Association of Medical Colleges has recommended that systems-based practice be incorporated into medical schools’ curricula. However, experiential learning in systems-based practice, including practical strategies to improve the quality and efficiency of clinical care, is often absent from or inconsistently included in medical education. Intervention A multidisciplinary clinician and non-clinician faculty team partnered with a cardiology outpatient clinic to design a nine-month clerkship for first-year medical students focused on systems-based practice, delivery of clinical care, and strategies to improve the quality and efficiency of clinical operations. The clerkship was called the Action Research Program. In 2013–2014, eight trainees participated in educational seminars, research activities, and nine-week clinic rotations. A qualitative process and outcome evaluation drew on interviews with students, clinic staff, and supervising physicians, as well as students’ detailed field notes. Context The Action Research Program was developed and implemented at the University of California, San Francisco, an academic medical center in the U.S. All educational activities took place at the university’s medical school and at the medical center’s cardiology outpatient clinic. Outcome Students reported and demonstrated increased understanding of how care delivery systems work, improved clinical skills, growing confidence in interactions with patients, and appreciation for patients’ experiences. Clinicians reported increased efficiency at the clinic level and improved performance and job satisfaction among medical assistants as a result of their unprecedented mentoring role with students. Some clinicians felt burdened when students shadowed them and asked questions during interactions with patients. Most student-led improvement projects were not fully implemented. Lessons Learned The Action Research Program is a small pilot project that demonstrates an innovative pairing of experiential and didactic training in systems-based practice. Lessons learned include the need for dedicated time and faculty support for students’ improvement projects, which were the least successful aspect of the program. We recommend that future projects aiming to combine clinical training and quality improvement projects designate distinct blocks of time for trainees to pursue each of these activities independently. In 2014–2015, the University of California, San Francisco School of Medicine incorporated key features of the Action Research Program into the standard curriculum, with plans to build upon this foundation in future curricular innovations. PMID:27064720

The Action Research Program: Experiential Learning in Systems-Based Practice for First-Year Medical Students.

PubMed

Ackerman, Sara L; Boscardin, Christy; Karliner, Leah; Handley, Margaret A; Cheng, Sarah; Gaither, Thomas W; Hagey, Jill; Hennein, Lauren; Malik, Faizan; Shaw, Brian; Trinidad, Norver; Zahner, Greg; Gonzales, Ralph

2016-01-01

Systems-based practice focuses on the organization, financing, and delivery of medical services. The American Association of Medical Colleges has recommended that systems-based practice be incorporated into medical schools' curricula. However, experiential learning in systems-based practice, including practical strategies to improve the quality and efficiency of clinical care, is often absent from or inconsistently included in medical education. A multidisciplinary clinician and nonclinician faculty team partnered with a cardiology outpatient clinic to design a 9-month clerkship for 1st-year medical students focused on systems-based practice, delivery of clinical care, and strategies to improve the quality and efficiency of clinical operations. The clerkship was called the Action Research Program. In 2013-2014, 8 trainees participated in educational seminars, research activities, and 9-week clinic rotations. A qualitative process and outcome evaluation drew on interviews with students, clinic staff, and supervising physicians, as well as students' detailed field notes. The Action Research Program was developed and implemented at the University of California, San Francisco, an academic medical center in the United States. All educational activities took place at the university's medical school and at the medical center's cardiology outpatient clinic. Students reported and demonstrated increased understanding of how care delivery systems work, improved clinical skills, growing confidence in interactions with patients, and appreciation for patients' experiences. Clinicians reported increased efficiency at the clinic level and improved performance and job satisfaction among medical assistants as a result of their unprecedented mentoring role with students. Some clinicians felt burdened when students shadowed them and asked questions during interactions with patients. Most student-led improvement projects were not fully implemented. The Action Research Program is a small pilot project that demonstrates an innovative pairing of experiential and didactic training in systems-based practice. Lessons learned include the need for dedicated time and faculty support for students' improvement projects, which were the least successful aspect of the program. We recommend that future projects aiming to combine clinical training and quality improvement projects designate distinct blocks of time for trainees to pursue each of these activities independently. In 2014-2015, the University of California, San Francisco School of Medicine incorporated key features of the Action Research Program into the standard curriculum, with plans to build upon this foundation in future curricular innovations.

Adaptive and accelerated tracking-learning-detection

NASA Astrophysics Data System (ADS)

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

2013-08-01

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

Accurate Descriptions of Hot Flow Behaviors Across β Transus of Ti-6Al-4V Alloy by Intelligence Algorithm GA-SVR

NASA Astrophysics Data System (ADS)

Wang, Li-yong; Li, Le; Zhang, Zhi-hua

2016-09-01

Hot compression tests of Ti-6Al-4V alloy in a wide temperature range of 1023-1323 K and strain rate range of 0.01-10 s-1 were conducted by a servo-hydraulic and computer-controlled Gleeble-3500 machine. In order to accurately and effectively characterize the highly nonlinear flow behaviors, support vector regression (SVR) which is a machine learning method was combined with genetic algorithm (GA) for characterizing the flow behaviors, namely, the GA-SVR. The prominent character of GA-SVR is that it with identical training parameters will keep training accuracy and prediction accuracy at a stable level in different attempts for a certain dataset. The learning abilities, generalization abilities, and modeling efficiencies of the mathematical regression model, ANN, and GA-SVR for Ti-6Al-4V alloy were detailedly compared. Comparison results show that the learning ability of the GA-SVR is stronger than the mathematical regression model. The generalization abilities and modeling efficiencies of these models were shown as follows in ascending order: the mathematical regression model < ANN < GA-SVR. The stress-strain data outside experimental conditions were predicted by the well-trained GA-SVR, which improved simulation accuracy of the load-stroke curve and can further improve the related research fields where stress-strain data play important roles, such as speculating work hardening and dynamic recovery, characterizing dynamic recrystallization evolution, and improving processing maps.

Learning to rank diversified results for biomedical information retrieval from multiple features.

PubMed

Wu, Jiajin; Huang, Jimmy; Ye, Zheng

2014-01-01

Different from traditional information retrieval (IR), promoting diversity in IR takes consideration of relationship between documents in order to promote novelty and reduce redundancy thus to provide diversified results to satisfy various user intents. Diversity IR in biomedical domain is especially important as biologists sometimes want diversified results pertinent to their query. A combined learning-to-rank (LTR) framework is learned through a general ranking model (gLTR) and a diversity-biased model. The former is learned from general ranking features by a conventional learning-to-rank approach; the latter is constructed with diversity-indicating features added, which are extracted based on the retrieved passages' topics detected using Wikipedia and ranking order produced by the general learning-to-rank model; final ranking results are given by combination of both models. Compared with baselines BM25 and DirKL on 2006 and 2007 collections, the gLTR has 0.2292 (+16.23% and +44.1% improvement over BM25 and DirKL respectively) and 0.1873 (+15.78% and +39.0% improvement over BM25 and DirKL respectively) in terms of aspect level of mean average precision (Aspect MAP). The LTR method outperforms gLTR on 2006 and 2007 collections with 4.7% and 2.4% improvement in terms of Aspect MAP. The learning-to-rank method is an efficient way for biomedical information retrieval and the diversity-biased features are beneficial for promoting diversity in ranking results.

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

PubMed

Hofmann, Bjørn

2009-07-23

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

Twelve tips for utilizing principles of learning to support medical education.

PubMed

Cutting, Maris F; Saks, Norma Susswein

2012-01-01

Research in the cognitive sciences on learning and memory conducted across a range of domains, settings, and age groups has resulted in the identification and formulation of a set of generic learning principles. These learning principles have proven relevant and applicable to a wide range of learning situations in a variety of settings, and can be useful in supporting medical education. They can provide guidance to medical students for efficient and effective study, and can be helpful to faculty to support instructional planning and decisions relating to curriculum. This article discusses evidence-based principles of learning and their relationship to effective learning, teaching, pedagogy and curriculum development. We reviewed important principles of learning to determine those most relevant to improving medical student learning, guiding faculty toward more effective teaching, and in designing a curriculum. Our analysis has resulted in the articulation of key learning principles and specific strategies that are broadly applicable to medical school learning, teaching, and instructional planning. The twelve tips highlight principles of learning that can be effectively applied in the complex learning environment of medical education.

Less is more: latent learning is maximized by shorter training sessions in auditory perceptual learning.

PubMed

Molloy, Katharine; Moore, David R; Sohoglu, Ediz; Amitay, Sygal

2012-01-01

The time course and outcome of perceptual learning can be affected by the length and distribution of practice, but the training regimen parameters that govern these effects have received little systematic study in the auditory domain. We asked whether there was a minimum requirement on the number of trials within a training session for learning to occur, whether there was a maximum limit beyond which additional trials became ineffective, and whether multiple training sessions provided benefit over a single session. We investigated the efficacy of different regimens that varied in the distribution of practice across training sessions and in the overall amount of practice received on a frequency discrimination task. While learning was relatively robust to variations in regimen, the group with the shortest training sessions (∼8 min) had significantly faster learning in early stages of training than groups with longer sessions. In later stages, the group with the longest training sessions (>1 hr) showed slower learning than the other groups, suggesting overtraining. Between-session improvements were inversely correlated with performance; they were largest at the start of training and reduced as training progressed. In a second experiment we found no additional longer-term improvement in performance, retention, or transfer of learning for a group that trained over 4 sessions (∼4 hr in total) relative to a group that trained for a single session (∼1 hr). However, the mechanisms of learning differed; the single-session group continued to improve in the days following cessation of training, whereas the multi-session group showed no further improvement once training had ceased. Shorter training sessions were advantageous because they allowed for more latent, between-session and post-training learning to emerge. These findings suggest that efficient regimens should use short training sessions, and optimized spacing between sessions.

Less Is More: Latent Learning Is Maximized by Shorter Training Sessions in Auditory Perceptual Learning

PubMed Central

Molloy, Katharine; Moore, David R.; Sohoglu, Ediz; Amitay, Sygal

2012-01-01

Background The time course and outcome of perceptual learning can be affected by the length and distribution of practice, but the training regimen parameters that govern these effects have received little systematic study in the auditory domain. We asked whether there was a minimum requirement on the number of trials within a training session for learning to occur, whether there was a maximum limit beyond which additional trials became ineffective, and whether multiple training sessions provided benefit over a single session. Methodology/Principal Findings We investigated the efficacy of different regimens that varied in the distribution of practice across training sessions and in the overall amount of practice received on a frequency discrimination task. While learning was relatively robust to variations in regimen, the group with the shortest training sessions (∼8 min) had significantly faster learning in early stages of training than groups with longer sessions. In later stages, the group with the longest training sessions (>1 hr) showed slower learning than the other groups, suggesting overtraining. Between-session improvements were inversely correlated with performance; they were largest at the start of training and reduced as training progressed. In a second experiment we found no additional longer-term improvement in performance, retention, or transfer of learning for a group that trained over 4 sessions (∼4 hr in total) relative to a group that trained for a single session (∼1 hr). However, the mechanisms of learning differed; the single-session group continued to improve in the days following cessation of training, whereas the multi-session group showed no further improvement once training had ceased. Conclusions/Significance Shorter training sessions were advantageous because they allowed for more latent, between-session and post-training learning to emerge. These findings suggest that efficient regimens should use short training sessions, and optimized spacing between sessions. PMID:22606309

Research on cardiovascular disease prediction based on distance metric learning

NASA Astrophysics Data System (ADS)

Ni, Zhuang; Liu, Kui; Kang, Guixia

2018-04-01

Distance metric learning algorithm has been widely applied to medical diagnosis and exhibited its strengths in classification problems. The k-nearest neighbour (KNN) is an efficient method which treats each feature equally. The large margin nearest neighbour classification (LMNN) improves the accuracy of KNN by learning a global distance metric, which did not consider the locality of data distributions. In this paper, we propose a new distance metric algorithm adopting cosine metric and LMNN named COS-SUBLMNN which takes more care about local feature of data to overcome the shortage of LMNN and improve the classification accuracy. The proposed methodology is verified on CVDs patient vector derived from real-world medical data. The Experimental results show that our method provides higher accuracy than KNN and LMNN did, which demonstrates the effectiveness of the Risk predictive model of CVDs based on COS-SUBLMNN.

Machine learning action parameters in lattice quantum chromodynamics

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

Shanahan, Phiala; Trewartha, Daneil; Detmold, William

Numerical lattice quantum chromodynamics studies of the strong interaction underpin theoretical understanding of many aspects of particle and nuclear physics. Such studies require significant computing resources to undertake. A number of proposed methods promise improved efficiency of lattice calculations, and access to regions of parameter space that are currently computationally intractable, via multi-scale action-matching approaches that necessitate parametric regression of generated lattice datasets. The applicability of machine learning to this regression task is investigated, with deep neural networks found to provide an efficient solution even in cases where approaches such as principal component analysis fail. Finally, the high information contentmore » and complex symmetries inherent in lattice QCD datasets require custom neural network layers to be introduced and present opportunities for further development.« less

Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach.

PubMed

Gómez-Bombarelli, Rafael; Aguilera-Iparraguirre, Jorge; Hirzel, Timothy D; Duvenaud, David; Maclaurin, Dougal; Blood-Forsythe, Martin A; Chae, Hyun Sik; Einzinger, Markus; Ha, Dong-Gwang; Wu, Tony; Markopoulos, Georgios; Jeon, Soonok; Kang, Hosuk; Miyazaki, Hiroshi; Numata, Masaki; Kim, Sunghan; Huang, Wenliang; Hong, Seong Ik; Baldo, Marc; Adams, Ryan P; Aspuru-Guzik, Alán

2016-10-01

Virtual screening is becoming a ground-breaking tool for molecular discovery due to the exponential growth of available computer time and constant improvement of simulation and machine learning techniques. We report an integrated organic functional material design process that incorporates theoretical insight, quantum chemistry, cheminformatics, machine learning, industrial expertise, organic synthesis, molecular characterization, device fabrication and optoelectronic testing. After exploring a search space of 1.6 million molecules and screening over 400,000 of them using time-dependent density functional theory, we identified thousands of promising novel organic light-emitting diode molecules across the visible spectrum. Our team collaboratively selected the best candidates from this set. The experimentally determined external quantum efficiencies for these synthesized candidates were as large as 22%.

Machine learning action parameters in lattice quantum chromodynamics

DOE PAGES

Shanahan, Phiala; Trewartha, Daneil; Detmold, William

2018-05-16

Numerical lattice quantum chromodynamics studies of the strong interaction underpin theoretical understanding of many aspects of particle and nuclear physics. Such studies require significant computing resources to undertake. A number of proposed methods promise improved efficiency of lattice calculations, and access to regions of parameter space that are currently computationally intractable, via multi-scale action-matching approaches that necessitate parametric regression of generated lattice datasets. The applicability of machine learning to this regression task is investigated, with deep neural networks found to provide an efficient solution even in cases where approaches such as principal component analysis fail. Finally, the high information contentmore » and complex symmetries inherent in lattice QCD datasets require custom neural network layers to be introduced and present opportunities for further development.« less

Physics Teachers' Professional Development in the Project "physics in Context"

NASA Astrophysics Data System (ADS)

Mikelskis-Seifert, Silke; Duit, Reinders

2013-06-01

Developing teachers' ways of thinking about "good" instruction as well as their views of the teaching and learning process is generally seen as essential for improving teaching behaviour and implementation of more efficient teaching and learning settings. Major deficiencies of German physics instruction as revealed by a nationwide video-study on the practice of physics instruction are addressed. Teachers participating in the project are made familiar with recent views of efficient instruction on the one hand and develop context-based instructional settings on the other. The evaluation resulted in partly encouraging findings. However, it also turned out that a number of teachers' ways of thinking about good instruction did only develop to a somewhat limited degree. The most impressive changes occurred for teachers who enjoyed the most intensive coaching.

Physics Teachers' Professional Development in the Project "physics in Context"

NASA Astrophysics Data System (ADS)

Mikelskis-Seifert, Silke; Duit, Reinders

2012-12-01

Developing teachers' ways of thinking about "good" instruction as well as their views of the teaching and learning process is generally seen as essential for improving teaching behaviour and implementation of more efficient teaching and learning settings. Major deficiencies of German physics instruction as revealed by a nationwide video-study on the practice of physics instruction are addressed. Teachers participating in the project are made familiar with recent views of efficient instruction on the one hand and develop context-based instructional settings on the other. The evaluation resulted in partly encouraging findings. However, it also turned out that a number of teachers' ways of thinking about good instruction did only develop to a somewhat limited degree. The most impressive changes occurred for teachers who enjoyed the most intensive coaching.

Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach

NASA Astrophysics Data System (ADS)

Gómez-Bombarelli, Rafael; Aguilera-Iparraguirre, Jorge; Hirzel, Timothy D.; Duvenaud, David; MacLaurin, Dougal; Blood-Forsythe, Martin A.; Chae, Hyun Sik; Einzinger, Markus; Ha, Dong-Gwang; Wu, Tony; Markopoulos, Georgios; Jeon, Soonok; Kang, Hosuk; Miyazaki, Hiroshi; Numata, Masaki; Kim, Sunghan; Huang, Wenliang; Hong, Seong Ik; Baldo, Marc; Adams, Ryan P.; Aspuru-Guzik, Alán

2016-10-01

Virtual screening is becoming a ground-breaking tool for molecular discovery due to the exponential growth of available computer time and constant improvement of simulation and machine learning techniques. We report an integrated organic functional material design process that incorporates theoretical insight, quantum chemistry, cheminformatics, machine learning, industrial expertise, organic synthesis, molecular characterization, device fabrication and optoelectronic testing. After exploring a search space of 1.6 million molecules and screening over 400,000 of them using time-dependent density functional theory, we identified thousands of promising novel organic light-emitting diode molecules across the visible spectrum. Our team collaboratively selected the best candidates from this set. The experimentally determined external quantum efficiencies for these synthesized candidates were as large as 22%.

Robotic Mitral Valve Repair: The Learning Curve.

PubMed

Goodman, Avi; Koprivanac, Marijan; Kelava, Marta; Mick, Stephanie L; Gillinov, A Marc; Rajeswaran, Jeevanantham; Brzezinski, Anna; Blackstone, Eugene H; Mihaljevic, Tomislav

Adoption of robotic mitral valve surgery has been slow, likely in part because of its perceived technical complexity and a poorly understood learning curve. We sought to correlate changes in technical performance and outcome with surgeon experience in the "learning curve" part of our series. From 2006 to 2011, two surgeons undertook robotically assisted mitral valve repair in 458 patients (intent-to-treat); 404 procedures were completed entirely robotically (as-treated). Learning curves were constructed by modeling surgical sequence number semiparametrically with flexible penalized spline smoothing best-fit curves. Operative efficiency, reflecting technical performance, improved for (1) operating room time for case 1 to cases 200 (early experience) and 400 (later experience), from 414 to 364 to 321 minutes (12% and 22% decrease, respectively), (2) cardiopulmonary bypass time, from 148 to 102 to 91 minutes (31% and 39% decrease), and (3) myocardial ischemic time, from 119 to 75 to 68 minutes (37% and 43% decrease). Composite postoperative complications, reflecting safety, decreased from 17% to 6% to 2% (63% and 85% decrease). Intensive care unit stay decreased from 32 to 28 to 24 hours (13% and 25% decrease). Postoperative stay fell from 5.2 to 4.5 to 3.8 days (13% and 27% decrease). There were no in-hospital deaths. Predischarge mitral regurgitation of less than 2+, reflecting effectiveness, was achieved in 395 (97.8%), without correlation to experience; return-to-work times did not change substantially with experience. Technical efficiency of robotic mitral valve repair improves with experience and permits its safe and effective conduct.

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.

Prediction task guided representation learning of medical codes in EHR.

PubMed

Cui, Liwen; Xie, Xiaolei; Shen, Zuojun

2018-06-18

There have been rapidly growing applications using machine learning models for predictive analytics in Electronic Health Records (EHR) to improve the quality of hospital services and the efficiency of healthcare resource utilization. A fundamental and crucial step in developing such models is to convert medical codes in EHR to feature vectors. These medical codes are used to represent diagnoses or procedures. Their vector representations have a tremendous impact on the performance of machine learning models. Recently, some researchers have utilized representation learning methods from Natural Language Processing (NLP) to learn vector representations of medical codes. However, most previous approaches are unsupervised, i.e. the generation of medical code vectors is independent from prediction tasks. Thus, the obtained feature vectors may be inappropriate for a specific prediction task. Moreover, unsupervised methods often require a lot of samples to obtain reliable results, but most practical problems have very limited patient samples. In this paper, we develop a new method called Prediction Task Guided Health Record Aggregation (PTGHRA), which aggregates health records guided by prediction tasks, to construct training corpus for various representation learning models. Compared with unsupervised approaches, representation learning models integrated with PTGHRA yield a significant improvement in predictive capability of generated medical code vectors, especially for limited training samples. Copyright © 2018. Published by Elsevier Inc.

Multi-agent Reinforcement Learning Model for Effective Action Selection

NASA Astrophysics Data System (ADS)

Youk, Sang Jo; Lee, Bong Keun

Reinforcement learning is a sub area of machine learning concerned with how an agent ought to take actions in an environment so as to maximize some notion of long-term reward. In the case of multi-agent, especially, which state space and action space gets very enormous in compared to single agent, so it needs to take most effective measure available select the action strategy for effective reinforcement learning. This paper proposes a multi-agent reinforcement learning model based on fuzzy inference system in order to improve learning collect speed and select an effective action in multi-agent. This paper verifies an effective action select strategy through evaluation tests based on Robocop Keep away which is one of useful test-beds for multi-agent. Our proposed model can apply to evaluate efficiency of the various intelligent multi-agents and also can apply to strategy and tactics of robot soccer system.

Highly undersampled MR image reconstruction using an improved dual-dictionary learning method with self-adaptive dictionaries.

PubMed

Li, Jiansen; Song, Ying; Zhu, Zhen; Zhao, Jun

2017-05-01

Dual-dictionary learning (Dual-DL) method utilizes both a low-resolution dictionary and a high-resolution dictionary, which are co-trained for sparse coding and image updating, respectively. It can effectively exploit a priori knowledge regarding the typical structures, specific features, and local details of training sets images. The prior knowledge helps to improve the reconstruction quality greatly. This method has been successfully applied in magnetic resonance (MR) image reconstruction. However, it relies heavily on the training sets, and dictionaries are fixed and nonadaptive. In this research, we improve Dual-DL by using self-adaptive dictionaries. The low- and high-resolution dictionaries are updated correspondingly along with the image updating stage to ensure their self-adaptivity. The updated dictionaries incorporate both the prior information of the training sets and the test image directly. Both dictionaries feature improved adaptability. Experimental results demonstrate that the proposed method can efficiently and significantly improve the quality and robustness of MR image reconstruction.

What millennial medical students say about flipped learning.

PubMed

Pettit, Robin K; McCoy, Lise; Kinney, Marjorie

2017-01-01

Flipped instruction is gaining popularity in medical schools, but there are unanswered questions such as the optimum amount of the curriculum to flip and whether flipped sessions should be mandatory. We were in a unique position to evaluate feedback from first-year medical students who had experienced both flipped and lecture-based courses during their first semester of medical school. A key finding was that the students preferred a variety of different learning formats over an "all or nothing" learning format. Learning format preferences did not necessarily align with perceptions of which format led to better course exam performance. Nearly 70% of respondents wanted to make their own decisions regarding attendance. Candid responses to open-ended survey prompts reflected millennial preferences for choice, flexibility, efficiency, and the ability to control the pace of their learning, providing insight to guide curricular improvements.

Learning Negotiation Policies Using IB3 and Bayesian Networks

NASA Astrophysics Data System (ADS)

Nalepa, Gislaine M.; Ávila, Bráulio C.; Enembreck, Fabrício; Scalabrin, Edson E.

This paper presents an intelligent offer policy in a negotiation environment, in which each agent involved learns the preferences of its opponent in order to improve its own performance. Each agent must also be able to detect drifts in the opponent's preferences so as to quickly adjust itself to their new offer policy. For this purpose, two simple learning techniques were first evaluated: (i) based on instances (IB3) and (ii) based on Bayesian Networks. Additionally, as its known that in theory group learning produces better results than individual/single learning, the efficiency of IB3 and Bayesian classifier groups were also analyzed. Finally, each decision model was evaluated in moments of concept drift, being the drift gradual, moderate or abrupt. Results showed that both groups of classifiers were able to effectively detect drifts in the opponent's preferences.

VLSI Design of SVM-Based Seizure Detection System With On-Chip Learning Capability.

PubMed

Feng, Lichen; Li, Zunchao; Wang, Yuanfa

2018-02-01

Portable automatic seizure detection system is very convenient for epilepsy patients to carry. In order to make the system on-chip trainable with high efficiency and attain high detection accuracy, this paper presents a very large scale integration (VLSI) design based on the nonlinear support vector machine (SVM). The proposed design mainly consists of a feature extraction (FE) module and an SVM module. The FE module performs the three-level Daubechies discrete wavelet transform to fit the physiological bands of the electroencephalogram (EEG) signal and extracts the time-frequency domain features reflecting the nonstationary signal properties. The SVM module integrates the modified sequential minimal optimization algorithm with the table-driven-based Gaussian kernel to enable efficient on-chip learning. The presented design is verified on an Altera Cyclone II field-programmable gate array and tested using the two publicly available EEG datasets. Experiment results show that the designed VLSI system improves the detection accuracy and training efficiency.

Development and implementation of a radiation therapy incident learning system compatible with local workflow and a national taxonomy.

PubMed

Montgomery, Logan; Fava, Palma; Freeman, Carolyn R; Hijal, Tarek; Maietta, Ciro; Parker, William; Kildea, John

2018-01-01

Collaborative incident learning initiatives in radiation therapy promise to improve and standardize the quality of care provided by participating institutions. However, the software interfaces provided with such initiatives must accommodate all participants and thus are not optimized for the workflows of individual radiation therapy centers. This article describes the development and implementation of a radiation therapy incident learning system that is optimized for a clinical workflow and uses the taxonomy of the Canadian National System for Incident Reporting - Radiation Treatment (NSIR-RT). The described incident learning system is a novel version of an open-source software called the Safety and Incident Learning System (SaILS). A needs assessment was conducted prior to development to ensure SaILS (a) was intuitive and efficient (b) met changing staff needs and (c) accommodated revisions to NSIR-RT. The core functionality of SaILS includes incident reporting, investigations, tracking, and data visualization. Postlaunch modifications of SaILS were informed by discussion and a survey of radiation therapy staff. There were 240 incidents detected and reported using SaILS in 2016 and the number of incidents per month tended to increase throughout the year. An increase in incident reporting occurred after switching to fully online incident reporting from an initial hybrid paper-electronic system. Incident templating functionality and a connection with our center's oncology information system were incorporated into the investigation interface to minimize repetitive data entry. A taskable actions feature was also incorporated to document outcomes of incident reports and has since been utilized for 36% of reported incidents. Use of SaILS and the NSIR-RT taxonomy has improved the structure of, and staff engagement with, incident learning in our center. Software and workflow modifications informed by staff feedback improved the utility of SaILS and yielded an efficient and transparent solution to categorize incidents with the NSIR-RT taxonomy. © 2017 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

Alcoa North American Extrusions Implements Energy Use Assessments at Multiple Facilities: Office of Industrial Technologies (OIT) BestPractices Aluminum Assessment Case Study

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

U.S. Department of Energy

2001-08-05

This case study is the latest in a series on industrial firms who are implementing energy efficient technologies and system improvements into their manufacturing processes. The case studies document the activities, savings, and lessons learned on these projects.

Function modeling improves the efficiency of spatial modeling using big data from remote sensing

Treesearch

John Hogland; Nathaniel Anderson

2017-01-01

Spatial modeling is an integral component of most geographic information systems (GISs). However, conventional GIS modeling techniques can require substantial processing time and storage space and have limited statistical and machine learning functionality. To address these limitations, many have parallelized spatial models using multiple coding libraries and have...

A Learning Theory Conceptual Foundation for Using Capture Technology in Teaching

ERIC Educational Resources Information Center

Berardi, Victor; Blundell, Greg

2014-01-01

Lecture capture technologies are increasingly being used by instructors, programs, and institutions to deliver online lectures and courses. This lecture capture movement is important as it increases access to education opportunities that were not possible before, it can improve efficiency, and it can increase student engagement. However, this is…

Hot Technologies for K-12 Schools: The 2005 Guide for Technology Decision Makers. COSN's Emerging Technologies Series

ERIC Educational Resources Information Center

Vockley, Martha, Ed.

2004-01-01

As technology companies introduce innovative products and services for the education market, school districts have the opportunity to invest in technologies designed to improve instruction and operations--from teaching, learning and assessments to organizational efficiency. Perhaps the greatest promise of anticipated technologies is their…

Building School Culture One Week at a Time

ERIC Educational Resources Information Center

Zoul, Jeffrey

2010-01-01

Use Friday Focus memos to motivate and engage your staff every week, and help create a school culture focused on the growth of students "and" teachers. Easy to understand and implement, Friday Focus memos offer an effective and efficient way to improve student learning, staff development, and school culture from within. Written by educational…

Carbon Smackdown: Cookstoves for the developing world

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

Ashok Gadgil, Kayje Booker, and Adam Rausch

2010-07-07

In this June 30, 2010 Berkeley Lab summer lecture, learn how efficient cookstoves for the developing world — from Darfur to Ethiopia and beyond — are reducing carbon dioxide emissions, saving forests, and improving health. Berkeley Lab's Ashok Gadgil, Kayje Booker, and Adam Rausch discuss why they got started in this great challenge and what's next.

The Elyria Schools First: An Initiative To Unleash a Community's Potential Empowering Children To Learn.

ERIC Educational Resources Information Center

Elyria City Board of Education, OH.

Total Quality Management (TQM) is a process and strategy designed to improve an organization's effectiveness and efficiency. The Elyria Schools, named as Ohio's model urban school district in 1991, uses TQM to implement updated strategic goals through a process emphasizing teamwork, best knowledge, prevention, and commitment to continuous…

Evaluation Study of Competencies of Secondary School Teachers in Punjab in the Context of Classroom Management

ERIC Educational Resources Information Center

Saeed, Safia

2009-01-01

There are so many characteristics and traits of personality and all the characteristics, qualities and competencies need training, grooming, improvement and development. The best classroom environment is one that results in efficient learning. Discipline involves employing guidance and teaching techniques to encourage students to become…

Novel Transparent Urinary Tract Simulator Improves Teaching of Urological Operation Skills at a Single Institution.

PubMed

Zhong, Xiao; Wang, Pingxian; Feng, Jiayu; Hu, Wengang; Huang, Chibing

2015-01-01

This randomized controlled study compared a novel transparent urinary tract simulator with the traditional opaque urinary tract simulator as an aid for efficiently teaching urological surgical procedures. Senior medical students were tested on their understanding of urological theory before and after lectures concerning urinary system disease. The students received operative training using the transparent urinary tract simulator (experimental group, n = 80) or the J3311 opaque plastic urinary tract simulator (control, n = 80), specifically in catheterization and retrograde double-J stent implantation. The operative training was followed by a skills test and student satisfaction survey. The test scores for theory were similar between the two groups, before and after training. Students in the experimental group performed significantly better than those in the control group on the procedural skills test, and also had significantly better self-directed learning skills, analytical skills, and greater motivation to learn. During the initial step of training, the novel transparent urinary tract simulator significantly improved the efficiency of teaching urological procedural skills compared with the traditional opaque device. © 2015 S. Karger AG, Basel.

IDEAL: Images Across Domains, Experiments, Algorithms and Learning

NASA Astrophysics Data System (ADS)

Ushizima, Daniela M.; Bale, Hrishikesh A.; Bethel, E. Wes; Ercius, Peter; Helms, Brett A.; Krishnan, Harinarayan; Grinberg, Lea T.; Haranczyk, Maciej; Macdowell, Alastair A.; Odziomek, Katarzyna; Parkinson, Dilworth Y.; Perciano, Talita; Ritchie, Robert O.; Yang, Chao

2016-11-01

Research across science domains is increasingly reliant on image-centric data. Software tools are in high demand to uncover relevant, but hidden, information in digital images, such as those coming from faster next generation high-throughput imaging platforms. The challenge is to analyze the data torrent generated by the advanced instruments efficiently, and provide insights such as measurements for decision-making. In this paper, we overview work performed by an interdisciplinary team of computational and materials scientists, aimed at designing software applications and coordinating research efforts connecting (1) emerging algorithms for dealing with large and complex datasets; (2) data analysis methods with emphasis in pattern recognition and machine learning; and (3) advances in evolving computer architectures. Engineering tools around these efforts accelerate the analyses of image-based recordings, improve reusability and reproducibility, scale scientific procedures by reducing time between experiments, increase efficiency, and open opportunities for more users of the imaging facilities. This paper describes our algorithms and software tools, showing results across image scales, demonstrating how our framework plays a role in improving image understanding for quality control of existent materials and discovery of new compounds.

Reinforcement learning interfaces for biomedical database systems.

PubMed

Rudowsky, I; Kulyba, O; Kunin, M; Parsons, S; Raphan, T

2006-01-01

Studies of neural function that are carried out in different laboratories and that address different questions use a wide range of descriptors for data storage, depending on the laboratory and the individuals that input the data. A common approach to describe non-textual data that are referenced through a relational database is to use metadata descriptors. We have recently designed such a prototype system, but to maintain efficiency and a manageable metadata table, free formatted fields were designed as table entries. The database interface application utilizes an intelligent agent to improve integrity of operation. The purpose of this study was to investigate how reinforcement learning algorithms can assist the user in interacting with the database interface application that has been developed to improve the performance of the system.

Optimization of internet content filtering-Combined with KNN and OCAT algorithms

NASA Astrophysics Data System (ADS)

Guo, Tianze; Wu, Lingjing; Liu, Jiaming

2018-04-01

The face of the status quo that rampant illegal content in the Internet, the result of traditional way to filter information, keyword recognition and manual screening, is getting worse. Based on this, this paper uses OCAT algorithm nested by KNN classification algorithm to construct a corpus training library that can dynamically learn and update, which can be improved on the filter corpus for constantly updated illegal content of the network, including text and pictures, and thus can better filter and investigate illegal content and its source. After that, the research direction will focus on the simplified updating of recognition and comparison algorithms and the optimization of the corpus learning ability in order to improve the efficiency of filtering, save time and resources.

Energy Efficiency Financing for Low- and Moderate-Income Households: Current State of the Market, Issues, and Opportunities

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

Leventis, Greg; Kramer, Chris; Schwartz, Lisa

Ensuring that low- and moderate-income (LMI) households have access to energy efficiency is equitable, provides energy savings as a resource to meet energy needs, and can support multiple policy goals, such as affordable energy, job creation, and improved public health. Although the need is great, many LMI households may not be able to afford efficiency improvements or may be inhibited from adopting efficiency for other reasons. Decision-makers across the country are currently exploring the challenges and potential solutions to ramping up adoption of efficiency in LMI households, including the use of financing. The report’s objective is to offer state andmore » local policymakers, state utility regulators, program administrators, financial institutions, consumer advocates and other LMI stakeholders with an understanding of: -The relationship between LMI communities and financing for energy efficiency, including important considerations for its use such as consumer protections -The larger programmatic context of grant-based assistance and other related resources supporting LMI household energy efficiency -Lessons learned from existing energy efficiency financing programs serving LMI households -Financing products used by these programs and their relative advantages and disadvantages in addressing barriers to financing or to energy efficiency uptake for LMI households« less

Improving disease management in the United Kingdom: what can be learned from U.S. experience?

PubMed

Florin, Dominique; Lewis, Richard; Rosen, Rebecca

2004-10-01

Chronic diseases are a large and growing burden for health services. The British National Health Service is set up in a way that has many advantages in the management of chronic diseases, but lessons from U.S. managed care plans show that several changes could be implemented to improve the efficiency and effectiveness of care for patients with chronic conditions. These include the introduction of limited market competition; financial incentives, particularly across primary and secondary care; increased patient self-help; and improved clinician-manager relationships.

Efficient probabilistic inference in generic neural networks trained with non-probabilistic feedback.

PubMed

Orhan, A Emin; Ma, Wei Ji

2017-07-26

Animals perform near-optimal probabilistic inference in a wide range of psychophysical tasks. Probabilistic inference requires trial-to-trial representation of the uncertainties associated with task variables and subsequent use of this representation. Previous work has implemented such computations using neural networks with hand-crafted and task-dependent operations. We show that generic neural networks trained with a simple error-based learning rule perform near-optimal probabilistic inference in nine common psychophysical tasks. In a probabilistic categorization task, error-based learning in a generic network simultaneously explains a monkey's learning curve and the evolution of qualitative aspects of its choice behavior. In all tasks, the number of neurons required for a given level of performance grows sublinearly with the input population size, a substantial improvement on previous implementations of probabilistic inference. The trained networks develop a novel sparsity-based probabilistic population code. Our results suggest that probabilistic inference emerges naturally in generic neural networks trained with error-based learning rules.Behavioural tasks often require probability distributions to be inferred about task specific variables. Here, the authors demonstrate that generic neural networks can be trained using a simple error-based learning rule to perform such probabilistic computations efficiently without any need for task specific operations.

How Do Learning Outcomes, Assessments and Student Engagement in a Fully Online Geoscience Laboratory Compare to Those Of The Original Hands-on Exercise?

NASA Astrophysics Data System (ADS)

Jones, F. M.

2015-12-01

In a third year geoscience elective for BSc majors, we adapted several active f2f learning strategies for an equivalent fully online version of the course. In particular, we converted a hands-on laboratory including analysis and interpretation of hand-specimens, sketching results and peer-to-peer discussion of scientific implications. This study compares learning outcomes in both formats and describes resources that make engaging, effective and efficient learning experiences for large classes in an asynchronous online environment. Our two hypotheses are: 1) a hands-on geology lab exercise can be converted for efficient fully online use without sacrificing feedback and assessment opportunities; 2) students find either the f2f or DE versions equally effective and enjoyable as learning experiences. Key components are an authentic context, interactive resources including sketching, strategies that enable efficient assessment and feedback on solo and group work, and asynchronous yet productive interaction with peers. Students in the f2f class handle real rock and fossil specimens, work with peers in the lab and classroom, and deliver most results including annotated figures on paper. DE students complete identical tasks using interactive high resolution figures and videos of specimens. Solo work is first delivered for automated assessment and feedback, then students engage asynchronously in small groups to improve results and discuss implications. Chronostratigraphy and other interpretations are sketched on prepared template images using a simple open-source sketching app that ensures equal access and consistent results that are efficient to assess by peers and instructors. Learning outcomes based on subsequent quizzes, sketches, and lab results (paper for f2f students and automated data entry for DE students), show that f2f and online students demonstrate knowledge and scientific interpretations of comparable quality. Effective engagement and group work are demonstrated for f2f students using video and survey data, and for DE students using learning management system tracking data and similar survey data. Finally, these initiatives are shown to be scalable to classes of many students by comparing the time required for instructors to run and grade the lab in both settings.

Productivity, part 2: cloud storage, remote meeting tools, screencasting, speech recognition software, password managers, and online data backup.

PubMed

Lackey, Amanda E; Pandey, Tarun; Moshiri, Mariam; Lalwani, Neeraj; Lall, Chandana; Bhargava, Puneet

2014-06-01

It is an opportune time for radiologists to focus on personal productivity. The ever increasing reliance on computers and the Internet has significantly changed the way we work. Myriad software applications are available to help us improve our personal efficiency. In this article, the authors discuss some tools that help improve collaboration and personal productivity, maximize e-learning, and protect valuable digital data. Published by Elsevier Inc.

Learning Efficiency: Identifying Individual Differences in Learning Rate and Retention in Healthy Adults.

PubMed

Zerr, Christopher L; Berg, Jeffrey J; Nelson, Steven M; Fishell, Andrew K; Savalia, Neil K; McDermott, Kathleen B

2018-06-01

People differ in how quickly they learn information and how long they remember it, yet individual differences in learning abilities within healthy adults have been relatively neglected. In two studies, we examined the relation between learning rate and subsequent retention using a new foreign-language paired-associates task (the learning-efficiency task), which was designed to eliminate ceiling effects that often accompany standardized tests of learning and memory in healthy adults. A key finding was that quicker learners were also more durable learners (i.e., exhibited better retention across a delay), despite studying the material for less time. Additionally, measures of learning and memory from this task were reliable in Study 1 ( N = 281) across 30 hr and Study 2 ( N = 92; follow-up n = 46) across 3 years. We conclude that people vary in how efficiently they learn, and we describe a reliable and valid method for assessing learning efficiency within healthy adults.

Integrating Machine Learning into Space Operations

NASA Astrophysics Data System (ADS)

Kelly, K. G.

There are significant challenges with managing activities in space, which for the scope of this paper are primarily the identification of objects in orbit, maintaining accurate estimates of the orbits of those objects, detecting changes to those orbits, warning of possible collisions between objects and detection of anomalous behavior. The challenges come from the large amounts of data to be processed, which is often incomplete and noisy, limitations on the ability to influence objects in space and the overall strategic importance of space to national interests. The focus of this paper is on defining an approach to leverage the improved capabilities that are possible using state of the art machine learning in a way that empowers operations personnel without sacrificing the security and mission assurance associated with manual operations performed by trained personnel. There has been significant research in the development of algorithms and techniques for applying machine learning in this domain, but deploying new techniques into such a mission critical domain is difficult and time consuming. Establishing a common framework could improve the efficiency with which new techniques are integrated into operations and the overall effectiveness at providing improvements.

Learning pathology using collaborative vs. individual annotation of whole slide images: a mixed methods trial.

PubMed

Sahota, Michael; Leung, Betty; Dowdell, Stephanie; Velan, Gary M

2016-12-12

Students in biomedical disciplines require understanding of normal and abnormal microscopic appearances of human tissues (histology and histopathology). For this purpose, practical classes in these disciplines typically use virtual microscopy, viewing digitised whole slide images in web browsers. To enhance engagement, tools have been developed to enable individual or collaborative annotation of whole slide images within web browsers. To date, there have been no studies that have critically compared the impact on learning of individual and collaborative annotations on whole slide images. Junior and senior students engaged in Pathology practical classes within Medical Science and Medicine programs participated in cross-over trials of individual and collaborative annotation activities. Students' understanding of microscopic morphology was compared using timed online quizzes, while students' perceptions of learning were evaluated using an online questionnaire. For senior medical students, collaborative annotation of whole slide images was superior for understanding key microscopic features when compared to individual annotation; whilst being at least equivalent to individual annotation for junior medical science students. Across cohorts, students agreed that the annotation activities provided a user-friendly learning environment that met their flexible learning needs, improved efficiency, provided useful feedback, and helped them to set learning priorities. Importantly, these activities were also perceived to enhance motivation and improve understanding. Collaborative annotation improves understanding of microscopic morphology for students with sufficient background understanding of the discipline. These findings have implications for the deployment of annotation activities in biomedical curricula, and potentially for postgraduate training in Anatomical Pathology.

Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies.

PubMed

Hansen, Katja; Montavon, Grégoire; Biegler, Franziska; Fazli, Siamac; Rupp, Matthias; Scheffler, Matthias; von Lilienfeld, O Anatole; Tkatchenko, Alexandre; Müller, Klaus-Robert

2013-08-13

The accurate and reliable prediction of properties of molecules typically requires computationally intensive quantum-chemical calculations. Recently, machine learning techniques applied to ab initio calculations have been proposed as an efficient approach for describing the energies of molecules in their given ground-state structure throughout chemical compound space (Rupp et al. Phys. Rev. Lett. 2012, 108, 058301). In this paper we outline a number of established machine learning techniques and investigate the influence of the molecular representation on the methods performance. The best methods achieve prediction errors of 3 kcal/mol for the atomization energies of a wide variety of molecules. Rationales for this performance improvement are given together with pitfalls and challenges when applying machine learning approaches to the prediction of quantum-mechanical observables.

Airport Flight Departure Delay Model on Improved BN Structure Learning

NASA Astrophysics Data System (ADS)

Cao, Weidong; Fang, Xiangnong

An high score prior genetic simulated annealing Bayesian network structure learning algorithm (HSPGSA) by combining genetic algorithm(GA) with simulated annealing algorithm(SAA) is developed. The new algorithm provides not only with strong global search capability of GA, but also with strong local hill climb search capability of SAA. The structure with the highest score is prior selected. In the mean time, structures with lower score are also could be choice. It can avoid efficiently prematurity problem by higher score individual wrong direct growing population. Algorithm is applied to flight departure delays analysis in a large hub airport. Based on the flight data a BN model is created. Experiments show that parameters learning can reflect departure delay.

Supervised Variational Relevance Learning, An Analytic Geometric Feature Selection with Applications to Omic Datasets.

PubMed

Boareto, Marcelo; Cesar, Jonatas; Leite, Vitor B P; Caticha, Nestor

2015-01-01

We introduce Supervised Variational Relevance Learning (Suvrel), a variational method to determine metric tensors to define distance based similarity in pattern classification, inspired in relevance learning. The variational method is applied to a cost function that penalizes large intraclass distances and favors small interclass distances. We find analytically the metric tensor that minimizes the cost function. Preprocessing the patterns by doing linear transformations using the metric tensor yields a dataset which can be more efficiently classified. We test our methods using publicly available datasets, for some standard classifiers. Among these datasets, two were tested by the MAQC-II project and, even without the use of further preprocessing, our results improve on their performance.

Role of Big Data and Machine Learning in Diagnostic Decision Support in Radiology.

PubMed

Syeda-Mahmood, Tanveer

2018-03-01

The field of diagnostic decision support in radiology is undergoing rapid transformation with the availability of large amounts of patient data and the development of new artificial intelligence methods of machine learning such as deep learning. They hold the promise of providing imaging specialists with tools for improving the accuracy and efficiency of diagnosis and treatment. In this article, we will describe the growth of this field for radiology and outline general trends highlighting progress in the field of diagnostic decision support from the early days of rule-based expert systems to cognitive assistants of the modern era. Copyright © 2018 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Teaching customer-centric operations management - evidence from an experiential learning-oriented mass customisation class

NASA Astrophysics Data System (ADS)

Medini, Khaled

2018-01-01

The increase of individualised customer demands and tough competition in the manufacturing sector gave rise to more customer-centric operations management such as products and services (mass) customisation. Mass customisation (MC), which inherits the 'economy of scale' from mass production (MP), aims to meet specific customer demands with near MP efficiency. Such an overarching concept has multiple impacts on operations management. This requires highly qualified and multi-skilled engineers who are well prepared for managing MC. Therefore, this concept should be properly addressed by engineering education curricula which needs to keep up with the emerging business trends. This paper introduces a novel course about MC and variety in operations management which recalls several Experiential Learning (EL) practices consistently with the principle of an active learning. The paper aims to analyse to which extent EL can improve the efficiency of the teaching methods and the retention rate in the context of operations management. The proposed course is given to engineering students whose' perceptions are collected using semi-structured questionnaires and analysed quantitatively and qualitatively. The paper highlights the relevance (i) of teaching MC, and (ii) of active learning in engineering education, through the specific application in the domain of MC.

Characterization and reconstruction of 3D stochastic microstructures via supervised learning.

PubMed

Bostanabad, R; Chen, W; Apley, D W

2016-12-01

The need for computational characterization and reconstruction of volumetric maps of stochastic microstructures for understanding the role of material structure in the processing-structure-property chain has been highlighted in the literature. Recently, a promising characterization and reconstruction approach has been developed where the essential idea is to convert the digitized microstructure image into an appropriate training dataset to learn the stochastic nature of the morphology by fitting a supervised learning model to the dataset. This compact model can subsequently be used to efficiently reconstruct as many statistically equivalent microstructure samples as desired. The goal of this paper is to build upon the developed approach in three major directions by: (1) extending the approach to characterize 3D stochastic microstructures and efficiently reconstruct 3D samples, (2) improving the performance of the approach by incorporating user-defined predictors into the supervised learning model, and (3) addressing potential computational issues by introducing a reduced model which can perform as effectively as the full model. We test the extended approach on three examples and show that the spatial dependencies, as evaluated via various measures, are well preserved in the reconstructed samples. © 2016 The Authors Journal of Microscopy © 2016 Royal Microscopical Society.

Sequential Multiple Assignment Randomized Trial (SMART) with Adaptive Randomization for Quality Improvement in Depression Treatment Program

PubMed Central

Chakraborty, Bibhas; Davidson, Karina W.

2015-01-01

Summary Implementation study is an important tool for deploying state-of-the-art treatments from clinical efficacy studies into a treatment program, with the dual goals of learning about effectiveness of the treatments and improving the quality of care for patients enrolled into the program. In this article, we deal with the design of a treatment program of dynamic treatment regimens (DTRs) for patients with depression post acute coronary syndrome. We introduce a novel adaptive randomization scheme for a sequential multiple assignment randomized trial of DTRs. Our approach adapts the randomization probabilities to favor treatment sequences having comparatively superior Q-functions used in Q-learning. The proposed approach addresses three main concerns of an implementation study: it allows incorporation of historical data or opinions, it includes randomization for learning purposes, and it aims to improve care via adaptation throughout the program. We demonstrate how to apply our method to design a depression treatment program using data from a previous study. By simulation, we illustrate that the inputs from historical data are important for the program performance measured by the expected outcomes of the enrollees, but also show that the adaptive randomization scheme is able to compensate poorly specified historical inputs by improving patient outcomes within a reasonable horizon. The simulation results also confirm that the proposed design allows efficient learning of the treatments by alleviating the curse of dimensionality. PMID:25354029

Efficiency in energy production and consumption

NASA Astrophysics Data System (ADS)

Kellogg, Ryan Mayer

This dissertation deals with economic efficiency in the energy industry and consists of three parts. The first examines how joint experience between pairs of firms working together in oil and gas drilling improves productivity. Part two asks whether oil producers time their drilling optimally by taking real options effects into consideration. Finally, I investigate the efficiency with which energy is consumed, asking whether extending Daylight Saving Time (DST) reduces electricity use. The chapter "Learning by Drilling: Inter-Firm Learning and Relationship Persistence in the Texas Oilpatch" examines how oil production companies and the drilling rigs they hire improve drilling productivity by learning through joint experience. I find that the joint productivity of a lead firm and its drilling contractor is enhanced significantly as they accumulate experience working together. Moreover, this result is robust to other relationship specificities and standard firm-specific learning-by-doing effects. The second chapter, "Drill Now or Drill Later: The Effect of Expected Volatility on Investment," investigates the extent to which firms' drilling behavior accords with a key prescription of real options theory: irreversible investments such as drilling should be deferred when the expected volatility of the investments' payoffs increases. I combine detailed data on oil drilling with expectations of future oil price volatility that I derive from the NYMEX futures options market. Conditioning on expected price levels, I find that oil production companies significantly reduce the number of wells they drill when expected price volatility is high. I conclude with "Daylight Time and Energy: Evidence from an Australian Experiment," co-authored with Hendrik Wolff. This chapter assesses DST's impact on electricity demand using a quasi-experiment in which parts of Australia extended DST in 2000 to facilitate the Sydney Olympics. We show that the extension did not reduce overall electricity consumption, but did cause a substantial intra-day shift in demand consistent with activity patterns that are tied to the clock rather than sunrise and sunset.

Neural Signatures of Phonetic Learning in Adulthood: A Magnetoencephalography Study

PubMed Central

Zhang, Yang; Kuhl, Patricia K.; Imada, Toshiaki; Iverson, Paul; Pruitt, John; Stevens, Erica B.; Kawakatsu, Masaki; Tohkura, Yoh'ichi; Nemoto, Iku

2010-01-01

The present study used magnetoencephalography (MEG) to examine perceptual learning of American English /r/ and /l/ categories by Japanese adults who had limited English exposure. A training software program was developed based on the principles of infant phonetic learning, featuring systematic acoustic exaggeration, multi-talker variability, visible articulation, and adaptive listening. The program was designed to help Japanese listeners utilize an acoustic dimension relevant for phonemic categorization of /r-l/ in English. Although training did not produce native-like phonetic boundary along the /r-l/ synthetic continuum in the second language learners, success was seen in highly significant identification improvement over twelve training sessions and transfer of learning to novel stimuli. Consistent with behavioral results, pre-post MEG measures showed not only enhanced neural sensitivity to the /r-l/ distinction in the left-hemisphere mismatch field (MMF) response but also bilateral decreases in equivalent current dipole (ECD) cluster and duration measures for stimulus coding in the inferior parietal region. The learning-induced increases in neural sensitivity and efficiency were also found in distributed source analysis using Minimum Current Estimates (MCE). Furthermore, the pre-post changes exhibited significant brain-behavior correlations between speech discrimination scores and MMF amplitudes as well as between the behavioral scores and ECD measures of neural efficiency. Together, the data provide corroborating evidence that substantial neural plasticity for second-language learning in adulthood can be induced with adaptive and enriched linguistic exposure. Like the MMF, the ECD cluster and duration measures are sensitive neural markers of phonetic learning. PMID:19457395

Learning in two butterfly species when using flowers of the tropical milkweed Asclepias curassavica: No benefits for pollination.

PubMed

Ramos, Bruna de Cássia Menezes; Rodríguez-Gironés, Miguel Angel; Rodrigues, Daniela

2017-08-08

The ability of insect visitors to learn to manipulate complex flowers has important consequences for foraging efficiency and plant fitness. We investigated learning by two butterfly species, Danaus erippus and Heliconius erato , as they foraged on the complex flowers of Asclepias curassavica , as well as the consequences for pollination. To examine learning with respect to flower manipulation, butterflies were individually tested during four consecutive days under insectary conditions. At the end of each test, we recorded the number of pollinaria attached to the body of each butterfly and scored visited flowers for numbers of removed and inserted pollinia. We also conducted a field study to survey D. erippus and H. erato visiting flowers of A. curassavica , as well as to record numbers of pollinaria attached to the butterflies' bodies, and surveyed A. curassavica plants in the field to inspect flowers for pollinium removal and insertion. Learning improves the ability of both butterfly species to avoid the nonrewarding flower parts and to locate nectar more efficiently. There were no experience effects, for either species, on the numbers of removed and inserted pollinia. Heliconius erato removed and inserted more pollinia than D. erippus . For both butterfly species, pollinium removal was higher than pollinium insertion. This study is the first to show that Danaus and Heliconius butterflies can learn to manipulate complex flowers, but this learning ability does not confer benefits to pollination in A. curassavica . © 2017 Botanical Society of America.

Learning accurate very fast decision trees from uncertain data streams

NASA Astrophysics Data System (ADS)

Liang, Chunquan; Zhang, Yang; Shi, Peng; Hu, Zhengguo

2015-12-01

Most existing works on data stream classification assume the streaming data is precise and definite. Such assumption, however, does not always hold in practice, since data uncertainty is ubiquitous in data stream applications due to imprecise measurement, missing values, privacy protection, etc. The goal of this paper is to learn accurate decision tree models from uncertain data streams for classification analysis. On the basis of very fast decision tree (VFDT) algorithms, we proposed an algorithm for constructing an uncertain VFDT tree with classifiers at tree leaves (uVFDTc). The uVFDTc algorithm can exploit uncertain information effectively and efficiently in both the learning and the classification phases. In the learning phase, it uses Hoeffding bound theory to learn from uncertain data streams and yield fast and reasonable decision trees. In the classification phase, at tree leaves it uses uncertain naive Bayes (UNB) classifiers to improve the classification performance. Experimental results on both synthetic and real-life datasets demonstrate the strong ability of uVFDTc to classify uncertain data streams. The use of UNB at tree leaves has improved the performance of uVFDTc, especially the any-time property, the benefit of exploiting uncertain information, and the robustness against uncertainty.

Progressive Dictionary Learning with Hierarchical Predictive Structure for Scalable Video Coding.

PubMed

Dai, Wenrui; Shen, Yangmei; Xiong, Hongkai; Jiang, Xiaoqian; Zou, Junni; Taubman, David

2017-04-12

Dictionary learning has emerged as a promising alternative to the conventional hybrid coding framework. However, the rigid structure of sequential training and prediction degrades its performance in scalable video coding. This paper proposes a progressive dictionary learning framework with hierarchical predictive structure for scalable video coding, especially in low bitrate region. For pyramidal layers, sparse representation based on spatio-temporal dictionary is adopted to improve the coding efficiency of enhancement layers (ELs) with a guarantee of reconstruction performance. The overcomplete dictionary is trained to adaptively capture local structures along motion trajectories as well as exploit the correlations between neighboring layers of resolutions. Furthermore, progressive dictionary learning is developed to enable the scalability in temporal domain and restrict the error propagation in a close-loop predictor. Under the hierarchical predictive structure, online learning is leveraged to guarantee the training and prediction performance with an improved convergence rate. To accommodate with the stateof- the-art scalable extension of H.264/AVC and latest HEVC, standardized codec cores are utilized to encode the base and enhancement layers. Experimental results show that the proposed method outperforms the latest SHVC and HEVC simulcast over extensive test sequences with various resolutions.

Collaborative learning of clinical skills in health professions education: the why, how, when and for whom.

PubMed

Tolsgaard, Martin G; Kulasegaram, Kulamakan M; Ringsted, Charlotte V

2016-01-01

This study is designed to provide an overview of why, how, when and for whom collaborative learning of clinical skills may work in health professions education. Collaborative learning of clinical skills may influence learning positively according to the non-medical literature. Training efficiency may therefore be improved if the outcomes of collaborative learning of clinical skills are superior or equivalent to those attained through individual learning. According to a social interaction perspective, collaborative learning of clinical skills mediates its effects through social interaction, motivation, accountability and positive interdependence between learners. Motor skills learning theory suggests that positive effects rely on observational learning and action imitation, and negative effects may include decreased hands-on experience. Finally, a cognitive perspective suggests that learning is dependent on cognitive co-construction, shared knowledge and reduced cognitive load. The literature on the collaborative learning of clinical skills in health science education is reviewed to support or contradict the hypotheses provided by the theories outlined above. Collaborative learning of clinical skills leads to improvements in self-efficacy, confidence and performance when task processing is observable or communicable. However, the effects of collaborative learning of clinical skills may decrease over time as benefits in terms of shared cognition, scaffolding and cognitive co-construction are outweighed by reductions in hands-on experience and time on task. Collaborative learning of clinical skills has demonstrated promising results in the simulated setting. However, further research into how collaborative learning of clinical skills may work in clinical settings, as well as into the role of social dynamics between learners, is required. © 2015 John Wiley & Sons Ltd.

Learning Efficiency versus Low IQ and/or Teachers' Ratings as Predictors of Reading Ability of "Mentally Defective" Children: A Longitudinal Study.

ERIC Educational Resources Information Center

Gupta, R. M.

1985-01-01

Low IQ should not be deemed as an index of poor learning ability. Information about middle school children's learning efficiency as measured by the Learning Efficiency Test Battery was found to be more useful for predicting reading ability than conventional types of assessment. (Author/RM)

Stochastic Averaging for Constrained Optimization With Application to Online Resource Allocation

NASA Astrophysics Data System (ADS)

Chen, Tianyi; Mokhtari, Aryan; Wang, Xin; Ribeiro, Alejandro; Giannakis, Georgios B.

2017-06-01

Existing approaches to resource allocation for nowadays stochastic networks are challenged to meet fast convergence and tolerable delay requirements. The present paper leverages online learning advances to facilitate stochastic resource allocation tasks. By recognizing the central role of Lagrange multipliers, the underlying constrained optimization problem is formulated as a machine learning task involving both training and operational modes, with the goal of learning the sought multipliers in a fast and efficient manner. To this end, an order-optimal offline learning approach is developed first for batch training, and it is then generalized to the online setting with a procedure termed learn-and-adapt. The novel resource allocation protocol permeates benefits of stochastic approximation and statistical learning to obtain low-complexity online updates with learning errors close to the statistical accuracy limits, while still preserving adaptation performance, which in the stochastic network optimization context guarantees queue stability. Analysis and simulated tests demonstrate that the proposed data-driven approach improves the delay and convergence performance of existing resource allocation schemes.

Effective e-learning for health professional and medical students: the experience with SIAS-Intelligent Tutoring System.

PubMed

Muñoz, Diana C; Ortiz, Alexandra; González, Carolina; López, Diego M; Blobel, Bernd

2010-01-01

Current e-learning systems are still inadequate to support the level of interaction, personalization and engagement demanded by clinicians, care givers, and the patient themselves. For effective e-learning to be delivered in the health context, collaboration between pedagogy and technology is required. Furthermore, e-learning systems should be flexible enough to be adapted to the students' needs, evaluated regularly, easy to use and maintain and provide students' feedback, guidelines and supporting material in different formats. This paper presents the implementation of an Intelligent Tutoring System (SIAS-ITS), and its evaluation compared to a traditional virtual learning platform (Moodle). The evaluation was carried out as a case study, in which the participants were separated in two groups, each group attending a virtual course on the WHO Integrated Management of Childhood Illness (IMCI) strategy supported by one of the two e-learning platforms. The evaluation demonstrated that the participants' knowledge level, pedagogical strategies used, learning efficiency and systems' usability were improved using the Intelligent Tutoring System.

Learning by Doing Approach in the Internet Environment to Improve the Teaching Efficiency of Information Technology

NASA Astrophysics Data System (ADS)

Zhang, X.-S.; Xie, Hua

This paper presents a learning-by-doing method in the Internet environment to enhance the results of information technology education by experimental work in the classroom of colleges. In this research, an practical approach to apply the "learning by doing" paradigm in Internet-based learning, both for higher educational environments and life-long training systems, taking into account available computer and network resources, such as blogging, podcasting, social networks, wiki etc. We first introduce the different phases in the learning process, which aimed at showing to the readers that the importance of the learning by doing paradigm, which is not implemented in many Internet-based educational environments. Secondly, we give the concept of learning by doing in the different perfective. Then, we identify the most important trends in this field, and give a real practical case for the application of this approach. The results show that the attempt methods are much better than traditional teaching methods.

Reinforcement Learning Trees

PubMed Central

Zhu, Ruoqing; Zeng, Donglin; Kosorok, Michael R.

2015-01-01

In this paper, we introduce a new type of tree-based method, reinforcement learning trees (RLT), which exhibits significantly improved performance over traditional methods such as random forests (Breiman, 2001) under high-dimensional settings. The innovations are three-fold. First, the new method implements reinforcement learning at each selection of a splitting variable during the tree construction processes. By splitting on the variable that brings the greatest future improvement in later splits, rather than choosing the one with largest marginal effect from the immediate split, the constructed tree utilizes the available samples in a more efficient way. Moreover, such an approach enables linear combination cuts at little extra computational cost. Second, we propose a variable muting procedure that progressively eliminates noise variables during the construction of each individual tree. The muting procedure also takes advantage of reinforcement learning and prevents noise variables from being considered in the search for splitting rules, so that towards terminal nodes, where the sample size is small, the splitting rules are still constructed from only strong variables. Last, we investigate asymptotic properties of the proposed method under basic assumptions and discuss rationale in general settings. PMID:26903687

Trends in extreme learning machines: a review.

PubMed

Huang, Gao; Huang, Guang-Bin; Song, Shiji; You, Keyou

2015-01-01

Extreme learning machine (ELM) has gained increasing interest from various research fields recently. In this review, we aim to report the current state of the theoretical research and practical advances on this subject. We first give an overview of ELM from the theoretical perspective, including the interpolation theory, universal approximation capability, and generalization ability. Then we focus on the various improvements made to ELM which further improve its stability, sparsity and accuracy under general or specific conditions. Apart from classification and regression, ELM has recently been extended for clustering, feature selection, representational learning and many other learning tasks. These newly emerging algorithms greatly expand the applications of ELM. From implementation aspect, hardware implementation and parallel computation techniques have substantially sped up the training of ELM, making it feasible for big data processing and real-time reasoning. Due to its remarkable efficiency, simplicity, and impressive generalization performance, ELM have been applied in a variety of domains, such as biomedical engineering, computer vision, system identification, and control and robotics. In this review, we try to provide a comprehensive view of these advances in ELM together with its future perspectives.

Improving the power of an efficacy study of a social and emotional learning program: application of generalizability theory to the measurement of classroom-level outcomes.

PubMed

Mashburn, Andrew J; Downer, Jason T; Rivers, Susan E; Brackett, Marc A; Martinez, Andres

2014-04-01

Social and emotional learning programs are designed to improve the quality of social interactions in schools and classrooms in order to positively affect students' social, emotional, and academic development. The statistical power of group randomized trials to detect effects of social and emotional learning programs and other preventive interventions on setting-level outcomes is influenced by the reliability of the outcome measure. In this paper, we apply generalizability theory to an observational measure of the quality of classroom interactions that is an outcome in a study of the efficacy of a social and emotional learning program called The Recognizing, Understanding, Labeling, Expressing, and Regulating emotions Approach. We estimate multiple sources of error variance in the setting-level outcome and identify observation procedures to use in the efficacy study that most efficiently reduce these sources of error. We then discuss the implications of using different observation procedures on both the statistical power and the monetary costs of conducting the efficacy study.

Marginal space learning for efficient detection of 2D/3D anatomical structures in medical images.

PubMed

Zheng, Yefeng; Georgescu, Bogdan; Comaniciu, Dorin

2009-01-01

Recently, marginal space learning (MSL) was proposed as a generic approach for automatic detection of 3D anatomical structures in many medical imaging modalities [1]. To accurately localize a 3D object, we need to estimate nine pose parameters (three for position, three for orientation, and three for anisotropic scaling). Instead of exhaustively searching the original nine-dimensional pose parameter space, only low-dimensional marginal spaces are searched in MSL to improve the detection speed. In this paper, we apply MSL to 2D object detection and perform a thorough comparison between MSL and the alternative full space learning (FSL) approach. Experiments on left ventricle detection in 2D MRI images show MSL outperforms FSL in both speed and accuracy. In addition, we propose two novel techniques, constrained MSL and nonrigid MSL, to further improve the efficiency and accuracy. In many real applications, a strong correlation may exist among pose parameters in the same marginal spaces. For example, a large object may have large scaling values along all directions. Constrained MSL exploits this correlation for further speed-up. The original MSL only estimates the rigid transformation of an object in the image, therefore cannot accurately localize a nonrigid object under a large deformation. The proposed nonrigid MSL directly estimates the nonrigid deformation parameters to improve the localization accuracy. The comparison experiments on liver detection in 226 abdominal CT volumes demonstrate the effectiveness of the proposed methods. Our system takes less than a second to accurately detect the liver in a volume.

MedTxting: Learning based and Knowledge Rich SMS-style Medical Text Contraction

PubMed Central

Liu, Feifan; Moosavinasab, Soheil; Houston, Thomas K.; Yu, Hong

2012-01-01

In mobile health (M-health), Short Message Service (SMS) has shown to improve disease related self-management and health service outcomes, leading to enhanced patient care. However, the hard limit on character size for each message limits the full value of exploring SMS communication in health care practices. To overcome this problem and improve the efficiency of clinical workflow, we developed an innovative system, MedTxting (available at http://medtxting.askhermes.org), which is a learning-based but knowledge-rich system that compresses medical texts in a SMS style. Evaluations on clinical questions and discharge summary narratives show that MedTxting can effectively compress medical texts with reasonable readability and noticeable size reduction. Findings in this work reveal potentials of MedTxting to the clinical settings, allowing for real-time and cost-effective communication, such as patient condition reporting, medication consulting, physicians connecting to share expertise to improve point of care. PMID:23304328

Dynamic frame resizing with convolutional neural network for efficient video compression

NASA Astrophysics Data System (ADS)

Kim, Jaehwan; Park, Youngo; Choi, Kwang Pyo; Lee, JongSeok; Jeon, Sunyoung; Park, JeongHoon

2017-09-01

In the past, video codecs such as vc-1 and H.263 used a technique to encode reduced-resolution video and restore original resolution from the decoder for improvement of coding efficiency. The techniques of vc-1 and H.263 Annex Q are called dynamic frame resizing and reduced-resolution update mode, respectively. However, these techniques have not been widely used due to limited performance improvements that operate well only under specific conditions. In this paper, video frame resizing (reduced/restore) technique based on machine learning is proposed for improvement of coding efficiency. The proposed method features video of low resolution made by convolutional neural network (CNN) in encoder and reconstruction of original resolution using CNN in decoder. The proposed method shows improved subjective performance over all the high resolution videos which are dominantly consumed recently. In order to assess subjective quality of the proposed method, Video Multi-method Assessment Fusion (VMAF) which showed high reliability among many subjective measurement tools was used as subjective metric. Moreover, to assess general performance, diverse bitrates are tested. Experimental results showed that BD-rate based on VMAF was improved by about 51% compare to conventional HEVC. Especially, VMAF values were significantly improved in low bitrate. Also, when the method is subjectively tested, it had better subjective visual quality in similar bit rate.

What can be Learned from Silage Breeding Programs?

NASA Astrophysics Data System (ADS)

Lorenz, Aaron J.; Coors, James G.

Improving the quality of cellulosic ethanol feedstocks through breeding and genetic manipulation could significantly impact the economics of this industry. Attaining this will require comprehensive and rapid characterization of large numbers of samples. There are many similarities between improving corn silage quality for dairy production and improving feedstock quality for cellulosic ethanol. It was our objective to provide insight into what is needed for genetic improvement of cellulosic feedstocks by reviewing the development and operation of a corn silage breeding program. We discuss the evolving definition of silage quality and relate what we have learned about silage quality to what is needed for measuring and improving feedstock quality. In addition, repeatability estimates of corn stover traits are reported for a set of hybrids. Repeatability of theoretical ethanol potential measured by near-infrared spectroscopy is high, suggesting that this trait may be easily improved through breeding. Just as cell wall digestibility has been factored into the latest measurements of silage quality, conversion efficiency should be standardized and included in indices of feedstock quality to maximize overall, economical energy availability.

A Cross-Correlated Delay Shift Supervised Learning Method for Spiking Neurons with Application to Interictal Spike Detection in Epilepsy.

PubMed

Guo, Lilin; Wang, Zhenzhong; Cabrerizo, Mercedes; Adjouadi, Malek

2017-05-01

This study introduces a novel learning algorithm for spiking neurons, called CCDS, which is able to learn and reproduce arbitrary spike patterns in a supervised fashion allowing the processing of spatiotemporal information encoded in the precise timing of spikes. Unlike the Remote Supervised Method (ReSuMe), synapse delays and axonal delays in CCDS are variants which are modulated together with weights during learning. The CCDS rule is both biologically plausible and computationally efficient. The properties of this learning rule are investigated extensively through experimental evaluations in terms of reliability, adaptive learning performance, generality to different neuron models, learning in the presence of noise, effects of its learning parameters and classification performance. Results presented show that the CCDS learning method achieves learning accuracy and learning speed comparable with ReSuMe, but improves classification accuracy when compared to both the Spike Pattern Association Neuron (SPAN) learning rule and the Tempotron learning rule. The merit of CCDS rule is further validated on a practical example involving the automated detection of interictal spikes in EEG records of patients with epilepsy. Results again show that with proper encoding, the CCDS rule achieves good recognition performance.

Development of an Efficient Identifier for Nuclear Power Plant Transients Based on Latest Advances of Error Back-Propagation Learning Algorithm

NASA Astrophysics Data System (ADS)

Moshkbar-Bakhshayesh, Khalil; Ghofrani, Mohammad B.

2014-02-01

This study aims to improve the performance of nuclear power plants (NPPs) transients training and identification using the latest advances of error back-propagation (EBP) learning algorithm. To this end, elements of EBP, including input data, initial weights, learning rate, cost function, activation function, and weights updating procedure are investigated and an efficient neural network is developed. Usefulness of modular networks is also examined and appropriate identifiers, one for each transient, are employed. Furthermore, the effect of transient type on transient identifier performance is illustrated. Subsequently, the developed transient identifier is applied to Bushehr nuclear power plant (BNPP). Seven types of the plant events are probed to analyze the ability of the proposed identifier. The results reveal that identification occurs very early with only five plant variables, whilst in the previous studies a larger number of variables (typically 15 to 20) were required. Modular networks facilitated identification due to its sole dependency on the sign of each network output signal. Fast training of input patterns, extendibility for identification of more transients and reduction of false identification are other advantageous of the proposed identifier. Finally, the balance between the correct answer to the trained transients (memorization) and reasonable response to the test transients (generalization) is improved, meeting one of the primary design criteria of identifiers.

Improving workplace safety training using a self-directed CPR-AED learning program.

PubMed

Mancini, Mary E; Cazzell, Mary; Kardong-Edgren, Suzan; Cason, Carolyn L

2009-04-01

Adequate training in cardiopulmonary resuscitation (CPR) and use of an automated external defibrillator (AED) is an important component of a workplace safety training program. Barriers to traditional in-classroom CPR-AED training programs include time away from work to complete training, logistics, learner discomfort over being in a classroom setting, and instructors who include information irrelevant to CPR. This study evaluated differences in CPR skills performance between employees who learned CPR using a self-directed learning (SDL) kit and employees who attended a traditional instructor-led course. The results suggest that the SDL kit yields learning outcomes comparable to those obtained with traditional instructor-led courses and is a more time-efficient tool for CPR-AED training. Furthermore, the SDL kit overcomes many of the barriers that keep individuals from learning CPR and appears to contribute to bystanders' confidently attempting resuscitation.

What millennial medical students say about flipped learning

PubMed Central

Pettit, Robin K; McCoy, Lise; Kinney, Marjorie

2017-01-01

Flipped instruction is gaining popularity in medical schools, but there are unanswered questions such as the optimum amount of the curriculum to flip and whether flipped sessions should be mandatory. We were in a unique position to evaluate feedback from first-year medical students who had experienced both flipped and lecture-based courses during their first semester of medical school. A key finding was that the students preferred a variety of different learning formats over an “all or nothing” learning format. Learning format preferences did not necessarily align with perceptions of which format led to better course exam performance. Nearly 70% of respondents wanted to make their own decisions regarding attendance. Candid responses to open-ended survey prompts reflected millennial preferences for choice, flexibility, efficiency, and the ability to control the pace of their learning, providing insight to guide curricular improvements. PMID:28769600

Enhanced attentional gain as a mechanism for generalized perceptual learning in human visual cortex.

PubMed

Byers, Anna; Serences, John T

2014-09-01

Learning to better discriminate a specific visual feature (i.e., a specific orientation in a specific region of space) has been associated with plasticity in early visual areas (sensory modulation) and with improvements in the transmission of sensory information from early visual areas to downstream sensorimotor and decision regions (enhanced readout). However, in many real-world scenarios that require perceptual expertise, observers need to efficiently process numerous exemplars from a broad stimulus class as opposed to just a single stimulus feature. Some previous data suggest that perceptual learning leads to highly specific neural modulations that support the discrimination of specific trained features. However, the extent to which perceptual learning acts to improve the discriminability of a broad class of stimuli via the modulation of sensory responses in human visual cortex remains largely unknown. Here, we used functional MRI and a multivariate analysis method to reconstruct orientation-selective response profiles based on activation patterns in the early visual cortex before and after subjects learned to discriminate small offsets in a set of grating stimuli that were rendered in one of nine possible orientations. Behavioral performance improved across 10 training sessions, and there was a training-related increase in the amplitude of orientation-selective response profiles in V1, V2, and V3 when orientation was task relevant compared with when it was task irrelevant. These results suggest that generalized perceptual learning can lead to modified responses in the early visual cortex in a manner that is suitable for supporting improved discriminability of stimuli drawn from a large set of exemplars. Copyright © 2014 the American Physiological Society.

A student-centered approach for developing active learning: the construction of physical models as a teaching tool in medical physiology.

PubMed

Rezende-Filho, Flávio Moura; da Fonseca, Lucas José Sá; Nunes-Souza, Valéria; Guedes, Glaucevane da Silva; Rabelo, Luiza Antas

2014-09-15

Teaching physiology, a complex and constantly evolving subject, is not a simple task. A considerable body of knowledge about cognitive processes and teaching and learning methods has accumulated over the years, helping teachers to determine the most efficient way to teach, and highlighting student's active participation as a means to improve learning outcomes. In this context, this paper describes and qualitatively analyzes an experience of a student-centered teaching-learning methodology based on the construction of physiological-physical models, focusing on their possible application in the practice of teaching physiology. After having Physiology classes and revising the literature, students, divided in small groups, built physiological-physical models predominantly using low-cost materials, for studying different topics in Physiology. Groups were followed by monitors and guided by teachers during the whole process, finally presenting the results in a Symposium on Integrative Physiology. Along the proposed activities, students were capable of efficiently creating physiological-physical models (118 in total) highly representative of different physiological processes. The implementation of the proposal indicated that students successfully achieved active learning and meaningful learning in Physiology while addressing multiple learning styles. The proposed method has proved to be an attractive, accessible and relatively simple approach to facilitate the physiology teaching-learning process, while facing difficulties imposed by recent requirements, especially those relating to the use of experimental animals and professional training guidelines. Finally, students' active participation in the production of knowledge may result in a holistic education, and possibly, better professional practices.

Teaching Baroreflex Physiology to Medical Students: A Comparison of Quiz-Based and Conventional Teaching Strategies in a Laboratory Exercise

ERIC Educational Resources Information Center

Berg, Ronan M. G.; Plovsing, Ronni R.; Damgaard, Morten

2012-01-01

Quiz-based and collaborative teaching strategies have previously been found to be efficient for the improving meaningful learning of physiology during lectures. These approaches have, however, not been investigated during laboratory exercises. In the present study, we compared the impact of solving quizzes individually and in groups with…

Carbon Smackdown: Cookstoves for the developing world

ScienceCinema

Ashok Gadgil, Kayje Booker, and Adam Rausch

2017-12-09

In this June 30, 2010 Berkeley Lab summer lecture, learn how efficient cookstoves for the developing world — from Darfur to Ethiopia and beyond — are reducing carbon dioxide emissions, saving forests, and improving health. Berkeley Lab's Ashok Gadgil, Kayje Booker, and Adam Rausch discuss why they got started in this great challenge and what's next.

Fourth Graders' Cognitive Processes and Learning Strategies for Reading Illustrated Biology Texts: Eye Movement Measurements

ERIC Educational Resources Information Center

Jian, Yu-Cin

2016-01-01

Previous research suggests that multiple representations can improve science reading comprehension. This facilitation effect is premised on the observation that readers can efficiently integrate information in text and diagram formats; however, this effect in young readers is still contested. Using eye-tracking technology and sequential analysis,…

Application of Data Envelopment Analysis on the Indicators Contributing to Learning and Teaching Performance

ERIC Educational Resources Information Center

Montoneri, Bernard; Lin, Tyrone T.; Lee, Chia-Chi; Huang, Shio-Ling

2012-01-01

This paper applies data envelopment analysis (DEA) to explore the quantitative relative efficiency of 18 classes of freshmen students studying a course of English conversation in a university of Taiwan from the academic year 2004-2006. A diagram of teaching performance improvement mechanism is designed to identify key performance indicators for…

Design, Development, and Maintenance of the GLOBE Program Website and Database

NASA Technical Reports Server (NTRS)

Brummer, Renate; Matsumoto, Clifford

2004-01-01

This is a 1-year (Fy 03) proposal to design and develop enhancements, implement improved efficiency and reliability, and provide responsive maintenance for the operational GLOBE (Global Learning and Observations to Benefit the Environment) Program website and database. This proposal is renewable, with a 5% annual inflation factor providing an approximate cost for the out years.

Improving Assessment Processes in Higher Education: Student and Teacher Perceptions of the Effectiveness of a Rubric Embedded in a LMS

ERIC Educational Resources Information Center

Atkinson, Doug; Lim, Siew Leng

2013-01-01

Students and teachers play different roles and thus have different perceptions about the effectiveness of assessment including structure, feedback, consistency, fairness and efficiency. In an undergraduate Business Information Systems course, a rubric was designed and semi-automated through a learning management system (LMS) to provide formative…

Revolution in Training Executive Review of Navy Training

DTIC Science & Technology

2001-08-08

AUTHOR(S) 5d. PROJECT NUMBER 5e. TASK NUMBER 5f. WORK UNIT NUMBER 7 . PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) United States Navy Chief of...telling us that investments in the learning of people pay off in improvements in profitability and employee effectiveness and satisfaction, and... 7 Efficiency and Cost-Effectiveness are Important

The Foreign-Language Teacher and Cognitive Psychology or Where Do We Go from Here?

ERIC Educational Resources Information Center

Rivers, Wilga M.

Research into the psychology of perception can uncover important discoveries for more efficient learning. There must be increased understanding of the processing of input and the pre-processing of output for improved language instruction. Educators must at the present time be extremely wary of basing what they do in the foreign-language classroom…

Overhauling Technical Handouts for Active Student Participation: A Model for Improving Lecture Efficiency and Increasing Attendance

ERIC Educational Resources Information Center

Jakee, Keith

2011-01-01

This instructional paper is intended to provide an alternative approach to developing lecture materials, including handouts and PowerPoint slides, successfully developed over several years. The principal objective is to aid in the bridging of traditional "chalk and talk" lecture approaches with more active learning techniques, especially in more…

Using a Review Book to Improve Knowledge Retention

ERIC Educational Resources Information Center

Elmas, Ridvan; Aydogdu, Bülent; Saban, Yakup

2017-01-01

This study has two primary objectives. The first one is preparation of an efficient review book including a series of activities, which will help fourth grade students exercise what they learned in the elementary science course in a year. The second objective is examination of the prepared book in the framework of student and teacher opinions. In…

How social network analysis can be used to monitor online collaborative learning and guide an informed intervention

PubMed Central

Fors, Uno; Tedre, Matti; Nouri, Jalal

2018-01-01

To ensure online collaborative learning meets the intended pedagogical goals (is actually collaborative and stimulates learning), mechanisms are needed for monitoring the efficiency of online collaboration. Various studies have indicated that social network analysis can be particularly effective in studying students’ interactions in online collaboration. However, research in education has only focused on the theoretical potential of using SNA, not on the actual benefits they achieved. This study investigated how social network analysis can be used to monitor online collaborative learning, find aspects in need of improvement, guide an informed intervention, and assess the efficacy of intervention using an experimental, observational repeated-measurement design in three courses over a full-term duration. Using a combination of SNA-based visual and quantitative analysis, we monitored three SNA constructs for each participant: the level of interactivity, the role, and position in information exchange, and the role played by each participant in the collaboration. On the group level, we monitored interactivity and group cohesion indicators. Our monitoring uncovered a non-collaborative teacher-centered pattern of interactions in the three studied courses as well as very few interactions among students, limited information exchange or negotiation, and very limited student networks dominated by the teacher. An intervention based on SNA-generated insights was designed. The intervention was structured into five actions: increasing awareness, promoting collaboration, improving the content, preparing teachers, and finally practicing with feedback. Evaluation of the intervention revealed that it has significantly enhanced student-student interactions and teacher-student interactions, as well as produced a collaborative pattern of interactions among most students and teachers. Since efficient and communicative activities are essential prerequisites for successful content discussion and for realizing the goals of collaboration, we suggest that our SNA-based approach will positively affect teaching and learning in many educational domains. Our study offers a proof-of-concept of what SNA can add to the current tools for monitoring and supporting teaching and learning in higher education. PMID:29566058

How social network analysis can be used to monitor online collaborative learning and guide an informed intervention.

PubMed

Saqr, Mohammed; Fors, Uno; Tedre, Matti; Nouri, Jalal

2018-01-01

To ensure online collaborative learning meets the intended pedagogical goals (is actually collaborative and stimulates learning), mechanisms are needed for monitoring the efficiency of online collaboration. Various studies have indicated that social network analysis can be particularly effective in studying students' interactions in online collaboration. However, research in education has only focused on the theoretical potential of using SNA, not on the actual benefits they achieved. This study investigated how social network analysis can be used to monitor online collaborative learning, find aspects in need of improvement, guide an informed intervention, and assess the efficacy of intervention using an experimental, observational repeated-measurement design in three courses over a full-term duration. Using a combination of SNA-based visual and quantitative analysis, we monitored three SNA constructs for each participant: the level of interactivity, the role, and position in information exchange, and the role played by each participant in the collaboration. On the group level, we monitored interactivity and group cohesion indicators. Our monitoring uncovered a non-collaborative teacher-centered pattern of interactions in the three studied courses as well as very few interactions among students, limited information exchange or negotiation, and very limited student networks dominated by the teacher. An intervention based on SNA-generated insights was designed. The intervention was structured into five actions: increasing awareness, promoting collaboration, improving the content, preparing teachers, and finally practicing with feedback. Evaluation of the intervention revealed that it has significantly enhanced student-student interactions and teacher-student interactions, as well as produced a collaborative pattern of interactions among most students and teachers. Since efficient and communicative activities are essential prerequisites for successful content discussion and for realizing the goals of collaboration, we suggest that our SNA-based approach will positively affect teaching and learning in many educational domains. Our study offers a proof-of-concept of what SNA can add to the current tools for monitoring and supporting teaching and learning in higher education.

Machine Learning Based Single-Frame Super-Resolution Processing for Lensless Blood Cell Counting

PubMed Central

Huang, Xiwei; Jiang, Yu; Liu, Xu; Xu, Hang; Han, Zhi; Rong, Hailong; Yang, Haiping; Yan, Mei; Yu, Hao

2016-01-01

A lensless blood cell counting system integrating microfluidic channel and a complementary metal oxide semiconductor (CMOS) image sensor is a promising technique to miniaturize the conventional optical lens based imaging system for point-of-care testing (POCT). However, such a system has limited resolution, making it imperative to improve resolution from the system-level using super-resolution (SR) processing. Yet, how to improve resolution towards better cell detection and recognition with low cost of processing resources and without degrading system throughput is still a challenge. In this article, two machine learning based single-frame SR processing types are proposed and compared for lensless blood cell counting, namely the Extreme Learning Machine based SR (ELMSR) and Convolutional Neural Network based SR (CNNSR). Moreover, lensless blood cell counting prototypes using commercial CMOS image sensors and custom designed backside-illuminated CMOS image sensors are demonstrated with ELMSR and CNNSR. When one captured low-resolution lensless cell image is input, an improved high-resolution cell image will be output. The experimental results show that the cell resolution is improved by 4×, and CNNSR has 9.5% improvement over the ELMSR on resolution enhancing performance. The cell counting results also match well with a commercial flow cytometer. Such ELMSR and CNNSR therefore have the potential for efficient resolution improvement in lensless blood cell counting systems towards POCT applications. PMID:27827837

The application of network teaching in applied optics teaching

NASA Astrophysics Data System (ADS)

Zhao, Huifu; Piao, Mingxu; Li, Lin; Liu, Dongmei

2017-08-01

Network technology has become a creative tool of changing human productivity, the rapid development of it has brought profound changes to our learning, working and life. Network technology has many advantages such as rich contents, various forms, convenient retrieval, timely communication and efficient combination of resources. Network information resources have become the new education resources, get more and more application in the education, has now become the teaching and learning tools. Network teaching enriches the teaching contents, changes teaching process from the traditional knowledge explanation into the new teaching process by establishing situation, independence and cooperation in the network technology platform. The teacher's role has shifted from teaching in classroom to how to guide students to learn better. Network environment only provides a good platform for the teaching, we can get a better teaching effect only by constantly improve the teaching content. Changchun university of science and technology introduced a BB teaching platform, on the platform, the whole optical classroom teaching and the classroom teaching can be improved. Teachers make assignments online, students learn independently offline or the group learned cooperatively, this expands the time and space of teaching. Teachers use hypertext form related knowledge of applied optics, rich cases and learning resources, set up the network interactive platform, homework submission system, message board, etc. The teaching platform simulated the learning interest of students and strengthens the interaction in the teaching.

E-learning to improve the drug prescribing in the hospitalized elderly patients: the ELICADHE feasibility pilot study.

PubMed

Franchi, C; Mari, D; Tettamanti, M; Pasina, L; Djade, C D; Mannucci, P M; Onder, G; Bernabei, R; Gussoni, G; Bonassi, S; Nobili, A

2014-08-01

E-learning is an efficient and cost-effective educational method. This study aimed at evaluating the feasibility of an educational e-learning intervention, focused on teaching geriatric pharmacology and notions of comprehensive geriatric assessment, to improve drug prescribing to hospitalized elderly patients. Eight geriatric and internal medicine wards were randomized to intervention (e-learning educational program) or control. Clinicians of the two groups had to complete a specific per group e-learning program in 30 days. Then, ten patients (aged ≥75 years) had to be consecutively enrolled collecting clinical data at hospital admission, discharge, and 3 months later. The quality of prescription was evaluated comparing the prevalence of potentially inappropriate medications through Beer's criteria and of potential drug-drug interactions through a specific computerized database. The study feasibility was confirmed by the high percentage (90 %) of clinicians who completed the e-learning program, the recruitment, and follow-up of all planned patients. The intervention was well accepted by all participating clinicians who judged positively (a mean score of >3 points on a scale of 5 points: 0 = useless; 5 = most useful) the specific contents, the methodology applied, the clinical relevance and utility of e-learning contents and tools for the evaluation of the appropriateness of drug prescribing. The pilot study met all the requested goals. The main study is currently ongoing and is planned to finish on July 2015.

Efficient multi-atlas abdominal segmentation on clinically acquired CT with SIMPLE context learning.

PubMed

Xu, Zhoubing; Burke, Ryan P; Lee, Christopher P; Baucom, Rebeccah B; Poulose, Benjamin K; Abramson, Richard G; Landman, Bennett A

2015-08-01

Abdominal segmentation on clinically acquired computed tomography (CT) has been a challenging problem given the inter-subject variance of human abdomens and complex 3-D relationships among organs. Multi-atlas segmentation (MAS) provides a potentially robust solution by leveraging label atlases via image registration and statistical fusion. We posit that the efficiency of atlas selection requires further exploration in the context of substantial registration errors. The selective and iterative method for performance level estimation (SIMPLE) method is a MAS technique integrating atlas selection and label fusion that has proven effective for prostate radiotherapy planning. Herein, we revisit atlas selection and fusion techniques for segmenting 12 abdominal structures using clinically acquired CT. Using a re-derived SIMPLE algorithm, we show that performance on multi-organ classification can be improved by accounting for exogenous information through Bayesian priors (so called context learning). These innovations are integrated with the joint label fusion (JLF) approach to reduce the impact of correlated errors among selected atlases for each organ, and a graph cut technique is used to regularize the combined segmentation. In a study of 100 subjects, the proposed method outperformed other comparable MAS approaches, including majority vote, SIMPLE, JLF, and the Wolz locally weighted vote technique. The proposed technique provides consistent improvement over state-of-the-art approaches (median improvement of 7.0% and 16.2% in DSC over JLF and Wolz, respectively) and moves toward efficient segmentation of large-scale clinically acquired CT data for biomarker screening, surgical navigation, and data mining. Copyright © 2015 Elsevier B.V. All rights reserved.

Thermodynamic efficiency of learning a rule in neural networks

NASA Astrophysics Data System (ADS)

Goldt, Sebastian; Seifert, Udo

2017-11-01

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

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

ERIC Educational Resources Information Center

Hursen, Cigdem; Fasli, Funda Gezer

2017-01-01

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

Improving labeling efficiency in automatic quality control of MRSI data.

PubMed

Pedrosa de Barros, Nuno; McKinley, Richard; Wiest, Roland; Slotboom, Johannes

2017-12-01

To improve the efficiency of the labeling task in automatic quality control of MR spectroscopy imaging data. 28'432 short and long echo time (TE) spectra (1.5 tesla; point resolved spectroscopy (PRESS); repetition time (TR)= 1,500 ms) from 18 different brain tumor patients were labeled by two experts as either accept or reject, depending on their quality. For each spectrum, 47 signal features were extracted. The data was then used to run several simulations and test an active learning approach using uncertainty sampling. The performance of the classifiers was evaluated as a function of the number of patients in the training set, number of spectra in the training set, and a parameter α used to control the level of classification uncertainty required for a new spectrum to be selected for labeling. The results showed that the proposed strategy allows reductions of up to 72.97% for short TE and 62.09% for long TE in the amount of data that needs to be labeled, without significant impact in classification accuracy. Further reductions are possible with significant but minimal impact in performance. Active learning using uncertainty sampling is an effective way to increase the labeling efficiency for training automatic quality control classifiers. Magn Reson Med 78:2399-2405, 2017. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

Cytopathology whole slide images and adaptive tutorials for senior medical students: a randomized crossover trial.

PubMed

Van Es, Simone L; Kumar, Rakesh K; Pryor, Wendy M; Salisbury, Elizabeth L; Velan, Gary M

2016-01-08

Diagnostic cytopathology is an essential part of clinical decision-making. However, due to a combination of factors including curriculum reform and shortage of pathologists to teach introductory cytopathology, this area of pathology receives little or no formal attention in most medical school curricula. We have previously described the successful use of efficient and effective digital learning resources, including whole slide images (WSI) and virtual microscopy adaptive tutorials (VMATs), to teach cytopathology to pathology specialist trainees - a group that had prior exposure to cytopathology in their day to day practice. Consequently, in the current study we attempted to demonstrate the efficiency and efficacy of this eLearning resource in a cohort of senior medical students that was completely naïve to the subject matter (cytopathology). We evaluated both the quantitative and qualitative impact of these digital educational materials for learning cytopathology compared with existing resources (e-textbooks and online atlases). The senior medical students were recruited from The University of New South Wales Australia for a randomized cross-over trial. Online assessments, administered after each arm of the trial, contained questions which related directly to a whole slide image. Two categories of questions in the assessments (focusing on either diagnosis or identification of cellular features) were utilized to determine efficacy. User experience and perceptions of efficiency were evaluated using online questionnaires containing Likert scale items and open-ended questions. For this cohort of senior medical students, virtual microscopy adaptive tutorials (VMATs) proved to be at least as effective as existing digital resources for learning cytopathology. Importantly, virtual microscopy adaptive tutorials had superior efficacy in facilitating accurate diagnosis on whole slide images. Student perceptions of VMATs were positive, particularly regarding the immediate feedback, interactivity and equity of learning which this learning resource provides. Virtual microscopy adaptive tutorials have the potential to improve the efficacy of learning microscopic pathology for medical students. The enhanced learning experience provided by these eLearning tools merits further investigation of their utility for other cohorts, including specialist trainees.

A Mixed-Methods Research Framework for Healthcare Process Improvement.

PubMed

Bastian, Nathaniel D; Munoz, David; Ventura, Marta

2016-01-01

The healthcare system in the United States is spiraling out of control due to ever-increasing costs without significant improvements in quality, access to care, satisfaction, and efficiency. Efficient workflow is paramount to improving healthcare value while maintaining the utmost standards of patient care and provider satisfaction in high stress environments. This article provides healthcare managers and quality engineers with a practical healthcare process improvement framework to assess, measure and improve clinical workflow processes. The proposed mixed-methods research framework integrates qualitative and quantitative tools to foster the improvement of processes and workflow in a systematic way. The framework consists of three distinct phases: 1) stakeholder analysis, 2a) survey design, 2b) time-motion study, and 3) process improvement. The proposed framework is applied to the pediatric intensive care unit of the Penn State Hershey Children's Hospital. The implementation of this methodology led to identification and categorization of different workflow tasks and activities into both value-added and non-value added in an effort to provide more valuable and higher quality patient care. Based upon the lessons learned from the case study, the three-phase methodology provides a better, broader, leaner, and holistic assessment of clinical workflow. The proposed framework can be implemented in various healthcare settings to support continuous improvement efforts in which complexity is a daily element that impacts workflow. We proffer a general methodology for process improvement in a healthcare setting, providing decision makers and stakeholders with a useful framework to help their organizations improve efficiency. Published by Elsevier Inc.

Differential theory of learning for efficient neural network pattern recognition

NASA Astrophysics Data System (ADS)

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

1993-09-01

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

Differential theory of learning for efficient neural network pattern recognition

NASA Astrophysics Data System (ADS)

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

1993-08-01

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

The iPad: tablet technology to support nursing and midwifery student learning: an evaluation in practice.

PubMed

Brown, Janie; McCrorie, Pamela

2015-03-01

This research explored the impact of tablet technology, in the form of Apple iPads, on undergraduate nursing and midwifery students' learning outcomes. In simulated clinical learning environments, first-year nursing students (n = 30) accessed apps and reference materials on iPads. Third-year nursing students (n = 88) referred to clinical guidelines to aid their decision making when problem solving. First-year midwifery students (n = 25) filmed themselves undertaking a skill and then immediately played back the video file. A total of 45 students completed an online questionnaire that allowed for qualitative comments. Students reported finding the use of iPads easy and that iPads provided point-of-care access to resources, ensuring an evidence-based approach to clinical decision making. iPads reportedly improved student efficiency and time management, while improving their ability to provide patient education. Students who used iPads for the purpose of formative self-assessment appreciated the immediate feedback and opportunity to develop clinical skills.

Rose garden promises of intelligent tutoring systems: Blossom or thorn

NASA Technical Reports Server (NTRS)

Shute, Valerie J.

1991-01-01

Intelligent tutoring systems (ITS) have been in existence for over a decade. However, few controlled evaluation studies have been conducted comparing the effectiveness of these systems to more traditional instruction methods. Two main promises of ITSs are examined: (1) Engender more effective and efficient learning in relation to traditional formats; and (2) Reduce the range of learning outcome measures where a majority of individuals are elevated to high performance levels. Bloom (1984) has referred to these as the two sigma problem; to achieve two standard deviation improvements with tutoring over traditional instruction methods. Four ITSs are discussed in relation to the two promises. These tutors have undergone systematic, controlled evaluations: (1) The LISP tutor (Anderson Farrell and Sauers, 1984); (2) Smithtown (Shute and Glaser, in press); (3) Sherlock (Lesgold, Lajoie, Bunzo and Eggan, 1990); and (4) The Pascal ITS (Bonar, Cunningham, Beatty and Well, 1988). Results show that these four tutors do accelerate learning with no degradation in final outcome. Suggestions for improvements to the design and evaluation of ITSs are discussed.

Machine Learning for Knowledge Extraction from PHR Big Data.

PubMed

Poulymenopoulou, Michaela; Malamateniou, Flora; Vassilacopoulos, George

2014-01-01

Cloud computing, Internet of things (IOT) and NoSQL database technologies can support a new generation of cloud-based PHR services that contain heterogeneous (unstructured, semi-structured and structured) patient data (health, social and lifestyle) from various sources, including automatically transmitted data from Internet connected devices of patient living space (e.g. medical devices connected to patients at home care). The patient data stored in such PHR systems constitute big data whose analysis with the use of appropriate machine learning algorithms is expected to improve diagnosis and treatment accuracy, to cut healthcare costs and, hence, to improve the overall quality and efficiency of healthcare provided. This paper describes a health data analytics engine which uses machine learning algorithms for analyzing cloud based PHR big health data towards knowledge extraction to support better healthcare delivery as regards disease diagnosis and prognosis. This engine comprises of the data preparation, the model generation and the data analysis modules and runs on the cloud taking advantage from the map/reduce paradigm provided by Apache Hadoop.

Speech recognition-based and automaticity programs to help students with severe reading and spelling problems.

PubMed

Higgins, Eleanor L; Raskind, Marshall H

2004-12-01

This study was conducted to assess the effectiveness of two programs developed by the Frostig Center Research Department to improve the reading and spelling of students with learning disabilities (LD): a computer Speech Recognition-based Program (SRBP) and a computer and text-based Automaticity Program (AP). Twenty-eight LD students with reading and spelling difficulties (aged 8 to 18) received each program for 17 weeks and were compared with 16 students in a contrast group who did not receive either program. After adjusting for age and IQ, both the SRBP and AP groups showed significant differences over the contrast group in improving word recognition and reading comprehension. Neither program showed significant differences over contrasts in spelling. The SRBP also improved the performance of the target group when compared with the contrast group on phonological elision and nonword reading efficiency tasks. The AP showed significant differences in all process and reading efficiency measures.

Using Unified Modelling Language (UML) as a process-modelling technique for clinical-research process improvement.

PubMed

Kumarapeli, P; De Lusignan, S; Ellis, T; Jones, B

2007-03-01

The Primary Care Data Quality programme (PCDQ) is a quality-improvement programme which processes routinely collected general practice computer data. Patient data collected from a wide range of different brands of clinical computer systems are aggregated, processed, and fed back to practices in an educational context to improve the quality of care. Process modelling is a well-established approach used to gain understanding and systematic appraisal, and identify areas of improvement of a business process. Unified modelling language (UML) is a general purpose modelling technique used for this purpose. We used UML to appraise the PCDQ process to see if the efficiency and predictability of the process could be improved. Activity analysis and thinking-aloud sessions were used to collect data to generate UML diagrams. The UML model highlighted the sequential nature of the current process as a barrier for efficiency gains. It also identified the uneven distribution of process controls, lack of symmetric communication channels, critical dependencies among processing stages, and failure to implement all the lessons learned in the piloting phase. It also suggested that improved structured reporting at each stage - especially from the pilot phase, parallel processing of data and correctly positioned process controls - should improve the efficiency and predictability of research projects. Process modelling provided a rational basis for the critical appraisal of a clinical data processing system; its potential maybe underutilized within health care.

Joint Dictionary Learning-Based Non-Negative Matrix Factorization for Voice Conversion to Improve Speech Intelligibility After Oral Surgery.

PubMed

Fu, Szu-Wei; Li, Pei-Chun; Lai, Ying-Hui; Yang, Cheng-Chien; Hsieh, Li-Chun; Tsao, Yu

2017-11-01

Objective: This paper focuses on machine learning based voice conversion (VC) techniques for improving the speech intelligibility of surgical patients who have had parts of their articulators removed. Because of the removal of parts of the articulator, a patient's speech may be distorted and difficult to understand. To overcome this problem, VC methods can be applied to convert the distorted speech such that it is clear and more intelligible. To design an effective VC method, two key points must be considered: 1) the amount of training data may be limited (because speaking for a long time is usually difficult for postoperative patients); 2) rapid conversion is desirable (for better communication). Methods: We propose a novel joint dictionary learning based non-negative matrix factorization (JD-NMF) algorithm. Compared to conventional VC techniques, JD-NMF can perform VC efficiently and effectively with only a small amount of training data. Results: The experimental results demonstrate that the proposed JD-NMF method not only achieves notably higher short-time objective intelligibility (STOI) scores (a standardized objective intelligibility evaluation metric) than those obtained using the original unconverted speech but is also significantly more efficient and effective than a conventional exemplar-based NMF VC method. Conclusion: The proposed JD-NMF method may outperform the state-of-the-art exemplar-based NMF VC method in terms of STOI scores under the desired scenario. Significance: We confirmed the advantages of the proposed joint training criterion for the NMF-based VC. Moreover, we verified that the proposed JD-NMF can effectively improve the speech intelligibility scores of oral surgery patients. Objective: This paper focuses on machine learning based voice conversion (VC) techniques for improving the speech intelligibility of surgical patients who have had parts of their articulators removed. Because of the removal of parts of the articulator, a patient's speech may be distorted and difficult to understand. To overcome this problem, VC methods can be applied to convert the distorted speech such that it is clear and more intelligible. To design an effective VC method, two key points must be considered: 1) the amount of training data may be limited (because speaking for a long time is usually difficult for postoperative patients); 2) rapid conversion is desirable (for better communication). Methods: We propose a novel joint dictionary learning based non-negative matrix factorization (JD-NMF) algorithm. Compared to conventional VC techniques, JD-NMF can perform VC efficiently and effectively with only a small amount of training data. Results: The experimental results demonstrate that the proposed JD-NMF method not only achieves notably higher short-time objective intelligibility (STOI) scores (a standardized objective intelligibility evaluation metric) than those obtained using the original unconverted speech but is also significantly more efficient and effective than a conventional exemplar-based NMF VC method. Conclusion: The proposed JD-NMF method may outperform the state-of-the-art exemplar-based NMF VC method in terms of STOI scores under the desired scenario. Significance: We confirmed the advantages of the proposed joint training criterion for the NMF-based VC. Moreover, we verified that the proposed JD-NMF can effectively improve the speech intelligibility scores of oral surgery patients.

Mind map learning for advanced engineering study: case study in system dynamics

NASA Astrophysics Data System (ADS)

Woradechjumroen, Denchai

2018-01-01

System Dynamics (SD) is one of the subjects that were use in learning Automatic Control Systems in dynamic and control field. Mathematical modelling and solving skills of students for engineering systems are expecting outcomes of the course which can be further used to efficiently study control systems and mechanical vibration; however, the fundamental of the SD includes strong backgrounds in Dynamics and Differential Equations, which are appropriate to the students in governmental universities that have strong skills in Mathematics and Scientifics. For private universities, students are weak in the above subjects since they obtained high vocational certificate from Technical College or Polytechnic School, which emphasize the learning contents in practice. To enhance their learning for improving their backgrounds, this paper applies mind maps based problem based learning to relate the essential relations of mathematical and physical equations. With the advantages of mind maps, each student is assigned to design individual mind maps for self-leaning development after they attend the class and learn overall picture of each chapter from the class instructor. Four problems based mind maps learning are assigned to each student. Each assignment is evaluated via mid-term and final examinations, which are issued in terms of learning concepts and applications. In the method testing, thirty students are tested and evaluated via student learning backgrounds in the past. The result shows that well-design mind maps can improve learning performance based on outcome evaluation. Especially, mind maps can reduce time-consuming and reviewing for Mathematics and Physics in SD significantly.

Transitioning a bachelor of science in nursing program to blended learning: Successes, challenges & outcomes.

PubMed

Posey, Laurie; Pintz, Christine

2017-09-01

To help address the challenges of providing undergraduate nursing education in an accelerated time frame, the Teaching and Transforming through Technology (T3) project was funded to transition a second-degree ABSN program to a blended learning format. The project has explored the use of blended learning to: enable flexible solutions to support teaching goals and address course challenges; provide students with new types of independent learning activities outside of the traditional classroom; increase opportunities for active learning in the classroom; and improve students' digital literacy and lifelong learning skills. Program evaluation included quality reviews of the redesigned courses, surveys of student perceptions, pre- and post-program assessment of students' digital literacy and interviews with faculty about their experiences with the new teaching methods. Adopting an established quality framework to guide course design and evaluation for quality contributed to the efficient and effective development of a high-quality undergraduate blended nursing program. Program outcomes and lessons learned are presented to inform future teaching innovation and research related to blended learning in undergraduate nursing education. Copyright © 2016 Elsevier Ltd. All rights reserved.

Trust-based Access Control in Virtual Learning Community

NASA Astrophysics Data System (ADS)

Wang, Shujuan; Liu, Qingtang

The virtual learning community is an important application pattern of E-Learning. It emphasizes the cooperation of the members in the community, the members would like to share their learning resources, to exchange their experience and complete the study task together. This instructional mode has already been proved as an effective way to improve the quality and efficiency of instruction. At the present time, the virtual learning communities are mostly designed using static access control policy by which the access permission rights are authorized by the super administrator, the super administrator assigns different rights to different roles, but the virtual and social characteristics of virtual learning community make information sharing and collaboration a complex problem, the community realizes its instructional goal only if the members in it believe that others will offer the knowledge they owned and believe the knowledge others offered is well-meaning and worthy. This paper tries to constitute an effective trust mechanism, which could promise favorable interaction and lasting knowledge sharing.

Barriers and decisions when answering clinical questions at the point of care: a grounded theory study.

PubMed

Cook, David A; Sorensen, Kristi J; Wilkinson, John M; Berger, Richard A

2013-11-25

Answering clinical questions affects patient-care decisions and is important to continuous professional development. The process of point-of-care learning is incompletely understood. To understand what barriers and enabling factors influence physician point-of-care learning and what decisions physicians face during this process. Focus groups with grounded theory analysis. Focus group discussions were transcribed and then analyzed using a constant comparative approach to identify barriers, enabling factors, and key decisions related to physician information-seeking activities. Academic medical center and outlying community sites. Purposive sample of 50 primary care and subspecialist internal medicine and family medicine physicians, interviewed in 11 focus groups. Insufficient time was the main barrier to point-of-care learning. Other barriers included the patient comorbidities and contexts, the volume of available information, not knowing which resource to search, doubt that the search would yield an answer, difficulty remembering questions for later study, and inconvenient access to computers. Key decisions were whether to search (reasons to search included infrequently seen conditions, practice updates, complex questions, and patient education), when to search (before, during, or after the clinical encounter), where to search (with the patient present or in a separate room), what type of resource to use (colleague or computer), what specific resource to use (influenced first by efficiency and second by credibility), and when to stop. Participants noted that key features of efficiency (completeness, brevity, and searchability) are often in conflict. Physicians perceive that insufficient time is the greatest barrier to point-of-care learning, and efficiency is the most important determinant in selecting an information source. Designing knowledge resources and systems to target key decisions may improve learning and patient care.

Assessing Complex Learning Objectives through Analytics

NASA Astrophysics Data System (ADS)

Horodyskyj, L.; Mead, C.; Buxner, S.; Semken, S. C.; Anbar, A. D.

2016-12-01

A significant obstacle to improving the quality of education is the lack of easy-to-use assessments of higher-order thinking. Most existing assessments focus on recall and understanding questions, which demonstrate lower-order thinking. Traditionally, higher-order thinking is assessed with practical tests and written responses, which are time-consuming to analyze and are not easily scalable. Computer-based learning environments offer the possibility of assessing such learning outcomes based on analysis of students' actions within an adaptive learning environment. Our fully online introductory science course, Habitable Worlds, uses an intelligent tutoring system that collects and responds to a range of behavioral data, including actions within the keystone project. This central project is a summative, game-like experience in which students synthesize and apply what they have learned throughout the course to identify and characterize a habitable planet from among hundreds of stars. Student performance is graded based on completion and accuracy, but two additional properties can be utilized to gauge higher-order thinking: (1) how efficient a student is with the virtual currency within the project and (2) how many of the optional milestones a student reached. In the project, students can use the currency to check their work and "unlock" convenience features. High-achieving students spend close to the minimum amount required to reach these goals, indicating a high-level of concept mastery and efficient methodology. Average students spend more, indicating effort, but lower mastery. Low-achieving students were more likely to spend very little, which indicates low effort. Differences on these metrics were statistically significant between all three of these populations. We interpret this as evidence that high-achieving students develop and apply efficient problem-solving skills as compared to lower-achieving student who use more brute-force approaches.

Machine Learning in Radiation Oncology: Opportunities, Requirements, and Needs

PubMed Central

Feng, Mary; Valdes, Gilmer; Dixit, Nayha; Solberg, Timothy D.

2018-01-01

Machine learning (ML) has the potential to revolutionize the field of radiation oncology, but there is much work to be done. In this article, we approach the radiotherapy process from a workflow perspective, identifying specific areas where a data-centric approach using ML could improve the quality and efficiency of patient care. We highlight areas where ML has already been used, and identify areas where we should invest additional resources. We believe that this article can serve as a guide for both clinicians and researchers to start discussing issues that must be addressed in a timely manner. PMID:29719815

A Brief Study on Autonomous Learning Mode in Self-study Center Based on Web

NASA Astrophysics Data System (ADS)

Tian, Xian Zhi

In the paper, the author has studied the autonomous learning ability and its reform of linguistic-major students. All of the studies are based on web in self-study center. As for the author, she has used the method of comparison and at the same time, she also used showing examples. In order to show the views clearly, the author has made investigation in English major and law major students. Thus she thinks that teaching reform is necessary for development of students and some effective ways can be used in improving teaching efficiency.

Test Generation Algorithm for Fault Detection of Analog Circuits Based on Extreme Learning Machine

PubMed Central

Zhou, Jingyu; Tian, Shulin; Yang, Chenglin; Ren, Xuelong

2014-01-01

This paper proposes a novel test generation algorithm based on extreme learning machine (ELM), and such algorithm is cost-effective and low-risk for analog device under test (DUT). This method uses test patterns derived from the test generation algorithm to stimulate DUT, and then samples output responses of the DUT for fault classification and detection. The novel ELM-based test generation algorithm proposed in this paper contains mainly three aspects of innovation. Firstly, this algorithm saves time efficiently by classifying response space with ELM. Secondly, this algorithm can avoid reduced test precision efficiently in case of reduction of the number of impulse-response samples. Thirdly, a new process of test signal generator and a test structure in test generation algorithm are presented, and both of them are very simple. Finally, the abovementioned improvement and functioning are confirmed in experiments. PMID:25610458

[Discussion of the Application of Micro-lecture in the Clinical Training 
of Thoracic Surgery].

PubMed

Li, Xiaofei; Lei, Jie

2018-04-20

Today, with the rapid development of network information technology, the micro-lecture plays a role in the teaching activities is becoming more and more important. The short and efficient teaching content of micro-lecture can be downloaded rapidly, expediently, and repeatedly, which improve the learning efficiency and independent learning capability. The clinical training of thoracic surgery elementarily remains at the scrabble stage. We require continuous reform and introduce new modes of teaching, which compatible with the development of society and the study habits of novice, to enhance the effectiveness of clinical training. In this paper, the concept, characteristic and advantage of micro-lecture was discussed, and the feasibility of application of micro-lecture in thoracic surgery teaching was also discussed. Our aim was to promote the application of micro-lecture in the clinical training of thoracic surgery reasonable and extensive.
.

Efficient hybrid evolutionary algorithm for optimization of a strip coiling process

NASA Astrophysics Data System (ADS)

Pholdee, Nantiwat; Park, Won-Woong; Kim, Dong-Kyu; Im, Yong-Taek; Bureerat, Sujin; Kwon, Hyuck-Cheol; Chun, Myung-Sik

2015-04-01

This article proposes an efficient metaheuristic based on hybridization of teaching-learning-based optimization and differential evolution for optimization to improve the flatness of a strip during a strip coiling process. Differential evolution operators were integrated into the teaching-learning-based optimization with a Latin hypercube sampling technique for generation of an initial population. The objective function was introduced to reduce axial inhomogeneity of the stress distribution and the maximum compressive stress calculated by Love's elastic solution within the thin strip, which may cause an irregular surface profile of the strip during the strip coiling process. The hybrid optimizer and several well-established evolutionary algorithms (EAs) were used to solve the optimization problem. The comparative studies show that the proposed hybrid algorithm outperformed other EAs in terms of convergence rate and consistency. It was found that the proposed hybrid approach was powerful for process optimization, especially with a large-scale design problem.

Machine learning properties of binary wurtzite superlattices

DOE PAGES

Pilania, G.; Liu, X. -Y.

2018-01-12

The burgeoning paradigm of high-throughput computations and materials informatics brings new opportunities in terms of targeted materials design and discovery. The discovery process can be significantly accelerated and streamlined if one can learn effectively from available knowledge and past data to predict materials properties efficiently. Indeed, a very active area in materials science research is to develop machine learning based methods that can deliver automated and cross-validated predictive models using either already available materials data or new data generated in a targeted manner. In the present paper, we show that fast and accurate predictions of a wide range of propertiesmore » of binary wurtzite superlattices, formed by a diverse set of chemistries, can be made by employing state-of-the-art statistical learning methods trained on quantum mechanical computations in combination with a judiciously chosen numerical representation to encode materials’ similarity. These surrogate learning models then allow for efficient screening of vast chemical spaces by providing instant predictions of the targeted properties. Moreover, the models can be systematically improved in an adaptive manner, incorporate properties computed at different levels of fidelities and are naturally amenable to inverse materials design strategies. Finally, while the learning approach to make predictions for a wide range of properties (including structural, elastic and electronic properties) is demonstrated here for a specific example set containing more than 1200 binary wurtzite superlattices, the adopted framework is equally applicable to other classes of materials as well.« less

The Educational Kanban: promoting effective self-directed adult learning in medical education.

PubMed

Goldman, Stuart

2009-07-01

The author reviews the many forces that have driven contemporary medical education approaches to evaluation and places them in an adult learning theory context. After noting their strengths and limitations, the author looks to lessons learned from manufacturing on both efficacy and efficiency and explores how these can be applied to the process of trainee assessment in medical education.Building on this, the author describes the rationale for and development of the Educational Kanban (EK) at Children's Hospital Boston--specifically, how it was designed to integrate adult learning theory, Japanese manufacturing models, and educator observations into a unique form of teacher-student collaboration that allows for continuous improvement. It is a formative tool, built on the Accreditation Council for Graduate Medical Education's six core competencies, that guides educational efforts to optimize teaching and learning, promotes adult learner responsibility and efficacy, and takes advantage of the labor-intensive clinical educational setting. The author discusses how this model, which will be implemented in July 2009, will lead to training that is highly individualized, optimizes faculty and student educational efforts, and ultimately conserves faculty resources. A model EK is provided for general reference.The EK represents a novel approach to adult learning that will enhance educational effectiveness and efficiency and complement existing evaluative models. Described here in a specific graduate medical setting, it can readily be adapted and integrated into a wide range of undergraduate and graduate clinical educational environments.

Machine learning properties of binary wurtzite superlattices

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

Pilania, G.; Liu, X. -Y.

The burgeoning paradigm of high-throughput computations and materials informatics brings new opportunities in terms of targeted materials design and discovery. The discovery process can be significantly accelerated and streamlined if one can learn effectively from available knowledge and past data to predict materials properties efficiently. Indeed, a very active area in materials science research is to develop machine learning based methods that can deliver automated and cross-validated predictive models using either already available materials data or new data generated in a targeted manner. In the present paper, we show that fast and accurate predictions of a wide range of propertiesmore » of binary wurtzite superlattices, formed by a diverse set of chemistries, can be made by employing state-of-the-art statistical learning methods trained on quantum mechanical computations in combination with a judiciously chosen numerical representation to encode materials’ similarity. These surrogate learning models then allow for efficient screening of vast chemical spaces by providing instant predictions of the targeted properties. Moreover, the models can be systematically improved in an adaptive manner, incorporate properties computed at different levels of fidelities and are naturally amenable to inverse materials design strategies. Finally, while the learning approach to make predictions for a wide range of properties (including structural, elastic and electronic properties) is demonstrated here for a specific example set containing more than 1200 binary wurtzite superlattices, the adopted framework is equally applicable to other classes of materials as well.« less

Evaluation Of Sludge Heel Dissolution Efficiency With Oxalic Acid Cleaning At Savannah River Site

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

Sudduth, Christie; Vitali, Jason; Keefer, Mark

The chemical cleaning process baseline strategy at the Savannah River Site was revised to improve efficiency during future execution of the process based on lessons learned during previous bulk oxalic acid cleaning activities and to account for operational constraints imposed by safety basis requirements. These improvements were also intended to transcend the difficulties that arise from waste removal in higher rheological yield stress sludge tanks. Tank 12 implemented this improved strategy and the bulk oxalic acid cleaning efforts concluded in July 2013. The Tank 12 radiological removal results were similar to previous bulk oxalic acid cleaning campaigns despite the factmore » that Tank 12 contained higher rheological yield stress sludge that would make removal more difficult than the sludge treated in previous cleaning campaigns. No appreciable oxalate precipitation occurred during the cleaning process in Tank 12 compared to previous campaigns, which aided in the net volume reduction of 75-80%. Overall, the controls established for Tank 12 provide a template for an improved cleaning process.« less

Failing to learn from negative prediction errors: Obesity is associated with alterations in a fundamental neural learning mechanism.

PubMed

Mathar, David; Neumann, Jane; Villringer, Arno; Horstmann, Annette

2017-10-01

Prediction errors (PEs) encode the difference between expected and actual action outcomes in the brain via dopaminergic modulation. Integration of these learning signals ensures efficient behavioral adaptation. Obesity has recently been linked to altered dopaminergic fronto-striatal circuits, thus implying impairments in cognitive domains that rely on its integrity. 28 obese and 30 lean human participants performed an implicit stimulus-response learning paradigm inside an fMRI scanner. Computational modeling and psycho-physiological interaction (PPI) analysis was utilized for assessing PE-related learning and associated functional connectivity. We show that human obesity is associated with insufficient incorporation of negative PEs into behavioral adaptation even in a non-food context, suggesting differences in a fundamental neural learning mechanism. Obese subjects were less efficient in using negative PEs to improve implicit learning performance, despite proper coding of PEs in striatum. We further observed lower functional coupling between ventral striatum and supplementary motor area in obese subjects subsequent to negative PEs. Importantly, strength of functional coupling predicted task performance and negative PE utilization. These findings show that obesity is linked to insufficient behavioral adaptation specifically in response to negative PEs, and to associated alterations in function and connectivity within the fronto-striatal system. Recognition of neural differences as a central characteristic of obesity hopefully paves the way to rethink established intervention strategies: Differential behavioral sensitivity to negative and positive PEs should be considered when designing intervention programs. Measures relying on penalization of unwanted behavior may prove less effective in obese subjects than alternative approaches. Copyright © 2017 Elsevier Ltd. All rights reserved.

Acute stress differentially affects spatial configuration learning in high and low cortisol-responding healthy adults

PubMed Central

Meyer, Thomas; Smeets, Tom; Giesbrecht, Timo; Quaedflieg, Conny W. E. M.; Merckelbach, Harald

2013-01-01

Background Stress and stress hormones modulate memory formation in various ways that are relevant to our understanding of stress-related psychopathology, such as posttraumatic stress disorder (PTSD). Particular relevance is attributed to efficient memory formation sustained by the hippocampus and parahippocampus. This process is thought to reduce the occurrence of intrusions and flashbacks following trauma, but may be negatively affected by acute stress. Moreover, recent evidence suggests that the efficiency of visuo-spatial processing and learning based on the hippocampal area is related to PTSD symptoms. Objective The current study investigated the effect of acute stress on spatial configuration learning using a spatial contextual cueing task (SCCT) known to heavily rely on structures in the parahippocampus. Method Acute stress was induced by subjecting participants (N = 34) to the Maastricht Acute Stress Test (MAST). Following a counterbalanced within-subject approach, the effects of stress and the ensuing hormonal (i.e., cortisol) activity on subsequent SCCT performance were compared to SCCT performance following a no-stress control condition. Results Acute stress did not impact SCCT learning overall, but opposing effects emerged for high versus low cortisol responders to the MAST. Learning scores following stress were reduced in low cortisol responders, while high cortisol-responding participants showed improved learning. Conclusions The effects of stress on spatial configuration learning were moderated by the magnitude of endogenous cortisol secretion. These findings suggest a possible mechanism by which cortisol responses serve an adaptive function during stress and trauma, and this may prove to be a promising route for future research in this area. PMID:23671762

A Support Vector Learning-Based Particle Filter Scheme for Target Localization in Communication-Constrained Underwater Acoustic Sensor Networks

PubMed Central

Zhang, Chenglin; Yan, Lei; Han, Song; Guan, Xinping

2017-01-01

Target localization, which aims to estimate the location of an unknown target, is one of the key issues in applications of underwater acoustic sensor networks (UASNs). However, the constrained property of an underwater environment, such as restricted communication capacity of sensor nodes and sensing noises, makes target localization a challenging problem. This paper relies on fractional sensor nodes to formulate a support vector learning-based particle filter algorithm for the localization problem in communication-constrained underwater acoustic sensor networks. A node-selection strategy is exploited to pick fractional sensor nodes with short-distance pattern to participate in the sensing process at each time frame. Subsequently, we propose a least-square support vector regression (LSSVR)-based observation function, through which an iterative regression strategy is used to deal with the distorted data caused by sensing noises, to improve the observation accuracy. At the same time, we integrate the observation to formulate the likelihood function, which effectively update the weights of particles. Thus, the particle effectiveness is enhanced to avoid “particle degeneracy” problem and improve localization accuracy. In order to validate the performance of the proposed localization algorithm, two different noise scenarios are investigated. The simulation results show that the proposed localization algorithm can efficiently improve the localization accuracy. In addition, the node-selection strategy can effectively select the subset of sensor nodes to improve the communication efficiency of the sensor network. PMID:29267252

A Support Vector Learning-Based Particle Filter Scheme for Target Localization in Communication-Constrained Underwater Acoustic Sensor Networks.

PubMed

Li, Xinbin; Zhang, Chenglin; Yan, Lei; Han, Song; Guan, Xinping

2017-12-21

Target localization, which aims to estimate the location of an unknown target, is one of the key issues in applications of underwater acoustic sensor networks (UASNs). However, the constrained property of an underwater environment, such as restricted communication capacity of sensor nodes and sensing noises, makes target localization a challenging problem. This paper relies on fractional sensor nodes to formulate a support vector learning-based particle filter algorithm for the localization problem in communication-constrained underwater acoustic sensor networks. A node-selection strategy is exploited to pick fractional sensor nodes with short-distance pattern to participate in the sensing process at each time frame. Subsequently, we propose a least-square support vector regression (LSSVR)-based observation function, through which an iterative regression strategy is used to deal with the distorted data caused by sensing noises, to improve the observation accuracy. At the same time, we integrate the observation to formulate the likelihood function, which effectively update the weights of particles. Thus, the particle effectiveness is enhanced to avoid "particle degeneracy" problem and improve localization accuracy. In order to validate the performance of the proposed localization algorithm, two different noise scenarios are investigated. The simulation results show that the proposed localization algorithm can efficiently improve the localization accuracy. In addition, the node-selection strategy can effectively select the subset of sensor nodes to improve the communication efficiency of the sensor network.

Improving High School Students' Understanding of Potential Difference in Simple Electric Circuits

ERIC Educational Resources Information Center

Liegeois, Laurent; Chasseigne, G'erard; Papin, Sophie; Mullet, Etienne

2003-01-01

This paper reports two studies into the understanding of the concept of potential difference in the current-potential difference-resistance context among 8th-12th graders (Study 1), and the efficiency of a learning device derived from Social Judgment Theory (Study 2). These two studies showed that: (a) when asked to infer potential difference from…

Improved Learning Efficiency and Increased Student Collaboration through Use of Virtual Microscopy in the Teaching of Human Pathology

ERIC Educational Resources Information Center

Braun, Mark W.; Kearns, Katherine D.

2008-01-01

The implementation of virtual microscopy in the teaching of pathology at the Bloomington, Indiana extension of the Indiana University School of Medicine permitted the assessment of student attitudes, use and academic performance with respect to this new technology. A gradual and integrated approach allowed the parallel assessment with respect to…

Retrieval Practice Is an Efficient Method of Enhancing the Retention of Anatomy and Physiology Information

ERIC Educational Resources Information Center

Dobson, John L.

2013-01-01

Although a great deal of empirical evidence has indicated that retrieval practice is an effective means of promoting learning and memory, very few studies have investigated the strategy in the context of an actual class. The primary purpose of this study was to determine if a series of very brief retrieval quizzes could significantly improve the…

A Comparison between the Effectiveness of PBL and LBL on Improving Problem-Solving Abilities of Medical Students Using Questioning

ERIC Educational Resources Information Center

He, Yunfeng; Du, Xiangyun; Toft, Egon; Zhang, Xingli; Qu, Bo; Shi, Jiannong; Zhang, Huan; Zhang, Hui

2018-01-01

In daily patient-history taking and diagnosis practice, doctors ask questions to gather information from patients and narrow down diagnostic hypotheses. Training medical students to be efficient problem solvers through the use of questioning is therefore important. In this study, the effectiveness of problem-based learning (PBL) and lecture-based…

A Digital Game-Based Learning System for Energy Education: An Energy COnservation PET

ERIC Educational Resources Information Center

Yang, Jie Chi; Chien, Kun Huang; Liu, Tzu Chien

2012-01-01

Energy education has been conducted to equip learners with relevant energy conservation knowledge for many years. However, learners seldom put the knowledge into practice and even have few ideas about how to reduce energy consumption. To this end, there is a need to address this issue to improve the efficiency of energy education. One of the…

Do Computers Improve the Drawing of a Geometrical Figure for 10 Year-Old Children?

ERIC Educational Resources Information Center

Martin, Perrine; Velay, Jean-Luc

2012-01-01

Nowadays, computer aided design (CAD) is widely used by designers. Would children learn to draw more easily and more efficiently if they were taught with computerised tools? To answer this question, we made an experiment designed to compare two methods for children to do the same drawing: the classical "pen and paper" method and a CAD…

Mobile Technologies: Tools for Organizational Learning and Management in Schools. iPrincipals: Analyzing the Use of iPads by School Administrators

ERIC Educational Resources Information Center

Winslow, Joe; Dickerson, Jeremy; Lee, Cheng-Yuan; Geer, Gregory

2012-01-01

This paper reports findings from an evaluation of a district-wide initiative deploying iPads to school administrators (principals) to improve workflow efficiencies and promote technology leadership self-efficacy. The findings indicate that iPad utilization not only facilitated administrative tasks (memos, calendars, etc.), but also improved…

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

NASA Technical Reports Server (NTRS)

Lee, Hanbong

2016-01-01

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

Kaiser Permanente's performance improvement system, Part 4: Creating a learning organization.

PubMed

Schilling, Lisa; Dearing, James W; Staley, Paul; Harvey, Patti; Fahey, Linda; Kuruppu, Francesca

2011-12-01

In 2006, recognizing variations in performance in quality, safety, service, and efficiency, Kaiser Permanente leaders initiated the development of a performance improvement (PI) system. Kaiser Permanente has implemented a strategy for creating the systemic capacity for continuous improvement that characterizes a learning organization. Six "building blocks" were identified to enable Kaiser Permanente to make the transition to becoming a learning organization: real-time sharing of meaningful performance data; formal training in problem-solving methodology; workforce engagement and informal knowledge sharing; leadership structures, beliefs, and behaviors; internal and external benchmarking; and technical knowledge sharing. Putting each building block into place required multiple complex strategies combining top-down and bottom-up approaches. Although the strategies have largely been successful, challenges remain. The demand for real-time meaningful performance data can conflict with prioritized changes to health information systems. It is an ongoing challenge to teach PI, change management, innovation, and project management to all managers and staff without consuming too much training time. Challenges with workforce engagement include low initial use of tools intended to disseminate information through virtual social networking. Uptake of knowledge-sharing technologies is still primarily by innovators and early adopters. Leaders adopt new behaviors at varying speeds and have a range of abilities to foster an environment that is psychologically safe and stimulates inquiry. A learning organization has the capability to improve, and it develops structures and processes that facilitate the acquisition and sharing of knowledge.

Computer-Based Learning: Interleaving Whole and Sectional Representation of Neuroanatomy

ERIC Educational Resources Information Center

Pani, John R.; Chariker, Julia H.; Naaz, Farah

2013-01-01

The large volume of material to be learned in biomedical disciplines requires optimizing the efficiency of instruction. In prior work with computer-based instruction of neuroanatomy, it was relatively efficient for learners to master whole anatomy and then transfer to learning sectional anatomy. It may, however, be more efficient to continuously…

Clinical data miner: an electronic case report form system with integrated data preprocessing and machine-learning libraries supporting clinical diagnostic model research.

PubMed

Installé, Arnaud Jf; Van den Bosch, Thierry; De Moor, Bart; Timmerman, Dirk

2014-10-20

Using machine-learning techniques, clinical diagnostic model research extracts diagnostic models from patient data. Traditionally, patient data are often collected using electronic Case Report Form (eCRF) systems, while mathematical software is used for analyzing these data using machine-learning techniques. Due to the lack of integration between eCRF systems and mathematical software, extracting diagnostic models is a complex, error-prone process. Moreover, due to the complexity of this process, it is usually only performed once, after a predetermined number of data points have been collected, without insight into the predictive performance of the resulting models. The objective of the study of Clinical Data Miner (CDM) software framework is to offer an eCRF system with integrated data preprocessing and machine-learning libraries, improving efficiency of the clinical diagnostic model research workflow, and to enable optimization of patient inclusion numbers through study performance monitoring. The CDM software framework was developed using a test-driven development (TDD) approach, to ensure high software quality. Architecturally, CDM's design is split over a number of modules, to ensure future extendability. The TDD approach has enabled us to deliver high software quality. CDM's eCRF Web interface is in active use by the studies of the International Endometrial Tumor Analysis consortium, with over 4000 enrolled patients, and more studies planned. Additionally, a derived user interface has been used in six separate interrater agreement studies. CDM's integrated data preprocessing and machine-learning libraries simplify some otherwise manual and error-prone steps in the clinical diagnostic model research workflow. Furthermore, CDM's libraries provide study coordinators with a method to monitor a study's predictive performance as patient inclusions increase. To our knowledge, CDM is the only eCRF system integrating data preprocessing and machine-learning libraries. This integration improves the efficiency of the clinical diagnostic model research workflow. Moreover, by simplifying the generation of learning curves, CDM enables study coordinators to assess more accurately when data collection can be terminated, resulting in better models or lower patient recruitment costs.

A random forest learning assisted "divide and conquer" approach for peptide conformation search.

PubMed

Chen, Xin; Yang, Bing; Lin, Zijing

2018-06-11

Computational determination of peptide conformations is challenging as it is a problem of finding minima in a high-dimensional space. The "divide and conquer" approach is promising for reliably reducing the search space size. A random forest learning model is proposed here to expand the scope of applicability of the "divide and conquer" approach. A random forest classification algorithm is used to characterize the distributions of the backbone φ-ψ units ("words"). A random forest supervised learning model is developed to analyze the combinations of the φ-ψ units ("grammar"). It is found that amino acid residues may be grouped as equivalent "words", while the φ-ψ combinations in low-energy peptide conformations follow a distinct "grammar". The finding of equivalent words empowers the "divide and conquer" method with the flexibility of fragment substitution. The learnt grammar is used to improve the efficiency of the "divide and conquer" method by removing unfavorable φ-ψ combinations without the need of dedicated human effort. The machine learning assisted search method is illustrated by efficiently searching the conformations of GGG/AAA/GGGG/AAAA/GGGGG through assembling the structures of GFG/GFGG. Moreover, the computational cost of the new method is shown to increase rather slowly with the peptide length.

Elasmobranch cognitive ability: using electroreceptive foraging behaviour to demonstrate learning, habituation and memory in a benthic shark.

PubMed

Kimber, Joel A; Sims, David W; Bellamy, Patricia H; Gill, Andrew B

2014-01-01

Top predators inhabiting a dynamic environment, such as coastal waters, should theoretically possess sufficient cognitive ability to allow successful foraging despite unpredictable sensory stimuli. The cognition-related hunting abilities of marine mammals have been widely demonstrated. Having been historically underestimated, teleost cognitive abilities have also now been significantly demonstrated. Conversely, the abilities of elasmobranchs have received little attention, despite many species possessing relatively large brains comparable to some mammals. The need to determine what, if any, cognitive ability these globally distributed, apex predators are endowed with has been highlighted recently by questions arising from environmental assessments, specifically whether they are able to learn to distinguish between anthropogenic electric fields and prey bioelectric fields. We therefore used electroreceptive foraging behaviour in a model species, Scyliorhinus canicula (small-spotted catshark), to determine cognitive ability by analysing whether elasmobranchs are able to learn to improve foraging efficiency and remember learned behavioural adaptations. Positive reinforcement, operant conditioning was used to study catshark foraging behaviour towards artificial, prey-type electric fields (Efields). Catsharks rewarded with food for responding to Efields throughout experimental weeks were compared with catsharks that were not rewarded for responding in order to assess behavioural adaptation via learning ability. Experiments were repeated after a 3-week interval with previously rewarded catsharks this time receiving no reward and vice versa to assess memory ability. Positive reinforcement markedly and rapidly altered catshark foraging behaviour. Rewarded catsharks exhibited significantly more interest in the electrical stimulus than unrewarded catsharks. Furthermore, they improved their foraging efficiency over time by learning to locate and bite the electrodes to gain food more quickly. In contrast, unrewarded catsharks showed some habituation, whereby their responses to the electrodes abated and eventually entirely ceased, though they generally showed no changes in most foraging parameters. Behavioural adaptations were not retained after the interval suggesting learned behaviour was not memorised beyond the interval. Sequences of individual catshark search paths clearly illustrated learning and habituation behavioural adaptation. This study demonstrated learning and habituation occurring after few foraging events and a memory window of between 12 h and 3 weeks. These cognitive abilities are discussed in relation to diet, habitat, ecology and anthropogenic Efield sources.

Enabling multi-level relevance feedback on PubMed by integrating rank learning into DBMS.

PubMed

Yu, Hwanjo; Kim, Taehoon; Oh, Jinoh; Ko, Ilhwan; Kim, Sungchul; Han, Wook-Shin

2010-04-16

Finding relevant articles from PubMed is challenging because it is hard to express the user's specific intention in the given query interface, and a keyword query typically retrieves a large number of results. Researchers have applied machine learning techniques to find relevant articles by ranking the articles according to the learned relevance function. However, the process of learning and ranking is usually done offline without integrated with the keyword queries, and the users have to provide a large amount of training documents to get a reasonable learning accuracy. This paper proposes a novel multi-level relevance feedback system for PubMed, called RefMed, which supports both ad-hoc keyword queries and a multi-level relevance feedback in real time on PubMed. RefMed supports a multi-level relevance feedback by using the RankSVM as the learning method, and thus it achieves higher accuracy with less feedback. RefMed "tightly" integrates the RankSVM into RDBMS to support both keyword queries and the multi-level relevance feedback in real time; the tight coupling of the RankSVM and DBMS substantially improves the processing time. An efficient parameter selection method for the RankSVM is also proposed, which tunes the RankSVM parameter without performing validation. Thereby, RefMed achieves a high learning accuracy in real time without performing a validation process. RefMed is accessible at http://dm.postech.ac.kr/refmed. RefMed is the first multi-level relevance feedback system for PubMed, which achieves a high accuracy with less feedback. It effectively learns an accurate relevance function from the user's feedback and efficiently processes the function to return relevant articles in real time.

Enabling multi-level relevance feedback on PubMed by integrating rank learning into DBMS

PubMed Central

2010-01-01

Background Finding relevant articles from PubMed is challenging because it is hard to express the user's specific intention in the given query interface, and a keyword query typically retrieves a large number of results. Researchers have applied machine learning techniques to find relevant articles by ranking the articles according to the learned relevance function. However, the process of learning and ranking is usually done offline without integrated with the keyword queries, and the users have to provide a large amount of training documents to get a reasonable learning accuracy. This paper proposes a novel multi-level relevance feedback system for PubMed, called RefMed, which supports both ad-hoc keyword queries and a multi-level relevance feedback in real time on PubMed. Results RefMed supports a multi-level relevance feedback by using the RankSVM as the learning method, and thus it achieves higher accuracy with less feedback. RefMed "tightly" integrates the RankSVM into RDBMS to support both keyword queries and the multi-level relevance feedback in real time; the tight coupling of the RankSVM and DBMS substantially improves the processing time. An efficient parameter selection method for the RankSVM is also proposed, which tunes the RankSVM parameter without performing validation. Thereby, RefMed achieves a high learning accuracy in real time without performing a validation process. RefMed is accessible at http://dm.postech.ac.kr/refmed. Conclusions RefMed is the first multi-level relevance feedback system for PubMed, which achieves a high accuracy with less feedback. It effectively learns an accurate relevance function from the user’s feedback and efficiently processes the function to return relevant articles in real time. PMID:20406504

Dependency-based Siamese long short-term memory network for learning sentence representations

PubMed Central

Zhu, Wenhao; Ni, Jianyue; Wei, Baogang; Lu, Zhiguo

2018-01-01

Textual representations play an important role in the field of natural language processing (NLP). The efficiency of NLP tasks, such as text comprehension and information extraction, can be significantly improved with proper textual representations. As neural networks are gradually applied to learn the representation of words and phrases, fairly efficient models of learning short text representations have been developed, such as the continuous bag of words (CBOW) and skip-gram models, and they have been extensively employed in a variety of NLP tasks. Because of the complex structure generated by the longer text lengths, such as sentences, algorithms appropriate for learning short textual representations are not applicable for learning long textual representations. One method of learning long textual representations is the Long Short-Term Memory (LSTM) network, which is suitable for processing sequences. However, the standard LSTM does not adequately address the primary sentence structure (subject, predicate and object), which is an important factor for producing appropriate sentence representations. To resolve this issue, this paper proposes the dependency-based LSTM model (D-LSTM). The D-LSTM divides a sentence representation into two parts: a basic component and a supporting component. The D-LSTM uses a pre-trained dependency parser to obtain the primary sentence information and generate supporting components, and it also uses a standard LSTM model to generate the basic sentence components. A weight factor that can adjust the ratio of the basic and supporting components in a sentence is introduced to generate the sentence representation. Compared with the representation learned by the standard LSTM, the sentence representation learned by the D-LSTM contains a greater amount of useful information. The experimental results show that the D-LSTM is superior to the standard LSTM for sentences involving compositional knowledge (SICK) data. PMID:29513748

Computer-based learning: interleaving whole and sectional representation of neuroanatomy.

PubMed

Pani, John R; Chariker, Julia H; Naaz, Farah

2013-01-01

The large volume of material to be learned in biomedical disciplines requires optimizing the efficiency of instruction. In prior work with computer-based instruction of neuroanatomy, it was relatively efficient for learners to master whole anatomy and then transfer to learning sectional anatomy. It may, however, be more efficient to continuously integrate learning of whole and sectional anatomy. A study of computer-based learning of neuroanatomy was conducted to compare a basic transfer paradigm for learning whole and sectional neuroanatomy with a method in which the two forms of representation were interleaved (alternated). For all experimental groups, interactive computer programs supported an approach to instruction called adaptive exploration. Each learning trial consisted of time-limited exploration of neuroanatomy, self-timed testing, and graphical feedback. The primary result of this study was that interleaved learning of whole and sectional neuroanatomy was more efficient than the basic transfer method, without cost to long-term retention or generalization of knowledge to recognizing new images (Visible Human and MRI). Copyright © 2012 American Association of Anatomists.

Computer-Based Learning: Interleaving Whole and Sectional Representation of Neuroanatomy

PubMed Central

Pani, John R.; Chariker, Julia H.; Naaz, Farah

2015-01-01

The large volume of material to be learned in biomedical disciplines requires optimizing the efficiency of instruction. In prior work with computer-based instruction of neuroanatomy, it was relatively efficient for learners to master whole anatomy and then transfer to learning sectional anatomy. It may, however, be more efficient to continuously integrate learning of whole and sectional anatomy. A study of computer-based learning of neuroanatomy was conducted to compare a basic transfer paradigm for learning whole and sectional neuroanatomy with a method in which the two forms of representation were interleaved (alternated). For all experimental groups, interactive computer programs supported an approach to instruction called adaptive exploration. Each learning trial consisted of time-limited exploration of neuroanatomy, self-timed testing, and graphical feedback. The primary result of this study was that interleaved learning of whole and sectional neuroanatomy was more efficient than the basic transfer method, without cost to long-term retention or generalization of knowledge to recognizing new images (Visible Human and MRI). PMID:22761001

Technique adaptation, strategic replanning, and team learning during implementation of MR-guided brachytherapy for cervical cancer.

PubMed

Skliarenko, Julia; Carlone, Marco; Tanderup, Kari; Han, Kathy; Beiki-Ardakani, Akbar; Borg, Jette; Chan, Kitty; Croke, Jennifer; Rink, Alexandra; Simeonov, Anna; Ujaimi, Reem; Xie, Jason; Fyles, Anthony; Milosevic, Michael

MR-guided brachytherapy (MRgBT) with interstitial needles is associated with improved outcomes in cervical cancer patients. However, there are implementation barriers, including magnetic resonance (MR) access, practitioner familiarity/comfort, and efficiency. This study explores a graded MRgBT implementation strategy that included the adaptive use of needles, strategic use of MR imaging/planning, and team learning. Twenty patients with cervical cancer were treated with high-dose-rate MRgBT (28 Gy in four fractions, two insertions, daily MR imaging/planning). A tandem/ring applicator alone was used for the first insertion in most patients. Needles were added for the second insertion based on evaluation of the initial dosimetry. An interdisciplinary expert team reviewed and discussed the MR images and treatment plans. Dosimetry-trigger technique adaptation with the addition of needles for the second insertion improved target coverage in all patients with suboptimal dosimetry initially without compromising organ-at-risk (OAR) sparing. Target and OAR planning objectives were achieved in most patients. There were small or no systematic differences in tumor or OAR dosimetry between imaging/planning once per insertion vs. daily and only small random variations. Peer review and discussion of images, contours, and plans promoted learning and process development. Technique adaptation based on the initial dosimetry is an efficient approach to implementing MRgBT while gaining comfort with the use of needles. MR imaging and planning once per insertion is safe in most patients as long as applicator shifts, and large anatomical changes are excluded. Team learning is essential to building individual and programmatic competencies. Copyright © 2017 American Brachytherapy Society. Published by Elsevier Inc. All rights reserved.

Improved object optimal synthetic description, modeling, learning, and discrimination by GEOGINE computational kernel

NASA Astrophysics Data System (ADS)

Fiorini, Rodolfo A.; Dacquino, Gianfranco

2005-03-01

GEOGINE (GEOmetrical enGINE), a state-of-the-art OMG (Ontological Model Generator) based on n-D Tensor Invariants for n-Dimensional shape/texture optimal synthetic representation, description and learning, was presented in previous conferences elsewhere recently. Improved computational algorithms based on the computational invariant theory of finite groups in Euclidean space and a demo application is presented. Progressive model automatic generation is discussed. GEOGINE can be used as an efficient computational kernel for fast reliable application development and delivery in advanced biomedical engineering, biometric, intelligent computing, target recognition, content image retrieval, data mining technological areas mainly. Ontology can be regarded as a logical theory accounting for the intended meaning of a formal dictionary, i.e., its ontological commitment to a particular conceptualization of the world object. According to this approach, "n-D Tensor Calculus" can be considered a "Formal Language" to reliably compute optimized "n-Dimensional Tensor Invariants" as specific object "invariant parameter and attribute words" for automated n-Dimensional shape/texture optimal synthetic object description by incremental model generation. The class of those "invariant parameter and attribute words" can be thought as a specific "Formal Vocabulary" learned from a "Generalized Formal Dictionary" of the "Computational Tensor Invariants" language. Even object chromatic attributes can be effectively and reliably computed from object geometric parameters into robust colour shape invariant characteristics. As a matter of fact, any highly sophisticated application needing effective, robust object geometric/colour invariant attribute capture and parameterization features, for reliable automated object learning and discrimination can deeply benefit from GEOGINE progressive automated model generation computational kernel performance. Main operational advantages over previous, similar approaches are: 1) Progressive Automated Invariant Model Generation, 2) Invariant Minimal Complete Description Set for computational efficiency, 3) Arbitrary Model Precision for robust object description and identification.

Bayesian nonparametric dictionary learning for compressed sensing MRI.

PubMed

Huang, Yue; Paisley, John; Lin, Qin; Ding, Xinghao; Fu, Xueyang; Zhang, Xiao-Ping

2014-12-01

We develop a Bayesian nonparametric model for reconstructing magnetic resonance images (MRIs) from highly undersampled k -space data. We perform dictionary learning as part of the image reconstruction process. To this end, we use the beta process as a nonparametric dictionary learning prior for representing an image patch as a sparse combination of dictionary elements. The size of the dictionary and patch-specific sparsity pattern are inferred from the data, in addition to other dictionary learning variables. Dictionary learning is performed directly on the compressed image, and so is tailored to the MRI being considered. In addition, we investigate a total variation penalty term in combination with the dictionary learning model, and show how the denoising property of dictionary learning removes dependence on regularization parameters in the noisy setting. We derive a stochastic optimization algorithm based on Markov chain Monte Carlo for the Bayesian model, and use the alternating direction method of multipliers for efficiently performing total variation minimization. We present empirical results on several MRI, which show that the proposed regularization framework can improve reconstruction accuracy over other methods.

Using S-P Chart and Bloom Taxonomy to Develop Intelligent Formative Assessment Tool

ERIC Educational Resources Information Center

Chang, Wen-Chih; Yang, Hsuan-Che; Shih, Timothy K.; Chao, Louis R.

2009-01-01

E-learning provides a convenient and efficient way for learning. Formative assessment not only guides student in instruction and learning, diagnose skill or knowledge gaps, but also measures progress and evaluation. An efficient and convenient e-learning formative assessment system is the key character for e-learning. However, most e-learning…

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

PubMed Central

Hayashibe, Mitsuhiro; Shimoda, Shingo

2014-01-01

A human motor system can improve its behavior toward optimal movement. The skeletal system has more degrees of freedom than the task dimensions, which incurs an ill-posed problem. The multijoint system involves complex interaction torques between joints. To produce optimal motion in terms of energy consumption, the so-called cost function based optimization has been commonly used in previous works.Even if it is a fact that an optimal motor pattern is employed phenomenologically, there is no evidence that shows the existence of a physiological process that is similar to such a mathematical optimization in our central nervous system.In this study, we aim to find a more primitive computational mechanism with a modular configuration to realize adaptability and optimality without prior knowledge of system dynamics.We propose a novel motor control paradigm based on tacit learning with task space feedback. The motor command accumulation during repetitive environmental interactions, play a major role in the learning process. It is applied to a vertical cyclic reaching which involves complex interaction torques.We evaluated whether the proposed paradigm can learn how to optimize solutions with a 3-joint, planar biomechanical model. The results demonstrate that the proposed method was valid for acquiring motor synergy and resulted in energy efficient solutions for different load conditions. The case in feedback control is largely affected by the interaction torques. In contrast, the trajectory is corrected over time with tacit learning toward optimal solutions.Energy efficient solutions were obtained by the emergence of motor synergy. During learning, the contribution from feedforward controller is augmented and the one from the feedback controller is significantly minimized down to 12% for no load at hand, 16% for a 0.5 kg load condition.The proposed paradigm could provide an optimization process in redundant system with dynamic-model-free and cost-function-free approach. PMID:24616695

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

PubMed

Hayashibe, Mitsuhiro; Shimoda, Shingo

2014-01-01

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

Case Comparisons: An Efficient Way of Learning Radiology.

PubMed

Kok, Ellen M; de Bruin, Anique B H; Leppink, Jimmie; van Merriënboer, Jeroen J G; Robben, Simon G F

2015-10-01

Radiologists commonly use comparison films to improve their differential diagnosis. Educational literature suggests that this technique might also be used to bolster the process of learning to interpret radiographs. We investigated the effectiveness of three comparison techniques in medical students, whom we invited to compare cases of the same disease (same-disease comparison), cases of different diseases (different-disease comparison), disease images with normal images (disease/normal comparison), and identical images (no comparison/control condition). Furthermore, we used eye-tracking technology to investigate which elements of the two cases were compared by the students. We randomly assigned 84 medical students to one of four conditions and had them study different diseases on chest radiographs, while their eye movements were being measured. Thereafter, participants took two tests that measured diagnostic performance and their ability to locate diseases, respectively. Students studied most efficiently in the same-disease and different-disease comparison conditions: test 1, F(3, 68) = 3.31, P = .025, ηp(2) = 0.128; test 2, F(3, 65) = 2.88, P = .043, ηp(2) = 0.117. We found that comparisons were effected in 91% of all trials (except for the control condition). Comparisons between normal anatomy were particularly common (45.8%) in all conditions. Comparing cases can be an efficient way of learning to interpret radiographs, especially when the comparison technique used is specifically tailored to the learning goal. Eye tracking provided insight into the comparison process, by showing that few comparisons were made between abnormalities, for example. Copyright © 2015 AUR. Published by Elsevier Inc. All rights reserved.

Field Evaluation of the Performance of the RTU Challenge Unit: Daikin Rebel

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

Katipamula, Srinivas; Wang, W.; Ngo, Hung

2017-05-31

Packaged rooftop air-conditioning units (RTUs) are used in 44% (2.5 million) of all commercial buildings, serving over 57% (46 billion square feet) of the commercial building floor space in the United States (EIA 2012). The primary energy consumption associated with RTUs is over 2.2 quads annually. Therefore, even a small improvement in efficiency or part-load operation of these units can lead to significant reductions in energy use and carbon emissions. Starting in 2011, the U.S. Department of Energy’s (DOE’s) Building Technologies Office funded a series of projects related to RTUs. Some projects were intended to improve the operating efficiency ofmore » the existing RTUs, while others were focused on improving the operating efficiency of new units. This report documents the field-testing and comparison of the seasonal efficiency of a state-of-art RTU Challenge unit and a standard unit. Section II provides the background for the work. Section III describes the measurement and verification plan for the field tests. Section IV describes the measurement and verification evaluation plan. The results are described in Section V. The lessons learned and recommendations for future work are presented in Section VI. A list of references is provided in Section VII.« less

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

ERIC Educational Resources Information Center

Zufic, Janko; Kalpic, Damir

2009-01-01

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

A decade of imaging surgeons' brain function (part II): A systematic review of applications for technical and nontechnical skills assessment.

PubMed

Modi, Hemel Narendra; Singh, Harsimrat; Yang, Guang-Zhong; Darzi, Ara; Leff, Daniel Richard

2017-11-01

Functional neuroimaging technologies enable assessment of operator brain function and can deepen our understanding of skills learning, ergonomic optima, and cognitive processes in surgeons. Although there has been a critical mass of data detailing surgeons' brain function, this literature has not been reviewed systematically. A systematic search of original neuroimaging studies assessing surgeons' brain function and published up until November 2016 was conducted using Medline, Embase, and PsycINFO databases. Twenty-seven studies fulfilled the inclusion criteria, including 3 feasibility studies, 14 studies exploring the neural correlates of technical skill acquisition, and the remainder investigating brain function in the context of intraoperative decision-making (n = 1), neurofeedback training (n = 1), robot-assisted technology (n = 5), and surgical teaching (n = 3). Early stages of learning open surgical tasks (knot-tying) are characterized by prefrontal cortical activation, which subsequently attenuates with deliberate practice. However, with complex laparoscopic skills (intracorporeal suturing), prefrontal cortical engagement requires substantial training, and attenuation occurs over a longer time course, after years of refinement. Neurofeedback and interventions that improve neural efficiency may enhance technical performance and skills learning. Imaging surgeons' brain function has identified neural signatures of expertise that might help inform objective assessment and selection processes. Interventions that improve neural efficiency may target skill-specific brain regions and augment surgical performance. Copyright © 2017 Elsevier Inc. All rights reserved.

Comparing SVM and ANN based Machine Learning Methods for Species Identification of Food Contaminating Beetles.

PubMed

Bisgin, Halil; Bera, Tanmay; Ding, Hongjian; Semey, Howard G; Wu, Leihong; Liu, Zhichao; Barnes, Amy E; Langley, Darryl A; Pava-Ripoll, Monica; Vyas, Himansu J; Tong, Weida; Xu, Joshua

2018-04-25

Insect pests, such as pantry beetles, are often associated with food contaminations and public health risks. Machine learning has the potential to provide a more accurate and efficient solution in detecting their presence in food products, which is currently done manually. In our previous research, we demonstrated such feasibility where Artificial Neural Network (ANN) based pattern recognition techniques could be implemented for species identification in the context of food safety. In this study, we present a Support Vector Machine (SVM) model which improved the average accuracy up to 85%. Contrary to this, the ANN method yielded ~80% accuracy after extensive parameter optimization. Both methods showed excellent genus level identification, but SVM showed slightly better accuracy  for most species. Highly accurate species level identification remains a challenge, especially in distinguishing between species from the same genus which may require improvements in both imaging and machine learning techniques. In summary, our work does illustrate a new SVM based technique and provides a good comparison with the ANN model in our context. We believe such insights will pave better way forward for the application of machine learning towards species identification and food safety.

Implementation of Chaotic Gaussian Particle Swarm Optimization for Optimize Learning-to-Rank Software Defect Prediction Model Construction

NASA Astrophysics Data System (ADS)

Buchari, M. A.; Mardiyanto, S.; Hendradjaya, B.

2018-03-01

Finding the existence of software defect as early as possible is the purpose of research about software defect prediction. Software defect prediction activity is required to not only state the existence of defects, but also to be able to give a list of priorities which modules require a more intensive test. Therefore, the allocation of test resources can be managed efficiently. Learning to rank is one of the approach that can provide defect module ranking data for the purposes of software testing. In this study, we propose a meta-heuristic chaotic Gaussian particle swarm optimization to improve the accuracy of learning to rank software defect prediction approach. We have used 11 public benchmark data sets as experimental data. Our overall results has demonstrated that the prediction models construct using Chaotic Gaussian Particle Swarm Optimization gets better accuracy on 5 data sets, ties in 5 data sets and gets worse in 1 data sets. Thus, we conclude that the application of Chaotic Gaussian Particle Swarm Optimization in Learning-to-Rank approach can improve the accuracy of the defect module ranking in data sets that have high-dimensional features.

Curriculum renewal in child psychiatry.

PubMed

Hanson, M; Tiberius, R; Charach, A; Ulzen, T; Sackin, D; Jain, U; Reiter, S; Shomair, G

1999-11-01

To ensure uniform design and evaluation of a clerkship curriculum for child and adolescent psychiatry teaching common disorders and problems in an efficient manner across 5 teaching sites and to include structures for continuous improvement. The curriculum committee selected for course inclusion disorders and problems of child psychiatry that were commonly encountered by primary care physicians. Instruction methods that encouraged active student learning were selected. Course coordination across sites was encouraged by several methods: involving faculty, adopting a centralized examination format, and aligning teaching methods with examination format. Quantitative and qualitative methods were used to measure students' perceptions of the course's value. These evaluative results were reviewed, and course modifications were implemented and reevaluated. The average adjusted student return rate for course evaluation questionnaires for the 3-year study period was 63%. Clerks' ratings of course learning value demonstrated that the course improved significantly and continually across all sites, according to a Scheffé post-hoc analysis. Analysis of student statements from focus-group transcripts contributed to course modifications, such as the Brief Focused Interview (BFI). Our curriculum in child psychiatry, which focused on common problems and used active learning methods, was viewed as a valuable learning experience by clinical clerks. Curriculum coordination across multiple teaching sites was accomplished by including faculty in the process and by using specific teaching and examination strategies. Structures for continuous course improvement were effective.

Hospital benchmarking: are U.S. eye hospitals ready?

PubMed

de Korne, Dirk F; van Wijngaarden, Jeroen D H; Sol, Kees J C A; Betz, Robert; Thomas, Richard C; Schein, Oliver D; Klazinga, Niek S

2012-01-01

Benchmarking is increasingly considered a useful management instrument to improve quality in health care, but little is known about its applicability in hospital settings. The aims of this study were to assess the applicability of a benchmarking project in U.S. eye hospitals and compare the results with an international initiative. We evaluated multiple cases by applying an evaluation frame abstracted from the literature to five U.S. eye hospitals that used a set of 10 indicators for efficiency benchmarking. Qualitative analysis entailed 46 semistructured face-to-face interviews with stakeholders, document analyses, and questionnaires. The case studies only partially met the conditions of the evaluation frame. Although learning and quality improvement were stated as overall purposes, the benchmarking initiative was at first focused on efficiency only. No ophthalmic outcomes were included, and clinicians were skeptical about their reporting relevance and disclosure. However, in contrast with earlier findings in international eye hospitals, all U.S. hospitals worked with internal indicators that were integrated in their performance management systems and supported benchmarking. Benchmarking can support performance management in individual hospitals. Having a certain number of comparable institutes provide similar services in a noncompetitive milieu seems to lay fertile ground for benchmarking. International benchmarking is useful only when these conditions are not met nationally. Although the literature focuses on static conditions for effective benchmarking, our case studies show that it is a highly iterative and learning process. The journey of benchmarking seems to be more important than the destination. Improving patient value (health outcomes per unit of cost) requires, however, an integrative perspective where clinicians and administrators closely cooperate on both quality and efficiency issues. If these worlds do not share such a relationship, the added "public" value of benchmarking in health care is questionable.

Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis

NASA Astrophysics Data System (ADS)

Litjens, Geert; Sánchez, Clara I.; Timofeeva, Nadya; Hermsen, Meyke; Nagtegaal, Iris; Kovacs, Iringo; Hulsbergen-van de Kaa, Christina; Bult, Peter; van Ginneken, Bram; van der Laak, Jeroen

2016-05-01

Pathologists face a substantial increase in workload and complexity of histopathologic cancer diagnosis due to the advent of personalized medicine. Therefore, diagnostic protocols have to focus equally on efficiency and accuracy. In this paper we introduce ‘deep learning’ as a technique to improve the objectivity and efficiency of histopathologic slide analysis. Through two examples, prostate cancer identification in biopsy specimens and breast cancer metastasis detection in sentinel lymph nodes, we show the potential of this new methodology to reduce the workload for pathologists, while at the same time increasing objectivity of diagnoses. We found that all slides containing prostate cancer and micro- and macro-metastases of breast cancer could be identified automatically while 30-40% of the slides containing benign and normal tissue could be excluded without the use of any additional immunohistochemical markers or human intervention. We conclude that ‘deep learning’ holds great promise to improve the efficacy of prostate cancer diagnosis and breast cancer staging.

Strategies to improve industrial energy efficiency

NASA Astrophysics Data System (ADS)

O'Rielly, Kristine M.

A lack of technical expertise, fueled by a lack of positive examples, can lead to companies opting not to implement energy reduction projects unless mandated by legislation. As a result, companies are missing out on exceptional opportunities to improve not only their environmental record but also save considerably on fuel costs. This study investigates the broad topic of energy efficiency within the context of the industrial sector by means of a thorough review of existing energy reduction strategies and a demonstration of their successful implementation. The study begins by discussing current industrial energy consumption trends around the globe and within the Canadian manufacturing sector. This is followed by a literature review which outlines 3 prominent energy efficiency improvement strategies currently available to companies: 1) Waste heat recovery, 2) Idle power loss reduction and production rate optimization, and lastly 3) Auxiliary equipment operational performance. Next, a broad overview of the resources and tools available to organizations looking to improve their industrial energy efficiency is provided. Following this, several case studies are presented which demonstrate the potential benefits that are available to Canadian organizations looking to improve their energy efficiency. Lastly, a discussion of a number of issues and barriers pertaining to the wide-scale implementation of industrial efficiency strategies is presented. It discusses a number of potential roadblocks, including a lack of energy consumption monitoring and data transparency. While this topic has been well researched in the past in terms of the losses encountered during various general manufacturing process streams, practically no literature exists which attempts to provide real data from companies who have implemented energy efficiency strategies. By obtaining original data directly from companies, this thesis demonstrates the potential for companies to save money and reduce GHG (greenhouse gas) emissions through the implementation of energy efficiency projects and publishes numbers which are almost impossible to find directly. By publishing success stories, it is hoped that other companies, especially SMEs (small and medium enterprises) will be able to learn from these case studies and be inspired to embark on energy efficiency projects of their own.

Male bumblebees, Bombus terrestris, perform equally well as workers in a serial colour-learning task

PubMed Central

Wolf, Stephan; Chittka, Lars

2016-01-01

The learning capacities of males and females may differ with sex-specific behavioural requirements. Bumblebees provide a useful model system to explore how different lifestyles are reflected in learning abilities, because their (female but sterile) workers and males engage in fundamentally different behaviour routines. Bumblebee males, like workers, embark on active flower foraging but in contrast to workers they have to trade off their feeding with mate search, potentially affecting their abilities to learn and utilize floral cues efficiently during foraging. We used a serial colour-learning task with freely flying males and workers to compare their ability to flexibly learn visual floral cues with reward in a foraging scenario that changed over time. Male bumblebees did not differ from workers in both their learning speed and their ability to overcome previously acquired associations, when these ceased to predict reward. In all foraging tasks we found a significant improvement in choice accuracy in both sexes over the course of the training. In both sexes, the characteristics of the foraging performance depended largely on the colour difference of the two presented feeder types. Large colour distances entailed fast and reliable learning of the rewarding feeders whereas choice accuracy on highly similar colours improved significantly more slowly. Conversely, switching from a learned feeder type to a novel one was fastest for similar feeder colours and slow for highly different ones. Overall, we show that behavioural sex dimorphism in bumblebees did not affect their learning abilities beyond the mating context. We discuss the possible drivers and limitations shaping the foraging abilities of males and workers and implications for pollination ecology. We also suggest stingless male bumblebees as an advantageous alternative model system for the study of pollinator cognition. PMID:26877542

Compact Deep-Space Optical Communications Transceiver

NASA Technical Reports Server (NTRS)

Roberts, W. Thomas; Charles, Jeffrey R.

2009-01-01

Deep space optical communication transceivers must be very efficient receivers and transmitters of optical communication signals. For deep space missions, communication systems require high performance well beyond the scope of mere power efficiency, demanding maximum performance in relation to the precious and limited mass, volume, and power allocated. This paper describes the opto-mechanical design of a compact, efficient, functional brassboard deep space transceiver that is capable of achieving megabyte-per-second rates at Mars ranges. The special features embodied to enhance the system operability and functionality, and to reduce the mass and volume of the system are detailed. System tests and performance characteristics are described in detail. Finally, lessons learned in the implementation of the brassboard design and suggestions for improvements appropriate for a flight prototype are covered.

eLearning for health system leadership and management capacity building: a protocol for a systematic review

PubMed Central

Tudor Car, Lorainne; Atun, Rifat

2017-01-01

Introduction Health leadership and management capacity are essential for health system strengthening and for attaining universal health coverage by optimising the existing human, technological and financial resources. However, in health systems, health leadership and management training is not widely available. The use of information technology for education (ie, eLearning) could help address this training gap by enabling flexible, efficient and scalable health leadership and management training. We present a protocol for a systematic review on the effectiveness of eLearning for health leadership and management capacity building in improving health system outcomes. Methodology and analysis We will follow the Cochrane Collaboration methodology. We will search for experimental studies focused on the use of any type of eLearning modality for health management and leadership capacity building in all types of health workforce cadres. The primary outcomes of interest will be health outcomes, financial risk protection and user satisfaction. In addition, secondary outcomes of interest include the attainment of health system objectives of improved equity, efficiency, effectiveness and responsiveness. We will search relevant databases of published and grey literature as well as clinical trials registries from 1990 onwards without language restrictions. Two review authors will screen references, extract data and perform risk of bias assessment independently. Contingent on the heterogeneity of the collated literature, we will perform either a meta-analysis or a narrative synthesis of the collated data. Ethics and dissemination The systematic review will aim to inform policy makers, investors, health professionals, technologists and educators about the existing evidence, potential gaps in literature and the impact of eLearning for health leadership and management capacity building on health system outcomes. We will disseminate the review findings by publishing it as a peer-reviewed journal manuscript and conference abstracts. Trial registration number PROSPERO CRD42017056998 PMID:28827265

eLearning for health system leadership and management capacity building: a protocol for a systematic review.

PubMed

Tudor Car, Lorainne; Atun, Rifat

2017-08-21

Health leadership and management capacity are essential for health system strengthening and for attaining universal health coverage by optimising the existing human, technological and financial resources. However, in health systems, health leadership and management training is not widely available. The use of information technology for education (ie, eLearning) could help address this training gap by enabling flexible, efficient and scalable health leadership and management training. We present a protocol for a systematic review on the effectiveness of eLearning for health leadership and management capacity building in improving health system outcomes. We will follow the Cochrane Collaboration methodology. We will search for experimental studies focused on the use of any type of eLearning modality for health management and leadership capacity building in all types of health workforce cadres. The primary outcomes of interest will be health outcomes, financial risk protection and user satisfaction. In addition, secondary outcomes of interest include the attainment of health system objectives of improved equity, efficiency, effectiveness and responsiveness. We will search relevant databases of published and grey literature as well as clinical trials registries from 1990 onwards without language restrictions. Two review authors will screen references, extract data and perform risk of bias assessment independently. Contingent on the heterogeneity of the collated literature, we will perform either a meta-analysis or a narrative synthesis of the collated data. The systematic review will aim to inform policy makers, investors, health professionals, technologists and educators about the existing evidence, potential gaps in literature and the impact of eLearning for health leadership and management capacity building on health system outcomes. We will disseminate the review findings by publishing it as a peer-reviewed journal manuscript and conference abstracts. PROSPERO CRD42017056998. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

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

PubMed Central

Tobler, Philippe N.

2015-01-01

When we are learning to associate novel cues with outcomes, learning is more efficient if we take advantage of previously learned associations and thereby avoid redundant learning. The blocking effect represents this sort of efficiency mechanism and refers to the phenomenon in which a novel stimulus is blocked from learning when it is associated with a fully predicted outcome. Although there is sufficient evidence that this effect manifests itself when individuals learn about their own rewards, it remains unclear whether it also does when they learn about others’ rewards. We employed behavioral and neuroimaging methods to address this question. We demonstrate that blocking does indeed occur in the social domain and it does so to a similar degree as observed in the individual domain. On the neural level, activations in the medial prefrontal cortex (mPFC) show a specific contribution to blocking and learning-related prediction errors in the social domain. These findings suggest that the efficiency principle that applies to reward learning in the individual domain also applies to that in the social domain, with the mPFC playing a central role in implementing it. PMID:25326037

Deficit in implicit motor sequence learning among children and adolescents with spastic cerebral palsy.

PubMed

Gofer-Levi, Moran; Silberg, Tamar; Brezner, Amichai; Vakil, Eli

2013-11-01

Skill learning (SL) is learning as a result of repeated exposure and practice, which encompasses independent explicit (response to instructions) and implicit (response to hidden regularities) processes. Little is known about the effects of developmental disorders, such as Cerebral Palsy (CP), on the ability to acquire new skills. We compared performance of CP and typically developing (TD) children and adolescents in completing the serial reaction time (SRT) task, which is a motor sequence learning task, and examined the impact of various factors on this performance as indicative of the ability to acquire motor skills. While both groups improved in performance, participants with CP were significantly slower than TD controls and did not learn the implicit sequence. Our results indicate that SL in children and adolescents with CP is qualitatively and quantitatively different than that of their peers. Understanding the unique aspects of SL in children and adolescents with CP might help plan appropriate and efficient interventions. Copyright © 2013 Elsevier Ltd. All rights reserved.

Top-Down Visual Saliency via Joint CRF and Dictionary Learning.

PubMed

Yang, Jimei; Yang, Ming-Hsuan

2017-03-01

Top-down visual saliency is an important module of visual attention. In this work, we propose a novel top-down saliency model that jointly learns a Conditional Random Field (CRF) and a visual dictionary. The proposed model incorporates a layered structure from top to bottom: CRF, sparse coding and image patches. With sparse coding as an intermediate layer, CRF is learned in a feature-adaptive manner; meanwhile with CRF as the output layer, the dictionary is learned under structured supervision. For efficient and effective joint learning, we develop a max-margin approach via a stochastic gradient descent algorithm. Experimental results on the Graz-02 and PASCAL VOC datasets show that our model performs favorably against state-of-the-art top-down saliency methods for target object localization. In addition, the dictionary update significantly improves the performance of our model. We demonstrate the merits of the proposed top-down saliency model by applying it to prioritizing object proposals for detection and predicting human fixations.

Enhanced teaching and student learning through a simulator-based course in chemical unit operations design

NASA Astrophysics Data System (ADS)

Ghasem, Nayef

2016-07-01

This paper illustrates a teaching technique used in computer applications in chemical engineering employed for designing various unit operation processes, where the students learn about unit operations by designing them. The aim of the course is not to teach design, but rather to teach the fundamentals and the function of unit operation processes through simulators. A case study presenting the teaching method was evaluated using student surveys and faculty assessments, which were designed to measure the quality and effectiveness of the teaching method. The results of the questionnaire conclusively demonstrate that this method is an extremely efficient way of teaching a simulator-based course. In addition to that, this teaching method can easily be generalised and used in other courses. A student's final mark is determined by a combination of in-class assessments conducted based on cooperative and peer learning, progress tests and a final exam. Results revealed that peer learning can improve the overall quality of student learning and enhance student understanding.

Action-Driven Visual Object Tracking With Deep Reinforcement Learning.

PubMed

Yun, Sangdoo; Choi, Jongwon; Yoo, Youngjoon; Yun, Kimin; Choi, Jin Young

2018-06-01

In this paper, we propose an efficient visual tracker, which directly captures a bounding box containing the target object in a video by means of sequential actions learned using deep neural networks. The proposed deep neural network to control tracking actions is pretrained using various training video sequences and fine-tuned during actual tracking for online adaptation to a change of target and background. The pretraining is done by utilizing deep reinforcement learning (RL) as well as supervised learning. The use of RL enables even partially labeled data to be successfully utilized for semisupervised learning. Through the evaluation of the object tracking benchmark data set, the proposed tracker is validated to achieve a competitive performance at three times the speed of existing deep network-based trackers. The fast version of the proposed method, which operates in real time on graphics processing unit, outperforms the state-of-the-art real-time trackers with an accuracy improvement of more than 8%.

Active learning machine learns to create new quantum experiments.

PubMed

Melnikov, Alexey A; Poulsen Nautrup, Hendrik; Krenn, Mario; Dunjko, Vedran; Tiersch, Markus; Zeilinger, Anton; Briegel, Hans J

2018-02-06

How useful can machine learning be in a quantum laboratory? Here we raise the question of the potential of intelligent machines in the context of scientific research. A major motivation for the present work is the unknown reachability of various entanglement classes in quantum experiments. We investigate this question by using the projective simulation model, a physics-oriented approach to artificial intelligence. In our approach, the projective simulation system is challenged to design complex photonic quantum experiments that produce high-dimensional entangled multiphoton states, which are of high interest in modern quantum experiments. The artificial intelligence system learns to create a variety of entangled states and improves the efficiency of their realization. In the process, the system autonomously (re)discovers experimental techniques which are only now becoming standard in modern quantum optical experiments-a trait which was not explicitly demanded from the system but emerged through the process of learning. Such features highlight the possibility that machines could have a significantly more creative role in future research.

Efficient convolutional sparse coding

DOEpatents

Wohlberg, Brendt

2017-06-20

Computationally efficient algorithms may be applied for fast dictionary learning solving the convolutional sparse coding problem in the Fourier domain. More specifically, efficient convolutional sparse coding may be derived within an alternating direction method of multipliers (ADMM) framework that utilizes fast Fourier transforms (FFT) to solve the main linear system in the frequency domain. Such algorithms may enable a significant reduction in computational cost over conventional approaches by implementing a linear solver for the most critical and computationally expensive component of the conventional iterative algorithm. The theoretical computational cost of the algorithm may be reduced from O(M.sup.3N) to O(MN log N), where N is the dimensionality of the data and M is the number of elements in the dictionary. This significant improvement in efficiency may greatly increase the range of problems that can practically be addressed via convolutional sparse representations.

Optimal Learning for Efficient Experimentation in Nanotechnology and Biochemistry

DTIC Science & Technology

2015-12-22

AFRL-AFOSR-VA-TR-2016-0018 Optimal Learning for Efficient Experimentation in Nanotechnology , Biochemistry Warren Powell TRUSTEES OF PRINCETON...3. DATES COVERED (From - To) 01-07-2012 to 30-09-2015 4. TITLE AND SUBTITLE Optimal Learning for Efficient Experimentation in Nanotechnology and...in Nanotechnology and Biochemistry Principal Investigators: Warren B. Powell Princeton University Department of Operations Research and

A Novel Clustering Method Curbing the Number of States in Reinforcement Learning

NASA Astrophysics Data System (ADS)

Kotani, Naoki; Nunobiki, Masayuki; Taniguchi, Kenji

We propose an efficient state-space construction method for a reinforcement learning. Our method controls the number of categories with improving the clustering method of Fuzzy ART which is an autonomous state-space construction method. The proposed method represents weight vector as the mean value of input vectors in order to curb the number of new categories and eliminates categories whose state values are low to curb the total number of categories. As the state value is updated, the size of category becomes small to learn policy strictly. We verified the effectiveness of the proposed method with simulations of a reaching problem for a two-link robot arm. We confirmed that the number of categories was reduced and the agent achieved the complex task quickly.

Alert Triage v 0.1 beta

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

Doak, Justin E.; Ingram, Joe; Johnson, Josh

2016-01-06

In the cyber security operations of a typical organization, data from multiple sources are monitored, and when certain conditions in the data are met, an alert is generated in an alert management system. Analysts inspect these alerts to decide if any deserve promotion to an event requiring further scrutiny. This triage process is manual, time-consuming, and detracts from the in-depth investigation of events. We have created a software system that uses supervised machine learning to automatically prioritize these alerts. In particular we utilize active learning to make efficient use of the pool of unlabeled alerts, thereby improving the performance ofmore » our ranking models over passive learning. We have demonstrated the effectiveness of our system on a large, real-world dataset of cyber security alerts.« less

Learning from halophytes: physiological basis and strategies to improve abiotic stress tolerance in crops

PubMed Central

Shabala, Sergey

2013-01-01

Background Global annual losses in agricultural production from salt-affected land are in excess of US$12 billion and rising. At the same time, a significant amount of arable land is becoming lost to urban sprawl, forcing agricultural production into marginal areas. Consequently, there is a need for a major breakthrough in crop breeding for salinity tolerance. Given the limited range of genetic diversity in this trait within traditional crops, stress tolerance genes and mechanisms must be identified in extremophiles and then introduced into traditional crops. Scope and Conclusions This review argues that learning from halophytes may be a promising way of achieving this goal. The paper is focused around two central questions: what are the key physiological mechanisms conferring salinity tolerance in halophytes that can be introduced into non-halophyte crop species to improve their performance under saline conditions and what specific genes need to be targeted to achieve this goal? The specific traits that are discussed and advocated include: manipulation of trichome shape, size and density to enable their use for external Na+ sequestration; increasing the efficiency of internal Na+ sequestration in vacuoles by the orchestrated regulation of tonoplast NHX exchangers and slow and fast vacuolar channels, combined with greater cytosolic K+ retention; controlling stomata aperture and optimizing water use efficiency by reducing stomatal density; and efficient control of xylem ion loading, enabling rapid shoot osmotic adjustment while preventing prolonged Na+ transport to the shoot. PMID:24085482

AI User Support System for SAP ERP

NASA Astrophysics Data System (ADS)

Vlasov, Vladimir; Chebotareva, Victoria; Rakhimov, Marat; Kruglikov, Sergey

2017-10-01

An intelligent system for SAP ERP user support is proposed in this paper. It enables automatic replies on users’ requests for support, saving time for problem analysis and resolution and improving responsiveness for end users. The system is based on an ensemble of machine learning algorithms of multiclass text classification, providing efficient question understanding, and a special framework for evidence retrieval, providing the best answer derivation.

Improved neutron-gamma discrimination for a 3He neutron detector using subspace learning methods

DOE PAGES

Wang, C. L.; Funk, L. L.; Riedel, R. A.; ...

2017-02-10

3He gas based neutron linear-position-sensitive detectors (LPSDs) have been applied for many neutron scattering instruments. Traditional Pulse-Height Analysis (PHA) for Neutron-Gamma Discrimination (NGD) resulted in the neutron-gamma efficiency ratio on the orders of 10 5-10 6. The NGD ratios of 3He detectors need to be improved for even better scientific results from neutron scattering. Digital Signal Processing (DSP) analyses of waveforms were proposed for obtaining better NGD ratios, based on features extracted from rise-time, pulse amplitude, charge integration, a simplified Wiener filter, and the cross-correlation between individual and template waveforms of neutron and gamma events. Fisher linear discriminant analysis (FLDA)more » and three multivariate analyses (MVAs) of the features were performed. The NGD ratios are improved by about 10 2-10 3 times compared with the traditional PHA method. Finally, our results indicate the NGD capabilities of 3He tube detectors can be significantly improved with subspace-learning based methods, which may result in a reduced data-collection time and better data quality for further data reduction.« less

How Should Students Learn in the School Science Laboratory? The Benefits of Cooperative Learning

NASA Astrophysics Data System (ADS)

Raviv, Ayala; Cohen, Sarit; Aflalo, Ester

2017-07-01

Despite the inherent potential of cooperative learning, there has been very little research into its effectiveness in middle school laboratory classes. This study focuses on an empirical comparison between cooperative learning and individual learning in the school science laboratory, evaluating the quality of learning and the students' attitudes. The research included 67 seventh-grade students who undertook four laboratory experiments on the subject of "volume measuring skills." Each student engaged both in individual and cooperative learning in the laboratory, and the students wrote individual or group reports, accordingly. A total of 133 experiment reports were evaluated, 108 of which also underwent textual analysis. The findings show that the group reports were superior, both in terms of understanding the concept of "volume" and in terms of acquiring skills for measuring volume. The students' attitudes results were statistically significant and demonstrated that they preferred cooperative learning in the laboratory. These findings demonstrate that science teachers should be encouraged to implement cooperative learning in the laboratory. This will enable them to improve the quality and efficiency of laboratory learning while using a smaller number of experimental kits. Saving these expenditures, together with the possibility to teach a larger number of students simultaneously in the laboratory, will enable greater exposure to learning in the school science laboratory.

Efficiency Assessment of a Blended-Learning Educational Methodology in Engineering

NASA Astrophysics Data System (ADS)

Rogado, Ana Belén González; Conde, Ma José Rodríguez; Migueláñez, Susana Olmos; Riaza, Blanca García; Peñalvo, Francisco José García

The content of this presentation highlights the importance of an active learning methodology in engineering university degrees in Spain. We present of some of the outcomes from an experimental study carried out during the academic years 2007/08 and 2008/09 with engineering students (Technical Industrial Engineering: Mechanics, Civical Design Engineering: Civical building, Technical Architecture and Technical Engineering on Computer Management.) at the University of Salamanca. In this research we select a subject which is common for the four degrees: Computer Science. This study has the aim of contributing to the improvement of education and teaching methods for a better performance of students in Engineering.

Using Simulation in Interprofessional Education.

PubMed

Paige, John T; Garbee, Deborah D; Brown, Kimberly M; Rojas, Jose D

2015-08-01

Simulation-based training (SBT) is a powerful educational tool permitting the acquisition of surgical knowledge, skills, and attitudes at both the individual- and team-based level in a safe, nonthreatening learning environment at no risk to a patient. Interprofessional education (IPE), in which participants from 2 or more health or social care professions learn interactively, can help improve patient care through the promotion of efficient coordination, dissemination of advances in care across specialties and professions, and optimization of individual- and team-based function. Nonetheless, conducting SBT IPE sessions poses several tactical and strategic challenges that must be effectively overcome to reap IPE's benefits. Copyright © 2015 Elsevier Inc. All rights reserved.

Improving Education in Medical Statistics: Implementing a Blended Learning Model in the Existing Curriculum

PubMed Central

Milic, Natasa M.; Trajkovic, Goran Z.; Bukumiric, Zoran M.; Cirkovic, Andja; Nikolic, Ivan M.; Milin, Jelena S.; Milic, Nikola V.; Savic, Marko D.; Corac, Aleksandar M.; Marinkovic, Jelena M.; Stanisavljevic, Dejana M.

2016-01-01

Background Although recent studies report on the benefits of blended learning in improving medical student education, there is still no empirical evidence on the relative effectiveness of blended over traditional learning approaches in medical statistics. We implemented blended along with on-site (i.e. face-to-face) learning to further assess the potential value of web-based learning in medical statistics. Methods This was a prospective study conducted with third year medical undergraduate students attending the Faculty of Medicine, University of Belgrade, who passed (440 of 545) the final exam of the obligatory introductory statistics course during 2013–14. Student statistics achievements were stratified based on the two methods of education delivery: blended learning and on-site learning. Blended learning included a combination of face-to-face and distance learning methodologies integrated into a single course. Results Mean exam scores for the blended learning student group were higher than for the on-site student group for both final statistics score (89.36±6.60 vs. 86.06±8.48; p = 0.001) and knowledge test score (7.88±1.30 vs. 7.51±1.36; p = 0.023) with a medium effect size. There were no differences in sex or study duration between the groups. Current grade point average (GPA) was higher in the blended group. In a multivariable regression model, current GPA and knowledge test scores were associated with the final statistics score after adjusting for study duration and learning modality (p<0.001). Conclusion This study provides empirical evidence to support educator decisions to implement different learning environments for teaching medical statistics to undergraduate medical students. Blended and on-site training formats led to similar knowledge acquisition; however, students with higher GPA preferred the technology assisted learning format. Implementation of blended learning approaches can be considered an attractive, cost-effective, and efficient alternative to traditional classroom training in medical statistics. PMID:26859832

Improving Education in Medical Statistics: Implementing a Blended Learning Model in the Existing Curriculum.

PubMed

Milic, Natasa M; Trajkovic, Goran Z; Bukumiric, Zoran M; Cirkovic, Andja; Nikolic, Ivan M; Milin, Jelena S; Milic, Nikola V; Savic, Marko D; Corac, Aleksandar M; Marinkovic, Jelena M; Stanisavljevic, Dejana M

2016-01-01

Although recent studies report on the benefits of blended learning in improving medical student education, there is still no empirical evidence on the relative effectiveness of blended over traditional learning approaches in medical statistics. We implemented blended along with on-site (i.e. face-to-face) learning to further assess the potential value of web-based learning in medical statistics. This was a prospective study conducted with third year medical undergraduate students attending the Faculty of Medicine, University of Belgrade, who passed (440 of 545) the final exam of the obligatory introductory statistics course during 2013-14. Student statistics achievements were stratified based on the two methods of education delivery: blended learning and on-site learning. Blended learning included a combination of face-to-face and distance learning methodologies integrated into a single course. Mean exam scores for the blended learning student group were higher than for the on-site student group for both final statistics score (89.36±6.60 vs. 86.06±8.48; p = 0.001) and knowledge test score (7.88±1.30 vs. 7.51±1.36; p = 0.023) with a medium effect size. There were no differences in sex or study duration between the groups. Current grade point average (GPA) was higher in the blended group. In a multivariable regression model, current GPA and knowledge test scores were associated with the final statistics score after adjusting for study duration and learning modality (p<0.001). This study provides empirical evidence to support educator decisions to implement different learning environments for teaching medical statistics to undergraduate medical students. Blended and on-site training formats led to similar knowledge acquisition; however, students with higher GPA preferred the technology assisted learning format. Implementation of blended learning approaches can be considered an attractive, cost-effective, and efficient alternative to traditional classroom training in medical statistics.

A pedagogical shift from direct instruction: Technology-assisted inquiry learning (TAIL) in chemistry

NASA Astrophysics Data System (ADS)

Lou, Rena Zhihong

The purpose of this study was to develop a student-centered Technology-Assisted Inquiry Learning (TAIL) pedagogical approach and compare it with the traditional, teacher-centered, direct instruction approach in a chemistry classroom. The study investigated how the TAIL approach affected community college chemistry students' (n = 21) learning gains and perceptions during a 1.5-hour intervention when compared with the direct instruction approach. A mixed methodology was used that included both quantitative and qualitative analyses. Results led to the following three key findings for novice learners: (a) TAIL had a statistically significant effect on students' procedural application skills improvement when compared with direct instruction; (b) The magnitude of the between-group difference (Cohen's d = 1.41) indicated that TAIL had a cumulative effect on students' learning gains due to its ability to incorporate multiple components including Inquiry, Guidance, Technology, and Collaboration; (c) When combining measures of students' performance and perceived mental effort, TAIL demonstrated high-instructional efficiency with a significant difference in teaching factual knowledge and procedural applications when compared with direct instruction. In summary, the outcome of this study demonstrated both the effectiveness and efficiency of the TAIL approach as a student-centered pedagogy in teaching a basic scientific topic. This study provided a practical demonstration of the pedagogical shift in teaching science from teacher-centered direct instruction to student-centered learning by using computer software as a pedagogical agent. The results of the study contribute to the literature in the fields of guided inquiry learning pedagogy and technology-assisted science teaching.

Relatively effortless listening promotes understanding and recall of medical instructions in older adults

PubMed Central

DiDonato, Roberta M.; Surprenant, Aimée M.

2015-01-01

Communication success under adverse conditions requires efficient and effective recruitment of both bottom-up (sensori-perceptual) and top-down (cognitive-linguistic) resources to decode the intended auditory-verbal message. Employing these limited capacity resources has been shown to vary across the lifespan, with evidence indicating that younger adults out-perform older adults for both comprehension and memory of the message. This study examined how sources of interference arising from the speaker (message spoken with conversational vs. clear speech technique), the listener (hearing-listening and cognitive-linguistic factors), and the environment (in competing speech babble noise vs. quiet) interact and influence learning and memory performance using more ecologically valid methods than has been done previously. The results suggest that when older adults listened to complex medical prescription instructions with “clear speech,” (presented at audible levels through insertion earphones) their learning efficiency, immediate, and delayed memory performance improved relative to their performance when they listened with a normal conversational speech rate (presented at audible levels in sound field). This better learning and memory performance for clear speech listening was maintained even in the presence of speech babble noise. The finding that there was the largest learning-practice effect on 2nd trial performance in the conversational speech when the clear speech listening condition was first is suggestive of greater experience-dependent perceptual learning or adaptation to the speaker's speech and voice pattern in clear speech. This suggests that experience-dependent perceptual learning plays a role in facilitating the language processing and comprehension of a message and subsequent memory encoding. PMID:26106353

Optimisation of GaN LEDs and the reduction of efficiency droop using active machine learning

DOE PAGES

Rouet-Leduc, Bertrand; Barros, Kipton Marcos; Lookman, Turab; ...

2016-04-26

A fundamental challenge in the design of LEDs is to maximise electro-luminescence efficiency at high current densities. We simulate GaN-based LED structures that delay the onset of efficiency droop by spreading carrier concentrations evenly across the active region. Statistical analysis and machine learning effectively guide the selection of the next LED structure to be examined based upon its expected efficiency as well as model uncertainty. This active learning strategy rapidly constructs a model that predicts Poisson-Schrödinger simulations of devices, and that simultaneously produces structures with higher simulated efficiencies.

Learning with Computer-Based Multimedia: Gender Effects on Efficiency

ERIC Educational Resources Information Center

Pohnl, Sabine; Bogner, Franz X.

2012-01-01

Up to now, only a few studies in multimedia learning have focused on gender effects. While research has mostly focused on learning success, the effect of gender on instructional efficiency (IE) has not yet been considered. Consequently, we used a quasi-experimental design to examine possible gender differences in the learning success, mental…

Time and Learning Efficiency in Internet-Based Learning: A Systematic Review and Meta-Analysis

ERIC Educational Resources Information Center

Cook, David A.; Levinson, Anthony J.; Garside, Sarah

2010-01-01

Authors have claimed that Internet-based instruction promotes greater learning efficiency than non-computer methods. Objectives Determine, through a systematic synthesis of evidence in health professions education, how Internet-based instruction compares with non-computer instruction in time spent learning, and what features of Internet-based…

Investigation of learning environment for arithmetic word problems by problem posing as sentence integration in Indonesian language

NASA Astrophysics Data System (ADS)

Hasanah, N.; Hayashi, Y.; Hirashima, T.

2017-02-01

Arithmetic word problems remain one of the most difficult area of teaching mathematics. Learning by problem posing has been suggested as an effective way to improve students’ understanding. However, the practice in usual classroom is difficult due to extra time needed for assessment and giving feedback to students’ posed problems. To address this issue, we have developed a tablet PC software named Monsakun for learning by posing arithmetic word problems based on Triplet Structure Model. It uses the mechanism of sentence-integration, an efficient implementation of problem-posing that enables agent-assessment of posed problems. The learning environment has been used in actual Japanese elementary school classrooms and the effectiveness has been confirmed in previous researches. In this study, ten Indonesian elementary school students living in Japan participated in a learning session of problem posing using Monsakun in Indonesian language. We analyzed their learning activities and show that students were able to interact with the structure of simple word problem using this learning environment. The results of data analysis and questionnaire suggested that the use of Monsakun provides a way of creating an interactive and fun environment for learning by problem posing for Indonesian elementary school students.

The Cognitive Science of Learning: Concepts and Strategies for the Educator and Learner.

PubMed

Weidman, Joseph; Baker, Keith

2015-12-01

Education is the fundamental process used to develop and maintain the professional skills of physicians. Medical students, residents, and fellows are expected to learn considerable amounts of information as they progress toward board certification. Established practitioners must continue to learn in an effort to remain up-to-date in their clinical realm. Those responsible for educating these populations endeavor to teach in a manner that is effective, efficient, and durable. The study of learning and performance is a subdivision of the field of cognitive science that focuses on how people interpret and process information and how they eventually develop mastery. A deeper understanding of how individuals learn can empower both educators and learners to be more effective in their endeavors. In this article, we review a number of concepts found in the literature on learning and performance. We address both the theoretical principles and the practical applications of each concept. Cognitive load theory, constructivism, and analogical transfer are concepts particularly beneficial to educators. An understanding of goal orientation, metacognition, retrieval, spaced learning, and deliberate practice will primarily benefit the learner. When these concepts are understood and incorporated into education and study, the effectiveness of learning is significantly improved.

Teleglaucoma: improving access and efficiency for glaucoma care.

PubMed

Kassam, Faazil; Yogesan, Kanagasingam; Sogbesan, Enitan; Pasquale, Louis R; Damji, Karim F

2013-01-01

Teleglaucoma is the application of telemedicine for glaucoma. We review and present the current literature on teleglaucoma; present our experience with teleglaucoma programs in Alberta, Canada and Western Australia; and discuss the challenges and opportunities in this emerging field. Teleglaucoma is a novel area that was first explored a little over a decade ago and early studies highlighted the technical challenges of delivering glaucoma care remotely. Advanced technologies have since emerged that show great promise in providing access to underserviced populations. Additionally, these technologies can improve the efficiency of healthcare systems burdened with an increasing number of patients with glaucoma, and a limited supply of ophthalmologists. Additional benefits of teleglaucoma systems include e-learning and e-research. Further work is needed to fully validate and study the cost and comparative effectiveness of this approach relative to traditional models of healthcare.

Teleglaucoma: Improving Access and Efficiency for Glaucoma Care

PubMed Central

Kassam, Faazil; Yogesan, Kanagasingam; Sogbesan, Enitan; Pasquale, Louis R.; Damji, Karim F.

2013-01-01

Teleglaucoma is the application of telemedicine for glaucoma. We review and present the current literature on teleglaucoma; present our experience with teleglaucoma programs in Alberta, Canada and Western Australia; and discuss the challenges and opportunities in this emerging field. Teleglaucoma is a novel area that was first explored a little over a decade ago and early studies highlighted the technical challenges of delivering glaucoma care remotely. Advanced technologies have since emerged that show great promise in providing access to underserviced populations. Additionally, these technologies can improve the efficiency of healthcare systems burdened with an increasing number of patients with glaucoma, and a limited supply of ophthalmologists. Additional benefits of teleglaucoma systems include e-learning and e-research. Further work is needed to fully validate and study the cost and comparative effectiveness of this approach relative to traditional models of healthcare. PMID:23741133

Harnessing learning biases is essential for applying social learning in conservation.

PubMed

Greggor, Alison L; Thornton, Alex; Clayton, Nicola S

2017-01-01

Social learning can influence how animals respond to anthropogenic changes in the environment, determining whether animals survive novel threats and exploit novel resources or produce maladaptive behaviour and contribute to human-wildlife conflict. Predicting where social learning will occur and manipulating its use are, therefore, important in conservation, but doing so is not straightforward. Learning is an inherently biased process that has been shaped by natural selection to prioritize important information and facilitate its efficient uptake. In this regard, social learning is no different from other learning processes because it too is shaped by perceptual filters, attentional biases and learning constraints that can differ between habitats, species, individuals and contexts. The biases that constrain social learning are not understood well enough to accurately predict whether or not social learning will occur in many situations, which limits the effective use of social learning in conservation practice. Nevertheless, we argue that by tapping into the biases that guide the social transmission of information, the conservation applications of social learning could be improved. We explore the conservation areas where social learning is highly relevant and link them to biases in the cues and contexts that shape social information use. The resulting synthesis highlights many promising areas for collaboration between the fields and stresses the importance of systematic reviews of the evidence surrounding social learning practices.

Joint detection and localization of multiple anatomical landmarks through learning

NASA Astrophysics Data System (ADS)

Dikmen, Mert; Zhan, Yiqiang; Zhou, Xiang Sean

2008-03-01

Reliable landmark detection in medical images provides the essential groundwork for successful automation of various open problems such as localization, segmentation, and registration of anatomical structures. In this paper, we present a learning-based system to jointly detect (is it there?) and localize (where?) multiple anatomical landmarks in medical images. The contributions of this work exist in two aspects. First, this method takes the advantage from the learning scenario that is able to automatically extract the most distinctive features for multi-landmark detection. Therefore, it is easily adaptable to detect arbitrary landmarks in various kinds of imaging modalities, e.g., CT, MRI and PET. Second, the use of multi-class/cascaded classifier architecture in different phases of the detection stage combined with robust features that are highly efficient in terms of computation time enables a seemingly real time performance, with very high localization accuracy. This method is validated on CT scans of different body sections, e.g., whole body scans, chest scans and abdominal scans. Aside from improved robustness (due to the exploitation of spatial correlations), it gains a run time efficiency in landmark detection. It also shows good scalability performance under increasing number of landmarks.

An Energy-Efficient Multi-Tier Architecture for Fall Detection Using Smartphones.

PubMed

Guvensan, M Amac; Kansiz, A Oguz; Camgoz, N Cihan; Turkmen, H Irem; Yavuz, A Gokhan; Karsligil, M Elif

2017-06-23

Automatic detection of fall events is vital to providing fast medical assistance to the causality, particularly when the injury causes loss of consciousness. Optimization of the energy consumption of mobile applications, especially those which run 24/7 in the background, is essential for longer use of smartphones. In order to improve energy-efficiency without compromising on the fall detection performance, we propose a novel 3-tier architecture that combines simple thresholding methods with machine learning algorithms. The proposed method is implemented on a mobile application, called uSurvive, for Android smartphones. It runs as a background service and monitors the activities of a person in daily life and automatically sends a notification to the appropriate authorities and/or user defined contacts when it detects a fall. The performance of the proposed method was evaluated in terms of fall detection performance and energy consumption. Real life performance tests conducted on two different models of smartphone demonstrate that our 3-tier architecture with feature reduction could save up to 62% of energy compared to machine learning only solutions. In addition to this energy saving, the hybrid method has a 93% of accuracy, which is superior to thresholding methods and better than machine learning only solutions.

Progress in Energy Storage Technologies: Models and Methods for Policy Analysis

NASA Astrophysics Data System (ADS)

Matteson, Schuyler W.

Climate change and other sustainability challenges have led to the development of new technologies that increase energy efficiency and reduce the utilization of finite resources. To promote the adoption of technologies with social benefits, governments often enact policies that provide financial incentives at the point of purchase. In their current form, these subsidies have the potential to increase the diffusion of emerging technologies; however, accounting for technological progress can improve program success while decreasing net public investment. This research develops novel methods using experience curves for the development of more efficient subsidy policies. By providing case studies in the field of automotive energy storage technologies, this dissertation also applies the methods to show the impacts of incorporating technological progress into energy policies. Specific findings include learning-dependent tapering subsidies for electric vehicles based on the lithium-ion battery experience curve, the effects of residual learning rates in lead-acid batteries on emerging technology cost competitiveness, and a cascading diffusion assessment of plug-in hybrid electric vehicle subsidy programs. Notably, the results show that considering learning rates in policy development can save billions of dollars in public funds, while also lending insight into the decision of whether or not to subsidize a given technology.

The Use of Animated Videos to Illustrate Oral Solid Dosage Form Manufacturing in a Pharmaceutics Course.

PubMed

Yellepeddi, Venkata Kashyap; Roberson, Charles

2016-10-25

Objective. To evaluate the impact of animated videos of oral solid dosage form manufacturing as visual instructional aids on pharmacy students' perception and learning. Design. Data were obtained using a validated, paper-based survey instrument designed to evaluate the effectiveness, appeal, and efficiency of the animated videos in a pharmaceutics course offered in spring 2014 and 2015. Basic demographic data were also collected and analyzed. Assessment data at the end of pharmaceutics course was collected for 2013 and compared with assessment data from 2014, and 2015. Assessment. Seventy-six percent of the respondents supported the idea of incorporating animated videos as instructional aids for teaching pharmaceutics. Students' performance on the formative assessment in 2014 and 2015 improved significantly compared to the performance of students in 2013 whose lectures did not include animated videos as instructional aids. Conclusions. Implementing animated videos of oral solid dosage form manufacturing as instructional aids resulted in improved student learning and favorable student perceptions about the instructional approach. Therefore, use of animated videos can be incorporated in pharmaceutics teaching to enhance visual learning.

Deep learning classification in asteroseismology using an improved neural network: results on 15 000 Kepler red giants and applications to K2 and TESS data

NASA Astrophysics Data System (ADS)

Hon, Marc; Stello, Dennis; Yu, Jie

2018-05-01

Deep learning in the form of 1D convolutional neural networks have previously been shown to be capable of efficiently classifying the evolutionary state of oscillating red giants into red giant branch stars and helium-core burning stars by recognizing visual features in their asteroseismic frequency spectra. We elaborate further on the deep learning method by developing an improved convolutional neural network classifier. To make our method useful for current and future space missions such as K2, TESS, and PLATO, we train classifiers that are able to classify the evolutionary states of lower frequency resolution spectra expected from these missions. Additionally, we provide new classifications for 8633 Kepler red giants, out of which 426 have previously not been classified using asteroseismology. This brings the total to 14983 Kepler red giants classified with our new neural network. We also verify that our classifiers are remarkably robust to suboptimal data, including low signal-to-noise and incorrect training truth labels.

Combining active learning and semi-supervised learning techniques to extract protein interaction sentences.

PubMed

Song, Min; Yu, Hwanjo; Han, Wook-Shin

2011-11-24

Protein-protein interaction (PPI) extraction has been a focal point of many biomedical research and database curation tools. Both Active Learning and Semi-supervised SVMs have recently been applied to extract PPI automatically. In this paper, we explore combining the AL with the SSL to improve the performance of the PPI task. We propose a novel PPI extraction technique called PPISpotter by combining Deterministic Annealing-based SSL and an AL technique to extract protein-protein interaction. In addition, we extract a comprehensive set of features from MEDLINE records by Natural Language Processing (NLP) techniques, which further improve the SVM classifiers. In our feature selection technique, syntactic, semantic, and lexical properties of text are incorporated into feature selection that boosts the system performance significantly. By conducting experiments with three different PPI corpuses, we show that PPISpotter is superior to the other techniques incorporated into semi-supervised SVMs such as Random Sampling, Clustering, and Transductive SVMs by precision, recall, and F-measure. Our system is a novel, state-of-the-art technique for efficiently extracting protein-protein interaction pairs.

The Use of Animated Videos to Illustrate Oral Solid Dosage Form Manufacturing in a Pharmaceutics Course

PubMed Central

Roberson, Charles

2016-01-01

Objective. To evaluate the impact of animated videos of oral solid dosage form manufacturing as visual instructional aids on pharmacy students’ perception and learning. Design. Data were obtained using a validated, paper-based survey instrument designed to evaluate the effectiveness, appeal, and efficiency of the animated videos in a pharmaceutics course offered in spring 2014 and 2015. Basic demographic data were also collected and analyzed. Assessment data at the end of pharmaceutics course was collected for 2013 and compared with assessment data from 2014, and 2015. Assessment. Seventy-six percent of the respondents supported the idea of incorporating animated videos as instructional aids for teaching pharmaceutics. Students’ performance on the formative assessment in 2014 and 2015 improved significantly compared to the performance of students in 2013 whose lectures did not include animated videos as instructional aids. Conclusions. Implementing animated videos of oral solid dosage form manufacturing as instructional aids resulted in improved student learning and favorable student perceptions about the instructional approach. Therefore, use of animated videos can be incorporated in pharmaceutics teaching to enhance visual learning. PMID:27899837

Fresh frozen cadaver workshops for advanced vascular surgical training.

PubMed

Jansen, Shirley; Cowie, Margaret; Linehan, John; Hamdorf, Jeffery M

2014-11-01

Reduction in working hours, streamlined training schemes and increasing use of endovascular techniques has meant a reduction in operative experience for newer vascular surgical trainees, especially those exposures which are not routinely performed such as thoracoabdominal, thoracotomy and retroperitoneal aortic, for example. This paper describes an Advanced Anatomy of Exposure course which was designed and convened at the Clinical Training & Evaluation Centre in Western Australia and uses fresh frozen cadavers. Feedback was obtained from the participants who attended over three courses by questionnaire. Feedback was strongly positive for the course meeting both its learning outcomes and personal learning objectives, and in addition, making a significant contribution to specialty skills. Most participants thought the fresh frozen cadaveric model significantly improved the learning objectives for training. The fresh frozen cadaver is an excellent teaching model highly representative of the living open surgical scenario where advanced trainees and newly qualified consultants can improve their operative confidence and consequently patient safety in vascular surgery. An efficient fresh frozen cadaver teaching programme can benefit many health professionals simultaneously maximizing the use of donated human tissue. © 2013 Royal Australasian College of Surgeons.

A meta-learning system based on genetic algorithms

NASA Astrophysics Data System (ADS)

Pellerin, Eric; Pigeon, Luc; Delisle, Sylvain

2004-04-01

The design of an efficient machine learning process through self-adaptation is a great challenge. The goal of meta-learning is to build a self-adaptive learning system that is constantly adapting to its specific (and dynamic) environment. To that end, the meta-learning mechanism must improve its bias dynamically by updating the current learning strategy in accordance with its available experiences or meta-knowledge. We suggest using genetic algorithms as the basis of an adaptive system. In this work, we propose a meta-learning system based on a combination of the a priori and a posteriori concepts. A priori refers to input information and knowledge available at the beginning in order to built and evolve one or more sets of parameters by exploiting the context of the system"s information. The self-learning component is based on genetic algorithms and neural Darwinism. A posteriori refers to the implicit knowledge discovered by estimation of the future states of parameters and is also applied to the finding of optimal parameters values. The in-progress research presented here suggests a framework for the discovery of knowledge that can support human experts in their intelligence information assessment tasks. The conclusion presents avenues for further research in genetic algorithms and their capability to learn to learn.

Newly qualified doctors' perceptions of informal learning from nurses: implications for interprofessional education and practice.

PubMed

Burford, Bryan; Morrow, Gill; Morrison, Jill; Baldauf, Beate; Spencer, John; Johnson, Neil; Rothwell, Charlotte; Peile, Ed; Davies, Carol; Allen, Maggie; Illing, Jan

2013-09-01

Newly qualified doctors spend much of their time with nurses, but little research has considered informal learning during that formative contact. This article reports findings from a multiple case study that explored what newly qualified doctors felt they learned from nurses in the workplace. Analysis of interviews conducted with UK doctors in their first year of practice identified four overarching themes: attitudes towards working with nurses, learning about roles, professional hierarchies and learning skills. Informal learning was found to contribute to the newly qualified doctors' knowledge of their own and others' roles. A dynamic hierarchy was identified: one in which a "pragmatic hierarchy" recognising nurses' expertise was superseded by a "normative structural hierarchy" that reinforced the notion of medical dominance. Alongside the implicit learning of roles, nurses contributed to the explicit learning of skills and captured doctors' errors, with implications for patient safety. The findings are discussed in relation to professional socialisation. Issues of power between the professions are also considered. It is concluded that increasing both medical and nursing professions' awareness of informal workplace learning may improve the efficiency of education in restricted working hours. A culture in which informal learning is embedded may also have benefits for patient safety.

The algorithm for duration acceleration of repetitive projects considering the learning effect

NASA Astrophysics Data System (ADS)

Chen, Hongtao; Wang, Keke; Du, Yang; Wang, Liwan

2018-03-01

Repetitive project optimization problem is common in project scheduling. Repetitive Scheduling Method (RSM) has many irreplaceable advantages in the field of repetitive projects. As the same or similar work is repeated, the proficiency of workers will be correspondingly low to high, and workers will gain experience and improve the efficiency of operations. This is learning effect. Learning effect is one of the important factors affecting the optimization results in repetitive project scheduling. This paper analyzes the influence of the learning effect on the controlling path in RSM from two aspects: one is that the learning effect changes the controlling path, the other is that the learning effect doesn't change the controlling path. This paper proposes corresponding methods to accelerate duration for different types of critical activities and proposes the algorithm for duration acceleration based on the learning effect in RSM. And the paper chooses graphical method to identity activities' types and considers the impacts of the learning effect on duration. The method meets the requirement of duration while ensuring the lowest acceleration cost. A concrete bridge construction project is given to verify the effectiveness of the method. The results of this study will help project managers understand the impacts of the learning effect on repetitive projects, and use the learning effect to optimize project scheduling.

An Experimental Investigation into the Efficiency of Cooperative Learning with Consideration of Multiple Grouping Criteria

ERIC Educational Resources Information Center

Hsiung, C. -M.

2010-01-01

The present study conducts an experimental investigation to compare the efficiency of the cooperative learning method with that of the traditional learning method. A total of 42 engineering students are randomly assigned to the two learning conditions and are formed into mixed-ability groups comprising three team members. In addition to the…

Efficient Learning Algorithms with Limited Information

ERIC Educational Resources Information Center

De, Anindya

2013-01-01

The thesis explores efficient learning algorithms in settings which are more restrictive than the PAC model of learning (Valiant) in one of the following two senses: (i) The learning algorithm has a very weak access to the unknown function, as in, it does not get labeled samples for the unknown function (ii) The error guarantee required from the…

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

ERIC Educational Resources Information Center

Kim, ChanMin

2008-01-01

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

Conceptions of Efficiency: Applications in Learning and Problem Solving

ERIC Educational Resources Information Center

Hoffman, Bobby; Schraw, Gregory

2010-01-01

The purpose of this article is to clarify conceptions, definitions, and applications of learning and problem-solving efficiency. Conceptions of efficiency vary within the field of educational psychology, and there is little consensus as to how to define, measure, and interpret the efficiency construct. We compare three diverse models that differ…

Applying a Theory-Driven Framework to Guide Quality Improvement Efforts in Nursing Homes: The LOCK Model.

PubMed

Mills, Whitney L; Pimentel, Camilla B; Palmer, Jennifer A; Snow, A Lynn; Wewiorski, Nancy J; Allen, Rebecca S; Hartmann, Christine W

2018-05-08

Implementing quality improvement (QI) programs in nursing homes continues to encounter significant challenges, despite recognized need. QI approaches provide nursing home staff with opportunities to collaborate on developing and testing strategies for improving care delivery. We present a theory-driven and user-friendly adaptable framework and facilitation package to overcome existing challenges and guide QI efforts in nursing homes. The framework is grounded in the foundational concepts of strengths-based learning, observation, relationship-based teams, efficiency, and organizational learning. We adapted these concepts to QI in the nursing home setting, creating the "LOCK" framework. The LOCK framework is currently being disseminated across the Veterans Health Administration. The LOCK framework has five tenets: (a) Look for the bright spots, (b) Observe, (c) Collaborate in huddles, (d) Keep it bite-sized, and (e) facilitation. Each tenet is described. We also present a case study documenting how a fictional nursing home can implement the LOCK framework as part of a QI effort to improve engagement between staff and residents. The case study describes sample observations, processes, and outcomes. We also discuss practical applications for nursing home staff, the adaptability of LOCK for different QI projects, the specific role of facilitation, and lessons learned. The proposed framework complements national efforts to improve quality of care and quality of life for nursing home residents and may be valuable across long-term care settings and QI project types.

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

PubMed

Seid-Fatemi, Azade; Tobler, Philippe N

2015-05-01

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

Deep learning with coherent nanophotonic circuits

NASA Astrophysics Data System (ADS)

Shen, Yichen; Harris, Nicholas C.; Skirlo, Scott; Prabhu, Mihika; Baehr-Jones, Tom; Hochberg, Michael; Sun, Xin; Zhao, Shijie; Larochelle, Hugo; Englund, Dirk; Soljačić, Marin

2017-07-01

Artificial neural networks are computational network models inspired by signal processing in the brain. These models have dramatically improved performance for many machine-learning tasks, including speech and image recognition. However, today's computing hardware is inefficient at implementing neural networks, in large part because much of it was designed for von Neumann computing schemes. Significant effort has been made towards developing electronic architectures tuned to implement artificial neural networks that exhibit improved computational speed and accuracy. Here, we propose a new architecture for a fully optical neural network that, in principle, could offer an enhancement in computational speed and power efficiency over state-of-the-art electronics for conventional inference tasks. We experimentally demonstrate the essential part of the concept using a programmable nanophotonic processor featuring a cascaded array of 56 programmable Mach-Zehnder interferometers in a silicon photonic integrated circuit and show its utility for vowel recognition.

In-class use of clickers and clicker tests improve learning and enable instant feedback and retests via automated grading

NASA Astrophysics Data System (ADS)

Burnham, Nancy A.; Kadam, Snehalata V.; DeSilva, Erin

2017-11-01

An audience response system (‘clickers’) was gradually incorporated into introductory physics courses at Worcester Polytechnic Institute during the years 2011-14. Clickers were used in lectures, as a means of preparing for labs, and for collection of exam data and grading. Average student grades were 13.5% greater, as measured by comparing exam results with a previous year. Student acceptance of clickers was high, ranging from 66% to 95%, and grading time for exams was markedly reduced, from a full day to a few hours for approximately 150 students. The streamlined grading allowed for a second test on the same material for the students who failed the first one. These improvements have the immediate effects of engagement, learning, and efficiency, and ideally, they will also provide an environment in which more students will succeed in college and their careers.

A Health Belief Model-Social Learning Theory approach to adolescents' fertility control: findings from a controlled field trial.

PubMed

Eisen, M; Zellman, G L; McAlister, A L

1992-01-01

We evaluated an 8- to 12-hour Health Belief Model-Social Learning Theory (HBM-SLT)-based sex education program against several community- and school-based interventions in a controlled field experiment. Data on sexual and contraceptive behavior were collected from 1,444 adolescents unselected for gender, race/ethnicity, or virginity status in a pretest-posttest design. Over 60% completed the one-year follow-up. Multivariate analyses were conducted separately for each preintervention virginity status by gender grouping. The results revealed differential program impacts. First, for preintervention virgins, there were no gender or intervention differences in abstinence maintenance over the follow-up year. Second, female preintervention Comparison program virgins used effective contraceptive methods more consistently than those who attended the HBM-SLT program (p less than 0.01); among males, the intervention programs were equally effective. Third, both interventions significantly increased contraceptive efficiency for teenagers who were sexually active before attending the programs. For males, the HBM-SLT program led to significantly greater follow-up contraceptive efficiency than the Comparison program with preintervention contraceptive efficiency controlled (p less than 0.05); for females, the programs produced equivalent improvement. Implications for program planning and evaluation are discussed.

Energy Management in Smart Cities Based on Internet of Things: Peak Demand Reduction and Energy Savings.

PubMed

Mahapatra, Chinmaya; Moharana, Akshaya Kumar; Leung, Victor C M

2017-12-05

Around the globe, innovation with integrating information and communication technologies (ICT) with physical infrastructure is a top priority for governments in pursuing smart, green living to improve energy efficiency, protect the environment, improve the quality of life, and bolster economy competitiveness. Cities today faces multifarious challenges, among which energy efficiency of homes and residential dwellings is a key requirement. Achieving it successfully with the help of intelligent sensors and contextual systems would help build smart cities of the future. In a Smart home environment Home Energy Management plays a critical role in finding a suitable and reliable solution to curtail the peak demand and achieve energy conservation. In this paper, a new method named as Home Energy Management as a Service (HEMaaS) is proposed which is based on neural network based Q -learning algorithm. Although several attempts have been made in the past to address similar problems, the models developed do not cater to maximize the user convenience and robustness of the system. In this paper, authors have proposed an advanced Neural Fitted Q -learning method which is self-learning and adaptive. The proposed method provides an agile, flexible and energy efficient decision making system for home energy management. A typical Canadian residential dwelling model has been used in this paper to test the proposed method. Based on analysis, it was found that the proposed method offers a fast and viable solution to reduce the demand and conserve energy during peak period. It also helps reducing the carbon footprint of residential dwellings. Once adopted, city blocks with significant residential dwellings can significantly reduce the total energy consumption by reducing or shifting their energy demand during peak period. This would definitely help local power distribution companies to optimize their resources and keep the tariff low due to curtailment of peak demand.

Energy Management in Smart Cities Based on Internet of Things: Peak Demand Reduction and Energy Savings

PubMed Central

Moharana, Akshaya Kumar

2017-01-01

Around the globe, innovation with integrating information and communication technologies (ICT) with physical infrastructure is a top priority for governments in pursuing smart, green living to improve energy efficiency, protect the environment, improve the quality of life, and bolster economy competitiveness. Cities today faces multifarious challenges, among which energy efficiency of homes and residential dwellings is a key requirement. Achieving it successfully with the help of intelligent sensors and contextual systems would help build smart cities of the future. In a Smart home environment Home Energy Management plays a critical role in finding a suitable and reliable solution to curtail the peak demand and achieve energy conservation. In this paper, a new method named as Home Energy Management as a Service (HEMaaS) is proposed which is based on neural network based Q-learning algorithm. Although several attempts have been made in the past to address similar problems, the models developed do not cater to maximize the user convenience and robustness of the system. In this paper, authors have proposed an advanced Neural Fitted Q-learning method which is self-learning and adaptive. The proposed method provides an agile, flexible and energy efficient decision making system for home energy management. A typical Canadian residential dwelling model has been used in this paper to test the proposed method. Based on analysis, it was found that the proposed method offers a fast and viable solution to reduce the demand and conserve energy during peak period. It also helps reducing the carbon footprint of residential dwellings. Once adopted, city blocks with significant residential dwellings can significantly reduce the total energy consumption by reducing or shifting their energy demand during peak period. This would definitely help local power distribution companies to optimize their resources and keep the tariff low due to curtailment of peak demand. PMID:29206159

The Potential of Supplemental Instruction in Engineering Education: Creating Additional Peer-Guided Learning Opportunities in Difficult Compulsory Courses for First-Year Students

ERIC Educational Resources Information Center

Malm, Joakim; Bryngfors, Leif; Mörner, Lise-Lotte

2016-01-01

Supplemental Instruction (SI) can be an efficient way of improving student success in difficult courses. Here, a study is made on SI attached to difficult first-year engineering courses. The results show that both the percentage of students passing a difficult first-year engineering course, and scores on the course exams are considerably higher…