Correlation Filter Learning Toward Peak Strength for Visual Tracking.

PubMed

Sui, Yao; Wang, Guanghui; Zhang, Li

2018-04-01

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

MoLeNET Mobile Learning Conference 2009: Research Papers

ERIC Educational Resources Information Center

Guy Parker, Ed.

2010-01-01

The Mobile Learning Network (MoLeNET) is a unique collaborative approach to encouraging, supporting, expanding and promoting mobile learning, primarily in English post-14 education and training, via supported shared cost mobile learning projects. Collaboration at national level involves participating institutions and the Learning and Skills…

NASA Electronic Library System (NELS): The system impact of security

NASA Technical Reports Server (NTRS)

Mcgregor, Terry L.

1993-01-01

This paper discusses security issues as they relate to the NASA Electronic Library System which is currently in use as the repository system for AdaNET System Version 3 (ASV3) being operated by MountainNET, Inc. NELS was originally designed to provide for public, development, and secure collections and objects. The secure feature for collections and objects was deferred in the initial system for implementation at a later date. The NELS system is now 9 months old and many lessons have been learned about the use and maintenance of library systems. MountainNET has 9 months of experience in operating the system and gathering feedback from the ASV3 user community. The user community has expressed an interest in seeing security features implemented in the current system. The time has come to take another look at the whole issue of security for the NELS system. Two requirements involving security have been put forth by MountainNET for the ASV3 system. The first is to incorporate at the collection level a security scheme to allow restricted access to collections. This should be invisible to end users and be controlled by librarians. The second is to allow inclusion of applications which can be executed only by a controlled group of users; for example, an application which can be executed by librarians only. The requirements provide a broad framework in which to work. These requirements raise more questions than answers. To explore the impact of these requirements a top down approach will be used.

PatterNet: a system to learn compact physical design pattern representations for pattern-based analytics

NASA Astrophysics Data System (ADS)

Lutich, Andrey

2017-07-01

This research considers the problem of generating compact vector representations of physical design patterns for analytics purposes in semiconductor patterning domain. PatterNet uses a deep artificial neural network to learn mapping of physical design patterns to a compact Euclidean hyperspace. Distances among mapped patterns in this space correspond to dissimilarities among patterns defined at the time of the network training. Once the mapping network has been trained, PatterNet embeddings can be used as feature vectors with standard machine learning algorithms, and pattern search, comparison, and clustering become trivial problems. PatterNet is inspired by the concepts developed within the framework of generative adversarial networks as well as the FaceNet. Our method facilitates a deep neural network (DNN) to learn directly the compact representation by supplying it with pairs of design patterns and dissimilarity among these patterns defined by a user. In the simplest case, the dissimilarity is represented by an area of the XOR of two patterns. Important to realize that our PatterNet approach is very different to the methods developed for deep learning on image data. In contrast to "conventional" pictures, the patterns in the CAD world are the lists of polygon vertex coordinates. The method solely relies on the promise of deep learning to discover internal structure of the incoming data and learn its hierarchical representations. Artificial intelligence arising from the combination of PatterNet and clustering analysis very precisely follows intuition of patterning/optical proximity correction experts paving the way toward human-like and human-friendly engineering tools.

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

Brown, Carrie; LaRue, Anna; Pigman, Margaret

As more and more zero net energy (ZNE) buildings are built and monitored, we can learn from both careful case studies of individual projects as well as a broader perspective of trends over time. In a forum sponsored by Pacific Gas and Electric Company (PG&E), eight expert speakers discussed: results and lessons from monitoring occupied ZNE buildings; best practices for setting performance targets and getting actionable performance information, and; things that have surprised them about monitored ZNE buildings. This paper distills the content of the forum by laying out the most common hurdles that are encountered in setting up monitoringmore » projects, frequent performance issues that the monitoring uncovers, and lessons learned that can be applied to future projects.« less

Proceedings of the International Workshop on Multistrategy Learning (2nd) Held in Harpers Ferry, West Virginia on May 26-29, 1993

DTIC Science & Technology

1993-05-26

aic.gmu.edu, Tel: 703 993-1719, Fax: 703 993-3729 All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or... Public Health Data ....................... 309 John W. Sheppard Author Index vi I. General Issues 3 Multitype Inference in Multistrategy Task-adaptive...0.7 values were chosen because experience This description has appeared already in many showed they facilitated GenNet dynamics). publications (e.g. de

An exploratory qualitative study on perceptions about mosquito bed nets in the Niger Delta: what are the barriers to sustained use?

PubMed Central

Galvin, Kathleen T; Petford, Nick; Ajose, Frances; Davies, Dai

2011-01-01

Background: The effectiveness of malaria control programs is determined by an array of complex factors, including the acceptability and sustained use of preventative measures such as the bed net. A small-scale exploratory study was conducted in several locations in the Niger Delta region, Nigeria, to discover barriers against the use of bed nets, in the context of a current drive to scale up net use in Nigeria. Methods: A qualitative approach with a convenience sample was used. One to one interviews with mostly male adult volunteers were undertaken which explored typical living and sleeping arrangements, and perceptions about and barriers against the use of the mosquito prevention bed net. Results: Several key issues emerged from the qualitative data. Bed nets were not reported as widely used in this small sample. The reasons reported for lack of use included issues of convenience, especially net set up and dismantling; potential hazard and safety concerns; issues related to typical family composition and nature of accommodation; humid weather conditions; and perceptions of cost and effectiveness. Most barriers to net use concerned issues about everyday practical living and sleeping arrangements and perceptions about comfort. Interviewees identified were aware of malaria infection risks, but several also indicated certain beliefs that were barriers to net use. Conclusions: Successful control of malaria and scale up of insecticide-treated net coverage relies on community perceptions and practice. This small study has illuminated a number of important everyday life issues, which remain barriers to sustained net use, and has clarified further questions to be considered in net design and in future research studies. The study highlights the need for further research on the human concerns that contribute to sustained use of nets or, conversely, present significant barriers to their use. PMID:21544249

MirandaNet: A Learning Community--A Community of Learners.

ERIC Educational Resources Information Center

Cuthell, John

2002-01-01

Explains MirandaNet, a learning community of teachers and academics as agents of change who use information and communications technology to change their teaching and learning practice and to develop innovative models for continuing professional development. Discusses distributed cognition in an online community. (LRW)

SchNet - A deep learning architecture for molecules and materials

NASA Astrophysics Data System (ADS)

Schütt, K. T.; Sauceda, H. E.; Kindermans, P.-J.; Tkatchenko, A.; Müller, K.-R.

2018-06-01

Deep learning has led to a paradigm shift in artificial intelligence, including web, text, and image search, speech recognition, as well as bioinformatics, with growing impact in chemical physics. Machine learning, in general, and deep learning, in particular, are ideally suitable for representing quantum-mechanical interactions, enabling us to model nonlinear potential-energy surfaces or enhancing the exploration of chemical compound space. Here we present the deep learning architecture SchNet that is specifically designed to model atomistic systems by making use of continuous-filter convolutional layers. We demonstrate the capabilities of SchNet by accurately predicting a range of properties across chemical space for molecules and materials, where our model learns chemically plausible embeddings of atom types across the periodic table. Finally, we employ SchNet to predict potential-energy surfaces and energy-conserving force fields for molecular dynamics simulations of small molecules and perform an exemplary study on the quantum-mechanical properties of C20-fullerene that would have been infeasible with regular ab initio molecular dynamics.

Deep neural nets as a method for quantitative structure-activity relationships.

PubMed

Ma, Junshui; Sheridan, Robert P; Liaw, Andy; Dahl, George E; Svetnik, Vladimir

2015-02-23

Neural networks were widely used for quantitative structure-activity relationships (QSAR) in the 1990s. Because of various practical issues (e.g., slow on large problems, difficult to train, prone to overfitting, etc.), they were superseded by more robust methods like support vector machine (SVM) and random forest (RF), which arose in the early 2000s. The last 10 years has witnessed a revival of neural networks in the machine learning community thanks to new methods for preventing overfitting, more efficient training algorithms, and advancements in computer hardware. In particular, deep neural nets (DNNs), i.e. neural nets with more than one hidden layer, have found great successes in many applications, such as computer vision and natural language processing. Here we show that DNNs can routinely make better prospective predictions than RF on a set of large diverse QSAR data sets that are taken from Merck's drug discovery effort. The number of adjustable parameters needed for DNNs is fairly large, but our results show that it is not necessary to optimize them for individual data sets, and a single set of recommended parameters can achieve better performance than RF for most of the data sets we studied. The usefulness of the parameters is demonstrated on additional data sets not used in the calibration. Although training DNNs is still computationally intensive, using graphical processing units (GPUs) can make this issue manageable.

Earthquake Early Warning and Public Policy: Opportunities and Challenges

NASA Astrophysics Data System (ADS)

Goltz, J. D.; Bourque, L.; Tierney, K.; Riopelle, D.; Shoaf, K.; Seligson, H.; Flores, P.

2003-12-01

Development of an earthquake early warning capability and pilot project were objectives of TriNet, a 5-year (1997-2001) FEMA-funded project to develop a state-of-the-art digital seismic network in southern California. In parallel with research to assemble a protocol for rapid analysis of earthquake data and transmission of a signal by TriNet scientists and engineers, the public policy, communication and educational issues inherent in implementation of an earthquake early warning system were addressed by TriNet's outreach component. These studies included: 1) a survey that identified potential users of an earthquake early warning system and how an earthquake early warning might be used in responding to an event, 2) a review of warning systems and communication issues associated with other natural hazards and how lessons learned might be applied to an alerting system for earthquakes, 3) an analysis of organization, management and public policy issues that must be addressed if a broad-based warning system is to be developed and 4) a plan to provide earthquake early warnings to a small number of organizations in southern California as an experimental prototype. These studies provided needed insights into the social and cultural environment in which this new technology will be introduced, an environment with opportunities to enhance our response capabilities but also an environment with significant barriers to overcome to achieve a system that can be sustained and supported. In this presentation we will address the main public policy issues that were subjects of analysis in these studies. They include a discussion of the possible division of functions among organizations likely to be the principle partners in the management of an earthquake early warning system. Drawing on lessons learned from warning systems for other hazards, we will review the potential impacts of false alarms and missed events on warning system credibility, the acceptability of fully automated warning systems and equity issues associated with possible differential access to warnings. Finally, we will review the status of legal authorities and liabilities faced by organizations that assume various warning system roles and possible approaches to setting up a pilot project to introduce early warning. Our presentation will suggest that introducing an early warning system requires multi-disciplinary and multi-agency cooperation and thoughtful discussion among organizations likely to be providers and participants in an early warning system. Recalling our experience with earthquake prediction, we will look at early warning as a promising but unproven technology and recommend moving forward with caution and patience.

Measuring The Impact Of Motivation On Achievement And Course Completion Rates In MarineNet Distance Education

DTIC Science & Technology

2016-09-01

the Marine Corps. This research applies the learning theory of human motivation to archival MarineNet data to determine if motivation factors impact...motivations. Each type of motivation has a different effect on human learning and course outcomes. To test this theory, archival data from the MarineNet...demonstrate the similarities and dissimilarities that exist between civilian and Marine Corps DE programs as well as the gap in knowledge on human learning

Remembering Differently: Use of Memory Strategies among Net-Generation ESL Learners

ERIC Educational Resources Information Center

Shakarami, Alireza; Mardziah, H. Abdullah; Faiz, S. Abdullah; Tan, Bee Hoon

2011-01-01

Net-generation learners are growing up in an era when much of the learning, communication, socializing and ways of working take place through digital means. Living in this digital era may result in different ways of thinking, ways of approaching learning, strategies, and priorities. The Net-Geners therefore, need new skills and new strategies to…

Research Library Issues. RLI 293

ERIC Educational Resources Information Center

Baughman, M. Sue, Ed.

2018-01-01

This issue of "Research Library Issues" ("RLI") presents an introduction article and two companion articles, which highlight Net Neutrality. The introduction article, "Why Net Neutrality Matters and What Research Libraries Can Do about It" (Mary Lee Kennedy), explains that the fundamental intent of the open internet…

Conditions for Productive Learning in Networked Learning Environments: A Case Study from the VO@NET Project

ERIC Educational Resources Information Center

Ryberg, Thomas; Koottatep, Suporn; Pengchai, Petch; Dirckinck-Holmfeld, Lone

2006-01-01

In this article we bring together experiences from two international research projects: the Kaleidoscope ERT research collaboration and the VO@NET project. We do this by using a shared framework identified for cross-case analyses within the Kaleidoscope ERT to analyse a particular case in the VO@NET project, a training course called "Green…

17 CFR 420.2 - Definitions.

Code of Federal Regulations, 2010 CFR

2010-04-01

... positions in the specific security issue: (1) Cash/immediate net settled positions; (2) Net when-issued... appropriate for monitoring the impact of concentrations of positions in Treasury securities. (h) “Reportable...

ClimateNet: A Machine Learning dataset for Climate Science Research

NASA Astrophysics Data System (ADS)

Prabhat, M.; Biard, J.; Ganguly, S.; Ames, S.; Kashinath, K.; Kim, S. K.; Kahou, S.; Maharaj, T.; Beckham, C.; O'Brien, T. A.; Wehner, M. F.; Williams, D. N.; Kunkel, K.; Collins, W. D.

2017-12-01

Deep Learning techniques have revolutionized commercial applications in Computer vision, speech recognition and control systems. The key for all of these developments was the creation of a curated, labeled dataset ImageNet, for enabling multiple research groups around the world to develop methods, benchmark performance and compete with each other. The success of Deep Learning can be largely attributed to the broad availability of this dataset. Our empirical investigations have revealed that Deep Learning is similarly poised to benefit the task of pattern detection in climate science. Unfortunately, labeled datasets, a key pre-requisite for training, are hard to find. Individual research groups are typically interested in specialized weather patterns, making it hard to unify, and share datasets across groups and institutions. In this work, we are proposing ClimateNet: a labeled dataset that provides labeled instances of extreme weather patterns, as well as associated raw fields in model and observational output. We develop a schema in NetCDF to enumerate weather pattern classes/types, store bounding boxes, and pixel-masks. We are also working on a TensorFlow implementation to natively import such NetCDF datasets, and are providing a reference convolutional architecture for binary classification tasks. Our hope is that researchers in Climate Science, as well as ML/DL, will be able to use (and extend) ClimateNet to make rapid progress in the application of Deep Learning for Climate Science research.

38 CFR 8.11 - Cash value and policy loan.

Code of Federal Regulations, 2010 CFR

2010-07-01

... will be taken as the date of delivery. (c) All values, reserves and net single premiums on..., reserves, and net single premiums issued under the provisions of section 1922(a) of title 38 U.S.C., and on...: 38 U.S.C. 1904, 1906) (e) All values on insurance, reserves, and net single premiums issued under the...

Cooperate to Validate: OBSERVAL-NET Experts' Report on Validation of Non-Formal and Informal Learning (VNIL) 2013

ERIC Educational Resources Information Center

Weber Guisan, Saskia; Voit, Janine; Lengauer, Sonja; Proinger, Eva; Duvekot, Ruud; Aagaard, Kirsten

2014-01-01

The present publication is one of the outcomes of the OBSERVAL-NET project (follow-up of the OBSERVAL project). The main aim of OBSERVAL-NET was to set up a stakeholder-centric network of organisations supporting the validation of non-formal and informal learning in Europe based on the formation of national working groups in the 8 participating…

Cooperate to Validate. Observal-Net Experts' Report on Validation of Non-Formal and Informal Learning (VNIL) 2013

ERIC Educational Resources Information Center

Weber Guisan, Saskia; Voit, Janine; Lengauer, Sonja; Proinger, Eva; Duvekot, Ruud; Aagaard, Kirsten

2014-01-01

The present publication is one of the outcomes of the OBSERVAL-NET project (followup of the OBSERVAL project). The main aim of OBSERVAL-NET was to set up a stakeholder centric network of organisations supporting the validation of non-formal and informal learning in Europe based on the formation of national working groups in the 8 participating…

Applying Dynamic Fuzzy Petri Net to Web Learning System

ERIC Educational Resources Information Center

Chen, Juei-Nan; Huang, Yueh-Min; Chu, William

2005-01-01

This investigation presents a DFPN (Dynamic Fuzzy Petri Net) model to increase the flexibility of the tutoring agent's behaviour and thus provide a learning content structure for a lecture course. The tutoring agent is a software assistant for a single user, who may be an expert in an e-Learning course. Based on each learner's behaviour, the…

MoleculeNet: a benchmark for molecular machine learning† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c7sc02664a

PubMed Central

Wu, Zhenqin; Ramsundar, Bharath; Feinberg, Evan N.; Gomes, Joseph; Geniesse, Caleb; Pappu, Aneesh S.; Leswing, Karl

2017-01-01

Molecular machine learning has been maturing rapidly over the last few years. Improved methods and the presence of larger datasets have enabled machine learning algorithms to make increasingly accurate predictions about molecular properties. However, algorithmic progress has been limited due to the lack of a standard benchmark to compare the efficacy of proposed methods; most new algorithms are benchmarked on different datasets making it challenging to gauge the quality of proposed methods. This work introduces MoleculeNet, a large scale benchmark for molecular machine learning. MoleculeNet curates multiple public datasets, establishes metrics for evaluation, and offers high quality open-source implementations of multiple previously proposed molecular featurization and learning algorithms (released as part of the DeepChem open source library). MoleculeNet benchmarks demonstrate that learnable representations are powerful tools for molecular machine learning and broadly offer the best performance. However, this result comes with caveats. Learnable representations still struggle to deal with complex tasks under data scarcity and highly imbalanced classification. For quantum mechanical and biophysical datasets, the use of physics-aware featurizations can be more important than choice of particular learning algorithm. PMID:29629118

Threat driven modeling framework using petri nets for e-learning system.

PubMed

Khamparia, Aditya; Pandey, Babita

2016-01-01

Vulnerabilities at various levels are main cause of security risks in e-learning system. This paper presents a modified threat driven modeling framework, to identify the threats after risk assessment which requires mitigation and how to mitigate those threats. To model those threat mitigations aspects oriented stochastic petri nets are used. This paper included security metrics based on vulnerabilities present in e-learning system. The Common Vulnerability Scoring System designed to provide a normalized method for rating vulnerabilities which will be used as basis in metric definitions and calculations. A case study has been also proposed which shows the need and feasibility of using aspect oriented stochastic petri net models for threat modeling which improves reliability, consistency and robustness of the e-learning system.

Library learning space--empirical research and perspective.

PubMed

Littleton, Dawn; Rethlefsen, Melissa

2008-01-01

Navigate the Net columns offer navigation to Web sites of value to medical librarians. For this issue, the authors recognize that librarians are frequently challenged to justify the need for the physical space occupied by a library in the context of the wide availability of electronic resources, ubiquitous student laptops, and competition for space needed by other institutional priorities. While this trend started years ago, it continues to raise a number of important practical and philosophical questions for libraries and the institutions they serve. What is the library for? What is library space best used for? How does the concept of "Library as Place" support informed decisions for librarians and space planners? In this issue, Web-based resources are surveyed that address these questions for libraries generally and health sciences libraries more specifically.

24 CFR 1000.420 - May grants made by HUD under section 603 of NAHASDA be used to pay net interest costs incurred...

Code of Federal Regulations, 2010 CFR

2010-04-01

... section 603 of NAHASDA be used to pay net interest costs incurred when issuing notes or other obligations... Activities § 1000.420 May grants made by HUD under section 603 of NAHASDA be used to pay net interest costs incurred when issuing notes or other obligations? Yes. Other costs that can be paid using grant funds...

Teacher Candidates Research, Teach, and Learn in the Nation's First Net Zero School

ERIC Educational Resources Information Center

Murley, Lisa D.; Gandy, S. Kay; Huss, Jeanine M.

2017-01-01

Teacher candidates conducted field hours in the nation's first net zero school, which uses the same amount of energy, measured annually, as it produces. These teacher candidates saw firsthand integration of the net zero advantages by completing a Collaborative Research Project and a Net Zero Lesson, which incorporated the use of the net zero…

Place-Based Learning: Interactive Learning and Net-Zero Design

ERIC Educational Resources Information Center

Holser, Alec; Becker, Michael

2011-01-01

Food and conservation science curriculum, net-zero design and student-based building performance monitoring have come together in the unique and innovative new Music and Science Building for Oregon's Hood River Middle School. The school's Permaculture-based curriculum both informed the building design and was also transformed through the…

Saliency U-Net: A regional saliency map-driven hybrid deep learning network for anomaly segmentation

NASA Astrophysics Data System (ADS)

Karargyros, Alex; Syeda-Mahmood, Tanveer

2018-02-01

Deep learning networks are gaining popularity in many medical image analysis tasks due to their generalized ability to automatically extract relevant features from raw images. However, this can make the learning problem unnecessarily harder requiring network architectures of high complexity. In case of anomaly detection, in particular, there is often sufficient regional difference between the anomaly and the surrounding parenchyma that could be easily highlighted through bottom-up saliency operators. In this paper we propose a new hybrid deep learning network using a combination of raw image and such regional maps to more accurately learn the anomalies using simpler network architectures. Specifically, we modify a deep learning network called U-Net using both the raw and pre-segmented images as input to produce joint encoding (contraction) and expansion paths (decoding) in the U-Net. We present results of successfully delineating subdural and epidural hematomas in brain CT imaging and liver hemangioma in abdominal CT images using such network.

White blood cells identification system based on convolutional deep neural learning networks.

PubMed

Shahin, A I; Guo, Yanhui; Amin, K M; Sharawi, Amr A

2017-11-16

White blood cells (WBCs) differential counting yields valued information about human health and disease. The current developed automated cell morphology equipments perform differential count which is based on blood smear image analysis. Previous identification systems for WBCs consist of successive dependent stages; pre-processing, segmentation, feature extraction, feature selection, and classification. There is a real need to employ deep learning methodologies so that the performance of previous WBCs identification systems can be increased. Classifying small limited datasets through deep learning systems is a major challenge and should be investigated. In this paper, we propose a novel identification system for WBCs based on deep convolutional neural networks. Two methodologies based on transfer learning are followed: transfer learning based on deep activation features and fine-tuning of existed deep networks. Deep acrivation featues are extracted from several pre-trained networks and employed in a traditional identification system. Moreover, a novel end-to-end convolutional deep architecture called "WBCsNet" is proposed and built from scratch. Finally, a limited balanced WBCs dataset classification is performed through the WBCsNet as a pre-trained network. During our experiments, three different public WBCs datasets (2551 images) have been used which contain 5 healthy WBCs types. The overall system accuracy achieved by the proposed WBCsNet is (96.1%) which is more than different transfer learning approaches or even the previous traditional identification system. We also present features visualization for the WBCsNet activation which reflects higher response than the pre-trained activated one. a novel WBCs identification system based on deep learning theory is proposed and a high performance WBCsNet can be employed as a pre-trained network. Copyright © 2017. Published by Elsevier B.V.

Net Generation's Learning Styles in Nursing Education.

PubMed

Christodoulou, Eleni; Kalokairinou, Athina

2015-01-01

Numerous surveys have confirmed that emerging technologies and Web 2.0 tools have been a defining feature in the lives of current students, estimating that there is a fundamental shift in the way young people communicate, socialize and learn. Nursing students in higher education are characterized as digital literate with distinct traits which influence their learning styles. Millennials exhibit distinct learning preferences such as teamwork, experiential activities, structure, instant feedback and technology integration. Higher education institutions should be aware of the implications of the Net Generation coming to university and be prepared to meet their expectations and learning needs.

Correlational Neural Networks.

PubMed

Chandar, Sarath; Khapra, Mitesh M; Larochelle, Hugo; Ravindran, Balaraman

2016-02-01

Common representation learning (CRL), wherein different descriptions (or views) of the data are embedded in a common subspace, has been receiving a lot of attention recently. Two popular paradigms here are canonical correlation analysis (CCA)-based approaches and autoencoder (AE)-based approaches. CCA-based approaches learn a joint representation by maximizing correlation of the views when projected to the common subspace. AE-based methods learn a common representation by minimizing the error of reconstructing the two views. Each of these approaches has its own advantages and disadvantages. For example, while CCA-based approaches outperform AE-based approaches for the task of transfer learning, they are not as scalable as the latter. In this work, we propose an AE-based approach, correlational neural network (CorrNet), that explicitly maximizes correlation among the views when projected to the common subspace. Through a series of experiments, we demonstrate that the proposed CorrNet is better than AE and CCA with respect to its ability to learn correlated common representations. We employ CorrNet for several cross-language tasks and show that the representations learned using it perform better than the ones learned using other state-of-the-art approaches.

Teaching Russian Via Distance Learning, the EdNet Experience.

ERIC Educational Resources Information Center

Zsiray, Stephen W., Jr.; And Others

In Utah, the statewide distance education network (EdNet) enables students from five rural and suburban high schools to learn Russian and earn college credits. Courses in Russian are offered through a partnership involving the Cache County School District, Utah State University, and the Utah State Office of Education. Classes are taught on one…

Understanding the Roles of Online Meetings in a Net-Based Course

ERIC Educational Resources Information Center

Berge, O.; Fjuk, A.

2006-01-01

It is argued elsewhere that online learning environments constitute new conditions for carrying out collaborative learning activities. This article explores the roles of a series of online meetings in such an environment. The online meetings are arranged as part of a net-based course on object-oriented programming, and constitute a recurring…

Modulation of Perineuronal Nets and Parvalbumin with Developmental Song Learning

PubMed Central

Balmer, Timothy S.; Carels, Vanessa M.; Frisch, Jillian L.; Nick, Teresa A.

2009-01-01

Neural circuits and behavior are shaped during developmental phases of maximal plasticity known as sensitive or critical periods. Neural correlates of sensory critical periods have been identified, but their roles remain unclear. Factors that define critical periods in sensorimotor circuits and behavior are not known. Birdsong learning in the zebra finch occurs during a sensitive period similar to that for human speech. We now show that perineuronal nets, which correlate with sensory critical periods, surround parvalbumin-positive neurons in brain areas that are dedicated to singing. The percentage of both total and parvalbumin-positive neurons with perineuronal nets increased with development. In HVC (this acronym is the proper name), a song area important for sensorimotor integration, the percentage of parvalbumin neurons with perineuronal nets correlated with song maturity. Shifting the vocal critical period with tutor song deprivation decreased the percentage of neurons that were parvalbumin positive and the relative staining intensity of both parvalbumin and a component of perineuronal nets. Developmental song learning shares key characteristics with sensory critical periods, suggesting shared underlying mechanisms. PMID:19828802

Lessons from trial-based cost-effectiveness analyses of mental health interventions: why uncertainty about the outcome, estimate and willingness to pay matters.

PubMed

Hoch, Jeffrey S; Dewa, Carolyn S

2007-01-01

The principal aim of this article is to share lessons learned by the authors while conducting economic evaluations, using clinical trial data, of mental health interventions. These lessons are quite general and have clear relevance for pharmacoeconomic studies. In addition, we explore how net benefit regression can be used to enhance consideration of key issues when conducting an economic evaluation based on clinical trial data. The first study we discuss found that cost-effectiveness results varied markedly based on the choice of both the patient outcome and the willingness to pay for more of that outcome. The importance of willingness to pay was also highlighted in the results from the second study. Even with a set willingness-to-pay value, most of the time the probability that the new treatment was cost effective was not 100%. In the third study, the cost effectiveness of the new treatment varied by patient characteristics. These observations have important implications for pharmacoeconomic studies. Namely, analysts must carefully consider choice of patient outcome, willingness to pay, patient heterogeneity and the statistical uncertainty inherent in the data. Net benefit regression is a useful technique for exploring these crucial issues when undertaking an economic evaluation using patient-level data on both costs and effects.

AccrualNet: Addressing Low Accrual Via a Knowledge-Based, Community of Practice Platform

PubMed Central

Massett, Holly A.; Parreco, Linda K.; Padberg, Rose Mary; Richmond, Ellen S.; Rienzo, Marie E.; Leonard, Colleen E. Ryan; Quesenbery, Whitney; Killiam, H. William; Johnson, Lenora E.; Dilts, David M.

2011-01-01

Purpose: Present the design and initial evaluation of a unique, Web-enabled platform for the development of a community of practice around issues of oncology clinical trial accrual. Methods: The National Cancer Institute (NCI) conducted research with oncology professionals to identify unmet clinical trial accrual needs in the field. In response, a comprehensive platform for accrual resources, AccrualNet, was created by using an agile development process, storyboarding, and user testing. Literature and resource searches identified relevant content to populate the site. Descriptive statistics were tracked for resource and site usage. Use cases were defined to support implementation. Results: AccrualNet has five levels: (1) clinical trial macrostages (prestudy, active study, and poststudy); (2) substages (developing a protocol, selecting a trial, preparing to open, enrolling patients, managing the trial, retaining participants, and lessons learned); (3) strategies for each substage; (4) multiple activities for each strategy; and (5) multiple resources for each activity. Since its launch, AccrualNet has had more than 45,000 page views, with the Tools & Resources, Conversations, and Training sections being the most viewed. Total resources have increased 69%, to 496 items. Analysis of articles in the site reveals that 22% are from two journals and 46% of the journals supplied a single article. To date, there are 29 conversations with 43 posts. Four use cases are discussed. Conclusion: AccrualNet represents a unique, centralized comprehensive-solution platform to systematically capture accrual knowledge for all stages of a clinical trial. It is designed to foster a community of practice by encouraging users to share additional strategies, resources, and ideas. PMID:22379429

Stroking the Net Whale: A Constructivist Grounded Theory of Self-Regulated Learning in Virtual Social Spaces

ERIC Educational Resources Information Center

Kasperiuniene, Judita; Zydziunaite, Vilma; Eriksson, Malin

2017-01-01

This qualitative study explored the self-regulated learning (SRL) of teachers and their students in virtual social spaces. The processes of SRL were analyzed from 24 semi-structured individual interviews with professors, instructors and their students from five Lithuanian universities. A core category stroking the net whale showed the process of…

The Knowledge Building Paradigm: A Model of Learning for Net Generation Students

ERIC Educational Resources Information Center

Philip, Donald

2005-01-01

In this article Donald Philip describes Knowledge Building, a pedagogy based on the way research organizations function. The global economy, Philip argues, is driving a shift from older, industrial models to the model of the business as a learning organization. The cognitive patterns of today's Net Generation students, formed by lifetime exposure…

Greening the Net Generation: Outdoor Adult Learning in the Digital Age

ERIC Educational Resources Information Center

Walter, Pierre

2013-01-01

Adult learning today takes place primarily within walled classrooms or in other indoor settings, and often in front of various types of digital screens. As adults have adopted the digital technologies and indoor lifestyle attributed to the so-called "Net Generation," we have become detached from contact with the natural world outdoors.…

Learning the Norm of Internality: NetNorm, a Connectionist Model

ERIC Educational Resources Information Center

Thierry, Bollon; Adeline, Paignon; Pascal, Pansu

2011-01-01

The objective of the present article is to show that connectionist simulations can be used to model some of the socio-cognitive processes underlying the learning of the norm of internality. For our simulations, we developed a connectionist model which we called NetNorm (based on Dual-Network formalism). This model is capable of simulating the…

LiteNet: Lightweight Neural Network for Detecting Arrhythmias at Resource-Constrained Mobile Devices.

PubMed

He, Ziyang; Zhang, Xiaoqing; Cao, Yangjie; Liu, Zhi; Zhang, Bo; Wang, Xiaoyan

2018-04-17

By running applications and services closer to the user, edge processing provides many advantages, such as short response time and reduced network traffic. Deep-learning based algorithms provide significantly better performances than traditional algorithms in many fields but demand more resources, such as higher computational power and more memory. Hence, designing deep learning algorithms that are more suitable for resource-constrained mobile devices is vital. In this paper, we build a lightweight neural network, termed LiteNet which uses a deep learning algorithm design to diagnose arrhythmias, as an example to show how we design deep learning schemes for resource-constrained mobile devices. Compare to other deep learning models with an equivalent accuracy, LiteNet has several advantages. It requires less memory, incurs lower computational cost, and is more feasible for deployment on resource-constrained mobile devices. It can be trained faster than other neural network algorithms and requires less communication across different processing units during distributed training. It uses filters of heterogeneous size in a convolutional layer, which contributes to the generation of various feature maps. The algorithm was tested using the MIT-BIH electrocardiogram (ECG) arrhythmia database; the results showed that LiteNet outperforms comparable schemes in diagnosing arrhythmias, and in its feasibility for use at the mobile devices.

LiteNet: Lightweight Neural Network for Detecting Arrhythmias at Resource-Constrained Mobile Devices

PubMed Central

Zhang, Xiaoqing; Cao, Yangjie; Liu, Zhi; Zhang, Bo; Wang, Xiaoyan

2018-01-01

By running applications and services closer to the user, edge processing provides many advantages, such as short response time and reduced network traffic. Deep-learning based algorithms provide significantly better performances than traditional algorithms in many fields but demand more resources, such as higher computational power and more memory. Hence, designing deep learning algorithms that are more suitable for resource-constrained mobile devices is vital. In this paper, we build a lightweight neural network, termed LiteNet which uses a deep learning algorithm design to diagnose arrhythmias, as an example to show how we design deep learning schemes for resource-constrained mobile devices. Compare to other deep learning models with an equivalent accuracy, LiteNet has several advantages. It requires less memory, incurs lower computational cost, and is more feasible for deployment on resource-constrained mobile devices. It can be trained faster than other neural network algorithms and requires less communication across different processing units during distributed training. It uses filters of heterogeneous size in a convolutional layer, which contributes to the generation of various feature maps. The algorithm was tested using the MIT-BIH electrocardiogram (ECG) arrhythmia database; the results showed that LiteNet outperforms comparable schemes in diagnosing arrhythmias, and in its feasibility for use at the mobile devices. PMID:29673171

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

NASA Astrophysics Data System (ADS)

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

2014-12-01

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

Neural-Network-Development Program

NASA Technical Reports Server (NTRS)

Phillips, Todd A.

1993-01-01

NETS, software tool for development and evaluation of neural networks, provides simulation of neural-network algorithms plus computing environment for development of such algorithms. Uses back-propagation learning method for all of networks it creates. Enables user to customize patterns of connections between layers of network. Also provides features for saving, during learning process, values of weights, providing more-precise control over learning process. Written in ANSI standard C language. Machine-independent version (MSC-21588) includes only code for command-line-interface version of NETS 3.0.

NetEnquiry--A Competitive Mobile Learning Approach for the Banking Sector

ERIC Educational Resources Information Center

Beutner, Marc; Teine, Matthias; Gebbe, Marcel; Fortmann, Lara Melissa

2016-01-01

Initial and further education in the banking sector is becoming more and more important due to the fact that the regulations and the complexity in world of work and an international banking scene is increasing. In this article we provide the structures of and information on NetEnquiry, an innovative mobile learning environment in this field,…

Shadow netWorkspace: An Open Source Intranet for Learning Communities

ERIC Educational Resources Information Center

Laffey, James M.; Musser, Dale

2006-01-01

Shadow netWorkspace (SNS) is a web application system that allows a school or any type of community to establish an intranet with network workspaces for all members and groups. The goal of SNS has been to make it easy for schools and other educational organizations to provide network services in support of implementing a learning community. SNS is…

NETS - A NEURAL NETWORK DEVELOPMENT TOOL, VERSION 3.0 (MACINTOSH VERSION)

NASA Technical Reports Server (NTRS)

Phillips, T. A.

1994-01-01

NETS, A Tool for the Development and Evaluation of Neural Networks, provides a simulation of Neural Network algorithms plus an environment for developing such algorithms. Neural Networks are a class of systems modeled after the human brain. Artificial Neural Networks are formed from hundreds or thousands of simulated neurons, connected to each other in a manner similar to brain neurons. Problems which involve pattern matching readily fit the class of problems which NETS is designed to solve. NETS uses the back propagation learning method for all of the networks which it creates. The nodes of a network are usually grouped together into clumps called layers. Generally, a network will have an input layer through which the various environment stimuli are presented to the network, and an output layer for determining the network's response. The number of nodes in these two layers is usually tied to some features of the problem being solved. Other layers, which form intermediate stops between the input and output layers, are called hidden layers. NETS allows the user to customize the patterns of connections between layers of a network. NETS also provides features for saving the weight values of a network during the learning process, which allows for more precise control over the learning process. NETS is an interpreter. Its method of execution is the familiar "read-evaluate-print" loop found in interpreted languages such as BASIC and LISP. The user is presented with a prompt which is the simulator's way of asking for input. After a command is issued, NETS will attempt to evaluate the command, which may produce more prompts requesting specific information or an error if the command is not understood. The typical process involved when using NETS consists of translating the problem into a format which uses input/output pairs, designing a network configuration for the problem, and finally training the network with input/output pairs until an acceptable error is reached. NETS allows the user to generate C code to implement the network loaded into the system. This permits the placement of networks as components, or subroutines, in other systems. In short, once a network performs satisfactorily, the Generate C Code option provides the means for creating a program separate from NETS to run the network. Other features: files may be stored in binary or ASCII format; multiple input propagation is permitted; bias values may be included; capability to scale data without writing scaling code; quick interactive testing of network from the main menu; and several options that allow the user to manipulate learning efficiency. NETS is written in ANSI standard C language to be machine independent. The Macintosh version (MSC-22108) includes code for both a graphical user interface version and a command line interface version. The machine independent version (MSC-21588) only includes code for the command line interface version of NETS 3.0. The Macintosh version requires a Macintosh II series computer and has been successfully implemented under System 7. Four executables are included on these diskettes, two for floating point operations and two for integer arithmetic. It requires Think C 5.0 to compile. A minimum of 1Mb of RAM is required for execution. Sample input files and executables for both the command line version and the Macintosh user interface version are provided on the distribution medium. The Macintosh version is available on a set of three 3.5 inch 800K Macintosh format diskettes. The machine independent version has been successfully implemented on an IBM PC series compatible running MS-DOS, a DEC VAX running VMS, a SunIPC running SunOS, and a CRAY Y-MP running UNICOS. Two executables for the IBM PC version are included on the MS-DOS distribution media, one compiled for floating point operations and one for integer arithmetic. The machine independent version is available on a set of three 5.25 inch 360K MS-DOS format diskettes (standard distribution medium) or a .25 inch streaming magnetic tape cartridge in UNIX tar format. NETS was developed in 1989 and updated in 1992. IBM PC is a registered trademark of International Business Machines. MS-DOS is a registered trademark of Microsoft Corporation. DEC, VAX, and VMS are trademarks of Digital Equipment Corporation. SunIPC and SunOS are trademarks of Sun Microsystems, Inc. CRAY Y-MP and UNICOS are trademarks of Cray Research, Inc.

NETS - A NEURAL NETWORK DEVELOPMENT TOOL, VERSION 3.0 (MACHINE INDEPENDENT VERSION)

NASA Technical Reports Server (NTRS)

Baffes, P. T.

1994-01-01

NETS, A Tool for the Development and Evaluation of Neural Networks, provides a simulation of Neural Network algorithms plus an environment for developing such algorithms. Neural Networks are a class of systems modeled after the human brain. Artificial Neural Networks are formed from hundreds or thousands of simulated neurons, connected to each other in a manner similar to brain neurons. Problems which involve pattern matching readily fit the class of problems which NETS is designed to solve. NETS uses the back propagation learning method for all of the networks which it creates. The nodes of a network are usually grouped together into clumps called layers. Generally, a network will have an input layer through which the various environment stimuli are presented to the network, and an output layer for determining the network's response. The number of nodes in these two layers is usually tied to some features of the problem being solved. Other layers, which form intermediate stops between the input and output layers, are called hidden layers. NETS allows the user to customize the patterns of connections between layers of a network. NETS also provides features for saving the weight values of a network during the learning process, which allows for more precise control over the learning process. NETS is an interpreter. Its method of execution is the familiar "read-evaluate-print" loop found in interpreted languages such as BASIC and LISP. The user is presented with a prompt which is the simulator's way of asking for input. After a command is issued, NETS will attempt to evaluate the command, which may produce more prompts requesting specific information or an error if the command is not understood. The typical process involved when using NETS consists of translating the problem into a format which uses input/output pairs, designing a network configuration for the problem, and finally training the network with input/output pairs until an acceptable error is reached. NETS allows the user to generate C code to implement the network loaded into the system. This permits the placement of networks as components, or subroutines, in other systems. In short, once a network performs satisfactorily, the Generate C Code option provides the means for creating a program separate from NETS to run the network. Other features: files may be stored in binary or ASCII format; multiple input propagation is permitted; bias values may be included; capability to scale data without writing scaling code; quick interactive testing of network from the main menu; and several options that allow the user to manipulate learning efficiency. NETS is written in ANSI standard C language to be machine independent. The Macintosh version (MSC-22108) includes code for both a graphical user interface version and a command line interface version. The machine independent version (MSC-21588) only includes code for the command line interface version of NETS 3.0. The Macintosh version requires a Macintosh II series computer and has been successfully implemented under System 7. Four executables are included on these diskettes, two for floating point operations and two for integer arithmetic. It requires Think C 5.0 to compile. A minimum of 1Mb of RAM is required for execution. Sample input files and executables for both the command line version and the Macintosh user interface version are provided on the distribution medium. The Macintosh version is available on a set of three 3.5 inch 800K Macintosh format diskettes. The machine independent version has been successfully implemented on an IBM PC series compatible running MS-DOS, a DEC VAX running VMS, a SunIPC running SunOS, and a CRAY Y-MP running UNICOS. Two executables for the IBM PC version are included on the MS-DOS distribution media, one compiled for floating point operations and one for integer arithmetic. The machine independent version is available on a set of three 5.25 inch 360K MS-DOS format diskettes (standard distribution medium) or a .25 inch streaming magnetic tape cartridge in UNIX tar format. NETS was developed in 1989 and updated in 1992. IBM PC is a registered trademark of International Business Machines. MS-DOS is a registered trademark of Microsoft Corporation. DEC, VAX, and VMS are trademarks of Digital Equipment Corporation. SunIPC and SunOS are trademarks of Sun Microsystems, Inc. CRAY Y-MP and UNICOS are trademarks of Cray Research, Inc.

Sleep and the Price of Plasticity: From Synaptic and Cellular Homeostasis to Memory Consolidation and Integration

PubMed Central

Tononi, Giulio; Cirelli, Chiara

2014-01-01

Summary Sleep is universal, tightly regulated, and its loss impairs cognition. But why does the brain need to disconnect from the environment for hours every day? The synaptic homeostasis hypothesis (SHY) proposes that sleep is the price the brain pays for plasticity. During a waking episode, learning statistical regularities about the current environment requires strengthening connections throughout the brain. This increases cellular needs for energy and supplies, decreases signal-to-noise ratios, and saturates learning. During sleep, spontaneous activity renormalizes net synaptic strength and restores cellular homeostasis. Activity-dependent down-selection of synapses can also explain the benefits of sleep on memory acquisition, consolidation, and integration. This happens through the off-line, comprehensive sampling of statistical regularities incorporated in neuronal circuits over a lifetime. This review considers the rationale and evidence for SHY and points to open issues related to sleep and plasticity. PMID:24411729

Sleep and the price of plasticity: from synaptic and cellular homeostasis to memory consolidation and integration.

PubMed

Tononi, Giulio; Cirelli, Chiara

2014-01-08

Sleep is universal, tightly regulated, and its loss impairs cognition. But why does the brain need to disconnect from the environment for hours every day? The synaptic homeostasis hypothesis (SHY) proposes that sleep is the price the brain pays for plasticity. During a waking episode, learning statistical regularities about the current environment requires strengthening connections throughout the brain. This increases cellular needs for energy and supplies, decreases signal-to-noise ratios, and saturates learning. During sleep, spontaneous activity renormalizes net synaptic strength and restores cellular homeostasis. Activity-dependent down-selection of synapses can also explain the benefits of sleep on memory acquisition, consolidation, and integration. This happens through the offline, comprehensive sampling of statistical regularities incorporated in neuronal circuits over a lifetime. This Perspective considers the rationale and evidence for SHY and points to open issues related to sleep and plasticity. Copyright © 2014 Elsevier Inc. All rights reserved.

What Students Produce from the Net: Assessing Their Work.

ERIC Educational Resources Information Center

1996

Two papers that examine how to assess students' work in this age of electronic information sources include: "Students on the Net: Enhancing Learning through Authentic Assessment" (James Henri); and "Assessing Students' Work from the Net: An Impossible Dream?" (Paul Lupton) The first paper notes the problem of parents doing…

Status of Net Metering: Assessing the Potential to Reach Program Caps

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

Heeter, J.; Gelman, R.; Bird, L.

2014-09-01

Several states are addressing the issue of net metering program caps, which limit the total amount of net metered generating capacity that can be installed in a state or utility service territory. In this analysis, we examine net metering caps to gain perspective on how long net metering will be available in various jurisdictions under current policies. We also surveyed state practices and experience to understand important policy design considerations.

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

Heeter, J.; Bird, L.; Gelman, R.

Several states are addressing the issue of net metering program caps, which limit the total amount of net metered generating capacity that can be installed in a state or utility service territory. In this analysis, we examine net metering caps to gain perspective on how long net metering will be available in various jurisdictions under current policies. We also surveyed state practices and experience to understand important policy design considerations.

NETS

NASA Technical Reports Server (NTRS)

Baffes, Paul T.

1993-01-01

NETS development tool provides environment for simulation and development of neural networks - computer programs that "learn" from experience. Written in ANSI standard C, program allows user to generate C code for implementation of neural network.

Learning Petri net models of non-linear gene interactions.

PubMed

Mayo, Michael

2005-10-01

Understanding how an individual's genetic make-up influences their risk of disease is a problem of paramount importance. Although machine-learning techniques are able to uncover the relationships between genotype and disease, the problem of automatically building the best biochemical model or "explanation" of the relationship has received less attention. In this paper, I describe a method based on random hill climbing that automatically builds Petri net models of non-linear (or multi-factorial) disease-causing gene-gene interactions. Petri nets are a suitable formalism for this problem, because they are used to model concurrent, dynamic processes analogous to biochemical reaction networks. I show that this method is routinely able to identify perfect Petri net models for three disease-causing gene-gene interactions recently reported in the literature.

Neuromorphic learning of continuous-valued mappings in the presence of noise: Application to real-time adaptive control

NASA Technical Reports Server (NTRS)

Troudet, Terry; Merrill, Walter C.

1989-01-01

The ability of feed-forward neural net architectures to learn continuous-valued mappings in the presence of noise is demonstrated in relation to parameter identification and real-time adaptive control applications. Factors and parameters influencing the learning performance of such nets in the presence of noise are identified. Their effects are discussed through a computer simulation of the Back-Error-Propagation algorithm by taking the example of the cart-pole system controlled by a nonlinear control law. Adequate sampling of the state space is found to be essential for canceling the effect of the statistical fluctuations and allowing learning to take place.

LabNet: Toward A Community of Practice. Technology in Education Series.

ERIC Educational Resources Information Center

Ruopp, Richard, Ed.; And Others

Many educators advocate the use of projects in the science classroom. This document describes an effort (LabNet) that has successfully implemented a program that allows students to learn science using projects. Chapter 1, "An Introduction to LabNet" (Richard Ruopp, Megham Pfister), provides an initial framework for understanding the…

Application of neural nets in structural optimization

NASA Technical Reports Server (NTRS)

Berke, Laszlo; Hajela, Prabhat

1993-01-01

The biological motivation for Artificial Neural Net developments is briefly discussed, and the most popular paradigm, the feedforward supervised learning net with error back propagation training algorithm, is introduced. Possible approaches for utilization in structural optimization is illustrated through simple examples. Other currently ongoing developments for application in structural mechanics are also mentioned.

Confronting the Technological Pedagogical Knowledge of Finnish Net Generation Student Teachers

ERIC Educational Resources Information Center

Valtonen, Teemu; Pontinen, Susanna; Kukkonen, Jari; Dillon, Patrick; Vaisanen, Pertti; Hacklin, Stina

2011-01-01

The research reported here is concerned with a critical examination of some of the assumptions concerning the "Net Generation" capabilities of 74 first-year student teachers in a Finnish university. There are assumptions that: (i) Net Generation students are adept at learning through discovery and thinking in a hypertext-like manner…

Evaluation of the Texas Nutrition Education and Training Program for Federal Fiscal Year 1997.

ERIC Educational Resources Information Center

Ahmad, Mahassen

This report summarizes the results of the 1997 Texas Nutrition Education and Training (NET) program, one of the U.S. Department of Agriculture's Child Nutrition Programs. NET provides nutrition education and instructional resources for children and key individuals in their learning environment. NET's target population includes parents or…

Net Neutrality and Its Implications to Online Learning

ERIC Educational Resources Information Center

Yamagata-Lynch, Lisa C.; Despande, Deepa R.; Do, Jaewoo; Garty, Erin; Mastrogiovanni, Jason M.; Teagu, Stephanie J.

2017-01-01

In this article, we studied net neutrality as a complex sociocultural phenomenon that can affect the works of distance education scholars and online learners. We decided to take part in this research because many distance education scholars and learners take net neutrality for granted. We engaged in a qualitative investigation of US public…

Mathematics Hiding in the Nets for a Cube

ERIC Educational Resources Information Center

Jeon, Kyungsoon

2009-01-01

Whether they are third graders or teacher candidates, students can learn about perimeter and area while having fun manipulating two-dimensional figures into three-dimensional objects. In this article, the author describes a common mathematical activity for geometry students by creating nets for a cube. By making connections between nets in two…

Scaling Deep Learning Workloads: NVIDIA DGX-1/Pascal and Intel Knights Landing

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

Gawande, Nitin A.; Landwehr, Joshua B.; Daily, Jeffrey A.

Deep Learning (DL) algorithms have become ubiquitous in data analytics. As a result, major computing vendors --- including NVIDIA, Intel, AMD and IBM --- have architectural road-maps influenced by DL workloads. Furthermore, several vendors have recently advertised new computing products as accelerating DL workloads. Unfortunately, it is difficult for data scientists to quantify the potential of these different products. This paper provides a performance and power analysis of important DL workloads on two major parallel architectures: NVIDIA DGX-1 (eight Pascal P100 GPUs interconnected with NVLink) and Intel Knights Landing (KNL) CPUs interconnected with Intel Omni-Path. Our evaluation consists of amore » cross section of convolutional neural net workloads: CifarNet, CaffeNet, AlexNet and GoogleNet topologies using the Cifar10 and ImageNet datasets. The workloads are vendor optimized for each architecture. GPUs provide the highest overall raw performance. Our analysis indicates that although GPUs provide the highest overall performance, the gap can close for some convolutional networks; and KNL can be competitive when considering performance/watt. Furthermore, NVLink is critical to GPU scaling.« less

Values in the Net Neutrality Debate: Applying Content Analysis to Testimonies from Public Hearings

ERIC Educational Resources Information Center

Cheng, An-Shou

2012-01-01

The Net neutrality debate is an important telecommunications policy issue that closely tied to technological innovation, economic development, and information access. Existing studies on Net neutrality have focused primarily on technological requirements, economic analysis, and regulatory justifications. Since values, technology, and policy are…

Using deep learning for detecting gender in adult chest radiographs

NASA Astrophysics Data System (ADS)

Xue, Zhiyun; Antani, Sameer; Long, L. Rodney; Thoma, George R.

2018-03-01

In this paper, we present a method for automatically identifying the gender of an imaged person using their frontal chest x-ray images. Our work is motivated by the need to determine missing gender information in some datasets. The proposed method employs the technique of convolutional neural network (CNN) based deep learning and transfer learning to overcome the challenge of developing handcrafted features in limited data. Specifically, the method consists of four main steps: pre-processing, CNN feature extractor, feature selection, and classifier. The method is tested on a combined dataset obtained from several sources with varying acquisition quality resulting in different pre-processing steps that are applied for each. For feature extraction, we tested and compared four CNN architectures, viz., AlexNet, VggNet, GoogLeNet, and ResNet. We applied a feature selection technique, since the feature length is larger than the number of images. Two popular classifiers: SVM and Random Forest, are used and compared. We evaluated the classification performance by cross-validation and used seven performance measures. The best performer is the VggNet-16 feature extractor with the SVM classifier, with accuracy of 86.6% and ROC Area being 0.932 for 5-fold cross validation. We also discuss several misclassified cases and describe future work for performance improvement.

Framing U-Net via Deep Convolutional Framelets: Application to Sparse-View CT.

PubMed

Han, Yoseob; Ye, Jong Chul

2018-06-01

X-ray computed tomography (CT) using sparse projection views is a recent approach to reduce the radiation dose. However, due to the insufficient projection views, an analytic reconstruction approach using the filtered back projection (FBP) produces severe streaking artifacts. Recently, deep learning approaches using large receptive field neural networks such as U-Net have demonstrated impressive performance for sparse-view CT reconstruction. However, theoretical justification is still lacking. Inspired by the recent theory of deep convolutional framelets, the main goal of this paper is, therefore, to reveal the limitation of U-Net and propose new multi-resolution deep learning schemes. In particular, we show that the alternative U-Net variants such as dual frame and tight frame U-Nets satisfy the so-called frame condition which makes them better for effective recovery of high frequency edges in sparse-view CT. Using extensive experiments with real patient data set, we demonstrate that the new network architectures provide better reconstruction performance.

Net Warrior D10 Technology Report: Airborne Early Warning and Control (AEW&C) and Data Link Nodes

DTIC Science & Technology

2012-04-01

ADO ) approach to implementing Network Centric Warfare (NCW) through ‘learning by doing’. Net Warrior was conceived to address, through... frameworks are able to satisfy design needs of applications to produce stable mission and net centric systems. NW-D10 employed a SOA approach to...UNCLASSIFIED Net Warrior D10 Technology Report: Airborne Early Warning and Control (AEW&C) and Data Link Nodes Derek Dominish

Deep learning

NASA Astrophysics Data System (ADS)

Lecun, Yann; Bengio, Yoshua; Hinton, Geoffrey

2015-05-01

Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.

Deep learning.

PubMed

LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey

2015-05-28

Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.

78 FR 51709 - Notice of Availability of Draft Environmental Assessment on the Effects of Issuing an Incidental...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-08-21

... stake at one or both ends of the nets). All comments received will become part of the public record and... one of the above methods to ensure that we receive, document, and consider them. Comments sent by any... gill nets (i.e., passive gill net sets deployed with an anchor or stake at one or both ends of the nets...

Directory of ICT Resources for Teaching and Learning of Science, Mathematics and Language

ERIC Educational Resources Information Center

Abdon, Buenafe, Comp.; Henly, John, Comp.; Jeffrey, Marilyn, Comp.

2006-01-01

The UNESCO SchoolNet project, "Strengthening ICT in Schools and SchoolNet Project in ASEAN Setting", was initiated to assist teachers to integrate ICT into teaching and to facilitate participation of teachers and students in the Asia-Pacific region in SchoolNet telecollaboration activities. The project was launched in July 2003 and…

ExplaNet: A Collaborative Learning Tool and Hybrid Recommender System for Student-Authored Explanations

ERIC Educational Resources Information Center

Masters, Jessica; Madhyastha, Tara; Shakouri, Ali

2008-01-01

ExplaNet is a web-based, anonymous, asynchronous explanation-sharing network. Instructors post questions to the network and students submit explanatory answers. Students then view and rank the explanations submitted by their peers before optionally resubmitting a final and revised answer. Three classroom evaluations of ExplaNet showed that by…

Jamming the Phone Lines: Pencils, Notebooks, and Modems (Computers in the Classroom).

ERIC Educational Resources Information Center

Holvig, Kenneth C.

1989-01-01

Describes how BreadNet (a national computer network of English teachers) has come to dominate the routine of a high school class. Notes that BreadNet gives students new motivation to write, inquire, and learn. Describes classroom electronic writing exchanges and an electronic writers' workshop which posted essays on BreadNet. (RS)

The QuarkNet Collaboration: How "Doing Science" is Changing Science Education

NASA Astrophysics Data System (ADS)

Whelan, K.

2004-12-01

QuarkNet is a national initiative to involve high-school teachers and their students in real scientific research. Students and teachers assist in seeking to resolve some of the mysteries about the structure of matter and the fundamental forces of nature It is supported by the Department and Energy and the National Science Foundation. This long-term project, beginning its sixth year of implementation, has provided a successful framework that might be adapted to similar endeavors. It is an international collaboration of universities, high schools and research centers including CERN in Switzerland, and Fermilab, LBNL, and SLAC in the United States. The goals of this program include the involvement of students and teachers in authentic scientific research projects. By actually "doing science", they gain first hand knowledge of the research procedure and the inquiry method of learning. Teachers increase their content knowledge and enhance their teaching skills by solving scientific research problems through the inquiry method of learning. Students involved in this program learn fundamental physics and research-based skills through the analysis of real data. Particle physicists also benefit by being exposed to some of the current issues in science education. Through an understanding of National Science Education Standards, physicist-mentors are made aware of the needs of local science education and gain a better grasp of age appropriate content. The QuarkNet program was developed while consulting with research physicists throughout the United States. There are three main program areas that have been established-teacher research experiences, teacher development programs, and an online resource that makes available numerous inquiry-based activities. Select teachers are given eight-week appointments allowing them to gain first hand experience as a part of a scientific research team. Those teachers become lead teachers during the following summer and, along with physicist mentors, work with other teachers on a short research scenario or activity over a period of several weeks. The scenarios can then be adapted for classroom use at virtually any level. The QuarkNet website provides a wide variety of resources for teacher and student use including- samples of experimental data for use in inquiry based activities, venues for communication and collaboration between students, teachers and physicists, student publication areas where ideas can be exchanged, and numerous other resources, activities, and simulations. Currently, the QuarkNet program involves over 50 research institutions and hundreds of teachers. This year, we have also added a student research component at several of the centers. This component will be expanded in the coming years so that many more students will have the opportunity to become an active part and contributing member of a scientific research team.

Case-based explanation of non-case-based learning methods.

PubMed Central

Caruana, R.; Kangarloo, H.; Dionisio, J. D.; Sinha, U.; Johnson, D.

1999-01-01

We show how to generate case-based explanations for non-case-based learning methods such as artificial neural nets or decision trees. The method uses the trained model (e.g., the neural net or the decision tree) as a distance metric to determine which cases in the training set are most similar to the case that needs to be explained. This approach is well suited to medical domains, where it is important to understand predictions made by complex machine learning models, and where training and clinical practice makes users adept at case interpretation. PMID:10566351

Hacking Culture.

ERIC Educational Resources Information Center

Vigh, Pia

The paper presents the model behind net2art, a joint Nordic project of creating a platform for Nordic net art. The projects background and scope, organization, impact and experiences, funding structures, and copyright issues are covered. The paper argues that museums do not have a natural role in the distribution of net art (i.e., art that is made…

The Net Neutrality Debate: The Basics

ERIC Educational Resources Information Center

Greenfield, Rich

2006-01-01

Rich Greenfield examines the basics of today's net neutrality debate that is likely to be an ongoing issue for society. Greenfield states the problems inherent in the definition of "net neutrality" used by Common Cause: "Network neutrality is the principle that Internet users should be able to access any web content they choose and…

Simple Activities for Powerful Impact

NASA Astrophysics Data System (ADS)

LaConte, K.; Shupla, C. B.; Dusenbery, P.; Harold, J. B.; Holland, A.

2016-12-01

STEM education is having a transformational impact on libraries across the country. The STAR Library Education Network (STAR_Net) provides free Science-Technology Activities & Resources that are helping libraries to engage their communities in STEM learning experiences. Hear the results of a national 2015 survey of library and STEM professionals and learn what STEM programming is currently in place in public libraries and how libraries approach and implement STEM programs. Experience hands-on space science activities that are being used in library programs with multiple age groups. Through these hands-on activities, learners explore the nature of science and employ science and engineering practices, including developing and using models, planning and carrying out investigations, and engaging in argument from evidence (NGSS Lead States, 2013). Learn how STAR_Net can help you print (free!) mini-exhibits and educator guides. Join STAR_Net's online community and access STEM resources and webinars to work with libraries in your local community.

Automated Pathogenesis-Based Diagnosis of Lumbar Neural Foraminal Stenosis via Deep Multiscale Multitask Learning.

PubMed

Han, Zhongyi; Wei, Benzheng; Leung, Stephanie; Nachum, Ilanit Ben; Laidley, David; Li, Shuo

2018-02-15

Pathogenesis-based diagnosis is a key step to prevent and control lumbar neural foraminal stenosis (LNFS). It conducts both early diagnosis and comprehensive assessment by drawing crucial pathological links between pathogenic factors and LNFS. Automated pathogenesis-based diagnosis would simultaneously localize and grade multiple spinal organs (neural foramina, vertebrae, intervertebral discs) to diagnose LNFS and discover pathogenic factors. The automated way facilitates planning optimal therapeutic schedules and relieving clinicians from laborious workloads. However, no successful work has been achieved yet due to its extreme challenges since 1) multiple targets: each lumbar spine has at least 17 target organs, 2) multiple scales: each type of target organ has structural complexity and various scales across subjects, and 3) multiple tasks, i.e., simultaneous localization and diagnosis of all lumbar organs, are extremely difficult than individual tasks. To address these huge challenges, we propose a deep multiscale multitask learning network (DMML-Net) integrating a multiscale multi-output learning and a multitask regression learning into a fully convolutional network. 1) DMML-Net merges semantic representations to reinforce the salience of numerous target organs. 2) DMML-Net extends multiscale convolutional layers as multiple output layers to boost the scale-invariance for various organs. 3) DMML-Net joins a multitask regression module and a multitask loss module to prompt the mutual benefit between tasks. Extensive experimental results demonstrate that DMML-Net achieves high performance (0.845 mean average precision) on T1/T2-weighted MRI scans from 200 subjects. This endows our method an efficient tool for clinical LNFS diagnosis.

Expanding the Reach of Continuing Educational Offerings Through a Web-Based Virtual Network: The Experience of InspireNet.

PubMed

Frisch, Noreen C; Atherton, Pat; Borycki, Elizabeth M; Mickelson, Grace; Black, Agnes; Novak Lauscher, Helen; Cordeiro, Jennifer

2017-01-01

Virtual platforms using webinars, e-posters, e-newsletters, wikis and blogs connect people who have common interests in new ways. When those individuals are healthcare providers, a professional network that operates on a virtual platform can support their needs for learning, professional development and information currency. The practice of e-learning for continuing professional development is emerging , particularly in nursing where shift work shift inhibits their ability to attend conferences and classes. This article reports the experience of the InspireNet network that provided e-learning models to: 1) provide opportunities for healthcare providers to organize themselves into learning communities through development of electronic communities of practice; 2) support learning on demand; and 3) dramatically increase the reach of educational offerings.

The Relationships among Group Size, Participation, and Performance of Programming Language Learning Supported with Online Forums

ERIC Educational Resources Information Center

Shaw, Ruey-Shiang

2013-01-01

This study examined the relationships among group size, participation, and learning performance factors when learning a programming language in a computer-supported collaborative learning (CSCL) context. An online forum was used as the CSCL environment for learning the Microsoft ASP.NET programming language. The collaborative-learning experiment…

Automation of Presentation Record Production Based on Rich-Media Technology Using SNT Petri Nets Theory.

PubMed

Martiník, Ivo

2015-01-01

Rich-media describes a broad range of digital interactive media that is increasingly used in the Internet and also in the support of education. Last year, a special pilot audiovisual lecture room was built as a part of the MERLINGO (MEdia-rich Repository of LearnING Objects) project solution. It contains all the elements of the modern lecture room determined for the implementation of presentation recordings based on the rich-media technologies and their publication online or on-demand featuring the access of all its elements in the automated mode including automatic editing. Property-preserving Petri net process algebras (PPPA) were designed for the specification and verification of the Petri net processes. PPPA does not need to verify the composition of the Petri net processes because all their algebraic operators preserve the specified set of the properties. These original PPPA are significantly generalized for the newly introduced class of the SNT Petri process and agent nets in this paper. The PLACE-SUBST and ASYNC-PROC algebraic operators are defined for this class of Petri nets and their chosen properties are proved. The SNT Petri process and agent nets theory were significantly applied at the design, verification, and implementation of the programming system ensuring the pilot audiovisual lecture room functionality.

Automation of Presentation Record Production Based on Rich-Media Technology Using SNT Petri Nets Theory

PubMed Central

Martiník, Ivo

2015-01-01

Rich-media describes a broad range of digital interactive media that is increasingly used in the Internet and also in the support of education. Last year, a special pilot audiovisual lecture room was built as a part of the MERLINGO (MEdia-rich Repository of LearnING Objects) project solution. It contains all the elements of the modern lecture room determined for the implementation of presentation recordings based on the rich-media technologies and their publication online or on-demand featuring the access of all its elements in the automated mode including automatic editing. Property-preserving Petri net process algebras (PPPA) were designed for the specification and verification of the Petri net processes. PPPA does not need to verify the composition of the Petri net processes because all their algebraic operators preserve the specified set of the properties. These original PPPA are significantly generalized for the newly introduced class of the SNT Petri process and agent nets in this paper. The PLACE-SUBST and ASYNC-PROC algebraic operators are defined for this class of Petri nets and their chosen properties are proved. The SNT Petri process and agent nets theory were significantly applied at the design, verification, and implementation of the programming system ensuring the pilot audiovisual lecture room functionality. PMID:26258164

AccrualNet: Addressing Low Accrual Via a Knowledge-Based, Community of Practice Platform.

PubMed

Massett, Holly A; Parreco, Linda K; Padberg, Rose Mary; Richmond, Ellen S; Rienzo, Marie E; Leonard, Colleen E Ryan; Quesenbery, Whitney; Killiam, H William; Johnson, Lenora E; Dilts, David M

2011-11-01

Present the design and initial evaluation of a unique, Web-enabled platform for the development of a community of practice around issues of oncology clinical trial accrual. The National Cancer Institute (NCI) conducted research with oncology professionals to identify unmet clinical trial accrual needs in the field. In response, a comprehensive platform for accrual resources, AccrualNet, was created by using an agile development process, storyboarding, and user testing. Literature and resource searches identified relevant content to populate the site. Descriptive statistics were tracked for resource and site usage. Use cases were defined to support implementation. ACCRUALNET HAS FIVE LEVELS: (1) clinical trial macrostages (prestudy, active study, and poststudy); (2) substages (developing a protocol, selecting a trial, preparing to open, enrolling patients, managing the trial, retaining participants, and lessons learned); (3) strategies for each substage; (4) multiple activities for each strategy; and (5) multiple resources for each activity. Since its launch, AccrualNet has had more than 45,000 page views, with the Tools & Resources, Conversations, and Training sections being the most viewed. Total resources have increased 69%, to 496 items. Analysis of articles in the site reveals that 22% are from two journals and 46% of the journals supplied a single article. To date, there are 29 conversations with 43 posts. Four use cases are discussed. AccrualNet represents a unique, centralized comprehensive-solution platform to systematically capture accrual knowledge for all stages of a clinical trial. It is designed to foster a community of practice by encouraging users to share additional strategies, resources, and ideas.

A new neural net approach to robot 3D perception and visuo-motor coordination

NASA Technical Reports Server (NTRS)

Lee, Sukhan

1992-01-01

A novel neural network approach to robot hand-eye coordination is presented. The approach provides a true sense of visual error servoing, redundant arm configuration control for collision avoidance, and invariant visuo-motor learning under gazing control. A 3-D perception network is introduced to represent the robot internal 3-D metric space in which visual error servoing and arm configuration control are performed. The arm kinematic network performs the bidirectional association between 3-D space arm configurations and joint angles, and enforces the legitimate arm configurations. The arm kinematic net is structured by a radial-based competitive and cooperative network with hierarchical self-organizing learning. The main goal of the present work is to demonstrate that the neural net representation of the robot 3-D perception net serves as an important intermediate functional block connecting robot eyes and arms.

Kernelized Elastic Net Regularization: Generalization Bounds, and Sparse Recovery.

PubMed

Feng, Yunlong; Lv, Shao-Gao; Hang, Hanyuan; Suykens, Johan A K

2016-03-01

Kernelized elastic net regularization (KENReg) is a kernelization of the well-known elastic net regularization (Zou & Hastie, 2005). The kernel in KENReg is not required to be a Mercer kernel since it learns from a kernelized dictionary in the coefficient space. Feng, Yang, Zhao, Lv, and Suykens (2014) showed that KENReg has some nice properties including stability, sparseness, and generalization. In this letter, we continue our study on KENReg by conducting a refined learning theory analysis. This letter makes the following three main contributions. First, we present refined error analysis on the generalization performance of KENReg. The main difficulty of analyzing the generalization error of KENReg lies in characterizing the population version of its empirical target function. We overcome this by introducing a weighted Banach space associated with the elastic net regularization. We are then able to conduct elaborated learning theory analysis and obtain fast convergence rates under proper complexity and regularity assumptions. Second, we study the sparse recovery problem in KENReg with fixed design and show that the kernelization may improve the sparse recovery ability compared to the classical elastic net regularization. Finally, we discuss the interplay among different properties of KENReg that include sparseness, stability, and generalization. We show that the stability of KENReg leads to generalization, and its sparseness confidence can be derived from generalization. Moreover, KENReg is stable and can be simultaneously sparse, which makes it attractive theoretically and practically.

ChemNet: A Transferable and Generalizable Deep Neural Network for Small-Molecule Property Prediction

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

Goh, Garrett B.; Siegel, Charles M.; Vishnu, Abhinav

With access to large datasets, deep neural networks through representation learning have been able to identify patterns from raw data, achieving human-level accuracy in image and speech recognition tasks. However, in chemistry, availability of large standardized and labelled datasets is scarce, and with a multitude of chemical properties of interest, chemical data is inherently small and fragmented. In this work, we explore transfer learning techniques in conjunction with the existing Chemception CNN model, to create a transferable and generalizable deep neural network for small-molecule property prediction. Our latest model, ChemNet learns in a semi-supervised manner from inexpensive labels computed frommore » the ChEMBL database. When fine-tuned to the Tox21, HIV and FreeSolv dataset, which are 3 separate chemical tasks that ChemNet was not originally trained on, we demonstrate that ChemNet exceeds the performance of existing Chemception models, contemporary MLP models that trains on molecular fingerprints, and it matches the performance of the ConvGraph algorithm, the current state-of-the-art. Furthermore, as ChemNet has been pre-trained on a large diverse chemical database, it can be used as a universal “plug-and-play” deep neural network, which accelerates the deployment of deep neural networks for the prediction of novel small-molecule chemical properties.« less

Counterfactuals and Causal Models: Introduction to the Special Issue

ERIC Educational Resources Information Center

Sloman, Steven A.

2013-01-01

Judea Pearl won the 2010 Rumelhart Prize in computational cognitive science due to his seminal contributions to the development of Bayes nets and causal Bayes nets, frameworks that are central to multiple domains of the computational study of mind. At the heart of the causal Bayes nets formalism is the notion of a counterfactual, a representation…

Places to Go: Google's Search Results for "Net Generation"

ERIC Educational Resources Information Center

Downes, Stephen

2007-01-01

In his Places to Go column for a special issue on the Net Generation, Stephen Downes takes an unexpected trip--to Google. According to Downes, Google epitomizes the essence of the Net Generation. Infinitely searchable and adaptable, Google represents the spirit of a generation raised in the world of the Internet, a generation that adapts…

Searching for exoplanets using artificial intelligence

NASA Astrophysics Data System (ADS)

Pearson, Kyle A.; Palafox, Leon; Griffith, Caitlin A.

2018-02-01

In the last decade, over a million stars were monitored to detect transiting planets. Manual interpretation of potential exoplanet candidates is labor intensive and subject to human error, the results of which are difficult to quantify. Here we present a new method of detecting exoplanet candidates in large planetary search projects which, unlike current methods uses a neural network. Neural networks, also called "deep learning" or "deep nets" are designed to give a computer perception into a specific problem by training it to recognize patterns. Unlike past transit detection algorithms deep nets learn to recognize planet features instead of relying on hand-coded metrics that humans perceive as the most representative. Our convolutional neural network is capable of detecting Earth-like exoplanets in noisy time-series data with a greater accuracy than a least-squares method. Deep nets are highly generalizable allowing data to be evaluated from different time series after interpolation without compromising performance. As validated by our deep net analysis of Kepler light curves, we detect periodic transits consistent with the true period without any model fitting. Our study indicates that machine learning will facilitate the characterization of exoplanets in future analysis of large astronomy data sets.

A Study on Mobile Learning as a Learning Style in Modern Research Practice

ERIC Educational Resources Information Center

Joan, D. R. Robert

2013-01-01

Mobile learning is a kind of learning that takes place via a portable handheld electronic device. It also refers to learning via other kinds of mobile devices such as tablet computers, net-books and digital readers. The objective of mobile learning is to provide the learner the ability to assimilate learning anywhere and at anytime. Mobile devices…

The Customers' Perspective: The EdNET 98 Survey of Buyers and Managers of Educational Technology. Constructive Input for the Educational Technology Industry from the EdNET 98 Education Executives Advisory Board.

ERIC Educational Resources Information Center

Craighead, Donna; Bigham, Vicki Smith; Heller, Nelson B.

The EdNET 98 Education Executives Advisory Board, also known as Partners in Education Program (PEP), is a featured activity of the EdNET 98 Conference. Its focus is to bring educators and vendors together to share their perspectives about technology in education and discussion technology-related concerns and issues. This report presents results…

Event identification for KM3NeT/ARCA

NASA Astrophysics Data System (ADS)

Heid, Thomas; KM3NeT Collaboration

2017-09-01

KM3NeT is a large research infrastructure consisting of a network of deep-sea neutrino telescopes. KM3NeT/ARCA will be the instrument detecting high-energy neutrinos with energies above 100 TeV. This instrument gives a new opportunity to observe the neutrino sky with very high angular resolution to be able to detect neutrino point sources. Furthermore it will be possible to probe the flavour composition of neutrino fluxes, and hence production mechanisms, with so-far unreached precision. Neutrinos produce different event topologies in the detector according to their flavour, interaction channel and deposited energy. Machine-learning algorithms are able to learn features of topologies to discriminate them. In previous analyses only two event types were regarded, namely the shower and track topology. With good timing resolution and precise reconstruction algorithms it is possible to separate into more event types, for example the double bang topology produced by tau neutrinos. The final goal is to distinguish all three neutrino flavors as much as possible. To resolve this issue the KM3NeT collaboration uses deep neural networks trained with Monte Carlo events of all neutrino types. This contribution shows the ability of KM3NeT/ARCA to classify events in more than two neutrino event topologies. Furthermore, the borders between detectable classes are shown, such as the minimum distance the tau has to travel before decaying into a tau neutrino to be detected as double bang event.

26 CFR 1.172-4 - Net operating loss carrybacks and net operating loss carryovers.

Code of Federal Regulations, 2010 CFR

2010-04-01

... succeeding taxable years. (2) Periods of less than 12 months. A fractional part of a year which is a taxable... provisions—(1) Years to which loss may be carried—(i) In general. In order to compute the net operating loss... succeeding taxable years which are carrybacks or carryovers to the taxable year in issue. (ii) General rule...

Stealth Learning: Unexpected Learning Opportunities through Games

ERIC Educational Resources Information Center

Sharp, Laura A.

2012-01-01

Educators across the country struggle to create engaging, motivating learning environments for their Net Gen students. These learners expect instant gratification that traditional lectures do not provide. This leaves educators searching for innovative ways to engage students in order to encourage learning. One solution is for educators to use…

Free-access open-source e-learning in comprehensive neurosurgery skills training.

PubMed

Jotwani, Payal; Srivastav, Vinkle; Tripathi, Manjul; Deo, Rama Chandra; Baby, Britty; Damodaran, Natesan; Singh, Ramandeep; Suri, Ashish; Bettag, Martin; Roy, Tara Sankar; Busert, Christoph; Mehlitz, Marcus; Lalwani, Sanjeev; Garg, Kanwaljeet; Paul, Kolin; Prasad, Sanjiva; Banerjee, Subhashis; Kalra, Prem; Kumar, Subodh; Sharma, Bhavani Shankar; Mahapatra, Ashok Kumar

2014-01-01

Since the end of last century, technology has taken a front seat in dispersion of medical education. Advancements of technology in neurosurgery and traditional training methods are now being challenged by legal and ethical concerns of patient safety, resident work-hour restriction and cost of operating-room time. To supplement the existing neurosurgery education pattern, various e-learning platforms are introduced as structured, interactive learning system. This study focuses on the concept, formulation, development and impact of web based learning platforms dedicated to neurosurgery discipline to disseminate education, supplement surgical knowledge and improve skills of neurosurgeons. 'Neurosurgery Education and Training School (NETS), e-learning platform' has integration of web-based technologies like 'Content Management System' for organizing the education material and 'Learning Management System' for updating neurosurgeons. NETS discussion forum networks neurosurgeons, neuroscientists and neuro-technologists across the globe facilitating collaborative translational research. Multi-authored neurosurgical e-learning material supplements the deficiencies of regular time-bound education. Interactive open-source, global, free-access e-learning platform of NETS has around 1) 425 visitors/month from 73 countries; ratio of new visitors to returning visitors 42.3; 57.7 (2); 64,380 views from 190 subscribers for surgical videos, 3-D animation, graphics based training modules (3); average 402 views per post. The e-Learning platforms provide updated educational content that make them "quick, surf, find and extract" resources. e-Learning tools like web-based education, social interactive platform and question-answer forum will save unnecessary expenditure of time and travel of neurosurgeons seeking knowledge. The need for free access platforms is more pronounced for the neurosurgeons and patients in developing nations.

Automated Whole-Body Bone Lesion Detection for Multiple Myeloma on 68Ga-Pentixafor PET/CT Imaging Using Deep Learning Methods.

PubMed

Xu, Lina; Tetteh, Giles; Lipkova, Jana; Zhao, Yu; Li, Hongwei; Christ, Patrick; Piraud, Marie; Buck, Andreas; Shi, Kuangyu; Menze, Bjoern H

2018-01-01

The identification of bone lesions is crucial in the diagnostic assessment of multiple myeloma (MM). 68 Ga-Pentixafor PET/CT can capture the abnormal molecular expression of CXCR-4 in addition to anatomical changes. However, whole-body detection of dozens of lesions on hybrid imaging is tedious and error prone. It is even more difficult to identify lesions with a large heterogeneity. This study employed deep learning methods to automatically combine characteristics of PET and CT for whole-body MM bone lesion detection in a 3D manner. Two convolutional neural networks (CNNs), V-Net and W-Net, were adopted to segment and detect the lesions. The feasibility of deep learning for lesion detection on 68 Ga-Pentixafor PET/CT was first verified on digital phantoms generated using realistic PET simulation methods. Then the proposed methods were evaluated on real 68 Ga-Pentixafor PET/CT scans of MM patients. The preliminary results showed that deep learning method can leverage multimodal information for spatial feature representation, and W-Net obtained the best result for segmentation and lesion detection. It also outperformed traditional machine learning methods such as random forest classifier (RF), k -Nearest Neighbors ( k -NN), and support vector machine (SVM). The proof-of-concept study encourages further development of deep learning approach for MM lesion detection in population study.

Automated Whole-Body Bone Lesion Detection for Multiple Myeloma on 68Ga-Pentixafor PET/CT Imaging Using Deep Learning Methods

PubMed Central

Tetteh, Giles; Lipkova, Jana; Zhao, Yu; Li, Hongwei; Christ, Patrick; Buck, Andreas; Menze, Bjoern H.

2018-01-01

The identification of bone lesions is crucial in the diagnostic assessment of multiple myeloma (MM). 68Ga-Pentixafor PET/CT can capture the abnormal molecular expression of CXCR-4 in addition to anatomical changes. However, whole-body detection of dozens of lesions on hybrid imaging is tedious and error prone. It is even more difficult to identify lesions with a large heterogeneity. This study employed deep learning methods to automatically combine characteristics of PET and CT for whole-body MM bone lesion detection in a 3D manner. Two convolutional neural networks (CNNs), V-Net and W-Net, were adopted to segment and detect the lesions. The feasibility of deep learning for lesion detection on 68Ga-Pentixafor PET/CT was first verified on digital phantoms generated using realistic PET simulation methods. Then the proposed methods were evaluated on real 68Ga-Pentixafor PET/CT scans of MM patients. The preliminary results showed that deep learning method can leverage multimodal information for spatial feature representation, and W-Net obtained the best result for segmentation and lesion detection. It also outperformed traditional machine learning methods such as random forest classifier (RF), k-Nearest Neighbors (k-NN), and support vector machine (SVM). The proof-of-concept study encourages further development of deep learning approach for MM lesion detection in population study. PMID:29531504

[Supporting the Love, Marriage, and Child-Rearing of Persons with Schizophrenia].

PubMed

Ikebuchi, Emi

2015-01-01

Persons with schizophrenia and their families have strong interests and hopes for love, marriage, pregnancy, and child-rearing. These experiences often lead to recovery from schizophrenia. There are many partners with schizophrenia who enjoy fruitful lives even with their disability. However, only some persons can enter into such lives in the real world in Japan and other countries. This leads persons with schizophrenia to develop a discouraged and disappointed attitude, and also causes professionals of mental health to develop indifference or pessimism about these issues. Schizophrenics are thought to have interests in love and sexual behavior just as strong as the general population. I discuss with my patients about these issues and working life early in the course of treatment. Because they lose their chance to learn adult behavior in social lives with peers due to the beginning of schizophrenia, they need an opportunity to participate in a social situation to learn knowledge and skills of dating and related behaviors, and systematic education such as psycho-education and social skills training should be provided. Continuing married life and child-rearing require more support from experts with rich experience and knowledge. Psychiatrists are required to participate in shared decision-making about medication during pregnancy and breast-feeding, as well as provide knowledge on the benefits and risks of antipsychotics. Net-working with the family, professionals of child welfare, and the community is necessary to support child-rearing. Urakawa Bethel's House was introduced as a pioneering concept to support love, marriage, and child-rearing. Finally, professionals' negative or indifferent attitudes toward these issues are discussed in the setting of treatment. I hope that professionals of mental health will think about these issues from the standpoints of persons with schizophrenia and their families.

Validating Network Security Policies via Static Analysis of Router ACL Configuration

DTIC Science & Technology

2006-12-01

this research effort. A. SOFTWARE IMPLEMENTATION The system software was created with Java, using NetBeans IDE 5.0 [12]. NetBeans is a free, open...11. P. Gupta, and N. McKeown (2001), Algorithms for Packet Classification, IEEE Network, vol. 15, issue 2, pp. 24-32. 12, NetBeans (2006), Welcome to... NetBeans , http://www.netbeans.org, last accessed on 25 November 2006. 13. IANA.org (2006), Port Numbers, http://www.iana.org/assignments/port

Usefulness of an Internet-based thematic learning network: comparison of effectiveness with traditional teaching.

PubMed

Coma Del Corral, María Jesús; Guevara, José Cordero; Luquin, Pedro Abáigar; Peña, Horacio J; Mateos Otero, Juan José

2006-03-01

UniNet is an Internet-based thematic network for a virtual community of users (VCU). It supports one multidisciplinary community of doctoral students, who receive most of the courses on the network. The evident advantages of distance learning by Internet, in terms of costs, comfort, etc., require a previous evaluation of the system, focusing on the learning outcomes of the student. The aim was to evaluate the real learning of the students of doctorate courses, by comparing the effectiveness of distance learning in UniNet with traditional classroom-based teaching. Five doctorate courses were taught simultaneously to two independent groups of students in two ways: one, through the UniNet Network, and the other in a traditional classroom. The academic knowledge of students was evaluated at the beginning and end of each course. The difference in score was considered as a knowledge increase. The comparison was made using Student's t-test for independent groups. There were no significant statistical differences in the outcomes of the two groups of students. This suggests that both teaching systems were equivalent in increasing the knowledge of the students. Both educational methods, the traditional system and the online system in a thematic network, are effective and similar for increasing knowledge.

Application of modified Martinez-Silva algorithm in determination of net cover

NASA Astrophysics Data System (ADS)

Stefanowicz, Łukasz; Grobelna, Iwona

2016-12-01

In the article we present the idea of modifications of Martinez-Silva algorithm, which allows for determination of place invariants (p-invariants) of Petri net. Their generation time is important in the parallel decomposition of discrete systems described by Petri nets. Decomposition process is essential from the point of view of discrete system design, as it allows for separation of smaller sequential parts. The proposed modifications of Martinez-Silva method concern the net cover by p-invariants and are focused on two important issues: cyclic reduction of invariant matrix and cyclic checking of net cover.

Negative emissions—Part 3: Innovation and upscaling

NASA Astrophysics Data System (ADS)

Nemet, Gregory F.; Callaghan, Max W.; Creutzig, Felix; Fuss, Sabine; Hartmann, Jens; Hilaire, Jérôme; Lamb, William F.; Minx, Jan C.; Rogers, Sophia; Smith, Pete

2018-06-01

We assess the literature on innovation and upscaling for negative emissions technologies (NETs) using a systematic and reproducible literature coding procedure. To structure our review, we employ the framework of sequential stages in the innovation process, with which we code each NETs article in innovation space. We find that while there is a growing body of innovation literature on NETs, 59% of the articles are focused on the earliest stages of the innovation process, ‘research and development’ (R&D). The subsequent stages of innovation are also represented in the literature, but at much lower levels of activity than R&D. Distinguishing between innovation stages that are related to the supply of the technology (R&D, demonstrations, scale up) and demand for the technology (demand pull, niche markets, public acceptance), we find an overwhelming emphasis (83%) on the supply side. BECCS articles have an above average share of demand-side articles while direct air carbon capture and storage has a very low share. Innovation in NETs has much to learn from successfully diffused technologies; appealing to heterogeneous users, managing policy risk, as well as understanding and addressing public concerns are all crucial yet not well represented in the extant literature. Results from integrated assessment models show that while NETs play a key role in the second half of the 21st century for 1.5 °C and 2 °C scenarios, the major period of new NETs deployment is between 2030 and 2050. Given that the broader innovation literature consistently finds long time periods involved in scaling up and deploying novel technologies, there is an urgency to developing NETs that is largely unappreciated. This challenge is exacerbated by the thousands to millions of actors that potentially need to adopt these technologies for them to achieve planetary scale. This urgency is reflected neither in the Paris Agreement nor in most of the literature we review here. If NETs are to be deployed at the levels required to meet 1.5 °C and 2 °C targets, then important post-R&D issues will need to be addressed in the literature, including incentives for early deployment, niche markets, scale-up, demand, and—particularly if deployment is to be hastened—public acceptance.

Interactive Exhibits Foster Partnership and Engage Diverse Learners at Their Local Libraries

NASA Astrophysics Data System (ADS)

LaConte, K.; Dusenbery, P.; Fitzhugh, G.; Harold, J. B.; Holland, A.

2016-12-01

Learners frequently need to access increasingly complex information to help them understand our changing world. More and more libraries are transforming themselves into places where learners not only access STEM information, but interact with professionals and undertake hands-on learning. Libraries are beginning to position themselves as part of learning ecosystems that contribute to a collective impact on the community. Traveling STEM exhibits are catalyzing these partnerships and engaging students, families, and adults in repeat visits through an accessible venue: their public library. The impact of the STAR Library Education Network's (STAR_Net) Discover Earth: A Century of Change exhibit on partnerships, the circulation of STEM resources, and the engagement of learners was studied by an external evaluation team. The STAR_Net project's summative evaluation utilized mixed methods to investigate project implementation and its outcomes. Methods included pre- and post-exhibit surveys administered to staff from each library that hosted the exhibits; interviews with staff from host libraries; patron surveys; exhibit-related circulation records; web metrics regarding the online STAR_Net community of practice; and site visits. A subset of host libraries recruited professionals, who delivered programming that connected Earth systems science, weather, climate, and conservation themes from the exhibit to local issues. Library patrons improved their knowledge about STEM topics presented in the exhibits and associated programming, and patrons viewing the exhibit reflected the demographics of their communities. In a follow-up survey, patrons reported spending an average of 60 minutes looking at the exhibit over their cumulative visits to the library. In contrast, visitors might visit a museum only once to look at a comparably-sized traveling exhibit due to barriers such as cost and distance. Exhibit host libraries reported an increase in the circulation of Earth science materials of 27% while the exhibit was at their library. The summative evaluation results, as well as tips for working with your local library, will be shared.

Neural Network Development Tool (NETS)

NASA Technical Reports Server (NTRS)

Baffes, Paul T.

1990-01-01

Artificial neural networks formed from hundreds or thousands of simulated neurons, connected in manner similar to that in human brain. Such network models learning behavior. Using NETS involves translating problem to be solved into input/output pairs, designing network configuration, and training network. Written in C.

TIMSS 2011 Student and Teacher Predictors for Mathematics Achievement Explored and Identified via Elastic Net.

PubMed

Yoo, Jin Eun

2018-01-01

A substantial body of research has been conducted on variables relating to students' mathematics achievement with TIMSS. However, most studies have employed conventional statistical methods, and have focused on selected few indicators instead of utilizing hundreds of variables TIMSS provides. This study aimed to find a prediction model for students' mathematics achievement using as many TIMSS student and teacher variables as possible. Elastic net, the selected machine learning technique in this study, takes advantage of both LASSO and ridge in terms of variable selection and multicollinearity, respectively. A logistic regression model was also employed to predict TIMSS 2011 Korean 4th graders' mathematics achievement. Ten-fold cross-validation with mean squared error was employed to determine the elastic net regularization parameter. Among 162 TIMSS variables explored, 12 student and 5 teacher variables were selected in the elastic net model, and the prediction accuracy, sensitivity, and specificity were 76.06, 70.23, and 80.34%, respectively. This study showed that the elastic net method can be successfully applied to educational large-scale data by selecting a subset of variables with reasonable prediction accuracy and finding new variables to predict students' mathematics achievement. Newly found variables via machine learning can shed light on the existing theories from a totally different perspective, which in turn propagates creation of a new theory or complement of existing ones. This study also examined the current scale development convention from a machine learning perspective.

TIMSS 2011 Student and Teacher Predictors for Mathematics Achievement Explored and Identified via Elastic Net

PubMed Central

Yoo, Jin Eun

2018-01-01

A substantial body of research has been conducted on variables relating to students' mathematics achievement with TIMSS. However, most studies have employed conventional statistical methods, and have focused on selected few indicators instead of utilizing hundreds of variables TIMSS provides. This study aimed to find a prediction model for students' mathematics achievement using as many TIMSS student and teacher variables as possible. Elastic net, the selected machine learning technique in this study, takes advantage of both LASSO and ridge in terms of variable selection and multicollinearity, respectively. A logistic regression model was also employed to predict TIMSS 2011 Korean 4th graders' mathematics achievement. Ten-fold cross-validation with mean squared error was employed to determine the elastic net regularization parameter. Among 162 TIMSS variables explored, 12 student and 5 teacher variables were selected in the elastic net model, and the prediction accuracy, sensitivity, and specificity were 76.06, 70.23, and 80.34%, respectively. This study showed that the elastic net method can be successfully applied to educational large-scale data by selecting a subset of variables with reasonable prediction accuracy and finding new variables to predict students' mathematics achievement. Newly found variables via machine learning can shed light on the existing theories from a totally different perspective, which in turn propagates creation of a new theory or complement of existing ones. This study also examined the current scale development convention from a machine learning perspective. PMID:29599736

Learning Management Platform for CyberCIEGE

DTIC Science & Technology

2011-12-01

developments were done using the NetBeans Integrated Development Environment (IDE) 7.0, which is a free and open source IDE. Some of these...developments could be implemented using the GUI design features of NetBeans . However, it was not done so because the existing Campaign Analyzer code base...directory through a dialog window. Also, the existing directory structures are not consistent with NetBeans project management assumptions and thus

Training Analyses Supporting the Land Warrior and Ground Soldier Systems

DTIC Science & Technology

2009-07-01

unit with LW and MW expressed in terms of unit force effectiveness, impacts to the DOTMLPF domains, life cycle cost, and ability to mitigate Joint...other individual tasks, Soldier and/or leader, be added to NET; should any be eliminated? What methods of instruction/resources should remain the...presentation of the training observation results from the nine-day NET. Terminal Learning Objectives The NET POI ( Omega Training Group, 2006

Impact of eLearning course on nurses' professional competence in seclusion and restraint practices: 9-month follow-up results of a randomized controlled study (ISRCTN32869544).

PubMed

Kontio, R; Hätönen, H; Joffe, G; Pitkänen, A; Lahti, M; Välimäki, M

2013-04-01

eLearning may facilitate continuing vocational education, but data on the long-term effects of an eLearning course are lacking. The aim of this study was to explore the long-term impact of an eLearning course entitled ePsychNurse.Net on psychiatric nurses' professional competence in practicing seclusion and restraint and on their job satisfaction and general self-efficacy at 9-month follow-up. In a randomized controlled study, 12 wards were randomly assigned to the ePsychNurse.Net (intervention) or training as usual (control). Baseline and 9-month follow-up data on nurses' knowledge of coercion-related legislation, physical restraint and seclusion, their attitudes towards physical restraint and seclusion, job satisfaction and general self-efficacy were analysed for 137 completers (those who participated in the 9-month follow-up assessment). No between-group differences were found on any variable, with the exception of a change in attitude to seclusion in favour of the control group. The findings of the long-term effects did not differ from the immediate outcomes (3-month follow-up) and the improved level of knowledge acquired and further consolidation of that knowledge did not take place in the 6-month period after the 3-month ePsychNurse.Net course. The ePsychNurse.Net should be further developed and its future modifications will require additional studies, probably with some new outcome measures. © 2012 Blackwell Publishing.

Scaling deep learning workloads: NVIDIA DGX-1/Pascal and Intel Knights Landing

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

Gawande, Nitin A.; Landwehr, Joshua B.; Daily, Jeffrey A.

Deep Learning (DL) algorithms have become ubiquitous in data analytics. As a result, major computing vendors --- including NVIDIA, Intel, AMD, and IBM --- have architectural road-maps influenced by DL workloads. Furthermore, several vendors have recently advertised new computing products as accelerating large DL workloads. Unfortunately, it is difficult for data scientists to quantify the potential of these different products. This paper provides a performance and power analysis of important DL workloads on two major parallel architectures: NVIDIA DGX-1 (eight Pascal P100 GPUs interconnected with NVLink) and Intel Knights Landing (KNL) CPUs interconnected with Intel Omni-Path or Cray Aries. Ourmore » evaluation consists of a cross section of convolutional neural net workloads: CifarNet, AlexNet, GoogLeNet, and ResNet50 topologies using the Cifar10 and ImageNet datasets. The workloads are vendor-optimized for each architecture. Our analysis indicates that although GPUs provide the highest overall performance, the gap can close for some convolutional networks; and the KNL can be competitive in performance/watt. We find that NVLink facilitates scaling efficiency on GPUs. However, its importance is heavily dependent on neural network architecture. Furthermore, for weak-scaling --- sometimes encouraged by restricted GPU memory --- NVLink is less important.« less

Decision-making deficits in patients with chronic schizophrenia: Iowa Gambling Task and Prospect Valence Learning model.

PubMed

Kim, Myung-Sun; Kang, Bit-Na; Lim, Jae Young

2016-01-01

Decision-making is the process of forming preferences for possible options, selecting and executing actions, and evaluating the outcome. This study used the Iowa Gambling Task (IGT) and the Prospect Valence Learning (PVL) model to investigate deficits in risk-reward related decision-making in patients with chronic schizophrenia, and to identify decision-making processes that contribute to poor IGT performance in these patients. Thirty-nine patients with schizophrenia and 31 healthy controls participated. Decision-making was measured by total net score, block net scores, and the total number of cards selected from each deck of the IGT. PVL parameters were estimated with the Markov chain Monte Carlo sampling scheme in OpenBugs and BRugs, its interface to R, and the estimated parameters were analyzed with the Mann-Whitney U-test. The schizophrenia group received significantly lower total net scores compared to the control group. In terms of block net scores, an interaction effect of group × block was observed. The block net scores of the schizophrenia group did not differ across the five blocks, whereas those of the control group increased as the blocks progressed. The schizophrenia group obtained significantly lower block net scores in the fourth and fifth blocks of the IGT and selected cards from deck D (advantageous) less frequently than the control group. Additionally, the schizophrenia group had significantly lower values on the utility-shape, loss-aversion, recency, and consistency parameters of the PVL model. These results indicate that patients with schizophrenia experience deficits in decision-making, possibly due to failure in learning the expected value of each deck, and incorporating outcome experiences of previous trials into expectancies about options in the present trial.

50 CFR 648.91 - Monkfish regulated mesh areas and restrictions on gear and methods of fishing.

Code of Federal Regulations, 2010 CFR

2010-10-01

... while on a monkfish DAS. Except as provided in paragraph (c)(1)(ii) of this section, the minimum mesh size for any trawl net, including beam trawl nets, used by a vessel fishing under a monkfish DAS is 10... area being fished. (ii) Trawl nets while on a monkfish and NE multispecies DAS. Vessels issued a...

Introducing ISTE Learning: What Do You Want to Learn Today?

ERIC Educational Resources Information Center

Hayman, April

2011-01-01

This article introduces ISTE Learning, a new online professional development (PD) program designed specifically to make PD both fun and more easily accessible for busy educators. One thing that makes ISTE Learning different from everything else out there is that the NETS for students, teachers, and administrators are the cornerstone of everything…

Preparing for Emergency Situations

NASA Astrophysics Data System (ADS)

Asproth, Viveca; Amcoff Nyström, Christina

2010-11-01

Disaster relief can be seen as a dynamic multi actor process with actors both joining and leaving the relief work during the help and rescue phase after the disaster has occurred. Actors may be governmental agencies, non profit voluntary organisations or spontaneous helpers comprised of individual citizens or temporal groups of citizens. Hence, they will vary widely in agility, competence, resources, and endurance. To prepare for for disasters a net based Agora with simulation of emergency situations for mutual preparation, training, and organisational learning is suggested. Such an Agora will ensure future security by: -Rising awareness and preparedness of potential disaster responders by help of the components and resources in the netAgora environment; -Improving cooperation and coordination between responders; -Improving competence and performance of organisations involved in security issues; -Bridging cultural differences between responders from different organizations and different backgrounds. The developed models are intended to reflect intelligent anticipatory systems for human operator anticipation of future consequences. As a way to catch what should be included in this netbased Agora and to join the split pictures that is present, Team Syntegrity could be a helpful tool. The purpose of Team Syntegrity is to stimulate collaboration and incite cross fertilization and creativity. The difference between syntegration and other group work is that the participants are evenly and uniquely distributed and will collectively have the means, the knowledge, the experience, the perspectives, and the expertise, to deal with the topic. In this paper the possibilities with using Team Syntegrity in preparation for the development of a netbased Agora is discussed. We have identified that Team Syntegrity could be useful in the steps User Integration, Designing the netAgora environment, developing Test Scenarios, and assessment of netAgora environment.

Computers for Political Change: PeaceNet and Public Data Access.

ERIC Educational Resources Information Center

Downing, John D. H.

1989-01-01

Describes two computer communication projects: PeaceNet, devoted to peace issues; and Public Data Access, devoted to making U.S. government information more broadly available. Discusses the potential of new technology (computer communication) for grass-roots political movements. (SR)

Automatic white blood cell classification using pre-trained deep learning models: ResNet and Inception

NASA Astrophysics Data System (ADS)

Habibzadeh, Mehdi; Jannesari, Mahboobeh; Rezaei, Zahra; Baharvand, Hossein; Totonchi, Mehdi

2018-04-01

This works gives an account of evaluation of white blood cell differential counts via computer aided diagnosis (CAD) system and hematology rules. Leukocytes, also called white blood cells (WBCs) play main role of the immune system. Leukocyte is responsible for phagocytosis and immunity and therefore in defense against infection involving the fatal diseases incidence and mortality related issues. Admittedly, microscopic examination of blood samples is a time consuming, expensive and error-prone task. A manual diagnosis would search for specific Leukocytes and number abnormalities in the blood slides while complete blood count (CBC) examination is performed. Complications may arise from the large number of varying samples including different types of Leukocytes, related sub-types and concentration in blood, which makes the analysis prone to human error. This process can be automated by computerized techniques which are more reliable and economical. In essence, we seek to determine a fast, accurate mechanism for classification and gather information about distribution of white blood evidences which may help to diagnose the degree of any abnormalities during CBC test. In this work, we consider the problem of pre-processing and supervised classification of white blood cells into their four primary types including Neutrophils, Eosinophils, Lymphocytes, and Monocytes using a consecutive proposed deep learning framework. For first step, this research proposes three consecutive pre-processing calculations namely are color distortion; bounding box distortion (crop) and image flipping mirroring. In second phase, white blood cell recognition performed with hierarchy topological feature extraction using Inception and ResNet architectures. Finally, the results obtained from the preliminary analysis of cell classification with (11200) training samples and 1244 white blood cells evaluation data set are presented in confusion matrices and interpreted using accuracy rate, and false positive with the classification framework being validated with experiments conducted on poor quality blood images sized 320 × 240 pixels. The deferential outcomes in the challenging cell detection task, as shown in result section, indicate that there is a significant achievement in using Inception and ResNet architecture with proposed settings. Our framework detects on average 100% of the four main white blood cell types using ResNet V1 50 while also alternative promising result with 99.84% and 99.46% accuracy rate obtained with ResNet V1 152 and ResNet 101, respectively with 3000 epochs and fine-tuning all layers. Further statistical confusion matrix tests revealed that this work achieved 1, 0.9979, 0.9989 sensitivity values when area under the curve (AUC) scores above 1, 0.9992, 0.9833 on three proposed techniques. In addition, current work shows negligible and small false negative 0, 2, 1 and substantial false positive with 0, 0, 5 values in Leukocytes detection.

Learning of Rule Ensembles for Multiple Attribute Ranking Problems

NASA Astrophysics Data System (ADS)

Dembczyński, Krzysztof; Kotłowski, Wojciech; Słowiński, Roman; Szeląg, Marcin

In this paper, we consider the multiple attribute ranking problem from a Machine Learning perspective. We propose two approaches to statistical learning of an ensemble of decision rules from decision examples provided by the Decision Maker in terms of pairwise comparisons of some objects. The first approach consists in learning a preference function defining a binary preference relation for a pair of objects. The result of application of this function on all pairs of objects to be ranked is then exploited using the Net Flow Score procedure, giving a linear ranking of objects. The second approach consists in learning a utility function for single objects. The utility function also gives a linear ranking of objects. In both approaches, the learning is based on the boosting technique. The presented approaches to Preference Learning share good properties of the decision rule preference model and have good performance in the massive-data learning problems. As Preference Learning and Multiple Attribute Decision Aiding share many concepts and methodological issues, in the introduction, we review some aspects bridging these two fields. To illustrate the two approaches proposed in this paper, we solve with them a toy example concerning the ranking of a set of cars evaluated by multiple attributes. Then, we perform a large data experiment on real data sets. The first data set concerns credit rating. Since recent research in the field of Preference Learning is motivated by the increasing role of modeling preferences in recommender systems and information retrieval, we chose two other massive data sets from this area - one comes from movie recommender system MovieLens, and the other concerns ranking of text documents from 20 Newsgroups data set.

Utilizing Technology to Enhance Learning Environments: The Net Gen Student

ERIC Educational Resources Information Center

Muhammad, Amanda J.; Mitova, Mariana A.; Wooldridge, Deborah G.

2016-01-01

It is essential for instructors to understand the importance of classroom technology so they can prepare to use it to personalize students' learning. Strategies for choosing effective electronic tools are presented, followed by specific suggestions for designing enhanced personalized learning using electronic tools.

Learning the ropes at Richard B. Russell Dam: Net systems for medium head hydropower pumpback and generation fish sampling

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

Schilt, C.R.; Vu, P.D.; Nestler, J.M.

1995-12-31

At Richard B. Russell Dam on the Savannah River we have been monitoring the magnitude (numbers and masses) and species compositions as well as possible survival of fish entrained in operation of four 85 MW Francis pump turbines. In this paper we review our progress in net design for hydropower application. We also discuss basic net handling and introduce a method for net management in a very turbulent tailrace. This report is meant to share what we have learned at Russell Dam in hopes that it will facilitate similar efforts elsewhere. The commercial fishing industry has evolved methods of netmore » construction and handling that may be applied, with appropriate modification, at dams. The nets we use are most appropriately called trawls in that they have the form of a long sock placed over the penstock or draft tube. These nets are superficially similar to those used in commercial trawling for fish. Important differences are that: (1) the net remains relatively stationary while the water moves through it, not vice versa; (2) water velocities and turbulence are much greater at dams than in commercial fishing operations and (3) mesh sizes are much smaller for environmental sampling than for commercial trawling. And while a fouled trawl may stop the boat that pulls it, the water passed in generation or pumpback (about 140 ft. head at Russell) is for all practical purposes unstoppable. Our nets fish in a very turbulent discharge at 7,000 cu. ft./sec/turbine. Their strength and their ability to pass water effectively under all possible operating conditions are primary concerns. Trawl length, mesh sizes, and hanging ratios are important factors. Although we have had setbacks (usually in the form of torn nets) as this study has developed, we have incrementally improved our net design and handling. We review our net failures and the solutions we have found thus far in both construction and handling.« less

Growing a professional network to over 3000 members in less than 4 years: evaluation of InspireNet, British Columbia's virtual nursing health services research network.

PubMed

Frisch, Noreen; Atherton, Pat; Borycki, Elizabeth; Mickelson, Grace; Cordeiro, Jennifer; Novak Lauscher, Helen; Black, Agnes

2014-02-21

Use of Web 2.0 and social media technologies has become a new area of research among health professionals. Much of this work has focused on the use of technologies for health self-management and the ways technologies support communication between care providers and consumers. This paper addresses a new use of technology in providing a platform for health professionals to support professional development, increase knowledge utilization, and promote formal/informal professional communication. Specifically, we report on factors necessary to attract and sustain health professionals' use of a network designed to increase nurses' interest in and use of health services research and to support knowledge utilization activities in British Columbia, Canada. "InspireNet", a virtual professional network for health professionals, is a living laboratory permitting documentation of when and how professionals take up Web 2.0 and social media. Ongoing evaluation documents our experiences in establishing, operating, and evaluating this network. Overall evaluation methods included (1) tracking website use, (2) conducting two member surveys, and (3) soliciting member feedback through focus groups and interviews with those who participated in electronic communities of practice (eCoPs) and other stakeholders. These data have been used to learn about the types of support that seem relevant to network growth. Network growth exceeded all expectations. Members engaged with varying aspects of the network's virtual technologies, such as teams of professionals sharing a common interest, research teams conducting their work, and instructional webinars open to network members. Members used wikis, blogs, and discussion groups to support professional work, as well as a members' database with contact information and areas of interest. The database is accessed approximately 10 times per day. InspireNet public blog posts are accessed roughly 500 times each. At the time of writing, 21 research teams conduct their work virtually using the InspireNet platform; 10 topic-based Action Teams meet to address issues of mutual concern. Nursing and other health professionals, even those who rated themselves as computer literate, required significant mentoring and support in their efforts to adopt their practice to a virtual environment. There was a steep learning curve for professionals to learn to work in a virtual environment and to benefit from the available technologies. Virtual professional networks can be positioned to make a significant contribution to ongoing professional practice and to creating environments supportive of information sharing, mentoring, and learning across geographical boundaries. Nonetheless, creation of a Web 2.0 and social media platform is not sufficient, in and of itself, to attract or sustain a vibrant community of professionals interested in improving their practice. Essential support includes instruction in the use of Web-based activities and time management, a biweekly e-Newsletter, regular communication from leaders, and an annual face-to-face conference.

In Practice: Weaving the Campus Safety Net by Integrating Student Health Issues into the Curriculum

ERIC Educational Resources Information Center

Olson, Todd A.; Riley, Joan B.

2009-01-01

Georgetown University has developed an innovative approach to addressing student health and wellness issues through curriculum infusion--a collaborative pedagogy that introduces real-life health issues faced by college students into their academic courses.

Linking Research to Practice: FEWS NET and Its Use of Satellite Remote Sensing Data

NASA Technical Reports Server (NTRS)

Brown, Molly E.; Brickley, Elizabeth B.

2011-01-01

The purpose of the Famine Early Warning Systems Network (FEWS NET) is to collaborate with international, regional and national partners to provide timely and rigorous early warning and vulnerability information on emerging and evolving food security issues

Virtual Mobility in Higher Education. The UNED Campus Net Program

ERIC Educational Resources Information Center

Aguado, Teresa; Monge, Fernando; Del Olmo, Alicia

2014-01-01

We present the UNED Virtual Mobility Campus Net Program, implemented since 2012 in collaboration with European and Latin American universities. Program's objectives, participating institutions, procedures, and evaluation are exposed. Virtual mobility is understood as a meaningful strategy for intercultural learning by studying an undergraduate or…

A Large Scale Code Resolution Service Network in the Internet of Things

PubMed Central

Yu, Haining; Zhang, Hongli; Fang, Binxing; Yu, Xiangzhan

2012-01-01

In the Internet of Things a code resolution service provides a discovery mechanism for a requester to obtain the information resources associated with a particular product code immediately. In large scale application scenarios a code resolution service faces some serious issues involving heterogeneity, big data and data ownership. A code resolution service network is required to address these issues. Firstly, a list of requirements for the network architecture and code resolution services is proposed. Secondly, in order to eliminate code resolution conflicts and code resolution overloads, a code structure is presented to create a uniform namespace for code resolution records. Thirdly, we propose a loosely coupled distributed network consisting of heterogeneous, independent; collaborating code resolution services and a SkipNet based code resolution service named SkipNet-OCRS, which not only inherits DHT's advantages, but also supports administrative control and autonomy. For the external behaviors of SkipNet-OCRS, a novel external behavior mode named QRRA mode is proposed to enhance security and reduce requester complexity. For the internal behaviors of SkipNet-OCRS, an improved query algorithm is proposed to increase query efficiency. It is analyzed that integrating SkipNet-OCRS into our resolution service network can meet our proposed requirements. Finally, simulation experiments verify the excellent performance of SkipNet-OCRS. PMID:23202207

A large scale code resolution service network in the Internet of Things.

PubMed

Yu, Haining; Zhang, Hongli; Fang, Binxing; Yu, Xiangzhan

2012-11-07

In the Internet of Things a code resolution service provides a discovery mechanism for a requester to obtain the information resources associated with a particular product code immediately. In large scale application scenarios a code resolution service faces some serious issues involving heterogeneity, big data and data ownership. A code resolution service network is required to address these issues. Firstly, a list of requirements for the network architecture and code resolution services is proposed. Secondly, in order to eliminate code resolution conflicts and code resolution overloads, a code structure is presented to create a uniform namespace for code resolution records. Thirdly, we propose a loosely coupled distributed network consisting of heterogeneous, independent; collaborating code resolution services and a SkipNet based code resolution service named SkipNet-OCRS, which not only inherits DHT’s advantages, but also supports administrative control and autonomy. For the external behaviors of SkipNet-OCRS, a novel external behavior mode named QRRA mode is proposed to enhance security and reduce requester complexity. For the internal behaviors of SkipNet-OCRS, an improved query algorithm is proposed to increase query efficiency. It is analyzed that integrating SkipNet-OCRS into our resolution service network can meet our proposed requirements. Finally, simulation experiments verify the excellent performance of SkipNet-OCRS.

Overcrowding and diversion in the emergency department: the health care safety net unravels.

PubMed

Velianoff, George D

2002-03-01

Emergency department overcrowding and diversion of patients are serious problems that are symptomatic of larger health care system issues. Downsizing, government regulations, managed care, increased numbers of uninsured, and reimbursement decreases are issues that have created the overcrowding and diversion issues. The Emergency Medical Treatment and Active Labor Act (EMTALA), poor operations and hospital processes, unavailable inpatient beds and closures, consolidations and workforce shortages are also contributors to the overcrowding and diversion issues. Options and solutions are proposed to alleviate the problem, however, greater collaboration, changed work environments, and reimbursement structures need to be developed and instituted. The safety net of the US health system is unraveling, and without intervention, the emergency department will not be able to provide services to the public at any level of quality and efficiency.

www.teld.net: Online Courseware Engine for Teaching by Examples and Learning by Doing.

ERIC Educational Resources Information Center

Huang, G. Q.; Shen, B.; Mak, K. L.

2001-01-01

Describes TELD (Teaching by Examples and Learning by Doing), a Web-based online courseware engine for higher education. Topics include problem-based learning; project-based learning; case methods; TELD as a Web server; course materials; TELD as a search engine; and TELD as an online virtual classroom for electronic delivery of electronic…

A case study analysing the process of analogy-based learning in a teaching unit about simple electric circuits

NASA Astrophysics Data System (ADS)

Paatz, Roland; Ryder, James; Schwedes, Hannelore; Scott, Philip

2004-09-01

The purpose of this case study is to analyse the learning processes of a 16-year-old student as she learns about simple electric circuits in response to an analogy-based teaching sequence. Analogical thinking processes are modelled by a sequence of four steps according to Gentner's structure mapping theory (activate base domain, postulate local matches, connect them to a global match, draw candidate inferences). We consider whether Gentner's theory can be used to account for the details of this specific teaching/learning context. The case study involved video-taping teaching and learning activities in a 10th-grade high school course in Germany. Teaching used water flow through pipes as an analogy for electrical circuits. Using Gentner's theory, relational nets were created from the student's statements at different stages of her learning. Overall, these nets reflect the four steps outlined earlier. We also consider to what extent the learning processes revealed by this case study are different from previous analyses of contexts in which no analogical knowledge is available.

Second Language Teaching and Learning in the Net Generation

ERIC Educational Resources Information Center

Oxford, Raquel, Ed.; Oxford, Jeffrey, Ed.

2009-01-01

Today's young people--the Net Generation--have grown up with technology all around them. However, teachers cannot assume that students' familiarity with technology in general transfers successfully to pedagogical settings. This volume examines various technologies and offers concrete advice on how each can be successfully implemented in the second…

Neural architectures for robot intelligence.

PubMed

Ritter, H; Steil, J J; Nölker, C; Röthling, F; McGuire, P

2003-01-01

We argue that direct experimental approaches to elucidate the architecture of higher brains may benefit from insights gained from exploring the possibilities and limits of artificial control architectures for robot systems. We present some of our recent work that has been motivated by that view and that is centered around the study of various aspects of hand actions since these are intimately linked with many higher cognitive abilities. As examples, we report on the development of a modular system for the recognition of continuous hand postures based on neural nets, the use of vision and tactile sensing for guiding prehensile movements of a multifingered hand, and the recognition and use of hand gestures for robot teaching. Regarding the issue of learning, we propose to view real-world learning from the perspective of data-mining and to focus more strongly on the imitation of observed actions instead of purely reinforcement-based exploration. As a concrete example of such an effort we report on the status of an ongoing project in our laboratory in which a robot equipped with an attention system with a neurally inspired architecture is taught actions by using hand gestures in conjunction with speech commands. We point out some of the lessons learnt from this system, and discuss how systems of this kind can contribute to the study of issues at the junction between natural and artificial cognitive systems.

Open issues on G3 neuroendocrine neoplasms: back to the future.

PubMed

Zatelli, Maria Chiara; Guadagno, Elia; Messina, Erika; Lo Calzo, Fabio; Faggiano, Antongiulio; Colao, Annamaria

2018-06-01

The recent recognition that grade 3 (G3) neuroendocrine neoplasms (NENs) can be divided into two different categories according to the histopathological differentiation, that is G3 neuroendocrine tumors (NETs) and G3 neuroendocrine carcinomas (NECs) has generated a lot of interest concerning not only the diagnosis, but also the differential management of such new group of NENs. However, several issues need to be fully clarified in order to put G3 NETs and G3 NECs in the right place. The aim of this review is to focus on those issues that are still undetermined starting from the current knowledge, evaluating the available evidence and the possible clinical implications. © 2018 Society for Endocrinology.

Compensation Still Matters: Language Learning Strategies in Third Millennium ESL Learners

ERIC Educational Resources Information Center

Shakarami, Alireza; Hajhashemi, Karim; Caltabiano, Nerina J.

2017-01-01

Digital media play enormous roles in much of the learning, communication, socializing, and ways of working for "Net-Generation" learners who are growing up in a wired world. Living in this digital era may require different ways of communicating, thinking, approaching learning, prioritizing strategies, interpersonally communicating, and…

Digital Learning: Strengthening and Assessing 21st Century Skills, Grades 5-8

ERIC Educational Resources Information Center

Serim, Ferdi

2012-01-01

This comprehensive book offers a practical pathway for developing twenty-first-century skills while simultaneously strengthening content-area learning. "Digital Learning" contains a wealth of research-based practices to integrate the International Society for Technology in Education (ISTE) National Education Technology Standards (NETS) for both…

Modernising Education and Training: Mobilising Technology for Learning

ERIC Educational Resources Information Center

Attewell, Jill; Savill-Smith, Carol; Douch, Rebecca; Parker, Guy

2010-01-01

In recent years there have been amazing advances in consumer technology. The Mobile Learning Network (MoLeNET) initiative has enabled colleges and schools to harness some of this technology in order to modernise aspects of teaching, learning and training. The result has been improvements in learner engagement, retention, achievement and…

Net-Based Training for Physicians

ERIC Educational Resources Information Center

Jokela, Paivi; Karlsudd, Peter

2009-01-01

In order to ensure and increase access to high-quality learning opportunities it is becoming more and more common to integrate e-learning into health-related environments. The rapid development of these new learning environments also requires continuous monitoring and evaluation, to guarantee the quality of the health-care education. In this…

Scaling Deep Learning workloads: NVIDIA DGX-1/Pascal and Intel Knights Landing

DOE PAGES

Gawande, Nitin A.; Daily, Jeff A.; Siegel, Charles; ...

2018-05-05

Deep Learning (DL) algorithms have become ubiquitous in data analytics. As a result, major computing vendors—including NVIDIA, Intel, AMD, and IBM—have architectural road maps influenced by DL workloads. Furthermore, several vendors have recently advertised new computing products as accelerating large DL workloads. Unfortunately, it is difficult for data scientists to quantify the potential of these different products. Here, this article provides a performance and power analysis of important DL workloads on two major parallel architectures: NVIDIA DGX-1 (eight Pascal P100 GPUs interconnected with NVLink) and Intel Knights Landing (KNL) CPUs interconnected with Intel Omni-Path or Cray Aries. Our evaluation consistsmore » of a cross section of convolutional neural net workloads: CifarNet, AlexNet, GoogLeNet, and ResNet50 topologies using the Cifar10 and ImageNet datasets. The workloads are vendor-optimized for each architecture. We use sequentially equivalent implementations to maintain iso-accuracy between parallel and sequential DL models. Our analysis indicates that although GPUs provide the highest overall performance, the gap can close for some convolutional networks; and the KNL can be competitive in performance/watt. We find that NVLink facilitates scaling efficiency on GPUs. However, its importance is heavily dependent on neural network architecture. Furthermore, for weak-scaling—sometimes encouraged by restricted GPU memory—NVLink is less important.« less

Scaling Deep Learning workloads: NVIDIA DGX-1/Pascal and Intel Knights Landing

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

Gawande, Nitin A.; Daily, Jeff A.; Siegel, Charles

Deep Learning (DL) algorithms have become ubiquitous in data analytics. As a result, major computing vendors—including NVIDIA, Intel, AMD, and IBM—have architectural road maps influenced by DL workloads. Furthermore, several vendors have recently advertised new computing products as accelerating large DL workloads. Unfortunately, it is difficult for data scientists to quantify the potential of these different products. Here, this article provides a performance and power analysis of important DL workloads on two major parallel architectures: NVIDIA DGX-1 (eight Pascal P100 GPUs interconnected with NVLink) and Intel Knights Landing (KNL) CPUs interconnected with Intel Omni-Path or Cray Aries. Our evaluation consistsmore » of a cross section of convolutional neural net workloads: CifarNet, AlexNet, GoogLeNet, and ResNet50 topologies using the Cifar10 and ImageNet datasets. The workloads are vendor-optimized for each architecture. We use sequentially equivalent implementations to maintain iso-accuracy between parallel and sequential DL models. Our analysis indicates that although GPUs provide the highest overall performance, the gap can close for some convolutional networks; and the KNL can be competitive in performance/watt. We find that NVLink facilitates scaling efficiency on GPUs. However, its importance is heavily dependent on neural network architecture. Furthermore, for weak-scaling—sometimes encouraged by restricted GPU memory—NVLink is less important.« less

CGBayesNets: Conditional Gaussian Bayesian Network Learning and Inference with Mixed Discrete and Continuous Data

PubMed Central

Weiss, Scott T.

2014-01-01

Bayesian Networks (BN) have been a popular predictive modeling formalism in bioinformatics, but their application in modern genomics has been slowed by an inability to cleanly handle domains with mixed discrete and continuous variables. Existing free BN software packages either discretize continuous variables, which can lead to information loss, or do not include inference routines, which makes prediction with the BN impossible. We present CGBayesNets, a BN package focused around prediction of a clinical phenotype from mixed discrete and continuous variables, which fills these gaps. CGBayesNets implements Bayesian likelihood and inference algorithms for the conditional Gaussian Bayesian network (CGBNs) formalism, one appropriate for predicting an outcome of interest from, e.g., multimodal genomic data. We provide four different network learning algorithms, each making a different tradeoff between computational cost and network likelihood. CGBayesNets provides a full suite of functions for model exploration and verification, including cross validation, bootstrapping, and AUC manipulation. We highlight several results obtained previously with CGBayesNets, including predictive models of wood properties from tree genomics, leukemia subtype classification from mixed genomic data, and robust prediction of intensive care unit mortality outcomes from metabolomic profiles. We also provide detailed example analysis on public metabolomic and gene expression datasets. CGBayesNets is implemented in MATLAB and available as MATLAB source code, under an Open Source license and anonymous download at http://www.cgbayesnets.com. PMID:24922310

CGBayesNets: conditional Gaussian Bayesian network learning and inference with mixed discrete and continuous data.

PubMed

McGeachie, Michael J; Chang, Hsun-Hsien; Weiss, Scott T

2014-06-01

Bayesian Networks (BN) have been a popular predictive modeling formalism in bioinformatics, but their application in modern genomics has been slowed by an inability to cleanly handle domains with mixed discrete and continuous variables. Existing free BN software packages either discretize continuous variables, which can lead to information loss, or do not include inference routines, which makes prediction with the BN impossible. We present CGBayesNets, a BN package focused around prediction of a clinical phenotype from mixed discrete and continuous variables, which fills these gaps. CGBayesNets implements Bayesian likelihood and inference algorithms for the conditional Gaussian Bayesian network (CGBNs) formalism, one appropriate for predicting an outcome of interest from, e.g., multimodal genomic data. We provide four different network learning algorithms, each making a different tradeoff between computational cost and network likelihood. CGBayesNets provides a full suite of functions for model exploration and verification, including cross validation, bootstrapping, and AUC manipulation. We highlight several results obtained previously with CGBayesNets, including predictive models of wood properties from tree genomics, leukemia subtype classification from mixed genomic data, and robust prediction of intensive care unit mortality outcomes from metabolomic profiles. We also provide detailed example analysis on public metabolomic and gene expression datasets. CGBayesNets is implemented in MATLAB and available as MATLAB source code, under an Open Source license and anonymous download at http://www.cgbayesnets.com.

DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection.

PubMed

Ouyang, Wanli; Zeng, Xingyu; Wang, Xiaogang; Qiu, Shi; Luo, Ping; Tian, Yonglong; Li, Hongsheng; Yang, Shuo; Wang, Zhe; Li, Hongyang; Loy, Chen Change; Wang, Kun; Yan, Junjie; Tang, Xiaoou

2016-07-07

In this paper, we propose deformable deep convolutional neural networks for generic object detection. This new deep learning object detection framework has innovations in multiple aspects. In the proposed new deep architecture, a new deformation constrained pooling (def-pooling) layer models the deformation of object parts with geometric constraint and penalty. A new pre-training strategy is proposed to learn feature representations more suitable for the object detection task and with good generalization capability. By changing the net structures, training strategies, adding and removing some key components in the detection pipeline, a set of models with large diversity are obtained, which significantly improves the effectiveness of model averaging. The proposed approach improves the mean averaged precision obtained by RCNN [16], which was the state-of-the-art, from 31% to 50.3% on the ILSVRC2014 detection test set. It also outperforms the winner of ILSVRC2014, GoogLeNet, by 6.1%. Detailed component-wise analysis is also provided through extensive experimental evaluation, which provides a global view for people to understand the deep learning object detection pipeline.

A Bayesian generative model for learning semantic hierarchies

PubMed Central

Mittelman, Roni; Sun, Min; Kuipers, Benjamin; Savarese, Silvio

2014-01-01

Building fine-grained visual recognition systems that are capable of recognizing tens of thousands of categories, has received much attention in recent years. The well known semantic hierarchical structure of categories and concepts, has been shown to provide a key prior which allows for optimal predictions. The hierarchical organization of various domains and concepts has been subject to extensive research, and led to the development of the WordNet domains hierarchy (Fellbaum, 1998), which was also used to organize the images in the ImageNet (Deng et al., 2009) dataset, in which the category count approaches the human capacity. Still, for the human visual system, the form of the hierarchy must be discovered with minimal use of supervision or innate knowledge. In this work, we propose a new Bayesian generative model for learning such domain hierarchies, based on semantic input. Our model is motivated by the super-subordinate organization of domain labels and concepts that characterizes WordNet, and accounts for several important challenges: maintaining context information when progressing deeper into the hierarchy, learning a coherent semantic concept for each node, and modeling uncertainty in the perception process. PMID:24904452

Overview of codes and tools for nuclear engineering education

NASA Astrophysics Data System (ADS)

Yakovlev, D.; Pryakhin, A.; Medvedeva, L.

2017-01-01

The recent world trends in nuclear education have been developed in the direction of social education, networking, virtual tools and codes. MEPhI as a global leader on the world education market implements new advanced technologies for the distance and online learning and for student research work. MEPhI produced special codes, tools and web resources based on the internet platform to support education in the field of nuclear technology. At the same time, MEPhI actively uses codes and tools from the third parties. Several types of the tools are considered: calculation codes, nuclear data visualization tools, virtual labs, PC-based educational simulators for nuclear power plants (NPP), CLP4NET, education web-platforms, distance courses (MOOCs and controlled and managed content systems). The university pays special attention to integrated products such as CLP4NET, which is not a learning course, but serves to automate the process of learning through distance technologies. CLP4NET organizes all tools in the same information space. Up to now, MEPhI has achieved significant results in the field of distance education and online system implementation.

Detection of colorectal masses in CT colonography: application of deep residual networks for differentiating masses from normal colon anatomy

NASA Astrophysics Data System (ADS)

Näppi, Janne J.; Hironaka, Toru; Yoshida, Hiroyuki

2018-02-01

Even though the clinical consequences of a missed colorectal cancer far outweigh those of a missed polyp, there has been little work on computer-aided detection (CADe) for colorectal masses in CT colonography (CTC). One of the problems is that it is not clear how to manually design mathematical image-based features that could be used to differentiate effectively between masses and certain types of normal colon anatomy such as ileocecal valves (ICVs). Deep learning has demonstrated ability to automatically determine effective discriminating features in many image-based problems. Recently, residual networks (ResNets) were developed to address the practical problems of constructing deep network architectures for optimizing the performance of deep learning. In this pilot study, we compared the classification performance of a conventional 2D-convolutional ResNet (2D-ResNet) with that of a volumetric 3D-convolutional ResNet (3D-ResNet) in differentiating masses from normal colon anatomy in CTC. For the development and evaluation of the ResNets, 695 volumetric images of biopsy-proven colorectal masses, ICVs, haustral folds, and rectal tubes were sampled from 196 clinical CTC cases and divided randomly into independent training, validation, and test datasets. The training set was expanded by use of volumetric data augmentation. Our preliminary results on the 140 test samples indicate that it is feasible to train a deep volumetric 3D-ResNet for performing effective image-based discriminations in CTC. The 3D-ResNet slightly outperformed the 2D-ResNet in the discrimination of masses and normal colon anatomy, but the statistical difference between their very high classification accuracies was not significant. The highest classification accuracy was obtained by combining the mass-likelihood estimates of the 2D- and 3D-ResNets, which enabled correct classification of all of the masses.

Exploring nursing e-learning systems success based on information system success model.

PubMed

Chang, Hui-Chuan; Liu, Chung-Feng; Hwang, Hsin-Ginn

2011-12-01

E-learning is thought of as an innovative approach to enhance nurses' care service knowledge. Extensive research has provided rich information toward system development, courses design, and nurses' satisfaction with an e-learning system. However, a comprehensive view in understanding nursing e-learning system success is an important but less focused-on topic. The purpose of this research was to explore net benefits of nursing e-learning systems based on the updated DeLone and McLean's Information System Success Model. The study used a self-administered questionnaire to collected 208 valid nurses' responses from 21 of Taiwan's medium- and large-scale hospitals that have implemented nursing e-learning systems. The result confirms that the model is sufficient to explore the nurses' use of e-learning systems in terms of intention to use, user satisfaction, and net benefits. However, while the three exogenous quality factors (system quality, information quality, and service quality) were all found to be critical factors affecting user satisfaction, only information quality showed a direct effect on the intention to use. This study provides useful insights for evaluating nursing e-learning system qualities as well as an understanding of nurses' intentions and satisfaction related to performance benefits.

Norepinephrine Transporter Heterozygous Knockout Mice Exhibit Altered Transport and Behavior

PubMed Central

Fentress, HM; Klar, R; Krueger, JK; Sabb, T; Redmon, SN; Wallace, NM; Shirey-Rice, JK; Hahn, MK

2013-01-01

The norepinephrine (NE) transporter (NET) regulates synaptic NE availability for noradrenergic signaling in the brain and sympathetic nervous system. Although genetic variation leading to a loss of NET expression has been implicated in psychiatric and cardiovascular disorders, complete NET deficiency has not been found in people, limiting the utility of NET knockout mice as a model for genetically-driven NET dysfunction. Here, we investigate NET expression in NET heterozygous knockout male mice (NET+/−), demonstrating that they display an ~50% reduction in NET protein levels. Surprisingly, these mice display no significant deficit in NET activity, assessed in hippocampal and cortical synaptosomes. We found that this compensation in NET activity was due to enhanced activity of surface-resident transporters, as opposed to surface recruitment of NET protein or compensation through other transport mechanisms, including serotonin, dopamine or organic cation transporters. We hypothesize that loss of NET protein in the NET+/− mouse establishes an activated state of existing, surface NET proteins. NET+/− mice exhibit increased anxiety in the open field and light-dark box and display deficits in reversal learning in the Morris Water Maze. These data suggest recovery of near basal activity in NET+/− mice appears to be insufficient to limit anxiety responses or support cognitive performance that might involve noradrenergic neurotransmission. The NET+/− mice represent a unique model to study the loss and resultant compensatory changes in NET that may be relevant to behavior and physiology in human NET deficiency disorders. PMID:24102798

Clio in Cyberspace.

ERIC Educational Resources Information Center

Hindley, Meredith

1996-01-01

Provides an overview of the discussions, activities, procedures, and issues surrounding the more than 70 listservs run by H-Net. The Internet e-mail subscription network is open and free to both scholars and the public. Includes a list of all the H-Net listservs and a representative copy of one list's operating rules. (MJP)

The power of neural nets

NASA Technical Reports Server (NTRS)

Ryan, J. P.; Shah, B. H.

1987-01-01

Implementation of the Hopfield net which is used in the image processing type of applications where only partial information about the image may be available is discussed. The image classification type of algorithm of Hopfield and other learning algorithms, such as the Boltzmann machine and the back-propagation training algorithm, have many vital applications in space.

Reviewing the Need for Gaming in Education to Accommodate the Net Generation

ERIC Educational Resources Information Center

Bekebrede, G.; Warmelink, H. J. G.; Mayer, I. S.

2011-01-01

There is a growing interest in the use of simulations and games in Dutch higher education. This development is based on the perception that students belong to the "gamer generation" or "net generation": a generation that has grown up with computer games and other technology affecting their preferred learning styles, social…

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

The technology necessary to build net zero energy buildings (NZEBs) is ready and available today, however, building to net zero energy performance levels can be challenging. Energy efficiency measures, onsite energy generation resources, load matching and grid interaction, climatic factors, and local policies vary from location to location and require unique methods of constructing NZEBs. It is recommended that Components start looking into how to construct and operate NZEBs now as there is a learning curve to net zero construction and FY 2020 is just around the corner.

It's in the Bag: Digital Backpacks for Project-Based Learning

ERIC Educational Resources Information Center

Basham, James D.; Perry, Ernest; Meyer, Helen

2011-01-01

When it comes to technology, many schools know what they want. They want targeted and scalable solutions that enhance learning and meet the NETS.S. And the teachers in those schools want simple, strategic instructional frameworks for developing their students' basic and digital age skills while meeting diverse learning needs. But as many…

The Knowledge Web: Learning and Collaborating on the Net. Open and Distance Learning Series.

ERIC Educational Resources Information Center

Eisenstadt, Marc, Ed.; Vincent, Tom, Ed.

This book contains a collection of examples of new and effective uses of the World Wide Web in education from the Knowledge Media Institute (KMi) at the Open University (Great Britain). The publication is organized in three main sections--"Learning Media,""Collaboration and Presence," and "Knowledge Systems on the…

Teaching and Learning Collocation in Adult Second and Foreign Language Learning

ERIC Educational Resources Information Center

Boers, Frank; Webb, Stuart

2018-01-01

Perhaps the greatest challenge to creating a research timeline on teaching and learning collocation is deciding how wide to cast the net in the search for relevant publications. For one thing, the term "collocation" does not have the same meaning for all (applied) linguists and practitioners (Barfield & Gyllstad 2009) (see timeline).…

Pedagogical Significance of Wikis: Towards Gaining Effective Learning Outcomes

ERIC Educational Resources Information Center

Hewege, Chandana Rathnasiri; Perera, Liyanage Chamila Roshani

2013-01-01

Purpose: The purpose of this paper is to explore the effectiveness and pedagogical implications of integrating wikis into the curriculum and the subsequent learning outcomes of a group of Net-Gens who enrolled in an International Marketing course. The research problem of the study is: "What are the learning outcomes and pedagogical…

A Holistic Approach to Scoring in Complex Mobile Learning Scenarios

ERIC Educational Resources Information Center

Gebbe, Marcel; Teine, Matthias; Beutner, Marc

2016-01-01

Interactive dialogues are key elements for designing authentic and motivating learning situations, and in combination with learning analysis they provide educators and users with the opportunity to track information related to professional competences, but mind-sets as well. This paper offers exemplary insights into the project NetEnquiry that is…

A Theory of Causal Learning in Children: Causal Maps and Bayes Nets

ERIC Educational Resources Information Center

Gopnik, Alison; Glymour, Clark; Sobel, David M.; Schulz, Laura E.; Kushnir, Tamar; Danks, David

2004-01-01

The authors outline a cognitive and computational account of causal learning in children. They propose that children use specialized cognitive systems that allow them to recover an accurate "causal map" of the world: an abstract, coherent, learned representation of the causal relations among events. This kind of knowledge can be perspicuously…

A 3D Active Learning Application for NeMO-Net, the NASA Neural Multi-Modal Observation and Training Network for Global Coral Reef Assessment

NASA Technical Reports Server (NTRS)

van den Bergh, Jarrett; Schutz, Joey; Li, Alan; Chirayath, Ved

2017-01-01

NeMO-Net, the NASA neural multi-modal observation and training network for global coral reef assessment, is an open-source deep convolutional neural network and interactive active learning training software aiming to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology as well as mapping of spatial distribution. We present an interactive video game prototype for tablet and mobile devices where users interactively label morphology classifications over mm-scale 3D coral reef imagery captured using fluid lensing to create a dataset that will be used to train NeMO-Nets convolutional neural network. The application currently allows for users to classify preselected regions of coral in the Pacific and will be expanded to include additional regions captured using our NASA FluidCam instrument, presently the highest-resolution remote sensing benthic imaging technology capable of removing ocean wave distortion, as well as lower-resolution airborne remote sensing data from the ongoing NASA CORAL campaign. Active learning applications present a novel methodology for efficiently training large-scale Neural Networks wherein variances in identification can be rapidly mitigated against control data. NeMO-Net periodically checks users input against pre-classified coral imagery to gauge their accuracy and utilize in-game mechanics to provide classification training. Users actively communicate with a server and are requested to classify areas of coral for which other users had conflicting classifications and contribute their input to a larger database for ranking. In partnering with Mission Blue and IUCN, NeMO-Net leverages an international consortium of subject matter experts to classify areas of confusion identified by NeMO-Net and generate additional labels crucial for identifying decision boundary locations in coral reef assessment.

A 3D Active Learning Application for NeMO-Net, the NASA Neural Multi-Modal Observation and Training Network for Global Coral Reef Assessment

NASA Astrophysics Data System (ADS)

van den Bergh, J.; Schutz, J.; Chirayath, V.; Li, A.

2017-12-01

NeMO-Net, the NASA neural multi-modal observation and training network for global coral reef assessment, is an open-source deep convolutional neural network and interactive active learning training software aiming to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology as well as mapping of spatial distribution. We present an interactive video game prototype for tablet and mobile devices where users interactively label morphology classifications over mm-scale 3D coral reef imagery captured using fluid lensing to create a dataset that will be used to train NeMO-Net's convolutional neural network. The application currently allows for users to classify preselected regions of coral in the Pacific and will be expanded to include additional regions captured using our NASA FluidCam instrument, presently the highest-resolution remote sensing benthic imaging technology capable of removing ocean wave distortion, as well as lower-resolution airborne remote sensing data from the ongoing NASA CORAL campaign.Active learning applications present a novel methodology for efficiently training large-scale Neural Networks wherein variances in identification can be rapidly mitigated against control data. NeMO-Net periodically checks users' input against pre-classified coral imagery to gauge their accuracy and utilizes in-game mechanics to provide classification training. Users actively communicate with a server and are requested to classify areas of coral for which other users had conflicting classifications and contribute their input to a larger database for ranking. In partnering with Mission Blue and IUCN, NeMO-Net leverages an international consortium of subject matter experts to classify areas of confusion identified by NeMO-Net and generate additional labels crucial for identifying decision boundary locations in coral reef assessment.

Accounting for Excess Purchase Price: Goodwill or Expense? Instructional Issues.

ERIC Educational Resources Information Center

Reed, Ronald O.; Elsea, John; Lilly, Martha S.

2000-01-01

Presents the issue of the accounting practice used when a business is acquired by another for a price exceeding its net assets. Discusses implications for instruction in financial accounting. (Contains 25 references.) (SK)

Meta-Analytic Evidence for a Reversal Learning Effect on the Iowa Gambling Task in Older Adults.

PubMed

Pasion, Rita; Gonçalves, Ana R; Fernandes, Carina; Ferreira-Santos, Fernando; Barbosa, Fernando; Marques-Teixeira, João

2017-01-01

Iowa Gambling Task (IGT) is one of the most widely used tools to assess economic decision-making. However, the research tradition on aging and the Iowa Gambling Task (IGT) has been mainly focused on the overall performance of older adults in relation to younger or clinical groups, remaining unclear whether older adults are capable of learning along the task. We conducted a meta-analysis to examine older adults' decision-making on the IGT, to test the effects of aging on reversal learning (45 studies) and to provide normative data on total and block net scores (55 studies). From the accumulated empirical evidence, we found an average total net score of 7.55 (±25.9). We also observed a significant reversal learning effect along the blocks of the IGT, indicating that older adults inhibit the prepotent response toward immediately attractive options associated with high losses, in favor of initially less attractive options associated with long-run profit. During block 1, decisions of older adults led to a negative gambling net score, reflecting the expected initial pattern of risk-taking. However, the shift toward more safe options occurred between block 2 (small-to-medium effect size) and blocks 3, 4, 5 (medium-to-large effect size). These main findings highlight that older adults are able to move from the initial uncertainty, when the possible outcomes are unknown, to decisions based on risk, when the outcomes are learned and may be used to guide future adaptive decision-making.

Saliency Detection and Deep Learning-Based Wildfire Identification in UAV Imagery.

PubMed

Zhao, Yi; Ma, Jiale; Li, Xiaohui; Zhang, Jie

2018-02-27

An unmanned aerial vehicle (UAV) equipped with global positioning systems (GPS) can provide direct georeferenced imagery, mapping an area with high resolution. So far, the major difficulty in wildfire image classification is the lack of unified identification marks, the fire features of color, shape, texture (smoke, flame, or both) and background can vary significantly from one scene to another. Deep learning (e.g., DCNN for Deep Convolutional Neural Network) is very effective in high-level feature learning, however, a substantial amount of training images dataset is obligatory in optimizing its weights value and coefficients. In this work, we proposed a new saliency detection algorithm for fast location and segmentation of core fire area in aerial images. As the proposed method can effectively avoid feature loss caused by direct resizing; it is used in data augmentation and formation of a standard fire image dataset 'UAV_Fire'. A 15-layered self-learning DCNN architecture named 'Fire_Net' is then presented as a self-learning fire feature exactor and classifier. We evaluated different architectures and several key parameters (drop out ratio, batch size, etc.) of the DCNN model regarding its validation accuracy. The proposed architecture outperformed previous methods by achieving an overall accuracy of 98%. Furthermore, 'Fire_Net' guarantied an average processing speed of 41.5 ms per image for real-time wildfire inspection. To demonstrate its practical utility, Fire_Net is tested on 40 sampled images in wildfire news reports and all of them have been accurately identified.

Automated Detection of Diabetic Retinopathy using Deep Learning.

PubMed

Lam, Carson; Yi, Darvin; Guo, Margaret; Lindsey, Tony

2018-01-01

Diabetic retinopathy is a leading cause of blindness among working-age adults. Early detection of this condition is critical for good prognosis. In this paper, we demonstrate the use of convolutional neural networks (CNNs) on color fundus images for the recognition task of diabetic retinopathy staging. Our network models achieved test metric performance comparable to baseline literature results, with validation sensitivity of 95%. We additionally explored multinomial classification models, and demonstrate that errors primarily occur in the misclassification of mild disease as normal due to the CNNs inability to detect subtle disease features. We discovered that preprocessing with contrast limited adaptive histogram equalization and ensuring dataset fidelity by expert verification of class labels improves recognition of subtle features. Transfer learning on pretrained GoogLeNet and AlexNet models from ImageNet improved peak test set accuracies to 74.5%, 68.8%, and 57.2% on 2-ary, 3-ary, and 4-ary classification models, respectively.

Logical Differential Prediction Bayes Net, improving breast cancer diagnosis for older women.

PubMed

Nassif, Houssam; Wu, Yirong; Page, David; Burnside, Elizabeth

2012-01-01

Overdiagnosis is a phenomenon in which screening identities cancer which may not go on to cause symptoms or death. Women over 65 who develop breast cancer bear the heaviest burden of overdiagnosis. This work introduces novel machine learning algorithms to improve diagnostic accuracy of breast cancer in aging populations. At the same time, we aim at minimizing unnecessary invasive procedures (thus decreasing false positives) and concomitantly addressing overdiagnosis. We develop a novel algorithm. Logical Differential Prediction Bayes Net (LDP-BN), that calculates the risk of breast disease based on mammography findings. LDP-BN uses Inductive Logic Programming (ILP) to learn relational rules, selects older-specific differentially predictive rules, and incorporates them into a Bayes Net, significantly improving its performance. In addition, LDP-BN offers valuable insight into the classification process, revealing novel older-specific rules that link mass presence to invasive, and calcification presence and lack of detectable mass to DCIS.

Norepinephrine transporter heterozygous knockout mice exhibit altered transport and behavior.

PubMed

Fentress, H M; Klar, R; Krueger, J J; Sabb, T; Redmon, S N; Wallace, N M; Shirey-Rice, J K; Hahn, M K

2013-11-01

The norepinephrine (NE) transporter (NET) regulates synaptic NE availability for noradrenergic signaling in the brain and sympathetic nervous system. Although genetic variation leading to a loss of NET expression has been implicated in psychiatric and cardiovascular disorders, complete NET deficiency has not been found in people, limiting the utility of NET knockout mice as a model for genetically driven NET dysfunction. Here, we investigate NET expression in NET heterozygous knockout male mice (NET(+/-) ), demonstrating that they display an approximately 50% reduction in NET protein levels. Surprisingly, these mice display no significant deficit in NET activity assessed in hippocampal and cortical synaptosomes. We found that this compensation in NET activity was due to enhanced activity of surface-resident transporters, as opposed to surface recruitment of NET protein or compensation through other transport mechanisms, including serotonin, dopamine or organic cation transporters. We hypothesize that loss of NET protein in the NET(+/-) mouse establishes an activated state of existing surface NET proteins. The NET(+/-) mice exhibit increased anxiety in the open field and light-dark box and display deficits in reversal learning in the Morris water maze. These data suggest that recovery of near basal activity in NET(+/-) mice appears to be insufficient to limit anxiety responses or support cognitive performance that might involve noradrenergic neurotransmission. The NET(+/-) mice represent a unique model to study the loss and resultant compensatory changes in NET that may be relevant to behavior and physiology in human NET deficiency disorders. © 2013 John Wiley & Sons Ltd and International Behavioural and Neural Genetics Society.

Little Experience with ICT: Are They Really the Net Generation Student-Teachers?

ERIC Educational Resources Information Center

So, Hyo-Jeong; Choi, Hyungshin; Lim, Wei Ying; Xiong, Yao

2012-01-01

The aim of this study is to investigate the complexity of past experiences with ICT, pedagogical beliefs, and attitude toward ICT in education that the Net Generation student teachers have about their intention to teach and learn with technology. This study has a particular focus on their lived experiences as school students where ICT related…

Building Capacity through Sustainable Engagement: Lessons for the Learning Community from the "GraniteNet" Project

ERIC Educational Resources Information Center

Arden, Catherine; McLachlan, Kathryn; Cooper, Trevor

2009-01-01

This paper reports an exploration into critical success factors for the sustainability of the partnership between the University of Southern Queensland and the Stanthorpe community during the GraniteNet Phoenix Project--the first phase of a three-phase participatory action research project conducted during 2007-2008. The concepts of learning…

E-Learning Technologies: Employing Matlab Web Server to Facilitate the Education of Mathematical Programming

ERIC Educational Resources Information Center

Karagiannis, P.; Markelis, I.; Paparrizos, K.; Samaras, N.; Sifaleras, A.

2006-01-01

This paper presents new web-based educational software (webNetPro) for "Linear Network Programming." It includes many algorithms for "Network Optimization" problems, such as shortest path problems, minimum spanning tree problems, maximum flow problems and other search algorithms. Therefore, webNetPro can assist the teaching process of courses such…

Test Bank. NetNews. Volume 8, Number 1, Winter 2008

ERIC Educational Resources Information Center

LDA of Minnesota, 2008

2008-01-01

Minnesota Adult Basic Education (ABE) providers are mandated to use CASAS (Comprehensive Adult Student Assessment System) Reading or Math or TABE (Tests for Adult Basic Education) Reading or Math. This issue of "NetNews" introduces the Test Bank: a variety of informal reading, spelling, and writing assessments available for Minnesota ABE…

Intermediate Decoding Skills. NetNews. Volume 4, Number 4

ERIC Educational Resources Information Center

LDA of Minnesota, 2004

2004-01-01

Intermediate decoding refers to word analysis skills that are beyond a beginning, one-syllable level as described in an earlier NetNews issue, yet are just as important for building adult level reading proficiency. Research from secondary settings indicates that struggling readers in middle school or high school programs often read between the…

High Throughput Measurement of Extracellular DNA Release and Quantitative NET Formation in Human Neutrophils In Vitro.

PubMed

Sil, Payel; Yoo, Dae-Goon; Floyd, Madison; Gingerich, Aaron; Rada, Balazs

2016-06-18

Neutrophil granulocytes are the most abundant leukocytes in the human blood. Neutrophils are the first to arrive at the site of infection. Neutrophils developed several antimicrobial mechanisms including phagocytosis, degranulation and formation of neutrophil extracellular traps (NETs). NETs consist of a DNA scaffold decorated with histones and several granule markers including myeloperoxidase (MPO) and human neutrophil elastase (HNE). NET release is an active process involving characteristic morphological changes of neutrophils leading to expulsion of their DNA into the extracellular space. NETs are essential to fight microbes, but uncontrolled release of NETs has been associated with several disorders. To learn more about the clinical relevance and the mechanism of NET formation, there is a need to have reliable tools capable of NET quantitation. Here three methods are presented that can assess NET release from human neutrophils in vitro. The first one is a high throughput assay to measure extracellular DNA release from human neutrophils using a membrane impermeable DNA-binding dye. In addition, two other methods are described capable of quantitating NET formation by measuring levels of NET-specific MPO-DNA and HNE-DNA complexes. These microplate-based methods in combination provide great tools to efficiently study the mechanism and regulation of NET formation of human neutrophils.

Tracking the Stages of Learning: Concept Maps as Representations of Liminal Space

ERIC Educational Resources Information Center

Cuthell, John; Preston, Christina

2012-01-01

The concept of liminal space has recently been applied to ways of learning: the learning journey through this space encounters difficulties and misunderstandings, that are resolved as knowledge is mastered. Since 1992 the MirandaNet Fellowship, a growing international community of educators, has investigated the ways in which this relates to the…

Diagrams and Math Notation in E-Learning: Growing Pains of a New Generation

ERIC Educational Resources Information Center

Smith, Glenn Gordon; Ferguson, David

2004-01-01

Current e-learning environments are ill-suited to college mathematics. Instructors/students struggle to post diagrams and math notation. A new generation of math-friendly e-learning tools, including WebEQ, bundled with Blackboard 6, and NetTutor's Whiteboard, address these problems. This paper compares these two systems using criteria for ideal…

Neomillennial User Experience Design Strategies: Utilizing Social Networking Media to Support "Always On" Learning Styles

ERIC Educational Resources Information Center

Baird, Derek E.; Fisher, Mercedes

2006-01-01

Raised in the "always on" world of interactive media, the Internet, and digital messaging technologies, today's student has different expectations and learning styles than previous generations. This net-centric generation values their ability to use the Web to create a self-paced, customized, on-demand learning path that includes multiple forms of…

Time, Space and Structure in an E-Learning and E-Mentoring Project

ERIC Educational Resources Information Center

Loureiro-Koechlin, Cecilia; Allan, Barbara

2010-01-01

This study focuses on a project, "EMPATHY Net-Works," which developed a learning community as a means of encouraging women to progress into employment and management positions in the logistics and supply chain industries (LaSCI). Learning activities were organised in the form of a taught module containing face-to-face and online elements and…

Cognitive Presence in Virtual Collaborative Learning: Assessing and Improving Critical Thinking in Online Discussion Forums

ERIC Educational Resources Information Center

Beckmann, Jennifer; Weber, Peter

2016-01-01

Purpose: The purpose of this study is to introduce a virtual collaborative learning setting called "Net Economy", which we established as part of an international learning network of currently six universities, and present our approach to continuously improve the course in each cycle. Design/ Methodology/Approach: Using the community of…

Cognitive Presence in Virtual Collaborative Learning: Assessing and Improving Critical Thinking in Online Discussion Forums

ERIC Educational Resources Information Center

Beckmann, Jennifer; Weber, Peter

2015-01-01

The paper introduces a virtual collaborative learning setting called "Net Economy," which we established as part of an international learning network of currently seven universities. Using the Community of Inquiry framework as guidance and Canonical Action Research (CAR) as the chosen research design, the discussion forum of the online…

IrisDenseNet: Robust Iris Segmentation Using Densely Connected Fully Convolutional Networks in the Images by Visible Light and Near-Infrared Light Camera Sensors

PubMed Central

Arsalan, Muhammad; Naqvi, Rizwan Ali; Kim, Dong Seop; Nguyen, Phong Ha; Owais, Muhammad; Park, Kang Ryoung

2018-01-01

The recent advancements in computer vision have opened new horizons for deploying biometric recognition algorithms in mobile and handheld devices. Similarly, iris recognition is now much needed in unconstraint scenarios with accuracy. These environments make the acquired iris image exhibit occlusion, low resolution, blur, unusual glint, ghost effect, and off-angles. The prevailing segmentation algorithms cannot cope with these constraints. In addition, owing to the unavailability of near-infrared (NIR) light, iris recognition in visible light environment makes the iris segmentation challenging with the noise of visible light. Deep learning with convolutional neural networks (CNN) has brought a considerable breakthrough in various applications. To address the iris segmentation issues in challenging situations by visible light and near-infrared light camera sensors, this paper proposes a densely connected fully convolutional network (IrisDenseNet), which can determine the true iris boundary even with inferior-quality images by using better information gradient flow between the dense blocks. In the experiments conducted, five datasets of visible light and NIR environments were used. For visible light environment, noisy iris challenge evaluation part-II (NICE-II selected from UBIRIS.v2 database) and mobile iris challenge evaluation (MICHE-I) datasets were used. For NIR environment, the institute of automation, Chinese academy of sciences (CASIA) v4.0 interval, CASIA v4.0 distance, and IIT Delhi v1.0 iris datasets were used. Experimental results showed the optimal segmentation of the proposed IrisDenseNet and its excellent performance over existing algorithms for all five datasets. PMID:29748495

IrisDenseNet: Robust Iris Segmentation Using Densely Connected Fully Convolutional Networks in the Images by Visible Light and Near-Infrared Light Camera Sensors.

PubMed

Arsalan, Muhammad; Naqvi, Rizwan Ali; Kim, Dong Seop; Nguyen, Phong Ha; Owais, Muhammad; Park, Kang Ryoung

2018-05-10

The recent advancements in computer vision have opened new horizons for deploying biometric recognition algorithms in mobile and handheld devices. Similarly, iris recognition is now much needed in unconstraint scenarios with accuracy. These environments make the acquired iris image exhibit occlusion, low resolution, blur, unusual glint, ghost effect, and off-angles. The prevailing segmentation algorithms cannot cope with these constraints. In addition, owing to the unavailability of near-infrared (NIR) light, iris recognition in visible light environment makes the iris segmentation challenging with the noise of visible light. Deep learning with convolutional neural networks (CNN) has brought a considerable breakthrough in various applications. To address the iris segmentation issues in challenging situations by visible light and near-infrared light camera sensors, this paper proposes a densely connected fully convolutional network (IrisDenseNet), which can determine the true iris boundary even with inferior-quality images by using better information gradient flow between the dense blocks. In the experiments conducted, five datasets of visible light and NIR environments were used. For visible light environment, noisy iris challenge evaluation part-II (NICE-II selected from UBIRIS.v2 database) and mobile iris challenge evaluation (MICHE-I) datasets were used. For NIR environment, the institute of automation, Chinese academy of sciences (CASIA) v4.0 interval, CASIA v4.0 distance, and IIT Delhi v1.0 iris datasets were used. Experimental results showed the optimal segmentation of the proposed IrisDenseNet and its excellent performance over existing algorithms for all five datasets.

Processing of chromatic information in a deep convolutional neural network.

PubMed

Flachot, Alban; Gegenfurtner, Karl R

2018-04-01

Deep convolutional neural networks are a class of machine-learning algorithms capable of solving non-trivial tasks, such as object recognition, with human-like performance. Little is known about the exact computations that deep neural networks learn, and to what extent these computations are similar to the ones performed by the primate brain. Here, we investigate how color information is processed in the different layers of the AlexNet deep neural network, originally trained on object classification of over 1.2M images of objects in their natural contexts. We found that the color-responsive units in the first layer of AlexNet learned linear features and were broadly tuned to two directions in color space, analogously to what is known of color responsive cells in the primate thalamus. Moreover, these directions are decorrelated and lead to statistically efficient representations, similar to the cardinal directions of the second-stage color mechanisms in primates. We also found, in analogy to the early stages of the primate visual system, that chromatic and achromatic information were segregated in the early layers of the network. Units in the higher layers of AlexNet exhibit on average a lower responsivity for color than units at earlier stages.

Impact of eLearning course on nurses' professional competence in seclusion and restraint practices: a randomized controlled study (ISRCTN32869544).

PubMed

Kontio, R; Lahti, M; Pitkänen, A; Joffe, G; Putkonen, H; Hätönen, H; Katajisto, J; Välimäki, M

2011-11-01

Education on the care of aggressive and disturbed patients is fragmentary. eLearning could ensure the quality of such education, but data on its impact on professional competence in psychiatry are lacking. The aim of this study was to explore the impact of ePsychNurse.Net, an eLearning course, on psychiatric nurses' professional competence in seclusion and restraint and on their job satisfaction and general self-efficacy. In a randomized controlled study, 12 wards were randomly assigned to ePsychNurse.Net (intervention) or education as usual (control). Baseline and 3-month follow-up data on nurses' knowledge of coercion-related legislation, physical restraint and seclusion, their attitudes towards physical restraint and seclusion, job satisfaction and general self-efficacy were analysed for 158 completers. Knowledge (primary outcome) of coercion-related legislation improved in the intervention group, while knowledge of physical restraint improved and knowledge of seclusion remained unchanged in both groups. General self-efficacy improved in the intervention group also attitude to seclusion in the control group. In between-group comparison, attitudes to seclusion (one of secondary outcomes) favoured the control group. Although the ePsychNurse.Net demonstrated only slight advantages over conventional learning, it may be worth further development with, e.g. flexible time schedule and individualized content. © 2011 Blackwell Publishing.

Combining deep residual neural network features with supervised machine learning algorithms to classify diverse food image datasets.

PubMed

McAllister, Patrick; Zheng, Huiru; Bond, Raymond; Moorhead, Anne

2018-04-01

Obesity is increasing worldwide and can cause many chronic conditions such as type-2 diabetes, heart disease, sleep apnea, and some cancers. Monitoring dietary intake through food logging is a key method to maintain a healthy lifestyle to prevent and manage obesity. Computer vision methods have been applied to food logging to automate image classification for monitoring dietary intake. In this work we applied pretrained ResNet-152 and GoogleNet convolutional neural networks (CNNs), initially trained using ImageNet Large Scale Visual Recognition Challenge (ILSVRC) dataset with MatConvNet package, to extract features from food image datasets; Food 5K, Food-11, RawFooT-DB, and Food-101. Deep features were extracted from CNNs and used to train machine learning classifiers including artificial neural network (ANN), support vector machine (SVM), Random Forest, and Naive Bayes. Results show that using ResNet-152 deep features with SVM with RBF kernel can accurately detect food items with 99.4% accuracy using Food-5K validation food image dataset and 98.8% with Food-5K evaluation dataset using ANN, SVM-RBF, and Random Forest classifiers. Trained with ResNet-152 features, ANN can achieve 91.34%, 99.28% when applied to Food-11 and RawFooT-DB food image datasets respectively and SVM with RBF kernel can achieve 64.98% with Food-101 image dataset. From this research it is clear that using deep CNN features can be used efficiently for diverse food item image classification. The work presented in this research shows that pretrained ResNet-152 features provide sufficient generalisation power when applied to a range of food image classification tasks. Copyright © 2018 Elsevier Ltd. All rights reserved.

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.

Including safety-net providers in integrated delivery systems: issues and options for policymakers.

PubMed

Witgert, Katherine; Hess, Catherine

2012-08-01

Health care reform legislation has spurred efforts to develop integrated health care delivery systems that seek to coordinate the continuum of health services. These systems may be of particular benefit to patients who face barriers to accessing care or have multiple health conditions. But it remains to be seen how safety-net providers, including community health centers and public hospitals--which have long experience in caring for these vulnerable populations--will be included in integrated delivery systems. This issue brief explores key considerations for incorporating safety-net providers into integrated delivery systems and discusses the roles of state and federal agencies in sup­porting and testing models of integrated care delivery. The authors conclude that the most important principles in creating integrated delivery systems for vulnerable populations are: (1) an emphasis on primary care; (2) coordination of all care, including behavioral, social, and public health services; and (3) accountability for population health outcomes.

Machine-learning in astronomy

NASA Astrophysics Data System (ADS)

Hobson, Michael; Graff, Philip; Feroz, Farhan; Lasenby, Anthony

2014-05-01

Machine-learning methods may be used to perform many tasks required in the analysis of astronomical data, including: data description and interpretation, pattern recognition, prediction, classification, compression, inference and many more. An intuitive and well-established approach to machine learning is the use of artificial neural networks (NNs), which consist of a group of interconnected nodes, each of which processes information that it receives and then passes this product on to other nodes via weighted connections. In particular, I discuss the first public release of the generic neural network training algorithm, called SkyNet, and demonstrate its application to astronomical problems focusing on its use in the BAMBI package for accelerated Bayesian inference in cosmology, and the identification of gamma-ray bursters. The SkyNet and BAMBI packages, which are fully parallelised using MPI, are available at http://www.mrao.cam.ac.uk/software/.

Machine Learning Approaches for Detecting Diabetic Retinopathy from Clinical and Public Health Records.

PubMed

Ogunyemi, Omolola; Kermah, Dulcie

2015-01-01

Annual eye examinations are recommended for diabetic patients in order to detect diabetic retinopathy and other eye conditions that arise from diabetes. Medically underserved urban communities in the US have annual screening rates that are much lower than the national average and could benefit from informatics approaches to identify unscreened patients most at risk of developing retinopathy. Using clinical data from urban safety net clinics as well as public health data from the CDC's National Health and Nutrition Examination Survey, we examined different machine learning approaches for predicting retinopathy from clinical or public health data. All datasets utilized exhibited a class imbalance. Classifiers learned on the clinical data were modestly predictive of retinopathy with the best model having an AUC of 0.72, sensitivity of 69.2% and specificity of 55.9%. Classifiers learned on public health data were not predictive of retinopathy. Successful approaches to detecting latent retinopathy using machine learning could help safety net and other clinics identify unscreened patients who are most at risk of developing retinopathy and the use of ensemble classifiers on clinical data shows promise for this purpose.

An electronic consumer health library: NetWellness.

PubMed Central

Guard, R; Haag, D; Kaya, B; Marine, S; Morris, T; Schick, L; Shoemaker, S

1996-01-01

NetWellness is a community-based, consumer-defined grant program supporting the delivery of electronic health information to rural residents of southern Ohio and urban and suburban communities in the Greater Cincinnati tri-state region. NetWellness is a collaboratively developed and publicly and privately funded demonstration project. Information is delivered via ISDN, standard dial, dedicated network connections, and the Internet. TriState Online (Greater Cincinnati's Free-Net) and other southern Ohio Free-Nets are key access points in the larger project communities. The other access points are more than forty workstations distributed at public sites throughout the project's primary geographical area. Design strengths and limitations, training initiatives, technical issues, and the project's impact on medical librarianship are examined in this paper. Also discussed are ways of determining community needs and interest, building political alliances, finding and developing funding sources, and overcoming technical obstacles. NetWellness's Internet address is: http:@www.netwellness.org. PMID:8913548

Biopathways representation and simulation on hybrid functional petri net.

PubMed

Matsuno, Hiroshi; Tanaka, Yukiko; Aoshima, Hitoshi; Doi, Atsushi; Matsui, Mika; Miyano, Satoru

2011-01-01

The following two matters should be resolved in order for biosimulation tools to be accepted by users in biology/medicine: (1) remove issues which are irrelevant to biological importance, and (2) allow users to represent biopathways intuitively and understand/manage easily the details of representation and simulation mechanism. From these criteria, we firstly define a novel notion of Petri net called Hybrid Functional Petri Net (HFPN). Then, we introduce a software tool, Genomic Object Net, for representing and simulating biopathways, which we have developed by employing the architecture of HFPN. In order to show the usefulness of Genomic Object Net for representing and simulating biopathways, we show two HFPN representations of gene regulation mechanisms of Drosophila melanogaster (fruit fly) circadian rhythm and apoptosis induced by Fas ligand. The simulation results of these biopathways are also correlated with biological observations. The software is available to academic users from http://www.GenomicObject.Net/.

Advantages and Disadvantages of Native- and Nonnative-English-Speaking Teachers: Student Perceptions in Hong Kong

ERIC Educational Resources Information Center

Ma, Lai Ping Florence

2012-01-01

The Native English Teachers (NETs) Scheme has been in place for over 20 years in secondary schools in Hong Kong and yet how students perceive these teachers is under-researched. This article reports a study which analyses student perceptions of the advantage and disadvantages of learning English from NETs and their non-native counterparts, local…

An Expanded Study of Net Generation Perceptions on Privacy and Security on Social Networking Sites (SNS)

ERIC Educational Resources Information Center

Lawler, James P.; Molluzzo, John C.; Doshi, Vijal

2012-01-01

Social networking on the Internet continues to be a frequent avenue of communication, especially among Net Generation consumers, giving benefits both personal and professional. The benefits may be eventually hindered by issues in information gathering and sharing on social networking sites. This study evaluates the perceptions of students taking a…

Dyscalculia Defined. NetNews. Volume 5, Number 4

ERIC Educational Resources Information Center

LDA of Minnesota, 2005

2005-01-01

The focus of this issue of "NetNews" is dyscalculia, or math disability. Most of the attention over the years has been on reading and writing difficulties, thus leading to the belief that math difficulties are not very common or serious. However, it has been estimated that about 6% of school-age children experience significant math difficulties.…

50 CFR 648.104 - Gear restrictions.

Code of Federal Regulations, 2010 CFR

2010-10-01

... through April 30, per trip, must fish with nets that have a minimum mesh size of 5.5-inch (14.0-cm...). Vessels fishing under the LOA shall not fish west of the line. Vessels issued a permit under § 648.4(a)(3.... (ii) [Reserved] (2) Vessels fishing with a two-seam otter trawl fly net with the following...

NeMO-Net & Fluid Lensing: The Neural Multi-Modal Observation & Training Network for Global Coral Reef Assessment Using Fluid Lensing Augmentation of NASA EOS Data

NASA Technical Reports Server (NTRS)

Chirayath, Ved

2018-01-01

We present preliminary results from NASA NeMO-Net, the first neural multi-modal observation and training network for global coral reef assessment. NeMO-Net is an open-source deep convolutional neural network (CNN) and interactive active learning training software in development which will assess the present and past dynamics of coral reef ecosystems. NeMO-Net exploits active learning and data fusion of mm-scale remotely sensed 3D images of coral reefs captured using fluid lensing with the NASA FluidCam instrument, presently the highest-resolution remote sensing benthic imaging technology capable of removing ocean wave distortion, as well as hyperspectral airborne remote sensing data from the ongoing NASA CORAL mission and lower-resolution satellite data to determine coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. Aquatic ecosystems, particularly coral reefs, remain quantitatively misrepresented by low-resolution remote sensing as a result of refractive distortion from ocean waves, optical attenuation, and remoteness. Machine learning classification of coral reefs using FluidCam mm-scale 3D data show that present satellite and airborne remote sensing techniques poorly characterize coral reef percent living cover, morphology type, and species breakdown at the mm, cm, and meter scales. Indeed, current global assessments of coral reef cover and morphology classification based on km-scale satellite data alone can suffer from segmentation errors greater than 40%, capable of change detection only on yearly temporal scales and decameter spatial scales, significantly hindering our understanding of patterns and processes in marine biodiversity at a time when these ecosystems are experiencing unprecedented anthropogenic pressures, ocean acidification, and sea surface temperature rise. NeMO-Net leverages our augmented machine learning algorithm that demonstrates data fusion of regional FluidCam (mm, cm-scale) airborne remote sensing with global low-resolution (m, km-scale) airborne and spaceborne imagery to reduce classification errors up to 80% over regional scales. Such technologies can substantially enhance our ability to assess coral reef ecosystems dynamics.

Key Information-Problem Solving Skills to Learn in Secondary Education: A Qualitative, Multi-Case Study

ERIC Educational Resources Information Center

Argelagós, Esther; Pifarré, Manoli

2016-01-01

Internet has become one of the most important information sources for students' personal and academic life. In addition, the World Wide Web is receiving increased attention in education because of its potential to support new forms of learning. However, using the information from the net for learning requires the development of a set of abilities…

"Cast Your Net Widely": Three Steps to Expanding and Refining Your Problem before Action Learning Application

ERIC Educational Resources Information Center

Reese, Simon R.

2015-01-01

This paper reflects upon a three-step process to expand the problem definition in the early stages of an action learning project. The process created a community-powered problem-solving approach within the action learning context. The simple three steps expanded upon in the paper create independence, dependence, and inter-dependence to aid the…

Meta-Analytic Evidence for a Reversal Learning Effect on the Iowa Gambling Task in Older Adults

PubMed Central

Pasion, Rita; Gonçalves, Ana R.; Fernandes, Carina; Ferreira-Santos, Fernando; Barbosa, Fernando; Marques-Teixeira, João

2017-01-01

Iowa Gambling Task (IGT) is one of the most widely used tools to assess economic decision-making. However, the research tradition on aging and the Iowa Gambling Task (IGT) has been mainly focused on the overall performance of older adults in relation to younger or clinical groups, remaining unclear whether older adults are capable of learning along the task. We conducted a meta-analysis to examine older adults' decision-making on the IGT, to test the effects of aging on reversal learning (45 studies) and to provide normative data on total and block net scores (55 studies). From the accumulated empirical evidence, we found an average total net score of 7.55 (±25.9). We also observed a significant reversal learning effect along the blocks of the IGT, indicating that older adults inhibit the prepotent response toward immediately attractive options associated with high losses, in favor of initially less attractive options associated with long-run profit. During block 1, decisions of older adults led to a negative gambling net score, reflecting the expected initial pattern of risk-taking. However, the shift toward more safe options occurred between block 2 (small-to-medium effect size) and blocks 3, 4, 5 (medium-to-large effect size). These main findings highlight that older adults are able to move from the initial uncertainty, when the possible outcomes are unknown, to decisions based on risk, when the outcomes are learned and may be used to guide future adaptive decision-making. PMID:29075222

Impaired clearance of neutrophils extracellular trap (NET) may induce detrimental tissular effect.

PubMed

Anjos, Paula M F; Fagundes-Netto, Fernanda S; Volpe, Caroline M O; Nogueira-Machado, Jose A

2014-01-01

Neutrophils Extracellular Trap (NET) is composed of nuclear chromatin with hyper segmentation of nuclear lobes, citrullination of histone-associated DNA and mixing with cytoplasmic proteins including the enzyme myeloperoxidase. It is believed that neutrophils trap can kill microorganisms and constitutes a new form of innate defense. However, in some conditions, NET formation may be detrimental to the organism due to its association with autoantibody formation. Thus, NETs can be beneficial or detrimental depending of the DNA clearance recent registered patents describing the processes, products, methods and therapeutic indications of the neutrophil extracellular trap (NET) phenomenon have been reported. The patents US8710039; EP2465536; EP2651440; US20130302345; US20140099648; US20130183662; WO2012166611; and RU2463349C2, related to NETosis, suggest an association between NET formation and autoimmunity. However, its function is still not fully understood. Some parasites have learned to escape from NET using nucleases. NET persistence could be due to a possible enzymatic inhibition as suggested in Grabar´s theory for explaining the induction of physiologic or pathologic autoantibodies. In the present mini-review NET persistence due to impairment in the homeostasis clearance of DNA is discussed.

Predicting multicellular function through multi-layer tissue networks

PubMed Central

Zitnik, Marinka; Leskovec, Jure

2017-01-01

Abstract Motivation: Understanding functions of proteins in specific human tissues is essential for insights into disease diagnostics and therapeutics, yet prediction of tissue-specific cellular function remains a critical challenge for biomedicine. Results: Here, we present OhmNet, a hierarchy-aware unsupervised node feature learning approach for multi-layer networks. We build a multi-layer network, where each layer represents molecular interactions in a different human tissue. OhmNet then automatically learns a mapping of proteins, represented as nodes, to a neural embedding-based low-dimensional space of features. OhmNet encourages sharing of similar features among proteins with similar network neighborhoods and among proteins activated in similar tissues. The algorithm generalizes prior work, which generally ignores relationships between tissues, by modeling tissue organization with a rich multiscale tissue hierarchy. We use OhmNet to study multicellular function in a multi-layer protein interaction network of 107 human tissues. In 48 tissues with known tissue-specific cellular functions, OhmNet provides more accurate predictions of cellular function than alternative approaches, and also generates more accurate hypotheses about tissue-specific protein actions. We show that taking into account the tissue hierarchy leads to improved predictive power. Remarkably, we also demonstrate that it is possible to leverage the tissue hierarchy in order to effectively transfer cellular functions to a functionally uncharacterized tissue. Overall, OhmNet moves from flat networks to multiscale models able to predict a range of phenotypes spanning cellular subsystems. Availability and implementation: Source code and datasets are available at http://snap.stanford.edu/ohmnet. Contact: jure@cs.stanford.edu PMID:28881986

Counting the costs: Comparing depot medroxyprogesterone acetate and norethisterone oenanthate utilisation patterns in South Africa

PubMed Central

2001-01-01

Background In South Africa, where health care resources are limited, it is important to ensure that drugs provision and use is rational. The Essential Drug List includes depot medroxyprogesterone acetate (DMPA) and norethisterone oenanthate (NET-EN) as injectable progestagen-only contraceptives (IPCs), and both products are extensively used. Objectives and Methods Utilisation patterns of the injectable contraceptive products DMPA and NET-EN are compared in the context of current knowledge of the safety and efficacy of these agents. Utilisation patterns were analysed by means of a Pareto (ABC) analysis of IPCs issued from 4 South African provincial pharmaceutical depots over 3 financial years. A case study from rural KwaZulu-Natal, South Africa, is used to examine utilisation patterns and self-reported side effects experienced by 187 women using IPCs. Results IPCs accounted for a substantial share of total state expenditure on drugs. While more DMPA than NET-EN was issued, NET-EN distribution from 2 depots increased over the 3-year period. Since DMPA was cheaper, if all NET-EN clients in the 1999/2000 financial year (annualised) had used DMPA, the 4 depots could have saved 4.95 million South African Rands on product acquisition costs alone. The KZN case study showed slightly more NET-EN (54%) than DMPA (46%) use; no significant differences in self-reported side effects; and that younger women were more likely to use NET-EN than DMPA (p = 0.0001). Conclusions Providing IPCs on the basis of age is not appropriate or cost effective. Rational use of these products should include consideration of the cost of prescribing one over another. PMID:11401729

A learning collaborative of CMHCs and CHCs to support integration of behavioral health and general medical care.

PubMed

Vannoy, Steven D; Mauer, Barbara; Kern, John; Girn, Kamaljeet; Ingoglia, Charles; Campbell, Jeannie; Galbreath, Laura; Unützer, Jürgen

2011-07-01

Integration of general medical and mental health services is a growing priority for safety-net providers. The authors describe a project that established a one-year learning collaborative focused on integration of services between community health centers (CHCs) and community mental health centers (CMHCs). Specific targets were treatment for general medical and psychiatric symptoms related to depression, bipolar disorder, alcohol use disorders, and metabolic syndrome. This observational study used mixed methods. Quantitative measures included 15 patient-level health indicators, practice self-assessment of resources and support for chronic disease self-management, and participant satisfaction. Sixteen CHC-CMHC pairs were selected for the learning collaborative series. One pair dropped out because of personnel turnover. All teams increased capacity on one or more patient health indicators. CHCs scored higher than CMHCs on support for chronic disease self-management. Participation in the learning collaborative increased self-assessment scores for CHCs and CMHCs. Participant satisfaction was high. Observations by faculty indicate that quality improvement challenges included tracking patient-level outcomes, workforce issues, and cross-agency communication. Even though numerous systemic barriers were encountered, the findings support existing literature indicating that the learning collaborative is a viable quality improvement approach for enhancing integration of general medical and mental health services between CHCs and CMHCs. Real-world implementation of evidence-based guidelines presents challenges often absent in research. Technical resources and support, a stable workforce with adequate training, and adequate opportunities for collaborator communications are particular challenges for integrating behavioral and general medical services across CHCs and CMHCs.

Dependability and performability analysis

NASA Technical Reports Server (NTRS)

Trivedi, Kishor S.; Ciardo, Gianfranco; Malhotra, Manish; Sahner, Robin A.

1993-01-01

Several practical issues regarding specifications and solution of dependability and performability models are discussed. Model types with and without rewards are compared. Continuous-time Markov chains (CTMC's) are compared with (continuous-time) Markov reward models (MRM's) and generalized stochastic Petri nets (GSPN's) are compared with stochastic reward nets (SRN's). It is shown that reward-based models could lead to more concise model specifications and solution of a variety of new measures. With respect to the solution of dependability and performability models, three practical issues were identified: largeness, stiffness, and non-exponentiality, and a variety of approaches are discussed to deal with them, including some of the latest research efforts.

Home-School Links: Networking the Learning Community.

ERIC Educational Resources Information Center

1996

The topic of networking the learning community with home-school links is addressed in four papers: "Internet Access via School: Expectations of Students and Parents" (Roy Crotty); "The School Library as Community Information Gateway" (Megan Perry); "Rural Access to the Internet" (Ken Eustace); and "NetDay '96:…

Using Web 2.0 to Support the Active Learning Experience

ERIC Educational Resources Information Center

Williams, Jo; Chinn, Susan J.

2009-01-01

Increased attention to student engagement and active learning strategies have become particularly relevant in today's classroom environments. These approaches are also considered to be meaningful when teaching "net generation" students who have different styles and expectations. This study attempts to address these challenges through the…

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

PubMed

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

2017-02-01

Automated segmentation of breast and fibroglandular tissue (FGT) is required for various computer-aided applications of breast MRI. Traditional image analysis and computer vision techniques, such atlas, template matching, or, edge and surface detection, have been applied to solve this task. However, applicability of these methods is usually limited by the characteristics of the images used in the study datasets, while breast MRI varies with respect to the different MRI protocols used, in addition to the variability in breast shapes. All this variability, in addition to various MRI artifacts, makes it a challenging task to develop a robust breast and FGT segmentation method using traditional approaches. Therefore, in this study, we investigated the use of a deep-learning approach known as "U-net." We used a dataset of 66 breast MRI's randomly selected from our scientific archive, which includes five different MRI acquisition protocols and breasts from four breast density categories in a balanced distribution. To prepare reference segmentations, we manually segmented breast and FGT for all images using an in-house developed workstation. We experimented with the application of U-net in two different ways for breast and FGT segmentation. In the first method, following the same pipeline used in traditional approaches, we trained two consecutive (2C) U-nets: first for segmenting the breast in the whole MRI volume and the second for segmenting FGT inside the segmented breast. In the second method, we used a single 3-class (3C) U-net, which performs both tasks simultaneously by segmenting the volume into three regions: nonbreast, fat inside the breast, and FGT inside the breast. For comparison, we applied two existing and published methods to our dataset: an atlas-based method and a sheetness-based method. We used Dice Similarity Coefficient (DSC) to measure the performances of the automated methods, with respect to the manual segmentations. Additionally, we computed Pearson's correlation between the breast density values computed based on manual and automated segmentations. The average DSC values for breast segmentation were 0.933, 0.944, 0.863, and 0.848 obtained from 3C U-net, 2C U-nets, atlas-based method, and sheetness-based method, respectively. The average DSC values for FGT segmentation obtained from 3C U-net, 2C U-nets, and atlas-based methods were 0.850, 0.811, and 0.671, respectively. The correlation between breast density values based on 3C U-net and manual segmentations was 0.974. This value was significantly higher than 0.957 as obtained from 2C U-nets (P < 0.0001, Steiger's Z-test with Bonferoni correction) and 0.938 as obtained from atlas-based method (P = 0.0016). In conclusion, we applied a deep-learning method, U-net, for segmenting breast and FGT in MRI in a dataset that includes a variety of MRI protocols and breast densities. Our results showed that U-net-based methods significantly outperformed the existing algorithms and resulted in significantly more accurate breast density computation. © 2016 American Association of Physicists in Medicine.

Discover Earth: An earth system science program for libraries and their communities

NASA Astrophysics Data System (ADS)

Curtis, L.; Dusenbery, P.

2010-12-01

The view from space has deepened our understanding of Earth as a global, dynamic system. Instruments on satellites and spacecraft, coupled with advances in ground-based research, have provided us with astonishing new perspectives of our planet. Now more than ever, enhancing the public’s understanding of Earth’s physical and biological systems is vital to helping citizens make informed policy decisions especially when they are faced with the consequences of global climate change. In spite of this relevance, there are many obstacles to achieving broad public understanding of key earth system science (ESS) concepts. Strategies for addressing climate change can only succeed with the full engagement of the general public. As reported by U.S. News and World Report in 2010, small towns in rural America are emerging as the front line in the climate change debate in the country. The Space Science Institute’s National Center for Interactive Learning (NCIL) in partnership with the American Library Association (ALA), the Lunar and Planetary Institute (LPI), and the National Girls Collaborative Project (NGCP) have received funding from NSF to develop a national project called the STAR Library Education Network: a hands-on learning program for libraries and their communities (or STAR-Net for short). STAR stands for Science-Technology, Activities and Resources. There are two distinct components of STAR-Net: Discover Earth and Discover Tech. While the focus for education reform is on school improvement, there is considerable research that supports the role that out-of-school experiences can play in student achievement. Libraries provide an untapped resource for engaging underserved youth and their families in fostering an appreciation and deeper understanding of science and technology topics. The overarching goal of the project is to reach underserved youth and their families with informal STEM learning experiences. The Discover Earth part of STAR_Net will produce ESS resources and inquiry-based activities that libraries can use to enrich the exhibit experience (focused on weather and climate). Additional resources will be provided through partnerships with relevant professional science organizations (e.g. American Geophysical Union) that will provide speakers for host library events and webinars. Online and in-person workshops will be conducted for library staff with a focus on increasing content knowledge and improving facilitation expertise. This presentation will provide an overview of the Discover Earth project and how it will address climate change issues, engage AGU scientists, and impact rural libraries nationwide.

Learning in Adolescence. The Andover Review: A Journal for Secondary Education, Spring 1978. [Special Issue]. [And] Learning in Adolescence II. The Andover Review: A Journal for Secondary Education, Fall 1978. [Special Issue].

ERIC Educational Resources Information Center

Brown, William H., Ed.

The document comprises two issues of a journal devoted to learning and adolescence. Each issue contains articles which were contributed by participants in a conference on learning and adolescence held at Phillips Academy, Andover, Massachusetts, in 1977. Articles in the Spring issue deal with formation of adolescents' values, observations of…

Is there a "net generation" in veterinary medicine? A comparative study on the use of the Internet and Web 2.0 by students and the veterinary profession.

PubMed

Tenhaven, Christoph; Tipold, Andrea; Fischer, Martin R; Ehlers, Jan P

2013-01-01

Informal and formal lifelong learning is essential at university and in the workplace. Apart from classical learning techniques, Web 2.0 tools can be used. It is controversial whether there is a so-called net generation amongst people under 30. To test the hypothesis that a net generation among students and young veterinarians exists. An online survey of students and veterinarians was conducted in the German-speaking countries which was advertised via online media and traditional print media. 1780 people took part in the survey. Students and veterinarians have different usage patterns regarding social networks (91.9% vs. 69%) and IM (55.9% vs. 24.5%). All tools were predominantly used passively and in private, to a lesser extent also professionally and for studying. The use of Web 2.0 tools is useful, however, teaching information and media skills, preparing codes of conduct for the internet and verification of user generated content is essential.

Usability inspection to improve an electronic provincial medication repository.

PubMed

Kitson, Nicole A; Price, Morgan; Bowen, Michael; Lau, Francis

2013-01-01

Medication errors are a significant source of actual and potential harm for patients. Community medication records have the potential to reduce medication errors, but they can also introduce unintended consequences when there is low fit to task (low cognitive fit). PharmaNet is a provincially managed electronic repository that contains the records for community-based pharmacy-dispensed medications in British Columbia. This research explores the usability of PharmaNet, as a representative community-based medication repository. We completed usability inspections of PharmaNet through vendor applications. Vendor participants were asked to complete activity-driven scenarios, which highlighted aspects of medication management workflow. Screen recording was later reviewed. Heuristics were applied to explore usability issues and improvement opportunities. Usability inspection was conducted with four PharmaNet applications. Ninety-six usability issues were identified; half of these had potential implications for patient safety. These were primarily related to login and logout procedures; display of patient name; display of medications; update and display of alert information; and the changing or discontinuation of medications. PharmaNet was designed primarily to support medication dispensing and billing activities by community pharmacies, but is also used to support care providers with monitoring and prescribing activities. As such, some of the features do not have a strong fit for other clinical activities. To improve fit, we recommend: having a Current Medications List and Displaying Medication Utilization Charts.

The Design of NetSecLab: A Small Competition-Based Network Security Lab

ERIC Educational Resources Information Center

Lee, C. P.; Uluagac, A. S.; Fairbanks, K. D.; Copeland, J. A.

2011-01-01

This paper describes a competition-style of exercise to teach system and network security and to reinforce themes taught in class. The exercise, called NetSecLab, is conducted on a closed network with student-formed teams, each with their own Linux system to defend and from which to launch attacks. Students are expected to learn how to: 1) install…

17 CFR 270.2a-4 - Definition of “current net asset value” for use in computing periodically the current price of...

Code of Federal Regulations, 2010 CFR

2010-04-01

... AND REGULATIONS, INVESTMENT COMPANY ACT OF 1940 § 270.2a-4 Definition of “current net asset value” for... value of any redeemable security issued by a registered investment company used in computing... which reflects calculations, whether or not recorded in the books of account, made substantially in...

Intercreativity: Mapping Online Activism

NASA Astrophysics Data System (ADS)

Meikle, Graham

How do activists use the Internet? This article maps a wide range of activist practice and research by applying and developing Tim Berners-Lee's concept of ‘intercreativity' (1999). It identifies four dimensions of Net activism: intercreative texts, tactics, strategies and networks. It develops these through examples of manifestations of Net activism around one cluster of issues: support campaigns for refugees and asylum seekers.

Alexnet Feature Extraction and Multi-Kernel Learning for Objectoriented Classification

NASA Astrophysics Data System (ADS)

Ding, L.; Li, H.; Hu, C.; Zhang, W.; Wang, S.

2018-04-01

In view of the fact that the deep convolutional neural network has stronger ability of feature learning and feature expression, an exploratory research is done on feature extraction and classification for high resolution remote sensing images. Taking the Google image with 0.3 meter spatial resolution in Ludian area of Yunnan Province as an example, the image segmentation object was taken as the basic unit, and the pre-trained AlexNet deep convolution neural network model was used for feature extraction. And the spectral features, AlexNet features and GLCM texture features are combined with multi-kernel learning and SVM classifier, finally the classification results were compared and analyzed. The results show that the deep convolution neural network can extract more accurate remote sensing image features, and significantly improve the overall accuracy of classification, and provide a reference value for earthquake disaster investigation and remote sensing disaster evaluation.

Nonparametric Representations for Integrated Inference, Control, and Sensing

DTIC Science & Technology

2015-10-01

Learning (ICML), 2013. [20] Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, and Trevor Darrell. DeCAF: A deep ...unlimited. Multi-layer feature learning “SuperVision” Convolutional Neural Network (CNN) ImageNet Classification with Deep Convolutional Neural Networks...to develop a new framework for autonomous operations that will extend the state of the art in distributed learning and modeling from data, and

Small Private Online Research: A Proposal for A Numerical Methods Course Based on Technology Use and Blended Learning

ERIC Educational Resources Information Center

Cepeda, Francisco Javier Delgado

2017-01-01

This work presents a proposed model in blended learning for a numerical methods course evolved from traditional teaching into a research lab in scientific visualization. The blended learning approach sets a differentiated and flexible scheme based on a mobile setup and face to face sessions centered on a net of research challenges. Model is…

Facilitating Autonomy and Creativity in Second Language Learning through Cyber-Tasks, Hyperlinks and Net-Surfing

ERIC Educational Resources Information Center

Akinwamide, T. K.; Adedara, O. G.

2012-01-01

The digitalization of academic interactions and collaborations in this present technologically conscious world is making collaborations between technology and pedagogy in the teaching and learning processes to display logical and systematic reasoning rather than the usual stereotyped informed decisions. This simply means, pedagogically, learning…

Whose Classroom Is It, Anyway? Improvisation as a Teaching Tool

ERIC Educational Resources Information Center

Berk, Ronald A.; Trieber, Rosalind H.

2009-01-01

Improvisational techniques derived from the experiences in improvisational theatre can be adapted for the college classroom to leverage the characteristics of the Net Generation, their multiple intelligences and learning styles, and the variety of collaborative learning activities already in place in a learner-centered environment. When…

Net Results: Online Protocols Boost Group Learning Potential

ERIC Educational Resources Information Center

Dichter, Alan; Zydney, Janet Mannheimer

2015-01-01

Educators have begun to use protocols to facilitate professional development in online spaces--partly because people need to connect from different places, but also to take advantage of new environments for learning. For example, asynchronous tools, such as discussion forums, blogs, or Google+, where participants post messages to one another at…

Designing Online Learning Communities of Practice: A Democratic Perspective

ERIC Educational Resources Information Center

Sorensen, Elsebeth Korsgaard; Murchu, Daithi O.

2004-01-01

This study addresses the problem of designing an appropriate learning space or architecture for distributed online courses using net-based communication technologies. We apply Wenger's criteria to explore, identify and discuss the design architectures of two online courses from two comparable online Master's programmes, developed and delivered in…

Improving Virtual Collaborative Learning through Canonical Action Research

ERIC Educational Resources Information Center

Weber, Peter; Lehr, Christian; Gersch, Martin

2014-01-01

Virtual collaboration continues to gain in significance and is attracting attention also as virtual collaborative learning (VCL) in education. This paper addresses aspects of VCL that we identified as critical in a series of courses named "Net Economy": (1) technical infrastructure, (2) motivation and collaboration, and (3) assessment…

K-12 Computer Networking.

ERIC Educational Resources Information Center

ERIC Review, 1993

1993-01-01

The "ERIC Review" is published three times a year and announces research results, publications, and new programs relevant to each issue's theme topic. This issue explores computer networking in elementary and secondary schools via two principal articles: "Plugging into the 'Net'" (Michael B. Eisenberg and Donald P. Ely); and…

75 FR 26822 - Sunshine Act; Notice of Meeting

Federal Register 2010, 2011, 2012, 2013, 2014

2010-05-12

... Committee; (iii) briefing on the Investor as Owner Subcommittee's environmental, social, and governance disclosure workplan; (iv) update on certain issues involved in financial reform legislation; (v) discussion... money market funds and the issue of net asset value (``NAV''), including a presentation by SEC staff...

Neuromorphic learning of continuous-valued mappings from noise-corrupted data. Application to real-time adaptive control

NASA Technical Reports Server (NTRS)

Troudet, Terry; Merrill, Walter C.

1990-01-01

The ability of feed-forward neural network architectures to learn continuous valued mappings in the presence of noise was demonstrated in relation to parameter identification and real-time adaptive control applications. An error function was introduced to help optimize parameter values such as number of training iterations, observation time, sampling rate, and scaling of the control signal. The learning performance depended essentially on the degree of embodiment of the control law in the training data set and on the degree of uniformity of the probability distribution function of the data that are presented to the net during sequence. When a control law was corrupted by noise, the fluctuations of the training data biased the probability distribution function of the training data sequence. Only if the noise contamination is minimized and the degree of embodiment of the control law is maximized, can a neural net develop a good representation of the mapping and be used as a neurocontroller. A multilayer net was trained with back-error-propagation to control a cart-pole system for linear and nonlinear control laws in the presence of data processing noise and measurement noise. The neurocontroller exhibited noise-filtering properties and was found to operate more smoothly than the teacher in the presence of measurement noise.

Inductive Learning Approaches for Improving Pilot Awareness of Aircraft Faults

NASA Technical Reports Server (NTRS)

Spikovska, Lilly; Iverson, David L.; Poll, Scott; Pryor, anna

2005-01-01

Neural network flight controllers are able to accommodate a variety of aircraft control surface faults without detectable degradation of aircraft handling qualities. Under some faults, however, the effective flight envelope is reduced; this can lead to unexpected behavior if a pilot performs an action that exceeds the remaining control authority of the damaged aircraft. The goal of our work is to increase the pilot s situational awareness by informing him of the type of damage and resulting reduction in flight envelope. Our methodology integrates two inductive learning systems with novel visualization techniques. One learning system, the Inductive Monitoring System (IMS), learns to detect when a simulation includes faulty controls, while two others, Inductive Classification System (INCLASS) and multiple binary decision tree system (utilizing C4.5), determine the type of fault. In off-line training using only non-failure data, IMS constructs a characterization of nominal flight control performance based on control signals issued by the neural net flight controller. This characterization can be used to determine the degree of control augmentation required in the pitch, roll, and yaw command channels to counteract control surface failures. This derived information is typically sufficient to distinguish between the various control surface failures and is used to train both INCLASS and C4.5. Using data from failed control surface flight simulations, INCLASS and C4.5 independently discover and amplify features in IMS results that can be used to differentiate each distinct control surface failure situation. In real-time flight simulations, distinguishing features learned during training are used to classify control surface failures. Knowledge about the type of failure can be used by an additional automated system to alter its approach for planning tactical and strategic maneuvers. The knowledge can also be used directly to increase the pilot s situational awareness and inform manual maneuver decisions. Our multi-modal display of this information provides speech output to issue control surface failure warnings to a lesser-used communication channel and provides graphical displays with pilot-selectable !eve!s of details to issues additional information about the failure. We also describe a potential presentation for flight envelope reduction that can be viewed separately or integrated with an existing attitude indicator instrument. Preliminary results suggest that the inductive approach is capable of detecting that a control surface has failed and determining the type of fault. Furthermore, preliminary evaluations suggest that the interface discloses a concise summary of this information to the pilot.

Optimizing Sampling Design to Deal with Mist-Net Avoidance in Amazonian Birds and Bats

PubMed Central

Marques, João Tiago; Ramos Pereira, Maria J.; Marques, Tiago A.; Santos, Carlos David; Santana, Joana; Beja, Pedro; Palmeirim, Jorge M.

2013-01-01

Mist netting is a widely used technique to sample bird and bat assemblages. However, captures often decline with time because animals learn and avoid the locations of nets. This avoidance or net shyness can substantially decrease sampling efficiency. We quantified the day-to-day decline in captures of Amazonian birds and bats with mist nets set at the same location for four consecutive days. We also evaluated how net avoidance influences the efficiency of surveys under different logistic scenarios using re-sampling techniques. Net avoidance caused substantial declines in bird and bat captures, although more accentuated in the latter. Most of the decline occurred between the first and second days of netting: 28% in birds and 47% in bats. Captures of commoner species were more affected. The numbers of species detected also declined. Moving nets daily to minimize the avoidance effect increased captures by 30% in birds and 70% in bats. However, moving the location of nets may cause a reduction in netting time and captures. When moving the nets caused the loss of one netting day it was no longer advantageous to move the nets frequently. In bird surveys that could even decrease the number of individuals captured and species detected. Net avoidance can greatly affect sampling efficiency but adjustments in survey design can minimize this. Whenever nets can be moved without losing netting time and the objective is to capture many individuals, they should be moved daily. If the main objective is to survey species present then nets should still be moved for bats, but not for birds. However, if relocating nets causes a significant loss of netting time, moving them to reduce effects of shyness will not improve sampling efficiency in either group. Overall, our findings can improve the design of mist netting sampling strategies in other tropical areas. PMID:24058579

Neural network and letter recognition

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

Lee, Hue Yeon.

Neural net architectures and learning algorithms that recognize hand written 36 alphanumeric characters are studied. The thin line input patterns written in 32 x 32 binary array are used. The system is comprised of two major components, viz. a preprocessing unit and a Recognition unit. The preprocessing unit in turn consists of three layers of neurons; the U-layer, the V-layer, and the C-layer. The functions of the U-layer is to extract local features by template matching. The correlation between the detected local features are considered. Through correlating neurons in a plane with their neighboring neurons, the V-layer would thicken themore » on-cells or lines that are groups of on-cells of the previous layer. These two correlations would yield some deformation tolerance and some of the rotational tolerance of the system. The C-layer then compresses data through the Gabor transform. Pattern dependent choice of center and wavelengths of Gabor filters is the cause of shift and scale tolerance of the system. Three different learning schemes had been investigated in the recognition unit, namely; the error back propagation learning with hidden units, a simple perceptron learning, and a competitive learning. Their performances were analyzed and compared. Since sometimes the network fails to distinguish between two letters that are inherently similar, additional ambiguity resolving neural nets are introduced on top of the above main neural net. The two dimensional Fourier transform is used as the preprocessing and the perceptron is used as the recognition unit of the ambiguity resolver. One hundred different person's handwriting sets are collected. Some of these are used as the training sets and the remainders are used as the test sets.« less

SEMANTIC3D.NET: a New Large-Scale Point Cloud Classification Benchmark

NASA Astrophysics Data System (ADS)

Hackel, T.; Savinov, N.; Ladicky, L.; Wegner, J. D.; Schindler, K.; Pollefeys, M.

2017-05-01

This paper presents a new 3D point cloud classification benchmark data set with over four billion manually labelled points, meant as input for data-hungry (deep) learning methods. We also discuss first submissions to the benchmark that use deep convolutional neural networks (CNNs) as a work horse, which already show remarkable performance improvements over state-of-the-art. CNNs have become the de-facto standard for many tasks in computer vision and machine learning like semantic segmentation or object detection in images, but have no yet led to a true breakthrough for 3D point cloud labelling tasks due to lack of training data. With the massive data set presented in this paper, we aim at closing this data gap to help unleash the full potential of deep learning methods for 3D labelling tasks. Our semantic3D.net data set consists of dense point clouds acquired with static terrestrial laser scanners. It contains 8 semantic classes and covers a wide range of urban outdoor scenes: churches, streets, railroad tracks, squares, villages, soccer fields and castles. We describe our labelling interface and show that our data set provides more dense and complete point clouds with much higher overall number of labelled points compared to those already available to the research community. We further provide baseline method descriptions and comparison between methods submitted to our online system. We hope semantic3D.net will pave the way for deep learning methods in 3D point cloud labelling to learn richer, more general 3D representations, and first submissions after only a few months indicate that this might indeed be the case.

Learning fuzzy information in a hybrid connectionist, symbolic model

NASA Technical Reports Server (NTRS)

Romaniuk, Steve G.; Hall, Lawrence O.

1993-01-01

An instance-based learning system is presented. SC-net is a fuzzy hybrid connectionist, symbolic learning system. It remembers some examples and makes groups of examples into exemplars. All real-valued attributes are represented as fuzzy sets. The network representation and learning method is described. To illustrate this approach to learning in fuzzy domains, an example of segmenting magnetic resonance images of the brain is discussed. Clearly, the boundaries between human tissues are ill-defined or fuzzy. Example fuzzy rules for recognition are generated. Segmentations are presented that provide results that radiologists find useful.

Smartphones as Experimental Tools: Different Methods to Determine the Gravitational Acceleration in Classroom Physics by Using Everyday Devices

ERIC Educational Resources Information Center

Kuhn, Jochen; Vogt, Patrik

2013-01-01

New media technology becomes more and more important for our daily life as well as for teaching physics. Within the scope of our N.E.T. research project we develop experiments using New Media Experimental Tools (N.E.T.) in physics education and study their influence on students learning abilities. We want to present the possibilities e.g. of…

Application of Convolution Neural Network to the forecasts of flare classification and occurrence using SOHO MDI data

NASA Astrophysics Data System (ADS)

Park, Eunsu; Moon, Yong-Jae

2017-08-01

A Convolutional Neural Network(CNN) is one of the well-known deep-learning methods in image processing and computer vision area. In this study, we apply CNN to two kinds of flare forecasting models: flare classification and occurrence. For this, we consider several pre-trained models (e.g., AlexNet, GoogLeNet, and ResNet) and customize them by changing several options such as the number of layers, activation function, and optimizer. Our inputs are the same number of SOHO)/MDI images for each flare class (None, C, M and X) at 00:00 UT from Jan 1996 to Dec 2010 (total 1600 images). Outputs are the results of daily flare forecasting for flare class and occurrence. We build, train, and test the models on TensorFlow, which is well-known machine learning software library developed by Google. Our major results from this study are as follows. First, most of the models have accuracies more than 0.7. Second, ResNet developed by Microsoft has the best accuracies : 0.86 for flare classification and 0.84 for flare occurrence. Third, the accuracies of these models vary greatly with changing parameters. We discuss several possibilities to improve the models.

The Federal Budget: Current and Upcoming Issues

DTIC Science & Technology

2009-12-31

90 Milton Friedman , Capitalism and Freedom (Chicago: Univ. of Chicago Press, 1962), pp. 75-84. . The Federal Budget: Current...government made significant financial interventions aimed at alleviating economic recession. The final costs of federal responses to this turmoil will...benefits paid net of Social Security payroll taxes collected and the U.S. Postal Service’s net balance) the (on-budget) FY2008 federal deficit was

Keynote: FarNet Ten Years On--The Past, Present, and Future for Distance Learners

ERIC Educational Resources Information Center

Alexander-Bennett, Carolyn

2016-01-01

This think piece by Carolyn Alexander-Bennett is a reflection of her keynote speech at DEANZ2016 conference, which was held from 17-20th April at the University of Waikato, New Zealand. In her speech Carolyn revisits the issues, developments, and technology trends that led to the birth of FarNet (an online cluster of schools catering for the…

Diabetes and pancreatic neuroendocrine tumours: Which interplays, if any?

PubMed

Gallo, Marco; Ruggeri, Rosaria Maddalena; Muscogiuri, Giovanna; Pizza, Genoveffa; Faggiano, Antongiulio; Colao, Annamaria

2018-06-01

Pancreatic neuroendocrine tumours (PanNETs) represent an uncommon type of pancreatic neoplasm, whose incidence is increasing worldwide. As per exocrine pancreatic cancer, a relationship seems to exist between PanNETs and glycaemic alterations. Diabetes mellitus (DM) or impaired glucose tolerance often occurs in PanNET patients as a consequence of hormonal hypersecretion by the tumour, specifically affecting glucose metabolism, or due to tumour mass effects. On the other hand, pre-existing DM may represent a risk factor for developing PanNETs and is likely to worsen the prognosis of such patients. Moreover, the surgical and/or pharmacological treatment of the tumour itself may impair glucose tolerance, as well as antidiabetic therapies may impact tumour behaviour and patients outcome. Differently from exocrine pancreatic tumours, few data are available for PanNETs as yet on this issue. In the present review, the bidirectional association between glycaemic disorders and PanNETs has been extensively examined, since the co-existence of both diseases in the same individual represents a further challenge for the clinical management of PanNETs. Copyright © 2018 Elsevier Ltd. All rights reserved.

Clinical Trial Design in Neuroendocrine Tumors.

PubMed

Halperin, Daniel M; Yao, James C

2016-02-01

Neuroendocrine tumors (NETs) present tremendous opportunities for productive clinical investigation, but substantial challenges as well. Investigators must be aware of common pitfalls in study design, informed by an understanding of the history of trials in the field, to make the best use of available data and our patient volunteers. We believe the salient issues in clinical trial design and interpretation in the NET field are patient homogeneity, standardized response assessment, and rigorous design and execution. Whether designing or interpreting a study in patients with NET, these principles should drive assessment. Copyright © 2016 Elsevier Inc. All rights reserved.

Understanding Game-Based Learning Cultures: Introduction to Special Issue

ERIC Educational Resources Information Center

Engerman, Jason A.; Carr-Chellman, Alison

2017-01-01

This special issue expands our understanding of teaching and learning through video game play, with specific attention to culture. The issue gives insight into the ways educators, researchers, and developers should be discussing and designing for impactful learner-centered game-based learning experiences. The issue features forward-thinking…

A New Method for Measuring Text Similarity in Learning Management Systems Using WordNet

ERIC Educational Resources Information Center

Alkhatib, Bassel; Alnahhas, Ammar; Albadawi, Firas

2014-01-01

As text sources are getting broader, measuring text similarity is becoming more compelling. Automatic text classification, search engines and auto answering systems are samples of applications that rely on text similarity. Learning management systems (LMS) are becoming more important since electronic media is getting more publicly available. As…

Army Learning Concept 2015: These Are Not the Droids You Are Looking For

DTIC Science & Technology

2011-06-07

instruction, gaming, video , interactive multimedia instruction, virtual worlds, massively multiplayer online games, simulations, and others.” A wide...games, buying stuff on E-Bay and surfing the net for porn ? “These are not the “an” droids we are looking for…” 24 ALC 2015 should enhance learning

Segmenting the Net-Generation: Embracing the Next Level of Technology

ERIC Educational Resources Information Center

Smith, Russell K.

2014-01-01

A segmentation study is used to partition college students into groups that are more or less likely to adopt tablet technology as a learning tool. Because the college population chosen for study presently relies upon laptop computers as their primary learning device, tablet technology represents a "next step" in technology. Student…

Leading in Reading: Nebraska District Nets Success with Evidence-Based Learning

ERIC Educational Resources Information Center

Mueller, Melanie; Hanson, Ron

2014-01-01

Mueller and Hanson report on a continuous improvement process taking place in the Papillion-La Vista School District in Papillion, Nebraska, where a proactive stance to improved learning for all students focuses directly on the human element as the change agent. The district has implemented a systemic and systematic continuous improvement process…

A Collaborative Model for Influencing the Technology Integration Behaviors and Dispositions of Preservice Social Studies Teachers

ERIC Educational Resources Information Center

Jones, Raymond; Cunningham, Ann; Stewart, Loraine Moses

2005-01-01

Collaboration among faculty can enhance the learning experience for preservice teachers and reinforce the integral role of technology in teaching, learning, and professional development in social studies education. Organized around the Performance Profiles outlined by the National Educational Technology Standards for Teachers (NETS[middle dot]T),…

Analysis of matters associated with the transferring of object-oriented applications to platform .Net using C# programming language

NASA Astrophysics Data System (ADS)

Sarsimbayeva, S. M.; Kospanova, K. K.

2015-11-01

The article provides the discussion of matters associated with the problems of transferring of object-oriented Windows applications from C++ programming language to .Net platform using C# programming language. C++ has always been considered to be the best language for the software development, but the implicit mistakes that come along with the tool may lead to infinite memory leaks and other errors. The platform .Net and the C#, made by Microsoft, are the solutions to the issues mentioned above. The world economy and production are highly demanding applications developed by C++, but the new language with its stability and transferability to .Net will bring many advantages. An example can be presented using the applications that imitate the work of queuing systems. Authors solved the problem of transferring of an application, imitating seaport works, from C++ to the platform .Net using C# in the scope of Visual Studio.

Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks.

PubMed

Lakhani, Paras; Sundaram, Baskaran

2017-08-01

Purpose To evaluate the efficacy of deep convolutional neural networks (DCNNs) for detecting tuberculosis (TB) on chest radiographs. Materials and Methods Four deidentified HIPAA-compliant datasets were used in this study that were exempted from review by the institutional review board, which consisted of 1007 posteroanterior chest radiographs. The datasets were split into training (68.0%), validation (17.1%), and test (14.9%). Two different DCNNs, AlexNet and GoogLeNet, were used to classify the images as having manifestations of pulmonary TB or as healthy. Both untrained and pretrained networks on ImageNet were used, and augmentation with multiple preprocessing techniques. Ensembles were performed on the best-performing algorithms. For cases where the classifiers were in disagreement, an independent board-certified cardiothoracic radiologist blindly interpreted the images to evaluate a potential radiologist-augmented workflow. Receiver operating characteristic curves and areas under the curve (AUCs) were used to assess model performance by using the DeLong method for statistical comparison of receiver operating characteristic curves. Results The best-performing classifier had an AUC of 0.99, which was an ensemble of the AlexNet and GoogLeNet DCNNs. The AUCs of the pretrained models were greater than that of the untrained models (P < .001). Augmenting the dataset further increased accuracy (P values for AlexNet and GoogLeNet were .03 and .02, respectively). The DCNNs had disagreement in 13 of the 150 test cases, which were blindly reviewed by a cardiothoracic radiologist, who correctly interpreted all 13 cases (100%). This radiologist-augmented approach resulted in a sensitivity of 97.3% and specificity 100%. Conclusion Deep learning with DCNNs can accurately classify TB at chest radiography with an AUC of 0.99. A radiologist-augmented approach for cases where there was disagreement among the classifiers further improved accuracy. © RSNA, 2017.

StarNet: An application of deep learning in the analysis of stellar spectra

NASA Astrophysics Data System (ADS)

Kielty, Collin; Bialek, Spencer; Fabbro, Sebastien; Venn, Kim; O'Briain, Teaghan; Jahandar, Farbod; Monty, Stephanie

2018-06-01

In an era when spectroscopic surveys are capable of collecting spectra for hundreds of thousands of stars, fast and efficient analysis methods are required to maximize scientific impact. These surveys provide a homogeneous database of stellar spectra that are ideal for machine learning applications. In this poster, we present StarNet: a convolutional neural network model applied to the analysis of both SDSS-III APOGEE DR13 and synthetic stellar spectra. When trained on synthetic spectra alone, the calculated stellar parameters (temperature, surface gravity, and metallicity) are of excellent precision and accuracy for both APOGEE data and synthetic data, over a wide range of signal-to-noise ratios. While StarNet was developed using the APOGEE observed spectra and corresponding ASSeT synthetic grid, we suggest that this technique is applicable to other spectral resolutions, spectral surveys, and wavelength regimes. As a demonstration of this, we present a StarNet model trained on lower resolution, R=6000, IR synthetic spectra, describing the spectra delivered by Gemini/NIFS and the forthcoming Gemini/GIRMOS instrument (PI Sivanandam, UToronto). Preliminary results suggest that the stellar parameters determined from this low resolution StarNet model are comparable in precision to the high-resolution APOGEE results. The success of StarNet at lower resolution can be attributed to (1) a large training set of synthetic spectra (N ~200,000) with a priori stellar labels, and (2) the use of the entire spectrum in the solution rather than a few weighted windows, which are common methods in other spectral analysis tools (e.g. FERRE or The Cannon). Remaining challenges in our StarNet applications include rectification, continuum normalization, and wavelength coverage. Solutions to these problems could be used to guide decisions made in the development of future spectrographs, spectroscopic surveys, and data reduction pipelines, such as for the future MSE.

Communicator, 1997.

ERIC Educational Resources Information Center

Bortolussi, Vicki, Ed.

1997-01-01

The CAG "Communicator" focus is on serving gifted students in California. This document consists of the four issues of "communicator" issued during 1997. Featured articles include: (1) "The Gifted Student At Risk. It Can't Be True" (Judy Roseberry); (2) "Tech Net-Technology and At-Risk Students" (Judy Lieb); (3) "Reviving Ophelia: Saving the…

Investigating Technical and Pedagogical Usability Issues of Collaborative Learning with Wikis

ERIC Educational Resources Information Center

Hadjerrouit, Said

2012-01-01

Wikis have been recently promoted as tools that foster collaborative learning. However, there has been little research devoted to the criteria that are suitable to address issues pertinent to collaborative learning. This paper proposes a set of criteria to explore technical and pedagogical usability issues of collaborative learning with wikis. The…

Exploring E-Learning. IES Report 376.

ERIC Educational Resources Information Center

Pollard, E.; Hillage, J.

This guide summarizes current research and commentary on e-learning, examining the key issues facing organizations exploring e-learning for employee development. The guide contains six sections. The first section provides an introduction to the issue of e-learning and a summary of the issues discussed in the remainder of the guide. Section 2…

Action Learning--An Experiential Tool for Solving Organizational Issues

ERIC Educational Resources Information Center

Kinsey, Sharon B.

2011-01-01

Action Learning can be effectively used in both large and small businesses and organizations by employees, stakeholders, or volunteers through this "learning by doing" approach to evaluate an issue or issues of importance to the organization. First developed in the 1940s, Action Learning has increasingly been used as a method to explore questions…

Contextual Learning Issues.

ERIC Educational Resources Information Center

1999

This document contains four symposium papers on contextual learning issues. "Learning to Learn Strategies of Successful Real Estate Professionals: Implications for Learning in the Workplace" (Margot B. Weinstein) describes a multicase study in which a model called the Individual Learning System was used to identify the strategies and…

Agile convolutional neural network for pulmonary nodule classification using CT images.

PubMed

Zhao, Xinzhuo; Liu, Liyao; Qi, Shouliang; Teng, Yueyang; Li, Jianhua; Qian, Wei

2018-04-01

To distinguish benign from malignant pulmonary nodules using CT images is critical for their precise diagnosis and treatment. A new Agile convolutional neural network (CNN) framework is proposed to conquer the challenges of a small-scale medical image database and the small size of the nodules, and it improves the performance of pulmonary nodule classification using CT images. A hybrid CNN of LeNet and AlexNet is constructed through combining the layer settings of LeNet and the parameter settings of AlexNet. A dataset with 743 CT image nodule samples is built up based on the 1018 CT scans of LIDC to train and evaluate the Agile CNN model. Through adjusting the parameters of the kernel size, learning rate, and other factors, the effect of these parameters on the performance of the CNN model is investigated, and an optimized setting of the CNN is obtained finally. After finely optimizing the settings of the CNN, the estimation accuracy and the area under the curve can reach 0.822 and 0.877, respectively. The accuracy of the CNN is significantly dependent on the kernel size, learning rate, training batch size, dropout, and weight initializations. The best performance is achieved when the kernel size is set to [Formula: see text], the learning rate is 0.005, the batch size is 32, and dropout and Gaussian initialization are used. This competitive performance demonstrates that our proposed CNN framework and the optimization strategy of the CNN parameters are suitable for pulmonary nodule classification characterized by small medical datasets and small targets. The classification model might help diagnose and treat pulmonary nodules effectively.

Internet-Mediated Learning in Public Affairs Programs: Issues and Implications.

ERIC Educational Resources Information Center

Rahm, Dianne; Reed, B. J.; Rydl, Teri L.

1999-01-01

An overview of Internet-mediated learning in public affairs programs identifies issues for faculty, students, and administrators, including intellectual property rights, instructional issues, learning approaches, student expectations, logistics and support, complexity of coordination, and organizational control. (DB)

AggNet: Deep Learning From Crowds for Mitosis Detection in Breast Cancer Histology Images.

PubMed

Albarqouni, Shadi; Baur, Christoph; Achilles, Felix; Belagiannis, Vasileios; Demirci, Stefanie; Navab, Nassir

2016-05-01

The lack of publicly available ground-truth data has been identified as the major challenge for transferring recent developments in deep learning to the biomedical imaging domain. Though crowdsourcing has enabled annotation of large scale databases for real world images, its application for biomedical purposes requires a deeper understanding and hence, more precise definition of the actual annotation task. The fact that expert tasks are being outsourced to non-expert users may lead to noisy annotations introducing disagreement between users. Despite being a valuable resource for learning annotation models from crowdsourcing, conventional machine-learning methods may have difficulties dealing with noisy annotations during training. In this manuscript, we present a new concept for learning from crowds that handle data aggregation directly as part of the learning process of the convolutional neural network (CNN) via additional crowdsourcing layer (AggNet). Besides, we present an experimental study on learning from crowds designed to answer the following questions. 1) Can deep CNN be trained with data collected from crowdsourcing? 2) How to adapt the CNN to train on multiple types of annotation datasets (ground truth and crowd-based)? 3) How does the choice of annotation and aggregation affect the accuracy? Our experimental setup involved Annot8, a self-implemented web-platform based on Crowdflower API realizing image annotation tasks for a publicly available biomedical image database. Our results give valuable insights into the functionality of deep CNN learning from crowd annotations and prove the necessity of data aggregation integration.

Squeeze-SegNet: a new fast deep convolutional neural network for semantic segmentation

NASA Astrophysics Data System (ADS)

Nanfack, Geraldin; Elhassouny, Azeddine; Oulad Haj Thami, Rachid

2018-04-01

The recent researches in Deep Convolutional Neural Network have focused their attention on improving accuracy that provide significant advances. However, if they were limited to classification tasks, nowadays with contributions from Scientific Communities who are embarking in this field, they have become very useful in higher level tasks such as object detection and pixel-wise semantic segmentation. Thus, brilliant ideas in the field of semantic segmentation with deep learning have completed the state of the art of accuracy, however this architectures become very difficult to apply in embedded systems as is the case for autonomous driving. We present a new Deep fully Convolutional Neural Network for pixel-wise semantic segmentation which we call Squeeze-SegNet. The architecture is based on Encoder-Decoder style. We use a SqueezeNet-like encoder and a decoder formed by our proposed squeeze-decoder module and upsample layer using downsample indices like in SegNet and we add a deconvolution layer to provide final multi-channel feature map. On datasets like Camvid or City-states, our net gets SegNet-level accuracy with less than 10 times fewer parameters than SegNet.

Frames of Reference for the Assessment of Learning Disabilities: New Views on Measurement Issues.

ERIC Educational Resources Information Center

Lyon, G. Reid, Ed.

This book offers 27 papers addressing critical issues in the assessment of students with all kinds of learning disabilities. Papers have the following titles and authors: "Critical Issues in the Measurement of Learning Disabilities" (G. Reid Lyon); "A Matrix of Decision Points in the Measurement of Learning Disabilities" (Barbara K. Keogh);…

Use of the EpiNet database for observational study of status epilepticus in Auckland, New Zealand.

PubMed

Bergin, Peter; Jayabal, Jayaganth; Walker, Elizabeth; Davis, Suzanne; Jones, Peter; Dalziel, Stuart; Yates, Kim; Thornton, Vanessa; Bennett, Patricia; Wilson, Kaisa; Roberts, Lynair; Litchfield, Rhonda; Te Ao, Braden; Parmer, Priya; Feigin, Valery; Jost, Jeremy; Beghi, Ettore; Rossetti, Andrea O

2015-08-01

The EpiNet project has been established to facilitate investigator-initiated clinical research in epilepsy, to undertake epidemiological studies, and to simultaneously improve the care of patients who have records created within the EpiNet database. The EpiNet database has recently been adapted to collect detailed information regarding status epilepticus. An incidence study is now underway in Auckland, New Zealand in which the incidence of status epilepticus in the greater Auckland area (population: 1.5 million) will be calculated. The form that has been developed for this study can be used in the future to collect information for randomized controlled trials in status epilepticus. This article is part of a Special Issue entitled "Status Epilepticus". Copyright © 2015 Elsevier Inc. All rights reserved.

TopologyNet: Topology based deep convolutional and multi-task neural networks for biomolecular property predictions

PubMed Central

2017-01-01

Although deep learning approaches have had tremendous success in image, video and audio processing, computer vision, and speech recognition, their applications to three-dimensional (3D) biomolecular structural data sets have been hindered by the geometric and biological complexity. To address this problem we introduce the element-specific persistent homology (ESPH) method. ESPH represents 3D complex geometry by one-dimensional (1D) topological invariants and retains important biological information via a multichannel image-like representation. This representation reveals hidden structure-function relationships in biomolecules. We further integrate ESPH and deep convolutional neural networks to construct a multichannel topological neural network (TopologyNet) for the predictions of protein-ligand binding affinities and protein stability changes upon mutation. To overcome the deep learning limitations from small and noisy training sets, we propose a multi-task multichannel topological convolutional neural network (MM-TCNN). We demonstrate that TopologyNet outperforms the latest methods in the prediction of protein-ligand binding affinities, mutation induced globular protein folding free energy changes, and mutation induced membrane protein folding free energy changes. Availability: weilab.math.msu.edu/TDL/ PMID:28749969

Is there a “net generation” in veterinary medicine? A comparative study on the use of the Internet and Web 2.0 by students and the veterinary profession

PubMed Central

Tenhaven, Christoph; Tipold, Andrea; Fischer, Martin R.; Ehlers, Jan P.

2013-01-01

Introduction: Informal and formal lifelong learning is essential at university and in the workplace. Apart from classical learning techniques, Web 2.0 tools can be used. It is controversial whether there is a so-called net generation amongst people under 30. Aims: To test the hypothesis that a net generation among students and young veterinarians exists. Methods: An online survey of students and veterinarians was conducted in the German-speaking countries which was advertised via online media and traditional print media. Results: 1780 people took part in the survey. Students and veterinarians have different usage patterns regarding social networks (91.9% vs. 69%) and IM (55.9% vs. 24.5%). All tools were predominantly used passively and in private, to a lesser extent also professionally and for studying. Outlook: The use of Web 2.0 tools is useful, however, teaching information and media skills, preparing codes of conduct for the internet and verification of user generated content is essential. PMID:23467682

Teaching for Better Learning: A Blended Learning Pilot Project with First-Year Geography Undergraduates

ERIC Educational Resources Information Center

Moore, Niamh; Gilmartin, Mary

2010-01-01

Internationally, recognition is growing that the transition between post-primary and higher education is raising a number of challenges for both students and educators. Simultaneously with growing class sizes, resources have become more constrained and there is a new set of expectations from the "net generation" (Mohanna, 2007, p. 211…

Educational Technology-Related Performance of Teaching Faculty in Higher Education: Implications for eLearning Management

ERIC Educational Resources Information Center

Larbi-Apau, Josephine A.; Guerra-Lopez, Ingrid; Moseley, James L.; Spannaus, Timothy; Yaprak, Attila

2017-01-01

The study examined teaching faculty's educational technology-related performances (ETRP) as a measure for predicting eLearning management in Ghana. A total of valid data (n = 164) were collected and analyzed on applied ISTE-NETS-T Performance Standards using descriptive and ANOVA statistics. Results showed an overall moderate performance with the…

Using Blogging to Enhance the Initiation of Students into Academic Research

ERIC Educational Resources Information Center

Chong, Eddy K. M.

2010-01-01

For the net-generation students learning in a Web 2.0 world, research is often equated with Googling and approached with a mindset accustomed to cut-and-paste practices. Recognizing educators' concern over such students' learning dispositions on the one hand, and the educational affordances of blogging on the other, this study examines the use of…

Does "Sneaky Fox" Facilitate Learning? Examining the Effects of Seductive Details in Physical Education

ERIC Educational Resources Information Center

Shen, Bo; McCaughtry, Nate; Martin, Jeffrey; Dillion, Suzanna

2006-01-01

While seductive details are enjoyable, they are unimportant content or activities intentionally inserted to make class fun and interesting. The purpose of this study was to examine the effect of seductive details on students' learning of net games in physical education. Participants were 240 middle school students. A videotaped lesson example…

Problems of Implementing SCORM in an Enterprise Distance Learning Architecture: SCORM Incompatibility across Multiple Web Domains.

ERIC Educational Resources Information Center

Engelbrecht, Jeffrey C.

2003-01-01

Delivering content to distant users located in dispersed networks, separated by firewalls and different web domains requires extensive customization and integration. This article outlines some of the problems of implementing the Sharable Content Object Reference Model (SCORM) in the Marine Corps' Distance Learning System (MarineNet) and extends…

Usage of Mobile Phone Applications and Its Impact on Teaching and Learning

ERIC Educational Resources Information Center

Davidovitch, Nitza; Yavich, Roman

2018-01-01

This study continues studies on the concept of leisure as culture dependent -- between tradition and modernity, while focusing on the usage of mobile phone applications and its impact on teaching and learning within a unique population. The study examined the association between having NetSpark on one's Smartphone and utilization of spare time…

Social Networking Services in E-Learning

ERIC Educational Resources Information Center

Weber, Peter; Rothe, Hannes

2016-01-01

This paper is a report on the findings of a study conducted on the use of the social networking service NING in a cross-location e-learning setting named "Net Economy." We describe how we implemented NING as a fundamental part of the setting through a special phase concept and team building approach. With the help of user statistics, we…

Interactive Multimedia in University Teaching and Learning: Some Pointers to Help Promote Discussion of Design Criteria.

ERIC Educational Resources Information Center

Trevitt, Chris

This paper addresses criteria in the design and development of computer-based courseware. The term "interactive multimedia" describes both the technology and the demands placed on the user. It implies that the user becomes actively engaged with the subject, thereby improving the likelihood that net learning takes place. However, nothing…

Social Networking Services in E-Learning

ERIC Educational Resources Information Center

Weber, Peter; Rothe, Hannes

2012-01-01

This paper is a report on the findings of a study conducted on the use of the social networking service NING in a cross-location e-learning setting named "Net Economy." We describe how we implemented NING as a fundamental part of the setting through a special phase concept and team building approach. With the help of user statistics, we examine…

Cognitive Analysis of Meaning and Acquired Mental Representations as an Alternative Measurement Method Technique to Innovate E-Assessment

ERIC Educational Resources Information Center

Morales-Martinez, Guadalupe Elizabeth; Lopez-Ramirez, Ernesto Octavio; Castro-Campos, Claudia; Villarreal-Treviño, Maria Guadalupe; Gonzales-Trujillo, Claudia Jaquelina

2017-01-01

Empirical directions to innovate e-assessments and to support the theoretical development of e-learning are discussed by presenting a new learning assessment system based on cognitive technology. Specifically, this system encompassing trained neural nets that can discriminate between students who successfully integrated new knowledge course…

Helping Second Language Literature Learners Overcome E-Learning Difficulties: LET-NET Team Teaching with Online Peer Interaction

ERIC Educational Resources Information Center

Wu, Pin-Hsiang Natalie; Marek, Michael W.

2013-01-01

This study presents and discusses results from an EFL second language literature program in which the instructional design included a team teaching scheme, blended learning practice, and computer-mediated peer-interaction. The team teaching plan used a Mandarin speaking English teacher and a Native English-speaking teacher collaborating and…

The Effect of Cross-Curricular Instruction on Reading Comprehension

ERIC Educational Resources Information Center

Aslan, Yasin

2016-01-01

Cross-curricular objectives serve as a kind of "safety net" for core objectives. Firstly, cross-curricular objectives refer to competencies that do not pertain to the content of one or more subjects, but that can be taught, practised and applied in it, such as learning to learn and social skills. Secondly, certain cross-curricular final…

3 Steps to Great Coaching: A Simple but Powerful Instructional Coaching Cycle Nets Results

ERIC Educational Resources Information Center

Knight, Jim; Elford, Marti; Hock, Michael; Dunekack, Devona; Bradley, Barbara; Deshler, Donald D.; Knight, David

2015-01-01

In this article the authors describe a three-step instructional coaching cycle that can helps coaches become more effective. The article provides the steps and related components to: (1) Identify; (2) Learn; and (3) Improve. While the instructional coaching cycle is only one effective coaching program, coaches also need professional learning that…

"Can You Hear Me, Hanoi?" Compensatory Mechanisms Employed in Synchronous Net-Based English Language Learning

ERIC Educational Resources Information Center

Cunningham, Una; Fagersten, Kristy Beers; Holmsten, Elin

2010-01-01

At Dalarna University, Sweden, modes of communication are offered at many points of Kenning's continuum with a web-based learning platform, including asynchronous document exchange and collaborative writing tools, e-mail, recorded lectures in various formats, live streamed lectures with the possibility of text questions to the lecturer in real…

Designing a Self-Contained Group Area Network for Ubiquitous Learning

ERIC Educational Resources Information Center

Chen, Nian-Shing; Kinshuk; Wei, Chun-Wang; Yang, Stephen J. H.

2008-01-01

A number of studies have evidenced that handheld devices are appropriate tools to facilitate face-to-face collaborative learning effectively because of the possibility of ample social interactions. Group Area Network, or GroupNet, proposed in this paper, uses handheld devices to fill the gap between Local Area Network and Body Area Network.…

Keys to Successful Diabetes Self-Management for Uninsured Patients: Social Support, Observational Learning, and Turning Points A Safety Net Providers’ Strategic Alliance Study

PubMed Central

Hanahan, Melissa A.; Werner, James J.; Tomsik, Phillip; Weirich, Stephen A.; Reichsman, Ann; Navracruz, Lisa; Clemons-Clark, Terri; Cella, Peggi; Terchek, Joshua; Munson, Michelle R.

2015-01-01

Objective To determine how medically uninsured patients with limited material resources successfully manage diabetes. Methods Clinicians at 5 safety net practices enrolled uninsured adult patients (N=26) with prior diagnosis of diabetes for 6 months or longer. Patients were interviewed about enabling factors, motivations, resources, and barriers. Chart reviews and clinician surveys supplemented interview data. Interview, survey, and chart review data were analyzed and findings were summarized. Results Two distinct groups of patients were investigated: 1) “successful,” defined as those with an HbA1c of ≤7% or a recent improvement of at least 2% (n=17); and 2) “unsuccessful,” defined as patients with HbA1c of ≥9% (n=9) without recent improvement. In comparison to unsuccessful patients, successful patients more often reported having friends or family with diabetes, sought information about the disease, used evidence-based self-management strategies, held an accurate perception of their own disease control, and experienced “turning point” events that motivated increased efforts in disease management. Conclusions Uninsured safety net patients who successfully managed diabetes learned from friends and family with diabetes and leveraged disease-related events into motivational turning points. It may be beneficial for clinicians to incorporate social learning and motivational enhancement into diabetes interventions to increase patients’ motivation for improved levels of self-management. PMID:21671529

Understanding the sources and effects of abandoned, lost, and discarded fishing gear on marine turtles in northern Australia.

PubMed

Wilcox, Chris; Heathcote, Grace; Goldberg, Jennifer; Gunn, Riki; Peel, David; Hardesty, Britta Denise

2015-02-01

Globally, 6.4 million tons of fishing gear are lost in the oceans annually. This gear (i.e., ghost nets), whether accidently lost, abandoned, or deliberately discarded, threatens marine wildlife as it drifts with prevailing currents and continues to entangle marine organisms indiscriminately. Northern Australia has some of the highest densities of ghost nets in the world, with up to 3 tons washing ashore per kilometer of shoreline annually. This region supports globally significant populations of internationally threatened marine fauna, including 6 of the 7 extant marine turtles. We examined the threat ghost nets pose to marine turtles and assessed whether nets associated with particular fisheries are linked with turtle entanglement by analyzing the capture rates of turtles and potential source fisheries from nearly 9000 nets found on Australia's northern coast. Nets with relatively larger mesh and smaller twine sizes (e.g., pelagic drift nets) had the highest probability of entanglement for marine turtles. Net size was important; larger nets appeared to attract turtles, which further increased their catch rates. Our results point to issues with trawl and drift-net fisheries, the former due to the large number of nets and fragments found and the latter due to the very high catch rates resulting from the net design. Catch rates for fine-mesh gill nets can reach as high as 4 turtles/100 m of net length. We estimated that the total number of turtles caught by the 8690 ghost nets we sampled was between 4866 and 14,600, assuming nets drift for 1 year. Ghost nets continue to accumulate on Australia's northern shore due to both legal and illegal fishing; over 13,000 nets have been removed since 2005. This is an important and ongoing transboundary threat to biodiversity in the region that requires attention from the countries surrounding the Arafura and Timor Seas. © 2014 Society for Conservation Biology.

Thinking about Distributed Learning? Issues and Questions To Ponder.

ERIC Educational Resources Information Center

Sorg, Steven

2001-01-01

Introduces other articles in this issue devoted to distributed learning at metropolitan universities. Discusses issues that institutions should address if considering distributed learning: institutional goals and strategic plans, faculty development needs and capabilities, student support services, technical and personnel infrastructure, policies,…

Implementation of Service-Learning in Business Education: Issues and Challenges

ERIC Educational Resources Information Center

Poon, Patrick; Chan, Tsang Sing; Zhou, Lianxi

2011-01-01

This paper examines the issues and challenges in the implementation of service-learning in undergraduate business education. It also provides an assessment of the students' learning efficacy and outcomes over time through the service-learning participation. Service-learning is a pedagogical approach that integrates academic learning and community…

Capital Structure and Stock Returns

ERIC Educational Resources Information Center

Welch, Ivo

2004-01-01

U.S. corporations do not issue and repurchase debt and equity to counteract the mechanistic effects of stock returns on their debt-equity ratios. Thus over one- to five-year horizons, stock returns can explain about 40 percent of debt ratio dynamics. Although corporate net issuing activity is lively and although it can explain 60 percent of debt…

10 CFR Appendix A to Part 30 - Criteria Relating to Use of Financial Tests and Parent Company Guarantees for Providing...

Code of Federal Regulations, 2010 CFR

2010-01-01

... company must have: (i) A current rating for its most recent bond issuance of AAA, AA, A, or BBB as issued by Standard and Poor's or Aaa, Aa, A, or Baa as issued by Moody's; and (ii) Tangible net worth each...

Network or Net Worth? Deconstructing the Knowledge Society

ERIC Educational Resources Information Center

Dyer, Maxine

2012-01-01

One of the major issues facing humanity in the twenty-first century is how the increasing effects of globalisation will play out in relation to existing societal and global inequalities. At the very crux of this issue are the terms "knowledge society" and "knowledge economy", two terms employed in a variety of different…

Recapture Heterogeneity in Cliff Swallows: Increased Exposure to Mist Nets Leads to Net Avoidance

PubMed Central

Roche, Erin A.; Brown, Charles R.; Brown, Mary Bomberger; Lear, Kristen M.

2013-01-01

Ecologists often use mark-recapture to estimate demographic variables such as abundance, growth rate, or survival for samples of wild animal populations. A common assumption underlying mark-recapture is that all animals have an equal probability of detection, and failure to meet or correct for this assumption–as when certain members of the population are either easier or more difficult to capture than other animals–can lead to biased and inaccurate demographic estimates. We built within-year and among-years Cormack-Jolly-Seber recaptures-only models to identify causes of capture heterogeneity for a population of colonially nesting cliff swallows (Petrochelidon pyrrhonota) caught using mist-netting as a part of a 20-year mark-recapture study in southwestern Nebraska, U.S.A. Daily detection of cliff swallows caught in stationary mist nets at their colony sites declined as the birds got older and as the frequency of netting at a site within a season increased. Experienced birds’ avoidance of the net could be countered by sudden disturbances that startled them into a net, such as when we dropped a net over the side of a bridge or flushed nesting cliff swallows into a stationary net positioned at a colony entrance. Our results support the widely held, but seldom tested, belief that birds learn to avoid stationary mist nets over time, but also show that modifications of traditional field methods can reduce this source of recapture heterogeneity. PMID:23472138

A course designed for undergraduate biochemistry students to learn about cultural diversity issues.

PubMed

Benore-Parsons, Marilee

2006-09-01

Biology, biochemistry, and other science students are well trained in science and familiar with how to conduct and evaluate scientific experiments. They are less aware of cultural issues or how these will impact their careers in research, education, or as professional health care workers. A course was developed for advanced undergraduate science majors to learn about diversity issues in a context that would be relevant to them, entitled "Diversity Issues in Health Care: Treatment and Research." Learning objectives included: developing awareness of current topics concerning diversity issues in health care; learning how research is carried out in health care, including pharmaceutical research, clinical trials, and social research; and learning about health care practices. Lectures and projects included readings on laboratory and clinical research, as well as literature on legal, race, gender, language, age, and income issues in health care research and clinical practice. Exams, papers, and a service learning project were used to determine the final course grade. Assessment indicated student understanding of diversity issues was improved, and the material was relevant. Copyright © 2006 International Union of Biochemistry and Molecular Biology, Inc.

Marketing netcoatings for aquaculture.

PubMed

Martin, Robert J

2014-10-17

Unsustainable harvesting of natural fish stocks is driving an ever growing marine aquaculture industry. Part of the aquaculture support industry is net suppliers who provide producers with nets used in confining fish while they are grown to market size. Biofouling must be addressed in marine environments to ensure maximum product growth by maintaining water flow and waste removal through the nets. Biofouling is managed with copper and organic biocide based net coatings. The aquaculture industry provides a case study for business issues related to entry of improved fouling management technology into the marketplace. Several major hurdles hinder entry of improved novel technologies into the market. The first hurdle is due to the structure of business relationships. Net suppliers can actually cut their business profits dramatically by introducing improved technologies. A second major hurdle is financial costs of registration and demonstration of efficacy and quality product with a new technology. Costs of registration are prohibitive if only the net coatings market is involved. Demonstration of quality product requires collaboration and a team approach between formulators, net suppliers and farmers. An alternative solution is a vertically integrated business model in which the support business and product production business are part of the same company.

Marketing Netcoatings for Aquaculture

PubMed Central

Martin, Robert J.

2014-01-01

Unsustainable harvesting of natural fish stocks is driving an ever growing marine aquaculture industry. Part of the aquaculture support industry is net suppliers who provide producers with nets used in confining fish while they are grown to market size. Biofouling must be addressed in marine environments to ensure maximum product growth by maintaining water flow and waste removal through the nets. Biofouling is managed with copper and organic biocide based net coatings. The aquaculture industry provides a case study for business issues related to entry of improved fouling management technology into the marketplace. Several major hurdles hinder entry of improved novel technologies into the market. The first hurdle is due to the structure of business relationships. Net suppliers can actually cut their business profits dramatically by introducing improved technologies. A second major hurdle is financial costs of registration and demonstration of efficacy and quality product with a new technology. Costs of registration are prohibitive if only the net coatings market is involved. Demonstration of quality product requires collaboration and a team approach between formulators, net suppliers and farmers. An alternative solution is a vertically integrated business model in which the support business and product production business are part of the same company. PMID:25329615

GeNets: a unified web platform for network-based genomic analyses.

PubMed

Li, Taibo; Kim, April; Rosenbluh, Joseph; Horn, Heiko; Greenfeld, Liraz; An, David; Zimmer, Andrew; Liberzon, Arthur; Bistline, Jon; Natoli, Ted; Li, Yang; Tsherniak, Aviad; Narayan, Rajiv; Subramanian, Aravind; Liefeld, Ted; Wong, Bang; Thompson, Dawn; Calvo, Sarah; Carr, Steve; Boehm, Jesse; Jaffe, Jake; Mesirov, Jill; Hacohen, Nir; Regev, Aviv; Lage, Kasper

2018-06-18

Functional genomics networks are widely used to identify unexpected pathway relationships in large genomic datasets. However, it is challenging to compare the signal-to-noise ratios of different networks and to identify the optimal network with which to interpret a particular genetic dataset. We present GeNets, a platform in which users can train a machine-learning model (Quack) to carry out these comparisons and execute, store, and share analyses of genetic and RNA-sequencing datasets.

Tracking How Science Resources Result in Educator- and Community-Level Outcomes

NASA Astrophysics Data System (ADS)

Dusenbery, P.; Harold, J. B.; Fitzhugh, G.; LaConte, K.; Holland, A.

2017-12-01

Learners frequently need to access increasingly complex information to help them understand our changing world. More and more libraries are transforming themselves into places where learners not only access STEM information, but interact with professionals and undertake hands-on learning. Libraries are beginning to position themselves as part of learning ecosystems that contribute to a collective impact on the community. Traveling STEM exhibits are catalyzing these partnerships and engaging students, families, and adults in repeat visits through an accessible venue: their public library. This talk will explore impacts from two STAR Library Network's (STAR_Net) exhibitions (Discover Earth and Discover Tech) on partnerships, the circulation of STEM resources, and the engagement of learners. The STAR_Net project's summative evaluation utilized mixed methods to investigate project implementation and its outcomes. Methods included pre- and post-exhibit surveys administered to staff from each library that hosted the exhibits; interviews with staff from host libraries; patron surveys; exhibit-related circulation records; web metrics regarding the online STAR_Net community of practice; and site visits. The latter provides a more complete view of impacts on the community, including underserved audiences. NASA@ My Library is a new STAR_Net initiative, which provides STEM facilitation kits, training, and other resources to 75 libraries nationwide. Initial results will be presented that show high levels of engagement by librarians and strong response rate from patrons on surveys.

Perspectives on learning through research on critical issues-based science center exhibitions

NASA Astrophysics Data System (ADS)

Pedretti, Erminia G.

2004-07-01

Recently, science centers have created issues-based exhibitions as a way of communicating socioscientific subject matter to the public. Research in the last decade has investigated how critical issues-based installations promote more robust views of science, while creating effective learning environments for teaching and learning about science. The focus of this paper is to explore research conducted over a 10-year period that informs our understanding of the nature of learning through these experiences. Two specific exhibitions - Mine Games and A Question of Truth - provide the context for discussing this research. Findings suggest that critical issues-based installations challenge visitors in different ways - intellectually and emotionally. They provide experiences beyond usual phenomenon-based exhibitions and carry the potential to enhance learning by personalizing subject matter, evoking emotion, stimulating dialogue and debate, and promoting reflexivity. Critical issues-based exhibitions serve as excellent environments in which to explore the nature of learning in these nonschool settings.

Processing Ocean Images to Detect Large Drift Nets

NASA Technical Reports Server (NTRS)

Veenstra, Tim

2009-01-01

A computer program processes the digitized outputs of a set of downward-looking video cameras aboard an aircraft flying over the ocean. The purpose served by this software is to facilitate the detection of large drift nets that have been lost, abandoned, or jettisoned. The development of this software and of the associated imaging hardware is part of a larger effort to develop means of detecting and removing large drift nets before they cause further environmental damage to the ocean and to shores on which they sometimes impinge. The software is capable of near-realtime processing of as many as three video feeds at a rate of 30 frames per second. After a user sets the parameters of an adjustable algorithm, the software analyzes each video stream, detects any anomaly, issues a command to point a high-resolution camera toward the location of the anomaly, and, once the camera has been so aimed, issues a command to trigger the camera shutter. The resulting high-resolution image is digitized, and the resulting data are automatically uploaded to the operator s computer for analysis.

New World Health Organization guidance helps protect breastfeeding as a human right.

PubMed

Grummer-Strawn, Laurence M; Zehner, Elizabeth; Stahlhofer, Marcus; Lutter, Chessa; Clark, David; Sterken, Elisabeth; Harutyunyan, Susanna; Ransom, Elizabeth I

2017-10-01

Written by the WHO/UNICEF NetCode author group, the comment focuses on the need to protect families from promotion of breast-milk substitutes and highlights new WHO Guidance on Ending Inappropriate Promotion of Foods for Infants and Young Children. The World Health Assembly welcomed this Guidance in 2016 and has called on all countries to adopt and implement the Guidance recommendations. NetCode, the Network for Global Monitoring and Support for Implementation of the International Code of Marketing of Breast-milk Substitutes and Subsequent Relevant World Health Assembly Resolutions, is led by the World Health Organization and the United Nations Children's Fund. NetCode members include the International Baby Food Action Network, World Alliance for Breastfeeding Action, Helen Keller International, Save the Children, and the WHO Collaborating Center at Metropol University. The comment frames the issue as a human rights issue for women and children, as articulated by a statement from the United Nations Office of the High Commissioner for Human Rights. © 2017 The Authors. Maternal and Child Nutrition Published by John Wiley & Sons, Ltd.

Learning, Motivation, and Transfer: Successful Teacher Professional Development

ERIC Educational Resources Information Center

McDonald, Lex

2012-01-01

In this article, I am concerned with three key issues of teacher professional development--teacher learning, motivation, and transfer of learning. Each issue has received minimal attention in teacher professional development literature. The three issues are discussed, and a model of an integrative professional development approach is outlined,…

Research Issues in Evaluating Learning Pattern Development in Higher Education

ERIC Educational Resources Information Center

Richardson, John T. E.

2013-01-01

This article concludes the special issue of "Studies in Educational Evaluation" concerned with "Evaluating learning pattern development in higher education" by discussing research issues that have emerged from the previous contributions. The article considers in turn: stability versus variability in learning patterns; old versus new analytic…

Invariant-feature-based adaptive automatic target recognition in obscured 3D point clouds

NASA Astrophysics Data System (ADS)

Khuon, Timothy; Kershner, Charles; Mattei, Enrico; Alverio, Arnel; Rand, Robert

2014-06-01

Target recognition and classification in a 3D point cloud is a non-trivial process due to the nature of the data collected from a sensor system. The signal can be corrupted by noise from the environment, electronic system, A/D converter, etc. Therefore, an adaptive system with a desired tolerance is required to perform classification and recognition optimally. The feature-based pattern recognition algorithm architecture as described below is particularly devised for solving a single-sensor classification non-parametrically. Feature set is extracted from an input point cloud, normalized, and classifier a neural network classifier. For instance, automatic target recognition in an urban area would require different feature sets from one in a dense foliage area. The figure above (see manuscript) illustrates the architecture of the feature based adaptive signature extraction of 3D point cloud including LIDAR, RADAR, and electro-optical data. This network takes a 3D cluster and classifies it into a specific class. The algorithm is a supervised and adaptive classifier with two modes: the training mode and the performing mode. For the training mode, a number of novel patterns are selected from actual or artificial data. A particular 3D cluster is input to the network as shown above for the decision class output. The network consists of three sequential functional modules. The first module is for feature extraction that extracts the input cluster into a set of singular value features or feature vector. Then the feature vector is input into the feature normalization module to normalize and balance it before being fed to the neural net classifier for the classification. The neural net can be trained by actual or artificial novel data until each trained output reaches the declared output within the defined tolerance. In case new novel data is added after the neural net has been learned, the training is then resumed until the neural net has incrementally learned with the new novel data. The associative memory capability of the neural net enables the incremental learning. The back propagation algorithm or support vector machine can be utilized for the classification and recognition.

Non-accidental properties, metric invariance, and encoding by neurons in a model of ventral stream visual object recognition, VisNet.

PubMed

Rolls, Edmund T; Mills, W Patrick C

2018-05-01

When objects transform into different views, some properties are maintained, such as whether the edges are convex or concave, and these non-accidental properties are likely to be important in view-invariant object recognition. The metric properties, such as the degree of curvature, may change with different views, and are less likely to be useful in object recognition. It is shown that in a model of invariant visual object recognition in the ventral visual stream, VisNet, non-accidental properties are encoded much more than metric properties by neurons. Moreover, it is shown how with the temporal trace rule training in VisNet, non-accidental properties of objects become encoded by neurons, and how metric properties are treated invariantly. We also show how VisNet can generalize between different objects if they have the same non-accidental property, because the metric properties are likely to overlap. VisNet is a 4-layer unsupervised model of visual object recognition trained by competitive learning that utilizes a temporal trace learning rule to implement the learning of invariance using views that occur close together in time. A second crucial property of this model of object recognition is, when neurons in the level corresponding to the inferior temporal visual cortex respond selectively to objects, whether neurons in the intermediate layers can respond to combinations of features that may be parts of two or more objects. In an investigation using the four sides of a square presented in every possible combination, it was shown that even though different layer 4 neurons are tuned to encode each feature or feature combination orthogonally, neurons in the intermediate layers can respond to features or feature combinations present is several objects. This property is an important part of the way in which high capacity can be achieved in the four-layer ventral visual cortical pathway. These findings concerning non-accidental properties and the use of neurons in intermediate layers of the hierarchy help to emphasise fundamental underlying principles of the computations that may be implemented in the ventral cortical visual stream used in object recognition. Copyright © 2018 Elsevier Inc. All rights reserved.

Surfing the Net: Children + Problem Solving = Giftedness. How Can Ill-Structured Problems Take Advantage of a Child's Natural Curiosity?

ERIC Educational Resources Information Center

Berger, Sandra

2000-01-01

This article discusses using a Problem-Based Learning (PBL) curriculum to engage gifted learners. The benefits of PBL are described and a list of seven useful Web sites that explain PBL and provide examples of problems that can be used to excite gifted children about learning is provided. (CR)

Assessing the Effectiveness of E-Learning Integration in College Physics in the Alamo Community Colleges District

ERIC Educational Resources Information Center

Zhou, Qiaoying

2012-01-01

Academic achievement and student participation in physics are lower than desired. Research has shown that there is a shortage of college students entering science and technology fields such as physics. E-learning may provide the technology-oriented Net Generation learner an option for taking courses such as physics in a course modality with which…

Learning and Skills Development in a Virtual Class of Educommunication Based on Educational Proposals and Interactions

ERIC Educational Resources Information Center

Bohorquez Sotelo, Maria Cristina; Rodriguez Mendoza, Brigitte Julieth; Vega, Sandra Milena; Roja Higuera, Naydu Shirley; Barbosa Gomez, Luisa Fernanda

2016-01-01

In the present paper we describe the analysis of qualitative and quantitative data from asynchronous learning networks, the virtual forums that take place in VirtualNet 2.0, the platform of the University Manuela Beltran (UMB), inside the course of Educommunication, from the master of Digital technologies applied to education. Here, we performed a…

Understanding Randomness and Its Impact on Student Learning: Lessons Learned from Building the Biology Concept Inventory (BCI)

ERIC Educational Resources Information Center

Garvin-Doxas, Kathy; Klymkowsky, Michael W.

2008-01-01

While researching student assumptions for the development of the Biology Concept Inventory (BCI; http://bioliteracy.net), we found that a wide class of student difficulties in molecular and evolutionary biology appears to be based on deep-seated, and often unaddressed, misconceptions about random processes. Data were based on more than 500…

Introduction to Evidence Centered Design and Lessons Learned from Its Application in a Global E-Learning Program

ERIC Educational Resources Information Center

Behrens, John T.; Mislevy, Robert J.; Bauer, Malcolm; Williamson, David M.; Levy, Roy

2004-01-01

This article introduces the assessment and deployment contexts of the Networking Performance Skill System (NetPASS) project and the articles in this section that report on findings from this endeavor. First, the educational context of the Cisco Networking Academy Program is described. Second, the basic outline of Evidence Centered Design is…

Health Videos: MedlinePlus

MedlinePlus Videos and Cool Tools

... to achieve this important distinction for online health information and services. Learn more about A.D.A. ... on the Net Foundation (www.hon.ch). The information provided herein should not be used during any ...

Structured feedback on students' concept maps: the proverbial path to learning?

PubMed

Joseph, Conran; Conradsson, David; Nilsson Wikmar, Lena; Rowe, Michael

2017-05-25

Good conceptual knowledge is an essential requirement for health professions students, in that they are required to apply concepts learned in the classroom to a variety of different contexts. However, the use of traditional methods of assessment limits the educator's ability to correct students' conceptual knowledge prior to altering the educational context. Concept mapping (CM) is an educational tool for evaluating conceptual knowledge, but little is known about its use in facilitating the development of richer knowledge frameworks. In addition, structured feedback has the potential to develop good conceptual knowledge. The purpose of this study was to use Kinchin's criteria to assess the impact of structured feedback on the graphical complexity of CM's by observing the development of richer knowledge frameworks. Fifty-eight physiotherapy students created CM's targeting the integration of two knowledge domains within a case-based teaching paradigm. Each student received one round of structured feedback that addressed correction, reinforcement, forensic diagnosis, benchmarking, and longitudinal development on their CM's prior to the final submission. The concept maps were categorized according to Kinchin's criteria as either Spoke, Chain or Net representations, and then evaluated against defined traits of meaningful learning. The inter-rater reliability of categorizing CM's was good. Pre-feedback CM's were predominantly Chain structures (57%), with Net structures appearing least often. There was a significant reduction of the basic Spoke- structured CMs (P = 0.002) and a significant increase of Net-structured maps (P < 0.001) at the final evaluation (post-feedback). Changes in structural complexity of CMs appeared to be indicative of broader knowledge frameworks as assessed against the meaningful learning traits. Feedback on CM's seemed to have contributed towards improving conceptual knowledge and correcting naive conceptions of related knowledge. Educators in medical education could therefore consider using CM's to target individual student development.

[Automated Assessment for Bone Age of Left Wrist Joint in Uyghur Teenagers by Deep Learning].

PubMed

Hu, T H; Huo, Z; Liu, T A; Wang, F; Wan, L; Wang, M W; Chen, T; Wang, Y H

2018-02-01

To realize the automated bone age assessment by applying deep learning to digital radiography （DR） image recognition of left wrist joint in Uyghur teenagers, and explore its practical application value in forensic medicine bone age assessment. The X-ray films of left wrist joint after pretreatment, which were taken from 245 male and 227 female Uyghur nationality teenagers in Uygur Autonomous Region aged from 13.0 to 19.0 years old, were chosen as subjects. And AlexNet was as a regression model of image recognition. From the total samples above, 60％ of male and female DR images of left wrist joint were selected as net train set, and 10％ of samples were selected as validation set. As test set, the rest 30％ were used to obtain the image recognition accuracy with an error range in ±1.0 and ±0.7 age respectively, compared to the real age. The modelling results of deep learning algorithm showed that when the error range was in ±1.0 and ±0.7 age respectively, the accuracy of the net train set was 81.4% and 75.6% in male, and 80.5% and 74.8% in female, respectively. When the error range was in ±1.0 and ±0.7 age respectively, the accuracy of the test set was 79.5% and 71.2% in male, and 79.4% and 66.2% in female, respectively. The combination of bone age research on teenagers' left wrist joint and deep learning, which has high accuracy and good feasibility, can be the research basis of bone age automatic assessment system for the rest joints of body. Copyright© by the Editorial Department of Journal of Forensic Medicine.

Dual System for Enhancing Cognitive Abilities of Children with ADHD Using Leap Motion and eye-Tracking Technologies.

PubMed

Garcia-Zapirain, Begoña; de la Torre Díez, Isabel; López-Coronado, Miguel

2017-07-01

Attention Deficit Hyperactivity Disorder (ADHD) is a brain disorder marked by an ongoing pattern of inattention and/or hyperactivity-impulsivity that affects with development or functioning. It affects 3-5% of all American and European children. The objective of this paper is to develop and test a dual system for the rehabilitation of cognitive functions in children with ADHD. A technological platform has been developed using the ". NET framework", which makes use of two physiological sensors, -an eye-tracker and a hand gesture recognition sensor- in order to provide children with the opportunity to develop their learning and attention skills. The two physiological sensors we utilized for the development are the Tobii X1 Light Eye Tracker and the Leap Motion. SUS and QUIS questionnaires have been carried out. 19 users tested the system and the average age was 10.88 years (SD = 3.14). The results obtained after tests were performed were quite positive and hopeful. The learning of the users caused by the system and the interfaces item got a high punctuation with a mean of 7.34 (SD = 1.06) for SUS questionnaire and 7.73 (SD = 0.6) for QUIS questionnaire. We didn't find differences between boys and girls. The developed multimodal rehabilitation system can help to children with attention deficit and learning issues. Moreover, the teachers may utilize this system to track the progression of their students and see their behavior.

Deep learning and face recognition: the state of the art

NASA Astrophysics Data System (ADS)

Balaban, Stephen

2015-05-01

Deep Neural Networks (DNNs) have established themselves as a dominant technique in machine learning. DNNs have been top performers on a wide variety of tasks including image classification, speech recognition, and face recognition.1-3 Convolutional neural networks (CNNs) have been used in nearly all of the top performing methods on the Labeled Faces in the Wild (LFW) dataset.3-6 In this talk and accompanying paper, I attempt to provide a review and summary of the deep learning techniques used in the state-of-the-art. In addition, I highlight the need for both larger and more challenging public datasets to benchmark these systems. Despite the ability of DNNs and autoencoders to perform unsupervised feature learning, modern facial recognition pipelines still require domain specific engineering in the form of re-alignment. For example, in Facebook's recent DeepFace paper, a 3D "frontalization" step lies at the beginning of the pipeline. This step creates a 3D face model for the incoming image and then uses a series of affine transformations of the fiducial points to "frontalize" the image. This step enables the DeepFace system to use a neural network architecture with locally connected layers without weight sharing as opposed to standard convolutional layers.6 Deep learning techniques combined with large datasets have allowed research groups to surpass human level performance on the LFW dataset.3, 5 The high accuracy (99.63% for FaceNet at the time of publishing) and utilization of outside data (hundreds of millions of images in the case of Google's FaceNet) suggest that current face verification benchmarks such as LFW may not be challenging enough, nor provide enough data, for current techniques.3, 5 There exist a variety of organizations with mobile photo sharing applications that would be capable of releasing a very large scale and highly diverse dataset of facial images captured on mobile devices. Such an "ImageNet for Face Recognition" would likely receive a warm welcome from researchers and practitioners alike.

Chinese Children's Reading Acquisition: Theoretical and Pedagogical Issues.

ERIC Educational Resources Information Center

Li, Wenling, Ed.; Gaffney, Janet S., Ed.; Packard, Jerome L., Ed.

This book provides comprehensive resources for the critical discussion of major issues in learning to read Chinese from a child acquisition perspective. It is divided into 4 parts and 11 chapters. Part 1, "Theoretical Perspectives on Learning to Read" includes "Current Issues in Learning To Read Chinese" (Ovid J.L. Tzeng),…

Issues of Learning and Knowledge in Technology Education

ERIC Educational Resources Information Center

McCormick, Robert

2004-01-01

This article examines issues that arise from learning and knowledge in technology education. The issues examined are, first, the definition of technological knowledge and what the nature of that knowledge should be, where the concern is with "how" we define and think about that knowledge, especially in the context of how students learn and use…

Ethical Issues Associated with the Use of Interactive Technology in Learning Environments.

ERIC Educational Resources Information Center

Bork, Alfred

1988-01-01

Discusses general social, moral, and ethical issues connected with computers in education; considers ethical issues related to the development of computer-based learning materials; and examines the use of the computer as a medium for ethical and moral education. Highlights include equity of access, games and learning, and cultural bias. (seven…

The National Issues Forum: Bridging the Human Gap through Innovative Learning.

ERIC Educational Resources Information Center

McMahan, Eva M.

The National Issues Forum model, a series of community based public discussions on key domestic issues, may be a partial solution to the "human gap" between growing complexity and a capacity to deal with it, by exemplifying "innovative learning." To engage in innovative learning, characterized by anticipation and participation,…

Learning Outcomes between Socioscientific Issues-Based Learning and Conventional Learning Activities

ERIC Educational Resources Information Center

Wongsri, Piyaluk; Nuangchalerm, Prasart

2010-01-01

Problem statement: Socioscientific issues-based learning activity is essential for scientific reasoning skills and it could be used for analyzing problems be applied to each situation for more successful and suitable. The purposes of this research aimed to compare learning achievement, analytical thinking and moral reasoning of seventh grade…

Understanding the Behavioral Intention to Play Austronesian Learning Games: From the Perspectives of Learning Outcome, Service Quality, and Hedonic Value

ERIC Educational Resources Information Center

Hsiao, Kuo-Lun; Huang, Tien-Chi; Chen, Mu-Yen; Chiang, Nien-Ting

2018-01-01

Although ubiquitous learning is a novel and creative teaching approach, two key issues inhibit its success overall: a lack of appropriate learning strategies regarding learning objectives, and ineffective learning tools for receiving knowledge regarding the chosen subjects. To address these issues, we develops and designs a game-based educational…

Image Reconstruction is a New Frontier of Machine Learning.

PubMed

Wang, Ge; Ye, Jong Chu; Mueller, Klaus; Fessler, Jeffrey A

2018-06-01

Over past several years, machine learning, or more generally artificial intelligence, has generated overwhelming research interest and attracted unprecedented public attention. As tomographic imaging researchers, we share the excitement from our imaging perspective [item 1) in the Appendix], and organized this special issue dedicated to the theme of "Machine learning for image reconstruction." This special issue is a sister issue of the special issue published in May 2016 of this journal with the theme "Deep learning in medical imaging" [item 2) in the Appendix]. While the previous special issue targeted medical image processing/analysis, this special issue focuses on data-driven tomographic reconstruction. These two special issues are highly complementary, since image reconstruction and image analysis are two of the main pillars for medical imaging. Together we cover the whole workflow of medical imaging: from tomographic raw data/features to reconstructed images and then extracted diagnostic features/readings.

2011 Japanese Nuclear Incident

EPA Pesticide Factsheets

EPA’s RadNet system monitored the environmental radiation levels in the United States and parts of the Pacific following the Japanese Nuclear Incident. Learn about EPA’s response and view historical laboratory data and news releases.

Higher education in health promotion in Europe: a comparative analysis of master's level training programmes using HP-Source.net.

PubMed

König, Claudia; Mittelmark, Maurice B

2008-01-01

This report summarises opportunities in Europe for master's degree level training in health promotion. Using data available at www.HP-Source.net, 105 study programmes at 71 institutions, spread over 20 European countries, were identified that include health promotion as a main subject. The programmes were analysed along a number of dimensions, including title, learning objectives, curricula, learning and teaching methods, entry requirements, duration, accreditation, language(s) of instruction and participation in European educational structures. The present analysis reveals great diversity along all these dimensions, but also several clusters of programmes that offer quite similar education in health promotion. Of special interest is the range of options available in Europe for length of study, ranging from one to two years, with part-time as well as full-time options.

Is a PBL curriculum a better nutrient medium for student-generated learning issues than a PBL island?

PubMed

Gehlhar, K; Wüller, A; Lieverscheidt, H; Fischer, M R; Schäfer, T

2010-12-01

Problem based learning (PBL) is often introduced in curricula in form of short segments. In the literature the value of these PBL-islands is doubted. In order to gain more insight in this curricular approach, we compared student generated learning issues, from a 7-week PBL-island introduced in a traditional curriculum (PBL-I), with the gold standard of a PBL-based model-curriculum (PBL-B) existing in parallel at the same University (Ruhr-University Bochum, Germany). Both tracks use five identical PBL-cases. Thousand seven hundred and three student-generated learning issues of 252 tutorial groups (193 PBL-I and 59 PBL-B groups with six to seven students per group) were analysed in seven different categories. Results showed that overall there were no substantial differences between both curricula. PBL-B students generated more problem-related and less basic science clinical learning issues than PBL-I students, but in both groups learning issues were related to the same number of different subjects. Furthermore, students in the PBL-curriculum tend to generate little less but slightly better phrased issues. Taken together, we found no substantial evidence with respect to student-generated learning issues that could prove that students cannot work with the PBL-method, even if it is introduced later in the curriculum and last only for a short period of time.

Nickel (II) nitrate hexahydrate triggered canine neutrophil extracellular traps release in vitro.

PubMed

Wei, Zhengkai; Zhang, Xu; Wang, Yanan; Wang, Jingjing; Fu, Yunhe; Yang, Zhengtao

2018-05-30

Nickel (II) nitrate hexahydrate (Ni) is a common heavy metal material in battery manufacturing, electroplating alloy parts and ceramic staining, therefore we frequently contact with Ni-related products in daily life. In this study, we aimed to investigate the effects of Ni on neutrophils extracellular traps (NETs) release by canine polymorphonuclear neutrophils (PMNs). The structure of Ni-induced NETs was observed by fluorescence confocal microscopy. Ni-triggered NETs release was quantified by Pico Green ® and fluorescence microplate reader. In addition, the inhibitors of NADPH oxidase, ERK1/2-, p38 - signaling pathways were used for preliminary inquiry into the potential mechanism of this process. The results showed that Ni markedly triggered the formation of NETs-like structures, and these structures were mainly consisted of DNA decorated with NE and MPO. Furthermore, quantification experiments showed that Ni significantly increased NETs formation compared to control groups. These results forcefully confirmed that nickel nitrate possesses the ability to induce NETs formation. However, inhibiting the NADPH oxidase, ERK1/2- and p38 MAPK-signaling pathways did not significantly change the quantitation of Ni-induced NETs release. To our knowledge, this study is the first report of Ni-triggered NETs release in vitro, which might provide an entirely new mechanism of several diseases and health issues induced by nickel overexposure. Copyright © 2018. Published by Elsevier Ltd.

Evaluation of a deep learning architecture for MR imaging prediction of ATRX in glioma patients

NASA Astrophysics Data System (ADS)

Korfiatis, Panagiotis; Kline, Timothy L.; Erickson, Bradley J.

2018-02-01

Predicting mutation/loss of alpha-thalassemia/mental retardation syndrome X-linked (ATRX) gene utilizing MR imaging is of high importance since it is a predictor of response and prognosis in brain tumors. In this study, we compare a deep neural network approach based on a residual deep neural network (ResNet) architecture and one based on a classical machine learning approach and evaluate their ability in predicting ATRX mutation status without the need for a distinct tumor segmentation step. We found that the ResNet50 (50 layers) architecture, pre trained on ImageNet data was the best performing model, achieving an accuracy of 0.91 for the test set (classification of a slice as no tumor, ATRX mutated, or mutated) in terms of f1 score in a test set of 35 cases. The SVM classifier achieved 0.63 for differentiating the Flair signal abnormality regions from the test patients based on their mutation status. We report a method that alleviates the need for extensive preprocessing and acts as a proof of concept that deep neural network architectures can be used to predict molecular biomarkers from routine medical images.

Peptidylarginine deiminase 4: a nuclear button triggering neutrophil extracellular traps in inflammatory diseases and aging.

PubMed

Wong, Siu Ling; Wagner, Denisa D

2018-06-20

Peptidylarginine deiminase 4 (PAD4) is a nuclear citrullinating enzyme that is critically involved in the release of decondensed chromatin from neutrophils as neutrophil extracellular traps (NETs). NETs, together with fibrin, are implicated in host defense against pathogens; however, the formation of NETs (NETosis) has injurious effects that may outweigh their protective role. For example, PAD4 activity produces citrullinated neoantigens that promote autoimmune diseases, such as rheumatoid arthritis, to which PAD4 is genetically linked and where NETosis is prominent. NETs are also generated in basic sterile inflammatory responses that are induced by many inflammatory stimuli, including cytokines, hypoxia, and activated platelets. Mice that lack PAD4-deficient in NETosis-serve as an excellent tool with which to study the importance of NETs in disease models. In recent years, animal and human studies have demonstrated that NETs contribute to the etiology and propagation of many common noninfectious diseases, the focus of our review. We will discuss the role of NETs in thrombotic and cardiovascular disease, the induction of NETs by cancers and its implications for cancer progression and cancer-associated thrombosis, and elevated NETosis in diabetes and its negative impact on wound healing, and will propose a link between PAD4/NETs and age-related organ fibrosis. We identify unresolved issues and new research directions.-Wong, S. L., Wagner, D. D. Peptidylarginine deiminase 4: a nuclear button triggering neutrophil extracellular traps in inflammatory diseases and aging.

Application of Deep Learning Architectures for Accurate and Rapid Detection of Internal Mechanical Damage of Blueberry Using Hyperspectral Transmittance Data.

PubMed

Wang, Zhaodi; Hu, Menghan; Zhai, Guangtao

2018-04-07

Deep learning has become a widely used powerful tool in many research fields, although not much so yet in agriculture technologies. In this work, two deep convolutional neural networks (CNN), viz. Residual Network (ResNet) and its improved version named ResNeXt, are used to detect internal mechanical damage of blueberries using hyperspectral transmittance data. The original structure and size of hypercubes are adapted for the deep CNN training. To ensure that the models are applicable to hypercube, we adjust the number of filters in the convolutional layers. Moreover, a total of 5 traditional machine learning algorithms, viz. Sequential Minimal Optimization (SMO), Linear Regression (LR), Random Forest (RF), Bagging and Multilayer Perceptron (MLP), are performed as the comparison experiments. In terms of model assessment, k-fold cross validation is used to indicate that the model performance does not vary with the different combination of dataset. In real-world application, selling damaged berries will lead to greater interest loss than discarding the sound ones. Thus, precision, recall, and F1-score are also used as the evaluation indicators alongside accuracy to quantify the false positive rate. The first three indicators are seldom used by investigators in the agricultural engineering domain. Furthermore, ROC curves and Precision-Recall curves are plotted to visualize the performance of classifiers. The fine-tuned ResNet/ResNeXt achieve average accuracy and F1-score of 0.8844/0.8784 and 0.8952/0.8905, respectively. Classifiers SMO/ LR/RF/Bagging/MLP obtain average accuracy and F1-score of 0.8082/0.7606/0.7314/0.7113/0.7827 and 0.8268/0.7796/0.7529/0.7339/0.7971, respectively. Two deep learning models achieve better classification performance than the traditional machine learning methods. Classification for each testing sample only takes 5.2 ms and 6.5 ms respectively for ResNet and ResNeXt, indicating that the deep learning framework has great potential for online fruit sorting. The results of this study demonstrate the potential of deep CNN application on analyzing the internal mechanical damage of fruit.

Application of Deep Learning Architectures for Accurate and Rapid Detection of Internal Mechanical Damage of Blueberry Using Hyperspectral Transmittance Data

PubMed Central

Hu, Menghan; Zhai, Guangtao

2018-01-01

Deep learning has become a widely used powerful tool in many research fields, although not much so yet in agriculture technologies. In this work, two deep convolutional neural networks (CNN), viz. Residual Network (ResNet) and its improved version named ResNeXt, are used to detect internal mechanical damage of blueberries using hyperspectral transmittance data. The original structure and size of hypercubes are adapted for the deep CNN training. To ensure that the models are applicable to hypercube, we adjust the number of filters in the convolutional layers. Moreover, a total of 5 traditional machine learning algorithms, viz. Sequential Minimal Optimization (SMO), Linear Regression (LR), Random Forest (RF), Bagging and Multilayer Perceptron (MLP), are performed as the comparison experiments. In terms of model assessment, k-fold cross validation is used to indicate that the model performance does not vary with the different combination of dataset. In real-world application, selling damaged berries will lead to greater interest loss than discarding the sound ones. Thus, precision, recall, and F1-score are also used as the evaluation indicators alongside accuracy to quantify the false positive rate. The first three indicators are seldom used by investigators in the agricultural engineering domain. Furthermore, ROC curves and Precision-Recall curves are plotted to visualize the performance of classifiers. The fine-tuned ResNet/ResNeXt achieve average accuracy and F1-score of 0.8844/0.8784 and 0.8952/0.8905, respectively. Classifiers SMO/ LR/RF/Bagging/MLP obtain average accuracy and F1-score of 0.8082/0.7606/0.7314/0.7113/0.7827 and 0.8268/0.7796/0.7529/0.7339/0.7971, respectively. Two deep learning models achieve better classification performance than the traditional machine learning methods. Classification for each testing sample only takes 5.2 ms and 6.5 ms respectively for ResNet and ResNeXt, indicating that the deep learning framework has great potential for online fruit sorting. The results of this study demonstrate the potential of deep CNN application on analyzing the internal mechanical damage of fruit. PMID:29642454

Neural Network and Letter Recognition.

NASA Astrophysics Data System (ADS)

Lee, Hue Yeon

Neural net architectures and learning algorithms that recognize hand written 36 alphanumeric characters are studied. The thin line input patterns written in 32 x 32 binary array are used. The system is comprised of two major components, viz. a preprocessing unit and a Recognition unit. The preprocessing unit in turn consists of three layers of neurons; the U-layer, the V-layer, and the C -layer. The functions of the U-layer is to extract local features by template matching. The correlation between the detected local features are considered. Through correlating neurons in a plane with their neighboring neurons, the V-layer would thicken the on-cells or lines that are groups of on-cells of the previous layer. These two correlations would yield some deformation tolerance and some of the rotational tolerance of the system. The C-layer then compresses data through the 'Gabor' transform. Pattern dependent choice of center and wavelengths of 'Gabor' filters is the cause of shift and scale tolerance of the system. Three different learning schemes had been investigated in the recognition unit, namely; the error back propagation learning with hidden units, a simple perceptron learning, and a competitive learning. Their performances were analyzed and compared. Since sometimes the network fails to distinguish between two letters that are inherently similar, additional ambiguity resolving neural nets are introduced on top of the above main neural net. The two dimensional Fourier transform is used as the preprocessing and the perceptron is used as the recognition unit of the ambiguity resolver. One hundred different person's handwriting sets are collected. Some of these are used as the training sets and the remainders are used as the test sets. The correct recognition rate of the system increases with the number of training sets and eventually saturates at a certain value. Similar recognition rates are obtained for the above three different learning algorithms. The minimum error rate, 4.9% is achieved for alphanumeric sets when 50 sets are trained. With the ambiguity resolver, it is reduced to 2.5%. In case that only numeral sets are trained and tested, 2.0% error rate is achieved. When only alphabet sets are considered, the error rate is reduced to 1.1%.

Methodological issues affecting the study of fish parasites. II. Sampling method affects ectoparasite studies.

PubMed

Kvach, Yuriy; Ondračková, Markéta; Janáč, Michal; Jurajda, Pavel

2016-08-31

In this study, we assessed the impact of sampling method on the results of fish ectoparasite studies. Common roach Rutilus rutilus were sampled from the same gravel pit in the River Dyje flood plain (Czech Republic) using 3 different sampling methods, i.e. electrofishing, beach seining and gill-netting, and were examined for ectoparasites. Not only did fish caught by electrofishing have more of the most abundant parasites (Trichodina spp., Gyrodactylus spp.) than those caught by beach seining or gill-netting, they also had relatively rich parasite infracommunities, resulting in a significantly different assemblage composition, presumably as parasites were lost through handling and 'manipulation' in the net. Based on this, we recommend electrofishing as the most suitable method to sample fish for parasite community studies, as data from fish caught with gill-nets and beach seines will provide a biased picture of the ectoparasite community, underestimating ectoparasite abundance and infracommunity species richness.

A Petri-net coordination model for an intelligent mobile robot

NASA Technical Reports Server (NTRS)

Wang, F.-Y.; Kyriakopoulos, K. J.; Tsolkas, A.; Saridis, G. N.

1990-01-01

The authors present a Petri net model of the coordination level of an intelligent mobile robot system (IMRS). The purpose of this model is to specify the integration of the individual efforts on path planning, supervisory motion control, and vision systems that are necessary for the autonomous operation of the mobile robot in a structured dynamic environment. This is achieved by analytically modeling the various units of the system as Petri net transducers and explicitly representing the task precedence and information dependence among them. The model can also be used to simulate the task processing and to evaluate the efficiency of operations and the responsibility of decisions in the coordination level of the IMRS. Some simulation results on the task processing and learning are presented.

The Design and Implementation of Network Teaching Platform Basing on .NET

NASA Astrophysics Data System (ADS)

Yanna, Ren

This paper addresses the problem that students under traditional teaching model have poor operation ability and studies in depth the network teaching platform in domestic colleges and universities, proposing the design concept of network teaching platform of NET + C # + SQL excellent course and designing the overall structure, function module and back-end database of the platform. This paper emphatically expounds the use of MD5 encryption techniques in order to solve data security problems and the assessment of student learning using ADO.NET database access technology as well as the mathematical formula. The example shows that the network teaching platform developed by using WEB application technology has higher safety and availability, and thus improves the students' operation ability.

Accidental bait: do deceased fish increase freshwater turtle bycatch in commercial fyke nets?

PubMed

Larocque, Sarah M; Watson, Paige; Blouin-Demers, Gabriel; Cooke, Steven J

2012-07-01

Bycatch of turtles in passive inland fyke net fisheries has been poorly studied, yet bycatch is an important conservation issue given the decline in many freshwater turtle populations. Delayed maturity and low natural adult mortality make turtles particularly susceptible to population declines when faced with additional anthropogenic adult mortality such as bycatch. When turtles are captured in fyke nets, the prolonged submergence can lead to stress and subsequent drowning. Fish die within infrequently checked passive fishing nets and dead fish are a potential food source for many freshwater turtles. Dead fish could thus act as attractants and increase turtle captures in fishing nets. We investigated the attraction of turtles to decomposing fish within fyke nets in eastern Ontario. We set fyke nets with either 1 kg of one-day or five-day decomposed fish, or no decomposed fish in the cod-end of the net. Decomposing fish did not alter the capture rate of turtles or fish, nor did it alter the species composition of the catch. Thus, reducing fish mortality in nets using shorter soak times is unlikely to alter turtle bycatch rates since turtles were not attracted by the dead fish. Interestingly, turtle bycatch rates increased as water temperatures did. Water temperature also influences turtle mortality by affecting the duration turtles can remain submerged. We thus suggest that submerged nets to either not be set or have reduced soak times in warm water conditions (e.g., >20 °C) as turtles tend to be captured more frequently and cannot withstand prolonged submergence.

Integrating Research, Teaching and Learning: Preparing the Future National STEM Faculty

NASA Astrophysics Data System (ADS)

Hooper, E. J.; Pfund, C.; Mathieu, R.

2010-08-01

A network of universities (Howard, Michigan State, Texas A&M, University of Colorado at Boulder, University of Wisconsin-Madison, Vanderbilt) have created a National Science Foundation-funded network to prepare a future national STEM (science, technology, engineering, mathematics) faculty committed to learning, implementing, and advancing teaching techniques that are effective for the wide range of students enrolled in higher education. The Center for the Integration of Research, Teaching and Learning (CIRTL; http://www.cirtl.net) develops, implements and evaluates professional development programs for future and current faculty. The programs comprise graduate courses, internships, and workshops, all integrated within campus learning communities. These elements are unified and guided by adherence to three core principles, or pillars: "Teaching as Research," whereby research skills are applied to evaluating and advancing undergraduate learning; "Learning through Diversity," in which the diversity of students' backgrounds and experiences are used as a rich resource to enhance teaching and learning; and "Learning Communities" that foster shared learning and discovery among students, and between future and current faculty within a department or institution. CIRTL established a laboratory for testing its ideas and practices at the University of Wisconsin-Madison, known as the Delta Program in Research, Teaching and Learning (http://www.delta.wisc.edu). The program offers project-based graduate courses, research mentor training, and workshops for post-docs, staff, and faculty. In addition, graduate students and post-docs can partner with a faculty member in a teaching-as-research internship to define and tackle a specific teaching and learning problem. Finally, students can obtain a Delta Certificate as testimony to their engagement in and commitment to teaching and learning. Delta has proved very successful, having served over 1500 UW-Madison instructors from graduate students to full professors. UW-Madison values the program to the point of now funding it internally.

EvoBuild: A Quickstart Toolkit for Programming Agent-Based Models of Evolutionary Processes

NASA Astrophysics Data System (ADS)

Wagh, Aditi; Wilensky, Uri

2018-04-01

Extensive research has shown that one of the benefits of programming to learn about scientific phenomena is that it facilitates learning about mechanisms underlying the phenomenon. However, using programming activities in classrooms is associated with costs such as requiring additional time to learn to program or students needing prior experience with programming. This paper presents a class of programming environments that we call quickstart: Environments with a negligible threshold for entry into programming and a modest ceiling. We posit that such environments can provide benefits of programming for learning without incurring associated costs for novice programmers. To make this claim, we present a design-based research study conducted to compare programming models of evolutionary processes with a quickstart toolkit with exploring pre-built models of the same processes. The study was conducted in six seventh grade science classes in two schools. Students in the programming condition used EvoBuild, a quickstart toolkit for programming agent-based models of evolutionary processes, to build their NetLogo models. Students in the exploration condition used pre-built NetLogo models. We demonstrate that although students came from a range of academic backgrounds without prior programming experience, and all students spent the same number of class periods on the activities including the time students took to learn programming in this environment, EvoBuild students showed greater learning about evolutionary mechanisms. We discuss the implications of this work for design research on programming environments in K-12 science education.

The Search for the Learning-Centered College. New Expeditions: Charting the Second Century of Community Colleges. Issues Paper No. 9.

ERIC Educational Resources Information Center

Flynn, William J.

Whether the topic is the learning revolution, a learning college for the 21st century, the learning organization, or the growth of franchised learning centers throughout the country, we are in the grip of learning mania. This issue has galvanized higher education to such an extent that suddenly it is unfashionable to mention teaching without…

Introduction to Nuclear Fusion Power and the Design of Fusion Reactors. An Issue-Oriented Module.

ERIC Educational Resources Information Center

Fillo, J. A.

This three-part module focuses on the principles of nuclear fusion and on the likely nature and components of a controlled-fusion power reactor. The physical conditions for a net energy release from fusion and two approaches (magnetic and inertial confinement) which are being developed to achieve this goal are described. Safety issues associated…

39 CFR 3010.42 - Contents of notice of agreement in support of a negotiated service agreement.

Code of Federal Regulations, 2011 CFR

2011-07-01

... agreement. (a) Whenever the Postal Service proposes to establish or change rates or fees and/or the Mail... public notice of the planned changes has been issued or will be issued at least 45 days before the... position or operations of the Postal Service. The projection of change in net financial position as a...

Willingness to pay for insecticide-treated nets in Berehet District, Amhara Region, Northern Ethiopia: implication of social marketing.

PubMed

Aleme, Adisu; Girma, Eshetu; Fentahun, Netsanet

2014-01-01

Understanding the feasibility of achieving widespread coverage with Insecticide-Treated Nets has to be preceded by learning how people value the Insecticide-Treated Nets and estimating the potential demand and willingness to pay so that sustainability of the intervention can be assured. The objective of this study was to determine willingness to pay for Insecticide-Treated Nets among households in Berehet District, Northern Ethiopia. A community-based cross-sectional study was conducted using both quantitative and qualitative methods in five randomly selected Kebeles from January-February 2012. Open ended contingent valuation technique with follow-up method was used. Qualitative data were collected through focus group discussions and observation methods. Binary logistic regression was used to determine the association between dependent and independent variables. The average number of individuals per Insecticide-Treated Nets was 3.83. Nearly 68.5% persons had willingness to buy Insecticide-Treated Nets if they have access to these Nets. The median maximum price a person is willingness to pay for blue rectangular Insecticide-Treated Net was 20 ETB. People had willingness to pay 30 ETB for blue and white conical insecticide-treated nets. Working on knowledge of malaria (OR=0.68, CI (0.47, 0.98; p<0.05), perceived benefit of Insecticide-Treated Nets (OR=0.28, CI (0.2-0.4; p<0.05), perceived susceptibility (OR=0.64(0.44-0.93; p<0.05) and perceived severity of malaria (OR=0.65(0.47-0.91, p<0.05) had significant association with a willingness to pay Insecticide-Treated Nets. Respondents who prefer Kebele/place/ to buy Insecticide-Treated Net for rectangular shape had a significant association with a willingness to pay for Insecticide-Treated Nets (OR=1.92, CI= 1.07-3.92). Promotions, products, price and place had significant association with willingness to pay for Insecticide-Treated Nets. Designing a social marketing strategy helps ensure sustainable supply of Insecticide-Treated Nets and proper use of Insecticide-Treated Nets.

Found in Translation: Exporting Patient-Centered Communication and Small Group Teaching Skills to China

PubMed Central

Blatt, Benjamin; Kallenberg, Gene; Lang, Forrest; Mahoney, Patrick; Patterson, JoEllen; Dugan, Beverly; Sun, Shaobang

2009-01-01

The Chinese Medical Doctor's Association asked us to develop a train-the-trainers program in doctor-patient communication and in teaching skills for a select group of Chinese health care professionals, who would then serve as trainers for practicing physicians throughout China. The request came in the context of increasing doctor-patient friction related, in part, to the dissolution of the socialist health care safety net in China. In this article we recount the implementation of our 5-day training program in Beijing. We explore cross-cultural issues that arose in presenting the program's two principal training domains: small group teaching and patient-centered doctor-patient communication. We also explore the linguistic challenges we encountered as non-Chinese speaking teachers. Finally, we reflect on the lessons learned from this project that may be of value to others called upon to export Western doctor-patient communications training to other cultures. In this age of increasing globalization, cross-cultural sharing of medical education represents a growing trend. PMID:20165520

[Three years of vertical networking in the German Network of Competence on Viral Hepatitis: what can we learn from the usage of different information tools?].

PubMed

Meyer, S; Manns, M P; Wedemeyer, H

2006-01-01

Treatment of patients with viral hepatitis is time-consuming and sometimes complicated. Several sources of information have been established as internet sites, however systematic analyses of the need and use of these information platforms are lacking. The Competence Network on Viral Hepatitis (Hep-Net) was established in 2002 and offers a telephone hotline, an e-mail service and a home page with frequently asked questions (FAQs). On the internet pages "FAQs" 38 125 hits have been registered on single "question-answer-units" within three years. In a half of the cases the question are associated with modes of transmission. In contrast, patient's questions in e-mail and the telephone hotline are mainly dealing with the treatment of hepatitis C. More than 46 % of the physician's questions referred to unclearness in indications for treatment. Questions on specific medical problems of individual patients and, in e-mail, also on legal issues played a major part. Many of these questions have not been addressed in the actual guidelines in Germany. In summary, the Hep-Net vertical networking tools represent a fast, quality-assured endorsement to the daily management of patients with viral hepatitis. The combination of internet, e-mail service and telephone hotline gives consideration to individual necessities of patients and physicians. However, the detailed analysis of usage also shows limitations of current guidelines which should be considered during updating.

Operational problems of Haniwa net as a form of social capital: interdependence between human networks of physicians and information networks.

PubMed

Maeda, Minoru; Araki, Sanae; Suzuki, Muneou; Umemoto, Katsuhiro; Kai, Yukiko; Araki, Kenji

2012-10-01

In August 2009, Miyazaki Health and Welfare Network (Haniwa Net, hereafter referred to as "the Net"), centrally led by University of Miyazaki Hospital (UMH), adopted a center hospital-based system offering a unilateral linkage that enables the viewing of UMH's medical records through a web-based browser (electronic medical records (EMR)). By the end of December 2010, the network had developed into a system of 79 collaborating physicians from within the prefecture. Beginning in August 2010, physicians in 12 medical institutions were visited and asked to speak freely on the operational issues concerning the Net. Recordings and written accounts were coded using the text analysis software MAXQDA 10 to understand the actual state of operations. Analysis of calculations of Kendall's rank correlation confirmed that the interdependency between human networks and information networks is significant. At the same time, while the negative opinions concerning the functions of the Net were somewhat conspicuous, the results showed a correlation between requests and proposals for operational improvements of the Net, clearly indicating the need for a more user-friendly system and a better viewer.

An Improved Sparse Representation over Learned Dictionary Method for Seizure Detection.

PubMed

Li, Junhui; Zhou, Weidong; Yuan, Shasha; Zhang, Yanli; Li, Chengcheng; Wu, Qi

2016-02-01

Automatic seizure detection has played an important role in the monitoring, diagnosis and treatment of epilepsy. In this paper, a patient specific method is proposed for seizure detection in the long-term intracranial electroencephalogram (EEG) recordings. This seizure detection method is based on sparse representation with online dictionary learning and elastic net constraint. The online learned dictionary could sparsely represent the testing samples more accurately, and the elastic net constraint which combines the 11-norm and 12-norm not only makes the coefficients sparse but also avoids over-fitting problem. First, the EEG signals are preprocessed using wavelet filtering and differential filtering, and the kernel function is applied to make the samples closer to linearly separable. Then the dictionaries of seizure and nonseizure are respectively learned from original ictal and interictal training samples with online dictionary optimization algorithm to compose the training dictionary. After that, the test samples are sparsely coded over the learned dictionary and the residuals associated with ictal and interictal sub-dictionary are calculated, respectively. Eventually, the test samples are classified as two distinct categories, seizure or nonseizure, by comparing the reconstructed residuals. The average segment-based sensitivity of 95.45%, specificity of 99.08%, and event-based sensitivity of 94.44% with false detection rate of 0.23/h and average latency of -5.14 s have been achieved with our proposed method.

Using the mixed media according to internet-based on the instructional multimedia for developing students' learning achievements in biology course on foundational cell issue of secondary students at the 10th grade level in Rangsit University demonstration school

NASA Astrophysics Data System (ADS)

Kangloan, Pichet; Chayaburakul, Kanokporn; Santiboon, Toansakul

2018-01-01

The aims of this research study were 1) to develop students' learning achievements in biology course on foundational cell issue, 2) to examine students' satisfactions of their learning activities through the mixed media according to internet-based multi-instruction in biology on foundational cell issue at the 10th grade level were used in the first semester in the academic year 2014, which a sample size of 17 students in Rangsit University Demonstration School with cluster random sampling was selected. Students' learning administrations were instructed with the 3-instructional lesson plans according to the 5-Step Ladder Learning Management Plan (LLMP) namely; the maintaining lesson plan on the equilibrium of cell issue, a lesson plan for learning how to communicate between cell and cell division. Students' learning achievements were assessed with the 30-item Assessment of Learning Biology Test (ALBT), students' perceptions of their satisfactions were satisfied with the 20-item Questionnaire on Students Satisfaction (QSS), and students' learning activities were assessed with the Mixed Media Internet-Based Instruction (MMIBI) on foundational cell issue was designed. The results of this research study have found that: statistically significant of students' post-learning achievements were higher than their pre-learning outcomes and indicated that the differences were significant at the .05 level. Students' performances of their satisfaction to their perceptions toward biology class with the mixed media according to internet-based multi instruction in biology on foundational cell issue were the highest level and evidence of average mean score as 4.59.

Baccalaureate nursing students' perspectives of peer tutoring in simulation laboratory, a Q methodology study.

PubMed

Li, Ting; Petrini, Marcia A; Stone, Teresa E

2018-02-01

The study aim was to identify the perceived perspectives of baccalaureate nursing students toward the peer tutoring in the simulation laboratory. Insight into the nursing students' experiences and baseline data related to their perception of peer tutoring will assist to improve nursing education. Q methodology was applied to explore the students' perspectives of peer tutoring in the simulation laboratory. A convenience P-sample of 40 baccalaureate nursing students was used. Fifty-eight selected Q statements from each participant were classified into the shape of a normal distribution using an 11-point bipolar scale form with a range from -5 to +5. PQ Method software analyzed the collected data. Three discrete factors emerged: Factor I ("Facilitate or empower" knowledge acquisition), Factor II ("Safety Net" Support environment), and Factor III ("Mentoring" learn how to learn). The findings of this study support and indicate that peer tutoring is an effective supplementary strategy to promote baccalaureate students' knowledge acquisition, establishing a supportive safety net and facilitating their abilities to learn in the simulation laboratory. Copyright © 2017 Elsevier Ltd. All rights reserved.

The Impact of an Authentic Science Experience on STEM Identity: A Preliminary Analysis of YouthAstroNet and MicroObservatory Telescope Network Participant Data

NASA Astrophysics Data System (ADS)

Dussault, Mary E.; Wright, Erika A.; Sadler, Philip; Sonnert, Gerhard; ITEAMS II Team

2018-01-01

Encouraging students to pursue careers in science, technology, engineering, and mathematics (STEM) is a high priority for national K-12 education improvement initiatives in the United States. Many educators have claimed that a promising strategy for nurturing early student interest in STEM is to engage them in authentic inquiry experiences. “Authentic” refers to investigations in which the questions are of genuine interest and importance to students, and the inquiry more closely resembles the way real science is done. Science education researchers and practitioners at the Harvard-Smithsonian Center for Astrophysics have put this theory into action with the development of YouthAstroNet, a nationwide online learning community of middle-school aged students, educators, and STEM professionals that features the MicroObservatory Robotic Telescope Network, professional image analysis software, and complementary curricula for use in a variety of learning settings. This preliminary study examines factors that influence YouthAstroNet participants' Science Affinity, STEM Identity, and STEM Career Interest, using the matched pre/post survey results of 261 participants as the data source. The pre/post surveys included some 40 items measuring affinity, identity, knowledge, and career interest. In addition, the post intervention instrument included a number of items in which students reported the instructional strategies they experienced as part of the program. A simple analysis of pre-post changes in affinity and interest revealed very little significant change, and for those items where a small pre-post effect was observed, the average change was most often negative. However, after accounting for students' different program treatment experiences and for their prior attitudes and interests, a predictor of significant student gains in Affinity, STEM Identity, Computer/Math Identity, and STEM Career Interest could be identified. This was the degree to which students reported using and experiencing the primary "authentic" learning activities of the YouthAstroNet program.

Learning as Issue Framing in Agricultural Innovation Networks

ERIC Educational Resources Information Center

Tisenkopfs, Talis; Kunda, Ilona; Šumane, Sandra

2014-01-01

Purpose: Networks are increasingly viewed as entities of learning and innovation in agriculture. In this article we explore learning as issue framing in two agricultural innovation networks. Design/methodology/approach: We combine frame analysis and social learning theories to analyse the processes and factors contributing to frame convergence and…

The Effects of Socio-Scientific Issue Based Inquiry Learning on Pupils' Representations of Landscape

ERIC Educational Resources Information Center

Kärkkäinen, Sirpa; Keinonen, Tuula; Kukkonen, Jari; Juntunen, Seija; Ratinen, Ilkka

2017-01-01

Research has demonstrated that socio-scientific issues based inquiry learning has significant advantages for learning outcomes and students' motivation. Further, a successful understanding of landscapes in environmental and geographical education can be achieved by combining informal learning environments with school education. Therefore this case…

Legal Issues in Experiential Education. PANEL Resource Paper #3.

ERIC Educational Resources Information Center

Goldstein, Michael B.

Legal issues relevant to experiential learning are identified to help program administrators know when to seek expert assistance and advice. Much of the law of experiential learning is based on specific statutory provisions and decisions. The student involved in experiential learning may assume certain learning outcomes very different from those…

Integrating Augmented Reality Technology to Enhance Children's Learning in Marine Education

ERIC Educational Resources Information Center

Lu, Su-Ju; Liu, Ying-Chieh

2015-01-01

Marine education comprises rich and multifaceted issues. Raising general awareness of marine environments and issues demands the development of new learning materials. This study adapts concepts from digital game-based learning to design an innovative marine learning program integrating augmented reality (AR) technology for lower grade primary…

Journal of College Reading and Learning, Volume XIX, 1986.

ERIC Educational Resources Information Center

O'Hear, Michael F., Ed.; And Others

1986-01-01

Addressing issues on developmental education, instructional and learning methods, learning assistance and academic support, and reading and research, this issue of the Journal of College Reading and Learning includes the following articles: "Moving the Mountain to Mohammed: Study Skills Tutoring in the Residence Halls" (J. L. Rogers); "Memory…

[Families Involved in Learning.

ERIC Educational Resources Information Center

Ashby, Nicole, Ed.

2001-01-01

This issue of "Community Update" focuses on families involved in learning. The first article briefly discusses the "Ready to Read, Ready to Learn" White House summit that highlighted new research on early childhood learning. The center spread of this issue offers "Priming the Primary Educator: A Look at L. A. County's Parent Involvement Programs"…

Current Issues in the Neurology and Genetics of Learning-Related Traits and Disorders: Introduction to the Special Issue.

ERIC Educational Resources Information Center

Gilger, Jeffrey W.

2001-01-01

This introductory article briefly describes each of the following eight articles in this special issue on the neurology and genetics of learning related disorders. It notes the greater appreciation of learning disability as a set of complex disorders with broad and intricate neurological bases and of the large individual differences in how these…

Deep feature classification of angiomyolipoma without visible fat and renal cell carcinoma in abdominal contrast-enhanced CT images with texture image patches and hand-crafted feature concatenation.

PubMed

Lee, Hansang; Hong, Helen; Kim, Junmo; Jung, Dae Chul

2018-04-01

To develop an automatic deep feature classification (DFC) method for distinguishing benign angiomyolipoma without visible fat (AMLwvf) from malignant clear cell renal cell carcinoma (ccRCC) from abdominal contrast-enhanced computer tomography (CE CT) images. A dataset including 80 abdominal CT images of 39 AMLwvf and 41 ccRCC patients was used. We proposed a DFC method for differentiating the small renal masses (SRM) into AMLwvf and ccRCC using the combination of hand-crafted and deep features, and machine learning classifiers. First, 71-dimensional hand-crafted features (HCF) of texture and shape were extracted from the SRM contours. Second, 1000-4000-dimensional deep features (DF) were extracted from the ImageNet pretrained deep learning model with the SRM image patches. In DF extraction, we proposed the texture image patches (TIP) to emphasize the texture information inside the mass in DFs and reduce the mass size variability. Finally, the two features were concatenated and the random forest (RF) classifier was trained on these concatenated features to classify the types of SRMs. The proposed method was tested on our dataset using leave-one-out cross-validation and evaluated using accuracy, sensitivity, specificity, positive predictive values (PPV), negative predictive values (NPV), and area under receiver operating characteristics curve (AUC). In experiments, the combinations of four deep learning models, AlexNet, VGGNet, GoogleNet, and ResNet, and four input image patches, including original, masked, mass-size, and texture image patches, were compared and analyzed. In qualitative evaluation, we observed the change in feature distributions between the proposed and comparative methods using tSNE method. In quantitative evaluation, we evaluated and compared the classification results, and observed that (a) the proposed HCF + DF outperformed HCF-only and DF-only, (b) AlexNet showed generally the best performances among the CNN models, and (c) the proposed TIPs not only achieved the competitive performances among the input patches, but also steady performance regardless of CNN models. As a result, the proposed method achieved the accuracy of 76.6 ± 1.4% for the proposed HCF + DF with AlexNet and TIPs, which improved the accuracy by 6.6%p and 8.3%p compared to HCF-only and DF-only, respectively. The proposed shape features and TIPs improved the HCFs and DFs, respectively, and the feature concatenation further enhanced the quality of features for differentiating AMLwvf from ccRCC in abdominal CE CT images. © 2018 American Association of Physicists in Medicine.

Compression of deep convolutional neural network for computer-aided diagnosis of masses in digital breast tomosynthesis

NASA Astrophysics Data System (ADS)

Samala, Ravi K.; Chan, Heang-Ping; Hadjiiski, Lubomir; Helvie, Mark A.; Richter, Caleb; Cha, Kenny

2018-02-01

Deep-learning models are highly parameterized, causing difficulty in inference and transfer learning. We propose a layered pathway evolution method to compress a deep convolutional neural network (DCNN) for classification of masses in DBT while maintaining the classification accuracy. Two-stage transfer learning was used to adapt the ImageNet-trained DCNN to mammography and then to DBT. In the first-stage transfer learning, transfer learning from ImageNet trained DCNN was performed using mammography data. In the second-stage transfer learning, the mammography-trained DCNN was trained on the DBT data using feature extraction from fully connected layer, recursive feature elimination and random forest classification. The layered pathway evolution encapsulates the feature extraction to the classification stages to compress the DCNN. Genetic algorithm was used in an iterative approach with tournament selection driven by count-preserving crossover and mutation to identify the necessary nodes in each convolution layer while eliminating the redundant nodes. The DCNN was reduced by 99% in the number of parameters and 95% in mathematical operations in the convolutional layers. The lesion-based area under the receiver operating characteristic curve on an independent DBT test set from the original and the compressed network resulted in 0.88+/-0.05 and 0.90+/-0.04, respectively. The difference did not reach statistical significance. We demonstrated a DCNN compression approach without additional fine-tuning or loss of performance for classification of masses in DBT. The approach can be extended to other DCNNs and transfer learning tasks. An ensemble of these smaller and focused DCNNs has the potential to be used in multi-target transfer learning.

Computational intelligence in earth sciences and environmental applications: issues and challenges.

PubMed

Cherkassky, V; Krasnopolsky, V; Solomatine, D P; Valdes, J

2006-03-01

This paper introduces a generic theoretical framework for predictive learning, and relates it to data-driven and learning applications in earth and environmental sciences. The issues of data quality, selection of the error function, incorporation of the predictive learning methods into the existing modeling frameworks, expert knowledge, model uncertainty, and other application-domain specific problems are discussed. A brief overview of the papers in the Special Issue is provided, followed by discussion of open issues and directions for future research.

An application of deep learning in the analysis of stellar spectra

NASA Astrophysics Data System (ADS)

Fabbro, S.; Venn, K. A.; O'Briain, T.; Bialek, S.; Kielty, C. L.; Jahandar, F.; Monty, S.

2018-04-01

Spectroscopic surveys require fast and efficient analysis methods to maximize their scientific impact. Here, we apply a deep neural network architecture to analyse both SDSS-III APOGEE DR13 and synthetic stellar spectra. When our convolutional neural network model (StarNet) is trained on APOGEE spectra, we show that the stellar parameters (temperature, gravity, and metallicity) are determined with similar precision and accuracy as the APOGEE pipeline. StarNet can also predict stellar parameters when trained on synthetic data, with excellent precision and accuracy for both APOGEE data and synthetic data, over a wide range of signal-to-noise ratios. In addition, the statistical uncertainties in the stellar parameter determinations are comparable to the differences between the APOGEE pipeline results and those determined independently from optical spectra. We compare StarNet to other data-driven methods; for example, StarNet and the Cannon 2 show similar behaviour when trained with the same data sets; however, StarNet performs poorly on small training sets like those used by the original Cannon. The influence of the spectral features on the stellar parameters is examined via partial derivatives of the StarNet model results with respect to the input spectra. While StarNet was developed using the APOGEE observed spectra and corresponding ASSET synthetic data, we suggest that this technique is applicable to other wavelength ranges and other spectral surveys.

Digital Media & Learning in Afterschool. MetLife Foundation Afterschool Alert. Issue Brief No. 58

ERIC Educational Resources Information Center

Afterschool Alliance, 2013

2013-01-01

The Afterschool Alliance, in partnership with MetLife Foundation, is proud to present the final issue brief in its latest series of four issue briefs examining critical issues facing middle school youth and the vital role afterschool programs play in addressing these issues. This brief explores afterschool and digital learning. At the core of…

Transforming the Twenty-First-Century Campus to Enhance the Net-Generation Student Learning Experience: Using Evidence-Based Design to Determine What Works and Why in Virtual/Physical Teaching Spaces

ERIC Educational Resources Information Center

Fisher, Kenn; Newton, Clare

2014-01-01

The twenty-first century has seen the rapid emergence of wireless broadband and mobile communications devices which are inexorably changing the way people communicate, collaborate, create and transfer knowledge. Yet many higher education campus learning environments were designed and built in the nineteenth and twentieth centuries prior to…

Lessons Learned From Methodological Validation Research in E-Epidemiology.

PubMed

Kesse-Guyot, Emmanuelle; Assmann, Karen; Andreeva, Valentina; Castetbon, Katia; Méjean, Caroline; Touvier, Mathilde; Salanave, Benoît; Deschamps, Valérie; Péneau, Sandrine; Fezeu, Léopold; Julia, Chantal; Allès, Benjamin; Galan, Pilar; Hercberg, Serge

2016-10-18

Traditional epidemiological research methods exhibit limitations leading to high logistics, human, and financial burden. The continued development of innovative digital tools has the potential to overcome many of the existing methodological issues. Nonetheless, Web-based studies remain relatively uncommon, partly due to persistent concerns about validity and generalizability. The objective of this viewpoint is to summarize findings from methodological studies carried out in the NutriNet-Santé study, a French Web-based cohort study. On the basis of the previous findings from the NutriNet-Santé e-cohort (>150,000 participants are currently included), we synthesized e-epidemiological knowledge on sample representativeness, advantageous recruitment strategies, and data quality. Overall, the reported findings support the usefulness of Web-based studies in overcoming common methodological deficiencies in epidemiological research, in particular with regard to data quality (eg, the concordance for body mass index [BMI] classification was 93%), reduced social desirability bias, and access to a wide range of participant profiles, including the hard-to-reach subgroups such as young (12.30% [15,118/122,912], <25 years) and old people (6.60% [8112/122,912], ≥65 years), unemployed or homemaker (12.60% [15,487/122,912]), and low educated (38.50% [47,312/122,912]) people. However, some selection bias remained (78.00% (95,871/122,912) of the participants were women, and 61.50% (75,590/122,912) had postsecondary education), which is an inherent aspect of cohort study inclusion; other specific types of bias may also have occurred. Given the rapidly growing access to the Internet across social strata, the recruitment of participants with diverse socioeconomic profiles and health risk exposures was highly feasible. Continued efforts concerning the identification of specific biases in e-cohorts and the collection of comprehensive and valid data are still needed. This summary of methodological findings from the NutriNet-Santé cohort may help researchers in the development of the next generation of high-quality Web-based epidemiological studies.

LIS Professionals as Knowledge Engineers.

ERIC Educational Resources Information Center

Poulter, Alan; And Others

1994-01-01

Considers the role of library and information science professionals as knowledge engineers. Highlights include knowledge acquisition, including personal experience, interviews, protocol analysis, observation, multidimensional sorting, printed sources, and machine learning; knowledge representation, including production rules and semantic nets;…

Collaborative Leadership and Motivation as Examples of Education for All Goals 1, 3, 4, and 6 at Work in Seven Schools in Trinidad

ERIC Educational Resources Information Center

Bissessar, Charmaine

2014-01-01

The underlying theoretical framework of this qualitative case study was leadership and motivation. The research questions were: What issues and challenges to effective teaching and learning do you have? What mechanisms do you use to resolve these teaching and learning issues? The issues surrounding teaching and learning were leadership and…

Inclusive E-Learning - Towards an Integrated System Design.

PubMed

Patzer, Yasmin; Pinkwart, Niels

2017-01-01

At first sight there seem to be issues combining technical accessibility guidelines and educational needs when designing inclusive E-Learning. Furthermore Universal Design for Learning seems to contradict individualization. In this paper we address both issues with an inclusive E-Learning design for the LAYA system, which targets disabled and non-disabled learners.

Learning from Sustainable Development: Education in the Light of Public Issues

ERIC Educational Resources Information Center

Van Poeck, Katrien; Vandenabeele, Joke

2012-01-01

Education for sustainable development plays an increasing role in environmental education policy and practice. In this article, we show how sustainable development is mainly seen as a goal that can be achieved by applying the proper processes of learning and how this learning perspective translates sustainability issues into learning problems of…

Personalized Learning in Wisconsin: FLIGHT Academy. Connect: Making Learning Personal

ERIC Educational Resources Information Center

Taege, Jeffrey; Krauter, Krista; Lees, Jonathan

2015-01-01

This field report is the third in a series produced by the Center on Innovations in Learning's League of Innovators. The series describes, discusses, and analyzes policies and practices that enable personalization in education. Issues of the series will present either issue briefs or, like this one, field reports on lessons learned by…

Learning Technologies: Affective and Social Issues in Computer-Supported Collaborative Learning

ERIC Educational Resources Information Center

Jones, Ann; Issroff, Kim

2005-01-01

This paper is concerned with "affective" issues in learning technologies in a collaborative context. Traditionally in learning there has been a division between cognition and affect: where cognition is concerned with skills and processes such as thinking and problem-solving and affect with emotional areas such as motivation, attitudes, feelings.…

A Framework for Institutional Adoption and Implementation of Blended Learning in Higher Education

ERIC Educational Resources Information Center

Graham, Charles R.; Woodfield, Wendy; Harrison, J. Buckley

2013-01-01

There has been rapid growth in blended learning implementation and research focused on course-level issues such as improved learning outcomes, but very limited research focused on institutional policy and adoption issues. More institutional-level blended learning research is needed to guide institutions of higher education in strategically…

Electronic Learning Communities: Issues and Practices.

ERIC Educational Resources Information Center

Reisman, Sorel, Ed.; Flores, John G., Ed.; Edge, Denzil, Ed.

This book provides information for researchers and practitioners on the current issues and best practices associated with electronic learning communities. Fourteen contributed chapters include: "Interactive Online Educational Experiences: E-volution of Graded Projects" (James Benjamin); "Hybrid Courses as Learning Communities"…

Collaborative innovations with rural and regional secondary teachers: enhancing student learning in mathematics

NASA Astrophysics Data System (ADS)

Pegg, John; Panizzon, Debra

2011-06-01

When questioned, secondary mathematics teachers in rural and regional schools in Australia refer to their limited opportunities to engage and share experiences with peers in other schools as an under-utilised and cost-effective mechanism to support their professional learning and enhance their students' learning. The paper reports on the creation and evaluation of a network of learning communities of rural secondary mathematics teachers around a common purpose—enhancement and increased engagement of student learning in mathematics. To achieve this goal, teams of teachers from six rural schools identified an issue hindering improved student learning of mathematics in their school. Working collaboratively with support from university personnel with expertise in curriculum, assessment and quality pedagogy, teachers developed and implemented strategies to address an identified issue in ways that were relevant to their teaching contexts. The research study identifies issues in mathematics of major concern to rural teachers of mathematics, the successes and challenges the teachers faced in working in learning communities on the issue they identified, and the efficacy of the professional learning model.

Reflection on Cuboid Net with Mathematical Learning Quality

NASA Astrophysics Data System (ADS)

Sari, Atikah; Suryadi, Didi; Syaodih, Ernawulan

2017-09-01

This research aims to formulate an alternative to the reflection in mathematics learning activities related to the activities of the professionalism of teachers motivated by a desire to improve the quality of learning. This study is a qualitative study using the Didactical Design research. This study was conducted in one of the elementary schools. The data collection techniques are triangulation with the research subject is teacher 5th grade. The results of this study indicate that through deep reflection, teachers can design learning design in accordance with the conditions of the class. Also revealed that teachers have difficulty in choosing methods of learning and contextual learning media. Based on the implementation of activities of reflection and make the learning design based on the results of reflection can be concluded that the quality of learning in the class will develop.

Public-private delivery of insecticide-treated nets: a voucher scheme in Volta Region, Ghana

PubMed Central

Kweku, Margaret; Webster, Jayne; Taylor, Ian; Burns, Susan; Dedzo, McDamien

2007-01-01

Background Coverage of vulnerable groups with insecticide-treated nets (ITNs) in Ghana, as in the majority of countries of sub-Saharan Africa is currently low. A voucher scheme was introduced in Volta Region as a possible sustainable delivery system for increasing this coverage through scale-up to other regions. Successful scale-up of public health interventions depends upon optimal delivery processes but operational research for delivery processes in large-scale implementation has been inadequate. Methods A simple tool was developed to monitor numbers of vouchers given to each health facility, numbers issued to pregnant women by the health staff, and numbers redeemed by the distributors back to the management agent. Three rounds of interviews were undertaken with health facility staff, retailers and pregnant women who had attended antenatal clinic (ANC). Results During the one year pilot 25,926 vouchers were issued to eligible women from clinics, which equates to 50.7% of the 51,658 ANC registrants during this time period. Of the vouchers issued 66.7% were redeemed by distributors back to the management agent. Initially, non-issuing of vouchers to pregnant women was mainly due to eligibility criteria imposed by the midwives; later in the year it was due to decisions of the pregnant women, and supply constraints. These in turn were heavily influenced by factors external to the programme: current household ownership of nets, competing ITN delivery strategies, and competition for the limited number of ITNs available in the country from major urban areas of other regions. Conclusion Both issuing and redemption of vouchers should be monitored as factors assumed to influence voucher redemption had an influence on issuing, and vice versa. More evidence is needed on how specific contextual factors influence the success of voucher schemes and other models of delivery of ITNs. Such an evidence base will facilitate optimal strategic decision making so that the delivery model with the best probability of success within a given context is implemented. Rigorous monitoring has an important role to play in the successful scaling-up of delivery of effective public health interventions. PMID:17274810

Ethical learning on international medical electives: a case-based analysis of medical student learning experiences.

PubMed

Bowsher, Gemma; Parry-Billings, Laura; Georgeson, Anna; Baraitser, Paula

2018-04-11

Students on international medical electives face complex ethical issues when undertaking clinical work. The variety of elective destinations and the culturally specific nature of clinical ethical issues suggest that pre-elective preparation could be supplemented by in-elective support. An online, asynchronous, case-based discussion was piloted to support ethical learning on medical student electives. We developed six scenarios from elective diaries to stimulate peer-facilitated discussions during electives. We evaluated the transcripts to assess whether transformative, experiential learning took place, assessing specifically for indications that 1) critical reflection, 2) reflective action and 3) reflective learning were taking place. We also completed a qualitative thematic content analysis of the discussions. Of forty-one extended comments, nine responses showed evidence of transformative learning (Mezirow stage three). The thematic analysis identified five themes: adopting a position on ethical issues without overt analysis; presenting issues in terms of their effects on students' ability to complete tasks; describing local contexts and colleagues as "other"; difficulty navigating between individual and structural issues, and overestimation of the impact of individual action on structures and processes. Results suggest a need to: frame ethical learning on elective so that it builds on earlier ethical programmes in the curriculum, and encourages students to adopt structured approaches to complex ethical issues including cross-cultural negotiation and to enhance global health training within the curriculum.

Opportunity to discuss ethical issues during clinical learning experience.

PubMed

Palese, Alvisa; Gonella, Silvia; Destrebecq, Anne; Mansutti, Irene; Terzoni, Stefano; Morsanutto, Michela; Altini, Pietro; Bevilacqua, Anita; Brugnolli, Anna; Canzan, Federica; Ponte, Adriana Dal; De Biasio, Laura; Fascì, Adriana; Grosso, Silvia; Mantovan, Franco; Marognolli, Oliva; Nicotera, Raffaela; Randon, Giulia; Tollini, Morena; Saiani, Luisa; Grassetti, Luca; Dimonte, Valerio

2018-01-01

Undergraduate nursing students have been documented to experience ethical distress during their clinical training and felt poorly supported in discussing the ethical issues they encountered. Research aims: This study was aimed at exploring nursing students' perceived opportunity to discuss ethical issues that emerged during their clinical learning experience and associated factors. An Italian national cross-sectional study design was performed in 2015-2016. Participants were invited to answer a questionnaire composed of four sections regarding: (1) socio-demographic data, (2) previous clinical learning experiences, (3) current clinical learning experience quality and outcomes, and (4) the opportunity to discuss ethical issues with nurses in the last clinical learning experience (from 0 - 'never' to 3 - 'very much'). Participants and research context: Participants were 9607 undergraduate nursing students who were attending 95 different three-year Italian baccalaureate nursing programmes, located at 27 universities in 15 Italian regions. Ethical considerations: This study was conducted in accordance with the Human Subject Research Ethics Committee guidelines after the research protocol was approved by an ethics committee. Overall, 4707 (49%) perceived to have discussed ethical issues 'much' or 'very much'; among the remaining, 3683 (38.3%) and 1217 (12.7%) students reported the perception of having discussed, respectively, 'enough' or 'never' ethical issues emerged in the clinical practice. At the multivariate logistic regression analysis explaining 38.1% of the overall variance, the factors promoting ethical discussion were mainly set at the clinical learning environment levels (i.e. increased learning opportunities, self-directed learning, safety and nursing care quality, quality of the tutorial strategies, competences learned and supervision by a clinical nurse). In contrast, being male was associated with a perception of less opportunity to discuss ethical issues. Nursing faculties should assess the clinical environment prerequisites of the settings as a context of student experience before deciding on their accreditation. Moreover, the nursing faculty and nurse managers should also enhance competence with regard to discussing ethical issues with students among clinical nurses by identifying factors that hinder this learning opportunity in daily practice.

Real-time adaptive off-road vehicle navigation and terrain classification

NASA Astrophysics Data System (ADS)

Muller, Urs A.; Jackel, Lawrence D.; LeCun, Yann; Flepp, Beat

2013-05-01

We are developing a complete, self-contained autonomous navigation system for mobile robots that learns quickly, uses commodity components, and has the added benefit of emitting no radiation signature. It builds on the au­tonomous navigation technology developed by Net-Scale and New York University during the Defense Advanced Research Projects Agency (DARPA) Learning Applied to Ground Robots (LAGR) program and takes advantage of recent scientific advancements achieved during the DARPA Deep Learning program. In this paper we will present our approach and algorithms, show results from our vision system, discuss lessons learned from the past, and present our plans for further advancing vehicle autonomy.

Computer Assisted Chronic Disease Management: Does It Work? A Pilot Study Using Mixed Methods

PubMed Central

Jones, Kay M.; Biezen, Ruby; Piterman, Leon

2013-01-01

Background. Key factors for the effective chronic disease management (CDM) include the availability of practical and effective computer tools and continuing professional development/education. This study tested the effectiveness of a computer assisted chronic disease management tool, a broadband-based service known as cdmNet in increasing the development of care plans for patients with chronic disease in general practice. Methodology. Mixed methods are the breakthrough series methodology (workshops and plan-do-study-act cycles) and semistructured interviews. Results. Throughout the intervention period a pattern emerged suggesting GPs use of cdmNet initially increased, then plateaued practice nurses' and practice managers' roles expanded as they became more involved in using cdmNet. Seven main messages emerged from the GP interviews. Discussion. The overall use of cdmNet by participating GPs varied from “no change” to “significant change and developing many the GPMPs (general practice management plans) using cdmNet.” The variation may be due to several factors, not the least, allowing GPs adequate time to familiarise themselves with the software and recognising the benefit of the team approach. Conclusion. The breakthrough series methodology facilitated upskilling GPs' management of patients diagnosed with a chronic disease and learning how to use the broadband-based service cdmNet. PMID:24959576

Army Net Zero: Energy Roadmap and Program Summary, Fiscal Year 2013 (Brochure)

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

Not Available

The U.S. Army (Army) partnered with the National Renewable Energy Laboratory (NREL) and the U.S. Army Corps of Engineers to assess opportunities for increasing energy security through improved energy efficiency and optimized renewable energy strategies at nine installations across the Army's portfolio. Referred to as Net Zero Energy Installations (NZEIs), these projects demonstrate and validate energy efficiency and renewable energy technologies with approaches that can be replicated across DOD and other Federal agencies, setting the stage for broad market adoption. This report summarizes the results of the energy project roadmaps developed by NREL, shows the progress each installation could makemore » in achieving Net Zero Energy by 2020, and presents lessons learned and unique challenges from each installation.« less

Radio frequency interference mitigation using deep convolutional neural networks

NASA Astrophysics Data System (ADS)

Akeret, J.; Chang, C.; Lucchi, A.; Refregier, A.

2017-01-01

We propose a novel approach for mitigating radio frequency interference (RFI) signals in radio data using the latest advances in deep learning. We employ a special type of Convolutional Neural Network, the U-Net, that enables the classification of clean signal and RFI signatures in 2D time-ordered data acquired from a radio telescope. We train and assess the performance of this network using the HIDE &SEEK radio data simulation and processing packages, as well as early Science Verification data acquired with the 7m single-dish telescope at the Bleien Observatory. We find that our U-Net implementation is showing competitive accuracy to classical RFI mitigation algorithms such as SEEK's SUMTHRESHOLD implementation. We publish our U-Net software package on GitHub under GPLv3 license.

The Strategic Partners Network's Extraction: The XStrat.Net Project

NASA Astrophysics Data System (ADS)

Taifi, Nouha; Passiante, Giuseppina

The firms in the business environment have to choose adequate partners in order to sustain their competitive advantage and their economic performance. Plus, the creation of special communities consisting of these partners is essential for the life-long development of these latter and the firms creating them. The research project XStrat.Net aims at the identification of factors and indicators about the organizations for the modelling of intelligent agents -XStrat intelligent agents- and the engineering of a software -XStrat- to process these backbones intelligent agents. Through the use of the software, the firms will be able to select the needed partners for the creation of special communities for the purpose of learning, interest or innovation. The XStrat.Net project also intends to provide guidelines for the creation of the special communities.

Reported bed net ownership and use in social contacts is associated with uptake of bed nets for malaria prevention in pregnant women in Ghana.

PubMed

Ernst, Kacey C; Erly, Steven; Adusei, Charity; Bell, Melanie L; Kessie, David Komla; Biritwum-Nyarko, Alberta; Ehiri, John

2017-01-04

Despite progress made in the last decades, malaria persists as a pressing health issue in sub-Saharan Africa. Pregnant women are particularly vulnerable to infection and serious health outcomes for themselves and their unborn child. Risk can be mitigated through appropriate use of control measures such as insecticide-treated bed nets. Although social networks can influence uptake of preventive strategies, the role of social influence on bed net ownership has not been explored. During an evaluation of a bed net distribution programme, the influence of non-health care advisors on ownership and use of bed nets by pregnant women in Kumasi, Ghana was examined. Data were collected through in-person interviews with 300 pregnant women seeking antenatal care in an urban hospital in Kumasi, Ghana. Participants were asked about their bed net ownership, bed net use, and information about three personal contacts that they go to for pregnancy advice. Information about these advisors was combined into an influence score. Logistic regression models were used to determine the association between the score and bed net ownership. Those who owned a bed net were further assessed to determine if interpersonal influence was associated with self-reported sleeping under the bed net the previous night. Of the 294 women in the analysis, 229 (78%) reported owning bed nets. Of these bed net owners, 139 (61%) reported using a bed net the previous night. A dose response relationship was observed between the interpersonal influence score and bed net ownership and use. Compared to the lowest influence score, those with the highest influence score (>1 SD above the mean) were marginally more likely to own a bed net [OR = 2.37, 95% CI (0.87, 6.39)] and much more likely to use their bed net [5.38, 95% CI (1.89, 15.25)] after adjusting for other factors. Interpersonal influence appears to have modest impact on ownership and use of bed nets by pregnant women in an urban area of Ghana. Further investigations would need to be conducted to determine if the relationship is causal or if individuals who associate are simply more likely to have similar practices.

Hebbian learning of hand-centred representations in a hierarchical neural network model of the primate visual system.

PubMed

Born, Jannis; Galeazzi, Juan M; Stringer, Simon M

2017-01-01

A subset of neurons in the posterior parietal and premotor areas of the primate brain respond to the locations of visual targets in a hand-centred frame of reference. Such hand-centred visual representations are thought to play an important role in visually-guided reaching to target locations in space. In this paper we show how a biologically plausible, Hebbian learning mechanism may account for the development of localized hand-centred representations in a hierarchical neural network model of the primate visual system, VisNet. The hand-centered neurons developed in the model use an invariance learning mechanism known as continuous transformation (CT) learning. In contrast to previous theoretical proposals for the development of hand-centered visual representations, CT learning does not need a memory trace of recent neuronal activity to be incorporated in the synaptic learning rule. Instead, CT learning relies solely on a Hebbian learning rule, which is able to exploit the spatial overlap that naturally occurs between successive images of a hand-object configuration as it is shifted across different retinal locations due to saccades. Our simulations show how individual neurons in the network model can learn to respond selectively to target objects in particular locations with respect to the hand, irrespective of where the hand-object configuration occurs on the retina. The response properties of these hand-centred neurons further generalise to localised receptive fields in the hand-centred space when tested on novel hand-object configurations that have not been explored during training. Indeed, even when the network is trained with target objects presented across a near continuum of locations around the hand during training, the model continues to develop hand-centred neurons with localised receptive fields in hand-centred space. With the help of principal component analysis, we provide the first theoretical framework that explains the behavior of Hebbian learning in VisNet.

Hebbian learning of hand-centred representations in a hierarchical neural network model of the primate visual system

PubMed Central

Born, Jannis; Stringer, Simon M.

2017-01-01

A subset of neurons in the posterior parietal and premotor areas of the primate brain respond to the locations of visual targets in a hand-centred frame of reference. Such hand-centred visual representations are thought to play an important role in visually-guided reaching to target locations in space. In this paper we show how a biologically plausible, Hebbian learning mechanism may account for the development of localized hand-centred representations in a hierarchical neural network model of the primate visual system, VisNet. The hand-centered neurons developed in the model use an invariance learning mechanism known as continuous transformation (CT) learning. In contrast to previous theoretical proposals for the development of hand-centered visual representations, CT learning does not need a memory trace of recent neuronal activity to be incorporated in the synaptic learning rule. Instead, CT learning relies solely on a Hebbian learning rule, which is able to exploit the spatial overlap that naturally occurs between successive images of a hand-object configuration as it is shifted across different retinal locations due to saccades. Our simulations show how individual neurons in the network model can learn to respond selectively to target objects in particular locations with respect to the hand, irrespective of where the hand-object configuration occurs on the retina. The response properties of these hand-centred neurons further generalise to localised receptive fields in the hand-centred space when tested on novel hand-object configurations that have not been explored during training. Indeed, even when the network is trained with target objects presented across a near continuum of locations around the hand during training, the model continues to develop hand-centred neurons with localised receptive fields in hand-centred space. With the help of principal component analysis, we provide the first theoretical framework that explains the behavior of Hebbian learning in VisNet. PMID:28562618

Cognitive Issues in Learning Advanced Physics: An Example from Quantum Mechanics

NASA Astrophysics Data System (ADS)

Singh, Chandralekha; Zhu, Guangtian

2009-11-01

We are investigating cognitive issues in learning quantum mechanics in order to develop effective teaching and learning tools. The analysis of cognitive issues is particularly important for bridging the gap between the quantitative and conceptual aspects of quantum mechanics and for ensuring that the learning tools help students build a robust knowledge structure. We discuss the cognitive aspects of quantum mechanics that are similar or different from those of introductory physics and their implications for developing strategies to help students develop a good grasp of quantum mechanics.

Comparison of different deep learning approaches for parotid gland segmentation from CT images

NASA Astrophysics Data System (ADS)

Hänsch, Annika; Schwier, Michael; Gass, Tobias; Morgas, Tomasz; Haas, Benjamin; Klein, Jan; Hahn, Horst K.

2018-02-01

The segmentation of target structures and organs at risk is a crucial and very time-consuming step in radiotherapy planning. Good automatic methods can significantly reduce the time clinicians have to spend on this task. Due to its variability in shape and often low contrast to surrounding structures, segmentation of the parotid gland is especially challenging. Motivated by the recent success of deep learning, we study different deep learning approaches for parotid gland segmentation. Particularly, we compare 2D, 2D ensemble and 3D U-Net approaches and find that the 2D U-Net ensemble yields the best results with a mean Dice score of 0.817 on our test data. The ensemble approach reduces false positives without the need for an automatic region of interest detection. We also apply our trained 2D U-Net ensemble to segment the test data of the 2015 MICCAI head and neck auto-segmentation challenge. With a mean Dice score of 0.861, our classifier exceeds the highest mean score in the challenge. This shows that the method generalizes well onto data from independent sites. Since appropriate reference annotations are essential for training but often difficult and expensive to obtain, it is important to know how many samples are needed to properly train a neural network. We evaluate the classifier performance after training with differently sized training sets (50-450) and find that 250 cases (without using extensive data augmentation) are sufficient to obtain good results with the 2D ensemble. Adding more samples does not significantly improve the Dice score of the segmentations.

Evaluating predictive modeling’s potential to improve teleretinal screening participation in urban safety net clinics

PubMed Central

Ogunyemi, Omolola; Teklehaimanot, Senait; Patty, Lauren; Moran, Erin; George, Sheba

2013-01-01

Introduction Screening guidelines for diabetic patients recommend yearly eye examinations to detect diabetic retinopathy and other forms of diabetic eye disease. However, annual screening rates for retinopathy in US urban safety net settings remain low. Methods Using data gathered from a study of teleretinal screening in six urban safety net clinics, we assessed whether predictive modeling could be of value in identifying patients at risk of developing retinopathy. We developed and examined the accuracy of two predictive modeling approaches for diabetic retinopathy in a sample of 513 diabetic individuals, using routinely available clinical variables from retrospective medical record reviews. Bayesian networks and radial basis function (neural) networks were learned using ten-fold cross-validation. Results The predictive models were modestly predictive with the best model having an AUC of 0.71. Discussion Using routinely available clinical variables to predict patients at risk of developing retinopathy and to target them for annual eye screenings may be of some usefulness to safety net clinics. PMID:23920536

Evaluating predictive modeling's potential to improve teleretinal screening participation in urban safety net clinics.

PubMed

Ogunyemi, Omolola; Teklehaimanot, Senait; Patty, Lauren; Moran, Erin; George, Sheba

2013-01-01

Screening guidelines for diabetic patients recommend yearly eye examinations to detect diabetic retinopathy and other forms of diabetic eye disease. However, annual screening rates for retinopathy in US urban safety net settings remain low. Using data gathered from a study of teleretinal screening in six urban safety net clinics, we assessed whether predictive modeling could be of value in identifying patients at risk of developing retinopathy. We developed and examined the accuracy of two predictive modeling approaches for diabetic retinopathy in a sample of 513 diabetic individuals, using routinely available clinical variables from retrospective medical record reviews. Bayesian networks and radial basis function (neural) networks were learned using ten-fold cross-validation. The predictive models were modestly predictive with the best model having an AUC of 0.71. Using routinely available clinical variables to predict patients at risk of developing retinopathy and to target them for annual eye screenings may be of some usefulness to safety net clinics.

Using fuzzy logic to integrate neural networks and knowledge-based systems

NASA Technical Reports Server (NTRS)

Yen, John

1991-01-01

Outlined here is a novel hybrid architecture that uses fuzzy logic to integrate neural networks and knowledge-based systems. The author's approach offers important synergistic benefits to neural nets, approximate reasoning, and symbolic processing. Fuzzy inference rules extend symbolic systems with approximate reasoning capabilities, which are used for integrating and interpreting the outputs of neural networks. The symbolic system captures meta-level information about neural networks and defines its interaction with neural networks through a set of control tasks. Fuzzy action rules provide a robust mechanism for recognizing the situations in which neural networks require certain control actions. The neural nets, on the other hand, offer flexible classification and adaptive learning capabilities, which are crucial for dynamic and noisy environments. By combining neural nets and symbolic systems at their system levels through the use of fuzzy logic, the author's approach alleviates current difficulties in reconciling differences between low-level data processing mechanisms of neural nets and artificial intelligence systems.

Lifelong Learning and Adult Education. Special Issue in Memory of CIHED Advisory Board Member J. Roby Kidd.

ERIC Educational Resources Information Center

CIHED Newsletter, 1982

1982-01-01

This newsletter deals with lifelong learning and adult and continuing education. Included in the issue are the following articles: "The Learning Society," by Solveig M. Turner; "Adult Education at the Beginning of the 1980s," by J. Roby Kidd; "Lifelong Learning in an International Perspective: Selected Case Studies,"…

Understanding Work-Related Learning: The Case of ICT Workers

ERIC Educational Resources Information Center

Gijbels, David; Raemdonck, Isabel; Vervecken, Dries; Van Herck, Jonas

2012-01-01

Purpose: A central issue in the field of workplace learning is how work-related learning can be stimulated so that a powerful learning work environment is created. This paper seeks to further enlarge understanding on this issue. Based on the demand-control-support the aim is to investigate the influence of job-characteristics on the work-related…

Issues and Recommendations: A Report of the Learning Disabilities Task Force on Definition, Criteria and Identification Procedures.

ERIC Educational Resources Information Center

Iowa State Dept. of Education, Des Moines. Bureau of Special Education.

The report, developed by a special Iowa task force, examined issues of definition, criteria, and identification procedures for learning disabilities as a point of departure for the examination of current practices affecting learning disabled students in Iowa. The committee's working definintion of learning disabilities is presented as a basis for…

Selective Social Learning: New Perspectives on Learning from Others

ERIC Educational Resources Information Center

Koenig, Melissa A.; Sabbagh, Mark A.

2013-01-01

This special issue was motivated by the recent, wide-ranging interest in the development of children's selective social learning. Human beings have a far-reaching dependence on others for information, and the focus of this issue is on the processes by which children selectively and intelligently learn from others. It showcases some of the finest…

Statistical-learning strategies generate only modestly performing predictive models for urinary symptoms following external beam radiotherapy of the prostate: A comparison of conventional and machine-learning methods

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

Yahya, Noorazrul, E-mail: noorazrul.yahya@research.uwa.edu.au; Ebert, Martin A.; Bulsara, Max

Purpose: Given the paucity of available data concerning radiotherapy-induced urinary toxicity, it is important to ensure derivation of the most robust models with superior predictive performance. This work explores multiple statistical-learning strategies for prediction of urinary symptoms following external beam radiotherapy of the prostate. Methods: The performance of logistic regression, elastic-net, support-vector machine, random forest, neural network, and multivariate adaptive regression splines (MARS) to predict urinary symptoms was analyzed using data from 754 participants accrued by TROG03.04-RADAR. Predictive features included dose-surface data, comorbidities, and medication-intake. Four symptoms were analyzed: dysuria, haematuria, incontinence, and frequency, each with three definitions (grade ≥more » 1, grade ≥ 2 and longitudinal) with event rate between 2.3% and 76.1%. Repeated cross-validations producing matched models were implemented. A synthetic minority oversampling technique was utilized in endpoints with rare events. Parameter optimization was performed on the training data. Area under the receiver operating characteristic curve (AUROC) was used to compare performance using sample size to detect differences of ≥0.05 at the 95% confidence level. Results: Logistic regression, elastic-net, random forest, MARS, and support-vector machine were the highest-performing statistical-learning strategies in 3, 3, 3, 2, and 1 endpoints, respectively. Logistic regression, MARS, elastic-net, random forest, neural network, and support-vector machine were the best, or were not significantly worse than the best, in 7, 7, 5, 5, 3, and 1 endpoints. The best-performing statistical model was for dysuria grade ≥ 1 with AUROC ± standard deviation of 0.649 ± 0.074 using MARS. For longitudinal frequency and dysuria grade ≥ 1, all strategies produced AUROC>0.6 while all haematuria endpoints and longitudinal incontinence models produced AUROC<0.6. Conclusions: Logistic regression and MARS were most likely to be the best-performing strategy for the prediction of urinary symptoms with elastic-net and random forest producing competitive results. The predictive power of the models was modest and endpoint-dependent. New features, including spatial dose maps, may be necessary to achieve better models.« less

Career Issues in Organizations.

ERIC Educational Resources Information Center

1997

This document contains four papers from a symposium on career issues in organizations. "Learning During Downsizing: Stories from the Survivors" (Sharon J. Confessore) describes a study to demonstrate that survivors of corporate downsizings undertake learning activities and use many resources to accomplish the learning tasks.…

Distance Learning: What's Holding Back This Boundless Delivery System?

ERIC Educational Resources Information Center

Bruder, Isabelle

1989-01-01

Discusses distance learning, identifies who distance learners may be, and examines issues involved in establishing distance learning systems. Topics discussed include teacher concerns, including job security and certification; curriculum concerns, including state and local requirements and cross-cultural issues; cooperative development,…

dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs.

PubMed

Ma, Kede; Liu, Wentao; Liu, Tongliang; Wang, Zhou; Tao, Dacheng

2017-05-26

Objective assessment of image quality is fundamentally important in many image processing tasks. In this work, we focus on learning blind image quality assessment (BIQA) models which predict the quality of a digital image with no access to its original pristine-quality counterpart as reference. One of the biggest challenges in learning BIQA models is the conflict between the gigantic image space (which is in the dimension of the number of image pixels) and the extremely limited reliable ground truth data for training. Such data are typically collected via subjective testing, which is cumbersome, slow, and expensive. Here we first show that a vast amount of reliable training data in the form of quality-discriminable image pairs (DIP) can be obtained automatically at low cost by exploiting largescale databases with diverse image content. We then learn an opinion-unaware BIQA (OU-BIQA, meaning that no subjective opinions are used for training) model using RankNet, a pairwise learning-to-rank (L2R) algorithm, from millions of DIPs, each associated with a perceptual uncertainty level, leading to a DIP inferred quality (dipIQ) index. Extensive experiments on four benchmark IQA databases demonstrate that dipIQ outperforms state-of-the-art OU-BIQA models. The robustness of dipIQ is also significantly improved as confirmed by the group MAximum Differentiation (gMAD) competition method. Furthermore, we extend the proposed framework by learning models with ListNet (a listwise L2R algorithm) on quality-discriminable image lists (DIL). The resulting DIL Inferred Quality (dilIQ) index achieves an additional performance gain.

Teaching and Learning Issues in Mathematics in the Context of Nepal

ERIC Educational Resources Information Center

Panthi, Ram Krishna; Belbase, Shashidhar

2017-01-01

In this paper, we discussed major issues of mathematics teaching and learning in Nepal. The issues coming from theories such as social and radical constructivism suggest that teachers are not trained to use such approach in teaching mathematics, and there is a lack of teaching aids and materials and technological tools. The issues related to…

Heart Health: Learn the Truth About Your Heart

MedlinePlus

... Bar Home Current Issue Past Issues Cover Story Heart Health Learn the Truth About Your Heart Past Issues / Winter 2009 Table of Contents For ... turn Javascript on. Photo: iStock February is American Heart Month. Now is the time to make sure ...

BrainNetCNN: Convolutional neural networks for brain networks; towards predicting neurodevelopment.

PubMed

Kawahara, Jeremy; Brown, Colin J; Miller, Steven P; Booth, Brian G; Chau, Vann; Grunau, Ruth E; Zwicker, Jill G; Hamarneh, Ghassan

2017-02-01

We propose BrainNetCNN, a convolutional neural network (CNN) framework to predict clinical neurodevelopmental outcomes from brain networks. In contrast to the spatially local convolutions done in traditional image-based CNNs, our BrainNetCNN is composed of novel edge-to-edge, edge-to-node and node-to-graph convolutional filters that leverage the topological locality of structural brain networks. We apply the BrainNetCNN framework to predict cognitive and motor developmental outcome scores from structural brain networks of infants born preterm. Diffusion tensor images (DTI) of preterm infants, acquired between 27 and 46 weeks gestational age, were used to construct a dataset of structural brain connectivity networks. We first demonstrate the predictive capabilities of BrainNetCNN on synthetic phantom networks with simulated injury patterns and added noise. BrainNetCNN outperforms a fully connected neural-network with the same number of model parameters on both phantoms with focal and diffuse injury patterns. We then apply our method to the task of joint prediction of Bayley-III cognitive and motor scores, assessed at 18 months of age, adjusted for prematurity. We show that our BrainNetCNN framework outperforms a variety of other methods on the same data. Furthermore, BrainNetCNN is able to identify an infant's postmenstrual age to within about 2 weeks. Finally, we explore the high-level features learned by BrainNetCNN by visualizing the importance of each connection in the brain with respect to predicting the outcome scores. These findings are then discussed in the context of the anatomy and function of the developing preterm infant brain. Copyright © 2016 Elsevier Inc. All rights reserved.

Learning and the Net Generation

NASA Astrophysics Data System (ADS)

Duncan, D. K.; Rudolph, A. L.; Bruning, D.

2014-07-01

Most instructors believe that GPA, ethnicity, native English speaking ability, class year, family income, and whether parents have a college degree are important indicators of student success in Astro 101. Research shows, however, that the single most important factor in student learning is interactivity in the classroom. While new electronic media may have some important uses, research shows that electronic device usage in the classroom by students can negatively impact their course grades by as much five percent.

Projection decomposition algorithm for dual-energy computed tomography via deep neural network.

PubMed

Xu, Yifu; Yan, Bin; Chen, Jian; Zeng, Lei; Li, Lei

2018-03-15

Dual-energy computed tomography (DECT) has been widely used to improve identification of substances from different spectral information. Decomposition of the mixed test samples into two materials relies on a well-calibrated material decomposition function. This work aims to establish and validate a data-driven algorithm for estimation of the decomposition function. A deep neural network (DNN) consisting of two sub-nets is proposed to solve the projection decomposition problem. The compressing sub-net, substantially a stack auto-encoder (SAE), learns a compact representation of energy spectrum. The decomposing sub-net with a two-layer structure fits the nonlinear transform between energy projection and basic material thickness. The proposed DNN not only delivers image with lower standard deviation and higher quality in both simulated and real data, and also yields the best performance in cases mixed with photon noise. Moreover, DNN costs only 0.4 s to generate a decomposition solution of 360 × 512 size scale, which is about 200 times faster than the competing algorithms. The DNN model is applicable to the decomposition tasks with different dual energies. Experimental results demonstrated the strong function fitting ability of DNN. Thus, the Deep learning paradigm provides a promising approach to solve the nonlinear problem in DECT.

The underlying reasons for very high levels of bed net use, and higher malaria infection prevalence among bed net users than non-users in the Tanzanian city of Dar es Salaam: a qualitative study.

PubMed

Msellemu, Daniel; Shemdoe, Aloysia; Makungu, Christina; Mlacha, Yeromini; Kannady, Khadija; Dongus, Stefan; Killeen, Gerry F; Dillip, Angel

2017-10-23

Bed nets reduce malaria-related illness and deaths, by forming a protective barrier around people sleeping under them. When impregnated with long-lasting insecticide formulations they also repel or kill mosquitoes attempting to feed upon sleeping humans, and can even suppress entire populations of malaria vectors that feed predominantly upon humans. Nevertheless, an epidemiological study in 2012 demonstrated higher malaria prevalence among bed net users than non-users in urban Dar es Salaam, Tanzania. Focus group discussions were conducted with women from four selected wards of Dar es Salaam city, focusing on four major themes relating to bed net use behaviours: (1) reasons for bed net use, (2) reasons for not using bed nets, (3) stimuli or reminders for people to use a bed net (4) perceived reasons for catching malaria while using a bed net. An analytical method by framework grouping of relevant themes was used address key issues of relevance to the study objectives. Codes were reviewed and grouped into categories and themes. All groups said the main reason for bed net use was protection against malaria. Houses with well-screened windows, with doors that shut properly, and that use insecticidal sprays against mosquitoes, were said not to use bed nets, while frequent attacks from malaria was the main stimulus for people to use bed nets. Various reasons were mentioned as potential reasons that compromise bed net efficacy, the most common of which were: (1) bed net sharing by two or more people, especially if one occupant tends to come to bed late at night, and does not tuck in the net 71%; (2) one person shares the bed but does not use the net, moving it away from the side on which s/he sleeps 68%; (3) ineffective usage habits, called ulalavi, in which a sprawling sleeper either touches the net while sleeping up against it or leaves a limb hanging outside of it 68%. Less common reasons mentioned included: (1) Small bed nets which become un-tucked at night (31%); (2) Bed nets with holes large enough to allow mosquitoes to pass (28%); and (3) Going to bed late after already being bitten outdoors (24%). Behaviours associated with bed net use like; bed sharing, bed net non compliant-bedfellow, sleeping pattern like ulalavi and some physical bed net attributes compromise its effectiveness and supposedly increase of malaria infection to bed net users. While some well-screened houses looked to instigate low malaria prevalence to non-bed net users.

What does fault tolerant Deep Learning need from MPI?

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

Amatya, Vinay C.; Vishnu, Abhinav; Siegel, Charles M.

Deep Learning (DL) algorithms have become the {\\em de facto} Machine Learning (ML) algorithm for large scale data analysis. DL algorithms are computationally expensive -- even distributed DL implementations which use MPI require days of training (model learning) time on commonly studied datasets. Long running DL applications become susceptible to faults -- requiring development of a fault tolerant system infrastructure, in addition to fault tolerant DL algorithms. This raises an important question: {\\em What is needed from MPI for designing fault tolerant DL implementations?} In this paper, we address this problem for permanent faults. We motivate the need for amore » fault tolerant MPI specification by an in-depth consideration of recent innovations in DL algorithms and their properties, which drive the need for specific fault tolerance features. We present an in-depth discussion on the suitability of different parallelism types (model, data and hybrid); a need (or lack thereof) for check-pointing of any critical data structures; and most importantly, consideration for several fault tolerance proposals (user-level fault mitigation (ULFM), Reinit) in MPI and their applicability to fault tolerant DL implementations. We leverage a distributed memory implementation of Caffe, currently available under the Machine Learning Toolkit for Extreme Scale (MaTEx). We implement our approaches by extending MaTEx-Caffe for using ULFM-based implementation. Our evaluation using the ImageNet dataset and AlexNet neural network topology demonstrates the effectiveness of the proposed fault tolerant DL implementation using OpenMPI based ULFM.« less

KANSAS WIND POWERING AMERICAN STATE OUTREACH: KANSAS WIND WORKING GROUP

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

HAMMARLUND, RAY

2010-10-27

The Kansas Wind Working Group (WWG) is a 33-member group announced by former Governor Kathleen Sebelius on Jan. 7, 2008. Formed through Executive Order 08-01, the WWG will educate stakeholder groups with the current information on wind energy markets, technologies, economics, policies, prospects and issues. Governor Mark Parkinson serves as chair of the Kansas Wind Working Group. The group has been instrumental in focusing on the elements of government and coordinating government and private sector efforts in wind energy development. Those efforts have moved Kansas from 364 MW of wind three years ago to over 1000 MW today. Further, themore » Wind Working Group was instrumental in fleshing out issues such as a state RES and net metering, fundamental parts of HB 2369 that was passed and is now law in Kansas. This represents the first mandatory RES and net metering in Kansas history.« less

A Novel Electrochemical Membrane Bioreactor as a Potential Net Energy Producer for Sustainable Wastewater Treatment

PubMed Central

Wang, Yun-Kun; Sheng, Guo-Ping; Shi, Bing-Jing; Li, Wen-Wei; Yu, Han-Qing

2013-01-01

One possible way to address both water and energy shortage issues, the two of major global challenges, is to recover energy and water resource from wastewater. Herein, a novel electrochemical membrane bioreactor (EMBR) was developed to recover energy from wastewater and meantime harvest clean water for reuse. With the help of the microorganisms in the biocatalysis and biodegradation process, net electricity could be recovered from a low-strength synthetic wastewater after estimating total energy consumption of this system. In addition, high-quality clean water was obtained for reuse. The results clearly demonstrate that, under the optimized operating conditions, it is possible to recover net energy from wastewater, while at the same time to harvest high-quality effluent for reuse with this novel wastewater treatment system. PMID:23689529

A simple and valuable approach for measuring customer satisfaction.

PubMed

Kinney, William C

2005-08-01

To determine the financial impact of poor customer satisfaction and the value of information gained from using a 1-question customer-satisfaction survey in a medical setting. A single-question customer-satisfaction survey was collected from customers presenting to an academic otolaryngology head and neck surgery outpatient clinic. The overall response rate was 25%, overall net promoter score was 67.3%, lowest net promoter score occurred on Wednesday and Friday, overall net potential referrals were 872, and potential lost revenue from dissatisfied customers equaled US 2.3 million dollars. A single-question customer-satisfaction survey may help identify areas of customer dissatisfaction that lead to a significant source of lost revenue. The competitive forces in today's health care environment require medical practices to address issues related to customer satisfaction.

Typology of College Student Perception on Institutional E-Learning Issues--An Extension Study of a Teacher's Typology in Taiwan

ERIC Educational Resources Information Center

Tao, Yu-Hui

2008-01-01

Recently, e-learning in Taiwan's higher education faces new challenges as the Ministry of Education begins to loosen its control over degree-awarding programs. Studies on stakeholder perceptions toward important e-learning issues become critical at this juncture for policy makers to make viable investment decisions toward e-learning programs.…

Avoiding Misinterpretations of Piaget and Vygotsky: Mathematical Teaching without Learning, Learning without Teaching, or Helpful Learning-Path Teaching?

ERIC Educational Resources Information Center

Fuson, Karen C.

2009-01-01

This article provides an overview of some perspectives about special issues in classroom mathematical teaching and learning that have stemmed from the huge explosion of research in children's mathematical thinking stimulated by Piaget. It concentrates on issues that are particularly important for less-advanced learners and for those who might be…

Banners for Books: "Mighty-Hearted" Kindergartners Take Action through Arts-Based Service Learning

ERIC Educational Resources Information Center

Montgomery, Sarah E.; Miller, Wendy; Foss, Page; Tallakson, Denise; Howard, Maria

2017-01-01

Teaching about the Universal Declaration of Human Rights, which was adopted by the United Nations General Assembly in 1948, is one way to support students' learning about issues of fairness. However, learning about this document is not enough. Students need to have experiences where they explore issues of justice and equity in order to learn about…

Development and Implementation of a Learning Object Repository for French Teaching and Learning: Issues and Promises

ERIC Educational Resources Information Center

Caws, Catherine

2008-01-01

This paper discusses issues surrounding the development of a learning object repository (FLORE) for teaching and learning French at the postsecondary level. An evaluation based on qualitative and quantitative data was set up in order to better assess how second-language (L2) students in French perceived the integration of this new repository into…

Queering College Writing: Writing Students' Learning of LGB Issues Using the Internet as an Instructional Tool

ERIC Educational Resources Information Center

Burnes, Theodore R.

2007-01-01

How do writing teachers use technology to help students learn about lesbian, gay, and bisexual (LGB) issues? What is the nature of writing students' learning about LGB sexual orientations and academic writing when the Internet is used as a learning tool? Participants completed a questionnaire in which they reflected on a writing assignment…

Who Gets to Be a Writer? Exploring Identity and Learning Issues in Becoming a Fiction Author

ERIC Educational Resources Information Center

Gouthro, Patricia A.

2014-01-01

Drawing upon a research study on lifelong learning, citizenship, and fiction writing, this paper explores issues around identity and learning in becoming a fiction author. Five main thematic areas are discussed: (1) envisioning a writing career, (2) compelled to write, (3) learning the craft, (4) getting published, and (5) online identity. The…

77 FR 46127 - Interim Staff Guidance on Changes to the Generic Aging Lessons Learned (GALL) Report Revision 2...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-08-02

... Aging Lessons Learned (GALL) Report Revision 2 AMP XI.M41, ``Buried and Underground Piping and Tanks... AMPs in NUREG-1801, Revision 2, ``Generic Aging Lessons Learned (GALL) Report,'' and the NRC staff's... issues LR-ISG to communicate insights and lessons learned and to address emergent issues not covered in...

NeMO-Net: The Neural Multi-Modal Observation and Training Network for Global Coral Reef Assessment

NASA Technical Reports Server (NTRS)

Chirayath, Ved

2017-01-01

In the past decade, coral reefs worldwide have experienced unprecedented stresses due to climate change, ocean acidification, and anthropomorphic pressures, instigating massive bleaching and die-off of these fragile and diverse ecosystems. Furthermore, remote sensing of these shallow marine habitats is hindered by ocean wave distortion, refraction and optical attenuation, leading invariably to data products that are often of low resolution and signal-to-noise (SNR) ratio. However, recent advances in UAV and Fluid Lensing technology have allowed us to capture multispectral 3D imagery of these systems at sub-cm scales from above the water surface, giving us an unprecedented view of their growth and decay. Exploiting the fine-scaled features of these datasets, machine learning methods such as MAP, PCA, and SVM can not only accurately classify the living cover and morphology of these reef systems (below 8 percent error), but are also able to map the spectral space between airborne and satellite imagery, augmenting and improving the classification accuracy of previously low-resolution datasets. We are currently implementing NeMO-Net, the first open-source deep convolutional neural network (CNN) and interactive active learning and training software to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. NeMO-Net will be built upon the QGIS platform to ingest UAV, airborne and satellite datasets from various sources and sensor capabilities, and through data-fusion determine the coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. To achieve this, we will exploit virtual data augmentation, the use of semi-supervised learning, and active learning through a tablet platform allowing for users to manually train uncertain or difficult to classify datasets. The project will make use of Pythons extensive libraries for machine learning, as well as extending integration to GPU and High-End Computing Capability (HECC) on the Pleiades supercomputing cluster, located at NASA Ames. The project is being supported by NASAs Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST-16) Program.

NeMO-Net The Neural Multi-Modal Observation Training Network for Global Coral Reef Assessment

NASA Technical Reports Server (NTRS)

Li, Alan; Chirayath, Ved

2017-01-01

In the past decade, coral reefs worldwide have experienced unprecedented stresses due to climate change, ocean acidification, and anthropomorphic pressures, instigating massive bleaching and die-off of these fragile and diverse ecosystems. Furthermore, remote sensing of these shallow marine habitats is hindered by ocean wave distortion, refraction and optical attenuation, leading invariably to data products that are often of low resolution and signal-to-noise (SNR) ratio. However, recent advances in UAV and Fluid Lensing technology have allowed us to capture multispectral 3D imagery of these systems at sub-cm scales from above the water surface, giving us an unprecedented view of their growth and decay. Exploiting the fine-scaled features of these datasets, machine learning methods such as MAP, PCA, and SVM can not only accurately classify the living cover and morphology of these reef systems (below 8 error), but are also able to map the spectral space between airborne and satellite imagery, augmenting and improving the classification accuracy of previously low-resolution datasets.We are currently implementing NeMO-Net, the first open-source deep convolutional neural network (CNN) and interactive active learning and training software to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. NeMO-Net will be built upon the QGIS platform to ingest UAV, airborne and satellite datasets from various sources and sensor capabilities, and through data-fusion determine the coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. To achieve this, we will exploit virtual data augmentation, the use of semi-supervised learning, and active learning through a tablet platform allowing for users to manually train uncertain or difficult to classify datasets. The project will make use of Pythons extensive libraries for machine learning, as well as extending integration to GPU and High-End Computing Capability (HECC) on the Pleiades supercomputing cluster, located at NASA Ames. The project is being supported by NASAs Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST-16) Program.

NeMO-Net - The Neural Multi-Modal Observation & Training Network for Global Coral Reef Assessment

NASA Astrophysics Data System (ADS)

Li, A. S. X.; Chirayath, V.; Segal-Rosenhaimer, M.; Das, K.

2017-12-01

In the past decade, coral reefs worldwide have experienced unprecedented stresses due to climate change, ocean acidification, and anthropomorphic pressures, instigating massive bleaching and die-off of these fragile and diverse ecosystems. Furthermore, remote sensing of these shallow marine habitats is hindered by ocean wave distortion, refraction and optical attenuation, leading invariably to data products that are often of low resolution and signal-to-noise (SNR) ratio. However, recent advances in UAV and Fluid Lensing technology have allowed us to capture multispectral 3D imagery of these systems at sub-cm scales from above the water surface, giving us an unprecedented view of their growth and decay. Exploiting the fine-scaled features of these datasets, machine learning methods such as MAP, PCA, and SVM can not only accurately classify the living cover and morphology of these reef systems (below 8% error), but are also able to map the spectral space between airborne and satellite imagery, augmenting and improving the classification accuracy of previously low-resolution datasets.We are currently implementing NeMO-Net, the first open-source deep convolutional neural network (CNN) and interactive active learning and training software to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. NeMO-Net will be built upon the QGIS platform to ingest UAV, airborne and satellite datasets from various sources and sensor capabilities, and through data-fusion determine the coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. To achieve this, we will exploit virtual data augmentation, the use of semi-supervised learning, and active learning through a tablet platform allowing for users to manually train uncertain or difficult to classify datasets. The project will make use of Python's extensive libraries for machine learning, as well as extending integration to GPU and High-End Computing Capability (HECC) on the Pleiades supercomputing cluster, located at NASA Ames. The project is being supported by NASA's Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST-16) Program.

Tools for Atmospheric Radiative Transfer: Streamer and FluxNet. Revised

NASA Technical Reports Server (NTRS)

Key, Jeffrey R.; Schweiger, Axel J.

1998-01-01

Two tools for the solution of radiative transfer problems are presented. Streamer is a highly flexible medium spectral resolution radiative transfer model based on the plane-parallel theory of radiative transfer. Capable of computing either fluxes or radiances, it is suitable for studying radiative processes at the surface or within the atmosphere and for the development of remote-sensing algorithms. FluxNet is a fast neural network-based implementation of Streamer for computing surface fluxes. It allows for a sophisticated treatment of radiative processes in the analysis of large data sets and potential integration into geophysical models where computational efficiency is an issue. Documentation and tools for the development of alternative versions of Fluxnet are available. Collectively, Streamer and FluxNet solve a wide variety of problems related to radiative transfer: Streamer provides the detail and sophistication needed to perform basic research on most aspects of complex radiative processes while the efficiency and simplicity of FluxNet make it ideal for operational use.

Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning

PubMed Central

Hoo-Chang, Shin; Roth, Holger R.; Gao, Mingchen; Lu, Le; Xu, Ziyue; Nogues, Isabella; Yao, Jianhua; Mollura, Daniel

2016-01-01

Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets (i.e. ImageNet) and the revival of deep convolutional neural networks (CNN). CNNs enable learning data-driven, highly representative, layered hierarchical image features from sufficient training data. However, obtaining datasets as comprehensively annotated as ImageNet in the medical imaging domain remains a challenge. There are currently three major techniques that successfully employ CNNs to medical image classification: training the CNN from scratch, using off-the-shelf pre-trained CNN features, and conducting unsupervised CNN pre-training with supervised fine-tuning. Another effective method is transfer learning, i.e., fine-tuning CNN models (supervised) pre-trained from natural image dataset to medical image tasks (although domain transfer between two medical image datasets is also possible). In this paper, we exploit three important, but previously understudied factors of employing deep convolutional neural networks to computer-aided detection problems. We first explore and evaluate different CNN architectures. The studied models contain 5 thousand to 160 million parameters, and vary in numbers of layers. We then evaluate the influence of dataset scale and spatial image context on performance. Finally, we examine when and why transfer learning from pre-trained ImageNet (via fine-tuning) can be useful. We study two specific computeraided detection (CADe) problems, namely thoraco-abdominal lymph node (LN) detection and interstitial lung disease (ILD) classification. We achieve the state-of-the-art performance on the mediastinal LN detection, with 85% sensitivity at 3 false positive per patient, and report the first five-fold cross-validation classification results on predicting axial CT slices with ILD categories. Our extensive empirical evaluation, CNN model analysis and valuable insights can be extended to the design of high performance CAD systems for other medical imaging tasks. PMID:26886976

Hammer toe repair - discharge

MedlinePlus

... first to achieve this important distinction for online health information and services. Learn more about A.D.A. ... Ethics and subscribes to the principles of the Health on the Net Foundation (www.hon.ch). The information provided herein should not be used during any ...

Third-space Architecture for Learning in 3D

DTIC Science & Technology

2011-01-01

wind, and geothermal ( Fogg , 1997). A viable Mars ecosystem rests on whether energy resources can be harnessed profitably. In other words, net...Lessons in curriculum, instruction, assessment, and professional development. Mahwah, NJ: Erlbaum. Fogg , M. J. (1997). The utility of geothermal

An Automation Framework for Neural Nets that Learn

ERIC Educational Resources Information Center

Kilmer, W. L.; Arbib, M. A.

1973-01-01

A discussion of several types of formal neurons, many of whose functions are modifiable by their own input stimuli. The language of finite automata is used to mathematicize the problem of adaptation sufficiently to remove some ambiguities of Brindley's approach. (Author)

Service-Learning in Afterschool: Helping Students Grow and Communities Prosper. MetLife Foundation Afterschool Alert. Issue Brief No. 52

ERIC Educational Resources Information Center

Afterschool Alliance, 2011

2011-01-01

The Afterschool Alliance, in partnership with MetLife Foundation, is proud to present the third in a series of four issue briefs examining critical issues facing middle school youth and the vital role afterschool programs play in addressing these issues. This brief focuses on service-learning opportunities for middle schoolers. Pairing service…

Contextual Learning Issues.

ERIC Educational Resources Information Center

1996

This document consists of four papers presented at a symposium on contextual learning issues moderated by John Henschke at the 1996 conference of the Academy of Human Resource Development (AHRD). "Self-Directed Learning in Organizations: An Analysis of Policies and Practices of Seven Resource Companies in Western Canada" (H. K. Morris…

Learning from Multiple Collaborating Intelligent Tutors: An Agent-based Approach.

ERIC Educational Resources Information Center

Solomos, Konstantinos; Avouris, Nikolaos

1999-01-01

Describes an open distributed multi-agent tutoring system (MATS) and discusses issues related to learning in such open environments. Topics include modeling a one student-many teachers approach in a computer-based learning context; distributed artificial intelligence; implementation issues; collaboration; and user interaction. (Author/LRW)

Collective Perspectives on Issues Affecting Learning Disabilities. Position Papers and Statements.

ERIC Educational Resources Information Center

National Joint Committee on Learning Disabilities, Baltimore, MD.

Position papers of the National Joint Committee on Learning Disabilities during 1981-1994 and information about this committee's history, mission, and operational procedures are presented. The position papers and statements are as follows: "Learning Disabilities: Issues on Definition" (1981); "In-service Programs in Learning…

Contextual Learning Issues. [Concurrent Symposium Session at AHRD Annual Conference, 1997.

ERIC Educational Resources Information Center

1997

This document contains four papers from a symposium on contextual learning issues. In "Creating Mosaics: The Interrelationships between Knowledge and Context" (Barbara J. Daley), nurses report using information from training programs to create a knowledge base for professional practice. "Analysis of Action Learning Experiences…

Learning Disabilities: Issues and Recommendations for Research

ERIC Educational Resources Information Center

Brainard, Suzanne Gage, Ed.

Presented are eight author contributed papers on research needs in the neuropsychological, socio-environmental, and educational aspects of learning disabilities. Issues focused on in the papers and conference include the definition of learning disabilities, the role of screening in prevention or remediation, and whether curriculum should focus on…

New Technology and Lifelong Learning.

ERIC Educational Resources Information Center

Thorpe, Mary

Key issues related to the relationship between new technology and lifelong learning in the United Kingdom and elsewhere were identified through reviews of the literature on information and communications technology (ICT) and the literature on lifelong learning. Two overarching issues related to the interplay of new technology and lifelong learning…

Design of a Blended Learning Environment: Considerations and Implementation Issues

ERIC Educational Resources Information Center

Gedik, Nuray; Kiraz, Ercan; Ozden, M. Yasar

2013-01-01

This study identified critical issues in the design of a blended learning environment by examining basic design considerations and implementation issues. Following a design-based research approach with the phenomenological tradition of qualitative research, the study investigated instructor experiences relating to the design, development, and…

Deep learning application: rubbish classification with aid of an android device

NASA Astrophysics Data System (ADS)

Liu, Sijiang; Jiang, Bo; Zhan, Jie

2017-06-01

Deep learning is a very hot topic currently in pattern recognition and artificial intelligence researches. Aiming at the practical problem that people usually don't know correct classifications some rubbish should belong to, based on the powerful image classification ability of the deep learning method, we have designed a prototype system to help users to classify kinds of rubbish. Firstly the CaffeNet Model was adopted for our classification network training on the ImageNet dataset, and the trained network was deployed on a web server. Secondly an android app was developed for users to capture images of unclassified rubbish, upload images to the web server for analyzing backstage and retrieve the feedback, so that users can obtain the classification guide by an android device conveniently. Tests on our prototype system of rubbish classification show that: an image of one single type of rubbish with origin shape can be better used to judge its classification, while an image containing kinds of rubbish or rubbish with changed shape may fail to help users to decide rubbish's classification. However, the system still shows promising auxiliary function for rubbish classification if the network training strategy can be optimized further.

Information systems - Issues in global habitability

NASA Technical Reports Server (NTRS)

Norman, S. D.; Brass, J. A.; Jones, H.; Morse, D. R.

1984-01-01

The present investigation is concerned with fundamental issues, related to information considerations, which arise in an interdisciplinary approach to questions of global habitability. Information system problems and issues are illustrated with the aid of an example involving biochemical cycling and biochemical productivity. The estimation of net primary production (NPP) as an important consideration in the overall global habitability issue is discussed. The NPP model requires three types of data, related to meteorological information, a land surface inventory, and the vegetation structure. Approaches for obtaining and processing these data are discussed. Attention is given to user requirements, information system requirements, workstations, network communications, hardware/software access, and data management.

Management of neuroendocrine tumors.

PubMed

Chung, Clement

2016-11-01

Current strategies for managing neuroendocrine tumors (NETs) in adult patients are reviewed, with a focus on medication safety concerns. NETs usually originate in the gastrointestinal or bronchopulmonary tract. Symptoms due to hormonal hypersecretion often occur in patients with foregut or midgut NETs or liver metastases. Surgical resection is recommended for most localized NETs, while systemic cytotoxic chemotherapy is typically used for high-grade and pancreatic tumors. The standard of care for metastatic NETs is somatostatin analog therapy with octreotide (available in both short- and long-acting formulations) or a depot formulation of lanreotide. Everolimus and sunitinib are targeted therapies with approved indications for use in treating advanced pancreatic NETs. Some patients with liver-predominant disease or liver metastases may undergo regional chemoembolization procedures. Pharmacists should be cognizant of differences between newer and older chemoembolization agents and procedures, as well as differences between somatostatin analog products used as medications and the radiolabelled forms used in diagnostic scintigraphy. Other medication safety issues in NET management arise during perioperative supportive care, patient education, compliance counseling, and management of adverse effects of targeted therapies and chemotherapy, including stomatitis, hyperthyroidism, and hand-foot skin reaction. Somatostatin analog therapy is the mainstay for management of locally advanced or metastatic NETs. Liver-directed therapy is an option for localized unresectable disease; platinum-based chemotherapy is the first-line treatment for poorly differentiated tumors. Optimal sequencing of these treatments and targeted therapies such as everolimus and tyrosine kinase inhibitors remains to be elucidated. Copyright © 2016 by the American Society of Health-System Pharmacists, Inc. All rights reserved.

Learning Languages: The Journal of the National Network for Early Language Learning, 1996-1997.

ERIC Educational Resources Information Center

Learning Languages: The Journal of the National Network for Early Language Learning, 1997

1997-01-01

This document consists of the three issues of the journal "Learning Languages" published during volume year 2. These issues contain the following major articles: "Minneapolis and Brittany: Children Bridge Geographical and Social Differences Through Technology" (Janine Onffroy Shelley); "Student Reasons for Studying…

Achieving Quality Learning in Higher Education.

ERIC Educational Resources Information Center

Nightingale, Peggy; O'Neil, Mike

This volume on quality learning in higher education discusses issues of good practice particularly action learning and Total Quality Management (TQM)-type strategies and illustrates them with seven case studies in Australia and the United Kingdom. Chapter 1 discusses issues and problems in defining quality in higher education. Chapter 2 looks at…

Study Offers Keen Insights into Professional Development Research

ERIC Educational Resources Information Center

Killion, Joellen

2017-01-01

Joellen Killion is senior advisor to Learning Forward. In each issue of "The Learning Professional", Killion explores a recent research study to help practitioners understand the impact of particular professional learning practices on student outcomes. In this Issue Mary Kennedy conducts a review and analysis of the research on…

Learning from Dealing with Real World Problems

ERIC Educational Resources Information Center

Akcay, Hakan

2017-01-01

The purpose of this article is to provide an example of using real world issues as tools for science teaching and learning. Using real world issues provides students with experiences in learning in problem-based environments and encourages them to apply their content knowledge to solving current and local problems.

Current Status of Research in Teaching and Learning Evolution: I. Philosophical/Epistemological Issues

ERIC Educational Resources Information Center

Smith, Mike U.

2010-01-01

Scholarship that addresses teaching and learning about evolution has rapidly increased in recent years. This review of that scholarship first addresses the philosophical/epistemological issues that impinge on teaching and learning about evolution, including the proper philosophical goals of evolution instruction; the correlational and possibly…

Excelsior: Leadership in Teaching and Learning. Volume 1, Number 1, Fall/Winter 2006

ERIC Educational Resources Information Center

Lassonde, Cynthia A., Ed.

2006-01-01

"Excelsior: Leadership in Teaching and Learning" provides a forum to explore issues related to teaching and learning at public and independent colleges and universities with programs in teacher preparation. "Excelsior" solicits original, thought-provoking manuscripts of various formats, including papers presenting research on issues and practices…

75 FR 31494 - Self-Regulatory Organizations; NYSE Amex LLC; Notice of Filing and Immediate Effectiveness of...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-06-03

... net capital computations, FOCUS reports, ITSFEA forms and annual audited statements, all of which are... the protection of investors and the public interest, because the proposal raises no novel issues and...

After-Action Reports: Capturing Lessons Learned and Identifying Areas for Improvement. Lessons Learned from School Crises and Emergencies. Volume 2, Issue 1, 2007

ERIC Educational Resources Information Center

US Department of Education, 2007

2007-01-01

"Lessons Learned" is a series of publications that are a brief recounting of actual school emergencies and crises. This issue of "Lessons Learned" addresses after-action reports, which are an integral part of the emergency preparedness planning continuum and support effective crisis response. After-action reports have a threefold purpose. They…

Ethical Issues in E-Learning: Insights from the Application of Stakeholder Analysis in Three E-Learning Cases.

ERIC Educational Resources Information Center

Chozos, Polyneikis; Lytras, Miltos; Pouloudi, Nancy

The application of emerging digital technologies such as e-mail, the World Wide Web and the Internet in the educational setting has received wide acceptance all over the world. Both corporate and academic agendas have recognized the potential advantages of e-learning; however, as a new field, e-learning courses comes with important issues that…

Using Finance Policy To Reduce Barriers to Distance Learning. A Report and Recommendations in a Series on Distance Learning Policy Issues.

ERIC Educational Resources Information Center

Southern Regional Education Board, Atlanta, GA.

This study explored the ways in which state and system financing policies can advance the use of distance learning technologies and the goals outlined in other reports by the Distance Learning Policy Laboratory more effectively. The subcommittee on finance that examined the issue approached the task by establishing a framework that considered:…

A Mathematics Educator's Introduction to Rural Policy Issues

ERIC Educational Resources Information Center

Waters, Michael S., Ed.

2005-01-01

Most of the scholarship and commentary on mathematics education deals with issues of curriculum and instruction; this is understandable in a field logically belonging to the domain of curriculum and instruction. Moreover, issues of teaching and learning are compelling to people who love to learn and teach mathematics. Policy receives shorter…

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

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

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

1995-08-01

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

DMirNet: Inferring direct microRNA-mRNA association networks.

PubMed

Lee, Minsu; Lee, HyungJune

2016-12-05

MicroRNAs (miRNAs) play important regulatory roles in the wide range of biological processes by inducing target mRNA degradation or translational repression. Based on the correlation between expression profiles of a miRNA and its target mRNA, various computational methods have previously been proposed to identify miRNA-mRNA association networks by incorporating the matched miRNA and mRNA expression profiles. However, there remain three major issues to be resolved in the conventional computation approaches for inferring miRNA-mRNA association networks from expression profiles. 1) Inferred correlations from the observed expression profiles using conventional correlation-based methods include numerous erroneous links or over-estimated edge weight due to the transitive information flow among direct associations. 2) Due to the high-dimension-low-sample-size problem on the microarray dataset, it is difficult to obtain an accurate and reliable estimate of the empirical correlations between all pairs of expression profiles. 3) Because the previously proposed computational methods usually suffer from varying performance across different datasets, a more reliable model that guarantees optimal or suboptimal performance across different datasets is highly needed. In this paper, we present DMirNet, a new framework for identifying direct miRNA-mRNA association networks. To tackle the aforementioned issues, DMirNet incorporates 1) three direct correlation estimation methods (namely Corpcor, SPACE, Network deconvolution) to infer direct miRNA-mRNA association networks, 2) the bootstrapping method to fully utilize insufficient training expression profiles, and 3) a rank-based Ensemble aggregation to build a reliable and robust model across different datasets. Our empirical experiments on three datasets demonstrate the combinatorial effects of necessary components in DMirNet. Additional performance comparison experiments show that DMirNet outperforms the state-of-the-art Ensemble-based model [1] which has shown the best performance across the same three datasets, with a factor of up to 1.29. Further, we identify 43 putative novel multi-cancer-related miRNA-mRNA association relationships from an inferred Top 1000 direct miRNA-mRNA association network. We believe that DMirNet is a promising method to identify novel direct miRNA-mRNA relations and to elucidate the direct miRNA-mRNA association networks. Since DMirNet infers direct relationships from the observed data, DMirNet can contribute to reconstructing various direct regulatory pathways, including, but not limited to, the direct miRNA-mRNA association networks.

Temporal control of bidirectional lipid-droplet motion in Drosophila depends on the ratio of kinesin-1 and its co-factor Halo

PubMed Central

Arora, Gurpreet K.; Tran, Susan L.; Rizzo, Nicholas; Jain, Ankit; Welte, Michael A.

2016-01-01

ABSTRACT During bidirectional transport, individual cargoes move continuously back and forth along microtubule tracks, yet the cargo population overall displays directed net transport. How such transport is controlled temporally is not well understood. We analyzed this issue for bidirectionally moving lipid droplets in Drosophila embryos, a system in which net transport direction is developmentally controlled. By quantifying how the droplet distribution changes as embryos develop, we characterize temporal transitions in net droplet transport and identify the crucial contribution of the previously identified, but poorly characterized, transacting regulator Halo. In particular, we find that Halo is transiently expressed; rising and falling Halo levels control the switches in global distribution. Rising Halo levels have to pass a threshold before net plus-end transport is initiated. This threshold level depends on the amount of the motor kinesin-1: the more kinesin-1 is present, the more Halo is needed before net plus-end transport commences. Because Halo and kinesin-1 are present in common protein complexes, we propose that Halo acts as a rate-limiting co-factor of kinesin-1. PMID:26906417

Neural network based speech synthesizer: A preliminary report

NASA Technical Reports Server (NTRS)

Villarreal, James A.; Mcintire, Gary

1987-01-01

A neural net based speech synthesis project is discussed. The novelty is that the reproduced speech was extracted from actual voice recordings. In essence, the neural network learns the timing, pitch fluctuations, connectivity between individual sounds, and speaking habits unique to that individual person. The parallel distributed processing network used for this project is the generalized backward propagation network which has been modified to also learn sequences of actions or states given in a particular plan.

Generalization error analysis: deep convolutional neural network in mammography

NASA Astrophysics Data System (ADS)

Richter, Caleb D.; Samala, Ravi K.; Chan, Heang-Ping; Hadjiiski, Lubomir; Cha, Kenny

2018-02-01

We conducted a study to gain understanding of the generalizability of deep convolutional neural networks (DCNNs) given their inherent capability to memorize data. We examined empirically a specific DCNN trained for classification of masses on mammograms. Using a data set of 2,454 lesions from 2,242 mammographic views, a DCNN was trained to classify masses into malignant and benign classes using transfer learning from ImageNet LSVRC-2010. We performed experiments with varying amounts of label corruption and types of pixel randomization to analyze the generalization error for the DCNN. Performance was evaluated using the area under the receiver operating characteristic curve (AUC) with an N-fold cross validation. Comparisons were made between the convergence times, the inference AUCs for both the training set and the test set of the original image patches without corruption, and the root-mean-squared difference (RMSD) in the layer weights of the DCNN trained with different amounts and methods of corruption. Our experiments observed trends which revealed that the DCNN overfitted by memorizing corrupted data. More importantly, this study improved our understanding of DCNN weight updates when learning new patterns or new labels. Although we used a specific classification task with the ImageNet as example, similar methods may be useful for analysis of the DCNN learning processes, especially those that employ transfer learning for medical image analysis where sample size is limited and overfitting risk is high.

Copability, coping, and learning as focal concepts in the evaluation of computerised diabetes disease management.

PubMed

Boisen, Egil; Bygholm, Ann; Cavan, David; Hejlesen, Ole K

2003-07-01

Within diabetes care, the majority of health decisions are in the hands of the patient. Therefore, the concepts of disease management and self-care represent inescapable challenges for both patient and healthcare professionals, entailing a considerable amount of learning. Thus, a computerised diabetes disease management systems (CDDM) is to be seen not merely as tools for the medical treatment, but also as pedagogical tools to enhance patient competence. The unfortunate lack of success for most knowledge-based systems might be related to the problem of finding an adequate way of evaluating the systems from their development through the implementation phase to the daily clinical practice. The following presents the initial methodological considerations for evaluating the usefulness of a CDDM system called DiasNet, which is being implemented as a learning tool for patients. The evaluation of usefulness of a CDDM, we claim, entails clinical assessment taking into account the challenges and pitfalls in diabetes disease management. Drawing on activity theory, we suggest the concept of copability as a supplement to 'usability' and 'utility' when determining 'usefulness'. We maintain that it is necessary to ask how well the user copes with the new situation using the system. As ways to measure copability of DiasNet the concepts of coping and learning are discussed, as well as ways this methodology might inform systems development, implementation, and daily clinical practice.

Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data.

PubMed

Aliper, Alexander; Plis, Sergey; Artemov, Artem; Ulloa, Alvaro; Mamoshina, Polina; Zhavoronkov, Alex

2016-07-05

Deep learning is rapidly advancing many areas of science and technology with multiple success stories in image, text, voice and video recognition, robotics, and autonomous driving. In this paper we demonstrate how deep neural networks (DNN) trained on large transcriptional response data sets can classify various drugs to therapeutic categories solely based on their transcriptional profiles. We used the perturbation samples of 678 drugs across A549, MCF-7, and PC-3 cell lines from the LINCS Project and linked those to 12 therapeutic use categories derived from MeSH. To train the DNN, we utilized both gene level transcriptomic data and transcriptomic data processed using a pathway activation scoring algorithm, for a pooled data set of samples perturbed with different concentrations of the drug for 6 and 24 hours. In both pathway and gene level classification, DNN achieved high classification accuracy and convincingly outperformed the support vector machine (SVM) model on every multiclass classification problem, however, models based on pathway level data performed significantly better. For the first time we demonstrate a deep learning neural net trained on transcriptomic data to recognize pharmacological properties of multiple drugs across different biological systems and conditions. We also propose using deep neural net confusion matrices for drug repositioning. This work is a proof of principle for applying deep learning to drug discovery and development.

Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data

PubMed Central

Aliper, Alexander; Plis, Sergey; Artemov, Artem; Ulloa, Alvaro; Mamoshina, Polina; Zhavoronkov, Alex

2016-01-01

Deep learning is rapidly advancing many areas of science and technology with multiple success stories in image, text, voice and video recognition, robotics and autonomous driving. In this paper we demonstrate how deep neural networks (DNN) trained on large transcriptional response data sets can classify various drugs to therapeutic categories solely based on their transcriptional profiles. We used the perturbation samples of 678 drugs across A549, MCF‐7 and PC‐3 cell lines from the LINCS project and linked those to 12 therapeutic use categories derived from MeSH. To train the DNN, we utilized both gene level transcriptomic data and transcriptomic data processed using a pathway activation scoring algorithm, for a pooled dataset of samples perturbed with different concentrations of the drug for 6 and 24 hours. In both gene and pathway level classification, DNN convincingly outperformed support vector machine (SVM) model on every multiclass classification problem, however, models based on a pathway level classification perform better. For the first time we demonstrate a deep learning neural net trained on transcriptomic data to recognize pharmacological properties of multiple drugs across different biological systems and conditions. We also propose using deep neural net confusion matrices for drug repositioning. This work is a proof of principle for applying deep learning to drug discovery and development. PMID:27200455

Decision making in healthy participants on the Iowa Gambling Task: new insights from an operant approach.

PubMed

Bull, Peter N; Tippett, Lynette J; Addis, Donna Rose

2015-01-01

The Iowa Gambling Task (IGT) has contributed greatly to the study of affective decision making. However, researchers have observed high inter-study and inter-individual variability in IGT performance in healthy participants, and many are classified as impaired using standard criteria. Additionally, while decision-making deficits are often attributed to atypical sensitivity to reward and/or punishment, the IGT lacks an integrated sensitivity measure. Adopting an operant perspective, two experiments were conducted to explore these issues. In Experiment 1, 50 healthy participants completed a 200-trial version of the IGT which otherwise closely emulated Bechara et al.'s (1999) original computer task. Group data for Trials 1-100 closely replicated Bechara et al.'s original findings of high net scores and preferences for advantageous decks, suggesting that implementations that depart significantly from Bechara's standard IGT contribute to inter-study variability. During Trials 101-200, mean net scores improved significantly and the percentage of participants meeting the "impaired" criterion was halved. An operant-style stability criterion applied to individual data revealed this was likely related to individual differences in learning rate. Experiment 2 used a novel operant card task-the Auckland Card Task (ACT)-to derive quantitative estimates of sensitivity using the generalized matching law. Relative to individuals who mastered the IGT, persistent poor performers on the IGT exhibited significantly lower sensitivity to magnitudes (but not frequencies) of rewards and punishers on the ACT. Overall, our findings demonstrate the utility of operant-style analysis of IGT data and the potential of applying operant concurrent-schedule procedures to the study of human decision making.

Nematode.net update 2011: addition of data sets and tools featuring next-generation sequencing data

PubMed Central

Martin, John; Abubucker, Sahar; Heizer, Esley; Taylor, Christina M.; Mitreva, Makedonka

2012-01-01

Nematode.net (http://nematode.net) has been a publicly available resource for studying nematodes for over a decade. In the past 3 years, we reorganized Nematode.net to provide more user-friendly navigation through the site, a necessity due to the explosion of data from next-generation sequencing platforms. Organism-centric portals containing dynamically generated data are available for over 56 different nematode species. Next-generation data has been added to the various data-mining portals hosted, including NemaBLAST and NemaBrowse. The NemaPath metabolic pathway viewer builds associations using KOs, rather than ECs to provide more accurate and fine-grained descriptions of proteins. Two new features for data analysis and comparative genomics have been added to the site. NemaSNP enables the user to perform population genetics studies in various nematode populations using next-generation sequencing data. HelmCoP (Helminth Control and Prevention) as an independent component of Nematode.net provides an integrated resource for storage, annotation and comparative genomics of helminth genomes to aid in learning more about nematode genomes, as well as drug, pesticide, vaccine and drug target discovery. With this update, Nematode.net will continue to realize its original goal to disseminate diverse bioinformatic data sets and provide analysis tools to the broad scientific community in a useful and user-friendly manner. PMID:22139919

Where Are You Going in the Next Millennium?

ERIC Educational Resources Information Center

Hay, LeRoy E.

1999-01-01

Public education should no longer reflect agricultural or industrial era learning modes. Third-millennium administrators must recognize certain societal trends: the "net generation" of students, predominance of technology, electronic schools, the information deluge and the democratization of information, the age of convenience and…

Research: The Effect of Wetland Mitigation Banking on the Achievement of No-Net-Loss.

PubMed

BROWN; LANT

1999-04-01

/ This study determines whether the 68 wetland mitigation banks in existence in the United States through 1 January 1996 are achieving no-net-loss of wetland acreage nationally and regionally. Although 74% of the individual banks achieve no-net-loss by acreage, overall, wetland mitigation banks are projected to result in a net loss of 21,328 acres of wetlands nationally, 52% of the acreage in banks, as already credited wetland acreages are converted to otheruses. While most wetland mitigation banks are using appropriate compensation methods and ratios, several of the largest banks use preservation or enhancement, instead of restoration or creation. Most of these preservation/enhancement banks use minimum mitigation ratios of 1:1, which is much lower than ratios given in current guidelines. Assuming that mitigation occurs in these banks as preservation at the minimum allowable ratio, ten of these banks, concentrated in the western Gulf Coast region, will account for over 99% of projected net wetland acreage loss associated with banks. We conclude that wetland mitigation banking is a conceptually sound environmental policy and planning tool, but only if applied according to recently issued guidelines that ensure no-net-loss of wetland functions and values. Wetland mitigation banking inevitably leads to geographic relocation of wetlands, and therefore changes, either positively or negatively, the functions they perform and ecosystem services they provide. KEY WORDS: Mitigation banking; Wetlands; Army Corps of Engineers; No-net-loss

Maintenance and sustained use of insecticide-treated bednets and curtains three years after a controlled trial in western Kenya.

PubMed

Kachur, S P; Phillips-Howard, P A; Odhacha, A M; Ruebush, T K; Oloo, A J; Nahlen, B L

1999-11-01

In large experimental trials throughout Africa, insecticide-treated bednets and curtains have reduced child mortality in malaria-endemic communities by 15%-30%. While few questions remain about the efficacy of this intervention, operational issues around how to implement and sustain insecticide-treated materials (ITM) projects need attention. We revisited the site of a small-scale ITM intervention trial, 3 years after the project ended, to assess how local attitudes and practices had changed. Qualitative and quantitative methods, including 16 focus group discussions and a household survey (n = 60), were employed to assess use, maintenance, retreatment and perceptions of ITM and the insecticide in former study communities. Families that had been issued bednets were more likely to have kept and maintained them and valued bednets more highly than those who had been issued curtains. While most households retained their original bednets, none had treated them with insecticide since the intervention trial was completed 3 years earlier. Most of those who had been issued bednets repaired them, but none acquired new or replacement nets. In contrast, households that had been issued insecticide-treated curtains often removed them. Three (15%) of the households issued curtains had purchased one or more bednets since the study ended. In households where bednets had been issued, children 10 years of age and younger were a third as likely to sleep under a net as were adults (relative risk (RR) = 0. 32; 95% confidence interval (95%CI) = 0.19, 0.53). Understanding how and why optimal ITM use declined following this small-scale intervention trial can suggest measures that may improve the sustainability of current and future ITM efforts.

Extraction of Built-Up Areas Using Convolutional Neural Networks and Transfer Learning from SENTINEL-2 Satellite Images

NASA Astrophysics Data System (ADS)

Bramhe, V. S.; Ghosh, S. K.; Garg, P. K.

2018-04-01

With rapid globalization, the extent of built-up areas is continuously increasing. Extraction of features for classifying built-up areas that are more robust and abstract is a leading research topic from past many years. Although, various studies have been carried out where spatial information along with spectral features has been utilized to enhance the accuracy of classification. Still, these feature extraction techniques require a large number of user-specific parameters and generally application specific. On the other hand, recently introduced Deep Learning (DL) techniques requires less number of parameters to represent more abstract aspects of the data without any manual effort. Since, it is difficult to acquire high-resolution datasets for applications that require large scale monitoring of areas. Therefore, in this study Sentinel-2 image has been used for built-up areas extraction. In this work, pre-trained Convolutional Neural Networks (ConvNets) i.e. Inception v3 and VGGNet are employed for transfer learning. Since these networks are trained on generic images of ImageNet dataset which are having very different characteristics from satellite images. Therefore, weights of networks are fine-tuned using data derived from Sentinel-2 images. To compare the accuracies with existing shallow networks, two state of art classifiers i.e. Gaussian Support Vector Machine (SVM) and Back-Propagation Neural Network (BP-NN) are also implemented. Both SVM and BP-NN gives 84.31 % and 82.86 % overall accuracies respectively. Inception-v3 and VGGNet gives 89.43 % of overall accuracy using fine-tuned VGGNet and 92.10 % when using Inception-v3. The results indicate high accuracy of proposed fine-tuned ConvNets on a 4-channel Sentinel-2 dataset for built-up area extraction.

Representation in incremental learning

NASA Technical Reports Server (NTRS)

1993-01-01

Work focused on two areas in machine learning: representation for inductive learning and how to apply concept learning techniques to learning state preferences, which can represent search control knowledge for problem solving. Specifically, in the first area the issues of the effect of representation on learning, on how learning formalisms are biased, and how concept learning can benefit from the use of a hybrid formalism are addressed. In the second area, the issues of developing an agent to learn search control knowledge from the relative values of states, of the source of that qualitative information, and of the ability to use both quantitative and qualitative information in order to develop an effective problem-solving policy are examined.

Learning and Knowledge: A Dream or Nightmare for Employees

ERIC Educational Resources Information Center

Newman, Nadine; Newman, Dunstan

2015-01-01

Purpose: The paper aims to focus on the issues relating to the concepts of knowledge management (KM) and the learning organization and discusses the relationship between these concepts and the issues of power and control. It looks at Coopey's (1998) critical review of the "Foucauldian gloom" with regard to the learning organization and…

Addressing the Context of E-Learning: Using Transactional Distance Theory to Inform Design

ERIC Educational Resources Information Center

Benson, Robyn; Samarawickrema, Gayani

2009-01-01

The rapidly expanding range of options available for innovative e-learning approaches based on emerging technologies has given renewed importance to teaching and learning issues that have long been familiar to distance educators. These issues arise from the separation between learners, and between teacher and learners, which occurs when learning…

Learning Hebrew by Writing in English

ERIC Educational Resources Information Center

Jacobson, Rolf A.

2011-01-01

This essay explores a midrange teaching and learning issue regarding the teaching of biblical languages and one strategy for addressing the issue. Seminary students do not yield a great enough return in exchange for the investment they are required to make in learning biblical languages. Students invest great time and money, but they do not learn…

Accelerated Learning Options: A Promising Strategy for States. Policy Insights

ERIC Educational Resources Information Center

Michelau, Demaree

2006-01-01

This issue of Policy Insights draws on findings from WICHE's report Accelerated Learning Options: Moving the Needle on Access and Success, to lay out some of the important policy issues that decision makers might consider when adopting new state policy related to accelerated learning or modifying policies already in existence. The publication…

LEA in Private: A Privacy and Data Protection Framework for a Learning Analytics Toolbox

ERIC Educational Resources Information Center

Steiner, Christina M.; Kickmeier-Rust, Michael D.; Albert, Dietrich

2016-01-01

To find a balance between learning analytics research and individual privacy, learning analytics initiatives need to appropriately address ethical, privacy, and data protection issues. A range of general guidelines, model codes, and principles for handling ethical issues and for appropriate data and privacy protection are available, which may…

Measuring Cognitive and Metacognitive Regulatory Processes during Hypermedia Learning: Issues and Challenges

ERIC Educational Resources Information Center

Azevedo, Roger; Moos, Daniel C.; Johnson, Amy M.; Chauncey, Amber D.

2010-01-01

Self-regulated learning (SRL) with hypermedia environments involves a complex cycle of temporally unfolding cognitive and metacognitive processes that impact students' learning. We present several methodological issues related to treating SRL as an event and strengths and challenges of using online trace methodologies to detect, trace, model, and…

Special Issue: "Getting of Wisdom", Learning in Later Life

ERIC Educational Resources Information Center

Krašovec, Sabina Jelenc; Golding, Barry; Findsen, Brian; Schmidt-Hertha, Bernhard

2017-01-01

This specially themed ""Getting of Wisdom," Learning in Later Life" Edition of the "Australian Journal of Adult Learning" ("AJAL") is not so much concerned with the issue of ageing itself, but more about quality of life regardless of age. It is about taking, but also giving back as best as possible at any…

Creating Next Generation Blended Learning Environments Using Mixed Reality, Video Games and Simulations

ERIC Educational Resources Information Center

Kirkley, Sonny E.; Kirkley, Jamie R.

2005-01-01

In this article, the challenges and issues of designing next generation learning environments using current and emerging technologies are addressed. An overview of the issues is provided as well as design principles that support the design of instruction and the overall learning environment. Specific methods for creating cognitively complex,…

Online Education and Adult Learning: New Frontiers for Teaching Practices

ERIC Educational Resources Information Center

Kidd, Terry T., Ed.

2010-01-01

The expanding field of adult learning encompasses the study and practice of utilizing sound instructional design principals, technology, and learning theory as a means to solve educational challenges and human performance issues relating to adults, often occurring online. This book disseminates current issues and trends emerging in the field of…

To Succeed or Not to Succeed: A Critical Review of Issues in Learned Helplessness.

ERIC Educational Resources Information Center

Mark, Sandra Fay

1983-01-01

A critical analysis of theoretical and methodological issues in research on learned helplessness is presented. As studied in achievement settings using achievement tasks, learned helplessness is perceived as maladaptive behavior. It has not been studied as an adaptive response to situational demands. New directions and educational implications are…

Some Issues for Cooperative Learning and Intercultural Education: Reflections on Aspects of the Recent Work of Jagdish Gundara

ERIC Educational Resources Information Center

Bash, Leslie

2014-01-01

This paper connects the two fields of cooperative learning and intercultural education, focusing on the argument that cooperative learning strategies need to be equipped with intercultural understandings. There is a consideration of assumptions that effective cooperative pedagogical strategies require an engagement with challenging issues related…

High-Performance Sport, Learning and Culture: New Horizons for Sport Pedagogues?

ERIC Educational Resources Information Center

Penney, Dawn; McMahon, Jenny

2016-01-01

Background: Research in sport coaching and sport pedagogy including studies published in this special issue bring to the fore the relationship between learning and culture in contexts of high-performance sport. This paper acknowledged that how learning, culture and their relationship are conceptualised is a crucial issue for researchers and…

Curriculum Issues: Teaching and Learning for Sustainable Development in Developing Countries--Zimbabwe Case Study

ERIC Educational Resources Information Center

Dambudzo, Ignatius Isaac

2015-01-01

The study sought to investigate curriculum issues, teaching and learning for sustainable development in secondary schools in Zimbabwe. Education for sustainable development (ESD) aims at changing the approach to education by integrating principles, values, practices and needs in all forms of learning. Literature has documented the importance of…

Communication and Collaboration During Natural Disasters: The Lessons Learned From Past Experience. Lessons Learned From School Crises and Emergencies, Volume 3, Issue 2, 2008

ERIC Educational Resources Information Center

US Department of Education, 2008

2008-01-01

"Lessons Learned" is a series of publications that are a brief recounting of actual school emergencies and crises. This "Lessons Learned" issue focuses on the response and recovery efforts to wildfires by the San Diego County Office of Education (SDCOE) and its school and community partners. Natural disasters such as floods,…

Cinemeducation: A pilot student project using movies to help students learn medical professionalism.

PubMed

Lumlertgul, Nuttha; Kijpaisalratana, Naruchorn; Pityaratstian, Nuttorn; Wangsaturaka, Danai

2009-07-01

Using movies has been accepted worldwide as a tool to help students learn medical professionalism. In the second year, a group of medical students conducted the "Cinemeducation" project to promote professionalism in the "Medical Ethics and Critical Thinking" course. Five movies with professionalism issues were screened with 20-30 students attending each session. After the show, participants then were asked to reflect on what they had learned in terms of professionalism. Two students led group discussion emphasizing questioning and argumentation for 60 min. Additional learning issues emerging from each session were also explored in more depth and arranged into a report. In the Cinemeducation Project, medical students have learned five main ethical issues in each film, which were the doctor-patient relationship, informed consent and clinical trials in patients, management of genetic disorders, patient management, and brain death and organ transplantation. In addition to issues of professionalism, they also developed critical thinking and moral reasoning skills. Using a case-based scenario in movies has proven to be an effective and entertaining method of facilitating students with learning on professionalism.

Perineuronal nets and vocal plasticity in songbirds: A proposed mechanism to explain the difference between closed-ended and open-ended learning.

PubMed

Cornez, Gilles; Madison, Farrah N; Van der Linden, Annemie; Cornil, Charlotte; Yoder, Kathleen M; Ball, Gregory F; Balthazart, Jacques

2017-09-01

Perineuronal nets (PNN) are aggregations of chondroitin sulfate proteoglycans surrounding the soma and proximal processes of neurons, mostly GABAergic interneurons expressing parvalbumin. They limit the plasticity of their afferent synaptic connections. In zebra finches PNN develop in an experience-dependent manner in the song control nuclei HVC and RA (nucleus robustus arcopallialis) when young birds crystallize their song. Because songbird species that are open-ended learners tend to recapitulate each year the different phases of song learning until their song crystallizes at the beginning of the breeding season, we tested whether seasonal changes in PNN expression would be found in the song control nuclei of a seasonally breeding species such as the European starling. Only minimal changes in PNN densities and total number of cells surrounded by PNN were detected. However, comparison of the density of PNN and of PNN surrounding parvalbumin-positive cells revealed that these structures are far less numerous in starlings that show extensive adult vocal plasticity, including learning of new songs throughout the year, than in the closed-ended learner zebra finches. Canaries that also display some vocal plasticity across season but were never formally shown to learn new songs in adulthood were intermediate in this respect. Together these data suggest that establishment of PNN around parvalbumin-positive neurons in song control nuclei has diverged during evolution to control the different learning capacities observed in songbird species. This differential expression of PNN in different songbird species could represent a key cellular mechanism mediating species variation between closed-ended and open-ended learning strategies. © 2017 Wiley Periodicals, Inc. Develop Neurobiol 77: 975-994, 2017. © 2017 Wiley Periodicals, Inc.

Content Analysis of Student Essays after Attending a Problem-Based Learning Course: Facilitating the Development of Critical Thinking and Communication Skills in Japanese Nursing Students.

PubMed

Itatani, Tomoya; Nagata, Kyoko; Yanagihara, Kiyoko; Tabuchi, Noriko

2017-08-22

The importance of active learning has continued to increase in Japan. The authors conducted classes for first-year students who entered the nursing program using the problem-based learning method which is a kind of active learning. Students discussed social topics in classes. The purposes of this study were to analyze the post-class essay, describe logical and critical thinking after attended a Problem-Based Learning (PBL) course. The authors used Mayring's methodology for qualitative content analysis and text mining. In the description about the skills required to resolve social issues, seven categories were extracted: (recognition of diverse social issues), (attitudes about resolving social issues), (discerning the root cause), (multi-lateral information processing skills), (making a path to resolve issues), (processivity in dealing with issues), and (reflecting). In the description about communication, five categories were extracted: (simple statement), (robust theories), (respecting the opponent), (communication skills), and (attractive presentations). As the result of text mining, the words extracted more than 100 times included "issue," "society," "resolve," "myself," "ability," "opinion," and "information." Education using PBL could be an effective means of improving skills that students described, and communication in general. Some students felt difficulty of communication resulting from characteristics of Japanese.

Machine Learning Algorithms Outperform Conventional Regression Models in Predicting Development of Hepatocellular Carcinoma

PubMed Central

Singal, Amit G.; Mukherjee, Ashin; Elmunzer, B. Joseph; Higgins, Peter DR; Lok, Anna S.; Zhu, Ji; Marrero, Jorge A; Waljee, Akbar K

2015-01-01

Background Predictive models for hepatocellular carcinoma (HCC) have been limited by modest accuracy and lack of validation. Machine learning algorithms offer a novel methodology, which may improve HCC risk prognostication among patients with cirrhosis. Our study's aim was to develop and compare predictive models for HCC development among cirrhotic patients, using conventional regression analysis and machine learning algorithms. Methods We enrolled 442 patients with Child A or B cirrhosis at the University of Michigan between January 2004 and September 2006 (UM cohort) and prospectively followed them until HCC development, liver transplantation, death, or study termination. Regression analysis and machine learning algorithms were used to construct predictive models for HCC development, which were tested on an independent validation cohort from the Hepatitis C Antiviral Long-term Treatment against Cirrhosis (HALT-C) Trial. Both models were also compared to the previously published HALT-C model. Discrimination was assessed using receiver operating characteristic curve analysis and diagnostic accuracy was assessed with net reclassification improvement and integrated discrimination improvement statistics. Results After a median follow-up of 3.5 years, 41 patients developed HCC. The UM regression model had a c-statistic of 0.61 (95%CI 0.56-0.67), whereas the machine learning algorithm had a c-statistic of 0.64 (95%CI 0.60–0.69) in the validation cohort. The machine learning algorithm had significantly better diagnostic accuracy as assessed by net reclassification improvement (p<0.001) and integrated discrimination improvement (p=0.04). The HALT-C model had a c-statistic of 0.60 (95%CI 0.50-0.70) in the validation cohort and was outperformed by the machine learning algorithm (p=0.047). Conclusion Machine learning algorithms improve the accuracy of risk stratifying patients with cirrhosis and can be used to accurately identify patients at high-risk for developing HCC. PMID:24169273

Conceptual Metaphor and Embodied Cognition in Science Learning: Introduction to Special Issue

ERIC Educational Resources Information Center

Amin, Tamer G.; Jeppsson, Fredrik; Haglund, Jesper

2015-01-01

This special issue of "International Journal of Science Education" is based on the theme "Conceptual Metaphor and Embodied Cognition in Science Learning." The idea for this issue grew out of a symposium organized on this topic at the conference of the European Science Education Research Association (ESERA) in September 2013.…

Defining and Measuring Engagement and Learning in Science: Conceptual, Theoretical, Methodological, and Analytical Issues

ERIC Educational Resources Information Center

Azevedo, Roger

2015-01-01

Engagement is one of the most widely misused and overgeneralized constructs found in the educational, learning, instructional, and psychological sciences. The articles in this special issue represent a wide range of traditions and highlight several key conceptual, theoretical, methodological, and analytical issues related to defining and measuring…

Sociocultural Paradoxes and Issues in E-Learning Use in Higher Education Africa

ERIC Educational Resources Information Center

Njenga, James Kariuki

2018-01-01

Sociocultural issues are major contributing factors in mass acceptance and effective use of technology. These issues are often perceived to contradict the benefits the technology brings about. E-learning use in higher education in Africa, as a technology, faces some sociocultural barriers that contradict its promise and benefits. This paper…

Remote sensing of biomass and annual net aerial primary productivity of a salt marsh

NASA Technical Reports Server (NTRS)

Hardisky, M. A.; Klemas, V.; Daiber, F. C.; Roman, C. T.

1984-01-01

Net aerial primary productivity is the rate of storage of organic matter in above-ground plant issues exceeding the respiratory use by the plants during the period of measurement. It is pointed out that this plant tissue represents the fixed carbon available for transfer to and consumption by the heterotrophic organisms in a salt marsh or the estuary. One method of estimating annual net aerial primary productivity (NAPP) required multiple harvesting of the marsh vegetation. A rapid nondestructive remote sensing technique for estimating biomass and NAPP would, therefore, be a significant asset. The present investigation was designed to employ simple regression models, equating spectral radiance indices with Spartina alterniflora biomass to nondestructively estimate salt marsh biomass. The results of the study showed that the considered approach can be successfully used to estimate salt marsh biomass.

[Network of plastic neurons capable of forming conditioned reflexes ("membrane" model of learning)].

PubMed

Litvinov, E G; Frolov, A A

1978-01-01

Simple net neuronal model was suggested which was able to form the conditioning due to changes of the neuron excitability. The model was based on the following main concepts: (a) the conditioning formation should result in reduction of the firing threshold in the same neurons where the conditioning and reinforcement stimuli were converged, (b) neuron threshold may have only two possible states: initial and final ones, these were identical for all cells, the threshold may be changed only once from the initial value to the final one, (c) isomorphous relation may be introduced between some pair of arbitrary stimuli and some subset of the net neurons; any two pairs differing at least in one stimulus have unlike subsets of the convergent neurons. Stochastically organized neuronal net was used for analysis of the model. Considerable information capacity of the net gives the opportunity to consider that the conditioning formation is possible on the basis of the nervous cells. The efficienty of the model turn out to be comparable with the well known models where the conditioning formation was due to the modification of the synapses.

Multimedia Learning: Beyond Modality. Commentary.

ERIC Educational Resources Information Center

Reimann, P.

2003-01-01

Identifies and summarizes instructional messages in the articles in this theme issue and also identifies central theoretical issues, focusing on: (1) external representations; (2) dual coding theory; and (3) the effects of animations on learning. (SLD)

Measuring students' attitudes toward college education's role in addressing social issues.

PubMed

Weber, James E; Weber, Paula S; Craven, Barney L

2008-06-01

As service-learning projects have spread throughout academia, efforts to assess the service-learning experience have assumed a greater importance. The BERSI scale (Business Education's Role in addressing Social Issues) was developed as a measure of business students' attitudes toward social issues being addressed as part of a business education. As such, it was intended to be useful in assessing attitudinal outcomes of service learning. In order for the BERSI to be useful for nonbusiness students, the scale would need to be reconceptualized and revalidated. This study modified the BERSI items with a focus on college students in general rather than business students, making the resulting scale, College Education's Role in addressing Social Issues (CERSI), potentially helpful to service-learning researchers in a broader setting. The CERSI scale was then validated using standard techniques and normative data were reported.

Design issues for a reinforcement-based self-learning fuzzy controller

NASA Technical Reports Server (NTRS)

Yen, John; Wang, Haojin; Dauherity, Walter

1993-01-01

Fuzzy logic controllers have some often cited advantages over conventional techniques such as PID control: easy implementation, its accommodation to natural language, the ability to cover wider range of operating conditions and others. One major obstacle that hinders its broader application is the lack of a systematic way to develop and modify its rules and as result the creation and modification of fuzzy rules often depends on try-error or pure experimentation. One of the proposed approaches to address this issue is self-learning fuzzy logic controllers (SFLC) that use reinforcement learning techniques to learn the desirability of states and to adjust the consequent part of fuzzy control rules accordingly. Due to the different dynamics of the controlled processes, the performance of self-learning fuzzy controller is highly contingent on the design. The design issue has not received sufficient attention. The issues related to the design of a SFLC for the application to chemical process are discussed and its performance is compared with that of PID and self-tuning fuzzy logic controller.

Mentoring, Women in Engineering and Related Sciences, and MentorNet

NASA Astrophysics Data System (ADS)

Dockter, J.; Muller, C.

2003-12-01

Mentoring is a frequently employed strategy for retention of women in engineering and science. The power of mentoring is sometimes poorly understood, and mentoring is not always effectively practiced, however. At its strongest, mentoring is understood as a powerful learning process, which assures the intergenerational transfer of knowledge and "know-how" on an ongoing basis throughout one's life. Mentoring helps make explicit the tacit knowledge of a discipline and its professional culture, which is especially important for underrepresented groups. MentorNet (www.MentorNet.net), the E-Mentoring Network for Women in Engineering and Science, is a nonprofit organization focused on furthering women's progress in scientific and technical fields through the use of a dynamic, technology-supported mentoring program. Since 1998, nearly 10,000 undergraduate and graduate women studying engineering and related sciences at more than 100 colleges and universities across the U.S., and in several other nations, have been matched in structured, one-on-one, email-based mentoring relationships with male and female scientific and technical professionals working in industry and government. This poster will describe the MentorNet program, and provide findings of annual program evaluations related to outcomes for participants with particular focus on women in the planetary and earth sciences. We also address the development of the partnership of approximately 100 organizations currently involved in MentorNet and the value each gains from its affiliation. MentorNet is an ongoing effort which supports the interests of all organizations and individuals working to advance women in engineering and related sciences.

Key Issues and Policy Considerations in Promoting Lifelong Learning in Selected African Countries: Ethiopia, Kenya, Namibia, Rwanda and Tanzania. UIL Publication Series on Lifelong Learning Policies and Strategies. No. 1

ERIC Educational Resources Information Center

Walters, Shirley; Yang, Jim; Roslander, Peter

2014-01-01

This cross-national study focuses on key issues and policy considerations in promoting lifelong learning in Ethiopia, Kenya, Namibia, Rwanda, and Tanzania (the five African countries that took part in a pilot workshop on "Developing Capacity for Establishing Lifelong Learning Systems in UNESCO Member States: at the UNESCO Institute for…

Learning Careers/Learning Trajectories. Trends and Issues Alert.

ERIC Educational Resources Information Center

Kerka, Sandra

"Learning autobiography,""learning career," and "learning trajectory" are related descriptors for the process of developing attitudes toward learning and the origins of interests, learning styles, and learning processes. The learning career is composed of events, activities, and interpretations that develop individual…

A real time neural net estimator of fatigue life

NASA Technical Reports Server (NTRS)

Troudet, T.; Merrill, W.

1990-01-01

A neural net architecture is proposed to estimate, in real-time, the fatigue life of mechanical components, as part of the Intelligent Control System for Reusable Rocket Engines. Arbitrary component loading values were used as input to train a two hidden-layer feedforward neural net to estimate component fatigue damage. The ability of the net to learn, based on a local strain approach, the mapping between load sequence and fatigue damage has been demonstrated for a uniaxial specimen. Because of its demonstrated performance, the neural computation may be extended to complex cases where the loads are biaxial or triaxial, and the geometry of the component is complex (e.g., turbopump blades). The generality of the approach is such that load/damage mappings can be directly extracted from experimental data without requiring any knowledge of the stress/strain profile of the component. In addition, the parallel network architecture allows real-time life calculations even for high frequency vibrations. Owing to its distributed nature, the neural implementation will be robust and reliable, enabling its use in hostile environments such as rocket engines. This neural net estimator of fatigue life is seen as the enabling technology to achieve component life prognosis, and therefore would be an important part of life extending control for reusable rocket engines.

MED31/437: A Web-based Diabetes Management System: DiabNet

PubMed Central

Zhao, N; Roudsari, A; Carson, E

1999-01-01

Introduction A web-based system (DiabNet) was developed to provide instant access to the Electronic Diabetes Records (EDR) for end-users, and real-time information for healthcare professionals to facilitate their decision-making. It integrates portable glucometer, handheld computer, mobile phone and Internet access as a combined telecommunication and mobile computing solution for diabetes management. Methods: Active Server Pages (ASP) embedded with advanced ActiveX controls and VBScript were developed to allow remote data upload, retrieval and interpretation. Some advisory and Internet-based learning features, together with a video teleconferencing component make DiabNet web site an informative platform for Web-consultation. Results The evaluation of the system is being implemented among several UK Internet diabetes discussion groups and the Diabetes Day Centre at the Guy's & St. Thomas' Hospital. Many positive feedback are received from the web site demonstrating DiabNet is an advanced web-based diabetes management system which can help patients to keep closer control of self-monitoring blood glucose remotely, and is an integrated diabetes information resource that offers telemedicine knowledge in diabetes management. Discussion In summary, DiabNet introduces an innovative online diabetes management concept, such as online appointment and consultation, to enable users to access diabetes management information without time and location limitation and security concerns.

Responding To School Walkout Demonstrations. Lessons Learned From School Crises and Emergencies, Volume 3, Issue 1, 2008

ERIC Educational Resources Information Center

US Department of Education, 2008

2008-01-01

"Lessons Learned" is a series of publications that are a brief recounting of actual school emergencies and crises. This "Lessons Learned" issue examines the incidence of student walkout demonstrations and the various ways in which administrators, school staff, law enforcement, and the community at large can help keep youths…

Objects and Issues--A Sunday Supplement View of Continuing Professional Development in Higher Education

ERIC Educational Resources Information Center

Pegler, Chris

2005-01-01

This paper draws on the presentation of three online pilot "series" of learning objects aimed at offering university staff convenient updating opportunities around issues connected with e-learning. The "Hot Topics" format presented short themed sets (series) of learning objects to a wide-range of staff, encouraging sampling strategies to support…

Video and Second Language Learning. Special Issue.

ERIC Educational Resources Information Center

Gillespie, Junetta B., Ed.

1985-01-01

The extent to which video has come of age with respect to language learning is the focus of this special issue, which provides information on sources of materials and offers practical ideas for the effective and creative use of those materials in second language instruction. Articles include: "Video and Language Learning: A Medium Comes of Age"…

An Analysis of Technological Issues Emanating from Faculty Transition to a New Learning Management System

ERIC Educational Resources Information Center

Sanga, Mapopa William

2016-01-01

This case study investigated the process 119 faculty members underwent as they transitioned from using Desire to Learn (D2L) learning management system (LMS), to using Canvas LMS. Other than analyzing technological issues faculty members encountered while navigating various aspects of the Canvas interface, the study also analyzed technological…

Dialogue, Language and Identity: Critical Issues for Networked Management Learning

ERIC Educational Resources Information Center

Ferreday, Debra; Hodgson, Vivien; Jones, Chris

2006-01-01

This paper draws on the work of Mikhail Bakhtin and Norman Fairclough to show how dialogue is central to the construction of identity in networked management learning. The paper is based on a case study of a networked management learning course in higher education and attempts to illustrate how participants negotiate issues of difference,…

Issues in Integrating Information Technology in Learning and Teaching EFL: The Saudi Experience

ERIC Educational Resources Information Center

Al-Maini, Yousef Hamad

2013-01-01

The Saudi education system is facing a climate of change characterized by an interest in integrating new technology and educational approaches to improve teaching and learning. In this climate, the present paper explores the issues in integrating information technology in learning and teaching English as a foreign language (EFL) in government…

Information Security Trends and Issues in the Moodle E-Learning Platform: An Ethnographic Content Analysis

ERIC Educational Resources Information Center

Schultz, Christopher

2012-01-01

Empirical research on information security trends and practices in e-learning is scarce. Many articles that have been published apply basic information security concepts to e-learning and list potential threats or propose frameworks for classifying threats. The purpose of this research is to identify, categorize and understand trends and issues in…